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How to Calculate Forecast Accuracy for Stocked Items with a Lumpy Demand - a Case Study at Alfa Laval

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How to Calculate Forecast Accuracy for Stocked Items with a Lumpy Demand - a Case Study at Alfa Laval School of Innovation, Design and Engineering How to calculate forecast accuracy for stocked items with a lumpy demand - A case study at Alfa Laval Master thesis Advanced level, 30 credits Product and process development Production and logistic Elsa Ragnerstam Abstract Inventory management is an important part of a good functioning logistic. Nearly all the literature on optimal inventory management uses criteria of cost minimization and profit maximization. To have a well functioning forecasting system it is important to have a balance in the inventory. But, it exist different factors that can results in uncertainties and difficulties to maintain this balance. One important factor is the customers’ demand. Over half of the stocked items are in stock to prevent irregular orders and an uncertainty demand. The customers’ demand can be categorized into four categories: Smooth, Erratic, Intermittent and Lumpy. Items with a lumpy demand i.e. the items that are both intermittent and erratic are the hardest to manage and to forecast. The reason for this is that the quantity and demand for these items varies a lot. These items may also have periods of zero demand. Because of this, it is a challenge for companies to forecast these items. It is hard to manage the random values that appear at random intervals and leaving many periods with zero demand. Due to the lumpy demand, an ongoing problem for most organization is the inaccuracy of forecasts. It is almost impossible to predict exact forecasts. It does not matter how good the forecasts are or how complex the forecast techniques are, the instability of the markets confirm that the forecasts always will be wrong and that errors therefore always will exist. Therefore, we need to accept this but still work with this issue to keep the errors as minimal and small as possible. The purpose with measuring forecast errors is to identify single random errors and systematic errors that show if the forecast systematically is too high or too low. To calculate the forecast errors and measure the forecast accuracy also helps to dimensioning how large the safety stock should be and control that the forecast errors are within acceptable error margins. The research questions answered in this master thesis are: · How should one calculate forecast accuracy for stocked items with a lumpy demand? · How do companies measure forecast accuracy for stocked items with a lumpy demand, which are the differences between the methods? · What kind of information do one need to apply these methods? To collect data and answer the research questions, a literature study have been made to compare how different researchers and authors write about this specific topic. Two different types of case studies have also been made. Firstly, a benchmarking process was made to compare how different companies work with this issue. And secondly, a case study in form of a hypothesis test was been made to test the hypothesis based on the analysis from the literature review and the benchmarking process. The analysis of the hypothesis test finally generated a conclusion that shows that a combination of the measurements WAPE, Weighted Absolute Forecast Error, and CFE, Cumulative Forecast Error, is a solution to calculate forecast accuracy for items with a lumpy demand. The keywords that have been used to search for scientific papers are: lumpy demand, forecast accuracy, forecasting, forecast error. I Acknowledgement The master thesis was executed at Alfa Laval AB in Tumba, Stockholm. The thesis includes 30 credits and is written as a final exam at the engineering program with specialization on Production and logistics at Mälardalens University in Eskilstuna. I want to thank all those who have supported me through this work. Thanks, to the employees at Alfa Laval that with open arms made me feel welcomed, gave me new experiences and have made my execution of the master thesis to a memory for life. Thanks, to my family, classmates and other friends for the support and knowledge you have contributed with. Thanks, to the companies that have been a part of the benchmarking process. You all have been a major contributing factor to my final results. A special thanks, I want to give to my supervisor at Alfa Laval, Alexandra Essén, and to my supervisor at Mälardalens University, Antti Salonen. The knowledge and support you have contributed with, I will keep forever. Thank you! Eskilstuna, Sweden, 2015 Elsa Ragnerstam II Contents 1 Introduction 1 1.1 Background 1 1.2 Problem formulation 2 1.3 Purpose and research questions 3 1.4 Thesis