Safety Stock: save stock or save service? The design of a safety stock advice model based on end to end supply chain risks to improve On Shelf Availability
Author: D.J.E.A.J. van de Gazelle Master Thesis Project – Dion van de Gazelle 2
Safety Stock: save stock or save service? The design of a safety stock advice model based on end to end supply chain risks to improve On Shelf Availability Master Thesis Project Rotterdam 20 November 2009 Name: D.J.E.A.J. van de Gazelle Study number: 1151878 University: Delft University of Technology Faculty: Technology, Policy and Management Program: System Engineering Policy Analysis and Management Specialization: Logistics Company: Unilever
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Preface This master thesis project is the final project for the Master program System Engineering, Policy Analysis and Management with the specialization Logistics. The research is performed in cooperation with and for Unilever Benelux. I experienced this combination between theory and practice as very inspiring and I learned a lot from Unilever and the FMCG business besides the performed research. I’m sure that I will never forget this fantastic opportunity and I really enjoyed writing my master thesis project. This fantastic period was not possible with some indispensable support and therefore I want to thank some people. First I want to thank Patrick and Gökhan for their enormous support during the process and for their great practical input to improve my model and thesis project and for the introduction in the world of Unilever. From the TU Delft I want to thank Ron, Bert and Joseph for their scientific input, challenges and assistance in the role of my supervisory committee. Secondly, my colleagues on de Brug in Rotterdam for their input and help and for the very open and friendly work environment. Special thanks goes to Martijn who introduced me during my first days at Unilever and for the cooperation during our thesis projects. I also want to thank the customer service officer of Retailer X and some of his assistance for their cooperation and openness, this research could not be performed without their input. And last but not least, Silke, Myrthe and my parents, for their social support outside the project boundaries. Beside the project I had a fantastic time at Unilever were I learned a lot but also enjoyed some fantastic activities. I hereby thank Unilever Benelux for the given opportunities.
Dion van de Gazelle November 2009
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Guide to the reader
To get all nuances of the research performed it is advised to read the full report. All main thoughts, conclusions and recommendations are however highlighted as statements: Statement All statements together are the common thread throughout the argumentation structure of this report. However the report can also be read in several other ways when: Interested in the main conclusions it is advised to read the executive summary. If this caught your attention do not only look at Chapter 9 and 10: Evaluation and Conclusions and Recommendations but also at Chapter 11 the reflection. Interested in the methodology read section 1.2.4 and section 3.4.2 for the methodology of the stakeholders analysis This research furthermore resulted in the paper “A stock level advice model for the FMCG industry based on end to end supply chain risks” which is attached to this report but must be seen as a separate piece of work.
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Management Summary Unilever and Retailer X both have the intention to improve the service level to the consumer, measured in On Shelf Availability (OSA). Beside the focus on service, Retailer X also tries to reduce costs in the supply chain. These cost reductions and freeing capital are catalyzed by the credit crunch and can be realized by decreasing stock levels. The service level can be improved when stock levels are better adapted to supply chain risks. To gain insights in this stock level reduction an advice model is designed based on the risks in the supply chain. In this research an advice model is designed that can help to optimize the stock levels at the distribution centre of Retailer X and supports stock level decisions in relation with service to the consumer. This research focus is translated in two design objectives. • Design an advice model that can help to optimize the stock levels at the distribution centre (DC) of Retailer X for a desired service level • The advice model can function as a decision supporting tool and is a first step for further supply chain optimization between Unilever and Retailer X Based on an analysis of the current supply chain performance of Unilever and Retailer X an advice model is designed. This model generates product specific stock level advices but also generates insights in the supply chain risks. The advices can be used to optimize the current stock levels of Retailer X. The model also functions as a decision supporting tool for supply chain optimization and stock level considerations. To realize this decision supporting function, the tool must be accepted and comprehended by the end users Unilever and Retailer X. Therefore the stakeholder setting and process received the necessary attention in this research. The performed research gives an answer on the main research question: How could a stock-advice model for the retailer’s distribution centre contribute to optimize stock levels and improve the On-Shelf-Availability (OSA) of Unilever products based on end-to-end supply chain information involving costs and risks considerations? The availability of products at the shop shelves (OSA) is determined by several aspects. Product availability at the distribution centre (DC) is one of these aspects. This DC availability is formed by the stock levels, the main topic of this research. The risks, which influence these stock levels, can be grouped in three risk sub models: 1 Upstream risks, delivery performance Unilever; 2 Downstream risks, demand pattern and 3 DC risks, storage risks. The advice model is based on upstream and downstream risks for a desired service level; DC risks are not in the scope of this research. To generate a product specific advice, products with the same risk classifications are grouped. These groups are based on the product risk analysis where four characteristics have been defined: • Volume • Sourcing Unit • HPC (Home and Personal Care)/ Foods • Seasonality for food products
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Based on the current situation, the model generates average stock level advice of 4,4 days for a service level of 97,5% to the shops. The average advice for a service level of 99% is 5,1 days. The results of the advice model indicate that a product risk specific safety stock level has potential to decrease the total stock level. The simulation supports this potential and proves that an equal service is possible with lower stock levels. The model results and insights can help Unilever and Retailer X to take decisions based on costs and service. Besides this advice and service improvement, the model opens the way for further integration and cooperation of Unilever and Retailer X. Retailer X and Unilever both speak out their intentions for more openness. This is in line with literature; suggesting that transparency, information sharing and partnership can have positive influences on supply chain costs and performance. This cooperation is decisive for this research but also for further improvement of supply chain processes in the future. This cooperation and transparency demands attention to the involved stakeholders. Unilever and Retailer X are identified as key stakeholders within this multi actor setting. But in fact these stakeholders contain internal stakeholders, important for this research as well. This has consequences for the process to achieve the research objectives. The logistic account officer of Unilever has a crucial role as end user and in the data delivery and is therefore identified as an important stakeholder together with Retailer X. It is important to manage these stakeholders closely and keep them involved during all the steps in the process. Frequent meetings and presentations of progress ensured important input but also improved usability and trust in the model. Comprehension of the model is extremely important for the usability and trust. Does the audience understands what the modeler intended? To accomplish this understanding of the model intensive and regular contact with the end user is important. The set up of the model has been discussed several times with field experts and the end users, the LAO’s of Unilever. Vague aspects are clarified or taken away to ensure comprehension of the model. Service to consumers, measurable in OSA, is a big driver for Unilever and Retailer X. But service is limited by costs. The costs for improving service increase enormous for high service levels. Costs are not only related to storage and handling, but also risks and interest can be expressed in terms of costs. The storage costs dependent on the safety stock and therefore are related to desired service level. The higher the desired service level, the higher the necessary safety stock. Besides the safety stock, also the order pattern is of influence. When the supply chain coordinator orders 6 times a week the cycle stock is much lower then when he orders only once a week. On the other hand the retailer receives a discount when ordering on full pallets and more orders results in more order related costs. The reorder point determines the optimal point, in terms of costs, to send in an order and therefore determines the stock level. Combining the order advice with the advice model gives direction to the order behavior of the retailer. Transparency and openness can improve supply chain performance and therefore the insights and results of the model must definitely be shared with Retailer X. The model and model insights can support supply chain decisions based on costs and service considerations Unilever should be aware of the possibilities and dangers of this transparency. Creating trust, comprehensions and commitment in combination with clear agreements and mutual goals are vital for this process of intensive cooperation. The influence of Unilever on OSA is limited but improving. Focus on the store casefill together with Retailer X gives Unilever the opportunity to get more influence down in the supply chain. It’s the first step to further cooperation to improve the OSA level.
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Table of contents
Preface...... 5 Guide to the reader ...... 6 Management Summary ...... 7 Table of contents...... 9 Index of Tables and Figures ...... 13 Figures...... 13 Tables ...... 14 List of Abbreviations...... 15 1. Introduction ...... 17 1.1 Problem Sketch...... 17 1.2 Research Project ...... 17 1.2.1 Research Objective...... 17 1.2.2 Research Boundaries...... 19 1.2.3 Research Questions...... 20 1.2.4 Methodology of the Research ...... 21 1.2.5 Structure of the Report...... 22 I. Analysis Phase ...... 25 2. Analysis of On Shelf Availability...... 27 2.1 Introduction On Shelf Availability ...... 27 2.2 What is On Shelf Availability?...... 27 2.3 Effects of Out of Stocks on the Retailer and Manufacturer ...... 28 2.3.1 Consumer Reactions to Out of Stocks ...... 28 2.3.2 Underlying Causes of out of stocks ...... 29 2.4 Root Causes and Influences on OSA ...... 30 2.5 Costs of an Out of Stock...... 32 2.6 Measuring On Shelf Availability ...... 32 2.6.1 Manual Audit Method ...... 33 2.6.2 POS Sales Estimation...... 33 2.6.3 Perpetual Inventory Aggregation ...... 33 2.6.4 Choice and Set Up OSA Measure Method ...... 33 2.7 Round up On Shelf Availability Analysis ...... 34 3. Analysis of Unilever and Retailer X...... 37 3.1 Unilever Analysis...... 37 3.1.1 General Introduction Unilever ...... 37 3.1.2 Unilever Benelux in the Netherlands...... 37 3.1.3 Customer Service and Logistics...... 38
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3.1.4 Current OSA situation...... 39 3.2 Retailer X ...... 40 3.2.1 General Introduction Retailer X ...... 40 3.2.2 Stock Optimization in Economic Downturn...... 40 3.3 Business Processes between Unilever and Retailer X...... 42 3.4 Stakeholder Analysis...... 43 3.4.1 The multi Stakeholder Setting ...... 43 3.4.2 Methodology of Stakeholders Analysis...... 44 3.4.3 The stakeholders’ Action Field...... 44 3.4.4 Importance of Stakeholders...... 45 3.5 Transparency in the Supply Chain...... 46 3.6 Round up Analysis Unilever and Retailer X ...... 48 i. Round up Analysis Phase...... 51 II. Design and Modelling Phase ...... 53 4. Design Set Up Advice model ...... 55 4.1 Introduction Design...... 55 4.2 Store Casefill and OSA ...... 55 4.3 Risks in the Supply Chain ...... 56 4.3.1 Upstream Risks ...... 57 4.3.2 DC Risks ...... 57 4.3.3 Downstream Risks ...... 58 4.4 Data...... 58 4.4.1 Research Period, Product Selection and Data Measuring...... 58 4.4.2 Data Pollution...... 58 4.5 Round up Design Set Up...... 60 5. Analysis of Product Risks ...... 61 5.1 Introduction Product Risks...... 61 5.2 Analysis of Stock Level determining Risks...... 61 5.2.1 Upstream Risks ...... 61 5.2.2 DC Risks ...... 64 5.2.3 Downstream Risks ...... 64 5.3 Round Up Risks Analysis ...... 65 6. Modelling Quality...... 67 6.1 Introduction Modelling Quality ...... 67 6.2 Evaluation of the Design Objectives...... 67 6.3 Evaluation Framework Model Set Up...... 67 6.4 Round Up Modelling Quality ...... 69 7. Design of the Model...... 71
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7.1 Introduction Model Design...... 71 7.2 Model Design based on Theory ...... 71 7.3 Practical Design and Model Results ...... 74 7.4 Round up Model Design ...... 75 8. From Model to Practice ...... 77 8.1 Introduction Model to Practice...... 77 8.2 General Usability of Model ...... 77 8.2.1 Objectives of the Model...... 77 8.2.2 Upstream and Downstream Risks ...... 78 8.2.3 From Model to Tool...... 78 8.2.4 Drawbacks in Practice...... 80 8.3 Using the Model for Retailer X ...... 80 8.3.1 Share Insights of Research...... 81 8.3.2 Shifting Risks ...... 81 8.3.3 Conditions for Transparency...... 81 8.3.4 Next Steps...... 82 8.4 Round up From Model to Practice ...... 82 ii. Round up Design and Modelling Phase...... 85 III. Evaluation and Validation Phase ...... 87 9. Evaluation of the Research Design and Results ...... 89 9.1 Introduction Evaluation ...... 89 9.2 Evaluation of Design Objectives and Modelling Quality ...... 89 9.2.1 Evaluation Model Design Objectives...... 89 9.2.2 Syntactic Evaluation of the Model Design ...... 90 9.2.3 Semantic Evaluation of the Model Design ...... 90 9.2.4 Pragmatic Evaluation of the Model Design...... 91 9.3 Simulation and Validation ...... 91 9.3.1 Set up of the Simulation Model...... 91 9.3.2 Validation of the Advice Model ...... 92 9.4 Round up Evaluation and Validation ...... 94 10. Conclusions and Recommendations...... 97 10.1 Introduction Conclusions and Recommendations...... 97 10.2 Conclusions of the Research ...... 97 10.3 Recommendations for further Research and Logistic Policy...... 102 10.3.1 Recommendations for Unilever ...... 102 10.3.2 Recommendations for Research...... 102 11. Reflection ...... 105 11.1 Introduction Reflection...... 105 11.2 Reflection on Theory and Methodology ...... 105
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11.3 Reflection on Data and Results ...... 106 11.4 Personal Reflection ...... 108 iii. Round up Evaluation and Validation Phase...... 109 References ...... 113 Appendices...... 119 Appendix A Interview Retailer X...... 121 Appendix B Interview Customer Marketing Manager...... 122 Appendix C Stakeholder Analysis...... 123 Appendix D Product List ...... 129 Appendix E Relation between OSA and Store Casefill...... 130 Appendix F Risk Profile ...... 131 F1 Grouping Sourcing Units...... 131 F2 Analysis Product Variables ...... 135 F3 Factor Analysis ...... 137 Appendix G Relation Volume and Stock Level Unilever ...... 140 Appendix H Season Products ...... 141 Appendix J Group Specification...... 142 Appendix K Lead time grouping...... 143 K1 Example of Construction Lead Time Groups...... 143 K2 Results Input Analyzer Arena all Risk groups...... 144 K3 Results Input Analyzer Arena Group 14...... 145 K4 Results Input Analyzer Arena Group 17...... 146 Appendix L Analysis Demand Data ...... 147 Appendix M Model Results...... 151 Appendix N Simulation Model...... 154 Appendix O Simulation Results...... 155 Appendix P Scientific Article...... 160
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Index of Tables and Figures
Figures
Figure 1 Research scope of the project...... 19 Figure 2 Design methodology...... 22 Figure 3 Thesis report outline...... 23 Figure 4 European average consumer response to an Out of Stock ...... 28 Figure 5 Schematic representation of supply chain Unilever...... 38 Figure 6 Downward spiral due to credit crunch...... 41 Figure 7 Results stakeholder analysis...... 44 Figure 8 Causal model of risks determine On shelf availability...... 56 Figure 9 Conceptual model risks determine stock level...... 57 Figure 10 Four cornerstones of the modeling framework and the three connecting linguistic aspects...... 68 Figure 11 Interface advice tool Ordering frequency...... 79 Figure 12 Interface advice tool Available order moments...... 80 Figure 13 Conceptual model risks determine stock level...... 98 Figure 14 Preliminary scan of stakeholder network...... 123 Figure 15 Stakeholder classification matrix...... 128 Figure 16 Unilever products measured for the OSA project...... 129 Figure 17 Relation between products and stock level at Unilever...... 140 Figure 18 Relation between sales volatility and forecast error...... 141 Figure 19 Representation distribution lead time all products...... 144 Figure 20 Representation distribution lead time group 14...... 145 Figure 21 Representation distribution lead time group 17...... 146 Figure 22 Q Q plots demand pattern products from data set...... 149 Figure 23 Simulation model used to validate advice model...... 154
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Tables
Table 1 Direct losses for retailer and manufacturer for consumer reactions to an out of stock29 Table 2 Relevant root causes OSA...... 32 Table 3 Business transactions Unilever Retailer X...... 43 Table 4 Average stock level advice for data set...... 74 Table 5 Average stock level 24 and 48 hours deliveries...... 75 Table 6 Delivery performance Unilever to retailers...... 78 Table 7 Expected service simulation and advice model...... 93 Table 8 Average stock level advice...... 99 Table 9 Analysis of store casefill Retailer X...... 108 Table 10 Goals, interests and powers/resources of the stakeholders...... 125 Table 11 Resources, input and criticality of the stakeholders...... 127 Table 12 Results correlation analysis OSA and store casefill...... 130 Table 13 Model summary regression analysis sourcing units...... 131 Table 14 ANOVA regression sourcing units...... 132 Table 15 Regression coefficients sourcing units...... 133 Table 16 Classification souring units based on performance...... 134 Table 17 Model summary multiple regression analysis product characteristics...... 135 Table 18 ANOVA multiple regression analysis product characteristics...... 135 Table 19 Coefficients multiple regression analysis product characteristics...... 136 Table 20 Communalities factor analysis...... 138 Table 21 Explained variance by factors...... 138 Table 22 Factor matrix...... 138 Table 23 Rotated factor matrix...... 139 Table 24 Factor transformation matrix...... 139 Table 25 Group classification based on risk profile...... 142 Table 26 Example construction lead time group 1 and group 14...... 143 Table 27 Results Kolmogorov Smirnov normality test...... 150 Table 28 Results advice model...... 153 Table 29 Simulation results...... 158 Table 30 Comparison of simulation and model results...... 159
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List of Abbreviations BBD Best Before Date CSO Customer Service Officer CS&L Customer Service and Logistics DC Distribution Centre ESM European Supply Management FE Forecast Error FMCG Fast Moving Consumer Goods FPE Full Pallet Equivalents HPC Home and Personal Care KPI Key Performance Indicator LA Logistic Assistant LAO Logistic Account Officer LCDB Logistic Customer DataBase MCO Multi Country Organisation OOS Out of Stock OSA On Shelf Availability POS Point of Sales RFID Radio Frequency Identification Retailer X Retailer in the full service segment, focusing on quality discount and service SKU Stock Keeping Unit SU Sourcing Unit PI Perpetual Inventory USCC Unilever Supply Chain Company VMI Vendor Managed Inventory 3PLP Third Party Logistics Provider
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1. Introduction
1.1 Problem Sketch To achieve growth targets `Winning at the first moment of truth’ is an important goal for Unilever. Optimizing the presence and presentation of brands are key factors for this goal. A powerful way to create value and satisfaction is to keep shelves filled, but in practice Out of Stock (OOS) is still a frequent phenomenon (Roland Berger, 2002). The presence of products is necessary to fulfill customers demand and achieve growth (Unilever, 2006) and results in a good performance on customer service excellence. The presence of products is stated in the On Shelf Availability (OSA) rate. The OSA level presents the percentage of products which are available on the shop shelf. The Out of Stock level has an average of 8.3% worldwide; these OOS provoke significant lost sales for retailers and manufacturers every year (Corsten and Gruen, 2008). Research indicates that consumers buy half of the intended purchases when experiencing an OOS, this results in sales losses about 4% for a typical retailer (Corsten and Gruen, 2004). Research in the retail industry has proven that improving OSA is directly linked with incremental growth (Unilever, 2007). An OSA improvement of 3% results in approximately 1% incremental growth for Unilever (Unilever, 2008b). OSA is affected by the processes of the manufacturer, transporter and the retailer. However most of the OOS are caused by processes in the stores, international research indicates that all parties must cooperate to increase OSA (ECR Europe, 2003). The past years Unilever investigated the improvement options for OSA and concluded that they can improve OSA by advising retailers on processes and stocks. Unilever’s impact in the chain is limited from the retailer’s distribution centre (DC). Unilever is not capable and allowed to practically implement improvements at the DC and on the store floor, because this is the domain of the retailer. But Unilever tries to point the retailer’s behavior in the right direction through clear and transparent generated advice. From this information the following research question is deducted: How could a stock-advice model for the retailer’s distribution centre contribute to optimize stock levels and improve the On-Shelf-Availability (OSA) of Unilever products based on end-to-end supply chain information involving costs and risks considerations?
1.2 Research Project
1.2.1 Research Objective
The current situation of economic downturn provokes extra attention to stock reduction for all companies including retailers. The direct customers of Unilever (the retailers in the Benelux market) are all encountering the same problems and focus on reducing stocks but the main focus is still on customer service. Unilever has recognized this focus and is willingly to help
Master Thesis Project – Dion van de Gazelle 17 their customers to improve business performance. Research has also indicated that the current safety stock allocation in the retail supply chain is not optimal (Deloitte & Touche, 2002) and therefore deserves some extra attention. Besides the stock reductions of retailers Unilever also has a clear strategy focus on OSA improvement (service improvement) to increase turnover. There are numerous root causes for OOS in planning, forecasting, replenishment and ordering. Most of these causes arise at the shop floor or in the back store. But also upstream some OOS causes can be identified (Bharadwaj et al., 2002). For Unilever it is hard to influence the shop floor causes but upstream there are more options to intervene. Analyze and improve the upstream causes can be the start of an intense relation between Unilever and Retailer X. An intensification of the relation can lead to more, shop floor aimed, steering variables in the future. To start this process the research focuses upstream on adjusting stock levels but also has attention to the downstream risks reflected in the customers demand pattern. This end to end focus of the supply chain is a unique way to handle the risks in the supply chain. This unique integrated approach combines information, risks and uncertainties from the whole chain to improve supply performance. Better adjusted stock levels will result in lower stocks and possible OSA improvement and can be the start of further collaboration. This is an interesting challenge between lowering stocks and improving OSA in relation with accessory costs. This challenge can be translated in a problem statement: Problem Statement In order to free capital and reduce costs retailers are triggered to reduce stocks. Unilever and Retailer X have a clear focus on On-Shelf-Availability (OSA) improvement to increase turnover. Insights are needed to optimize and when possible minimize stock levels, in order to improve OSA of Unilever products, and with reflection to related costs. Supply chain decisions are made with a probabilistic view of the future. As a result, there is a necessity for decision support tools that can help managers understand the costs, benefits, and risks associated with various alternatives (Swaminathan, 1998). This research will identify which criteria are relevant to determine stock levels at the retailer’s DC and eventually deliver a model for stock level advice at the retailer’s DC. This advice model generates stock advice based on product specific risks. This advice can help to determine further tactical decisions. Reducing stock levels is one of the important objectives of this research but improving service is also indicated as a very important variable. Unilever and Retailer X can consider stock reductions but also reallocation of stocks to realize a higher service is an option. Keeping lower stock levels result in lower capital costs but increases handling costs. The results of the model can help to take decisions for these challenging considerations. The research investigates some of these future considerations and there will be provided a couple recommendations how to use the advice in practice and intensify cooperation like VMI (Vendor Managed Inventory). These options for the future are shortly mentioned in this research but this is not the main research goal. The research focus is clearly on improving service level and optimizing stock levels. The model must be used and understood by associates of Unilever and Retailer X. These associates will have different backgrounds, goals and work levels. Attention to this multi actor setting is important and frequent contact and information sharing will contribute to this. Recognizing the multi actor setting for stock level adjustments is important. Unilever and the retailer both have the intention to adjust stock levels. Unilever’s goal is creating an advice for the retailer and get insights on the supply chain risks from the model, but the retailer owns and controls the stocks.
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To create mutual trust and understanding the model will be validated by experts in the field from both actors. Now the research goals are set up and the approach is clear the research objective can be draught:
Research Objective Generate an advice model, trusted and accepted by Retailer X, for the stock levels at the retailers DC based on the current situation and explore the possible benefits of the model for further optimization of logistic processes of Unilever and Retailer X. The problem statement and research objective make explicitly that the focus of the research is on stock reduction in relation with OSA (service level) improvement with cost reflections. When possible the OSA level must be improved to increase turnover. The research boundaries will be discussed in the next section. In 1.2.3 the research questions are stated, which are necessary to achieve the research objective.
1.2.2 Research Boundaries Unilever’s practical influence stops at the retailer’s DC. But Unilever has some possibilities to stimulate the retailers in their logistic decisions and actions. The focus of the research is represented in figure 1. Important to mention is that this research covers the end to end risks of the whole supply chain. Of course the focus is on stock levels but all processes of the chain are taken into account from the sourcing unit (SU) until the shop floor. The project boundaries include the part of the information flow that influences stock level decisions. The distribution centre of the retailer is also part of the scope but is indicated with a dotted line due to the limited practical influence of Unilever on this part. The influences outside the scope are regarded as stable for the short term; and thus function as input. In the long term the production and product variables can change. The shops are important for the output. At this level the result of the given advice in terms of service can be measured in the OSA. This research focuses on the baseline sales. Promotions are outside the research boundaries because they can have a disturbing effect, due to temporary shifts in demand, on delivery performance. When stocks are reduced but losses do not outweigh the gains the advice is not successful. A balance must be found between stock level investments and service level (Johnston and Boylan, 1996). The OSA level should be increased when possible, while generating loss/cost reductions.
Figure 1 Research scope of the project.
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Information sharing can significantly improve the performances of the supply chain (Zhao et al., 2002). Transparency and information sharing is necessary to gain a complete vision about the risks in the whole chain and fit the different parts into one consistent chain. Literature points out that information sharing and communication is crucial for OSA improvement and stock level reductions (ECR Australasia, 2001). Figure 1 shows the boundaries of the research but also explicitly mentions the crucial transparency. The model not only gives the retailer stock advice but also has a convincing and informative role (section 3.4 and 8.1.4). Potential users must trust, understand and be able to use the model, before the advice can be considered successful. Firstly the interactions between the involved actors will be examined, To create this trust and understanding. Frequent contact with and clarification for the stakeholders is probably an outcome to handle this multi actor complexity. Beside these frequently updates, a workshop can be a useful tool to create the required comprehension and trust. 1.2.3 Research Questions To achieve the research objective stated in section 1.2.1 the following research question are formulated: Research Question How could a stock-advice model for the retailer’s distribution centre contribute to optimize stock levels and improving the On-Shelf-Availability (OSA) of Unilever products based on end-to-end supply chain information involving costs and risks considerations? In order to find an answer on the main research question several sub questions are set up: 1. Which major factors determine the On Shelf Availability of Unilever products? Method: Literature research of scientific publications, internal documents, field research. Analyze SAP data and logistic customer database (LCDB), research at sourcing units, DC’s and stores and interviews with stakeholders. Result: Insights in the factors influencing supply reliability to the DC’s of the retailers significantly. The influence can be used as input to determine the different product groups. Insights in the demand pattern at the shop floor per product. 2. Which product groups can be identified, categorized in behavior and properties? Method: Literature research of scientific publications and data analysis. Result: A risk profile in which all the import factors influencing the supply performance are mentioned. This risk profile is necessary to evaluate the different product categories and is an input for the advice model. The differentiation in behavior and properties of different products is used to form product groups. These groups are necessary to understand and find general conclusions about the behavior of individual products.
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3. a. Which stock advice, based on the current situation, can be generated by the model for different products groups regarding the current stock levels? b. Which costs are effected by the stock advice and how do the costs influence the process of stock level decisions? Method: Desk research, data assembling (SAP data/logistic customer database) and modeling. Result: The risk profile is used in a model that generates advice for the customers’ safety stock at the DC’s. When the safety stock is better adapted to the end to end risks of the supply chain the store casefill can improve. An improvement of the store casefill results in a better OSA and service to the customers. 4. What role does the stock level advice model have and which criteria must be developed to fulfill this role? Method: Desk research, stakeholder interviews. Result: A list of important modeling criteria (animation, usability). This list assures that the model will be accepted, understood and can be used by the retailer. 5. What are the effects and results of an intensifying transparency and communication between the involved stakeholders? Method: Field research, interviews with stakeholders, literature research of scientific publications. Result: A transparent process and model. Mutual understanding and agreement is necessary to make the insight of the model clear and accepted and acceptable for the client to use. 6. What changes in order behavior are recommended due to the new stock level advice regarding to the constructed product groups? Method: Field research and interviews with stakeholders and literature analysis of scientific publications. Result: Revaluation of the current used stock control and order patterns and advice to adapt or maintain this procedure, based on the new insights on safety stock levels for different product (groups). The answers on these questions are identified in this final thesis report and the conclusions are represented in section 10.1.
1.2.4 Methodology of the Research This chapter defines the methodology that will be used to gather an answer on the formulated research questions. The methodology is a framework for the research and can be used as a guideline in the research activities. Translating the problem statement and the research objective into a model can be considered as a design challenge. The design method used in this research is based on the complex multi actor and multi requirements methodology (Herder and Stikkelman, 2004).
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Figure 2 Design methodology (adapted from Herder and Stikkelman, 2004). The goals of the research are determined by the stakeholders (Chapter 3). A clear formulation of goals is essential to create a suitable and accepted design; and control afterwards if the design fits the desires of the stakeholders. The goals are translated into objectives and constraints. These objectives and constraints are matched with the design space. The design space is formed by the boundaries of the specific situation. The research boundaries in combination with time, data and money restrictions determine the design space for this research (Analysis phase). With respect to the design space first a conceptual model is designed (Chapter 4 and 5). This conceptual model is discussed with experts and stakeholders and when necessary adapted. The conceptual model is then translated in a real model (Chapter 7). This model is tested and discussed again with stakeholders and experts. After this evaluation the final model is designed and steps to use it in practice are provided (Chapter 7 and 8). The results of the model are evaluated in Chapter 9. The results are used to test how good the model fits with the design objectives (Chapter 10). Finally some method and research reflections are presented (Chapter 11).
1.2.5 Structure of the Report The report is split up in three phases: I. Analysis, II. Design and Modeling and III. Evaluation and Validation. The three phases have an introduction, conclusion (i., ii. and iii.) and are split up in different chapters. The first phase is a system analyses (chapter 2, 3 and 4). In this phase the requirements and decision space are formulated based on the analysis of On Shelf Availability, Unilever and Retailer X and a product analysis. The analysis will also draw attention to the multi actor setting of this problem. The tensions between the actors will discussed and options to handle these differences are formulated (Chapter 8). In chapter 5, start of the second phase, the risk profile, based on product characteristics which determine delivery performance, is set up. This profile mentions the factors that influence the delivery reliability to Retailer X. The risk profile is used to create product groups as input for the advice model. The service level of Retailer X depends on the delivery reliability. The stock policy at the retailers DC can handle these uncertainties. The objectives and constraints form
Master Thesis Project – Dion van de Gazelle 22 the solution space and are together with the risk profile input for the design. Besides this input a list of criteria will be formulated to evaluate the model (outcomes). The solution space is an input for the design of the model (start Design and Modeling phase, Chapter 4). In chapter 8 some suggestions to translate the model into practice are presented. The Evaluation and Validation phase starts with a validation of the model with a simulation (Chapter 9). The results of the model and the implications for Unilever and Retailer X are described in Chapter 10. In chapter 11 some reflections are written about the process and personal experiences of the thesis work.
Chapter 1 Introduction
Chapter 2 Chapter 3 Phase 1 Analysis Analysis of On-Shelf- Analysis of Unilever Availability and Retailer X
Chapter 4 Chapter 5 Chapter 6 Design Set Up Risk Analysis Product Modeling Quality Model Risks
Phase 2 Design & Modelling Chapter 9 Chapter 10 From Model to Conclusions and Practice Recommendations
Chapter 7 Design of the Model
Chapter 8 Chapter 11 Evaluation & Reflection Validation
Phase 3 Evaluation & Validation Figure 3 Thesis report outline.
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I. Analysis Phase
The analysis phase is the first part of the design process. In this phase the concept and implications of On Shelf Availability (Chapter 2) and the structure of and relations between Unilever and Retailer X (Chapter 3) are analyzed in detail. The results of this analysis are translated in design requirements and objectives and are used for the identification of the solution space. The analysis phase ends with a wrap up of the conclusions of the analysis phase in the round up section.
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2. Analysis of On-Shelf-Availability
2.1 Introduction On-Shelf-Availability The prior complaint of shoppers in supermarkets is the unavailability of products. Almost 26% of the shoppers often experience an out of stock (OOS) as an inconvenient event. Also the unavailability of promotion products (ranked 6 th in the shoppers’ complaints list) must be considered as an OOS event (EFMI Business School, 2008). This topic has been an interesting issue in academic logistic literature, with many authors discussing On Shelf Availability in relation with information sharing and collaborative store ordering (Pramatari and Miliotis, 2005), inventory management (Fleisch and Tellkamp, 2005); (Jammernegg and Reiner, 2007), two echelon supply chains (Nahmias and Smith, 1994) and Supply Chain Synchronization (Vlist van der, 2007). This chapter defines the concept of On Shelf Availability (2.2) and indicates the importance of this subject and the related problems and gives an overview of the most important and interesting academic literature and conclusions. First the problems and losses of OSA for manufactures and retailers will be discussed (2.3). Some attention will be given to customers’ reactions to and influences and root causes of out of stocks (2.4). The results of these reactions and causes on costs are described in section 2.5. Section 2.6 introduces different ways to measure the OSA level. The drawn conclusions of this chapter (2.7) are useful in the design phase for the set up of the risk profile and to identify product groups.
