Optimal Production Planning Strategy for Global CPG Company by Omar Mahmoud Sakr BSc Electromechanical Engineering, Alexandria University, Egypt

SUBMITTED TO THE PROGRAM IN SUPPLY CHAIN MANAGEMENT IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE IN SUPPLY CHAIN MANAGEMENT AT THE MASSACHUSETTS INSTITUTE OF TECHNOLOGY

June 2021 © 2021 Omar Mahmoud Sakr. All rights reserved. The authors hereby grant to MIT permission to reproduce and to distribute publicly paper and electronic copies of this capstone document in whole or in part in any medium now known or hereafter created.

Signature of Author: ______Department of Supply Chain Management May 14, 2021

Certified by: ______James B. Rice, Jr. Deputy Director, Center for Transportation and Logistics Capstone Advisor Certified by: ______Dr. Karla M. Gámez Pérez External Advisor Capstone Co-Advisor

Accepted by: ______Prof. Yossi Sheffi Director, Center for Transportation and Logistics Elisha Gray II Professor of Engineering Systems Professor, Civil and Environmental Engineering

Optimal Production Planning Strategy for Global CPG Company

by

Omar Mahmoud Sakr

Submitted to the Program in Supply Chain Management on May 14, 2021 in Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Supply Chain Management

ABSTRACT

Companies in the Consumer-Packaged Goods Industry are faced with a chronic dilemma: efficiency vs. agility. Companies must find the balance between scale, which creates cost-saving opportunities, and flexibility, which often incurs incremental costs. The main questions addressed in this capstone pertain to manufacturing and logistics decisions. Simply put, how much should the company produce, when and how, and therefore how much inventory should they hold? Following extensive cost structure analysis and mapping of the company’s supply chain network, a comprehensive Mixed Integer Linear Programming model was created. The model’s objective function is cost minimization, which it must attempt considering “current-state” inputs as well as multiple operational constraints. The results suggest that a hybrid production planning strategy between “level-production” and “demand-chase” is preferred, and can generate significant cost savings across the supply chain. In summary, this strategy can help companies enhance their efficiency and reduce costs when attempting to optimize total end-to-end supply chain costs, instead of using department-based budget management.

Capstone Advisor: James B. Rice, Jr. Title: Deputy Director, Center for Transportation and Logistics

Capstone Co-Advisor: Dr. Karla M. Gámez Pérez Title: External Advisor ACKNOWLEDGMENTS

In loving memory of Dr. Karla M. Gámez Pérez, who will always be remembered for her kindness, sincerity, and dedication. She will be truly missed.

To James Rice,

For his valuable advice, and for teaching me how to look at things for what they are.

To Dr. Nima Kazemi,

For the support in shaping the capstone at the start, and his endless reassurance.

To the XYZ Team,

For their consistent dedication and support in delivering this capstone.

To Dennis Sokoletski,

For co-starting the journey and trusting that I would see it through.

To My Family,

For their unwavering support and inspiration, thank you, always.

To My Extended Family,

My Friends: for their constant encouragement and sincerity.

To My Mars Family,

The IE team for their ongoing support. YM and OH: for believing in me.

TABLE OF CONTENTS

LIST OF FIGURES ______LIST OF TABLES ______LIST OF ACRONYMS ______1. INTRODUCTION ______7 1.1 Background ______7 1.2: Objective and Scope ______8 2. LITERATURE REVIEW ______9 2.1 Introduction ______9 2.2 Production Planning Types ______9 2.3 Production Planning in Literature ______11 2.4 Production Planning Model Considerations ______12 2.5 Comparative Matrix ______13 3. DATA AND METHODOLOGY ______15 3.1 Data Collection ______15 3.2 Quantitative Analysis ______16 3.3 Model Definition ______21 3.4 What-if Scenarios ______25 4. RESULTS AND DISCUSSION ______27 4.1 Model Results ______27 4.2 Discussion ______28 5. CONCLUSION ______33 REFERENCES ______

LIST OF FIGURES

Figure 1: Comparison between Making and Packing operations from demand and supply perspectives

Figure 2: Summary of APP inter-link with other elements

Figure 3: Summary of APP model types based on data uncertainty

Figure 4: The four main steps to be followed in the methodology

Figure 5: Four key pillars within the model scope and consideration

Figure 6: The split of site B cost elements into four main components

Figure 7: Model structure in Open Solver

Figure 8: The baseline of site B operating model: labor and crewing pattern

Figure 9: Model inputs: FWIP Demand and Opening Stock

Figure 10: Model constraints: Labor, Capacity, Inventory and Storage

Figure 11: Model decision variables: Crewing, Production Plan and Inventory

Figure 12: Overview of cost elements in as-is vs. baseline optimized comparison

Figure 13: Framework of scenario groups and subsequent scenarios

Figure 14: Scenario group 1: variation in DFC against the corresponding total and unit costs

Figure 15: Comparison between different scenario groups for different DFC targets

Figure 16: Comprehensive comparison of scenario group outcomes and financial benefits

LIST OF TABLES

Table 1: Production planning types based on time horizon and focus areas

Table 2: Main variables considered in APP models

Table 3: Comparison of APP models and solution methods with our proposed work

Table 4: TMC and T&W sub-elements, categorization and rationale

Table 5: List of decision variables, inputs and constraints to be used in the model

Table 6: Site B main manufacturing costs (variable cost sub-elements)

Table 7: Warehouse main T&W sub-elements’ unit costs

Table 8: Summary of the 20 distinct simulated scenarios

Table 9: Detailed cost comparison of model recommendations vs. S&OP outcome

Table 10: Summary of result of 20 scenarios including the baseline LIST OF ACRONYMS

CPG: consumer packaged goods x-supply: cross supply

LT: lead time

SC: supply chain

FWIP: finished work in progress

APP: aggregate production planning

MILP: mixed integer linear programming

TMC: total manufacturing costs

T&W: transportation and warehousing

OTDC: other total delivery costs

NMC: non-manufacturing costs

M&R: maintenance and repair

FG: finished goods

DFC: days forward coverage

S&OP: sales and operating plan

Optimal Production Planning Strategy for Global CPG Company Chapter 1 Introduction

This project aimed to address a chronic problem faced by many firms across a wide range of industries: the dilemma of cost-efficiency vs. agility. As firms grow larger, they start taking advantage of scale through various operations, such as leveraging size to negotiate better terms with suppliers and expanding operations through capital investments. While these economies of scale enable a firm to benefit from a cost-efficiency perspective, they often create a slow-moving behemoth, unable to react swiftly enough to the volatile and disruptive nature of market demand.

1.1 Background

XYZ is a global Consumer Packaged Goods (CPG) company, with significant operations worldwide. For the XYZ Europe team, this dilemma is a lingering one and has been adversely affecting their agility across the supply network, as well as impacting their financial metrics. There are two main manufacturing sites in Europe: L in Poland and B in Germany. The German site (B) is responsible for producing the “Elaborate” family, which are complex, high-end, premium products. Site B requires complicated, expensive manufacturing assets to produce the Elaborate products, in an operation known as “making”, which needs highly skilled labor and expensive assets. As speculated by Vollmer (2015), skilled labor is anticipated to be in high demand in Germany since it is one of the largest manufacturing hubs in Europe. Labor flexibility constraints and training requirements are extensive, which was one of the main drivers for selecting site B in this project. Site L conducts the more labor intensive “packing” operation, which is not as asset-heavy as making. The low flexibility in making means that capacity-increasing staffing decisions must be made well in advance and must be in full crews, due to the nature of the manufacturing assets and line crewing. Figure 1 highlights the differences between making and packing operations.