limitations 3 2 Approach and methodology 4 2.1 General about research study 4 2.1.1 Overall approach 5 2.2 Data collection 5 2.2.1 Literature review 5 2.2.2 Benchmarking 6 2.2.3 Hypothesis test 9 2.3 Qualitative and quantitative research 10 2.3.1 Qualitative research 10 2.3.2 Quantitative research 10 2.4 Validity and reliability 11 2.4.1 Validity 11 2.4.2 Reliability 12 3 Theoretical framework 14 3.1 Logistic and the logistic system 14 3.1.1 Inventory and working capital 15 3.1.2 Delivery service 16 3.1.3 ABC-classification 17 3.2 Forecasting 18 3.2.1 Modeling and categorizations of demand 18 3.2.2 Forecasting methods 22 3.2.3 Forecast errors and forecasts monitoring 24 3.2.4 Accuracy measuring techniques 29 4 Benchmarking 36 4.1 Data collection 36 4.1.1 Company one 36 4.1.2 Company two 37 4.1.3 Company three 38 4.1.4 Company four 38 4.2 The summary of the benchmarking 39 5 Analysis 41 5.1 Formulation of a hypothesis 51 6 The company Alfa Laval 52 6.1 How Alfa Laval works with inventory management 52 6.1.1 Re-classification 52 6.1.2 ABC-classification 53 6.1.3 Categorization of demand 53 6.1.4 Forecast 54 6.1.5 Annual demand 55 7 Hypothesis testing 56 7.1 Developing of the tool 56 7.2 How to use the tool 59 7.3 The results of the hypothesis test 60 7.3.1 The hypothesis 60 7.3.2 How the hypothesis test was performed 60 7.3.3 The result 60 III 8 Conclusions and discussion 63 8.1 Recommendations for further research 65 9 References 66 9.1 Articles 66 9.2 Books 68 9.3 Oral 70 9.4 Internet 70 List of figures Figure 1 - The logistic goal mix 15 Figure 2 - The root causes of inventory 16 Figure 3 - ABC-classification based on volume value 17 Figure 4 - Trend demand 19 Figure 5 - Season demand 19 Figure 6 - Cycle demand 19 Figure 7 - Level demand 19 Figure 8 - Random variety demand 19 Figure 9 - Categorization of demand 21 Figure 10 - Forecast error and planning horizons 25 Figure 11 - How the demand can variety verses systematically forecast errors 27 Figure 12 - Disadvantages with MAD 31 Figure 13 - Categorization of demand 54 List of tables Table 1 - Guidelines for the number of periods when using moving average 23 Table 2 - How Company one calculates forecast errors 37 Table 3 - The formulas Company one uses 37 Table 4 - How Company two calculates forecast errors 37 Table 5 - How Company two calculates forecast errors over time 38 Table 6 - How Company four works with forecast errors 39 Table 7 - Summary of the benchmarking process 40 Table 8 - The analysis of the hypothesis test 61 Table 9 - The connection between negative CFE and availability 61 List of appendices Appendix 1 - Extract of demand vs. forecasts for four items Appendix 2 - Extract from the availability report Appendix 3 - Extract from the developed tool Appendix 4 - The manual to the developed tool IV Abbreviations APE Absolute percentage error CFE Cumulative sum of forecast errors DC Distribution Centre L24M Last 24 months MAD Mean absolute deviation MAPE Mean absolute percentage error ME Mean error MPE Mean percentage error MPS Master production scheduling MRP Material replenishment planning MSE Mean squared error PGP Product Group Parts SSE Sum of squared errors WAPE Weighted absolute forecast error V 1 Introduction This introductory chapter will first of all describe the background for the thesis. Then the problem description will be presented as a result of the purpose and research questions. And finally, the project limitation for the thesis will be clarified. 1.1 Background Oskarsson et.al. (2006) say that during the last twenty years logistics has grown. From the beginning, logistics were about storage and transportations but today logistics is an important part for companies’ strategies for competitiveness. Today you can find many companies that have created and increase their competitiveness by smooth and good functioning logistics. Inventory management is an important part of a good functioning logistic. Koumanakos (2008) says that nearly all the literature on optimal inventory management uses criteria of cost minimization and profit maximization. The inventory manager’s goal is to minimizing the costs and maximizing the profits while satisfying the customers’ demand. To do so it is important to have a low inventory cost and at the same time be able to deliver what the customers want. Lumsden (1998) explains that you need a balance between availability of stocked items, low tied up capital and low processing cost and he calls it the “logistic goalmix”. Oskarsson, et.al. (2006) write about delivery service elements and one of them is availability in stock. They describe availability in stock as the number of orders or order lines that can be deliver on the customers’ requirements. Koumanakos (2008) explains how much inventory a company should have since the inventory is both an access and a debt.
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