2.2 What is On-Shelf-Availability? The On Shelf Availability (OSA) rate is a reflection of the percentage of available products on the shelves. There are only two options in OSA measurement: the product is available (100%) or it isn’t (0%). A product is available when the desired product is in a saleable condition when and where the customer wants it (Accenture, 2008). When the product is not available it is out of stock (OOS) or Void. A product is out of stock when there is a shelf tag and the consumer unit (product) is in the store catalogue, but there isn’t any inventory on shelf at the moment of the measurement. A product is Void when the consumer unit is in the store assortment and expected to be stocked, but at the moment of measurement there is neither a shelf tag nor any reference in the store catalogue or retailer system (Unilever, 2008b). The need to focus more on OSA improvement has drastically ascended over the past few years. The first reason for this ascend is the fact that consumers are less tolerant of OOS situations. Consumers are better informed and more willing to switch to another brand or store. The second reason is that the direct impact of OSA improvement actions has increased. Because retailers and manufacturers operate worldwide, solutions and adaptations have a far deeper impact against lower development and research costs (economy of scales). The third reason is formed by technological improvements. These developments opened new ways to improve OSA, other than traditionally solutions with ongoing costs of increased labor or greater inventory safety stocks (Bharadwaj et al., 2002). This renewed importance of OSA focus is acknowledged by an ECR study wherein reducing out of stocks is the third most important shopper need, after shorter queues and more promotions (ECR Europe, 2003). More recent investigations in the Dutch retail market show that out of stocks are ranked 1 st (for baseline) and 6 th (for promotions) in the 2008 consumer complaint list (EFMI Business School, 2008).
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Multiple international studies demonstrate that the average OOS extent is 8.3% in the grocery retail sector (Corsten and Gruen, 2003). This means that any customer is disappointed at least on one of every 13 products he intended to purchase. Fernie and Grant show that these figures are even higher in other industries. In bookshops 40 to 80% of the products are not available, for electronics these rates are 6 16% and for mobile phones 2 20% (Fernie and Grant, 2008). Although these high figures in other industries one should mention the relative high importance and effects of OOS in the retail industry.
2.3 Effects of Out-of-Stocks on the Retailer and Manufacturer In literature two different approaches are used to analyze consumer behaviour in relation with OSA. The research of (ECR Australasia, 2001), (ECR Europe, 2003) and (ECR UK, 2007),(Corsten and Gruen, 2004, 2008) and (Bharadwaj et al., 2002) are focused on the actions taken by consumers when encountering an out of stock. (Campo et al., 2000, Campo et al., 2003, 2004), (Emmelhainz et al., 1991) and (Sloot et al., 2005) focus on the drivers of consumer responses when faced with an out of stock. Both will be discussed below.
2.3.1 Consumer Reactions to Out-of-Stocks
An OOS provokes different consumer reactions. Consumers can switch size, variety, brand or store and delay or even cancel purchases. In total there are at least 15 possible actions for consumers when experiencing an OOS (Emmelhainz et al., 1991). Although there are multiple reactions to OOS it is possible to group these reactions in five behavior categories. Consumers can switch brand, purchase a different size or variety, delay purchase, switch to another store or cancel the purchase (Corsten and Gruen, 2004). The worldwide average on consumer response is presented in figure 4.
9% 16%
17% Substitute - Same brand Substitute - Different brand Sw itch to another store Delay purchase 31% Do not purchase item
27%
Figure 4 European average consumer response to an Out-of-Stock (Adapted from Corsten and Gruen, 2003).
All these different kinds of behavior provoke losses for the retailer and/or manufacturer. The retailer faces a direct loss when the consumer purchases the product in another store or
Master Thesis Project – Dion van de Gazelle 28 cancels the purchase. When the consumer switches brand or doesn’t purchase at all the manufacturer faces a direct loss (ECR Europe, 2003). Direct loss for retailer and manufacturer for consumer reactions to an Out-of-Stock Consumer Response Retailer Manufacturer 1. Buy item in another store Yes (Most problematic of No all five options for the retailer) 2. Delay purchase No (But negatively affects No (But negatively affects cash flow cash flow and inventory turns) and exaggerates demand fluctuation) 3. Substitute Same brand No (But there is partial loss No (But there is partial loss when consumer substitution when consumer substitution is smaller or cheaper) is smaller or cheaper) 4. Substitute Different brand No Yes (Most problematic of all five options for the manufacturer) 5. Do not purchase product Yes Yes Table 1 Direct losses for retailer and manufacturer for consumer reactions to an out-of-stock (Adapted from Bharadwaj et al., 2002). Beside these direct losses the retailer and manufacturer can also bear indirect losses. An OOS gives the consumer the `opportunity’ to try another store and brand. When a consumer is very product loyal, he can decide to switch store for a longer period and the retailer in that case also loses the sales of other products (Bharadwaj et al., 2002). Consumer reactions to OOS also result in supply chain inefficiencies. Switching brands, size and stores as well as delayed purchases provide an inaccurate pattern for the supply chain managers (Bharadwaj et al., 2002). These inaccuracies emerge amplified in the supply chain and cause forecasting, ordering, production and delivering problems resulting in new OOS.
2.3.2 Underlying Causes of out-of-stocks Beside the consumer reaction focus of Corsten and Gruen other authors focus on the drivers for these reactions. Campo et al. provide a list of underlying variables which can influence customers’ reactions to Out of Stocks (Campo et al., 2004): • Availability of acceptable alternatives • Item loyalty • Large regular pack, size that the household usually purchases • Store loyalty • Time constraint, restrictions on the time spent on shopping • Shopping frequency • Shopping attitude, degree to which the consumer finds grocery shopping an enjoyable task
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• Major shopping trip • Consumption rate, average household consumption • Product importance, degree to which the consumer finds having a stock at home important All these variables influence more or less consumer reactions when facing an out of stock. Because not all of these variables can be measured or are not important enough only some will be taken in account in this research. The most important variables for this research are: availability of alternatives, loyalty and consumption rate and product importance. When consumers have more acceptable alternatives they will have less problems with switching to another package size or brand. Product loyalty is also important, when consumers are very loyal they will not choose a substitute. Instead of changing their brand they will go to another store or maybe delay the purchase. This behavior depends on the product characteristics, customers for example switch of store for personal care; and for beer they switch brand (ECR Europe, 2003). Consumption rate and product importance are also important product characteristics and in combination with substitutes and loyalty they determine customer’s behavior.
2.4 Root Causes and Influences on OSA There are multiple root causes affecting OSA divided over the supply chain from end to end. Bharadwaj et al. constructed a list with 94 root causes for OOS. About 70 to 85% of these root causes are related to shop activities (Bharadwaj et al., 2002), (ECR Europe, 2003) and (Corsten and Gruen, 2008). Because Unilever has currently a limited or no influence on these causes they won’t be taken in account anymore during this research. As mentioned in the introduction this research is a first step to further cooperation. Therefore these causes are important for the future and after intensifying the relation it is recommended to investigate were improvements are possible. To give a complete overview all the major causes identified by multiple authors are mentioned. A list of specific root causes is provided which are in the scope of this research. The main group of root causes for OSA is identified by multiple authors (Bharadwaj et al., 2002), (Corsten and Gruen, 2004, 2008), (ECR Europe, 2003), (ECR Australasia, 2001) and (ECR UK, 2007). These causes can be split in two major groups (Accenture, 2008). Replenishment: • Shelf refilling Wrong product refill, too late reaction, not right location (DeHoratius et al., 2001). • Back of store problems Too much inventory, bad storing practices, inventory inaccuracy and `untraceable products’. • Inadequate shelf allocation Slow movers receive too much shelf space what results in not enough shelf space for the fast movers. A case study shows that 50% of the products in the store had 20 or more days of stock on shelf (ECR Europe, 2003). • Low replenishment frequency Probability of OOS increase with lower frequency.
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Planning and forecasting: • Demand underestimation Ordering too less products. • Introductions/relaunch Introductions of new products, there is a 2:1 OOS ratio of promoted vs non promoted OOS rates (Corsten and Gruen, 2003). Also relaunches with slight packaging changes or price changes. • Long order cycles Longer order cycles make the process less flexible. • Promotions/advertisements This often results in inadequate demand forecasting and ordering. Storage on a display and the normal shelf often result in an OSA decrease (Corsten and Gruen, 2008). From this list only the planning and forecasting root causes are relevant for this research. The other causes are not part of this research because they are out of the research scope; however they are very interesting and recommended for further research. It should also be noted that most of the promotion causes are a special form of demand underestimation. Beside these frequently discussed root causes (Corsten and Gruen, 2003) determined also some other relevant causes: • Data inaccuracy Lack of information about availability of products or lead time. • Incorrect ordering Ordering wrong products or quantities. Product availability at DC When there are no products available at the DC, they can not deliver to the stores.
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The last cause is the main topic of this research. Generating stock level advice can help to adapt stocks better to risks and demand fluctuations and will ensure that the number of available products at the DC is accurate. This product availability is linked to the delivery performance of the manufacturer and also handles the more upstream OSA causes (Accenture, 2008). Somewhere between 3 4% of the OOS causes can be directly attributed to insufficient production by the manufacturers (Bharadwaj et al., 2002). The given advice also deals with uncertainties in demand pattern and ordering. The final list of important and for this research relevant root causes for OSA is: Demand underestimation Introductions/relaunch Long order cycles Promotions/advertisements Data inaccuracy Incorrect ordering Product availability at DC Table 2 Relevant root causes OSA.
2.5 Costs of an Out-of-Stock Out of stocks cause losses for retailers and/or manufactures depending on consumers’ reactions. When consumers encounter an OOS and decide not to purchase the product at all (9% of the cases in Europe), this cost the entire industry €4 billion per year in turnover (ECR Europe, 2003). However there are no hard facts the costs for manufacturers are estimated on $23 million for every $ 1 billion of sales (Corsten and Gruen, 2008). Switching brand (maybe a cheaper brand), size (maybe smaller) or store is not even included in these figures. Corsten and Gruen (2003) argue that OOS lead to value destruction for the entire supply chain of 3.7% worldwide. It’s proven that it is difficult to translate these losses into costs and the impact of an OOS is often incorrectly measured (Liu and Zinn, 1998). Often forgotten are the costs of negative word of mouth or reputation (Thomas, 2002) and the duration of the time a customer is lost due to out of stocks. Schneider (2009) investigated the costs caused by low delivery performance of Unilever and proved that Unilever Benelux has a yearly turnover loss of 32,6 million Euro due to delivery problems. This proves that the actual influence on OSA is not as high in terms of service as in terms of money. Improving their own processes is less effective for Unilever then trying to improve downstream processes.
2.6 Measuring On-Shelf-Availability Data about OSA for different products is necessary to draw conclusions about the service in a later stage. This data can be gathered by measuring OSA of different products in different stores. The three most common ways of measuring OSA will be discussed now and the most important advantages and limitations will be mentioned. At the end of the section the choice for the actual performed method will be clarified.
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2.6.1 Manual Audit Method With the manual audit method the shelves are checked periodically on OOS. The auditor counts whether the product is available at the right shelf and at the right tag or not for a certain period. The total number of empty products spaces that are uncovered in that given period are divided by the total number of measurements in the category to provide a percentage of items that are OOS. This method has been used for years and provide a reliable benchmarks another advantage is that the method is trustworthy because seeing is believing. It is also a cost effective way to address OSA in a known problem area of the shelf. Count all misses as equal with no attention to duration or sale speed is a limitation. The lost revenue and consumer impact are not measured. The audits are very labor intensive. At last the time and frequency of the measurement and the selection of products need attention. Balance these count characteristics is necessary to get objective OSA measures (Corsten and Gruen, 2008).
2.6.2 POS Sales Estimation This method does not use physical audits but uses store scanners and inventory data. This method calculates how often a consumer encounters an OOS for an intended purchase. The number of OSA divided by the product on shelf + product OOS is OSA percentage. This method has an accuracy of 85 90% when validated by manual audits. The method works on historical sales and accurate POS (Point of Sales) data. POS data is used to update the inventory records of retailers to determine the replenishment quantity (Reid and Sanders, 2005). When the data of product availability on the shelf is not accurate this will have significant effects on the accuracy of the measurement. The initial set up costs are high due to the investments in a complex and expensive computer system and the trustworth will be lower because the limited transparency and the mathematical estimates that are used (Corsten and Gruen, 2008).
2.6.3 Perpetual Inventory Aggregation The third method is perpetual inventory (PI) measuring. PI systems track sales and when sales = 0 the item is OOS. This method has potential for the future in combination with automatically detection of OOS with Radio Frequency Identification (RFID) or a Rule Based System (Papakiriakopoulos, 2006). In the future it will be possible to detect OOS automatically, with Radio Frequency Identification (RFID). These methods are not possible and effective at the shops of Retailer X. Some authors argue that this method is suited better for store availability then On Shelf Availability and more important it has in this stage an accuracy below 50%. The low accuracy dismisses PI measuring directly (Corsten and Gruen, 2008).
2.6.4 Choice and Set Up OSA Measure Method POS estimation is the most promising of the three options. But Unilever doesn’t have access to the POS data of Retailer X and besides this; a complex and expensive computer system is necessary to make the method work. Therefore the manual audit method is chosen to measure OSA for this research. As mentioned before the balance of product and the frequency and time of measuring are very important to find accurate results. Brand and Cluster Management identified 88 SKU’s (Stock Keeping Units) as the most important for Unilever, whether it is based on volume,
Master Thesis Project – Dion van de Gazelle 33 growth, new introductions or any other reason. As for the volume, the combined volume for these SKU’s represented in 2008 around 20% of the total volume of Unilever in the Netherlands. Also 33 non Unilever SKU’s are measured to make good comparisons possible. Eventually the measuring is done for 6 different retailers. For each of these 6 retailers, the most important supermarkets had already been identified; based on volume, location, cooperation and other sales variables. In total, 412 supermarkets have been identified to do OSA measurements, on which the supermarket owners have agreed. A full overview of the products and of the supermarkets is provided in Appendix D. This research will only focus on the results and stores of Retailer X. The external agency, executing the measurements, has a rotation that covers each supermarket at least once in every 6 weeks and 40% of the measures must be done during peak hours. This is in accordance with the Unilever worldwide OSA measurement standards (Unilever, 2008b): • At least 800 store visits per quarter • At least 75 priority consumer units are measured (50 Unilever, 25 key competitors) • At least 40% of the checks is during peak hours • For each consumer unit is detected if it is on shelf, OOS or VOID. Measurement occurs by manual audits, in which investigators search for a product and consult (if necessary) store staff. This should resemble actual shopper behaviour, as shoppers also do not wait for more than a couple minutes before deciding to change the product. Data is uploaded monthly in a web based application, while additional data is sent per e mail. Further distinction between item and brand OOS, or between store and shelf OOS are not made. The first can be found by grouping some of the 88 products together (products of the same brand range obviously). Having information on store and shelf out of stocks would have been beneficial in order to locate the reason of an out of stock. However, this data is not available as this was not the initial intent of the measurements and as there is too little cooperation yet between the retailers and Unilever (Schneider, 2009).
2.7 Round up On-Shelf-Availability Analysis
The need to focus more on OSA improvement has drastically ascended the past few years. Consumers are less tolerant of OOS situations, the direct impact of OSA improvement actions has increased and technological improvements create some new possibilities. These developments opened new ways to improve OSA, other than traditionally solutions. An OOS provokes different consumer reactions. Consumers can switch size, variety, brand or store and delay or even cancel purchases. All these different kinds of behavior provoke losses for the retailer and/or manufacturer. When consumers encountering an OOS and decide not to purchase the product at all, this cost the entire industry €4 billion per year in turnover. Switching brand size or store is not even included in these figures. However there are some appropriate ways to determine the costs of an OOS there are no general figures available for the costs of an OOS on product level. Research has indicated that Unilever Benelux has a yearly turnover loss of 32,6 million Euro due to delivery problems There are multiple root causes affecting OSA divided over the supply chain from end to end. The most important and relevant root causes for this research are: Demand underestimation, introductions/relaunch, long order cycles, promotions/advertisements, data inaccuracy, incorrect ordering and product availability at the DC. All these root causes are reflected in the
Master Thesis Project – Dion van de Gazelle 34 demand and delivery pattern. Therefore they will be represented as demand and delivery/production data in this research. Measuring OSA is possible in three different ways. POS Sales data is very accurate but expensive and technical not possible for Retailer X. Perpetual Inventory (PI) aggregation is not accurate at all and therefore not useful. The manual audit method has been used for years and provides a reliable benchmark. Another advantage is that the method is trustworthy because seeing is believing. It is also a cost effective way to address OSA in a known problem area of the shelf. Counting all misses as equal with no attention to duration or sale speed is a limitation. Drawbacks are that the lost revenue and consumer impact are not measured and the method is very labor intensive. The OSA for 6 retailers is measured with manual audits for 88 Unilever and 32 non Unilever products in 412 supermarkets. Every quarter 800 visits are done with 40% of the measures within peak hours. This measuring set up must give a very realistic view of the real product availability at the shop level. The OSA measurements are used to get insights in the possibilities of improving store casefill (section 4.1) and are a perfect way to measure the influence of the advice model on service after the implementation. The measurements can be used as control mechanism for the effects of the stock advice on service.
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3. Analysis of Unilever and Retailer X The analysis on OSA and consumer behaviour, as performed in the previous chapter, is the first part of the analysis phase. The second part will give some special attention to the most important parties, within this research, Unilever and Retailer X (section 3.1). In this section also the underlying reasons for this research are made explicit. After the first brief introduction and description the processes within and between these two companies are discussed and represented in more detail in section 3.2. The multiple stakeholders, within and between Unilever and Retailer X, have all other interests, goals and powers in relation to the stock level advice. Mapping this information (section 3.3) can help to improve the process. In section 3.4 transparency within the supply chain and between the stakeholders is discussed. Section 3.5 provides a short round up and summaries all these aspects. Section 3.6 finally gives a wrap up of the whole analysis phase.
3.1 Unilever Analysis
3.1.1 General Introduction Unilever The Margarine Unie and the Lever brothers were both 19th century producers of products based on oil and fats, principally margarine and soap. At the beginning of the 20th century, this kind of industry grew so fast that the demand of raw materials nearly outstriped the supply. Many businesses formed trade associations, to manage the results of tough economic conditions and the First World War and to protect their shared interests. With business expanding fast, companies set up negotiations intending to stop others producing the same types of products. During these negotiations the Margarine Unie and the Lever brothers uncovered the potential of company integration and they agree to merge into Unilever. The first 30 years were characterized by product and brand development in their domestic and European markets. The world economy was expanding in the 1960’s and Unilever used and benefited from this trend with an ambitious acquisition program. Unilever became increasingly global, leading to one of the largest companies in the world today with an annual turnover of over € 40 billion (Unilever, 2008a). Unilever’s global brand portfolio consists of over 400 brands, of which 13 brands individually account for more than € 1 billion global turnover. Current strategies are increasingly focused on vitality and sustainability, adapted to current global developments and consumer trends. The company’s original Anglo Dutch structure is reflected by its structure of two parent companies (Unilever N.V. and Unilever PLC, which are respectively Dutch and English), operating as a single entity.
3.1.2 Unilever Benelux in the Netherlands Unilever Benelux is a Multi Country Organization (MCO) and is part of Unilever global. Unilever Benelux has 1.100 employees and realizes an annual turnover of € 1,8 billion. Unilever is considered as the number one producer of A brands in the Benelux. Unilever Benelux supplies almost all the important retailers of the Dutch market with 2.256 Stock Keeping Units (SKU’s). SKU’s are all unique products; differences in product characteristics like weight, volume or geographical location turn out into new SKU’s (Reid and Sanders, 2005). Unilever has 5 distribution centres (DC) in the Netherlands to deliver all these SKU’s. Those products partly coming from three Sourcing Units (SU) in the Netherlands, and partly from SU’s across the world. Unilever only produces premium brands, no private labels. Unilever’s Corporate Centre in Rotterdam, the 3 Sourcing Units (SUs), the R&D unit in Vlaardingen, and Unilever Foodsolutions (for the professional kitchen) are not part of Unilever Benelux.
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To ensure close customer contact and excellence service Unilever Benelux is grouped in four sections. These four sections are called customer teams and are responsible for one or multiple customers of Unilever: • Albert Heijn • Schuitema/SuperdeBoer • Superunie • Out of Home (e.g. gas stations, Bijenkorf) These three retailer teams are further divided into five clusters: • Home care ( e.g. Omo and Cif ) • Personal care ( e.g. Dove, Vaseline and Rexona) • Savoury and dressings ( e.g. Knorr, Conimex, Unox and Calvé ) • Spreads cooking category ( e.g. Becel and Blue Band ) • Ice cream and beverages ( e.g. Ola, Magnum and Lipton) These clusters can be grouped into HPC (Home & Personal Care) and Food (the other three clusters). Both the Unilever Supply Chain Company (USCC) in Switzerland and the MCO Unilever Benelux have important impacts and responsibilities related to the supply chain. The USSC owns the inventory of Unilever and is officially practical responsible for the supply chain from the supplier to the consumer, including the 3rd Party Logistics Provider (3PLP). The raw materials are purchased by the European Supply Management (ESM) on behalf of the Sourcing Units (SU). The USCC manages these purchases from the ESM and is able to optimize prices and volumes through economy of scales. The MCO determines the forecast for the SU’s. The SU’s are responsible for their own production planning based on the forecast and the stock level at the DC’s. The MCO responsibility officially ends at the customers DC, except at Albert Heijn for which Unilever manages the DC’s inventory with VMI.
Figure 5 Schematic representation of supply chain Unilever.
3.1.3 Customer Service and Logistics
The department of Customer Service and Logistics (CS&L) is responsible for the physical flow of goods to the customer and the information flow through the supply chain from the retailer to the SU. Direct contact with Unilever’s logistic providers is provided by CS&L managing 100 million cases, representing 130.000 orders, 66.000 drops (truck stop per route) and 700.000
Master Thesis Project – Dion van de Gazelle 38 pallet equivalents per year. These orders can be ordered in cases, layers on a pallet or by full pallets. All order quantities are measured in full pallet equivalents (FPE), and hence do not have to be integers. The logistics assistances are responsible for the planning and forecasting of promotions. They coordinate between planning, the retailer, 3PLP’s and Customer Development to synchronize forecast and real demand as much as possible.
3.1.4 Current OSA situation To achieve growth targets `Winning at the first moment of truth’ is an important goal for Unilever. Optimizing the presence and presentation of brands are key factors for this goal. Unilever is first and foremost interested in making their product available to the shopper (Schneider, 2009). The presence of products is necessary to fulfill customers demand and achieve growth (Unilever, 2006) and results in good performance on customer service excellence. Unilever’s performance in the supply chain is measured through the casefill. The casefill represents the correctly delivered cases to the retailers. Problems, failures or adjustments are recorded and a reason code is assigned. With these reason codes it is possible to analyze the failures for a certain period and investigate where improvements are possible and necessary. As represented in figure 5 the supply chain consists of multiple actors all influencing the performance of the whole chain. When Unilever delivers products at the retailer’s distribution centre, much can happen on the way to the shelf. These undesired ‘events’ cause OOS at the store shelves. About 70 to 85% of these root causes are related to shop activities (Bharadwaj et al., 2002). Availability of products and distribution agreements are based on shopper insights and research, together with brand/category strategy, and are negotiated and agreed between Unilever and the retailers. An important goal is to provide the shelf assortment as efficient as possible because it provides the range of sizes and variants which shoppers expect and want. The expectation here is that the SKU’s of Unilever will be present and available on the shelf. Shelf assortment is a way to attract shoppers and give them the opportunity to buy the product and create turnover. Shoppers make their purchasing decisions in the aisle, in front of the shelves, so it’s essential to ensure that all listed products are available on shelf. If items are out of stock, Unilever will lose shopper confidence and therefore sales volume and turnover (Unilever, 2006). Unilever Europe and the USCC decided, based on studies, that the topic of On Shelf Availability must receive a high priority for the countries. To grow this awareness in the MCO, it must be established how big the impact of Unilever is on OSA in the Benelux and what can be done to improve in this area. Unilever Benelux therefore wants more insights on OSA and OOS of certain key products at certain key customers, to reduce out of stocks at the shelf to an absolute minimum. The current OSA level of Unilever Benelux in the Netherlands is about 93,7%. An OSA improvement of 3% would result in a 1% incremental growth for Unilever (Unilever, 2007) There seems to be a consensus that an OOS level of about 2 percent must be accepted (Pramatari and Miliotis, 2005). So there still is a big opportunity and incentive to improve this OSA level. To find out how the different stores and retailers perform on shelf level Unilever started measuring at the Dutch retail market in March 2008. The set up of these measurements is already discussed in section 2.6.4. Possibly influential effects on the service are the stock levels at the DC’s of the retailers. The current stock levels are not adapted to customers demand and supply chain behavior. Retailers
Master Thesis Project – Dion van de Gazelle 39 have too high stock levels for some SKU’s (what results in extra costs) and too low stock level for other SKU’s (what results in OOS). For Unilever it is very interesting and worthwhile to find out which characteristics are important for OSA. When these risks are known they can be handled better and stock level policy can be used to overcome these risks. A model can optimize the stock levels for different SKU’s to handle the risks better and improve OSA.
3.2 Retailer X
3.2.1 General Introduction Retailer X The Dutch retail market is a very dynamic business environment compared to other EU markets. Repositioning, selling stores, the growing health attention and the threat of foreign competitors are part of the dynamic situation. The retailers have shown a fast growth the last years but where the translation was very successful the growth to performance stayed behind. Reasons for this stagnating performance are age, internationalization, market concentration or the nature of competition within the sector. Internationalization and consolidation are clear trends; price based competition between retailers is likely to be the key driver of retailer economics (Corstjens and Steele, 2008). The Netherlands has a high percentage of multiple retailers, low food outlet density and a high own label penetration (Fernie and Staines, 2001). These market characteristics combined with an extreme high density of population and roads have translated the Dutch retail market in efficient complex system. People spent a smaller part of their income on food every year; in combination with the stagnating population growth this makes the retail market a `push aside’ market (EFMI Business School, 2007). In this narrow market all actors try to differentiate and compete with each other, while new (foreign) competitors try to enter the market. Market share of all retailers will be stable at 45% of all food expenses for the next 10 years. Due to stagnating volume growth and inflation related prices, the turnover increase for the retailers will be fractional (EFMI Business School, 2007). A clear strategy and focus is important for Retailer X to survive this tentative playing field. The main focus of Retailer X is to optimize service level to the consumers (Appendix A). The consumer eventually buys the products and generates turnover. Retailer X tries with a high service level to keep the current customers and attract new customers in the near future. OSA is one of the service level KPI’s (key performance indicators), a higher OSA indicates a higher service level. In chapter 2 is explained that OOS are the prior complaints of customers in the Dutch retail sector. Therefore reducing OOS will have a positive influence on the customer service level. Retailers like Retailer X use safety stocks to safeguard some effects of the OOS causes. The safety stock is a buffer for the OSA causes in the supply chain. But keeping high stock levels bears higher costs for the retailer; and there is also a storage space limitation at the DC’s of Retailer X (section 3.2.2). So there are some constraints on the amount of stocks at the DC’s of Retailer X. Optimizing the operational costs is one of the most important operational strategies for retailers in the full service segment (EFMI Business School, 2007), and Retailer X also tries to minimize these operational costs. The relation between costs and stock levels will be discussed in the next section.
3.2.2 Stock Optimization in Economic Downturn The credit crunch of September 2008 has put pressure on turnovers and margins for a lot of companies. To compensate these downward trends all companies including retailers try to
Master Thesis Project – Dion van de Gazelle 40 reduce costs. Keeping stocks is crucial for almost every organization that offers products to customers (Haddock et al., 1994) and so also for the retail sector. Stocks are responsible for a big part of the operating costs and reducing stock levels can have a positive effect on the total costs (Huveneers, 2008). Interests are also raised for companies due to the credit crunch. The consequence of this higher interest is that capital is more expensive or not available at all. Stocks are a form of capital, so stocks are more expensive and also hold up cash. Thus in these times of economic downturn companies try to generate cash by focusing on stocks optimization (Hoeven van der, 2009). Therefore also retailers have a clear focus on reducing stock levels; for cost reduction and cash generating reasons.
Figure 6 Downward spiral due to credit crunch.
Beside this capital driven focus on stock reduction Retailer X has also space boundaries in its DC’s. Due to the growth of store numbers per retailer in the last decade and the increase of numbers of SKU’s for more service the space boundaries of the DC’s are under pressure. These space boundaries of the DC’s form a design space boundary (total stock can not increase) for the reallocation of stocks and can be reasons for stock reduction too. Stocks in the retailer supply chain can be divided in cycle and safety stock. Cycle stock is the result of ordering and producing goods in certain batches. Safety stock is used to preempt uncertainties in forecast and replenishment. One could argue that cycle stock is the price paid for inflexibility of the processes and safety stock is the price paid for the lack of information and transparency (Vlist van der, 2007). The costs of these stock levels are also dual. On the one hand there are capital costs of keeping a unit with a certain value in stock. On the other hand there are handing costs, risks for obsoletes and the lost of interest. Attention to these costs is necessary to find an optimal stock level. Retailer X currently holds stocks of one week for all SKU’s on average. These stocks are used to fill up deliver uncertainties from the manufacturers and the demand pattern. Not all SKU’s need the same level of safety stock, based on product specific supply chain risks. These higher stock levels result not only in higher costs but also influence the forecast and ordering pattern. A lot of these incorrect stock levels are caused due a lack of transparency between the retailer and manufacturer (Vlist van der, 2007). Retailers are not aware of the potential and possibilities of the manufacturer. The manufacturer on the other hand has no insights in, for example the stock levels at the distribution centers and the shopper demand pattern. So there is a clear potential to reduce stocks or to reallocate stocks to be more effective. Inventory management is a focal point of managing supply chain processes (Jammernegg and Reiner, 2007). So this new stock levels or allocation can have positive influences on costs, space utility, service level and company performance, especially during a credit crunch crisis.
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3.3 Business Processes between Unilever and Retailer X
In the previous section the cause for this research is discussed. The design of an advice model can help to optimize stock levels and improve service. This model is only suitable when it fits into the current business processes of Unilever and Retailer X. This section contains a detailed description of the business processes within the Unilever Retailer X supply chain. The structure of the description is based on the CPI Enterprise Modeling method (Barjis, 2009). But only the way of structuring the processes is used. The interactive part was not part of this analysis instead short interviews with experts in the field are used to gather information about the processes (Unilever employees). The results are represented schematic in table 3. All stores of Retailer X are separately responsible for their stocks and order processes. Each store must have a certain range and quantity of SKU’s depending on the store’s capacity. These appointments about range and quantities are made between the sales department of Unilever and the purchasing department of Retailer X; and are part of the yearly contract negotiations. When a stock controller (from a store) discovers that there are not enough products on shelf and in the stores back room to fulfill the expected demand, the stock controller sends an order to the main office of Retailer X. At the main office the orders of all stores are collected. A supply chain coordinator checks if there are sufficient stocks at the DC of Retailer X. Normally there is sufficient stock and the orders are delivered at the stores the next day (24 hours delivery lead time). When the stock level at the DC of Retailer X is not sufficient or is expected to drop below the minimum level (below the reorder point) the supply chain coordinator takes action. He orders the product at Unilever. Usually this order is round up in full pallets because Retailer X receives a discount when ordering at full pallets. The Unilever Customer Service Officer (CSO) checks the orders and decides based on the stock level at the Unilever DC (outsourced to 3 PLP) which quantity is delivered to Retailer X. Normally these orders are delivered at the retailer’s DC in 48 hours. Commonly all ordered products are delivered but in cases of low stock at the Unilever DC’s; the available stocks must divided among the different retailers or some retailers will get zero deliveries to safeguard the promotion volumes for other retailers. Besides the base deliveries the Logistic Assistant (LA) discuss on forehand the promotion volumes with Retailer X and forecasts the expected demand for these promotions. The LA sends via the Unilever systems the forecast to the planning department of Unilever. The planning department already made a baseline forecast based on historical data, time of the year and temperature. The demand for ice cream for example will increase substantial when the temperature rises above the 20 degrees. In the winter the demand for smoked sausage and peas soup is much higher then in summer. The planning department couples the promotion forecast with the baseline forecast. This forecasted demand is communicated with the different sourcing units. The sourcing units are free to plan their own production activities based on capacity and efficiency as long as they are capable to fulfill the demand forecasted by the planner. The finished products are transported by 3PLP (third party logistic provider) to the Unilever DC’s. Here the products are temporarily stored until an order from a retailer comes in. The CSO checks the order with the stock level and informs the 3PLP that the order can be delivered based on a 48 hours lead time. The CSO also sends an invoice to the retailer. The handling of these invoices is handled by the financial departments of Unilever and Retailer X. The 3PLP is responsible for storage and transportation from the Unilever DC to the DC of Retailer X. In the retailer’s DC the products are temporarily stored and/or repacked and then transported to the different stores. In the stores the roll containers are stripped and stored for a few hours. When the shelves are almost empty the shelves are replenished with the new products, and are
Master Thesis Project – Dion van de Gazelle 42 ready to fulfill consumer demand. The consumer buys the product and when the amount of products on the shelves and in the back store drop the process repeats itself. T1: Negotiate product range and quantity TA: Arrange promotions Initiator Sales UL Initiator Sales UL Executor Purchasing department Executor Promotion Retailer X T2: Check product availability store TB: Forecast promotion quantity Initiator Stock controller store Initiator Planner Executor Back store manager/replenisher Executor LA T3: Placing an order TC: Combine baseline and promotion Initiator Stock controller store Initiator Planner Executor Main office Retailer X Executor Planner T4: Check product availability DC Retailer X TD: Order to SU Initiator Stock controller store Initiator Planner Executor Supply chain coordinator DC Retailer X Executor Logistic manager SU T5: Send products to store TE: Production Initiator Logistic officer Retailer X Initiator Planner SU Executor 3PLP Executor SU operators T6: Placing an order at UL TF: Send products to DC UL Initiator Supply chain coordinator DC Retailer X Initiator Logistic manager SU Executor CSO UL Executor 3PLP T7: Check product availability DC UL Initiator CSO UL Executor CSO UL T8: Send products to Retailer X Initiator CSO UL Executor 3PLP T9: Pay the order Initiator CSO UL Executor Financial department Retailer X Table 3 Business transactions Unilever - Retailer X.