Figure 1 Comparison between Making and Packing operations from demand and supply perspectives

Additionally, site B supplies multiple other packing sites, both within the European region and in other regions worldwide (known as “cross-supply” or “x-supply”), which adds to the criticality of the site, and the need to have an agile yet cost-efficient manufacturing unit.

1.2 Objective and Scope

In summary, the main objective of this project was to create a model that attempts to find the optimal trade-off between flexibility and cost-efficiency, while delivering the required customer service level and taking into consideration all the associated manufacturing and inventory constraints (such as minimum stock coverage). This model intended to develop a production plan for site B, that addressed decision points related to workforce management, including crew hiring, crew release, crew size change or overtime operation. These decision points should be highlighted ahead of the time they need to be in effect, taking into consideration staffing lead times (LT) as well as time needed for stakeholder alignments.

The model also intended to provide proposals regarding inventory, such as when to expect inventory levels to rise above the standard operating levels and when to plan for additional storage capacity.

One of the first methodological steps was reviewing historical data and operating facts pertaining to the below aspects:

- Service level trends throughout the past year - Financial performance in site B throughout the past year - Understanding of the cost of different transactions (fixed vs variable costs) - Labor constraints, such as regulations and overtime considerations - Labor decisions and implications (e.g. LT for hiring additional crews) - Inventory guidelines and constraints

This review was followed by creating the first version of the model, considering the above constraints and the main objective function of optimizing/minimizing total supply chain (SC) costs. Next, model testing and verification were initiated, which included:

- Reviewing specific suggestions and the data on which they were based upon - Reviewing the impact of these suggestions (financial and operational) - Providing data as an input to the model to generate the model suggestions - Testing the impact of the model suggestions and fine-tuning the model parameters - Conducting sensitivity analysis on the model (e.g. changes to labor/inventory constraints)

The model provided useful suggestions that the XYZ Europe team may use to optimize SC costs, considering the target service levels and the numerous operational constraints. Comparing the model proposals with the recommendations of the typical Sales and Operating Plan (S&OP) outcome suggests that optimizing the production planning strategy can drive cost reductions of up to 15%. In addition to running the model with the current constraints (the baseline), several what-if scenarios were suggested and simulated, to get a better sense of the impact of changing different constraints on the total SC costs. Based on the analysis and the constraint values tested, the results suggest that changes in labor flexibility constraints may not yield much benefit. On the other hand, changes in the inventory-related constraints, mainly the target inventory coverage, can yield significant cost savings across the supply chain, and is worth further analysis and exploration by XYZ. Chapter 2 Literature Review

The goal of this capstone was to attempt to optimize production costs for the sponsor company XYZ, specifically labor and inventory costs. These costs form a large part of operating costs at site B, which was where the research was focused. Production planning optimization models use data to analyze levels of inventory, capacity, labor, and production, with the intention of calculating an optimal system given the operational constraints. These models can be extended to include multiple sites, where integrating these multiple sites can enable the interchange of workforce and inventory, facilitating demand fulfillment at lower costs (Hafezalkotob et al. 2019). In the Elaborate product category, finished work in process (FWIP) inventory is interchanged between sites, but we will not consider workforce interchange, as there are different regulations in different sites, not to mention the major geographical challenges.

2.1 Introduction

In this chapter, we discuss production planning in detail, focusing on production planning types and common categorizations based on planning horizons. We then discuss several approaches used in solving production planning models and various model types with different demand data and objectives. Finally, we present a synthesis of the literature reviewed and explain the scope and considerations of our model.

2.2 Production Planning Types

Production planning strategies are split into two fundamental approaches: level production and demand chase (Bateman, 2017). The basic concept of level production strategy is sustaining a smooth, stable operation resulting in consistent production output. While this approach reduces production costs and the need for flexibility (changeovers, overtime, etc.), it results in significantly high inventory levels during periods of low demand and high probability of stock-outs in periods of high demand. In contrast, the demand chase strategy is where production follows demand as closely as possible, hence the term “chase.” This method minimizes inventory variation and obsolescence since production closely follows forecasted demand. However, the corresponding large swings in production and subsequent flexibility requirements induce major cost implications and entail substantial operational complexity.

A hybrid strategy is often the most viable option, which blends the fundamentals of the two basic strategies and creates a realistic alternative that is usually the practical approach. Regardless of the strategy used, a key categorization of production planning is based on the planning horizon (see Table 1):

Table 1 Production planning types based on time horizon and focus areas

Planning Strategy Time Horizon Focus Areas Strategic > 1 year Network Design and Capital Investment Planning Portfolio Shaping and Development Tactical 1-18 months Shift Configuration and Inventory Projections Transportation Models Operational 4-8 weeks Production Run Size and Material Planning Order Quantities and Shipment-Carrier Allocation Within the overlap of the strategic and tactical planning horizons lies a powerful and prevalent planning model: aggregate production planning (APP). Other production planning categorizations used are based on the number of manufacturing sites in scope, as well as the number of products considered.

APP focuses on minimizing costs by optimizing levels of inventory, human resources, overtime wages, number of start-ups, underutilization of labor, and maximizing customer service levels (Baykasoglu, 2001). APP is a type of medium-term capacity planning that usually includes a time horizon of 6 to 18 months and has significant awareness from both supply chain practitioners and in academia (Remazanian et al., 2012). Researchers have developed different methodologies to solve APP models, with some pioneering studies proposing a linear decision rule and transportation methods (Jamalnia et al., 2019).

Figure 2 depicts the APP position amidst other types of production planning and control techniques, and their interconnected relationships from a holistic perspective. In the hierarchy of production planning activities, APP comes between long-term strategic planning decisions such as capital investment planning, and short-term decisions such as shop-floor scheduling practices (Jamalnia et al., 2019).

Figure 2 Summary of APP inter-link with other elements (Jamalnia, A., Yang J., Feili, A. 2017)

) Market Research Product Development Research &

& Demand Study Decisions Development

Range

-

Long

( Planning

Strategic Human Resource Demand Forecasting Capacity Planning

Planning

) Range - Sub-contracting Aggregate Production Raw Materials

(external capacity) Planning [APP] Inventory

Medium

Planning

( Tactical Tactical Master Production Finished Goods Schedule (MPS) Inventory

& MRP Strategy

)

Range

-

Short

( Planning

Detailed Production

Operational Operational Schedule

2.3 Production Planning Approaches in Literature

Several APP models with varying levels of detail have been introduced and deployed since the 1950s. Typical methods in approaching APP models can be classified into the following sets (Zaidan et al. 2019):

- Simulation - Linear decision rule - Linear programming - Transportation method - Management coefficient approach

In present day APP problems, there is a lot of uncertainty, due to inaccurate or missing input data in elements such as demand, cost, resources, and objective functions (Zaidan et al., 2019).

One of the most recent studies regarding APP models is by Cheraghalikhani et al. (2019), where two main classification groups were identified based on data uncertainty: deterministic and uncertain models. In deterministic models, the key parameters (labor costs, inventory costs, demand, etc.) pertaining to the optimization model are assumed to be known. Deterministic models are further sub-classified into single and multiple objective models (Liang, 2007), depending on the number of objective functions to be solved. Regarding uncertain models, these are sub-classified into two main families of uncertainty: stochastic and fuzzy models (Cheraghalikhani et al., 2019), which are both further sub-classified into single and multiple objective models. In general, stochastic models are ones whose uncertainty can be governed by known probability distributions, (Tang et al., 2003), while fuzzy models are associated with vague and imprecise decision-making, due to lack of specific information (Bellman and Zadeh, 1970). Figure 3 clarifies the different types of APP models (Cheraghalikhani et al., 2019).