3.4 Stakeholder Analysis The advice model will only be suitable and useful as it fit’s into the current situation. In the previous section the business processes are discussed but also the stakeholders have significant influences on the design of the model. In this section the multi stakeholder setting of the research will be analyzed. The first section (3.4.1) introduces the multi actor setting and clarifies the importance of this attention. In section 3.4.2 the methodology of the multi stakeholder analysis, based on (Enserink et al., 2008), is introduced. The section 3.4.4 presents the most import results of the analysis. In this section the results are also translated into recommendations and actions to handle the multi stakeholder analysis in practice. In Chapter 8 these recommendations are worked out in practical steps.
3.4.1 The multi Stakeholder Setting In the previous chapters is already carefully mentioned that our research field is situated in a multi stakeholder setting. Unilever and Retailer X are identified within this multi actor setting but in fact these stakeholders contain internal stakeholders important for this research as well. This has certain consequences for the process to achieve the research objectives. The importance of understanding the ‘culture’ of the chosen retailer is vital for the research. In most large retailers the HR Director, Operations Director or Supply Chain Director has responsibility for steering the cultural direction of the organization. They are strategic internal contacts with the capability to sponsor the project and should be contacted early in the process to obtain an overview of the corporate structure of the organization. Successful communication and roll out of the aims, benefits and results is dependent on having this sponsor in place. A stakeholder interview (Appendix A) can contribute to create this sponsor position. The purpose of stakeholder interviews is to gain an insight into the key priorities for the project from each person involved. These key priorities are assembled in the analysis of
Master Thesis Project – Dion van de Gazelle 43 goals, interests and powers of the stakeholders (see Appendix C) This information contributes to set targets and create a comprehensive project brief (Unilever, 2007).
3.4.2 Methodology of Stakeholders Analysis The implementation of a suitable solution depends on the identified stakeholders in relation with a certain problem situation. Therefore it’s important to be ware of the perceptions, goals and interests of the stakeholders in an early stage (Enserink et al., 2008). The problem situation is discussed in depth in the sections 3.1 and 3.2. An analysis of the multi actor setting will contribute to create the set up for these processes. First the internal and external multi stakeholder setting of the system will be revealed with a stakeholder analysis; to identify which stakeholders can provide input and who will be the end users. Also the powers and dependence of the different actors related to the situation are analyzed. The results of this analysis can contribute to find an accepted and suitable solution.
3.4.3 The stakeholders’ Action Field For the first goal, providing input to this research, the resources of the different stakeholders are of interest. For the second goal it is also important to have clear insights in the perception of the stakeholders on stock level adaptations, so what are their goals and interests in this research? The stakeholder analysis is performed according to Enserink et al. (2004). Figure 7 presents the conclusions important for this research from the results of the stakeholder analysis presented in Appendix C. Importance
Figure 7 Results stakeholder analysis. Figure 7 shows the multi actor setting of this research and ranks the different stakeholders on importance and influence. The data that forms the base of the input is essential for the research. Part of this input data is coming from inside Unilever; especially the Customer Service & Logistic department has asses to these data. The OSA project team is responsible for the OSA measurements; which also forms a big part of the input for the research. Minor data input comes from the planning, sales en marketing departments and the sourcing units and distribution centers. Important data from outside Unilever must be delivered by Retailer X. The
Master Thesis Project – Dion van de Gazelle 44 logistic account manager of Retailer X must give his authorization of the data sharing. In practice he is represented by the logistic account officer of Retailer X The LAO of Unilever is the end user of the advice model. He is responsible for generating stock level advices and the maintenance of the model. The logistic account officer of Retailer X is responsible for the logistic processes between Unilever and Retailer X and is in that way also end user. The logistic account officer must cooperate with the ordering department and DC’s of Retailer X to use the given advice and model insights in an effective way.
3.4.4 Importance of Stakeholders The fact that the logistic account officer of Unilever as main responsible person has such a crucial role as end user and in the data delivery makes him a very important stakeholder. The importance and the influence of this position are both very high in relation with the advice model research. Also the Customer Service & Logistics department has high importance and influences. To avoid problems and obstacles due to stakeholders during the research it is important to manage these stakeholders closely and keep them involved during the whole process. During the design phase some meetings are scheduled to discuss progress. This will provide not only useful input (practical experience, knowledge and implementation advice) but also improves and ensures usability and trust in the model. During these sessions the LAO’s learn the structure of the model and have the opportunity to ask questions and come up with possible addition. That makes not only the model but also the comprehension and trust of the LAO much stronger. The OSA project team and the distribution centre of Retailer X have a more facilitating role providing data as input. However they are still, together with the ordering department as potential user, important stakeholders. These stakeholders deserve attention and must be informed on a regular base and involved during the research process. The planning, marketing and sales department, distribution centre of Unilever and the SU have a less important role. These stakeholders are highly important for the Unilever business and so their goals and interest (Appendix C, table 10) should be kept in mind because these parties are not replaceable. The plans and results of the advice model will be introduced in an early stage to these parties to secure the necessary support. Some opposition for the model can be expected from the ordering department of Retailer X. The employees of this department from Retailer X are currently responsible for the planning and ordering from the DC. The advice model will challenge the current jobs of these employees and they may be reluctant to accept the outcomes of the model. The employees probably will show their use and try to counter against the model. The success of the model depends on in which degree these people can be convinced about the model outcomes. The ordering department is dependent from the advice model and they must trust and accept the model and the outcomes. So it’s important to manage this stakeholder closely and keep them involved during all the steps in the process. Problematic is that the only contact to Retailer X goes through the logistic account officer of Retailer X. He is responsible for the logistic operations between Unilever and Retailer X and must communicate the progress and goals of improvement projects with other departments of Retailer X. Sponsorship of the logistic account officer of Retailer X is very important for data input and acceptation and trust of the model inside Retailer X. In Chapter 8 some explicit steps will be presented to translate the model in practice. These steps can help to ensure comprehension and trust inside Unilever and Retailer X. In the coming chapters a reflection of the stakeholders and the steps in the design process can be expected.
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3.5 Transparency in the Supply Chain Increased openness and transparency are key factors in realizing supply chain improvements. In traditional supply chain inventory management orders are the only exchanged information between companies. Today’s information technology possibilities allow companies to interchange much more information. Cachon and Fisher state that (full) information sharing can have positive influences on supply chain costs and performance. A numerical study shows that supply chain costs are 2.2% lower on average with full information sharing compared to traditional information sharing (Cachon and Fisher, 2000). Zhao et al. uses a simulation study with different demand patterns and different levels of information sharing. The simulation (for a supplier with only one product) shows that the further degree of information sharing the further the supply chain performance will improve. The reason of this improvement is a better adaptation of production on demand, based on increased chain knowledge (Zhao et al., 2002). For Unilever this already is the case but more information about risks in the chain will improve supply chain performance further and will lead to extensive improvements. Zhao et al. only speaks about information sharing on forecasting and planned orders. This has positive effect for the total supply chain costs, but insides in stock levels risk will also upgrade this benefit. Lack of information sharing and a focus on local optimization are reasons for lower overall performance of the supply chain. Partnerships can contribute to match demand with supply more effectively (Fisher et al., 1994) but through differences in interests, optimization is rarely realized. The model build by Cachon and Fisher also demonstrates that there isn’t always a significant benefit of information sharing (Cachon and Fisher, 2000). A case study by the Coca Cola Retail Research group Europe also has indicated that sizeable cost reductions can be achieved when retailers and suppliers adopting collaborative logistical practices. However the collaboration is beneficial it is limited to a few key players. Cooperation should fit in the strategy of the companies and they must believe in cooperation as a way to reduce overall costs. The deep discount sector for example focus more on low prices then on supply consistencies and therefore a collaboration strategy is not suitable for these kind of companies (Walker, 1994). Losses due to supply and demand mismatches are a chronic problem in many supply chains. These losses include OOS costs, markdowns, transshipment, advertising and sale preparation costs, obsoletes and disposal costs. Collaboration or a partnership can help to understand common and different interests and sharing risks and rewards will result in higher business performance. Logistic alliances among different firms offer opportunities to improve customer service and at the same time realize lower distribution and storage operation costs (Bowersox, 1990). Information sharing provides substantial benefits to participating members. Information sharing offers individual members, on a strategic level, mutual understanding of competitive advantage. At the tactical level, the information integration helps the chain members to mitigate demand uncertainty and cope with decision making complexity at different levels of the planning horizon. Information sharing is also useful to handle with relational vulnerability of opportunistic behavior. Some chain members can take advantage in opportunistic ways from trade deals at the expense of the members due to managerial inertia. The underlying causes of managerial inertia are inappropriate measures of performance, outdated policies, asymmetric information and incentive misalignment. To resolve these problems chain members should simultaneously consider appropriate performance measures, integrated policies, information sharing and incentive alignment. The initiatives of appropriate performance measures and integrated policies address orientation issues and the initiatives of information sharing and incentive alignment address enabling issues. If orientation and enabling issues are aligned across the chain member, then potential benefits can be reaped successfully from an effective collaboration (Simatupang and Sridharan, 2002).
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When retailers have problems to coordinate their inventories the shared information is not valuable for the supplier. When the inventory is managed properly, the value of shared information increases. Information sharing, for example sharing risks in the supply chain, can be helpful for better management of the inventory. Better stock level adaptations in combination with further or even full information sharing eventually can improve the performance of the supply chain. Transparency in demand patterns and demand risks in combination with stock level in the chain give the supplier the opportunity to adjust production on it (Deloitte & Touche, 2002). You can also state that stocks can be adjusted to production characteristics and risks and demand risks. Vendor Managed Inventory (VMI) is an example of full information sharing between the supplier and retailer. VMI is an interesting option to further improve the supply chain of Unilever and Retailer X and deserves further research and attention in the future (Cachon and Fisher, 2000); (Achabal et al., 2000). Transparency, information sharing and partnership are different words for more openness between two or more companies in a supply chain. This openness can contribute to improve supply chain performance and reduce costs. This openness must fit in the companies’ strategy and culture. Retailer X and Unilever both speak out their intentions for more openness and see this as a potential way to improve supply chain performance. Better and faster information sharing is even one of the key points in the new Supply Chain Strategy of Unilever for the upcoming years (Unilever, 2009). Openness is essential to realize improvements within supply chain logistics and stock level management. This research must contribute to improve transparency based on information sharing. Especially for this stock optimization research Unilever and Retailer X share data about the risks in the supply chain. The upstream data is always available for Unilever but information about demand at the shop level is only shared for this research. To construct a safety stock level advice in the future it is necessary to share this information again also to keep the data up to date. A clear agreement in combination with mutual goals about data sharing in the future is essential. Sharing the retailer’s POS data provide enormous information for Unilever. This is very interesting option but Unilever has to compensate this extra information source in some way. The first steps of this process from model to practice are discussed in Chapter 8.
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3.6 Round up Analysis Unilever and Retailer X
In this chapter the underlying causes to perform this research are presented from the perspective of Unilever and Retailer X. First Unilever and Retailer X are introduced and their vision among the problem is explained. Unilever and Retailer X both have the intention to improve the service level to the consumer. This statement is translated in the first design goal: 1) Design a model to improve service to the consumer, based on product specific end-to- end supply chain risk. Beside the focus on service Retailer X also tries to reduce costs in the supply chain (Chapter 2). The reduction of costs and release pressure on capital can be realized by minimizing stock levels. Minimizing stock levels by better adaptations on risks in the supply chain can realize costs reduction without lowering the service level. The service level can even improve when stock levels are better adapted to supply chain risks. This leads to the second design goal: 2) Design a model to optimize stock levels with respect to the desired service level. Implementing a designed a model in practice is only successful when it fits well with the current business processes. The current processes between Unilever and Retailer X are analyzed and this information forms input and boundaries for the design. An advice model is suitable when it is comprehensive and trusted by the stakeholders. These aspects are the base for the other two design objectives: 3) Trust of the important stakeholders in the model and outcomes. 4) Comprehension of the model and the process. The multi stakeholder setting which surrounds the problem situation demands some extra attention. Unilever and Retailer X are identified within this multi actor setting but in fact these stakeholders contain internal stakeholders, important for this research as well. This has consequences for the process to achieve the research objectives. It’s important to involve strategic internal contacts with the capability to sponsor the project early in the process to obtain an overview of the corporate structure of the organization. The sponsors must provide data and knowledge input and helps to translate the model into practice by giving advices and communication to other departments. Successful communication and roll out of the aims, benefits and results is dependent on having this sponsor in place. The logistic account officer of Unilever has such a crucial role as end user and in the data delivery and is therefore an important stakeholder and sponsor. The importance and the influence of his position are both very high in relation with the advice model research. Also the Customer Service & Logistics department had high importance and influences. To avoid problems and obstacles due to stakeholders during the research it is important to manage these stakeholders closely and keep them involved during the whole process. The OSA project team and the distribution centre of Retailer X are more facilitating and deserve attention and must be informed on a regular base and involved during the research process. The planning, marketing and sales department, distribution centre of Unilever and the SU have a less important role, but their goals and interests should be respected because these parties are not replaceable. The logistic account officer of Retailer X has also a very important sponsoring role. Providing downstream data from Retailer X is crucial for the model. The logistic account officer of
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Retailer X must also comprehend and trust the model and translate and communicate this to other departments of Retailer X. The LAO’s (from Unilever and Retailer X) form together the connection between Unilever and Retailer X. Cooperation, trust and sponsorship of both parties is extremely important. Some opposition for the model can be expected from the ordering department of Retailer X. The success of the model depends on in which degree these people can be convinced about the model outcomes. The ordering department is also an end user and they must trust and accept the model and the outcomes. So it’s important to manage this stakeholder closely and keep them involved during all the steps. Regular meetings will not only provide vital input but also ensures and improves usability and trust. Transparency, information sharing and partnership can have positive influences on supply chain costs and performance. Openness can contribute to improve supply chain performance and reduce costs. This openness must fit in the companies’ strategy and culture. Retailer X and Unilever both speak out their intentions for more openness. Better and faster information sharing is even one of the key points in the new Supply Chain Strategy of Unilever for the upcoming years. Transparency is important to realize improvements within supply chain logistics and stock level management. In this chapter is analyzed what is necessary to make a suitable advice model that can be implemented and is trust and accepted by the different stakeholders. How the outcomes of this analysis can be used to translate the model into practice is discussed in Chapter 8. This chapter provides explicit steps to ensure a successful implementation of the model in practice.
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i. Round up Analysis Phase
The analysis phase is the first part of the design process. In Chapter 2 the methodology on On Shelf Availability is analyzed. The connection between OSA, the problem situation and the stakeholders is discussed in Chapter 3. From this chapter can be concluded that there has been a clear increased focus on OSA over the past few years driven by new technologies, consumer awareness and the impact of OSA. When a customer encounters an OOS he can switch size, variety, brand or store and delay or even cancels purchases. All these different kinds of behavior provoke losses for the retailer and/or manufacturer. When consumers encountering an OOS and decide not to purchase the product at all, this cost the entire industry €4 billion per year in turnover. Switching brand size or store is not even included in these figures. Losses due OOS have gigantic influences on the food business all over the world. Unilever and Retailer X both have the intention to improve the service level (OSA) to the consumer. Beside the focus on service Retailer X also tries (catalyzed by the credit crunch) to reduce costs in the supply chain. The reduction of costs and freeing capital can be realized by minimizing stock levels. Minimizing stock levels through adapt stock levels better to risks in the supply chain can realize costs reductions without lowering the service level. The service level can even improve when stock levels are better adapted to supply chain risks. These aspects form the goals and boundaries of this research. To realize this stock level reduction, advice can be given based on the risks in the supply chain. This main focus is translated into two design objectives: 1) Design a model to improve service to the consumer, based on product specific end-to- end supply chain risk.
2) Design a model to optimize stock levels with respect to the desired service level. The advice model should focus on these two objectives and the success depends strongly on the degree of fulfillment of these two objectives. Investigating which aspects are causing OOS is the first step in the design of this model. There are multiple causes affecting OSA scattered over the end to end supply chain. The most important and relevant root causes are: Demand underestimation, introductions/relaunch, long order cycles, promotions/advertisements, data inaccuracy, incorrect ordering and product availability at the DC. The last cause is the main subject of this research. All the other root causes are more or less reflected in the demand and delivery pattern. Therefore they will be represented as demand and delivery/production data in this research. Measuring the service at the shop floor is important to investigate the connection between stock levels and OSA. This can also help to measure effects on service of improvements in the future. The OSA (service to consumers at the shop floor) for 6 Unilever customers (also Retailer X) is measured with manual audits for 88 Unilever and 32 non Unilever products in 412 supermarkets. Every quarter 800 visits are done with 40% of the measures within peak hours. This measuring set up must give a very realistic view of the real product availability at the shop level.
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The design of the model can only be successfully implemented when it fits well with the current business process and is accepted and trusted by the important stakeholders. These aspects are the base for the other two design objectives: 3) Trust of the important stakeholders in the model and outcomes. 4) Comprehension of the model and the process. The multi stakeholder setting, that surrounds the stock level optimization process, demands some attention to achieve these objectives. Unilever and Retailer X are identified within this multi actor setting but in fact these stakeholders contain internal stakeholders important for this research as well. This has consequences for the process to achieve the research objectives. The logistic account officer of Unilever has a crucial role as end user and in the data delivery and is therefore an important stakeholder. The logistic account officer of Retailer X has also a very important sponsoring role. Providing downstream data from Retailer X is crucial for the model. The logistic account officer of Retailer X must also comprehend and trust the model and translate and communicate this to other departments of Retailer X. The LAO’s (from Unilever and Retailer X) form together the connection between Unilever and Retailer X. Cooperation, trust and sponsorship of both parties is extremely important. Some opposition for the model can be expected from the ordering department of Retailer X. The success of the model depends on in which degree these people can be convinced about the model outcomes. Retailer X and Unilever both speak out their intentions for more openness. This is in line with literature suggesting that transparency, information sharing and partnership can have positive influences on supply chain costs and performance. Openness can contribute to improve supply chain performance and reduce costs. Faster information sharing is even one of the key points in the new Supply Chain Strategy of Unilever for the upcoming years. Transparency is important to realize improvements within supply chain logistics and stock level management. The stakeholder analysis also already made clear that it is important to keep the logistic account officer (from Retailer X) very close to the process. In the next phase (Design and Modelling) the mutual design objectives and boundaries are used to design an advice model. The structure of the model is designed, based on product and production characteristics (Chapter 4 and 5). The set up and control for proper modelling is performed in Chapter 6. Integrated policies are more important on a higher level in the business and are out of scope for this research. In chapter 7 the model is constructed for drawing up stock level advices. The given advice is based on the risks within the whole supply chain and makes it not only possible to reduce stock levels but also improve OSA. How this advice model and the outcomes can contribute to improve service and take decisions in practice is discussed in Phase 3.
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II. Design and Modelling Phase
The analysis phase clarifies the focus of Unilever and Retailer X on service (OSA) and cost/stock level reductions. Information from the whole supply chain can be used to identify the risks and turned into a stock level advice. In the design phase the process and model to draw up a stock level advice is designed. The basic building blocks for the model are the involved supply chain risks, which are discussed in Chapter 4. There is also some attention to the data used to determine the supply chain risks. To simplify the method products with similar risks are grouped in risk profiles (Chapter 5). The criteria to ensure model quality are represented in Chapter 6. The design phase ends with a wrap up.
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4. Design Set Up Advice model
4.1 Introduction Design This chapter presents the design set up for the model which minimizes stocks, based on product specific risks with attention to service and costs. In the previous chapters is the focus on stock reductions and OSA improvement for Retailer X and Unilever driven through costs, sales, economic developments and service mentioned. In the current situation Retailer X holds one week stock in the DC’s for all SKU’s on average. But not all SKU’s have the same risks and deserve and receive the same attention and priority. Therefore stock levels can be adapted to fit better with the SKU’s risks. The design of the model is explained hereafter starting with the relation between store service and OSA (4.1). Section 4.2 mentions the end to end risks in the supply chain and how they are related to the model. Section 4.3 elaborates about the data that forms the input for this design.
4.2 Store Casefill and OSA Improving the service is an important objective of this research. OSA is a measure for the service to the customers. An increasing OSA has a positive effect on service to the customers and will results in more sales and more profit for both Retailer X and Unilever. When the stocks are better adapted to the end to end risks in the Supply Chain, the store casefill will improve because more risks are covered. There can be assumed that a better allocation of stocks results in a higher store casefill. The correlation between OSA and store casefill can help to control if a higher store casefill really results in a higher OSA level. There is a significant and positive relation between OSA and store casefill (Appendix E). The relation is only 0,063, which means that only 4% of the variance of OSA is explained by store casefill. The limitation of this influence was expected for the relation between Unilever and Retailer X (Schneider, 2009) but also based on general theory that 85% of the out of stocks originate at the shop floor (Bharadwaj et al., 2002). Improving store casefill will not have direct strong effects on OSA. However steering on the store casefill KPI is a first step in intensifying the cooperation between Unilever and Retailer X. This intensified relation has potential to realize supply chain improvements (section 3.5).
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4.3 Risks in the Supply Chain The relation between risks within the supply chain, service and costs are represented in a causal model (figure 8).
Production risks
Risks product - characteristics
- Delivery performance + Stock level Unilever
-
+ Retailer DC Risks
- - Responsiveness on Stock level Profit demand + DC retailer
- +
Volatility consumer demand Pattern
Store casefill +
+ OSA
Figure 8 Causal model of risks determine On-shelf-availability.
The production and product related risks are upstream in the Supply Chain, when these risks increase the delivery performance (service to the retailer) decreases. The stock level at Unilever functions like a buffer and has positive effects on the delivery performance. The delivery performance with the risks within the Retailer’s DC, the volatility of consumer demand and the stock level at the Retailer’s DC determine the responsiveness. A higher responsiveness implies better and faster reactions to store demand. The responsiveness determines the casefill to the stores. A more responsive SKU will have a higher store casefill. This store casefill has a positive relation with OSA (customer service); better store casefill will improve OSA (section 4.2). The stock levels at Unilever and Retailer X both influence the total costs in the supply chain. These levels offer service but against a certain price. OSA is also a form of service and improving this service actually increases profit because there are less lost sales.
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To generate a stock advice based on product specific risks the causal model must translated into an advice model. The model is cut up in three sub models the upstream risks, DC risks and downstream risks (figure 9).
Figure 9 Conceptual model risks determine stock level. These sub models can be combined in one complete model with three forms of output: stock level, OSA (service) and costs. The three sub models will now discussed in detail
4.3.1 Upstream Risks The upstream risks are product and production related risks upstream in the supply chain from the Retailer’s DC. These risks determine the possible lead time to the DC of Retailer X and the effects of these risks are represented in the delivery performance. The delivery performance is the percentage of on time delivered orders, the delivery time is standard 48 hours. There are several product characteristics like BBD, volume, factory which effecting the risks. In Chapter 5 will be analyzed which of these risks have significant influences on the delivery performance. For example; does BBD influence the delivery performance and if so which significant different categories can be identified? Based on these influences risk profiles will be constructed. The different products in the dataset (section 4.3) can divided in groups based on their risk profile. When those groups are formed the distribution of the lead time can be deducted (section 5.2.1). This distribution is the DNA of the risk involved in the upstream supply chain and input for the advice model, designed in chapter 7.
4.3.2 DC Risks When the orders arrive from Unilever at the DC of Retailer X some extra lead time and possible delays occurs through DC activities. Repacking of the products, internal transportation and temporary storage form these DC risks and result in a longer lead time. A
Master Thesis Project – Dion van de Gazelle 57 longer lead time, demands a higher stock level to handle this uncertainty. There is no clear data on the DC risks of Retailer X and therefore this effect is standardized as minimal. The DC risks therefore will not be taken in account and lie outside the research scope.
4.3.3 Downstream Risks The main risk on the downstream side of the supply chain is formed by the demand pattern. The demand pattern is expressed in average purchases per day. The variance of the purchases gives insights in behaviour of customers and the risks belonging to this behaviour. The distribution of the demand pattern is the third and last input part to determine the necessary safety stock for a certain probability interval in the advice model. The demand pattern is product specific therefore it is not possible to group the products in the same risk profiles as done for the upstream risks. The data for the downstream risks are provided by Retailer X. Due to some data gathering problems five products are not part of the downstream risks (Andrelon perfecte krul, Dove body nutri body milk 400ml, Hertog 1 liter slagroomijs, Unox stevige tomatensoep 800ml and Unox stevige erwtensoep 800ml).
4.4 Data
4.4.1 Research Period, Product Selection and Data Measuring The OSA data used for this research is derived from the measurements of the general OSA project. The independent agency Wfm is assigned by Unilever to measure OSA in the stores. Wfm measures OSA of 88 Unilever products and 30 competitors’ products in 412 stores. Those products are representative for the Unilever assortment. Wfm measures on different days and times. Because the OSA measurements just started in May 2008 there is some data pollution. In section 4.4.2 the data pollution is discussed and the polluted data is filtered out.
4.4.2 Data Pollution The OSA project just started in May 2008 and in this start up phase there have been made some mistakes mainly due tot the independent measure party Wfm. These data inaccuracies and the irrelevant data (this research only focus on Unilever products and Retailer X) must be filtered out to create a data set suitable for further analysis. The data used in this research is measured from week 19 2008 until week 5 in 2009 (38 weeks in total). This long measuring period in combination with scattered measuring on different days, times and seasons make the measurement very representative. The data filtering is a combined process with other OSA research within Unilever (Schneider, 2009). The first step to clean up the data is filtering out the non Unilever products. These products can be used in a comparative way, but because they are measured very poorly and there isn’t any end to end data the products are not part of this research. In the second step all measurements of products at stores which did not have contractual obligations to carry the product, related to the shelf size, are filtered out. If a store did carry the product, but was not obliged to by contracts, these measurements were also excluded as this might lead to incorrect measures.
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The third filter was to exclude long lasting void measurements, as these can never be the effect of supply chain disruptions, but are (as explained previously) mainly due to contractual issues and sales issues. It was deemed long lasting void, if • The product store combination were measured as ‘void’ at least from the last quarter of 2008 (hence at least 4 consecutive void measurements); or, • If there had never been a order for that product by Retailer X; or, • According to Nielsen purchasing data (information bought by Unilever from A.C. Nielsen containing sales data over all customers), there has not been any sales of that product at Retailer X. The fourth and final filter was to exclude product customer combinations on a case by case basis. Reasons for excluding these cases were, for example; • Temporary political bans on products several important SKU’s had been banned by Retailer X in the period of week 21 to week 30 of 2008, hence measurements in that time period had to be excluded; or, • Known incorrect measurements by the 3rd party – one product (Sun Tabs all in 1 30pc) had been relaunched during measurement period into Sun Tabs all in 1 30+50%pc. However, this had not been properly communicated to the 3rd party, resulting in incorrectly high levels of Void and Out of Stock. • Similar products interchanged by measurement agency. For example, some stores have the Dove Soap Tablet 2*100grams (not having the 4*100 grams), whilst some have it the other way around. This was sometimes measured to the letter (as two different products), but also sometimes the measurement agency measured it as if were the same product. As this showed a mixed picture, customers that have never ordered the 2*100 grams at Unilever were excluded. • Finally the Calve Pindakaas measurements are deleted from the list because this product was part of a restore value project. This restore value project caused unrealistic negative consequences for the delivery performance between week 40 and 45. Also the upstream lead time data needed a filter before using it in the model design. The lead time is calculated by subtracting the delivered quantity from the ordered quantity. When this number was zero, the lead time is considered as normal (48 hours). When the subtraction was negative the lead time is calculated by 48 hours plus 24 * the number of days until the next full delivery. The days between the next 100 % delivery are also mentioned as days with a lead time above 48 hours increasing with 24 hours every day. When there was no data, but clearly a failure in delivery, an estimation is done for the lead time in that period. Speed deliveries (in 24 hours) are not part of this research. Also the disturbing effect of temporary lower performance caused by promotions is filtered out. The data of the promotion weeks is not part of the total dataset. Volume is not used as weight factor for lead time. The lead time of products is not coupled on the volume of ordered and delivered products. The main reason is that the average expected lead time for an order is important for Retailer X. Volume as a weight factor can pull this average skew. Time is more important then volume. An example is an order of 1 box delivered 99 times on time (within 48 hours) and only once not on time. The volume lost is only one box, but the delay can be for three weeks, in terms of percentage is this one box miss gigantic. Therefore this is far more important than a miss of 500 boxes in a big order (3000+) which is resolved the next day. Of course there are also examples that can prove the opposite but for this research is chosen to not use volume as a weight factor also because this is common for this industry
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(and for example not in the perfume industry). The lead time is calculated per group based on the configuration of the risk profile (for an example see Appendix K). Now it is possible to get an idea of the lead time and the variance, in other words the upstream risks, of the different product groups. Data integrity for other figures is ensured by using internal Unilever systems; this data doesn’t need to be filtered so extensively as the On Shelf Availability measurements. The final list of products in the data for the risk profile is represented in Appendix D.
4.5 Round up Design Set Up
Improving the service is an important objective of this research. OSA is a measure for the service to the customers. An increasing OSA has a positive effect on service to the consumers and will results in more sales and more profit for both Retailer x and Unilever. When the stocks are better adapted to the end to end risks in the Supply Chain, the store casefill will improve because more risks are covered. Analysis had indicated that improvement of the store casefill has positive effects on OSA. The supply chain is represented as a risk model. There are thee types of risks influencing stock level: upstream risks, DC risks and downstream risks. The upstream risks are product and production related risks upstream in the supply chain from the Retailer’s DC. These risks determine the total lead time to the DC of Retailer X and the effects of these risks are represented in the delivery performance. Based on these influences risk profiles are constructed. The different products in the dataset can divided in groups based on their risk profile (Chapter 5). DC risks like repacking of the products, internal transportation and temporary storage, result in a longer lead time. A longer lead time means a higher stock level to handle this uncertainty. The main risk on the downstream side of the supply chain is formed by the demand pattern. The demand pattern expressed in the average purchases/day and the variance of the purchases gives insights in behaviour of consumers and the risks belonging to this behaviour. These risks are deducted from data; gathered from Wfm measurements, internal databases and Retailer X and are the input for the construction of the advice model.
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5. Analysis of Product Risks
5.1 Introduction Product Risks
In the previous chapter the different supply chain risks are described. This exploration in combination with the gathered and cleaned up data is used to design risk profiles in this chapter. First the possible risk factors are analyzed based on literature and professionals in the field (section 5.2). With a linear regression is analyzed which of these risk factors are significant. With help of factor analysis the risk characteristics are bundled and a risk profiles are created. The risk profiles are used to order products in different groups with the same upstream risk characteristics.
5.2 Analysis of Stock Level determining Risks In chapter 4 the different risk stages, upstream, downstream and DC, are mentioned. In this chapter these risks are analysed for the selected data related to Retailer X. An analysis of relation and the direction of the relation between independent risk variables and the delivery performance must give insights in the logistic performance of Unilever and then translated in risk profiles. The risks are the independent variables and the dependent variable is the delivery performance. The delivery performance is calculated with the following formulas: Delivery performance = 1 Deliver failures Delivery failures = (Total external failures + Total internal failures)/adjusted quantity The performance within Unilever is usually measured with the internal casefill. This figure only measures the failures caused by Unilever (Total internal failures) divided by the adjusted quantity (the confirmed and correct order quantity by the customer), failures without Unilever influence are not part of this number. Internal failures can be caused by low stock or inaccuracies in the order take process. Because this research takes all risks within the supply chain in account also the failures outside Unilever’s influence field (External failures) are taken in account. Examples of external failures are too early sent in orders or wrong ordered numbers. The analysis in this research is done based on the total delivery performance to cover all the risks in the order and deliver process. When a final list of factors with relevant influence on delivery performance is drawn a risk profile is created.
5.2.1 Upstream Risks Based on literature (Bharadwaj et al., 2002),(Schneider, 2009) and experts inside Unilever (the Logistic Account Officers) a list is created with factors, which possibly influence the delivery performance. 1. Responsiveness of Unilever SU’s – number of days within an urgent order possibly can produced. This number is estimated by the planning department of Unilever. 2. Sourcing Unit distance to Unilever DC – with help of the planning department SU and Unilever DC are identified per product. With help of googlemaps the distance via the road is calculated. 3. Volume (boxes per year for the 6 biggest customers of Unilever) – from LKDB (Logistieke Klanten Data Base).
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4. Stock level Unilever (weeks stock) – average stock level during the period wherein the data is collected. This is represented in weeks stock: the 4 weekly average divided by the 4 weekly average sales level. 5. ABC qualification – internal Pareto qualification based on volume and turnover 6. Sourcing Unit – the planning department is indicated which products are produced in which SU’s. 7. BBD – duration wherein a product can consumed, the BBD (Best Before Date) for HPC products is much longer. 8. Product category – Unilever is divided in five categories, based on other research the distinction between HPC (Home and Personal Care) and foods is made (Schneider, 2009). All these different factors can be considered as product variables. Every product has an ABC classification, is produced in a certain sourcing unit with a certain distance and so on. From literature and experts opinions there is suggested that there might be a connection and direction between all these variables and the delivery performance: 1. Responsiveness, if the responsiveness increases the delivery performance also increases 2. Sourcing Unit Distance, a shorter distance of the SU will result in a better delivery performance 3. Volume, higher volumes have less (relative) logistic problems and therefore will have a better delivery performance 4. Stock level UL, higher stocks can bear problems and the delivery performance will better 5. ABC qualification, more focus on A products must result in better delivery performance 6. Sourcing Unit, production performance of all SU differs, good performing SU’s results in a higher Delivery Performance 7. BBD, the longer the BBD the higher delivery performance is expected 8. Product category HPC products perform better than food products on delivery performance. Based on these different influences groups with different risks can be formed. The combination of risk factors forms a certain profile wherein products can be classified. The influences of SU’s are analyzed first, because too many groups will occur and multicollonearity is a problem, when all SU’s separately added in the analyses. The performance of all the SU’s is measured based on their delivery performance. Based on this performance the SU’s are divided in two groups: high performing SU’s and low performing SU’s. The high performing group has a performance above the Unilever performance target of 98%; the low performance group scores below 98% (see Appendix F1 for these sourcing unit group determining). The sourcing units are divided into two groups and the next step is to analyse which variables significantly influence the delivery performance of Unilever. This analysis must measure the influence of a specific variable, keeping the other variables stable. This can be achieved with help of a multiple regression analysis to analyze the relation between a dependent variable and multiple independent variables. This technique is now shortly introduced. Introduction of Multiple Regression Technique. Multiple linear regression is a technique to analyze the relation between a dependent variable and multiple independent variables. This method analyses the influence of one predictor on
Master Thesis Project – Dion van de Gazelle 62 the dependant variable under condition that the other predictors are stable. The regression equations consist of the unstandardized regression constant and the unstandardized regression coefficients multiplied with the predictor (Hair et al., 2006). This regression coefficient expresses how much the constant changes when the predictor changes with on entity, controlled for the other predictors (Molin, 2008). Results of the Product Risks Data Analysis. Sourcing Unit, volume and HPC/Foods are the product risk variables which are to set up risk profiles. A multiple regression analysis for the independent variables ABC qualification, Sourcing unit groups, SU distance, volume, BBD, responsiveness, stock level and HPC is used for this construction. The results of this analysis are represented in Appendix F2. With a factor analysis it is possible to group products based on the underlying predictors. With rotated factor analysis (principal axis factoring) the variables clearly load on three factors (see Appendix F3). This results in the following list of variables where the risk profile of the products is based on: 1. Sourcing Unit group 2. HPC 3. Volume There are 2 Sourcing Unit groups, a products is HPC or not and there are three volume groups. This risk profile now contains 2 * 2 * 3 = 12 groups. The volumes boundaries are different for HPC and foods (Schneider, 2009). The low volume HPC group is from 0 40.000, medium 40.000 100.000 and high volume is >100.000 collo. The foods boundaries are 0 120.000 for low volume, 120.000 200.000 for medium and > 200.000 for high volume. Seasonality For some products demand clearly depends on the season. The demand is not only dependent of the season but also the performance is influenced by the season. In the summer season for example there is a high demand on ice creams. However this demand is good to predict despite of good forecasting, planning and extra stocks there can appear some delivery problems through significant demand growth. In the data set such products are revealed by subtract the forecast error (difference between forecasted and real demand) from the sales volatility. When the outcome of this subtraction is high the product is good to predict despite the volatility of sales during the year. All the products with high volatility and low forecast error seem to be real seasonal products (See Appendix H). These products need some extra attention because both demand and supply are different in high and low season. Therefore some extra groups are added. These groups are only for foods products because a clear seasonal trend is not recognized at the HPC products. With this extra classification there are totally 18 groups. Appendix J shows the 83 products from the data set divided over the 18 groups. Lead time The lead time is determined for the separate risk groups, and is an important input variable for the advice model. The difference between the average product lead time and average group lead time is evaluated to control if the products fit into the groups based on lead time data. The difference between mean of the product and mean of the group is very small for the major part of the products. For Unox rookworst 275 mager, Knorr chicken tonight hawai, Unox leverpastei standard, Knorr dubbelpak groenten soep and Knorr eet kleur pasteuze
Master Thesis Project – Dion van de Gazelle 63 maaltijdmix texaanse bbq the difference of the mean is bigger then 0,3 days. This indicates that the lead time for these products differs significant from the mean group lead time. Andrelon krul control creme 200 ml and Andrelon cremespoeling 300 ml perfecte krul outweigh each other with a positive and negative average difference. This difference is also strengthened because there are only two products in this group. Differences in product average and group average determine the relative big influence of these products on the group average. Adding extra products to these groups is an option to test again if current mean lead time is representative for these groups. The high number of measurements of two day deliveries make it impossible to find out the distribution behind the lead time at once. The lead time distribution is split in two parts. The first part is the chance that the delivery is on time (in 2 days) or over time, this is described with a Bernoulli distribution. The second part describes the chance of the lead time when a delivery is not on time (above 2 days). The chance of a one time delivery is different for each group and determined by analyzing the percentage of on time deliveries regarding the total number of deliveries per group (for an example see Appendix K). There are not enough measurement points for all the groups separately, so the distribution is analyzed for all the over time data measurements. A drawback of this option is that the lead time for some products groups can over or underestimated. From the results of the Arena input analyze it’s plausible to accept the follow distribution (see Appendix K) for the whole group: 2, 5 + 16 ∗ β() 0, 476; 2, 54 . In case a product is not delivered on time, this β distribution represents the chance how long it will take to deliver the order. Group 14 and 17 have slightly different distributions (see Appendix K).
5.2.2 DC Risks
Between the upstream and downstream risks there are some risks related to the distribution centre. Unfortunately there is no clear information about risks related to the DC’s of Retailer X. Beside this information gap the most important DC risks do not influence the safety stock level. Picking mistakes for examples have certain influences on the performance of delivery to the stores but safety can not avoid certain mistakes. Despite the presence of enough (safety) stock an operator can make a picking mistake. Keeping more safety stock is more costly and doesn’t help to bear these problems. Therefore is chosen to keep the DC risk constant at 0 days.
5.2.3 Downstream Risks The downstream risks are formed by the demand pattern at the shop floor. A more volatile demand is (how bigger the variance) a bigger risk. This demand pattern and the corresponding variance are input for the advice model (see section 7.2). It’s important to use the real demand on the shop floor and not the order pattern of Retailer X in total (the total order size can flat out peaks caused by the order behaviour of the retailer). The availability of this data depends on the cooperation of Retailer X. The retailer decided to cooperate for this research but it is important to safeguard this cooperation also for the future. Clear improvements and trust in the model must convince Retailer X for cooperation in the future. Retailer X provided the demand pattern data on the shop floor (for week 19 2008 until week 5 2009). For 79 products is a demand pattern provided, what means that 4 products are not part of the data set (in the downstream side) anymore (see 4.3.3). Promotions can have disturbing effects on the standard pattern and therefore these weeks are filtered out of the data set. Determine the distribution behind the demand pattern is
Master Thesis Project – Dion van de Gazelle 64 important for the set up of the advice model (see section 7.2). In practice the demand pattern commonly has a normal distribution (Seger, 2006). Therefore a normal distribution is assumed for the demand dataset of Retailer X. To test this assumption the products are represented in Q Q plots (see Appendix L). These graphical normality tests seem the follow the normality test strictly (Vocht de, 2000). A Kolmogorov Smirnov (K S) test can give a decisive prediction of the normality of the dataset. This non parametric test examined if the distribution significant differs from a normal distribution. When the distribution significant differs from the normal distribution the asymptotic significance of the Z value in the K S test is below 0,05 (Vocht de, 2000). From every risk profile is the demand data of one random product analyzed with a Kolmogorov Smirnov test (see Appendix L). For the seasonal products is only a short period examined from June 2008 until September 2008. Figure 22 shows that the distributions are normal for the most products. Only Andrelon krul controle crème and Conimex kroepoek snack differ significantly from the normal distribution. The strong prove for normality of the majority of the products makes it fair to assume normality for all products, this also improves model simplicity. This test has proven that during the model construction (Chapter 7) can be assumed that the demand pattern has a normal distribution.
5.3 Round Up Risks Analysis The advice model is determined by three types of risks: upstream risks, DC risks and downstream risks. The upstream and downstream risks are analyzed based on a dataset, the DC risks are not part of this research because lack of data and the less significant relation with safety stock. On the upstream side of the model is first analyzed which product/production factors influence the delivery performance of products. The delivery performance contains both internal and external failures. The factors with significant influence are SU performance, BBD, Volume, Stock Level Unilever, HPC/foods and ABC qualification. The SU performance is analyzed separately because multicollinearity would otherwise influence the results. Based on this analysis two groups can be distinguished high performing SU (above 98%) and low performing SU (below 98%). This SU factor is together with other possible variables analyzed with a multiple regression analysis. A factor analysis grouped the significant variables in three main groups: SU performance, volume and HPC/foods. SU performance contents partly the stock level of Unilever but the height of stock level has no drastic influence (see Appendix G). Based on these factors 3 * 2 * 2 = 12 groups are formed. Seasonality for food products has also special characteristics and therefore 6 extra foods groups are formed. The 83 products of the dataset are divided over the 18 groups based on four product/production characteristics. • Sourcing Unit • Volume • HPC/Foods • Seasonality for foods products The lead time, which is an important input variable for the advice model, is determined for the separate risk profiles. Assumptions and data filtering are done when necessary. The high number of measurements of two day deliveries make it impossible to determine the distribution behind the lead time. The lead time distribution is split, first there is a chance that the delivery is on time (in 2 days) or over time; secondly there is a distribution representing how long an over time delivery will take. There are not enough measurement point for all the
Master Thesis Project – Dion van de Gazelle 65 groups, so the distribution is analyzed for all the over time data. This distribution has the following function 2, 5 + 16 ∗ β() 0, 476; 2, 54 . In case a product is not delivered on time, this β distribution represents the chance how long it will take to deliver the order. The downstream risks are formed by the demand pattern at the shop floor. This demand pattern and the corresponding variance are input for the advice model (see section 7.2). Retailer X provides these data and it is important to safeguard this cooperation also for the future. Clear improvements and trust in the model shall convince Retailer X for cooperation in the future. For 79 products a demand pattern is provided, promotions can have disturbing effects on the standard pattern and therefore these weeks are filtered out of the data set. With a Q Q plot and a Kolmogorov Smirnov (K S) test is justified that the demand pattern follows a normal distribution (Vocht de, 2000). For seasonal products it’s important to analyze a shorter period otherwise the volatility will have significant disturbing effects. With a shorter measuring period seasonal products also have a normal distribution.
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6. Modelling Quality
6.1 Introduction Modelling Quality A model is a reduction of the complex real world that instigates understanding and supports decision making. Reduction comprehends processes like assumptions and simplifications. A useful model not only provides insights to support decisions but must also be accepted and understood by the end users (subsection 3.4.4). This chapter not only mentions the outcome criteria wherefore the model must be evaluated but also introduces a framework to evaluate the quality of the (set up of the) model. The design objectives, which are determined in the analyses phase, will be used during the modeling phase as manual and for design feedbacks. The framework helps to get insides in the syntactic, semantic and pragmatic correctness and completeness of the model set up. The set up of this chapter is used in Chapter 9 to evaluate the design objectives.
6.2 Evaluation of the Design Objectives Most developers agree that the quality and satisfaction of a model depends greatly on the accuracy of specified requirements (Lindland et al., 1994). The first step in the design phase is the set up of a conceptual model. This step is taken in section 4.3. The system and system components are identified and the mutual relations are determined (figure 8). The relations are translated into a conceptual model, wherein the three different types of risks are represented (figure 9). In the analysis phase the design objectives are identified. 1) Design a model to improve service to the consumer, based on product specific end-to- end supply chain risk.
2) Design a model to optimize stock levels with respect to the desired service level. Optimizing stock levels based on the risks in the supply chain and improving service are the main objectives. The design objectives are the first two criteria two evaluate the design of the model. The outcomes of the model indicate in which degree these criteria are fulfilled.
6.3 Evaluation Framework Model Set Up Beside the feasible objectives of section 6.2, the model is only successful when other less feasible objectives are also accomplished. These objectives are also determined in the analysis phase and represented in the design objectives: 3) Trust of the important stakeholders in the model and outcomes. 4) Comprehension of the model and the process. These objectives are determined in the actor analysis (section 3.4). Cooperation, transparency, comprehension and trust in the model are the four most important issues for a successful
Master Thesis Project – Dion van de Gazelle 67 implementation and utilization. These design objectives are not feasible but are in fact related with the modeling and design quality. A framework to evaluate these less feasible design objectives is now introduced. Some authors argue that specification and requirements are too limiting to describe the process of (conceptual) modeling, even though they are used in mainstream terminology (Lindland et al., 1994). It is true that a focus on requirements and specifications is too limiting and has not enough depth to cover the whole design phase. Specifications and requirements are assumed to be clear and known on forehand, but in practice those requirements and specifications are not definite at all. They develop during the design and modeling process as the design methodology (figure 2) shows. The feedback loop makes it possible to rearrange objectives and the model when necessary. Lindland et al. developed a framework to safeguard quality in (conceptual) modeling, when requirements are not definite on forehand. This framework is based on linguistic concepts and applies them to the four aspects of modeling: language, domain, model and audience participation. The linguistic concepts are Syntax (relates model to language rules), Semantics (relates the model to the purpose of the language) and Pragmatics (relates the model to the interpretation of language and purpose by the audience). This concept is strongly linked to the less feasible objectives as discussed before. Semantic Syntactic quality quality
Modelling Domain Model language
Pragmatic quality
Actors/ audience
Figure 10 Four cornerstones of the modeling framework and the three connecting linguistic aspects (Adapted from Lindland et al., 1994). To safeguard quality it’s important to analyze and understand these aspects and their mutual relations. To judge and evaluate these aspects it is necessary to translate the goals and the means of the linguistic relations into feasible objectives. The only syntactic goal is syntactic correctness; the statements in the model must be represented according to the rules of the used (modeling) language. Error prevention, detection and correction are the means and can be performed by checking the syntax during and after the modeling phase.
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Semantic quality is the degree of correspondence between the model and the real world (Berg van den and Teeuw, 1997). The semantic goals to achieve high quality are validity and completeness. Validity depends on the correctness of statements and the consistency of the statements. Control the statements in the model with literature is one way to check validity. Simulation is another way to reach the same goal. Using both options shall lead to the strongest conclusions. Completeness is a very delicate concept because completeness in modeling is almost impossible due to simplification and assumptions. For completeness it is important that most important and significant aspects are part of the model. When these aspects are absent the model will not be accepted by the audience. Annotation and traceability are also parts of the completeness factor; the audience shall only consider a model reliable when modeling decisions/ actions are transparent. The pragmatic goal for a model is comprehension; understands the audience what the modeler intended. The pragmatic quality depends on the correspondence between the model and interpretation (Berg van den and Teeuw, 1997). The pragmatic quality can be improved by making the model easier to understand for the audience. Simplification, transparency but also simulation, examples and the use of colors are options to improve comprehension (based on (Lindland et al., 1994)). Syntactic, semantic and pragmatic goals are all related and when a decrease of quality of one of the aspects will also result in lower quality of the other aspects. Interdependency is high and therefore evaluation for modeling quality is extremely important.
6.4 Round Up Modelling Quality
In the analysis phase the design objectives for the model are determined. After the design and modeling phase can be evaluated if these objectives are realized. The first two design objectives can be evaluated with the model outcomes. 1) Design a model to improve service to the consumer, based on product specific end-to- end supply chain risk. 2) Design a model to optimize stock levels with respect to the desired service level. Other objectives, related to trust and comprehension of the model, also strongly determine the success of the model, and therefore deserve considerable attention: 3) Trust of the important stakeholders in the model and outcomes. 4) Comprehension of the model and the process. These objectives are not measurable with the model (outcomes). Therefore a linguistic based framework is used to safeguard the quality of these objectives. This will be realized by active and structured control and adaptation during the modeling and evaluation phase of syntactic, semantic and pragmatic aspect of the model. The evaluation of all the design objectives is executed in Chapter 9.
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7. Design of the Model
7.1 Introduction Model Design In the previous chapters the design objectives and boundaries, which form the criteria’s, for the model are discussed in depth. Optimizing the stock level and improving service are the most important design objectives of this research. But the model also has a communicative and informative function. In this chapter the model is designed and built to satisfy the design objectives within the design boundaries. In the next section the structure of the model, based on literature, is explained and there is some attention to the relation between the data en the choices of the model structure. The chapter ends with a description of an excel tool which will be used to determine the optimal safety stock level. This tool is developed together with field experts (LAO’s of Unilever) to ensure the practical workability of the tool and the acceptation of the end users.
7.2 Model Design based on Theory
Traditionally researchers investigated the optimization options of different parts within the supply chain. The last years there is a tendency towards research on performance, design and optimization of the supply chain as a whole (Beamon, 1998). An example of a subject where analyzing the whole supply chain is important is the optimization of inventory. Demand and supply are not constant over a given time period. To bear these deviations companies keep safety stock. Safety stock is defined as the stock kept to deal with uncertainties in demand and supply for the short term (Silver et al., 1985). As discussed in Chapter 5 product demand at the shop level and lead time are the critical variables to determine the safety stock level. There are several ways to determine the safety stock level. The most common, simple way to determine safety stock, based on these two variables, is described with the formula (Marmostein and Zinn, 1990):
2 SS= k ∗()σ2 ∗LT +( σ 2 ∗ DEM ) DEM LT SS = Safety stock k = Service factor σ DEM = Standard deviation demand DEM = Average demand per time period σ LT = Standard deviation lead time LT = Average lead time The safety stock is determined by the service factor k multiplied with de square root of lead time demand. The lead time demand represents the demand during the lead with deviations in lead time and demand. The service factor is a dimension free number and is represented by the value k. This number represent the desired service level and can be calculated in excel (NORMSINV) or tract from a standard table. The service factor k, for a chosen service level of 98% to the stores, is 2,053749. This formula and especially the k factor presumes that the lead time and demand have a normal distribution. Section 5.3 already shows that demand for the
Master Thesis Project – Dion van de Gazelle 71 most products has a normal distribution; and for utilization reasons is a normal demand distribution assumed for the other products. Lead time has seldom a normal distribution in practice. Because, the minimum lead time for Unilever is 48 hours (2 days) and the most observations have a lead time of 48 hours, a normal distribution is not expected for this particular research. Stochastic lead times seem to be a more realistic view of the reality and stochastic inventory models therefore received considerable attention (Johansen and Thorstenson, 1993). It is commonly accepted that the most lead times can be characterized with a gamma distribution (Seger, 2006). The k factor of these functions can not be analyzed analytical but there are approximation possibilities for solving the equations (Chen and Namit, 1999); (Corbett, 2001); (Das, 1976); (Johansen and Thorstenson, 1993). An advance of the gamma distributed lead time is the possibility of using computer simulation (Burgin, 1975) based on historical data (Eppen and Martin, 1988). The common formula to calculate the safety stock level is using a Q,r inventory model. The formula is based on two equations: Q=()() 2λ[] A + πη () rIC/ H( r )= QIC / πλ Some of the variables are expressed in costs; A for example is the cost of placing an order. When these different costs are not known the formula can not be used. In practice the lead time demand is often simplified as a normal distribution because it is analytical not possible to solve the formula for safety stock with a gamma distribution (Grubbström and Tang, 2006). This simplification has negative implications for the service, because due to using the normal distribution the safety stock level is underestimated what results in lower service in practice (Chopra et al., 2004), whereas using a gamma distribution often overestimates the reorder point (Grubbström and Tang, 2006). By both methods only the first two moments are taken into account. Grubbström and Tang developed and test an approximation method based on higher order moment for stochastic inventory. This method leads not only to very accurate results but is also applicable for other stochastic distributions like the β distribution. The approximation method (Grubbström and Tang, 2006) will now been introduced: The demand during lead time can be written as M = W∑ Y i i =1 where Yi represents the order size (demand pattern/downstream risks) in period i and M the random variable for the lead time (upstream risks). The first four central moments are µ( ) = µ( ) ∗ µ ( ) 1W 1 M 1 Y µ()()()()()= µ ∗ µ + µ ∗ µ 2 2W 1 M 2 Y 2 M 1 Y µ()()()()()()()()= µµ ∗ +3 µµµµµ ∗ ∗ + ∗ 3 3W 13 MY 21231 MYY MY µµ()()()= ∗3( ()()() µ −1∗ µµ2 + ) 41WM 1 M 24 YY
+µ()()()()()()()M ∗6( µµµ MY ∗2 ∗ Y +4 µµ YY ∗ + 3 µ Y 2 ) 2 112 13 2 +6µ()()() ∗ µ2 ∗ µ 3M 1 Y 2 Y +µ()() ∗ µ 4 4M 1 Y
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Where µ ()X is the ith central moment of the random variable X. The first and second i moment are the mean and variance. Among the higher order moment, the skewness β1 (measure of asymmetry of the curve) and kurtosis β2 (peakedness of a unimodal curve) are defined as:
β=µ()()2 µ 3 1 3X/ 2 X
β=µ()() µ 2 2 4X/ 2 X The data set contains a very high number of observations of deliveries of 2 days. Therefore the lead time is divided into two probability distributions. The first distribution is a Bernoulli distribution with a chance p that the lead time is exact 2 days and a chance p 1 that the lead time has another distribution. This other distribution is 2, 5 + 16 ∗ β() 0, 476; 2, 54 for all product groups except group 14 and 17. These groups have a different distribution in case the lead time is not 2 days. The distribution for group 14 is 2, 5 + 10 ∗ β() 0, 584;1, 62 for group 14 and 2, 5 + 13 ∗ β() 0, 548; 2, 56 for group 17 (section 5.2.1). The central moments of the lead time with these two formulas are determined with the equation: k= k ∗+ k ∗1− Exp E 2 pEY p Where Y is the β distribution, which describes lead time in case the lead time is not exactly 2 days. To determine the safety stock the standard formula can be used as an multiplication between the safety factor k (based on an approximation) and lead time demand W: SS=∗σ k()() =∗ k µ W2 W The safety factor k is dependant on the desired service level. In this section is already discussed how the k factor can be calculated with a given desired service level for the normal distributed lead time and demand. The k factor differs when the lead time has not a normal distribution but can not be determined analytically. An approximation formula is developed for a set of percentage points of the Pearson curve. This method can used to determine the k factor for a given skewness β1, kurtosis β2 and service level p (Bowman and Shenton, 1979): ( ) =π( ββ) π( ββ ) k p 1 1,2/ 2 1,2
r π() β β =ai() β β s ι 1, 2 ∑ r, s 1 2 0≤r + s ≤ 3 i Where a r, s depend on p. The maximum error of this approximation is 0,5% according to the research of Bowman and Shenton. Now it’s possible to translate this theory to a real model. In this model the demand has a normal distribution (see Appendix L) and the lead time has a twofold (Bernoulli and Beta) distribution. The data set contains a very high number of observations of deliveries in 2 days.
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Therefore the lead time is divided in two probability distributions. The first distribution is a Bernoulli distribution with a chance p that the lead time is exact 2 and a chance p 1 that the lead time has another distribution. This other distribution is 2, 5 + 16 ∗ β() 0, 476; 2, 54 for all product groups except group 14 and 17. These groups have a different distribution in case the lead time is not 2 days. The distribution for group 14 is 2, 5 + 10 ∗ β() 0, 584;1, 62 and 2, 5 + 13 ∗ β() 0, 548; 2, 56 for group 17 (section 5.2.1). The central moments of the lead with these two formulas are determined with the equation: k= k ∗+ k ∗1− Exp E 2 pEY p Where Y is the β distribution, which describes lead time in case the lead time is not exactly 2 days. The model is set up in excel and the results based on the lead time and demand pattern from the data set are presented in the next section.
7.3 Practical Design and Model Results The model generates advice for the safety stock and total stock (= safety stock + cycle stock) levels for a desired service degree to the stores based on the orders per week. The lead time is determined for the product groups (section 5.2.1) but the demand pattern is unique for all products (section 5.2.3). The model combines the lead time and the demand pattern and uses an approximation to determine the k factor (section 7.2). This k factor determines the safety stock level for a desired service level. In this model a k factor is determined for a service of 97,5% and 99%. The results of the model are represented in Appendix M. These outcomes presume that it’s possible for Retailer X to sent in orders every day, when necessary this can be adapted in the tool (section 8.2.3). The order frequency is determined by the order behaviour of the retailer. Due to discounts retailers try to order on full pallets (section 8.2.3). When necessary these presumptions can be changed based on the needs and characteristics of a retailer. The products in the data set are divided in groups (section 5.2.1). These groups have the same chance p of a lead time of exact 2 days and a chance 1 p that the lead time has a β distribution. For all products is determined which k factor must be used to calculate the safety stock level for a service of 97,5% and 99%. Multiply the k factor with the √(Lead time demand) gives a safety stock level advice in colli (boxes). The average results for the data set are presented in table 4. Service level 97,50% 99% Safety Stock (boxes) 165 219 Safety Stock (days) 2,2 2,9 Total Stock (boxes) 260 304 Total Stock (days) 4,4 5,1
Table 4 Average stock level advice for data set.
Dividing the safety stock in colli by the average demand per day gives the safety stock level in days. The average safety stock for the products in the data set based on the advice model is 2,15 days. The total stock based on the advice model is 4,4 days stock for a service level of 97,5% to the shops. For a service level of 99% a safety stock of 2,88 days is required with a total average stock of 5,1 days. The individual advices for the different products can be find
Master Thesis Project – Dion van de Gazelle 74 the table 28 (Appendix M) for a 97,5% and 99% service level. Because the total stock level depends strongly on the ordering pattern (cycle stock) it is difficult to draw conclusions about total stock levels. But the results of the advice model indicate that a product risk specific safety stock level has potential to decrease the total stock level. During design sessions with the LAO’s of Unilever the possibility of 24 hour deliveries came up. Shifting the standard lead time from 48 to 24 hours has possibly a positive effect on the stock levels. In the model this possibility is analysed and the results are represented in table 5. 48 hours delivery 24 hours delivery 97,50% 99% 97,50% 99% SS (days) Tot Stock (days) SS (days) Tot Stock (days) SS (days) Tot Stock (days) SS (days) Tot Stock (days) 6 order moments 2,15 4,37 2,88 5,1 2,04 4,26 2,78 5 3 order moments 2,54 4,76 3,4 5,62 2,46 4,68 3,3 5,52 2 order moments 3 5,22 4 6,22 2,98 5,2 3,85 6,07 Table 5 Average stock level 24 and 48 hours deliveries. This table shows the average safety and total stock levels for a standard delivery time of 24 and 48 hours. The results are represented for 6, 3 and 2 order moments per week. It is very clear that the total stock levels can not be decreased significant when the lead time changes from 48 to 24 hours. This is caused by two aspects: • The safety stock, necessary to bear the risks during lead time, decreases not significantly when delivery shifts from 24 to 48 hours • Total stock is determined for the major part by order frequency; lead time has only a minor effect on the total stock level Considering the extra costs, necessary to implement and achieve 24 hour deliveries, it is not worthwhile to implement 24 hour deliveries with the aim of stock level reductions. On the other hand 24 hour deliveries increase the responsiveness of Unilever, but in practice such special speed deliveries are already possible. In the conclusions (chapter 10) the results of this modelling phase will be analyzed in a broader perspective and the design objectives will be evaluated. But first the results of the model will be validated with a simulation. This simulation is built with the discrete simulation software program Arena. Chapter 9 describes how the simulation model is built and validates the results of the advice model.
7.4 Round up Model Design The advice model is based on group specific upstream risks (lead time) and product specific downstream risks (demand pattern). The model generates advice based on these risks for a desired service level. This desired service level is expressed in the k factor. Because the stochastic character of the lead time of our data it is not possible to use the standard k factor which presumes normality for the lead time. The k factor for stochastic lead times can not be determined analytical and therefore an approximation is used. The model gives product specific advice for safety and total stock levels for two desired service levels (97,5% and 99%). The order frequency is determined by the order behaviour of ordering on full pallets. The outcomes of the model have an average safety stock of 2,15 days and a total of 4,4 days total stock for a service level of 97,5% to the shops. For a service level of 99% a safety stock of 2,88 days is required with a total average stock of 5,1 days. The current average total stock level is one week. Because there is no product specific stock level data it is not possible to determine how much the total stock can decrease based on the stock advice. But the results of the advice model indicate that a product risk specific safety stock level has potential to decrease the total stock level.
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8. From Model to Practice
8.1 Introduction Model to Practice The designed model is capable to generate a product specific stock level advice based on upstream and downstream risks. The outcomes of the model can be used to adapt the current stock levels better to the risks in the supply chain. Better allocation of stock levels can decrease the total stock level, keeping the current service equal or reallocation can improve service. The model indicates these possibilities theoretically, but to ensure results the model must also be translated into practice. This chapter describes this translation from model to practice and indicates possible obstacles and chances. This chapter also describes wherefore the model can be used and indicates some limitations. In the section 8.2 there is attention to the generality of the model for other customers. Can Unilever use this model for other customers and which problems can be expected? Retailer X has a very important role in the translation of the model in practice and must be convinced of the validity and use of the model, as the conclusions of the stakeholder analysis show. In section 8.3 more about the relation between the model, Unilever and Retailer X will be discussed. The relation and tension between these stakeholders is part of this discussion.
8.2 General Usability of Model
8.2.1 Objectives of the Model The main goal of this research and the purpose of the model is to generate (safety) stock advice, based on end to end risks in the supply chain. Combining information about upstream and downstream risks can help to find an optimal allocation of stocks. With this reallocation a higher service to the stores or a lower total stock level can be achieved. Beside this main goal the research and the model are also a first step in a bigger process to improve OSA. The research gives more insights in the risks of both sides of the supply chain. When these insights are shared, increased transparency and openness can contribute to improve supply chain performance and reduce costs as we have seen in section 3.5. Openness about this part of the supply chain is a first step to improve supply chain processes in a bigger context and is the first step to generate trust between both stakeholders. With the insights and outcomes of this model the focus can change from delivery performance of Unilever to Retailer X’s DC to store casefill. From there it can be further extended to performance in the store it self. So actually this model can be used as support for further cooperation and openness for overall supply chain improvements. The advice model has twofold practical purposes: • providing a product and retailer specific (safety) stock advice • supporting tool for further optimization of the whole supply chain The last purpose has much potential for further supply chain improvements. Using the advice model can be a start up project for Unilever and Retailer X. The relation can be intensified and increased trust and transparency is a good step to further cooperation and mutual supply chain improvements. As described in section 3.5 openness, transparency and cooperation
Master Thesis Project – Dion van de Gazelle 77 between a manufacturer and retailer have a definite potential for supply chain optimization. But openness also contains the drawback of shifting risks and opportunely behaviour of one the stakeholders. Unilever should be aware of these risks and consider the consequences before starting the extended cooperation and information sharing. This will be discussed in more depth and in relation to Retailer X in section 8.3.
8.2.2 Upstream and Downstream Risks The model is not only applicable for Retailer X but can also help to improve the supply chain performance of other retailers. The research and design of the model is based on Retailer X specific data. The downstream risks are in fact product specific risks of Unilever products, caused by demand patterns at the shop floor (of Retailer X, in this research). These risks are always product and retailer specific and therefore can not be generalized. This specificness is a strength of the model but also a weakness because it is more labour intensive to generate advice. Specific data about the daily demand pattern at the shop floor per products is always necessary. The upstream risks are based on the delivery performance from Unilever to Retailer X. The products are divided into 18 different product groups based on their risk profile. The characteristics of the risk profiles (Volume, Sourcing Unit, HPC and Season) are non retailer specific characteristics. If the delivery performance to Retailer X is similar with the deliver performance to other retailers the risk groups maintain the same. When necessary it is possible to change the performances of the groups when the delivery performance is not similar. In general can the products also shift between groups when the volume of the product changes or for example the performance of the sourcing unit changes. Table 6 shows the delivery performance of the 6 most important retailers. The last row shows the average delivery performance of Unilever to retailers without Retailer X. Retailer X 98,60% Retailer A 98,40% Retailer B 99,10% Retailer C 98,90% Retailer D 98,80% Retailer E 99% Average 98,70% Table 6 Delivery performance Unilever to retailers. Table 6 shows that the delivery performance to Retailer X is quite in line with the performance to other retailers. The average delivery performance without Retailer X is 98,7% and the specific delivery performance of Retailer X is 98,6%. From this we can conclude that the risk profiles of the upstream risk can be used in general for other retailers. Specific adaptations for other retailers of adaptations caused by general characteristic changes are possible in the model.