Figure 3 Summary of APP model types based on data uncertainty (Cheraghalikhani et al. 2019)

Aggregate Production Planning Models

Deterministic Uncertain Models Models

Stochastic Fuzzy Models Models

Single Objective Multiple Objective Single Objective Multiple Objective Single Objective Multiple Objective

The APP model in this capstone was a deterministic one, with a single objective function targeting cost minimization. The choice of model was primarily due to the nature of the variables and constraints considered in the model, where elements are either explicitly known (such as labor costs, production rates, etc.) or can be logically and accurately assumed (such as demand, inventory storage costs, etc.). Researchers have not presented a comprehensive and general model to formulate real production environments, but mainly deal with discussing solution algorithms. Most APP models are relevant to single product and single stage systems, which are not highly compatible with real production systems. In this capstone, we created an APP model representing a realistic supply chain based on XYZ constraints and solved it using Mixed Integer Linear Programing (MILP).

This capstone considered a multi-period, multi-product, multi-machine model, with a focus on labor and inventory costs. Since there are operational decisions in this system, we formulated this model as a MILP one (Hung & Hu, 1998), due to the need for binary variables representing “yes-no” decision variables.

2.4 Production Planning Model Considerations

APP is complex because it requires coordination of interacting variables for the firm to respond to demand (Kumar & Suresh, 2009). The main APP variables are shown in Table 2 (Cheraghalikhani et al., 2019).

Table 2

Main variables considered in APP models

Market Demand For each period, demand to be satisfied by production or inventory Inventory Products that are held in stock in each period Production Capacity Maximum # of products that can be produced per period (machine and labor) Warehouse Space The warehouse capacity for holding inventory Costs of production consist of regular and overtime labor, as well as costs of Costs of Production inventory handling / holding Sub-contracting Hire the capacity of other firms temporarily to make components / parts Labor Level Number of workers in each period includes regular and overtime workers Recruiting of additional labor for peak production and laying-off unutilized Hiring / Layoff cost workers in low demand period to reduce costs Product Price Sales price of product

In addition to these main variables, some additional variables have been used in APP models. These variables have been considered in some studies (Cheraghalikhani et al., 2019), and we focused on only the relevant ones for the scope at site B:

- Multiple Product Items: In many APP models there is more than one product family, where several multiple product models exist in APP literature (Cheraghalikhani et al., 2019).

- Labor Characteristics: In production planning literature labor is typically modeled as a key resource in APP models (Mazzola et al., 1998). Some important characteristics of labor such as skill level, legal restrictions, training, productivity, and utilization are considered in APP models. These labor-related issues are classified as “labor characteristics”, with these sub issues:

o Labor Skill: In APP models, it is assumed that all workers are equivalent, which contradicts real-life situations where some workers are more valuable than others and thus not equal where hiring/releasing costs are considered. Some APP models have considered different labor types and thus costs in order to conform to reality (Fahimnia et al., 2005). o Legal Restrictions: Legislations in many countries have posted considerable restrictions on the release of labor, which are considered in some models (Toledo et al., 2013). o Labor Training (Cost and Time): Some labor training aspects such as length of training periods and training costs can be considered in APP models (Toledo et al., 2013) o Labor Utilization: Efficient labor utilization is important in realizing a profit in every job. Labor utilization is defined as the hours worked divided by the total available hours, and is considered as a labor characteristic in some APP models (Leung et al., 2007)

- Customer satisfaction level: Customer satisfaction/service level is defined as an organization's ability to consistently meet the needs and expectations of its customers, which is important since the profits that a business earns depends on it (Filho in 1999).

- Multiple Manufacturing Plant: APP models assume that products are produced in a single manufacturing plant, while global companies usually have multiple manufacturing plants for their productions (Leung & Chan, 2009). Normally, production costs such as labor cost and sub- contracting cost vary in different plants.

- Machine Utilization: amount of time the machine is used for production. Leung and Chan (2009) considered machine utilization as an objective function that must be maximized.

- Financial Concepts: Today’s tough financial conditions worldwide clearly demonstrate the changing emphasis and trade-off between products, facilities, capacities, work force and profitability in the industrial companies that struggle for survival. These financial conditions may be used in APP models as objective functions or constraints. Fung et al. (2003) and Tang et al. (2003) proposed APP models under financial constraints.

- Multiple Product Market: APP models usually consider a single market with customer demand and unique sale price, while there are companies which have multiple markets for selling their productions. In this case demand and sale price may vary for each market. Leung and Chan (2009) and Aliev et al. (2007) considered multiple product markets in their models.

2.5 Comparative Matrix

Various studies have discussed the different production planning types and common categorizations based on planning horizons. These studies have also deliberated on the multiple types of models used to solve APP problems, including both deterministic and stochastic models. For our capstone, we developed a deterministic model, due to the nature of the variable and constraint elements (known/assumed). Furthermore, the model was a single objective one, where the purpose of the model was to attempt to optimize production costs at site B, considering numerous variables and constraints.

This comprehensive synthesis matrix in Table 3 summarizes the different studies discussed in this review, showing the main elements considered in their models and how our own model stacks up against them.

Table 3

Comparison of APP models and solution methods with our proposed work

Author

plant

-

Model type Model Multi Horizon Planning Objective Type Demand Product OrderCapacity Size Cost Operation Facility Cost Holding Inventory Cost SafetyStock Level Service Cost Handling Inventory

Hafezalkotob et al. 2019 APP M T M D M x x x x

Baykasoglu, 2001 MILP S T M S S

Ramezanian et al. 2012 MILP S O M D M x x x

Cheraghalikhani et al. 2019 MILP M T S D M x x x X

Liang, 2007 MILP S S S F S x

Tang et al., 2003 MILP S S M S S x x x x

Leung et al., 2007 MILP M S S D M x x x

Deb et al., 2000 NSGA M T M S M x x x x

Osman Alp et al. 2016 H S T M S S x

Mula et al. 2006 MILP - S/T M S M x x x x Proposed Work MILP S T S D M x x x x x

Model Type APP = Aggregate Production Planning Multi-plant S = Single MILP = Mixed Integer Linear Programming M = Multiple NGSA = Non-dominated Sorting Genetic Algorithm H = Heuristics Planning Horizon S = Strategic Product S = Single T = Tactical M = Multiple O = Operational

Objective S = Single Demand Type D = Deterministic M = Multiple S = Stochastic F = Fuzzy

Chapter 3 Data and Methodology

The approach to attempting to minimize the supply chain costs of XYZ’s site B through optimized production planning is an analytical one, and has four key sequential steps, with specific focus on labor and inventory costs. Figure 4 illustrates the four main steps followed in the methodology.

Figure 4 The four main steps followed in the methodology

Data Quantatative Model What-if Collection Analysis Definition Scenarios

First, we needed to establish an understanding of the plant-specific status and the inter-dependencies at site B. To gain this understanding, we created a digital prototype for site B before attempting any optimization. The prototype was created through data analysis of various financial data, volume forecasts, production considerations and more. We organized several meetings with multiple experts from different departments within XYZ, to create and verify the connection between numerous data sources. After we created the digital prototype, we used it in a MILP model targeting optimizing inventory and labor costs in producing FWIP at site B. The main driver behind using a MILP approach is that there was a need to establish the usage of binary variables to represent multiple constraints, which has been confirmed through extensive literature review and research (Mirzapour et al., 2011). Thus, as the process at XYZ was profiled and explained by a variety of experts from multiple departments across the European division, presenting their experiences and views throughout, the model framework was developed.