8.2.3 From Model to Tool The stock advice model is based on theory and input data from Unilever and Retailer X. This model and the research where the model is based on, has provided some insights in the supply chain risks of Unilever and Retailer X. As described before these insights can help to support further cooperation and supply chain improvements in the future, but the main purpose is to provide a stock advice. To improve the suitability for Unilever the model is
Master Thesis Project – Dion van de Gazelle 78 extended into a tool. This tool fits well with the current supply chain processes of Unilever and makes it possible to adapt product or retailer specific characteristics. The most important parts of the tool will now be discussed. Product and production characteristics can change over time and maintenance is necessary to keep the tool useful. The tool is handed over to the end user, the LAO of Unilever. Some handover sessions in combination with a manual inform the LAO on how to use the tool and maintain it in the future. The manual describes the design of and theory behind the model and also explains how to make changes and maintain the tool. The upstream and downstream risks must be updated periodically. This is especially important for seasonal products. The demand pattern differs highly in high and low seasons and therefore the input data must be update every season. The LAO can also make manual changes when for example the delivery performance for a specific retailer differs from the data in the tool. Unilever currently advises her customers (the retailers) to order products when possible on full pallets. Ordering on full pallets decreases delivery complexity and handling costs. Retailers receive a discount every time when they order a full pallet. To make the tool coherent with this behaviour the order frequency is based on full pallet ordering. The number of colli (boxes) on a full pallet divided by the number of colli demand per day gives an indication of the order frequency.
Round up Order Average Round Up Round up order Colli per Demand in frequency Product daily fast slow frequency pallet pallets advice (per Demand movers movers advice in week) half days
Robijn color 1,5 l 75 51,43 0,69 4,11 4,11 4,11 4,0 Figure 11 Interface advice tool - Ordering frequency. In figure 11 a fragment of the advice tool is presented. In the tool pale blue cells must be filled out by the user (LAO of Unilever). The use of colours has a positive effect on the usability of the tool. For this interface (figure 11) the LAO can fill out the number of colli per pallet. The tool couples this automatically on daily demand of the product. In the interface example the number of colli per pallets for Robijn Color is 75 and the average daily demand is 51. The tool calculates that an order frequency of 4 times per week is expected for this product (see last cell). The maximum order frequency is once per day and the minimum is set on once per two weeks. This order pattern has a strong effect on the cycle stock and therefore also on the total stock advice. The order pattern based on full pallets realizes complexity and handling cost reductions but increases cycle stock and stock costs. Further research is necessary to find out were the optimum lies between full pallet ordering and costs, but this is out of the research scope. The tool can not only be adapted for retailer specific product , production and delivery characteristics but also for available order moments. The biggest customers of Unilever can order 6 days a week, but some of the smaller clients have less extended ordering options. Less ordering moments has implications for the safety stock. When a retailer can only order twice a week the safety stock is much higher then when he can order 6 times a week. In the tool an option is implemented where the user, the LAO of Unilever, can indicate the number of order moments per week.
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Extra lead Percentage Mean Advice # Available time orders in demand Variance order order caused by K-factor K-factor Lead time 48 hours (HE) per Demand moments moments limited (97,5%) (99%) demand /group day per week per week order moments
Robijn color 1,5 l 98,89% 51,43 448,43 4,00 6,00 6,00 2,183 2,872 1428,068 Figure 12 Interface advice tool - Available order moments. Figure 12 shows another fragment of the advice tool. In this interface the LAO must again fill out the pale blue cell. In this example the retailer can order 6 times per week (the maximum). When the LAO fills out this cell all the other cells (of available order moments per week) take over this value. When necessary it is even possible to change the number of order moments manual per product or product category. This makes it possible to generate a product and retailer specific stock advice.
8.2.4 Drawbacks in Practice The work intensiveness to maintain the lead time of the current groups is a drawback of the model structure. The lead time is calculated by subtracting the delivered quantity from the ordered quantity (see chapter 7). This is very work intensive and one should argue if this is necessary. The LAO of Unilever is recommended to investigate if this numbers can be generated automatically from the LCDB (Logistic Client Data Base). Another option is to use the internal casefill (percentage of order delivered on time) of Unilever, but as explained in section 5.2 the internal casefill measures not all failures and therefore these numbers are less suitable for the model. Important is not to use individual lead time figures but use the average of the group. The strength of the model is that it predicts lead time based on a risk profile. Products with the same risk profile have the same expected delivery performance. Using the average lead time of the group to determine the safety stock will flatten exceptions in positive and negative ways. Another drawback of the tool is that it only generates advice for a desired store casefill of 97,5 and 99%. When another store casefill is desired interpolation is needed (Grubbström and Tang, 2006). The model is designed for a Beta 1 value between 0 and 4. When Beta 1 is a little above 4 this will not have significant influences but when Beta lies very close to zero (< 0,3) another approximation table must used. Using the same approximation for a very small Beta 1, results in an underestimation of the necessary stock level. Tests have proven that Beta 1 is only very small when the retailer can order just once a week for 24 hours deliveries. The manual and the comments in the tool indicate this risk and help the user to adapt the approximation when necessary.
8.3 Using the Model for Retailer X In the previous section the general objectives and purposes of the model are discussed. The model is designed based on data of Retailer X and one of the design objectives is to provide a stock level advice for Retailer X. This section describes how Unilever can share the founded insights and how they can provide and share a stock level advice, based on the multi actor setting and mutual relations and tensions.
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8.3.1 Share Insights of Research Unilever should definitely share the insights and results of this research with Retailer X. Increased openness and transparency are key factors in realizing supply chain improvements (section 3.5). Unilever should use the obtained knowledge and cooperate with Retailer X to improve overall supply chain performance. The model and model insights can support supply chain decisions based on costs and service considerations. This cooperation is only possible when Retailer X also wants to cooperate. Retailer X and Unilever both spoke out their intentions for more openness on OSA improvement. As described in the general model purpose section, using the insights for further cooperation to achieve OSA improvements is one of the research and design objectives. Unilever, in practice the LAO’s, must communicate this very clear to Retailer X. This should give Retailer X perspectives for future supply chain improvements and in that sense costs reductions. Besides it also creates a mutual goal between Unilever and Retailer X to improve supply chain performance based on transparency and cooperation. In fact this is creating commitment of Retailer X and Unilever to a higher goal of overall supply chain improvement. Steering on OSA is only one specific example and several steps should be taken first. Beside this commitment Retailer X should also be convinced that the model can help to improve performance (in either stock reduction or service improvement). Retailer X only trusts the model and the outcomes when they get insights in the structure of the model. When the model is a black box for Retailer X they will not trust the outcomes unless they receive a guarantee of Unilever. Unilever will never provide this guarantee and information sharing about the structure is necessary to reach the unbearable trust. To create this trust and commitment a stepwise plan is identified in section 8.3.4.
8.3.2 Shifting Risks Another important aspect for Unilever is the shift of risks and responsibilities. For some products, risks shift from the retailer to Unilever, when the stocks are adapted to the optimal minimum. Retailer X expects from Unilever that the advised (safety) stocks are sufficient and assumes that they can achieve the desired service level to the stores. Parts of the risks are currently captured by the extra stock at the retailers’ DC. At optimal stock level all the risks shift to Unilever for these products. One uncommon failure at Unilever has definite consequences for Retailer X’s performance to the stores. On the other hand also risks shift from Unilever to Retailer X. Retailer X will have higher safety stocks for products with higher upstream and/or downstream risks. In fact risks shift from both sides and the overall robustness and responsiveness options are increased.
8.3.3 Conditions for Transparency Absolutely clear is that transparency and cooperation is necessary to understand the risks of both sides and to determine where problems maybe arise. Determine these possible obstacles (like limited store capacity, strong seasonal influences or order frequency) in an early stage is important and can also help to expect and react on these obstacles. Clear agreements (in contract form) and mutual trust are necessary to avoid or solve such problems. When both parties know which risks can be expected the impact of these risks will be much lower because both parties are prepared and know how to handle such situations. Clear agreements on sharing data are also essential. Sharing data give both parties important insights in the other company. These insights are the reason why cooperation can improve supply chain optimization but is also a reason why this optimization can fail. Transparency has only added value when strategic information is shared. These shared strategic information can also used against the other party in for example contract negotiations or comparisons with other companies. Very clear and strict agreements are necessary to avoid these kinds of
Master Thesis Project – Dion van de Gazelle 81 opportunistic behavior. This should be one of the most important process points for further cooperation between Unilever and Retailer X.
8.3.4 Next Steps To achieve further cooperation, transparency and fully exploit the benefits of the advice model and the insights of the research three steps are identified. These steps ensure the conditions for transparency and help to create commitment between Unilever and Retailer X. Step 1 The first step is informing Retailer X about the structure of the model. This helps the retailer to understand how the risks are connected to each other and supports to create trust in the model. These sessions can be supported with an animation of the simulation model. The LAO of Unilever must also mention to Retailer X that there is an indication that stock levels can be decreased with stable service. Further research and a pilot project for one or two product groups are practical actions. Unilever and Retailer X can set up a pilot for a low and high risk profile group. The effects of the advised safety stock levels on store casefill must be tested for several months. Based on the results of this pilot the model can be adapted or used for more product groups. Step 2 When Retailer X trusts and comprehends the structure and the outcomes of the model step 2 can be taken. Unilever and Retailer X must use the advice model in a broader context and use the insights of the research for further supply chain performance. There are already agreements about the OSA track and this model and further research can be part of such a broader track. This broader procedure will bring forward more and more influential improvements for the whole supply chain. Step 3 Steps three is recording the intentions of both parties and translate them in concrete goals and expectations. Beside the project goals and expectations the responsibilities and confidence of data must be recorded explicit. This creates commitment and on the same time it protects both Unilever and Retailer X for opportunistic behavior of the other party.
8.4 Round up From Model to Practice In this chapter the translation from the model into practice is described. The advice model has twofold practical purposes: • providing a product and retailer specific (safety) stock advice • supporting tool for further optimization of the whole supply chain The model design is based on data of Unilever and Retailer X, but is generally applicable on other retailers. The upstream risks of Retailer X are comparable with other retailers and the downstream risks are always product retailer specific. The work intensiveness of this product retailer specific data gathering is one of the drawbacks of the models structure. Currently it is not possible to extract the lead time direct from the LCDB. Unilever should investigate of this possibility. The advice model only generates advices for a desired service level of 97,5% and 99% to the stores. For other service levels interpolation is necessary. The designed model is turned into a tool for Unilever. This tool is handed over during several sessions to the end user (the LAO of Unilever). An extensive manual in combination with instructions and use of colors in the tool improve the usability of the tool for the user. Possibilities to change product or retailer characteristics increase the generality of the tool and give the end user options to make adaptations when necessary.
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The model and the research are set up to generate a stock level advice for Retailer X. Transparency and openness can improve supply chain performance and therefore the insights and results of the model must definitely be shared with Retailer X. Unilever should be aware of the possibilities and dangers of this transparency. Creating trust, comprehensions and commitment in combination with clear agreements and mutual goals are vital for this process of intensive cooperation. This model can be used as a first step to create trust and commitment. In the future this relation can be used and intensified for further supply chain improvements. Several steps are identified to achieve the purposes of the advice model.
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Master Thesis Project – Dion van de Gazelle 84 ii. Round up Design and Modelling Phase
In this phase a stock level advice model is designed and modelled based on the design objectives and boundaries as determined in the analysis phase. The supply chain is represented as a risk model, with upstream risks, DC risks and downstream risks. The upstream risks are product and production related risks upstream in the supply chain from the Retailer’s DC. The significant product/production characteristics are grouped based on their risk profile. With a factor analysis the significant variables are grouped into three factors SU performance, Volume and HPC/foods, based on these variables 12 risk profiles are constructed. Seasonality for food products has also special characteristics and therefore 6 extra foods groups are formed. The 83 products of the dataset are divided over the 18 groups based on their product/production characteristics. The lead time, an important input variable for the advice model, is determined for the separate groups. The lead time distribution is split, there is a chance that the delivery is on time (in 2 days) or the delivery is not on time (> 2 days); secondly a distribution is identified which determines the duration of the lead time > 2 days. In the case when a products is not delivered on time, the expected delivery time has the distribution: 2, 5 + 16 ∗ β() 0, 476; 2, 54 . DC risks like repacking of the products, internal transportation and temporary storage result in a longer lead time. Due to lack of data this influence is kept constant. The total lead time representing the upstream and DC risk are input for the advice model The downstream risks are determined by the demand pattern at the shop floor. This demand pattern and the corresponding variance are input for the advice model. This data is delivered by Retailer X and it is important to safeguard this cooperation also for the future. For 79 products a demand pattern is provided, promotions can have disturbing effects on the standard pattern and therefore these weeks are filtered out of the data set. Tests have proven that the demand patterns follow a normal distribution. For seasonal products it’s important to analyze a shorter period otherwise the volatility will have significant disturbing effects. With a shorter measure period, seasonal products also have a normal distribution. In the analysis phase the design objectives for the model are determined. After the design and modeling phase can be evaluated if these objectives are realized. The first two design objectives: 1) Design a model to improve service to the consumer, based on product specific end-to- end supply chain risk. 2) Design a model to optimize stock levels with respect to the desired service level. can be evaluated with the model outcomes. Other objectives, related to trust and comprehension of the model, also strongly determine the success of the model, and therefore deserve considerable attention: 3) Trust of the important stakeholders in the model and outcomes. 4) Comprehension of the model and the process. The achievement of these objectives is not measurable with the model (outcomes). Therefore a linguistic based framework is used to safeguard the quality of these objectives. This will be realized by active and structured control and adaptation during the modeling and evaluation
Master Thesis Project – Dion van de Gazelle 85 phase of syntactic, semantic and pragmatic aspect of the model. The evaluation of all four design objectives is executed in Chapter 9. The advise model is based on group specific upstream risks (lead time) and product specific downstream risks (demand pattern). The model generates advice based on these risks for a desired service level. This desired service level is expressed in the k factor. Because the stochastic character of the lead time of our data it is not possible to use the standard k factor which presumes normality for the lead time. The k factor for stochastic lead times can not be determined analytical and therefore an approximation is used. The model gives product specific advice for safety stock and total stock levels for two desired service levels (97,5% and 99%). Retailer X can order 6 times a week and the order frequency is based on ordering on full pallets. The outcomes of the model have an average safety stock of 2,15 days and a total of 4,4 days total stock for a service level of 97,5% to the shops. For a service level of 99% a safety stock of 2,88 days is required with a total average stock of 5,1 days. The current average total stock level is one week. Because there is no product specific stock level data it is not possible to determine how much the total stock can decrease based on the stock advice. But the results of the advice model indicate that a product risk specific safety stock level has potential to decrease the total stock level. The model is translated into a tool to improve usability and support the practical purposes of the model. The advice model has twofold practical purposes: • providing a product and retailer specific (safety) stock advice • supporting tool for further optimization of the whole supply chain The model design is based on data of Unilever and Retailer X, but is generally applicable on other retailers. The tool is handed over during several sessions to the end user (the LAO of Unilever) and a manual, instructions and the use of colors in the tool improve the usability of the tool for the user. Possibilities to change product or retailer characteristics increase the generality of the tool and give the end user options to make adaptations when necessary. The model and the research are set up to generate a stock level advice for Retailer X. Transparency and openness can improve supply chain performance and therefore the insights and results of the model must definitely be shared with Retailer X. The model and model insights can support supply chain decisions based on costs and service considerations Unilever should be aware of the possibilities and dangers of this transparency. Creating trust, comprehensions and commitment in combination with clear agreements and mutual goals are vital for this process of intensive cooperation. This model can be used as a first step to create trust and commitment. In the future this relation can be used and intensified for further supply chain improvements. Several steps are identified to achieve the purposes of the advice model. The second phase of design and modelling is finished. In the next phase the model results is validated with a simulation and the design objectives are evaluated based on the model outcomes and the modelling quality framework. The last phase ends with the conclusions and some recommendations for the future.
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III. Evaluation and Validation Phase
The design and the model are set up in the analysis and design and modeling phase. Now it is time to evaluate this process and to evaluate in what degree the research has given insights in the research objectives and research questions. The advice model is validated with a simulation in this phase. In Chapter 10 the conclusions are discussed and some recommendations for the future are indicated. At the end some critical notes are provided in the reflection (Chapter 11).
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9. Evaluation of the Research Design and Results
9.1 Introduction Evaluation In Chapter 7 the advice model is presented which can generate safety stock advice for the different products of the data set. In this chapter this advice is validated with a simulation. In section 9.2 the execution of the design objectives and modelling quality are discussed. In section 9.3 the results of the advice model are validated with a simulation model, for a number of products from the data set. At last section 9.4 rounds up the chapter with some conclusions.
9.2 Evaluation of Design Objectives and Modelling Quality In the analysis phase the design objectives for this research are identified. The first two objectives are model focussed and the evaluating of the execution of these objectives is based on the model. 1) Design a model to improve service to the consumer, based on product specific end-to- end supply chain risk. 2) Design a model to optimize stock levels with respect to the desired service level. 3) Trust of the important stakeholders in the model and outcomes. 4) Comprehension of the model and the process. The first two objectives are model focussed. As is discussed in Chapter 6 the evaluating of the execution of these objectives is based on the model. The third and fourth design objectives are related with modelling quality and are evaluated by the framework of Lindland, as introduced in Chapter 6. The evaluation start with the model focussed design objectives.
9.2.1 Evaluation Model Design Objectives Optimizing stock levels based on the risks in the supply chain and improving service are the main design objectives. These design objectives can be divided in three parts: service optimization, stock optimization and product specific end to end risks. The last part supports the other two and is in fact the first step to be taken. The risk profiles, based on the upstream product risk analysis (Chapter 5), are the first part of this design result. The analysis of the product retailer specific downstream risks forms the second part. The combination of both risks in the model show the end result of the end to end product specific analysis. The model generates advice based on a desired service level. This is optimal advice to reach a desired service level. The optimization is performed due the product specific risks. With no current product specific stock level data strong conclusions can not be drawn. For an average stock level of one week, decreased stock levels for some products are still sufficient to accomplish the desired service degree to the stores. For other products the stock level advice is above the average of one week. The model indicates that the overall stock level can be decreased. The effects on service, the last part of the design goals are hard to measure. In fact the service actually can be measured in practice, but the effects of stock reallocation on service are
Master Thesis Project – Dion van de Gazelle 89 unsure. A simulation can validate the performance of the advice model for service (section 9.3) but is not a guarantee. The stock levels are based on a desired service level, but this level can be influenced by inefficient logistic performance from DC to store. In practice Unilever and Retailer X can also choose to keep the total stock level constant and rearrange the set up of stock. Increasing stock levels of high risk products will have positive effects on the performed service to the consumer.
9.2.2 Syntactic Evaluation of the Model Design The only syntactic goal is syntactic correctness; the statements in the model must be represented according to the rules of the used (modeling) language (section 6.3). The given advice is based on the theory of approximate the k factor for higher order moments for stochastic inventory systems (Grubbström and Tang, 2006). To prevent errors the advice model is constructed first in Excel and then tested with an example given in the article of Grubbström and Tang. The answer provided by the advice model was equal to the answer in the example. This is a check and proves that the structure of the advice model is correct. Because the high number of measure points of exact two days lead time, it’s not possible to use the standard model structure for the data of this research. Therefore the order moments of the chance p and p 1 must counted up. In the first attempt the mean, variance, skewness and kurtosis of both parts were multiplied with their chance probability and then counted up. For example total mean = (mean y * p) + (mean z * (1 p)). This formula holds for the mean but not for the variance, skewness and kurtosis. Further investigation showed that the order moments must be multiplied with the chance p or 1 p and then counted up. This check detected and corrected the syntactic errors of the advice model and helped to redesign the model during the design phase.
9.2.3 Semantic Evaluation of the Model Design
Semantic quality is the degree of correspondence between the model and the real world (Berg van den and Teeuw, 1997). The semantic goals to achieve high quality are validity and completeness. Validity depends on the correctness of statements and the consistency of the statements. The correctness of the statements in the model is already discussed in section 9.2.2. Counting up the variance, mean and kurtosis is not correct according to theory. But counting up the different order moments, multiplied with their probability chance, is allowed and valid to do. In the first design of the model the 3 and 4 were also interpreted as the skewness and kurtosis. A dubble check with the article of Grubbström and Tang indicates that rd th µ σ 1,5 µ σ 2 3 and 4 in fact are the 3 and 4 moment. The skewness 3 / and kurtosis 4 / are based on the 3 rd and 4 th moment, but they are not the same. Identifying and correcting these statements ensure the correctness and validity of the advice model. The simulation as performed in section 9.3 is also a validity check. The simulation is a reduced model of the complex real situation and has some constraints but it validates the results of the advice model en shows that the results are plausible based on a simulation of the real situation. Completeness in modeling is almost impossible due to simplification and assumptions. For completeness and acceptance by audience it is important that most important and significant aspects are part of the model. The most important aspects, like demand and lead time fluctuations, desired service degree and number of orders per week are covered in the model. How these different aspects relate to each other is explained in the model and in a user manual. The model is also transmitted to a key user within Unilever who will be responsible for the updates and maintenance of the model. Some aspect like maximum order moments per week, 24 hours deliveries and ordering on full pallets are implemented in the tool for
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Unilever. These options not only extend the possibilities of the tool, but also provide extra insights in model behaviour.
9.2.4 Pragmatic Evaluation of the Model Design
The pragmatic goal for a model is comprehension; understands the audience what the modeler intended. To accomplish this understanding of the model, intensive and regular contact with the end user is important. The set up of the model is several times discussed with field experts and the end users, the LAO’s of Unilever. Vague aspects are clarified or taken away. When the end users came up with suggestions like limited delivery moments, full pallets ordering, 24 hours lead time, these suggestions are taken into account and implemented in the model to improve and ensure usability. When the final advice model was constructed, the results and model are presented to the end user and other logistic stakeholders to ensure comprehension, trust and take away vagueness. Finally the advice model is translated in an advice tool. This tool is assisted by the manual and comments. The manual in combination with the use of colors and limited data input, has strongly improved the usability and reduction of complexity. The use of colors and extended options for retailer specific characteristics can help to convince Retailer X of the possibilities of the model. The simple and clear interface helps to improve the comprehension of the audience, at Unilever and Retailer X.
9.3 Simulation and Validation
9.3.1 Set up of the Simulation Model The simulation results are used to validate the outcomes of the advice model. The simulation model (Appendix N) is built with the discrete modelling software Arena. The simulation is run for 8 products from different risk groups. Arena is developed for discrete modelling and is based on arrivals, queues and processes of entities and orders. The simulation model, which is represented in Appendix N, is dived in two parts. The first part simulates the consumer demand and the second part simulates the order and delivery process between Unilever and Retailer X. In this section the set up of the simulation is described. Part 1 Consumer demand The consumer demand part starts with a create block. This block creates the product demand on the shop floor described by a normal distribution, for example Robijn color NORM(50, 21). In the next block the stock level is checked. There are two options, the stock level is zero or the stock level is positive. When the stock level is positive the consumer buys the product and in the block update inventory, the stock level and inventory level (stock level + ordered quantity) are decreased by one product. In the next block the demand leaves the simulation through the disposal block Delivery. When the stock level is zero the demand leaves the simulation through the disposal block OOS, what means that the consumer demand was not fulfilled. Part 2 Order en delivery process The second part starts with the creation of an order. This block creates one entity every day; this represents the daily check of the current stock level. In the next block the order seizes the
Master Thesis Project – Dion van de Gazelle 91 order process. The order process is seized until the delivery of the order. Therefore it is not possible to send in multiple orders. Sending in multiple orders will overestimate delivery performance because re orders can be delivered before the initial order is delivered. After the seize order process block is checked if the stock level is higher then the reorder point. The reorder point is the average daily demand * standard lead time of two days plus the safety stock. If there is sufficient stock the entity is released from the order process and leaves the simulation through the disposal block. After the release the order process is seized by a new entity and the stock level is checked again. When the stock level is below the re order point an order is send in. When possible, retailers order on full pallets, the batch size is therefore determined by the number of boxes on a pallet. Every product is ordered at least once every two weeks, the batch size is set on 12 * daily demand for these products. There is a minimum of two days between every order, because the next order can only be send when the previous order is delivered. For frequent ordered products the batch size is increased to 2,5 * average daily demand. Otherwise some modelling inconsistencies will arise. Consequence of this changed batch size and order frequency is a higher total stock level compared to the average total stock level of the advice model. When an order is placed, the inventory position is increased with the batch size and the order entity goes to the 48 hours decide block. This block represents the chance p that Unilever delivers in exact 2 days and the chance 1 p that Unilever delivers according the distribution 2, 5 + 16 ∗ β() 0, 476; 2, 54 . These chances are both translated into lead time delay blocks. After these blocks the order is delivered and the stock level increases with the batch size. After the delivery the order is released from the order process and a new stock level check is possible. The order leaves the simulation through the dispose block.
9.3.2 Validation of the Advice Model In the previous section the simulation set up is discussed, the next step is to use simulation to validate the advice model results. The simulation run is performed for 8 products of the data set: Becel light 500, Ola cornetto classico, Robijn vloeibaar wit, Lipton thee forest fruit, Andrelon shampoo 300 ml iedere dag, Dove deodorant spray original, 500 ml Calve Statube mayo and Andrelon krul control crème. The products are random selected from different risk profiles. The simulation has a warm up period of 32 days. This is two times the longest order cycle of 16 days. The simulation length is set on 1825 days (5 years). This very long simulation length improves the accuracy of the modelling results. The number of replications is determined by the necessary reliability of the simulation. In this simulation five replications are run for a reliability of 99%. This is determined with the next formulas (Verbraeck and Valentin, 2005): µ=x − hx; + h
x = ∑ X/ n j j
2 sx2 () =∑() xxn − / -1 i i ht= sx( ) sx2( ) = sxn 2 () / n −1,1 −α / 2 ,
x h ht= sx( ) Where is the estimation of the mean and the half life value. The value of n −1,1 −α / 2 is determined from the table of the Studen t test with n 1 degrees of freedom for 99%
Master Thesis Project – Dion van de Gazelle 92 reliability. The number of replications is determined for the average total stock level of Ola cornetto classico and five test runs. x = 115 s( x ) = 0,225 n = 5 s( x ) = 0,010
t 0,99 = 2,821 h = 0,029 µ = [115 – 0,029; 115 + 0,029] The conclusion of this calculation is that for 5 replications the average stock level is between 114,97 and 115,03 with a reliability of 99%. This interval is smaller then the minimum order cycle of one day (stock level check without ordering) and so can be concluded that 5 replications produce a very reliable replication of stock levels. The results of the simulation are represented in Appendix O. The simulation goal is to validate the advice results of the stock level advice model. For 8 products is the performance simulated, for the stock level advice with a desired service of 97% and 99%. The reorder point (safety stock + average demand * standard lead time) and batch size (order size) are input for the model. The average simulation results are summarized in table 7. The difference between simulation and model service are 1,3% for the 97,5% advice and 0,2% for the 99% advice. Service Service Difference simulation model Total 98,8% 97,5% 1,3% 99,2% 99,0% 0,2% Table 7 Expected service simulation and advice model. The simulation results and expected model service are very similar for the 99% desired service level, with an average difference of only 0,2%. The simulation not only validates the average service but also the product specific results are very similar as presented in Appendix O. The service for a 97,5% safety stock advice is much higher in the simulation then expected from the model. The simulation indicates a 1,3% higher service then the desired model service. Besides validating the model, these outcomes also validate the statements of Grubbström and Tang that their approximations are more accurate for higher service levels (Grubbström and Tang, 2006). In table 29 of Appendix O also the average stock levels for the model and simulation are presented. The average stock levels, of Becel light 500 and 500 ml Calve Statube mayo, are much higher in the simulation because the order frequency is decreased and therefore the average cycle stock has increased. The other stock levels are very similar with difference of 3 to 4%. The Andrelon shampoo 300ml iedere dag has the biggest difference of 10%. This indicates that even when average stock levels are decreased, the service level can stay equal. The simulation validates the outcomes for the 99% service advice and indicates higher then expected service levels for the 97,5% advice. There can be concluded that the simulation confirms the results of the advice model.
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9.4 Round up Evaluation and Validation The first step in the design phase is the set up of a conceptual model. The system and system components are identified and the mutual relations are determined (figure 8). The relations are translated into a conceptual model, wherein the three different types of risks are represented (figure 9). In the analysis phase the design objectives are identified. 1) Design a model to improve service to the consumer, based on product specific end-to- end supply chain risk. 2) Design a model to optimize stock levels with respect to the desired service level. 3) Trust of the important stakeholders in the model and outcomes. 4) Comprehension of the model and the process. Optimizing stock levels based on the risks in the supply chain and improving service are the main design objectives. These design objectives can be divided in three parts: service optimization, stock optimization and product specific end to end risks. The risk profiles, based on the upstream product risk analysis are the first part of this design result. The analysis of the product retailer specific downstream risks forms the second part. The combination of both risks in the model show the end result of the end to end product specific analysis. The model generates advice based on a desired service level. The optimization is performed due the product specific risks. The model indicates that the overall stock level can be decreased. The effects on service, the last part of the design objectives are hard to measure. In fact the service can be measured in practice, but the effects of stock reallocation on service are not clear yet. Unilever and Retailer X can also choose to keep the total stock level constant and rearrange the set up of stock. Increasing stock levels of high risk products will have positive effects on the performed service to the consumer. Beside these feasible objectives the model is only successful when the other two less feasible objectives 3 and 4 are accomplished. These objectives are determined in the actor analysis (section 3.4). Cooperation, transparency, comprehension and trust in the model are the four most important issues for a successful implementation and utilization. The only syntactic goal is syntactic correctness; multiple checks detected and corrected the syntactic errors of the advice model and helped to redesign the model during the design phase.
Semantic quality is the degree of correspondence between the model and the real world. For completeness and acceptance by audience it is important that the most important and significant aspects are part of the model set up. The most important aspects like demand and lead time fluctuations, desired service degree and number of orders per week are covered in the model. How these different aspects relate to each other is explained in the user manual. The only missing part in the model is formed by the DC risks which are assumed insignificant.
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The model will be transmitted to a key user within Unilever, who will be responsible for the updates and maintenance of the model. To accomplish compression of the model intensive and regular contact with the end user is important. The set up of the model is several times discussed with field experts and the end users (the LAO’s of Unilever). Vague aspects are clarified or taken away. When the final advice model was constructed, the results and model are presented to the end user and other logistic stakeholders to ensure comprehension, trust and take away vagueness. Finally the advice model is translated in an advice tool, wherein only limited data input is necessary to improve usability and reduce complexity. The simulation model is built in the discrete modelling software Arena. The simulation results are used to validate the outcomes of the advice model. The simulation is run for 8 products with the generated stock level advice for a desired service of 97% and 99%. The reorder point (safety stock + average demand * standard lead time) and batch size (order size) are input for the model. The difference between simulation and model service are 1,3% for the generated advice of 97,5% service and 0,2% for the generated advice of 99% service. The simulation not only validates the average service but also the product specific results are very similar. The service for a 97,5% safety stock advice is higher in the simulation then expected from the model. The simulation indicates a 1,3% higher service then the desired model service. Besides validating the model, these outcomes also validate the statements of Grubbström and Tang that their approximations are more accurate for higher service levels. Another validation aspect is based on the average stock levels. The major part of the stock levels of the simulation is very similar with the model stock levels. For the most products the service is higher and the stock levels are lower at the simulation. This indicates that even when average stock levels are decreased, the service level can stay equal. The simulation validates the outcomes for the 99% service advice and indicates higher then expected service levels for the 97,5% advice. There can be concluded that the simulation confirms the results of the advice model.
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10. Conclusions and Recommendations
10.1 Introduction Conclusions and Recommendations In the previous chapter the model design is evaluated and the model results are validated. Now it is time to draw conclusions, based on the results of this research. The research questions determined the direction and structure of the research. In the conclusions answers are provided on the research questions. The research questions therefore form the structure of the conclusions section (10.2). Some recommendations are provided (section 10.3), for aspects out of the research scope and aspects which deserve further research.
10.2 Conclusions of the Research Service to the consumer is the most important driver for Unilever and Retailer X. Improving the service will result in fewer Out of Stocks on the shop floor and in less lost sales. Due to the credit crunch Retailer X focuses also on stock reductions but the service level must at least stay equal. This research has two objectives. • Design an advice model that can help to optimize the stock levels at the distribution centre (DC) of Retailer X for a desired service level • The advice model can function as a decision supporting tool and is a first step for further supply chain optimization between Unilever and Retailer X The stock level optimization or reallocation results in better service to the consumers, which can be measured in On Shelf Availability. The decision support function of the model and the insights provided through the model can also be used to realize service improvement. The performed research gives an answer on the main research question: How could a stock-advice model for the retailer’s distribution centre contribute to optimize stock levels and improve the On-Shelf-Availability (OSA) of Unilever products based on end-to-end supply chain information involving costs and risks considerations? Before an answer on the main question can be formulated, first the sub questions will be answered, based on the performed research. The sub questions (as set in section 1.2.3) will now be discussed and answered systematically. Mention that the sequence of the question numbers is somewhat different due to this systematic approach. The objective of the research is to design a model that gives stock level advice for the DC stock levels of Retailer X for a desired service level and provides insights in the supply chain. Before this model can be designed, investigation of the risks which influence the safety stock level is necessary. 1. Which major factors determine the On Shelf Availability of Unilever products? The availability of products at the shop shelves (OSA) is determined by several aspects. Product availability at the DC is one of these aspects. This DC availability is formed by the stock levels, the main topic of this research. The risks, which influence these stock levels, can be grouped in
Master Thesis Project – Dion van de Gazelle 97 three risk sub models, influencing the stock level at the DC of Retailer x: upstream risks, downstream risks and DC risks (section 4.3).
Figure 13 Conceptual model risks determine stock level. These three risks in combination with a desired service level to the stores determine the stock level at the Retailer’s DC. The downstream risks are formed by the demand pattern per product and can not be influenced by Unilever of Retailer X. The DC risks are formed by handling time and unique events at the Retailer’s DC. There is not much data to analyze this type of risk, therefore it is decided that this risk is not part of the research scope. The upstream risks are formed by the reliability of the supply of Unilever to the DC of Retailer X. Based on literature and experts a list is created of product/production characteristics which possibly influence the delivery performance of Unilever. The major characteristics which determine the On-Shelf-Availability are: • Responsiveness sourcing units • Sourcing unit distance • Volume • Stock level Unilever • ABC qualification (Pareto) • Sourcing unit • Best Before Date (BBD) • Seasonality • Product category (Home and Personal Care or Food). These characteristics influence the delivery performance significantly. Grouping products with the same risk characteristics can help to generate risk specific advice for a product. The identified characteristics can now be used to form product groups. 2. Which product groups can be identified, categorized in behavior and properties? To generate product specific advice, products with the same upstream risks are grouped in risk profiles. With a regression analysis is tested which theoretical characteristics are significant. SU, BBD, volume, ABC qualification, HCP/ Foods, seasonality and Unilever stock level are significant predictors of delivery performance. For usability reasons the characteristics are grouped with a factor analysis.
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The groups are formed based on 4 profile characteristics: • Volume • Sourcing unit • HPC / Foods • Seasonality for food products Based on these characteristics risk profiles are set. These risk profiles determine the different groups. The products in the data set of this research are divided among the different groups. Together with the product specific demand pattern a stock level advice model is designed and constructed. This model is able to provide advice for the current stock levels of Retailer X.
3 a. Which stock advice, based on the current situation, can be generated by the model for different products groups regarding the current stock levels? In the current situation Retailer X can send in orders every day to Unilever. The order frequency is based on the order behaviour of ordering on full pallets. The products are grouped based on the upstream risks (risk profile) but the difference in their demand pattern makes that the advice model generates a separate and specific advice per product. The safety stock is not only determined by risk, but also by the desired service level. In this research the model generates advice for a service level of 97,5% and 99% (table 8). The average safety stock for the products in the data set is 2,15 days. The average total stock level is 4,4 days (= Safety stock + cycle stock), based on the current order frequency for a service level of 97,5% to the shops. To gain a service level of 99% a safety stock of 2,88 days is required with a total average stock of 5,1 days. Service level 97,50% 99% Safety Stock (boxes) 165 219 Safety Stock (days) 2,2 2,9 Total Stock (boxes) 260 304 Total Stock (days) 4,4 5,1 Table 8 Average stock level advice.
Service to consumers (measurable in OSA) is a big driver for Unilever and Retailer X. But service is limited by costs. The costs for improving service increase enormously for high service levels. The costs to improve service from 90% to 91% are far lower then an improvement from 97% to 98%. The next step is to analyze how service improvement and costs are related. 3 b. Which costs are effected by the stock advice and how do the costs influence the process of stock level decisions? Storage, Risk and Interest There are not only storage costs for products, but also risks and interest can be expressed in terms of costs. The storage costs are dependent on the safety stock and therefore related to the desired service level. The higher the desired service level, the higher the necessary safety stock. Order Pattern Beside the safety stock also the order pattern is of influence. When the supply chain coordinator orders 6 times a week the cycle stock is much lower, when if he orders only once per week. On the other hand the retailer receives a discount when ordering on full pallets and
Master Thesis Project – Dion van de Gazelle 99 more orders results in more order costs. The reorder point determines the optimal (in terms of costs) point to send in an order and therefore determines the stock level. Combining the order advice with the advice model gives direction to the order behavior in combination with safety stock of Retailer X and helps to answer the question: 6. What changes in order behavior are recommended due to the new stock level advice regarding to the constructed product groups? Order and Stock Strategy Retailer X can use the combination of the order advice and the safety stock advice model to set up his order and stock strategy. Some choices must be made product specific, for some products service is far more important then costs and the extra costs do not weigh up to the advantages of the higher service. These however are strategic decisions of the retailer and can’t be made for them. Decision Supporting Tool The safety stock advice model can help Unilever and Retailer X to determine their strategy and reach their desired service (improvements). The advice model is not only a tool but also helps to understand the complexity and can help to test what effects decisions can have in a real situation. This is an import research objective and is related to the role of the advice model. 4 What role does the stock level advice model have and which criteria must be developed to fulfill this role? Modelling Quality and Usability The most important (syntactic) criteria is model correctness. The design must be supported by literature and built according to the rules of the model language. Semantic criteria like completeness and validity are also important. The model must represent the real situation and trusted as valid by the users and stakeholders. The advice model is checked on validity manually but also with a simulation. The simulation results ground the correctness of the model results. Besides these base criteria comprehension is extreme important for the usability and trust. Does the audience understands what the modeler intended? To accomplish this understanding of the model, intensive and regular contact with the end user is important. The set up of the model is several times discussed with field experts and the end users (the LAO’s of Unilever). Vague aspects are clarified or taken away. When the end users came up with suggestions (limited delivery moments, full pallets ordering, 24 hours lead time), these suggestions were taken into account and are implemented in the model to improve and ensure usability. When the final advice model was constructed, the results and model are presented to the end user and other logistic stakeholders to ensure comprehension, trust and take away vagueness. To improve usability and reduce complexity the advice model is translated in an advice tool, wherein only limited data input is necessary. The logistic account officer of Retailer X was also indicated as a possible sponsor for the project. In practice he was not willingly to take this sponsor position. This made it difficult to keep the LAO of Retailer X involved during the process. Retailer X agreed to cooperate and eventually delivered the necessary data, but a drawback is that they were not involved during the design of the model. Trust and Transparency Trust and understanding are very important and decisive for the acceptance and future use of the model. Transparency, information sharing and partnership can have positive influences on supply chain costs and performance. Openness can contribute to improve supply chain performance and reduce costs.
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5. What are the effects and results of an intensifying transparency and communication between the involved stakeholders? Intension for Openness Retailer X and Unilever both speak out their intentions for more openness. Better and faster information sharing is even one of the key points in the new Supply Chain Strategy of Unilever for the upcoming years. Transparency is important to realize improvements within supply chain logistics and stock level management. The stakeholder analysis (3.4) mentions that intense contact with the end users during the design process is very important. Data Sharing Unilever should be aware of the possibilities and dangers of this transparency. Creating trust, comprehensions and commitment in combination with clear agreements and mutual goals are vital for this process of intensive cooperation. This model can be used as a first step to create trust and commitment. In the future this relation can be used and intensified for further supply chain improvements. Several steps are identified to achieve the purposes of the advice model. Open Discussion The influence of Unilever on OSA is limited (section 2.4) but with this model combined with an order advice it’s possible for Unilever and Retailer X to focus on the service level to stores. For Unilever this helps to increase their influence with one step and opens the discussion on OSA at the shop level in the future. With help of the sub questions it’s possible to formulate an answer on the main question: How could a stock-advice model for the retailer’s distribution centre contribute to optimize stock levels and improve the On-Shelf-Availability (OSA) of Unilever products based on end-to-end supply chain information involving costs and risks considerations? The stock level advice model can give a product specific advice based on the risk profile for the upstream risks and the product specific demand pattern. Based on the current situation, the model generates an average stock level advice of 4,4 days for a service level of 97,5% to the shops. The average advice for a service level of 99% is 5,1 days. Retailer X indicates that the current average stock is around 6 days. There is no product specific stock level data for the current situation, but the results of the advice model indicate that a product risk specific safety stock level has potential to decrease the total stock level. The simulation supports this potential and proves that an equal service is possible with lower stock levels. Better adaptation of stock levels should cause a small improvement on the store casefill (service to the shops) as is described in section 4.2. The model results and insights can help Unilever and Retailer X to take decisions based on costs and service. Besides this advice and service improvement the model opens the way for further integration and cooperation of Unilever and Retailer X. The influence of Unilever on OSA is limited but improving and focus on the store casefill together gives Unilever the opportunity to get more influence down in the supply chain. It’s the first step to further cooperation to improve the OSA level.
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10.3 Recommendations for further Research and Logistic Policy Although this research project is finished, the insights of this research have identified other future work which can be performed. These recommendations are divided into three recommendations for Unilever and three recommendations for future research.
10.3.1 Recommendations for Unilever The first and most important recommendation is to establish, improve and extend the cooperation and openness with Retailer X. As described in the conclusions the advice model is a first step to further OSA improvement. Unilever currently only has a limited influence on OSA and extended cooperation and openness can and will increase this influence. The first step is steering together on improving store casefill. From here it’s maybe possible to even steer on back store service and eventually on OSA. The next steps to turn the model into practice, presented in section 8.3.4, can help to start this process. With increased cooperation and openness it is even possible to consider Vendor Managed Inventory (VMI), where Unilever can steer the stock levels of Retailer X. This system however bears a lot of investments in organization, employees and ICT and some experts inside the business doubt the impact of the added value. So here is plenty room for discussion and further investigation of the options. Unilever and Retailer X are interested in ongoing cooperation and therefore the second recommendation is to investigate the options and the benefits and drawbacks of implementing VMI or at least the frequency and intensity of data sharing. In section 2.4 some root causes for OOS are identified. These root causes where out of the research scope, because they can not be influenced by Unilever. When cooperation and openness is improved further, it is possible to examine these root causes and identify options to solve the root causes. Extended cooperation opens especially options for examining planning and forecast root causes like demand underestimation, introductions/relaunches, long order cycles and promotions. It’s even possible to investigate improvements on the replenishment level like shelf refilling, back store problems, inadequate shelf allocation and low replenishment frequency. This focus on replenishment is only possible when also the stores owners are part of the cooperation.
10.3.2 Recommendations for Research The fourth recommendation is aimed on promotions. Promotions are not part of this research, because the promotion characteristics influence the normal demand pattern and lead time. It would be worthwhile to also investigate lead time and demand pattern for promotion products. There are a lot of promotion related delivery problems. Promotions can be seen as an extra risk above the normal risk pattern. Adapting the safety stock level on this extra risk can help to improve service during promotions. The advice model is based on upstream and downstream risks. DC risks are not in the scope of the research but do have influences on stock levels. Re packing and handling cause extra lead time, which is not covered in the model. The stock level advices will be more accurate when the DC risks are also implemented in the model. Mention that logistic problems and picking mistakes influence the store casefill but can not be eliminated with higher safety stock levels. These risks come on top of the stock level risks, because they are not related to the height of the stock levels.
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The last recommendation is an extension of the model. The current model only includes service and stock level considerations. Stock and service level decisions are related with cost considerations. These costs are not part of the model yet. The model can support decision taking based on service and risk characteristics but implementing a cost aspect improves the quality of decision support power of the model. Adding a cost component therefore is recommended for further research. To finish this research some critical notes about the research and the process will be discussed in the next chapter.
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11. Reflection
11.1 Introduction Reflection This research report described how a stock level advice model can contribute to the improvement of On Shelf Availability. In the previous chapter the conclusions of this research are represented and there are some recommendations given, on how to use this research for further extension of the cooperation and for further research. Now it’s time to discuss and reflect on the performed research and the methods, theory and data which are used. Some (parts of the) reflections are more focused on practice (Unilever) and others more on science (TU Delft). The abbreviations UL and TU indicate this special focus and distinguish the different reflection parts. The first section of this chapter (11.2) consists of some reflections on the used theory and methodologies. In the second section (11.3) the data and outcomes will discussed and there is some reflection on how to interpret the outcomes. Some personal reflections are given in section 11.4.
11.2 Reflection on Theory and Methodology TU – Safety Stock Theory The advice model is based on an approximation to determine the service factor (k), by using higher order moments. The relative high number measurements with a lead time of exact two days make it impossible to find a distribution behind the lead time data. Therefore the distribution is split up in a Bernoulli distribution with p = 2 and (p 1) = β distribution, to describe the lead time. This solution is introduced to make the approximation of the k factor possible, but it not clear why other lead time researchers not have the same problems. In a lot of industries, especially in the food industry, delivery performance is very high. During lead time investigations, there will be a lot measure points at the minimum lead time. In such case it is not possible to find a distribution that determines the lead time. In this research the split up in two Bernoulli distributions is introduced for lead team research. Unclear is how other researchers have covered these problems and if they use another method to solve this problem. TU – Model Theory and Methodology In this research is chosen to design a model based on stock theory and approximation methodology. This method has several benefits. The first benefit is the product specific stock advice based on risk profiles and demand pattern. This risk profiles can used again for other products or retailers and therefore increases generality. Another benefit is the simplicity of the usability. The tool is simple and well structured and supports the user during the different advice generating steps. The last benefit is that this theory made it possible to use higher order moments and therefore the results are more accurate. The method also has some drawbacks. Despite the simplicity of the user interface, the calculation behind the tool is very complex. Hand over the model and clarify all the ins and outs of the model to the end user is difficult due this complexity. An approach with a standard safety stock formula is an option to reduce this complexity, but has negative consequences for the result accuracy. Simulation is an alternative methodology for this research. With simulation a less complex model structure is sufficient to provide an answer. Based on multiple simulation runs an optimal service level can be deducted based on service, stock levels and cost considerations. Another benefit of this method is that simulation and especially animation can help to create
Master Thesis Project – Dion van de Gazelle 105 user/client comprehension. A drawback is that the model can not be used without the designer. The only output is formed by the model results and model insights of the current situation. The end user can not adapt the simulation for other products or retailers without the designer. This research tried to incorporate the benefits of simulation through a simulation validation of the advice model results. UL – Generality One of the biggest benefits of the design set up is the generality. This general set up make it possible to use the research also for other retailers. The lead time is determined on the actual performance of lead time to Retailer X. the delivery performance of Retailer X is comparable with the delivery performance of other retailers. The upstream risk profiles are therefore general usable and a good set up for research on other retailers. The possibilities to adapt some product and retailer characteristics in the tool also support the generality of the model. The user should be careful when implementing these adaptations; otherwise the model behaviour can change dramatically. The manual and comments in the tool help the user to maintain and adapt the model. Colors are used to improve usability and reduce possible fill out failures when using the tool.
11.3 Reflection on Data and Results UL & TU – Regression Coefficients The product and production characteristics are analyzed with a regression analysis, to create a risk profile. The R square of the regression model is only 0,035, which is rather low. Although this low R square value some characteristics where significant due there high number of measure points. This low r square could be a problem when it is used to predict the delivery performance. In this research the regression is used to form groups and set up a risk profile for the upstream risks. The upstream risks, of these risk profiles, are analyzed based on lead time data. This lead time is not related with the regression analysis and therefore the low R square has not influence on the lead time or expected service level. So for this research the low R square is not a problem, but one should be careful to extend the conclusions taken based on the regression analysis.
UL & TU – Data Set In this research an initial data set of 88 products is used. During the research and design some products are filtered out of the data. The Calve pindakaas for example is filtered out because this product, with a special relaunch, was not representative for the baseline situation. The Unox erwtensoep is used during the lead time analysis, but is filtered out later, because there was no data for the demand pattern. This research works with this set of products because the OSA of these products is measured and one of the objectives was to determine if a stock level given advice can contribute to the improvement of OSA level. The effects on OSA can be measured when the model results are used by Unilever and Retailer X. When the risk profile was set up the products in the data set were divided among the groups based on their characteristics. However the data set was random and representative for the Unilever assortment not all groups contain equal number of products. Some groups don’t have even a single product. For these groups advice, supported by the model, can not be generated. Group 12 only contains one product, Calve knoflooksaus. This product had a very high delivery performance during the measuring period (99%), but based on the risk profile a lower delivery performance is expected. When generating advice for products out of the data set the lead time for. For the groups with only a small number (or zero) products it’s wise to extend the research and reconsider the lead time.
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UL & TU – Sponsorship Retailer X A major role for the Logistic Account Officer of Retailer X was expected at the start of this research. Retailer X focuses on stock reduction and service improvement, optimizing stock levels can support this focus. During the project the sponsorship of Retailer X disappeared. Retailer X indicates that they were not ready to start with the OSA improvement track at that moment. To regain their sponsorship the stock level research is set up as a pre track of the OSA track. The stock level research indicates possibilities for stock level reductions and the tool is a first step, as decisions supporting tool, for further cooperation and supply chain improvement. Retailer X confirms the benefits of this pre track and decided to cooperate on data sharing. This data was essential to continue the research. The drawback of this restricted sponsorship is that Retailer X was not closely involved during the set up of the structure. This non involvement during the set up makes it harder to convince Retailer X of the correct structure of the model. For further optimization or extension of the model, the sponsorship of Retailer X must definitely be extended into the set up phase. The mutual goals and agreements on the OSA track already confirm the importance of this statement. UL – A, B and C Products The gross of the products in the data set are A and B products (based on the pareto classification). There are some C products (low volumes, less importance) involved but the number is very low. This will influence the conclusions for low volume products. An extension of the number of low volume products can strengthen the outcomes and improve the reliability of the results. UL – Ordering Behaviour The advice model generates product specific advice, based on the group upstream risks and the product specific demand, for a desired service level. In this model Retailer X orders at full pallets when possible. Retailer X orders at least once every two weeks and maximal once a day. Based on economic order quantity and demand pattern the frequency of ordering is different for all products. Actual order pattern can be slightly different; the effects on the average advice will be minimal. UL – Beta distribution The Beta distribution which describes the lead time, in case Unilever delivers too late, is based on all the measurements with a lead time above two days. The consequence is that an average distribution in case of too late delivery is found. This average under or overestimates the responsiveness of some groups. This was a design choice based on the available data set, but with more products per group the Beta distribution can be made more group specific. UL & TU – K-factor Approximation Some reflection is also necessary for the outcomes in the model. The advice model uses an approximation to determine the k factor. The approximation table assumes that the β1 of the compound distribution is between 0 and 4. For the most products the β1 is in this range but some products are a little above 4. This will slightly influence the outcomes but shall not have enormous effects on the given advice. Special attention is necessary for the 24 hours advice when the retailer can order only once a week. The β1 then drops often below 0,3 and then the approximation of the k factor is less accurate. Another approximation must used in this case; this is also mentioned in the tool and the manual to help and protect the user against mistakes.
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UL – Improving Store Casefill The model results indicate that it is possible to decrease stock levels for a service of 99% to the stores. Currently Retailer X has a store casefill of 98,3%. Is it possible to decrease stock and achieve a higher or equal service? A short analysis shows how the reallocation of stocks make it possible to keep service equal and reduce the total average stock level. Total Below 99% Above 99% Average storecasefill 98,3 <99 >99 Average casefill UL 98,6 97,4 99,1 SS advice 2,9 3,1 2,7 Total Stock advice 5,11 5,48 4,83 Table 9 Analysis of store casefill Retailer X.
Table 9 shows the results of this analysis. The products of the data set are divided in a group with a store casefill above 99% and below 99%. The table indicates that the low store casefill group has a lower average casefill from Unilever then the high store casefill group. This indicates that the delivery performance has some effect on the store casefill. The table also shows that the safety and total stock advice is significant higher on average for the low store casefill group. This means that the given advice takes the extra risks in account. The advice gives a good indication for reallocation stocks and therefore the total average stock level can decrease where the service stays equal. Interesting is also that the store casefill is lower then the Unilever casefill. This means that Retailer X is also responsible for some problems of the store casefill. These (logistic) problems can not be solved with higher stock levels. The 99% service to the stores therefore must be seen as a theoretic service level. When Retailer X fails in their logistic performance, service will be lower then 99% no matter how high the stock levels are.
11.4 Personal Reflection After some reflection on theory and data it is now time for some personal reflection on the research. The first remark is about the tension between theory and practice. In theory there can be developed very strong and ingenious models with high expectations. But in practice one should work with the available possibilities and data. It was not easy to find a possibility and design an advice model based on stochastic lead times; where lead time not even could be described with one probability function. This search has taken some time and some pioneer work was done on the area of higher order moments of lead times with a combination of a Bernoulli and β distribution. The second remark is about the contact between me and Retailer X. when I started the research I taught that there were clear agreements about my project between Unilever and Retailer X. But in fact Retailer X was not so willingly to cooperate at all times and I expected to get more and intense contact with Retailer X. The lack of this contact made it hard to keep them close informed as end users. Presenting the options and the results of the model will make them aware of the possibilities and shall convince them to improve and extend further cooperation to improve OSA in the future, but their influence on the set up of the model is limited to some design constraints. Stronger and clear agreements about the project set up can bear such difficulties in the future as indicated in the stakeholder analysis and is mentioned in the next steps (section 8.3.4) to be taken to translate the model to practice.
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In the evaluation and validation phase is evaluated in which degree the design and the model objectives are accomplished. In the analysis phase the modeling objectives are identified 1) Design a model to improve service to the consumer, based on product specific end-to- end supply chain risk. 2) Design a model to optimize stock levels with respect to the desired service level. 3) Trust of the important stakeholders in the model and outcomes. 4) Comprehension of the model and the process. Optimizing stock levels based on the risks in the supply chain and improving service are the main design objectives. These design objectives can be divided in three parts: service optimization, stock optimization and product specific end to end risks. The risk profiles, based on the upstream product risk analysis are the first part of this design result. The analysis of the product retailer specific downstream risks forms the second part. The combination of both risks in the model show the end result of the end to end product specific analysis. The model generates an advice based on a desired service level. The optimization is performed due the product specific risks. The model indicates that the overall stock level can be decreased. The effects on service, the last part of the design objectives are hard to measure. In fact the service can be measured in practice, but the effects of stock reallocation on service are not clear yet. Unilever and Retailer X can also choose to keep the total stock level constant and rearrange the set up of stock. Increasing stock levels of high risk products will have positive effects on the performed service to the consumer. The syntactic goal is correctness; multiple checks detected and corrected the syntactic errors of the advice model and helped to redesign the model during the design phase. Semantic quality is the degree of correspondence between the model and the real world. The most important aspects like demand and lead time fluctuations, desired service degree and number of orders per week are covered in the model, and support completeness and acceptance by the audience. The only missing part in the model is formed by the DC risks which are assumed insignificant. To ensure compression of the model intensive and regular contact with the end user is important. The set up of the model is several times discussed with field experts and the end users (the LAO’s of Unilever). Vague aspects are clarified and the final results and model are presented to the end user and other logistic stakeholders to ensure comprehension, trust and take away vagueness. The translation from model into practice is very important to support this comprehension of the audience. Simulation is used to validate the outcomes of the advice model. For 8 products is the performance simulated, for the stock level advice with a desired service of 97% and 99%.The difference between simulation and model service are 1,3% for the 97,5% advice and 0,2% for the 99% advice. The simulation not only validates the average service but also the product specific results are very similar. Besides validating the model, these outcomes also validate the statements of Grubbström and Tang that their approximations are more accurate for higher service levels. Another validation aspect is based in the average stock levels. The most stock levels of the simulation are very similar with the model stock levels. There can be concluded that the simulation validates the outcomes for the 99% service advice and indicates higher then expected service levels for the
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97,5% stock advice. There can be concluded that the simulation confirms the results of the advice model.
Service to the consumer is the most important driver for Unilever and Retailer X. Improving the service will result in fewer Out of Stocks on the shop floor and in less lost sales. Due to the credit crunch Retailer X focus also on stock reductions, but the service level must at least stay equal. The goal of this research is to design an advice model that can help to optimize the stock level at the DC of Retailer X for a desired service level. Besides this main goal the research and the model should also function as a supporting tool and as a first step for further supply chain optimization. The stock level optimization or reallocation results in better service to the consumers, what can be measured in On Shelf Availability. The performed research gives an answer on the main research question: How could a stock-advice model for the retailer’s distribution centre contribute to optimize stock levels and improve the On-Shelf-Availability (OSA) of Unilever products based on end-to-end supply chain information involving costs and risks considerations? Upstream risks, downstream risks and DC risks in combination with a desired service level to the stores determine the stock level at the Retailer’s DC. Grouping products with the same risk characteristics can help to generate risk specific advice for a product. Based on the product risk analysis there are defined 4 significant risk characteristics: Volume, SU, HPC / Foods and seasonality for food products, forming 18 groups with the same risk profile. In the current situation Retailer X can send in orders to Unilever 6 times a week. The order frequency is based on the order behaviour to order on full pallets. The safety stock is not only determined by risks but also by the desired service level. In this research the model generates advice for a service level of 97,5% and 99%. The average safety stock for the products in the data set is 2,15 days. The average total stock level is 4,4 days (= Safety stock + cycle stock), based on the current order frequency for a service level of 97,5% to the shops. To gain a service level of 99% a safety stock of 2,88 days is required with a total average stock of 5,1 days. Service to consumers (measurable in OSA) is a big driver for Unilever and Retailer X. But service is limited by costs. The costs for improving service increase enormous for high service levels. The costs to improve service from 90% to 91% are far lower then an improvement from 97% to 98%. There are not only storage costs for products, but also risks and interest can be expressed in terms of costs. The storage costs are dependent on the safety stock and therefore related to desired service level. The higher the desired service level, the higher the necessary safety stock. Beside the safety stock is also the order pattern of influence. The reorder point determines the optimal (in terms of costs) point to send in an order and therefore determines the stock level. Combining the order advice with the advice model gives direction to the order behavior in combination with safety stock of Retailer X. The safety stock advice model can help Unilever and Retailer X to determine their strategy and reach their desired service (improvements). The advice model is not only a tool but also helps to understand the complexity and can help to test what effects decisions can have in a real situation. The advice model is checked on validity manual, but also with a simulation. The simulation results ground the correctness of the model results. Besides these base criteria comprehension is extreme important for the usability and trust. Understands the audience what the modeler intended? To accomplish this understanding of the model intensive and regular contact with
Master Thesis Project – Dion van de Gazelle 110 the end user is important. The set up of the model is several times discussed with field experts and the end users (the LAO’s of Unilever). Vagueness aspects are clarified or taken away. When the end users came up with suggestions (limited delivery moments, full pallets ordering, 24 hours lead time) are taken into account and are implemented in the model to improve and ensure usability. When the final advice model was constructed, the results and model are presented to the end user and other logistic stakeholders to ensure comprehension, trust and take away vagueness. To improve usability and reduce complexity the advice model is translated in an advice tool, wherein only limited data input is necessary. The logistic account officer of Retailer X was indicated as a possible sponsor of the project. In practice he was not willingly to take this sponsor position. This made it difficult to keep the LAO of Retailer X involved during the process. Retailer X agreed to cooperate and eventually delivered the necessary data, but a drawback is that they were not involved during the design of the model. Trust and understanding are very important and decisive for the acceptance and future use of the model. Openness can contribute to improve supply chain performance and reduce costs. Besides the advice and service improvement the model opens the way for further integration and cooperation of Unilever and Retailer X. Unilever and Retailer X both speak out their intentions for more openness. The influence of Unilever on OSA is limited but improving and focus on the store casefill together gives Unilever the opportunity to get more influence down in the supply chain. For Unilever this is increasing their influence with one step and opens the discussion on OSA at the shop level in the future. The steps provided in this research, to turn the model in practice, can help to start this process. These conclusions are translated in recommendations for further extending the cooperation and openness and investigating the options for data sharing and VMI. Further research is necessary on promotions, downstream OSA root causes and cost considerations.
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[33] Fernie, J. and Staines, H. (2001). Towards an understanding of European grocery supply chains. Journal of Retailing and Consumer Services, 8:p.29 36. [34] Fisher, M.L., Hammond, J.H., Obermeyer, W.R. and Raman, A. (1994). Making Supply Meet Demand in An Uncertain World. Harvard Business Review, 72 (3):p.83 93. [35] Fleisch, E. and Tellkamp, C. (2005). Inventory inaccuracy and supply chain performance: a simulation study of a retail supply chain. International Journal of Production Economics, 95 (3):p.273 385. [36] Grubbström, R.W. and Tang, O. (2006). On using higher-order moments for stochastic inventory systems. International Journal of Production Economics, (104):p.454 461. [37] Haddock, J., Iyer, T. and Nagar, A. (1994). A heuristic for inventory management of slow-moving items. Production Planning en Control, 5 (2):p.163 174. [38] Hair, J.F., Anderson, R.E., Black, W., Babin, B. and Tatham, R.L. (2006). Multivariate Data Analysis , Tulsa: PPC Books. [39] Herder, P.M. and Stikkelman, R.M. (2004). Methanol-based industrial cluster design: a study of design options and the design process. Industrial & Engineering Chemistry Research, 42:p.3879 3885. [40] Hoeven van der, M. (2009). Voorraadmanagement. Supply chain magazine, 7:p.18 22. [41] Huveneers, I. (2008). Voorraad politiek van slow-moving items , Hasselt, University of Hasselt. [42] Jammernegg, W. and Reiner, G. (2007). Performance improvement of supply chain processes by coordinated inventory and capacity management. International Journal Production Economics, 108:p.183 190. [43] Johansen, S.G. and Thorstenson, A. (1993). Optimal and approximate (Q, r) inventory policies with lost sales and gamma-distributed lead time. International Journal of Production Economics, (30):p.179 194. [44] Johnston, F.R. and Boylan, J.E. (1996). Variance law for inventory management. International Journal of Production Economics, 45:p.343 352. [45] Lindland, O.I., Sindre, G. and Sølvberg, A. (1994). Understanding Quality in Conceptual Modeling. IEEE Software, 11 (2):p.42 49. [46] Liu, P.C. and Zinn, W. (1998). Consumer response to retail stockouts. Journal of Business Logistics, 22 (1):p.49 71. [47] MacArthur, J. (1997). Stakeholder analysis in project planning: origins, applications and refinements of the method. Project Appraisal, 12 (4):p.251 265. [48] Marmostein, H. and Zinn, W. (1990). Comparing two alternative methods of determining safety stock levels: the demand and the forecast systems. Journal of Business Logistics, 11 (1):p.95 110.
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[49] Molin, E.J.E. (2008). SPM2330:Onderzoek en dataverwerking Lecture slides , Delft: TU Delft. [50] Nahmias, S. and Smith, S.A. (1994). Optimizing inventory levels in a two-echelon retailer system with partial lost sales. Management Science, 40 (5):p.582 596. [51] Papakiriakopoulos, A.D. (2006). Automatic Detection of Out-Of-Shelf Products in the Retail Sector Supply Chain. [52] Pramatari, K.C. and Miliotis, P. (2005). Increasing Shelf Availability Through Internet- Based Information Sharing and Collaborative Store Ordering , Athens: Department of Management Science and Technology. [53] Reid, R.D. and Sanders, N.R. (2005). Operations Management, an integrated approach , USA: John Wiley & Sons. [54] Roland Berger (2002). Full-Shelf Satisfaction, Reducing out-of-stocks in the grocery channel , Grocery Manufacturers of America (GMA). [55] Schneider, M. (2009). Out-of-Stock out of business , Enschede, TU Twente. [56] Seger, Y. (2006). Voorraadreductie via leveringen door verschillende leveranciers , Hasselt, University of Hasselt. [57] Silver, E.A., Pyke, D.F. and Peterson, R. (1985). Inventory management and production planning and scheduling , New York: Wiley. [58] Simatupang, T.M. and Sridharan, R. (2002). The Collaborative Supply Chain: A Scheme for Information Sharing and Incentive Alignment. The International Journal of Logistics Management, (13):p.15 30. [59] Sloot, L.M., Verhoef, P.C. and Franses, P.H. (2005). The impact of brand equity and the hedonic level of products on consumer stock-out reactions. Journal of Retailing, 81 (1):p.15 34. [60] Swaminathan, J.M. (1998). Modeling supply chain dynamics: A multiagent approach. Decision sciences, 29:p.607 632. [61] Thomas, D.J. (2002). Customer Service models for bricks, clicks and in between. Supply Chain Management: Models, applications and research directions, 62:p.19 32. [62] Unilever (2006). Unilever’s Practical Guide to PoP Performance Excellence , Rotterdam: Unilever. [63] Unilever (2007). 5 Step Approach to Excellence in On Shelf Availability , Rotterdam: Unilever. [64] Unilever (2008a). Annual report Unilever 2008: Vitaliteit toevoegen aan het leven , Rotterdam: Unilever. [65] Unilever (2008b). Unilever information standard on shelf availability, Rotterdam: Unilever.
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[66] Unilever (2009). Vision and Strategy , Rotterdam: Unilever. [67] Verbraeck, A. and Valentin, E.C. (2005). Discrete Modellen deel 2 , Delft: [68] Vlist van der, P. (2007). Synchronizing the Retail Supply Chain , Rotterdam, Erasmus University Rotterdam. [69] Vocht de, A. (2000). Basishandboek SPSS , Utrecht: A. de Bijleveld Press. [70] Walker, M. (1994). Supplier-Retailer Collaboration in European Grocery Distribution. Logistic Information Management, (7):p.23 27. [71] Zhao, X., Xie, J. and Leung, J. (2002). The impact of information sharing and ordering co-ordination on supply chain performance. Supply Chain Management: an International Journal, 7 (1):p.24 40.