The specific type of model used was an APP model, as it fits the planning time horizon and focuses on areas related to the project scope. APP focuses on minimizing costs by optimizing levels of inventory, labor, start-ups, asset underutilization, and maximizing customer service (Baykasoglu, 2001), and is a type of medium-term capacity planning that usually has a time horizon of 6-18 months. Researchers have developed different methodologies to solve APP models, with some studies proposing a linear decision rule and transportation methods, while other tackling APP models through MILP (Jamalnia et al., 2019). We used a deterministic model as the key parameters are either explicitly known (such as standard labor costs, overtime costs, etc.) or were practically assumed (such as handling and inventory holding costs).

3.1 Data Collection

The initial stages of the project included multiple meetings and sessions with XYZ personnel, mainly within the supply chain function. During that period, the main priority was on developing a sound understanding of current XYZ planning processes and ways of working, which was supplemented by a few training sessions held by supply chain planners responsible for the European region. Then, project scoping began, where we started directing efforts towards the main points that arose throughout the discussions, leading us to pin-point specific manufacturing plants and product families as the core focus area of the project. This moment is when we identified site B and the Elaborate product family as the focus areas in the project scope, due to high labor costs in site B and the high inventory value of the Elaborate product family. Next, we held a few key sessions with Finance and Operations experts within XYZ, in order to identify the cost structure of site B and the inter-dependency with operational parameters. This investigation included an extensive analysis of the different types of costs associated with the lifecycle of Elaborate products and identifying those relevant to the project (e.g. cost of raw materials is out of scope). In addition, we examined the previous fiscal year’s financial data to reach the cost drivers which will be fundamental elements of the model design. Then, through collaborative analysis with cross-functional teams at XYZ, as well as separately in weekly connection points, the different variables were allocated, identifying the objective function, decision variables, inputs, and constraints. The inputs and decision variables were extensively reviewed and studied, finalizing the variables that were imbedded into the model.

3.2 Quantitative Analysis

In this step, detailed quantitative analysis was conducted to clearly establish the model inputs, constraints and decision variables, as well as defining the model from mathematical and programming perspectives.

3.2.1 Model Inputs

While our model provided some guidance and suggestions regarding the cost-optimal production planning approach for site B, it cannot consider a myriad of variables/constraints. Our model was not an inventory-policy optimization one and thus did not investigate safety stock assumptions, lead time of materials or lead time variability. Moreover, the model did not evaluate current company processes or portfolio simplification (number of SKUs), as these were outside the project scope. Thus, to clarify our model scope, we compiled a list of the main considerations, grouped into four pillars depicted in Figure 5.

Figure 5 Four key pillars within the model scope and consideration

With the above-mentioned considerations, we concluded, along with our counterparts at XYZ, that the model scope should be adequate for solving the APP model. 3.2.2 Site B Cost Structure

As the next step, we reviewed the cost structure of the site B. These results served as a foundation for understanding the site cost structure and were the basis for the development of a deterministic model that relied on a combination of known data and assumptions. Throughout various working sessions and meetings with XYZ finance, operations, and supply chain experts, we compiled the full site cost structure. The baseline for the determination of the cost elements and for carving out the relevant aspects to the model, was fixed vs. variable cost analysis. This analysis mainly differentiates between fixed costs, which the optimization model cannot change, and variable costs, which the model will optimize. In summary, fixed costs are defined as costs that remain constant with changes in produced quantities (e.g. rental cost, salaries), while variable costs are ones that vary with production volumes, proportionally or otherwise (e.g. overtime costs). Figure 6 shows site B cost elements considered in the model.

Figure 6 The split of site B cost elements into four main components

Total Manufacturing Costs (TMC)

Non- Total Delivered Transportation Manufacturing & Warehousing Costs (NMC) Costs (TDC) (T&W)

Other Costs (OTDC)

Of the above-mentioned cost components, we considered only TMC and T&W, as these are within the project scope and are naturally affected by attempted production planning optimization at site B. Regarding OTDC and NMC, these include elements such as “Central Engineering Team” and “Material Scrap Rates”, which will neither be affected nor improved by the model. Within TMC and T&W, several sub-elements exist, of which some are fixed costs and others are variable. TMC is usually divided into Material Costs, Labor Costs, and Manufacturing Overheads. In our model, we needed to be more precise with manufacturing overheads, which we split into fixed and variable costs. At site B, the main variable overhead costs were machine Maintenance and Repair (M&R) costs. The fixed manufacturing overhead costs were depreciation, utilities, and other administrative costs.

Any sub-elements that are fixed costs were not impacted by the model, whether in constraints or in the objective function. In contrast, variable cost elements were the essence of our model, as these were affected by the model suggestions. Table 4 shows the sub-elements in TMC and T&W cost elements, further sub-classified into “fixed vs. variable” groups with rationale behind the categorizations.

Table 4 TMC and T&W sub-elements, categorization and rationale

Total Manufacturing Costs (TMC)

Sub-Element Fixed / Variable Rationale Personnel are paid monthly for a maximum of 20 working days, Labor (standard) Fixed regardless of actual working days Labor Overtime Variable Overtime costs incurred in weekends or bank holidays

Labor Extra Crew Variable Additional crew to increase the production capacity and output Depreciation costs can be calculated in multiple ways based on Depreciation Fixed accounting methods. XYZ uses linear depreciation, where cost is calculated by dividing asset acquisition price by estimated lifetime, and therefore it is independent of machine utilization Maintenance/ We assume that the maintenance / repair costs are changing Variable Repair proportionally depending on total annual production volume One machine can manufacture multiple products, one at a time. To switch products, it is necessary to do up to 8 hours of changeover. Changeover Fixed These costs are fixed as they have been pre-set and optimized by the site B scheduling team. These are costs associated with utility bills (e.g. electricity and gas), Utilities Fixed which largely remain constant irrespective of annual production. Services Fixed Minor contracted services such as cleaning/adminstration

Transportation and Warehousing (T&W)

Sub-Element Fixed / Variable Rationale Costs relating to preparing pallets for transport, such as packaging Handling Variable and shipment preparation Storage Variable Storing costs incurred in external warehouses managed by 3PL Costs relating to pallet transport from site B to Europe or cross- Transportation Variable regional sites, dependent on production quantity Impact of holding inventory on the working capital, dependent on Holding Variable duration (cost of capital) With a sound understanding of the site cost structure including fixed and variable sub-elements, the decision variables, inputs, and constraints were all identified, which enabled the building of the model structure in terms of mathematical formulation and programming (summarized in Table 5).

Table 5 List of decision variables, inputs, and constraints to be used in the model

Variable Decision Variable Input Constraint Labor (overtime) x Labor (extra crew) x Labor (reduced crew size) x FWIP Inventory x Demand (FWIP Europe and x-supply) x FWIP Opening Stock x Inventory Coverage Targets x Labor Throughput x Overtime Labor Throughput x Extra Crew Throughput x Reduced Crew Size Throughput x Maximum Overtime per Month x Contract Duration x Hiring and Training Lead Time x Crew Release Notice Period x Crew Size Change Lead Time x Machine Capacities x Warehouse Capacity x

The variable cost sub-elements pertaining to product logistics (T&W) were key considerations in the model and contributed to the inventory side of the equation, while the sub-elements belonging to manufacturing (TMC) built the production side. Throughout multiple discussions and working sessions with Operations and Planning experts in XYZ, we organized the different manufacturing costs that were used in the model, based on the previous fiscal year’s Income Statement (as summarized in Table 6).