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Appendices
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Appendix A Interview Retailer X
Introduction and orientation with Retailer X (23 04 2009) What strategy focus has Retailer X? The most important factor for our focus is the customer. The customer buys products and generates turnover and we will keep and expand this number of customers. We definitely focus on service to achieve this goal, so the customer is our main strategy focus. How is the current ordering process arranged? Store owners forecast their demand and sent in their order quantity. At the DC’s these quantities are collected and compared with the stock level and statistic and seasonal forecasts. When necessary a combined order is send to the manufacturers. Should stock level advice be helpful to improve this ordering process? There are people within Retailer X responsible for the stock levels. Their job is to determine these levels based on demand, lead time, statistics and history. So a total stock advice will not be necessary from their perspective. Some insights in the delivery side of the supply chain and the consequences on the lead time would be valuable and help for a better understanding of the delivery side. Are the DC’s well organized and equipped? The DC’s differ related to their function, age and surface space. In Breda there is a relative new DC with enough storage space and possibilities for adaptations. Other DC’s like the one in Elst have far less space and storage of extra stocks is not possible yet. When possible these stock levels should be reduced. We currently have stock levels of one week for the greater part of our SKU’s. What future perspectives do you see in relation to stock control? An analysis of our current stocks and the relation with service level to customers can be a first step to a more integrated end to end stock control. From there we can maybe in the near future explore the possibilities and benefits of VMI between Unilever and Retailer X, but at this moment we are not so far yet.
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Appendix B Interview Customer Marketing Manager Interview customer marketing manager Unilever (5 5 2009) How do you characterize Retailer X? Retailer X is a typical family retailer and the biggest part of their customers, are indeed families. Other retailers are also interested in this customer segment; the goal of Retailer X is to keep these customers in the stores. What strategy does Retailer X use to achieve this goal? Retailer X uses the value for money strategy. This value can received in three ways: Price, product volume (20% extra) or premium (gift by a purchase). In the marketing Retailer X focus especially on price and bulk. In their advertisements they explicitly mentioned low prices and high volumes for the family segment for example 10 kg bags of potatoes. What purchase behaviour of customers can be indicated? Customers use Retailer X for both their bulk as top up purchases. The bigger part are bulk purchases of families, what results in shopping peaks on Thursday, Friday and Saturday. This put also some extra pressure on the stocks for these days. Some customers go for their secondary shopping to price discounters and to the premium retailer in the Dutch market. Does the economic downturn has identifiable influence on the shopping pattern? In these times customers, especially in the lower segments, must consider about their purchase pattern. Some will switch from A brand to an own brand, others will focus on volume offers and there is also a part that switch to other (cheaper) retailers. A lot of customers shop both at price discounters and Retailer X. This makes customers think that Retailer X has a very brad assortment, but in fact this is not realistic.
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Appendix C Stakeholder Analysis
This analysis identifies the stakeholders and their perceptions, interests and goals related to stock level adaptations and OSA. The analysis is based on the methodology of Enserink et al. (2004). 1. Use a problem formulation as starting point In order to generate cash and reduce costs retailers are triggered to reduce stocks. Unilever has a clear focus on On Shelf Availability improvement to increase turnover. Insights are needed to optimize and when possibly minimize stock levels, in order to improve OSA of Unilever products with respect for the corresponding costs. 2. Preliminary scan of stakeholders In this stage a first global scan is done for stakeholders, possible influence, interested or providing input for the formulated problem. This scan is supported by literature, internal documents and expert opinions. There should be mentioned that the stakeholders are divided in two subgroups within or outside Unilever. Both parts deserve sufficient attention in the analysis. The preliminary scan of stakeholders is represented in figure 14.
Stakeholders outside Stakeholders inside Unilever Unilever
Sourcing Units Logistic account officer
Planning Ordering
OSA project
Distribution Centre Retailer X
Logistics
Stock level advice 3PLP (Third Party Marketing Logistics Provider)
Unilever
Store Sales Retailer X
3PLP
Distribution Centre UL Stores Replenishers Store manager Customers Customers
Figure 14 Preliminary scan of stakeholder network. 3) Identify Perceptions, Interests and Goals To translate the extensive list of stakeholders, identified in the preliminary scan, in useful information some further analysis is required. All these stakeholders have their own perception on the problem and have different interest and goals related to that same problem. Understanding these differences is a first step but the second step is using this information. In this table the interest of the different stakeholders are presented. How these interests are related to a safety stock advice is expressed in the stakeholders’ goals. In the last column is identified which powers the different stakeholders have to influence the process. In a further stage this information can be used to value the stakeholders.
Stakeholder Goal Interest Power/Recources
Internal stakeholders Unilever
Customer service Manage the logistic activities to optimize the Better adaptation of stock levels on Knowledge about and experience and logistics effectivity and efficieny of these processes. the risks in the supply chain can help with (logistic) processes and customer to improve service to the retailer. relations. OSA project Analyse the service performance on the shop floor; The research give some extra insides Delivers data input and experience unfold initiatives to improve the OSA. on OSA related to retailer X. Better with OSA measuring. adaptation can improve service and contribute to reach the OSA target. Planning Effective and efficient planning to ensure the Help to create risk profile by Crucial for availability of products at availabity of products at the right moment delivering data of upstream risks. the UL DC's; data upstream risks. Marketing Create and maintain a strong brand; develop On shelf unavailability has negative Influencing powers. the potential of a product with a clear strategy. effects for a brand; improving OSA has positive effects for the brand strength. Sales Realize as much sales volume per product as An improved service (OSA) is positive Influencing powers. possible. for the sales volumes. Distribution Centre UL Manage and store the Unilever stocks as cost Order pattern of Retailer X can Limited advise power, input data set. effective as possible. change due to knew stock levels at Retailer X. Sourcing Unit Produce effective and efficient the amount of The SU's performance forms Relative autonomy to determine products necessary to fullfill the retailers' demand. a big part of the upstream risks. the execution of the producing Imrpoving these performance leads processes;input data set. to deliver performance. External stakeholders
Logistic account officer Effective and efficient management of the Stock reductions creates more Important data about downstream supply proces of products from the production working capital and the improvement risks; potential enduser of the companies to the stores. of service increase revenues for advice model. Retailer X. Ordering Calculate, plan and order the amount of products Stock advice model can help to Block the cooperation during the demand by the stores; with as less as possible order a quantity better adapted on research; question the function and cycle stock. the risks in the whole supply chain; reliability of the model; End user. Distribution Centre X Efficient and effective store of products;minimize Better adapatation of the stock levels End user of the advice model; delivers cycle stock but alwys capable to deliver products improves efficiency and effectivity input data for the model. to the stores. of the processes. Store Increasing turnover trough sales growth and The service level to customers can The ordering process and quantity costs reductions. increase by better adapted stock is part of the downstream risks. levels at the DC level. This increase in service results in increase of turnover. Storemanager Fullfill the customers demand in assortment and Better service pleases the customers Limited powers. availability of products. and keeps them satisfied and in attached to the store. Replenisher Easy replenishing of all products, with less A higher degree of service from the None. extra proceedings and reliable deliveries. DC can result in better erplenish performance. Customers Availability of cheap and qualitative good products Higher OSA leads to higher customer Boycot products, their shop behavior of the brand intended to buy. satisfaction. is important for the performance of Retailer X and Unilever and determines the downstream risks. 3PLP Increase tranport volumes to realize turnover The new stock level configuration Knowledge about transportation. growth. may have some influences on the number of transport movements. Table 10 Goals, interests and powers/resources of the stakeholders.
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4) Network Analysis - Structure and Analyze Data The previous table already was a first step in structuring the data. The clear view of the goals, interests and powers and resources of the different stakeholders helps to understand the particular positions of the stakeholders. A clear view about the influence of the different stakeholders on the research and the importance to keep them close to the research are not given yet. It is therefore still necessary to get insights on this points based on the perceptions and resources and powers of the stakeholders for this research. In the table below a clear step is made to create insight in the importance the stakeholders. This is done by identifying the dependency on the stakeholder and the possibility to replace the stakeholder within this research. Stakeholder Power/Recources Possibility to replace Dependency Critical actor?
Internal stakeholders Unilever
Customer service Knowledge about and experience No High Yes and logistics with (logistic) processes and customer relations. OSA project Delivers data input and experience No High Yes with OSA measuring. Planning Crucial for availability of products at No Medium Yes/No the UL DC's; data upstream risks. Marketing Influencing powers. No Low No Sales Influencing powers. No Low No Distribution Centre UL Limited advise power, input data set. No Low No Sourcing Unit Relative autonomy to determine No Medium No the execution of the producing processes;input data set.
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External stakeholders
Logistic account officer Important data about downstream No Yes Yes risks; potential enduser of the advice model. Stock control Block the cooperation during the No Medium Yes research; question the function and reliability of the model. End user Distribution Centre X End user of the advice model; delivers No High Yes input data for the model.
Store The ordering process and quantity No Low No is part of the downstream risks. Storemanager Limited powers. Yes Low No
Replenisher None. Yes Low No Customers Boycot products, their shop behavior No Low No is important for the performance of Retailer X and Unilever and determines the downstream risks. 3PLP Knowledge about transportation. Yes Medium No Table 11 Resources, input and criticality of the stakeholders.
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5) Interpretation of Results From the overview created in the previous tables the most important stakeholders can be subtracted. The classification of these stakeholders is important for the communication internally in this research and externally to the stakeholders themselves. The stakeholders are ranked in a matrix based on their influences and importance (Figure 15). This matrix, based on MacArthur’s classification matrix (MacArthur, 1997), makes explicit how to treat the different actors to ensure the progression of the research. Importance
Figure 15 Stakeholder classification matrix (based on MacArthur, 1997).
Appendix D Product List
500ML Calve Statube Mayo saus Knorr chicken tonight hawai Adez mango/abrikoos Knorr chicken tonight zoetzuur Adez zwarte bessen/framboos Knorr droge sauskaas Andrelon cremespoeling 300 ml perfecte krul Knorr dubbelpak groenten soep Andrelon krul control creme 200 ml Knorr eet kleur pasteuze maaltijdmix texaanse bbq Andrelon shampoo 300 ml glans Knorr eet kleur soep rood Andrelon shampoo 300 ml iedere dag Knorr maaltijdmix bami Andy original 750 ml Knorr maaltijdmix macaroni Axe deodorant bodyspray 150 ml africa Knorr maaltijdmix nasi Axe deodorant bodyspray 150 ml vice Knorr vie appel/wortel/aardbei 6 pack Axe shower gel 250 ml shock Knorr vie sinaasappel/banaan/wortel 6 pack Becel bak & braad 400 ml Knorr wereldgerechten kip tandoori Becel dieet 500 Knorr wereldgerechten lasagne Becel light 500 Lipton ice tea lemon 1,5 l pak Becel pro.activ 500 Lipton ice tea sparkling regular 1,5 l Becel pro.activ minidrink naturel 6 pack Lipton thee forest fruit Becel pro.activ minidrink sinaasappel 6 pack Lipton thee green Blue band goede start 500 Lipton thee marocco Blue band wikkel 250 Ola cornetto classico 4 + 2 Bona Halfvol Kuip 500 gram Ola magnum classic 4 + 2 Calve partysaus knoflook Ola raket Calve partysaus whiskey cocktail Ola viennetta 650 ml vanilla Calve slasaus naturel Omo wit 1,5 l Cif cream citroen 500 ml Omo wit poeder 1,4 kg Conimex droge mix nasi goreng 48 grams zakje Rexona deospray 150 ml cotton dry Conimex ketjap manis 250 ml Robijn black velvet 1 l Conimex kroepoek snack naturel Robijn color 1,5 l Conimex kroepoeklange plaknaturel Robijn fleur en fijn 711 gram Conimex sate marinade 40 grams zakje Robijn stralend wit 1,5 l Croma wikkel 200 Robijn vloeibaar Wit 3 L Cup a soup champignonsoep 3 pack Robijn wasverzachter morgenfris dilute 2 l Cup a soup tomaat 3 pack Robijn wasverzachter puur & zacht 750 ml Dove body nutri body milk 400ml Sun spoelglans 750 ml Dove deodorant spray 150 ml invisible Unox knaks standaard Dove deodorant spray 150 ml original Unox leverpastei standaard Dove shower 500 ml cream oil douchecreme met verzorgende olien Unox rookworst 275 mager Dove shower 500 ml indulging cream shower Unox rookworst 275 standaard Dove sunshine lotion lichte huid 250ml Unox smac 200 gram standaard Dove wastablet (2 x 100 g) 12 st regular Unox soep in zak romige tomatensoep 570ml Glorix extra dikke bleek 750 ml Unox stevige erwtensoep 800ml Hertog 1 liter slagroomijs Unox stevige tomatensoep 800ml Hertog ovaal stroopwafelfeest Figure 16 Unilever products measured for the OSA project. Appendix E Relation between OSA and Store Casefill
Table 12 Results correlation analysis OSA and store casefill.
The correlation between OSA and store casefill can help to control if a higher store casefill really results in a higher OSA level. There is a significant and positive relation between OSA and store casefill. The significance level indicates that the correlation is in the 95% reliability interval. The relation is only 0,063, which means that only 4% of the variance of OSA is explained by store casefill. This shows that the influence of store casefill on OSA is small, but there is a significant influence.
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Appendix F Risk Profile
The sourcing units can not be analyzed separately with other product and production characteristics because the correlation between these variables is too high. Some SU’s for example only produce HPC products, the correlation with this variables is therefore 1. To avoid these multicollonearity a multi regression analysis on the data set of the research is used to group the SU’s in classes. The outcomes of these analysis and classification are presented in this Appendix (F1). This classification leads to two SU groups, this variables is together with the other relevant product characteristics analyzed with a multiple regression (F2). Goal of this regression is to reveal which variables significant determine the delivery performance of products. The outcomes are not used to predict the delivery performance but are used to classify characteristics in a risk profile. For usability reasons the significant characteristics are grouped with a factor analysis (F3).
F1 Grouping Sourcing Units
Table 13 Model summary regression analysis sourcing units.
Table 14 ANOVA regression sourcing units.
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Table 15 Regression coefficients sourcing units. A regression analysis is used to group the SU’s in two classes. The high performing class consists of SU where products are produced with an average delivery performance of 98% and higher. The low performing class consists of SU’s where products are produced with an average delivery performance below 98%. High performing SU Low performing SU Almelo Nelahozeves Bad Essen Aranguez Nyirbator Bydgoszcz Auerbach Oss Duppigheim Brussel Port Sunlight Emig Casale Poznan Leeds Buxtehude Pozzili Leioa Gloucester Rotterdam Lelystad Hellendoorn Senoble Nijkerk Heppenheim Sarre Union Thayngen Lieshout Vechta St. Vulbas Mannheim Warrington Table 16 Classification souring units based on performance.
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F2 Analysis Product Variables The sourcing unit group, as constructed in Appendix F1, together with the product characteristics Best Before Date (BBD), volume, stock level Unilever, Home and Personal Care (HPC)/ foods and ABC (pareto) classification are indicated as characteristics that possible determine delivery performance of products (section 5.2.1). In this appendix is analyzed with a multiple regression analysis if there is a significant link between a characteristic and delivery performance of a product for the data set of the research. A multiple regression analysis for the independent variables ABC qualification, Sourcing unit groups, SU distance, volume, BBD, responsiveness, stock level and HPC is used for this construction. The results of this analysis are represented in Appendix F2. For the variables ABC and SU groups a dummy code is used. The model as a whole and the constant are significant. Factor A and responsiveness are filtered out, because their significance level was above 0,05. This significance level is used to judge if the results are not just based on coincidence and/or that the factor doesn’t differ from the constant. The model is significant and has a R square (clarified variance) of 0,037 which is not high (only 3,7%). Not the R square but the direction of the relations is important for the analysis and all variables in the model are significant. So the conclusion is that these variables can used to create groups with different upstream risks. When all variables are used to create groups too many groups are formed because there are only 83 products in the data set. The enter method is used to filter only the significant characteristics. The results of this analysis are presented below.
Table 17 Model summary multiple regression analysis product characteristics.
Table 18 ANOVA multiple regression analysis product characteristics.
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Table 19 Coefficients multiple regression analysis product characteristics.
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F3 Factor Analysis In the pervious section the characteristics whit significant effect on delivery performance of products are analyzed. There are still quite a lot of characteristics left. Making groups with all these characteristics is complicated because there will be a very high number of groups for only 83 products in the data set. Beside this problem it is also for usability reasons recommended to group characteristics with a factor analysis. A factor analysis groups variables which the same underlying causes. The principal axis factoring is used because the goal is not data reduction (principal component analysis) but find factors. Rotation of the axis is used to get stronger results and to make drawing conclusions smoother. In this factor analysis is the strongest variable of a factor chosen to be representative for the other variables loading high on the same variable. These variables/characteristics are Volume, SU and HPC/Foods and are used further in the research design. The results of the factor analysis are represented here. The first step in a factor analysis is check if there are enough communalities above 0.25. Stocks Unilever is low but high enough to be taken in account. The performance classification of the SU’s is the first variable from the risk profiles. The second and third factor are harder to interpret. BBD and HPC highly correlate and the influence of volume loads on both the second and the third factor. C product is still a significant and has strong relations with stocks. In the data set only 7 C products are represented. The average stock level at Unilever was around 16 weeks for C products, where A and B only had an average of 6,5 weeks stocks. Because volume is theoretically and in the data coupled with the ABC qualification the choice is made in favour of volume, as factor in the risk profile. Another reason to choose for volume and not for stocks is that the factor stocks, is for a big part already explained by SU group. The performance of the SU’s is measured by the delivery performance and this delivery performance is determined by the SU performance and stock level at Unilever. So actually the stocks variable is already partly involved in the groups high and low performing SU’s. One could argue that stocks can have a positive effect on the performance of a SU in this way of measuring. Therefore is checked if there is moderating effect of stocks on the performance of SU’s. On average the low performing group has less stock then the high performing group. Buxtehude and Poznan are examples of high performing SU’s that have very high average stock levels (20,4 and 17,08 weeks). Those high averages are caused by two products with extremely high stock values: Dove Sunshine lotion (65 weeks) and Knorr Soep rood (43) weeks (see appendix G). The other products from these SU’s have common stock levels. From this there can be concluded that stock level don’t have a significant moderating effect on the performance of the SU’s. The conclusion is that SU, volume and HPC/Foods are chosen as factor variables.
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Table 20 Communalities factor analysis.
Table 21 Explained variance by factors.
Table 22 Factor matrix.
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Table 23 Rotated factor matrix.
Table 24 Factor transformation matrix.
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Appendix G Relation Volume and Stock Level Unilever
Figure 17 Relation between products and stock level at Unilever.
In this figure the volume is plotted against stocks. The red line indicates the linear relation between stock levels at Unilever and Volume. This line indicates that there is a negative linear relation, but the R square is too low for strong conclusions. Therefore the outliers are analyzed because stock level can have a positive effect on the performance of the SU. A higher stock can bear the production risks. Therefore is checked if there is moderating effect of stocks on the performance of SU’s. On average the low performing group has less stock then the high performing group (checked with SPSS). Buxtehude and Poznan are examples of high performing SU’s that have very high average stock levels (20,4 and 17,08 weeks). Those high averages are caused by two products with extremely high stock values: Dove Sunshine lotion (65 weeks) and Knorr Soep rood (43) weeks. The other products from these SU’s have common stock levels. From this there can be concluded that stock level don’t have a significant moderating effect on the performance of the SU’s. Appendix H Season Products
Figure 18 Relation between sales volatility and forecast error.
Figure 18 represents the relation between the forecast error (FE, difference between forecasted sales and actual sales) and de sales volatility. The red line indicates that there is a positive relation. The FE of Becel minidrink naturel (FE= 4,63) and sinaasappel (FE= 3,47 are very high and therefore disturbing. These outliers are filtered out for a better analysis. Figure 18 shows the plot of the sales volatility and FE of the different products. The products far above the line are possible seasonal products which are good to forecast but with an high yearly sales volatility. A closer look leads to the the follow products: 5 products Magnum classis volatility 1,20 FE 0,73 Raket volatility 1,12 Fe 0,75 Cornetto classico volatility 0,98 FE 0,52 Rookworst volatility 0,97 FE 0,59 Erwtensoep volatility 0,91 FE 0,57 These products are clearly seasonal products, either in reality as data. Therefore it is very reasonable to ad another group to the risk profile, a seasonal foods group. One important consequence for the model is that for these products a shorter time sequence must be used. Otherwise the results will not satisfying due to big difference in demand during the year.
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Appendix J Group Specification High Vol Med. Vol Low vol Dove shower 500 ml cream oil Adez zwarte bessen/framboos High perf. Becel bak & braad 400 ml A Andy original 750 ml B Adez mango/abrikoos B Axe shower gel 250 ml shock B B B Glorix extra dikke bleek 750 ml Becel pro.activ minidrink Dove body nutri body milk Becel pro.activ minidrink Becel dieet 500 B Cif cream citroen 500 ml A A naturel 6 pack B 400ml B sinaasappel 6 pack B Dove shower 500 ml indulging Cup a soup champignonsoep Dove sunshine lotion lichte Robijn black velvet 1 l B Becel light 500 A Bona Halfvol Kuip 500 gram C cream shower B A huid 250ml B Dove wastablet (2 x 100 g) 12 Robijn color 1,5 l A Becel pro.activ 500 B Omo wit 1,5 l B Cup a soup tomaat 3 pack B Calve slasaus naturel B st regular B Conimex droge mix nasi Robijn stralend wit 1,5 l A Blue band goede start 500 A Omo wit poeder 1,4 kg B Hertog 1 liter slagroomijs B Robijn vloeibaar Wit 3 L C goreng 48 B Robijn wasverzachter Hertog ovaal stroopwafelfeest Blue band wikkel 250 A Robijn fleur en fijn 711 gram B Knorr chicken tonight hawai B morgenfris dilute 2 l B B Robijn wasverzachter puur & Knorr chicken tonight Croma wikkel 200 A Knorr eet kleur soep rood C zacht 750 ml B zoetzuur B Knorr wereldgerechten kip Knorr maaltijdmix macaroni A Knorr maaltijdmix bami B tandoori A Ola viennetta 650 ml vanilla A Lipton thee marocco B Knorr maaltijdmix nasi B Unox stevige tomatensoep Unox knaks standaard A Lipton thee forest fruit C 800ml B Unox soep in zak romige Lipton thee green C tomatensoep 570ml A Unox smac 200 gram standaard B Seasonal products Seasonal products Seasonal products Lipton ice tea sparkling Lipton ice tea lemon 1,5 l pak regular 1,5 l B B Ola magnum classic 4 + 2 A Ola cornetto classico 4 + 2 B Unox stevige erwtensoep Ola raket A 800ml B Unox rookworst 275 mager B Unox rookworst 275 standaard B Andrelon shampoo 300 ml Conimex kroepoek snack Axe deodorant bodyspray 150 500ML Calve Statube Mayo Andrelon krul control creme Conimex ketjap manis 250 ml Low perf. glans A naturel A ml africa A saus B 200 ml A C Andrelon shampoo 300 ml Axe deodorant bodyspray 150 Calve partysaus whiskey Andrelon cremespoeling 300 Conimex kroepoeklange iedere dag A ml vice A cocktail B ml perfecte krul B plaknaturel B Dove deodorant spray 150 ml Knorr wereldgerechten Knorr droge sauskaas B original A lasagne B Dove deodorant spray 150 ml Knorr dubbelpak groenten Unox leverpastei standaard A invisible C soep B Rexona deospray 150 ml Knorr kleur pasteuze mlt.mix cotton dry B tex. bbq C Knorr vie appel/wortel/aardbei Sun spoelglans 750 ml B 6 pack A
Knorr vie sinaasappel/banaan/wortel B
Seasonal products Seasonal products Seasonal products Conimex sate marinade 40 Calve partysaus knoflook A grams zakje B HPC Foods HPC Foods HPC Foods Table 25 Group classification based on risk profile. Appendix K Lead time grouping
K1 Example of Construction Lead Time Groups
Group 1 Mean Std. Dev. 449 observations SU performance High Dove shower 500 ml cream oil douchecreme met verzorgende olien2 2 2 2,024 0,156 444 = 2 Volume High Robijn black velvet 1 l 6 5 4 3 2 2 2 2,111 0,570 HPC Yes Robijn color 1,5 l 2 2,000 0,000 Season No Robijn stralend wit 1,5 l 2 2 2 2,000 0,000 Robijn wasverzachter morgenfris dilute 2 l 2 2 2 2 2,000 0,000 Robijn wasverzachter puur & zacht 750 ml 2 2 2,000 0,000 Glorix extra dikke bleek 750 ml 2 2 2 2,000 0,000 2,024 0,262 Totaal
Group 14 Mean Std. Dev. 398 observations SU performance Low 500ML Calve Statube Mayo saus 2 2 2 2 2 2 2 2 2 2,113 0,635 365 = 2 Volume Medium Knorr wereldgerechten lasagne 2 2,041 0,200 HPC No Calve partysaus whiskey cocktail 2 2 2 2 2 2 2 2,269 0,861 Season No Unox leverpastei standaard 2 2 2,646 1,924 2,269 1,131 Totaal Table 26 Example construction lead time group 1 and group 14.
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K2 Results Input Analyzer Arena all Risk groups
Figure 19 Representation distribution lead time all products.
Distribution Summary Data Summary Distribution: Beta Number of Data Points = 266 Expression: 2.5 + 16 * BETA(0.476, 2.54) Min Data Value = 3 Square Error: 0.003181 Max Data Value = 18 Sample Mean = 5.03 Chi Square Test Sample Std Dev = 2.91 Number of intervals = 8 Degrees of freedom = 5 Histogram Summary Test Statistic = 5.41 Corresponding p value = 0.384 Histogram Range = 2.5 to 18.5 Number of Intervals = 16
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K3 Results Input Analyzer Arena Group 14
Figure 20 Representation distribution lead time group 14.
Distribution Summary Data Summary Distribution: Beta Number of Data Points = 34 Expression: 2.5 + 10 * BETA(0.584, 1.62) Min Data Value = 3 Square Error: 0.008139 Max Data Value = 12 Sample Mean = 5.15 Chi Square Test Sample Std Dev = 2.46 Number of intervals = 4 Degrees of freedom = 1 Histogram Summary Test Statistic = 1.26 Corresponding p value = 0.268 Histogram Range = 2.5 to 12.5 Number of Intervals = 10
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K4 Results Input Analyzer Arena Group 17
Figure 21 Representation distribution lead time group 17.
Distribution Summary Data Summary Distribution: Beta Number of Data Points = 79 Expression: 2.5 + 13 * BETA(0.548, 2.36) Min Data Value = 3 Square Error: 0.003469 Max Data Value = 15 Sample Mean = 4.95 Chi Square Test Sample Std Dev = 2.57 Number of intervals = 5 Degrees of freedom = 2 Histogram Summary Test Statistic = 2.63 Corresponding p value = 0.276 Histogram Range = 2.5 to 15.5 Number of Intervals = 13
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Appendix L Analysis Demand Data
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Figure 22 Q-Q plots demand pattern products from data set.
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Table 27 Results Kolmogorov-Smirnov normality test. The Q Q plots of the different products in combination with the Kilmogorov Smirnov test are a strong indication that the demand pattern of the products has a normal distribution. The demand pattern for Andrelon krul control crème probably not follows a normal distribution but in general there can be concluded that the demand pattern of products are normally distributed. For the seasonal products in this test a data for a shorter period is taken into account because the high volatility of demand during the year.