Table 6 Site B main manufacturing costs (variable cost sub-elements)

Service UoM Unit Rate (zł) Unit Rate (EUR) Overtime (weekday) Hr. 181 80 Labor Overtime (weekend) Hr. 255 110 Extra Crew (21 people) Hr. 255 110

Next, following several discussions and alignments with Finance and Logistics personnel at XYZ, the quantification of logistics costs was conducted, as summarized in Table 7.

Table 7 Warehouse main T&W sub-elements’ unit costs

Warehouse Activity Service UoM Unit Rate (zł) Unit Rate (EUR) Full pallets in Pallet 4.5 0.9 Handling Full pallet out Pallet 4.5 0.9 Storage Double-stacking storage / day Pallet 0.5 0.1 Holding Daily cost of capital Unit 81.5 16.2

3.2.3 Model Assumptions

Following detailed analysis of the site cost structure, the model assumptions were formulated and grouped into four categories, building on the four-pillar categorization we used for model considerations:

o Demand - Forecasts considered feature manual demand over-ride by planners - FG demand is converted to FWIP requirements - FWIP production must satisfy FG packaging requirements (to avoid complicating the model with safety stock and customer service level calculations) o Capacity - Preparation machines (e.g. assembly) are unconstrained - Standard daily throughput at 18, 000 units / day

o Manufacturing Costs (Labor and Other) - Standard shift duration is 8 hours / shift - Standard crewing pattern is 5 days / week - Monthly crew salary independent of working days (up to 20 / month) - Standard crew contract duration is 6 months - Overtime limited to 4 days / month for the standard crewing pattern - Overtime decision-to-implementation lead time is 2 weeks - Overtime costs assumed constant (no change between weekends and holidays) - Additional crew hiring decision-to-implementation lead time is 8 weeks Crew release to be communicated 4 weeks before contract end - Depreciation, Utility and Other costs considered fixed - M&R is considered variable

o Planning and Logistics - No FWIP shelf-life constraints considered - Phase-In Phase-Out (PIPO) of product families not considered - Packing lines (for FG production) sequencing out of scope - Storage costs incurred in all warehouses - Additional warehouse capacity possible at higher costs (due to rework)

3.3 Model Definition

In this section, the model mathematical formulation is stated, showing the objective function, decision variables and the constraints, followed by a portrayal of the model structure and simulated scenarios.

3.3.1 Mathematical Formulation

Sets 푗: 푇푦푝푒 표푓 푝푟표푑푢푐푡 푗 {1 … 푛} 푡: 푡푖푚푒 푓푟푎푚푒 (푚표푛푡ℎ푠 푡){1 … 12}

Parameters 푖_푥: 푖푛푣푒푛푡표푟푦 푒푥푡푟푎 푐표푠푡푠 푖_푠: 푖푛푣푒푛푡표푟푦 푠푡푎푛푑푎푟푑 푐표푠푡푠 퐷푗푡: 푑푒푚푎푛푑 표푓 푝푟표푑푢푐푡 푗 푎푡 푒푎푐ℎ 푚표푛푡ℎ 푡 푑_푡: 푡표푡푎푙 푐푎푙푒푛푑푎푟 푑푎푦푠 푝푒푟 푚표푛푡ℎ 푟_푡: 푐푟푒푤 푠푖푧푒 푏_푡: 푏푎푠푒 푐푟푒푤 푠푖푧푒 퐶푥표푡: 퐸푥푡푟푎 표푣푒푟 푡푖푚푒 푐표푠푡 퐶푥푐푡: 퐸푥푡푟푎 푐푟푒푤 푐표푠푡 퐶ℎ푖푡: 푃푎푙푙푒푡 ℎ푎푛푑푙푖푛푔 (푖푛)푐표푠푡 퐶ℎ표푡: 푃푎푙푙푒푡 ℎ푎푛푑푙푖푛푔 (표푢푡)푐표푠푡 푇푟푐푡: 푇푟푎푚푠푝표푟푡푎푡푖표푛 푐표푠푡 퐶푎푝푏푡: 푃푟표푑푢푐푡푖표푛 푏푎푠푒 푐푎푝푎푐푖푡푦 푒표푡: 퐸푥푡푟푎 푝푟표푑푢푐푡 푐푎푝푎푐푖푡푦 푑푢푒 푡표 표푣푒푟 푡푖푚푒 푎푡 푒푎푐ℎ 푚표푛푡ℎ 푡 푒푐ℎ푡: 퐸푥푡푟푎 푝푟표푑푢푐푡 푐푎푝푎푐푖푡푦 푑푢푒 푡표 푒푥푡푟푎 푐푟푒푤 ℎ푖푟푒 푑푒푐푖푠푖표푛 푎푡 푒푎푐ℎ 푚표푛푡ℎ 푡 푒푐푟푡: 푝푟표푑푐푡 푐푎푝푎푐푖푡푦 푟푒푑푢푐푡푖표푛 푑푢푒 푡표 푎 푐푟푒푤 푟푒푎푙푒푎푠푒 푑푒푐푖푠푖표푛 푎푡 푒푎푐ℎ 푚표푛푡ℎ 푡 푒푐푟푙1푡: 푝푟표푑푢푐푡 푐푎푝푎푐푖푡푦 푟푒푑푢푐푡푖표푛 푑푢푒 푡표 푎 푐푟푒푤 푟푒푙푒푎푠푒 푑푒푐푖푠푖표푛 (퐿표표푝 1) 푒푐푟푙2푡: 푝푟표푑푢푐푡 푐푎푝푎푐푖푡푦 푟푒푑푢푐푡푖표푛 푑푢푒 푡표 푎 푐푟푒푤 푟푒푙푒푎푠푒 푑푒푐푖푠푖표푛 (퐿표표푝 2) 푒푐푟푙3푡: 푝푟표푑푢푐푡 푐푎푝푎푐푖푡푦 푟푒푑푢푐푡푖표푛 푑푢푒 푡표 푎 푐푟푒푤 푟푒푙푒푎푠푒 푑푒푐푖푠푖표푛 (퐿표표푝 3) 푦푓푥: 푓푖푥푒푑 푚푎푛푢푓푎푐푡푢푟푖푛푔 푐표푠푡푠 (푑푒푝푟푒푐푖푎푡푖표푛 + 푢푡푖푙푖푡푖푒푠 + 표푡ℎ푒푟)