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Appendix M Model Results
Percentage Advice # order Available order Lead time SS (products) SS (days) 97,5% Total stock Total stock SS (products) SS (days) 99% Total stock Total stock orders in 48 moments per moments per K-factor (97,5%) K-factor (99%) demand 97,5% service service (97,5%) (97,5%) 99% service service to shops (99%) (99%) hours /group week week Group 1 Dove shower 500 ml cream oil douchecreme 98,89% 0,50 6,00 2,178 2,869 46,4 14,84 1,67 68,26 7,67 19,55 2,20 72,97 8,20 Robijn black velvet 1 l 98,89% 4,00 6,00 2,180 2,870 3270,5 124,67 1,64 181,54 2,39 164,15 2,16 221,02 2,91 Robijn color 1,5 l 98,89% 4,00 6,00 2,183 2,872 1428,1 82,50 1,60 121,07 2,35 108,53 2,11 147,11 2,86 Robijn stralend wit 1,5 l 98,89% 4,00 6,00 2,176 2,868 1520,5 84,85 1,69 122,48 2,44 111,84 2,23 149,47 2,98 Robijn wasverzachter morgenfris dilute 2 l 98,89% 6,00 6,00 2,196 2,877 2578,1 111,50 1,40 151,31 1,90 146,09 1,83 185,90 2,33 Robijn wasverzachter puur & zacht 750 ml 98,89% 3,00 6,00 2,189 2,875 1501,2 84,80 1,53 140,39 2,53 111,38 2,00 166,96 3,00 Glorix extra dikke bleek 750 ml 98,89% 6,00 6,00 2,166 2,862 3925,4 135,73 1,79 173,57 2,29 179,31 2,37 217,16 2,87
Group 2 Becel bak & braad 400 ml 99,01% 6,00 6,00 2,193 2,872 30052,1 380,21 1,37 518,98 1,87 497,80 1,79 636,56 2,29 Becel dieet 500 99,01% 6,00 6,00 2,195 2,872 19980,9 310,25 1,33 426,56 1,83 405,96 1,75 522,27 2,25 Becel light 500 99,01% 6,00 6,00 2,194 2,872 69393,5 577,99 1,35 791,92 1,85 756,50 1,77 970,44 2,27 Becel pro.activ 500 99,01% 4,50 6,00 2,194 2,872 9253,9 211,09 1,35 315,63 2,01 276,26 1,76 380,80 2,43 Blue band wikkel 250 99,01% 6,00 6,00 2,195 2,872 30650,5 384,21 1,34 527,53 1,84 502,79 1,75 646,11 2,25 Blue band goede start 500 99,01% 6,00 6,00 2,171 2,861 20682,6 312,27 1,68 405,06 2,18 411,40 2,22 504,19 2,72 Croma wikkel 200 99,01% 6,00 6,00 2,195 2,872 39625,8 436,98 1,33 601,71 1,83 571,71 1,74 736,44 2,24 Knorr wereldgerechten kip tandoori 99,01% 4,00 6,00 2,197 2,872 4389,3 145,55 1,28 230,71 2,03 190,30 1,68 275,46 2,43 Ola viennetta 650 ml vanilla 99,01% 3,50 6,00 2,056 2,724 23046,0 312,17 2,95 402,99 3,80 413,53 3,90 504,35 4,76 Unox knaks standaard 99,01% 6,00 6,00 2,176 2,863 27146,8 358,50 1,63 468,38 2,13 471,77 2,15 581,65 2,65 Unox soep in zak romige tomatensoep 570ml 99,01% 4,50 6,00 2,170 2,860 17762,2 289,26 1,69 403,15 2,36 381,16 2,23 495,06 2,90
Group 3 (Season period Oct., Nov.,Dec.) Lipton ice tea sparkling regular 1,5 l 96,61% 6,00 6,00 2,217 2,971 82803,7 638,07 2,15 786,55 2,65 854,83 2,88 1003,30 3,38 Ola magnum classic 4 + 2 96,61% 1,50 6,00 2,225 2,973 1424,9 84,00 2,05 166,13 4,05 112,22 2,73 194,34 4,73 Ola raket 96,61% 2,00 6,00 2,231 2,973 1570,1 88,38 1,97 155,58 3,47 117,81 2,63 185,01 4,13 Unox rookworst 275 standaard 96,61% 6,00 6,00 2,216 2,970 23822,2 342,05 2,16 421,07 2,66 458,43 2,90 537,46 3,40 Unox rookworst 275 mager 96,61% 6,00 6,00 2,226 2,973 13187,6 255,59 2,04 318,15 2,54 341,39 2,73 403,95 3,23
Group 4 Andy original 750 ml 96,88% 3,50 6,00 2,209 2,959 1757,1 92,61 2,15 129,52 3,01 124,02 2,88 160,93 3,74 Cif cream citroen 500 ml 96,88% 5,00 6,00 2,198 2,953 2574,9 111,55 2,27 141,00 2,87 149,82 3,05 179,27 3,65 Dove shower 500 ml indulging cream shower 96,88% 0,50 6,00 2,224 2,963 275,5 36,91 1,97 149,31 7,97 49,18 2,63 161,58 8,63 Omo wit poeder 1,4 kg 96,88% 1,00 6,00 2,212 2,960 429,7 45,86 2,12 110,87 5,12 61,36 2,83 126,37 5,83 Omo wit 1,5 l 96,88% 0,50 6,00 2,181 2,939 46,6 14,89 2,46 51,16 8,46 20,06 3,32 56,33 9,32 Robijn fleur en fijn 711 gram 96,88% 2,50 6,00 2,217 2,962 1222,2 77,51 2,06 122,72 3,26 103,53 2,75 148,74 3,95
Group 5 Adez mango/abrikoos 98,12% 2,50 6,00 2,185 2,902 1270,7 77,89 1,93 126,32 3,13 103,45 2,56 151,87 3,76 Becel pro.activ minidrink naturel 6 pack 98,12% 2,00 6,00 2,208 2,911 3043,9 121,83 1,63 233,72 3,13 160,59 2,15 272,48 3,65 Knorr chicken tonight hawai 98,12% 4,00 6,00 2,210 2,911 8942,0 208,94 1,61 306,26 2,36 275,26 2,12 372,58 2,87 Knorr chicken tonight zoetzuur 98,12% 2,50 6,00 2,206 2,910 3749,5 135,05 1,68 231,65 2,88 178,21 2,21 274,81 3,41 Knorr maaltijdmix macaroni 98,12% 4,00 6,00 2,213 2,911 9345,6 213,93 1,54 318,03 2,29 281,39 2,03 385,49 2,78 Lipton thee marocco 98,12% 1,50 6,00 2,198 2,908 640,1 55,61 1,78 118,08 3,78 73,58 2,36 136,04 4,36 Cup a soup tomaat 3 pack 98,12% 1,50 6,00 2,208 2,911 4990,1 155,97 1,64 346,29 3,64 205,62 2,16 395,94 4,16 Cup a soup champignonsoep 3 pack 98,12% 1,50 6,00 2,209 2,911 4327,7 145,30 1,63 323,96 3,63 191,49 2,14 370,15 4,14
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Group 6 (Season period Oct., Nov.,Dec.) Lipton ice tea lemon 1,5 l pak 96,63% 6,00 6,00 2,215 2,969 6133,0 173,50 2,17 213,56 2,67 232,53 2,90 272,60 3,40 Ola cornetto classico 4 + 2 96,63% 2,50 6,00 2,214 2,969 1155,7 75,27 2,18 116,65 3,38 100,92 2,93 142,31 4,13
Group 7 Axe shower gel 250 ml shock 97,30% 0,50 6,00 2,207 2,943 306,0 38,60 2,02 153,28 8,02 51,48 2,69 166,15 8,69 Dove sunshine lotion lichte huid 250ml 97,30% 0,50 6,00 2,157 2,904 7,2 5,77 2,56 19,30 8,56 7,77 3,45 21,30 9,45 Dove wastablet (2 x 100 g) 12 st regular 97,30% 0,50 6,00 2,122 2,848 227,8 32,03 3,10 94,03 9,10 42,99 4,16 104,99 10,16 Robijn vloeibaar Wit 3 L 97,30% 1,00 6,00 2,170 2,918 147,9 26,39 2,41 59,23 5,41 35,49 3,24 68,33 6,24
Group 8 Adez zwarte bessen/framboos 97,47% 1,50 6,00 2,179 2,920 667,4 56,29 2,25 106,29 4,25 75,44 3,02 125,44 5,02 Becel pro.activ minidrink sinaasappel 6 pack 97,47% 1,50 6,00 2,216 2,939 2265,0 105,46 1,83 220,62 3,83 139,86 2,43 255,02 4,43 Bona Halfvol Kuip 500 gram 97,47% 5,00 6,00 2,218 2,939 2945,9 120,38 1,80 160,49 2,40 159,51 2,39 199,62 2,99 Calve slasaus naturel 97,47% 5,50 6,00 2,192 2,929 4362,3 144,76 2,12 181,99 2,67 193,46 2,83 230,69 3,38 Conimex droge mix nasi goreng 48 gr. zakje 97,47% 2,00 6,00 2,221 2,939 628,4 55,66 1,76 103,13 3,26 73,67 2,33 121,13 3,83 Hertog ovaal stroopwafelfeest 97,47% 1,50 6,00 2,147 2,889 1973,0 95,36 2,59 168,92 4,59 128,31 3,49 201,87 5,49 Knorr eet kleur soep rood 97,47% 0,50 6,00 2,161 2,904 67,1 17,70 2,44 61,26 8,44 23,78 3,28 67,34 9,28 Knorr maaltijdmix bami 97,47% 1,50 6,00 2,218 2,939 1940,6 97,71 1,80 206,34 3,80 129,46 2,38 238,09 4,38 Knorr maaltijdmix nasi 97,47% 2,00 6,00 2,221 2,939 3257,6 126,75 1,75 235,13 3,25 167,74 2,32 276,11 3,82 Lipton thee forest fruit 97,47% 1,50 6,00 2,188 2,927 632,8 55,03 2,16 105,89 4,16 73,62 2,89 124,48 4,89 Lipton thee green 97,47% 0,50 6,00 2,168 2,911 163,6 27,72 2,37 97,97 8,37 37,23 3,18 107,48 9,18 Unox smac 200 gram standaard 97,47% 2,00 6,00 2,221 2,939 2390,8 108,62 1,74 202,15 3,24 143,70 2,30 237,22 3,80
Group 9 (Season period Oct., Nov.,Dec.) No products
Group 10 Andrelon shampoo 300 ml glans 98,46% 1,00 6,00 2,112 2,824 4247,2 137,65 2,49 303,30 5,49 184,05 3,33 349,71 6,33 Andrelon shampoo 300 ml iedere dag 98,46% 1,00 6,00 2,201 2,895 1480,5 84,67 1,58 245,83 4,58 111,40 2,07 272,56 5,07
Group 11 Conimex kroepoek snack naturel 90,15% 6,00 6,00 2,267 3,109 73926,0 616,35 3,08 716,45 3,58 845,38 4,22 945,47 4,72
Group 12 (Season period Oct., Nov.,Dec.) Calve partysaus knoflook 99,90% 5,50 6,00 2,093 2,760 17765,4 278,98 1,95 356,84 2,50 367,92 2,58 445,78 3,12
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Group 13 Sun spoelglans 750 ml 95,82% 1,50 6,00 2,232 3,001 957,6 69,06 2,24 130,80 4,24 92,86 3,01 154,59 5,01 Axe deodorant bodyspray 150 ml africa 95,82% 1,00 6,00 2,239 3,001 3318,2 128,98 2,13 310,36 5,13 172,90 2,86 354,28 5,86 Rexona deospray 150 ml cotton dry 95,82% 0,50 6,00 2,232 3,001 470,5 48,41 2,24 178,36 8,24 65,09 3,01 195,03 9,01 Axe deodorant bodyspray 150 ml vice 95,82% 0,50 6,00 2,224 2,998 582,2 53,66 2,34 191,38 8,34 72,34 3,15 210,06 9,15 Dove deodorant spray 150 ml original 95,82% 0,50 6,00 2,234 3,001 2315,0 107,48 2,21 399,56 8,21 144,39 2,97 436,47 8,97 Dove deodorant spray 150 ml invisible 95,82% 0,50 6,00 2,236 3,001 1224,1 78,22 2,18 293,26 8,18 105,00 2,93 320,04 8,93
Group 14 500ML Calve Statube Mayo saus 91,71% 3,00 6,00 2,139 2,880 24606,0 335,58 2,63 463,37 3,63 451,78 3,54 579,57 4,54 Knorr wereldgerechten lasagne 91,71% 2,50 6,00 2,140 2,882 9553,4 209,17 2,66 303,60 3,86 281,70 3,58 376,12 4,78 Calve partysaus whiskey cocktail 91,71% 5,50 6,00 2,177 2,904 59849,9 532,50 3,77 609,51 4,32 710,34 5,03 787,35 5,58 Unox leverpastei standaard 91,71% 5,50 6,00 2,139 2,880 22240,9 319,06 2,63 385,27 3,17 429,54 3,54 495,75 4,08
Group 15 (Season period Oct., Nov.,Dec.) No products
Group 16 Andrelon krul control creme 200 ml 82,56% 0,50 6,00 2,315 3,142 634,3 58,32 4,27 140,22 10,27 79,12 5,80 161,03 11,80
Group 17 Knorr dubbelpak groenten soep 88,38% 2,00 6,00 2,203 2,997 2882,7 118,30 3,05 176,46 4,55 160,92 4,15 219,08 5,65 Knorr vie appel/wortel/aardbei 6 pack 88,38% 1,00 6,00 2,207 2,999 4231,8 143,57 3,23 276,98 6,23 195,11 4,39 328,52 7,39 Knorr vie sinaasappel/banaan/wortel 6 pack 88,38% 0,50 6,00 2,204 2,997 1679,5 90,31 3,07 267,07 9,07 122,84 4,17 299,60 10,17 Knorr droge sauskaas 88,38% 1,00 6,00 2,202 2,996 6814,4 181,79 2,98 364,55 5,98 247,29 4,06 430,05 7,06 Knorr eet kleur pasteuze maaltijdmix tex. bbq 88,38% 0,50 6,00 2,210 2,999 52,0 15,93 3,35 44,48 9,35 21,62 4,54 50,18 10,54 Conimex kroepoeklange plaknaturel 88,38% 6,00 6,00 2,200 2,990 8417,6 201,81 2,83 237,49 3,33 274,35 3,84 310,04 4,34 Conimex ketjap manis 250 ml 88,38% 4,00 6,00 2,201 2,993 11151,4 232,40 2,90 292,46 3,65 316,08 3,95 376,14 4,70
Group 18 (Season period Oct., Nov.,Dec.) Conimex sate marinade 40 grams zakje 88,04% 0,50 6,00 2,279 3,123 6064,5 177,46 3,58 475,03 9,58 243,18 4,90 540,75 10,90 Average 164,64 2,15 259,95 4,37 219,16 2,88 314,47 5,10 Table 28 Results advice model.
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Appendix N Simulation Model
Figure 23 Simulation model used to validate advice model.
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Appendix O Simulation Results Simulation results Service 97,5% Service 99%
Becel light 500 Becel light 500
Total 772711 Percentage Avg Stock level Total 771522 Percentage Avg Stock level replication 1 On time 761426 98,5% 1059 replication 1 On time 762028 98,8% 1208 Too late 11285 1,5% Too late 9494 1,2%
replication 2 Total 779916 Percentage Avg Stock level replication 2 Total 774398 Percentage Avg Stock level On time 770214 98,8% 1060 On time 772229 99,7% 1229 Too late 9702 1,2% Too late 2169 0,3%
replication 3 Total 773612 Percentage Avg Stock level replication 3 Total 776569 Percentage Avg Stock level On time 761165 98,4% 1044 On time 771466 99,3% 1201 Too late 12447 1,6% Too late 5103 0,7%
replication 4 Total 775923 Percentage Avg Stock level replication 4 Total 771404 Percentage Avg Stock level On time 763633 98,4% 1061 On time 762756 98,9% 1211 Too late 12290 1,6% Too late 8648 1,1%
replication 5 Total 775211 Percentage Avg Stock level replication 5 Total 776483 Percentage Avg Stock level On time 761411 98,2% 1054 On time 766783 98,8% 1222 Too late 13800 1,8% Too late 9700 1,2% Ola cornetto classico Ola cornetto classico
Total 59919 Percentage Avg Stock level Total 60690 Percentage Avg Stock level replication 1 On time 58639 97,9% 117 replication 1 On time 59963 98,8% 142 Too late 1280 2,1% Too late 727 1,2%
replication 2 Total 61850 Percentage Avg Stock level replication 2 Total 62378 Percentage Avg Stock level On time 60576 97,9% 116 On time 61738 99,0% 140 Too late 1274 2,1% Too late 640 1,0%
replication 3 Total 60918 Percentage Avg Stock level replication 3 Total 60850 Percentage Avg Stock level On time 59588 97,8% 113 On time 59908 98,5% 138 Too late 1330 2,2% Too late 942 1,5%
replication 4 Total 60402 Percentage Avg Stock level replication 4 Total 60531 Percentage Avg Stock level On time 58963 97,6% 116 On time 59861 98,9% 141 Too late 1439 2,4% Too late 670 1,1%
replication 5 Total 61073 Percentage Avg Stock level replication 5 Total 60829 Percentage Avg Stock level On time 59174 96,9% 114 On time 59017 97,0% 138 Too late 1899 3,1% Too late 1812 3,0%
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Robijn vloeibaar wit Robijn vloeibaar wit
Total 19267 Percentage Avg Stock level Total 19420 Percentage Avg Stock level replication 1 On time 19057 98,9% 57 replication 1 On time 19331 99,5% 65 Too late 210 1,1% Too late 89 0,5% replication 2 Total 19667 Percentage Avg Stock level replication 2 Total 19812 Percentage Avg Stock level On time 19585 99,6% 57 On time 19793 99,9% 66 Too late 82 0,4% Too late 19 0,1% replication 3 Total 19415 Percentage Avg Stock level replication 3 Total 19393 Percentage Avg Stock level On time 19303 99,4% 57 On time 19258 99,3% 65 Too late 112 0,6% Too late 135 0,7% replication 4 Total 18862 Percentage Avg Stock level replication 4 Total 18717 Percentage Avg Stock level On time 18762 99,5% 57 On time 18585 99,3% 66 Too late 100 0,5% Too late 132 0,7% replication 5 Total 19270 Percentage Avg Stock level replication 5 Total 19325 Percentage Avg Stock level On time 19188 99,6% 57 On time 19282 99,8% 65 Too late 82 0,4% Too late 43 0,2% Lipton thee forest fruit Lipton thee forest fruit
Total 44246 Percentage Avg Stock level Total 44689 Percentage Avg Stock level replication 1 On time 43862 99,1% 110 replication 1 On time 44318 99,2% 129 Too late 384 0,9% Too late 371 0,8% replication 2 Total 45114 Percentage Avg Stock level replication 2 Total 45396 Percentage Avg Stock level On time 44831 99,4% 109 On time 45316 99,8% 130 Too late 283 0,6% Too late 80 0,2% replication 3 Total 44631 Percentage Avg Stock level replication 3 Total 44704 Percentage Avg Stock level On time 43712 97,9% 108 On time 44191 98,9% 128 Too late 919 2,1% Too late 513 1,1% replication 4 Total 43782 Percentage Avg Stock level replication 4 Total 43996 Percentage Avg Stock level On time 43505 99,4% 111 On time 43825 99,6% 130 Too late 277 0,6% Too late 171 0,4% replication 5 Total 44614 Percentage Avg Stock level replication 5 Total 44648 Percentage Avg Stock level On time 44307 99,3% 109 On time 44488 99,6% 128 Too late 307 0,7% Too late 160 0,4%
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Andrelon shampoo 300 ml iedere dag Andrelon shampoo 300 ml iedere dag
Total 96610 Percentage Avg Stock level Total 96599 Percentage Avg Stock level replication 1 On time 96101 99,5% 220 replication 1 On time 96386 99,8% 248 Too late 509 0,5% Too late 213 0,2% replication 2 Total 98798 Percentage Avg Stock level replication 2 Total 98694 Percentage Avg Stock level On time 98571 99,8% 220 On time 98581 99,9% 246 Too late 227 0,2% Too late 113 0,1% replication 3 Total 96541 Percentage Avg Stock level replication 3 Total 96704 Percentage Avg Stock level On time 96331 99,8% 221 On time 96636 99,9% 247 Too late 210 0,2% Too late 68 0,1% replication 4 Total 95932 Percentage Avg Stock level replication 4 Total 96222 Percentage Avg Stock level On time 95471 99,5% 221 On time 96147 99,9% 250 Too late 461 0,5% Too late 75 0,1% replication 5 Total 96390 Percentage Avg Stock level replication 5 Total 96743 Percentage Avg Stock level On time 95494 99,1% 220 On time 96421 99,7% 247 Too late 896 0,9% Too late 322 0,3% Dove deodorant spray original Dove deodorant spray original
Total 87830 Percentage Avg Stock level Total 87828 Percentage Avg Stock level replication 1 On time 87467 99,6% 384 replication 1 On time 87548 99,7% 419 Too late 363 0,4% Too late 280 0,3% replication 2 Total 88876 Percentage Avg Stock level replication 2 Total 88840 Percentage Avg Stock level On time 88224 99,3% 381 On time 88534 99,7% 418 Too late 652 0,7% Too late 306 0,3% replication 3 Total 88260 Percentage Avg Stock level replication 3 Total 88288 Percentage Avg Stock level On time 87553 99,2% 381 On time 88116 99,8% 422 Too late 707 0,8% Too late 172 0,2% replication 4 Total 86816 Percentage Avg Stock level replication 4 Total 88900 Percentage Avg Stock level On time 86503 99,6% 386 On time 88592 99,7% 432 Too late 313 0,4% Too late 308 0,3% replication 5 Total 88138 Percentage Avg Stock level replication 5 Total 88018 Percentage Avg Stock level On time 87646 99,4% 381 On time 87345 99,2% 416 Too late 492 0,6% Too late 673 0,8%
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500 ml Calve Statube mayo 500 ml Calve Statube mayo
Total 230875 Percentage Avg Stock level Total 231891 Percentage Avg Stock level replication 1 On time 222646 96,4% 549 replication 1 On time 225703 97,3% 656 Too late 8229 3,6% Too late 6188 2,7%
replication 2 Total 233898 Percentage Avg Stock level replication 2 Total 232847 Percentage Avg Stock level On time 231166 98,8% 563 On time 229381 98,5% 669 Too late 2732 1,2% Too late 3466 1,5%
replication 3 Total 232039 Percentage Avg Stock level replication 3 Total 230926 Percentage Avg Stock level On time 225320 97,1% 550 On time 226981 98,3% 667 Too late 6719 2,9% Too late 3945 1,7%
replication 4 Total 228773 Percentage Avg Stock level replication 4 Total 229230 Percentage Avg Stock level On time 224972 98,3% 567 On time 227263 99,1% 685 Too late 3801 1,7% Too late 1967 0,9%
replication 5 Total 231555 Percentage Avg Stock level replication 5 Total 230714 Percentage Avg Stock level On time 229676 99,2% 568 On time 228776 99,2% 677 Too late 1879 0,8% Too late 1938 0,8% Andrelon krul control crème Andrelon krul control crème
Total 24528 Percentage Avg Stock level Total 24368 Percentage Avg Stock level replication 1 On time 24459 99,7% 137 replication 1 On time 24267 99,6% 155 Too late 69 0,3% Too late 101 0,4%
replication 2 Total 24869 Percentage Avg Stock level replication 2 Total 24876 Percentage Avg Stock level On time 24752 99,5% 136 On time 24732 99,4% 155 Too late 117 0,5% Too late 144 0,6%
replication 3 Total 24427 Percentage Avg Stock level replication 3 Total 24654 Percentage Avg Stock level On time 23914 97,9% 132 On time 24398 99,0% 152 Too late 513 2,1% Too late 256 1,0%
replication 4 Total 24005 Percentage Avg Stock level replication 4 Total 24031 Percentage Avg Stock level On time 23754 99,0% 134 On time 23923 99,6% 156 Too late 251 1,0% Too late 108 0,4%
replication 5 Total 24760 Percentage Avg Stock level replication 5 Total 24634 Percentage Avg Stock level On time 24544 99,1% 137 On time 24545 99,6% 157 Too late 216 0,9% Too late 89 0,4% Table 29 Simulation results.
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The simulation set up has a replication of 1825 days (5 year), with a warm up period of 32 days. There are 5 replications performed per product to improve the quality of the validation.
Average Average Average Average Service Service Service Service Difference stock level stock level Difference Difference stock level stock level Difference simulation model simulation model simulation model simulation model
Becel light 500 98,5% 97,5% 1,0% 1056 792 264 99,1% 99,0% 0,1% 1214 970 244 Ola cornetto classico 97,6% 97,5% 0,1% 115 117 -2 98,4% 99,0% -0,6% 140 142 -2 Robijn vloeibaar wit 99,4% 97,5% 1,9% 57 59 -2 99,6% 99,0% 0,6% 65 68 -3 Lipton thee forest fruit 99,0% 97,5% 1,5% 109 106 3 99,4% 99,0% 0,4% 129 124 5 Andrelon shampoo 300 ml iedere dag 99,5% 97,5% 2,0% 221 246 -25 99,8% 99,0% 0,8% 248 273 -25 Dove deodorant spray original 99,4% 97,5% 1,9% 383 400 -17 99,6% 99,0% 0,6% 421 436 -15 500 ml Calve Statube mayo 98,0% 97,5% 0,5% 559 463 96 98,5% 99,0% -0,5% 671 580 91 Andrelon krul control crème 99,0% 97,5% 1,5% 135 140 -5 99,4% 99,0% 0,4% 155 161 -6 Total 98,8% 97,5% 1,3% 99,2% 99,0% 0,2% Table 30 Comparison of simulation and model results.
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Appendix P Scientific Article
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A stock level advice model for the FMCG industry based on end-to-end supply chain risks
Dion J.E.A.J van de Gazelle Faculty of Technology, Policy analysis and Management, Delft University of Technology
Abstract Stock levels determine the service and costs in the supply chain of Fast Moving Consumer Goods companies. These companies have a strong focus on service and recently also on stock reduction. Reconsidering the height of stock levels and reallocation of stocks can have positive influences on costs, space utility, service and company performance. A clear set up for a stock level optimization model is lacking for Fast Moving Consumer Goods companies. This paper describes a case study based on data from a retailer and manufacturer. In the paper is exposed how stock levels can be optimized based on the end-to-end supply chain risks. Simulation shows that the average stock level can decrease while service stays equal. So with reallocation of the current stock it’s possible to improve the service.
Keywords : Safety stock, advice model, end-to-end supply chain risks, openness, stochastic lead time, service, FMCG
1. Introduction aspects. Stock availability at the DC is Companies in the Fast Moving one of these aspects. This study focuses Consumer Goods (FMCG) industry on the relation between service (OSA) have a strong focus on service. Service and stock levels. to the consumer is expressed in On- Keeping stocks is crucial for almost Shelf-Availability (OSA). The prior every organization that offers products annoyance of shoppers in supermarkets to customers (Haddock et al., 1994) also is the unavailability of products. Almost for the retail sector. Stock can be divided 26% of the shoppers often considers an in cycle stock and safety stock. Cycle Out-of-Stock (OOS) as an inconvenient stock is the result of ordering and event (EFMI Business School, 2008). This producing goods in certain batches. topic has been an interesting issue in Safety stock is used to preempt academic logistic literature, with many uncertainties in forecast and authors discussing On-Shelf- replenishment. One could argue that Availability in relation with information cycle stock is the price paid for sharing and collaborative store ordering inflexibility of the processes and safety (Pramatari and Miliotis, 2005), inventory stock is the price paid for the lack of management (Fleisch and Tellkamp, information and transparency (Vlist van 2005); (Jammernegg and Reiner, 2007), der, 2007). Stocks are responsible for a two echelon supply chains (Nahmias big part of the operating costs and and Smith, 1994) and Supply Chain reducing stock levels can have a positive Synchronization (Vlist van der, 2007). effect on the total costs (Huveneers, The availability of products at the shop 2008). Retailers are focusing on cost shelves (OSA) is determined by several reductions, catalyzed by the credit crunch. Stock reduction is an option to first step in the design set up (section 2). reduce costs, but may not influence the In section 3 the determination of the service to the consumer. This results in a safety stock level is discussed. The strong retailer focus on: translation of the safety stock level theory and supply chain risks in an • Service improvement advice model is made explicit in section • Stock level optimization 4. The results of a case study are analyzed and discussed in section 5. Safety stock is used to bear Finally, the conclusions are drawn in uncertainties in the supply chain. But section 6. not all Stock Keeping Units (SKU’s) have the same risks and therefore don’t 2. Analysis of supply chain risks need the same level of safety stock. The total stock level is formed by the Higher stock levels result in higher costs cycle stock and safety stock. Cycle stock but also influence the forecast and is determined by the order pattern of the ordering pattern. The incorrect stock retailer. The order pattern is determined levels are the consequence of limited by order cost considerations. Safety transparency between the retailer and stock is used to safeguard service from manufacturer (Vlist van der, 2007). uncertainties in demand, storage and Retailers are often not aware of the deliveries of the manufacturer. potential and possibilities of the manufacturer. The manufacturer has no insights in the stock levels at the Retailer X DC’s Risks distribution centers and the consumer demand pattern. Reallocation of stocks, + based on product specific supply chain risks can improve the service to the + + Stock level DC shops and indirect to the consumers. So Upstream Risks Downstream Risks Retailer X there is a clear potential to reduce stocks or arrange stocks in another way to be more effective. Inventory management is a focal point of managing supply +/ chain processes (Jammernegg and Figure 24 Stock influencing supply chain risks. Reiner, 2007). The objective of this paper is to The uncertainties in the demand pattern provide a model design for a stock level form the downstream risks and the advice model. The purpose of this delivery uncertainties the upstream advice model is to improve service risks. The storage risks are out of the levels and optimize stock levels. research scope and not implemented in The safety stock level is determined the design set up (Gazelle van de, 2009). by the supply chain risks and the The safety stock is determined by desired service level to the stores. An upstream and downstream risks and the analysis of the supply chain risks is the desired service level to the shops. To
162 design a general design set up, risk profiles for the upstream risks have This formula and especially the service been determined. The risks of these factor ( k ) presumes that the lead time different profiles are expressed in lead and demand have a normal distribution. time. This set up is described in section Lead time has seldom a normal 4. The demand risks are unique for all distribution in practice. Stochastic lead products and are deducted from the times seem to be a more realistic view of demand pattern at the shop floor. The the reality and stochastic inventory advice model combines the group lead models therefore received considerable time and product specific demand attention (Johansen and Thorstenson, pattern to generate a stock level advice. 1993). It is commonly accepted that the A lack of transparency and most lead times can be characterized openness is responsible for the current with a gamma distribution (Seger, 2006). high stock levels in the supply chain. A An advance of the gamma distributed better fit between risks and safety stock lead time is the possibility of using can be reached by transparency and computer simulation (Burgin, 1975) openness. Sharing information of based on historical data (Eppen and supply chain risks by the retailer and Martin, 1988). The common formula to manufacturer is necessary to adjust calculate the safety stock level is using a stocks on product specific risks. Q,r inventory model. The formula is based on two equations: 3. Determine the safety stock Q=()() 2λ[] A + πη () rIC/ The upstream and downstream risks of H( r )= QIC / πλ the products are used to determine the safety stock, necessary to achieve the Some of the variables are expressed in desired service level. The most costs; unfortunately these costs are not traditional way to determine the safety always known. stock level is based on lead time and This formula based on the gamma demand variables (Marmostein and distribution can not be analyzed Zinn, 1990): analytically, but some approximation
possibilities for solving the equations 2 SS= k ∗()σ2 ∗LT +( σ 2 ∗ DEM ) are available (Chen and Namit, 1999); DEM LT (Corbett, 2001); (Das, 1976); (Johansen
and Thorstenson, 1993). SS = Safety stock In practice the lead time demand k = Service factor is often simplified as a normal σ = Standard deviation demand DEM distribution because it is analytically not DEM = Average demand per time possible to solve the formula for safety period stock with a gamma distribution σ (Grubbström and Tang, 2006). This LT = Standard deviation lead time simplification has negative implications LT = Average lead time for the service. When the normal
163 distribution is used, the safety stock β=µ()() µ 2 2 4X/ 2 X level is underestimated what results in lower service in practice (Chopra et al., To determine the safety stock the 2004), whereas using a gamma standard formula can be used as a distribution often overestimates the multiplication between the safety factor reorder point (Grubbström and Tang, k (based on an approximation) and 2006). Grubbström and Tang developed lead time demand W: an approximation method based on higher order moment for stochastic SS=∗σ k()()W =∗ k µ W inventory: 2
The demand during lead time can be The safety factor k is dependant on the written as desired service level. The k factor M differs when the lead time is not W= Y ∑ i normally distributed and can not be i =1 determined analytically. An Where Yi represents the order size approximation formula is developed for (demand pattern) in period i and M a set of percentage points of the Pearson represents the random variable for the curve. This method can be used to lead time. The first four central determine the safety k factor for a given moments are skewness β1, kurtosis β2 and service
µ( ) = µ( ) ∗ µ ( ) level p (Bowman and Shenton, 1979): 1W 1 M 1 Y
( ) =π( ββ) π( ββ ) 2 k p / µ()()()()()= µ ∗ µ + µ ∗ µ 1 1,2 2 1,2 2W 1 M 2 Y 2 M 1 Y r π() β β =ai() β β s ι 1, 2 ∑ r, s 1 2 3 ≤ + ≤ µ()()()()()()()()W= µµ MY ∗ +3 µµµµµ MYY ∗ ∗ + MY ∗ 0r s 3 3 13 21231 The maximum error of this
2 approximation is 0,5% according to the µµ()()()WM= ∗3( ()()() µ M −1∗ µµ YY + ) 41 1 24 research of Bowman and Shenton. The 2 2 +µ()()()()()()()M ∗( 6 µµµ MY ∗ ∗ Y +4 µµ YY ∗ + 3 µ Y ) 2 112 13 2 method leads not only to very accurate +6µ()()() ∗ µ2 ∗ µ 3M 1 Y 2 Y results but is also applicable for other +µ()() ∗ µ 4 stochastic distributions like the β- 4M 1 Y distribution. When the distribution for lead time and demand pattern is known, µ ( ) the advice model can be designed. Where i X is the ith central moment of the random variable X. Among the
higher-order moment, the skewness β1 4. Model design (measure of asymmetry of the curve)
and kurtosis β2 (peakedness of a The translation from risk analysis and unimodal curve) are defined as: safety stock theory in an advice model is 2 3 divided in three steps. The set up of a β=µ()()X/ µ X 1 3 2 case study supports the explanation of
164 the three steps. In the first step (1) the Group 3 upstream risks are used to construct risk SU performance High profiles. In step 2 the lead time for the Volume High HPC No different risk profiles is determined. The Season Yes product demand pattern is inserted in Figure 25 Example of risk profile Nr. 3. the model in the third step (3). The products in this risk profile are 1. The first step in the design is the produced in a high performing sourcing construction of a risk profile for the unit. The sales volume is in the highest upstream risks. The upstream risks are category and it is a seasonal foods product and production related risks products. upstream in the supply chain from the Now the risk profiles are set up, Retailer’s DC. This first step requires the lead time per risk profile can be input and openness from the determined. manufacturer. From literature several important characteristics are indicated 2. In step 2 the 83 products of the which influence the delivery dataset of the case study are divided performance of the manufacturer over the 18 groups. Data about the (Bharadwaj et al., 2002); (Schneider, orders of the different products is used 2009). With a regression analysis has to uncover the probability distribution been verified that the characteristics: behind the lead time per group. The Sourcing unit performance, Best-Before- data set contains a very high number of Date (BBD), Volume, Stock level observations with a lead time of 2 days. Unilever, Home and Personal Care Therefore the lead time is divided in (HPC)/foods, ABC (Pareto) classification two probability distributions. The first and Seasonality have significant effects distribution is a Bernoulli distribution on the expected delivery performance. with a chance p that the lead time is For usability convenience the exact 2 and a chance p-1 that the lead significant variables are grouped into time is described by a distribution. This four factors. distribution is described by 2,5 +16∗β( 0,476;2,54 ) . Now the central • Sourcing unit performance moments for the lead time can be • Volume deducted with help of the equation: • HPC/foods
• Seasonality for food products Exk= E 2 k ∗+ pEY k ∗1− p p The risk profiles of the upstream risks are constructed for the case study based Where Y is the β-distribution, which on these characteristics. The result is a describes the lead time in case the lead set of 18 risk profiles. An example of time is not exactly 2 days. risk profile is represented in figure 2.
165 3. The third step is inserting the behaviour of the retailer. Frequent demand pattern in the model. The ordering decreases cycle stocks but accuracy of the model improves when results in higher ordering costs. The the actual demand on the shop floor is advice model and model insights can be used. This data requires openness from used to support logistic decisions like the retailer. The demand patterns of the stock levels and order frequency. 83 products from the dataset are The provided advice is validated with a analyzed for this purpose. As expected discrete simulation. The simulation is the demand pattern of most products run for 8 products with the generated have a normal distribution. For seasonal stock level advice for a desired service products some deviations are found, of 97% and 99%. The reorder point caused by high volatility of sales during (safety stock + average demand * the year. When a shorter measuring standard lead time) and batch size period is taken in account (for example (order size) are input for the model. The only high season for ice cream) these difference between the simulation and deviations disappear. model service are 1,3% for the generated Finishing this third step means also advice of 97,5% service and 0,2% for the the completion of the model set up. generated advice of 99% service. Now safety stock advice can be generated based on input and openness The service for a 97,5% safety stock from the manufacturer and retailer (van generated advice is higher in the de Gazelle, 2009). simulation then expected from the model. The simulation indicates a 5. Results service of 98,8%, which is higher then The design of the advice model as the desired model service of 97,5%. introduced in the previous section is Besides validating the model, these translated in a model. The lead time and outcomes also validate the statements of demand risks are combined in the Grubbström and Tang that their model and product specific advice is approximations are more accurate for generated. higher service levels. The simulation results indicate that even when average Service level stock levels are decreased, the service 97,50% 99% level can stay equal. The simulation Safety Stock (boxes) 165 219 Safety Stock (days) 2,2 2,9 validates the outcomes for the 99% Total Stock (boxes) 260 304 service advice and indicates higher then Total Stock (days) 4,4 5,1 expected service levels for the 97,5% Table 31 Average stock level advice. advice. The conclusion is that the simulation confirms and validates the Table 1 shows the average safety stock results of the advice model (Gazelle van levels advice of 2,2 days for a service of de, 2009). 97,5% and 4,4 days for a service level of 99% (Gazelle van de, 2009). The total stock is also determined by the order
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Johansen, S.G. and Thorstenson, A. (1993). Optimal and approximate (Q, r) inventory policies with lost sales and
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