Variables 퐼푗푡: 푂푛 ℎ푎푛푑 푖푛푣푒푛푡표푟푦 표푓 푝푟표푑푢푐푡 푗 푎푡 푚표푛푡ℎ 푡 퐼푚푖푛푗푡: 푀푖푛푖푚푚푢푛 푖푛푣푒푛푡표푟푦 푙푒푣푒푙 표푓 푝푟표푑푢푐푡 푗 푎푡 푚표푛푡ℎ 푡 퐼푏푗푡: 퐴푚표푢푛푡 표푓 퐼푛푣푒푛푡표푟푦 푠푡표푟푒푑 푏푒푙표푤 푙표푐푎푡푖표푛 푐푎푝푎푐푖푡푦 (푖. 푒. 35,000) 퐼푎푗푡: 퐴푚표푢푛푡 표푓 퐼푛푣푒푛푡표푟푦 푠푡표푟푒푑 푎푏표푣푒 푙표푐푎푡푖표푛 푐푎푝푎푐푖푡푦 (푖. 푒. 35,000) 푃푗푡: 푃푙푎푛푛푒푑 푝푟표푑푢푐푡푖표푛 표푓 푝푟표푑푢푐푡 푗 푎푡 푚표푛푡ℎ 푡 푥푒표푡: 퐸푥푡푟푎 표푣푒푟 푡푖푚푒 푎푡 푒푎푐ℎ 푚표푛푡ℎ 푡 푥푒푐ℎ푡: 퐸푥푡푟푎 푐푟푒푤 ℎ푖푟푒 푑푒푐푖푠푖표푛 푎푡 푒푎푐ℎ 푚표푛푡ℎ 푡 푥푒푐푟푡: 퐶푟푒푤 푟푒푙푒푎푠푒 푑푒푐푖푠푖표푛 푎푡 푒푎푐ℎ 푚표푛푡ℎ 푡 푥푒푐푟푙1푡: 퐿표표푝 1 푐푟푒푤 푠푖푧푒 푟푒푑푢푐푡푖표푛 푑푒푐푖푠푖표푛 푎푡 푒푎푐ℎ 푚표푛푡ℎ 푡 푥푒푐푟푙2푡: 퐿표표푝 2 푐푟푒푤 푠푖푧푒 푟푒푑푢푐푡푖표푛푑푒푐푖푠푖표푛 푎푡 푒푎푐ℎ 푚표푛푡ℎ 푡 푥푒푐푟푙3푡: 퐿표표푝 3 푐푟푒푤 푠푖푧푒 푟푒푑푢푐푡푖표푛푑푒푐푖푠푖표푛 푎푡 푒푎푐ℎ 푚표푛푡ℎ 푡 퐶푎푝푣푡: 푃푟표푑푢푐푡푖표푛 푣푎푟푖푎푏푙푒 푐푎푝푎푐푖푡푦

Objective Function

푛 12 푛 12 12 12 푀푖푛 푧 ∶ ∑푗 ∑푡 퐼푏푗푡 ∗ 푖푠 + ∑푗 ∑푡 퐼푎푗푡 ∗ 푖푥 + ∑푡 푒표푡 ∗ 푐표푥푡 + ∑푡 푒푐ℎ푡 ∗ Equation 1 12 12 푛 12 12 푐푥푐푡 + ∑푡 푃푗푡 ∗ 푐ℎ푖푡 + ∑푡 푃푗푡 ∗ 푡푟푡 + ∑푗 ∑푡 퐷푗푡 ∗ 푐ℎ표푡 − ∑푡 푒푐푟푡 ∗ 푐푥푐푡 − 12 12 12 ∑푡 푒푐푟푙1푡 ∗ 푐푥푐푡 − ∑푡 푒푐푟푙2푡 ∗ 푐푥푐푡 − ∑푡 푒푐푟푙3푡 ∗ 푐푥푐푡 + ∗ 푦푓푥

Constraints

푰풋풕 = 푰풃풋풕 + 푰풂풋풕 ∀풋, ∀풕 Equation 2

푰풋ퟏ = 푰ퟎ + 푷풋ퟏ − 푫풋ퟏ ∀풋, ∀풕 = ퟏ Equation 3

푰풋풕 = 푰풕−ퟏ + 푷풋풕 − 푫풋풕 ∀풋, ∀풕, 풕 ≠ ퟏ Equation 4

푰풋풕 ≥ 푰풎풊풏풋풕 ∀풋, ∀풕 Equation 5 풏

∑ 푷풋풕 ≤ 푪풂풑풃풕 + 푪풂풑풗풕 ∀풕 Equation 6 풋=ퟏ 푪풂풑풗 = 풙풆풐 ∗ 풆풐 + 풙풆풄풉 ∗ 풆풄풉 − 풙풆풄풓 ∗ 풆풄풓 − 풙풆풄풓ퟏ 풕 풕 풕 풕 풕 풕 풕 풕 ∀풕 Equation 7 ∗ 풆풄풓ퟏ풕 − 풙풆풄풓ퟐ풕 ∗ 풆풄풓ퟐ풕 − 풙풆풄풓ퟑ풕 ∗ 풆풄풓ퟑ풕

풙풆풐풕 ≤ ퟏퟐ ∀풕 Equation 8

풙풆풄풉풕 , 풙풆풄풓풕, 풙풆풄풓ퟏ풕, 풙풆풄풓ퟐ풕, 풙풆풄풓ퟑ풕 ∈ {ퟎ, ퟏ} ∀풕 Equation 9

푰풋풕 , 푰풃풋풕 , 푰풂풋풕 , 푰풋ퟏ , 푷풋풕 , 푪풂풑풗풕 , 풙풐풗풕 ≥ ퟎ ∀풋, ∀풕 Equation 10

Equation 1 contains the model objective function, whose purpose is minimizing total SC costs. The equation contains all elements relating to SC costs; labor (fixed and variable), fixed costs (depreciation, utilities, other), storage (standard and extra rework), handling (in and out), transportation and inventory holding (cost of capital). Equations 2-4 show the opening inventory stock, which add production volumes to the previous month’s opening stock and subtract demand volumes. Equation 5 represents the minimum inventory constraint, where the on-hand inventory must exceed the minimum target coverage.

Regarding manufacturing (labor) constraints, equation 6 represents the capacity constraint, where the monthly production cannot exceed the monthly capacity. The monthly capacity, which is shown in equation 7, is calculated based on the base capacity as well as any incremental/decremental capacities because of workforce-related changes. With regards to overtime, equation 8 constraints the maximum overtime to 12 shifts per month (four days). Finally, equations 9 and 10 consist of the non-negative and binary variable constraints, where all decision variables related to crew hiring, crew release and crew size changes are binary, demonstrating the decision-making policies.

3.3.2 Model Structure

The model structure was created using “Open Solver”, which is an Excel add-in featuring several advanced, programming-based solvers such as “CBC”, “COIN-OR” and “Gurobi”. The main drivers behind using Open Solver were the friendly user interface, which could be more helpful to XYZ in the future, in addition to the utilization of advanced solvers that can tackle large linear and non-linear models (where Excel solver typically fails). Figure 7 shows the main interface of Open Solver. Figure 7 Model structure in Open Solver

As mentioned, the model objective, inputs, constraints, and assumptions were mapped out, clearly defining the model structure. The key components of the model in terms of current state, inputs, constraints, and decision variables are depicted in Figures 8-11.

Figure 8 The baseline of site B operating model: labor and crewing pattern

OBJECTIVE FUNCTION Month-Year Mar-21 OBJECTIVE FUNCTION COMPONENTS INPUTS Changed all referenced cells to this sheet (different sheets not supported by Excel Solver or Open Solver) DECISION VARIABLES Standard Excel Solver cannot solve this model (errors: "too many variables" or "problem too large") CONSTRAINTS Advanced Open Solver fully functional (Installed Gurobi & Couenne solvers via Advanced Open Solver)

CALENDAR Mar-21 Apr-21 May-21 Jun-21 Jul-21 Aug-21 Sep-21 Oct-21 Nov-21 Dec-21 Jan-22 Feb-22 Total Calendar Days 31 30 31 30 31 31 30 31 30 31 31 28 Total Available Days (5+2) 23 20 20 23 22 23 23 22 23 23 23 23 Holidays 0 0 1 0 2 2 0 1 2 4 2 0 Available Days 23 20 19 23 20 21 23 21 21 19 21 23 Available Shifts 69 60 57 69 60 63 69 63 63 57 63 69

Base Crew (total) 231 231 231 231 231 231 231 231 231 231 231 231 Crew Size 21 21 21 21 21 21 21 21 21 21 21 21 Base Productivity (GUs / crew) 180,300 180,300 180,300 180,300 180,300 180,300 180,300 180,300 180,300 180,300 180,300 180,300 Base Capacity (GUs / day) 1,983,300 1,983,300 1,983,300 1,983,300 1,983,300 1,983,300 1,983,300 1,983,300 1,983,300 1,983,300 1,983,300 1,983,300 Base Capacity (GUs / month) 45,615,900 39,666,000 37,682,700 45,615,900 39,666,000 41,649,300 45,615,900 41,649,300 41,649,300 37,682,700 41,649,300 45,615,900

Figure 9 Model inputs: FWIP Demand and Opening Stock

FWIP DEMAND / Requirements Product Family Mar-21 Apr-21 May-21 Jun-21 Jul-21 Aug-21 Sep-21 Oct-21 Nov-21 Dec-21 Jan-22 Feb-22 265 Crt ------303 Crt 2,500,478 2,275,656 3,855,742 4,820,310 4,224,497 3,817,627 2,834,194 2,836,348 2,499,967 1,814,407 1,852,015 1,880,449 303-11 Crt 1,247,798 1,315,467 1,299,078 2,366,631 1,673,073 1,833,225 1,929,998 2,277,581 2,096,851 1,964,135 2,056,171 1,765,213 312 Crt - 67,039 4,513,054 4,360,037 3,819,229 3,152,180 2,932,949 2,088,170 1,633,406 1,602,521 1,537,979 1,295,271 313 Crt - - - - - 479,826 408,429 370,599 358,879 265,778 179,996 162,868 313-4 Crt 4,401,347 4,724,535 4,556,074 3,956,518 6,036,789 7,015,039 6,645,513 8,229,782 8,041,317 7,611,013 7,417,012 6,526,123 436 Crt - - - - - 206,935 187,086 100,279 105,150 55,382 67,980 78,847 436-3/4 Crt 1,274,621 1,783,027 2,218,600 1,697,307 2,416,025 2,505,331 2,645,135 3,108,316 2,554,243 2,597,906 2,795,143 2,628,829 436-5 Crt 2,001,409 1,354,510 1,527,190 1,539,037 1,581,046 1,618,955 1,938,598 2,220,799 1,765,640 1,735,521 1,999,815 1,719,305 510 Crt - - 251,349 423,081 509,749 506,997 511,460 495,921 385,712 310,449 435,390 395,431 511 Crt 11,350,053 12,129,868 8,441,035 8,237,295 12,093,902 12,327,760 12,695,866 13,198,366 10,667,062 9,129,537 10,833,861 9,765,911 Fem 610 Crt - - - - - 21,130 25,563 26,853 25,007 17,547 9,980 6,000 Joy 596 Crt ------3,769 36,214 31,843 SG 556 Crt 118,146 68,717 136,887 79,893 357,237 479,374 444,776 488,952 562,132 678,970 855,834 780,020 SG 556-8 Crt 1,184,014 1,589,479 1,569,022 1,392,411 1,765,028 1,770,952 1,858,693 1,974,822 1,864,266 1,663,210 1,961,267 1,749,913 TOTAL DEMAND 24,077,866 25,308,298 28,368,031 28,872,520 34,476,575 35,735,331 35,058,260 37,416,788 32,559,632 29,450,145 32,038,657 28,786,023 SPARE CAPACITY 21,538,034 6,763,842 1,884,142 8,659,521 (2,404,435) (1,843,224) 2,473,781 (94,688) (2,097,518) (2,301,299) (1,576,543) 4,989,359 Handling cost kEUR € 0.8 € 0.8 € 0.9 € 1.0 € 1.1 € 1.2 € 1.2 € 1.2 € 1.1 € 1.0 € 1.1 € 1.0

INVENTORY

Opening Stock Product Family Mar-21 Apr-21 May-21 Jun-21 Jul-21 Aug-21 Sep-21 Oct-21 Nov-21 Dec-21 Jan-22 Feb-22

318,526 265 Crt 318,526 318,526 318,526 318,526 318,526 318,526 318,526 318,526 318,526 318,526 318,526 318,526 1,089,473 303 Crt 3,226,423 3,034,208 4,975,151 6,427,080 5,450,964 4,925,970 3,778,925 3,659,804 3,333,289 2,341,170 2,389,697 2,686,356 1,684,684 303-11 Crt 1,610,062 1,753,956 1,676,230 3,155,508 2,158,804 2,365,452 2,573,331 2,938,814 2,795,801 4,709,295 2,653,124 2,521,733 13,242,740 312 Crt 13,242,740 13,175,701 8,662,647 5,813,383 4,928,037 4,067,329 4,782,583 2,694,413 2,177,875 2,067,769 1,984,489 1,850,387 3,524,794 313 Crt 3,524,794 3,524,794 3,524,794 3,524,794 3,524,794 3,044,968 2,636,539 2,265,940 1,907,061 1,641,283 1,461,287 1,298,419 6,123,737 313-4 Crt 5,679,157 6,299,380 5,878,805 8,220,210 7,789,405 9,051,663 9,306,907 10,619,074 10,721,756 9,820,662 9,570,338 9,323,033 3,529,764 436 Crt 3,529,764 3,529,764 3,529,764 3,529,764 3,529,764 3,322,829 3,135,743 3,035,464 2,930,314 2,874,932 2,806,952 2,728,105 838,192 436-3/4 Crt 1,644,672 2,377,369 2,862,710 5,533,477 3,117,452 3,232,685 3,526,847 4,010,730 3,405,657 3,352,137 3,606,636 3,755,470 634,868 436-5 Crt 2,582,463 1,806,013 1,970,568 3,621,105 2,040,059 2,088,974 2,584,797 2,865,547 2,354,187 2,239,382 2,580,407 2,456,150 1,392,095 510 Crt 1,392,095 1,392,095 1,140,746 1,670,936 1,161,187 654,190 681,947 639,898 514,283 400,579 561,794 564,901 9,763,029 511 Crt 14,645,230 16,173,157 10,891,658 10,983,060 16,310,745 15,906,787 16,927,821 17,030,150 15,663,779 11,943,983 13,979,175 13,951,301 185,056 Fem 610 Crt 185,056 185,056 185,056 185,056 185,056 163,926 138,363 111,510 86,503 68,956 58,976 52,976 217,411 Joy 596 Crt 217,411 217,411 217,411 217,411 217,411 217,411 217,411 217,411 217,411 213,642 177,428 145,585 732,872 SG 556 Crt 614,726 546,009 409,122 1,455,158 1,097,921 618,547 593,035 630,906 749,509 876,090 1,104,302 1,114,314 1,571,862 SG 556-8 Crt 1,527,760 2,119,305 2,024,545 1,856,548 2,277,456 2,285,099 2,478,257 2,548,157 2,485,688 4,491,934 2,530,667 2,499,876 44,849,103 TOTAL STOCK LEVEL 53,940,880 56,452,745 48,267,732 56,512,016 54,107,581 52,264,357 53,681,032 53,586,345 49,661,639 47,360,341 45,783,797 45,267,132 Figure 10 Model constraints: Labor, Capacity, Inventory, and Storage

integer Extra OT (shifts/month) 0≤,12≥ 0 0 0 0 0 0 0 0 0 0 0 0 INTEGER (0-12) Extra Capacity from OT ------Extra OT cost kEUR € - € - € - € - € - € - € - € - € - € - € - € - € - binary Extra Crew Hire Decision 0 0 0 0 0 0 0 0 0 0 0 0 BINARY (0,1) Increased Capacity from Extra Crew hire ------Extra Crew (shift) cost kEUR € - € - € - € - € - € - € - € - € - € - € - € - € - integer Crew (shift) Release Decision 2≥ 2 - - - - - 2 - - - - - INTEGER (0-2) Reduced Capacity (Crew Release) - 4,327,200 4,327,200 4,327,200 4,327,200 4,327,200 4,327,200 4,327,200 4,327,200 4,327,200 4,327,200 4,327,200 Released Crew cost kEUR € - € (270) € (270) € (270) € (270) € (270) € (270) € (270) € (270) € (270) € (270) € (270) € (2,970) binary Loop 1 Crew Size Reduction Decision ------1 - - - - BINARY (0,1) Loop 1 Reduced Capacity (Reduced Crew Size) ------3,429,993 3,103,327 3,429,993 3,756,659 Reduced Crew Size cost EUR € - € - € - € - € - € - € - € - € (96) € (96) € (96) € (96) € (386) binary Loop 2 Crew Size Reduction Decision ------1 - - - - BINARY (0,1) Loop 2 Reduced Capacity (Reduced Crew Size) ------3,429,993 3,103,327 3,429,993 3,756,659 Reduced Crew Size cost EUR € - € - € - € - € - € - € - € - € (96) € (96) € (96) € (96) € (386) binary Loop 3 Crew Size Reduction Decision 1 ------BINARY (0,1) Loop 3 Reduced Capacity (Reduced Crew Size) - 3,266,660 3,103,327 3,756,659 3,266,660 3,429,993 3,756,659 - - - - - Reduced Crew Size cost EUR € - € (96) € (96) € (96) € (96) € (96) € (96) € - € - € - € - € - € (579)

Extra crew hire decision 0 0 0 0 0 0 0 0 0 0 0 0 <= <= <= <= <= <= <= <= <= <= <= <= ≤ 1 1 1 1 1 1 1 1 1 1 1 1

Extra Crew Released 1st Period 2 2 2 2 2 2 Extra Crew Released 2nd Period ------Extra Crew Released 3rd Period ------Extra Crew Released 4ft Period ------Extra Crew Released 5th Period ------Extra Crew Released 6th Period ------Extra Crew Released 7th Period 2 2 2 2 2 Extra Crew Released 8th Period - - - - Extra Crew Released 9th Period - - - Extra Crew Released 10th Period - - Extra Crew Released 11th Period - Extra crew release decision 0 2 2 2 2 2 2 2 2 2 2 2 <= <= <= <= <= <= <= <= <= <= <= <= ≤ 2 2 2 2 2 2 2 2 2 2 2 2

TOTAL PRODCUTION 33,169,643 27,820,164 20,183,018 37,116,804 32,072,140 33,892,107 36,474,935 37,322,100 28,634,928 27,148,846 30,462,114 28,269,358 <= <= <= <= <= <= <= <= <= <= <= <= <= TOTAL CAPACITY ≤ 45,615,900 32,072,140 30,252,173 37,532,041 32,072,140 33,892,107 37,532,041 37,322,100 30,462,114 27,148,846 30,462,114 33,775,382 >= >= >= >= >= >= >= >= >= >= >= >= ≥ 0 0 0 0 0 0 0 0 0 0 0 0

MINIMUM STOCK LEVEL Product Family Mar-21 Apr-21 May-21 Jun-21 Jul-21 Aug-21 Sep-21 Oct-21 Nov-21 Dec-21 Jan-22 Feb-22 265 Crt ------303 Crt 3,226,423 3,034,208 4,975,151 6,427,080 5,450,964 4,925,970 3,778,925 3,659,804 3,333,289 2,341,170 2,389,697 2,686,356 303-11 Crt 1,610,062 1,753,956 1,676,230 3,155,508 2,158,804 2,365,452 2,573,331 2,938,814 2,795,801 2,534,368 2,653,124 2,521,733 312 Crt - 89,385 5,823,295 5,813,383 4,928,037 4,067,329 3,910,599 2,694,413 2,177,875 2,067,769 1,984,489 1,850,387 313 Crt - - - - - 619,130 544,572 478,192 478,505 342,939 232,253 232,669 313-4 Crt 5,679,157 6,299,380 5,878,805 5,275,357 7,789,405 9,051,663 8,860,684 10,619,074 10,721,756 9,820,662 9,570,338 9,323,033 436 Crt - - - - - 267,013 249,448 129,392 140,200 71,461 87,716 112,639 436-3/4 Crt ≥ 1,644,672 2,377,369 2,862,710 2,263,076 3,117,452 3,232,685 3,526,847 4,010,730 3,405,657 3,352,137 3,606,636 3,755,470 436-5 Crt 2,582,463 1,806,013 1,970,568 2,052,049 2,040,059 2,088,974 2,584,797 2,865,547 2,354,187 2,239,382 2,580,406 2,456,150 510 Crt - - 324,321 564,108 657,741 654,190 681,947 639,898 514,283 400,579 561,794 564,901 511 Crt 14,645,230 16,173,157 10,891,658 10,983,060 15,605,035 15,906,787 16,927,821 17,030,150 14,222,749 11,780,048 13,979,175 13,951,301 Fem 610 Crt - - - - - 27,265 34,084 34,649 33,343 22,641 12,877 8,571 Joy 596 Crt ------4,863 46,728 45,490 SG 556 Crt 152,446 91,623 176,628 106,524 460,951 618,547 593,035 630,906 749,509 876,090 1,104,302 1,114,314 SG 556-8 Crt 1,527,760 2,119,305 2,024,545 1,856,548 2,277,455 2,285,099 2,478,257 2,548,157 2,485,688 2,146,077 2,530,667 2,499,876 TOTAL MINIMUM STOCK LEVEL 31,068,214 33,744,397 36,603,911 38,496,693 44,485,903 46,110,105 46,744,347 48,279,726 43,412,843 38,000,187 41,340,203 41,122,890

TOTAL STORED STOCK (INVENTORY) 53,940,880 56,452,746 48,267,731 56,512,016 54,107,581 52,264,357 53,681,032 53,586,343 49,661,640 47,360,340 45,783,798 45,267,132 Standard storage capacity (GUs) 35,000,000 35,000,000 35,000,000 35,000,000 35,000,000 35,000,000 35,000,000 35,000,000 35,000,000 35,000,000 35,000,000 35,000,000 Extra storage needed (GUs) 18,940,880 21,452,746 13,267,731 21,512,016 19,107,581 17,264,357 18,681,032 18,586,343 14,661,640 12,360,340 10,783,798 10,267,132 Extra storage needed (Y/N) Y Y Y Y Y Y Y Y Y Y Y Y Figure 11 Model decision variables: Crewing, Production Plan, and Inventory

integer Extra OT (shifts/month) 0≤,12≥ 0 0 0 0 0 0 0 0 0 0 0 0 Extra Capacity from OT ------Extra OT cost kEUR € - € - € - € - € - € - € - € - € - € - € - € - binary Extra Crew Hire Decision 0 0 0 0 0 0 0 0 0 0 0 0 Increased Capacity from Extra Crew hire ------Extra Crew (shift) cost kEUR € - € - € - € - € - € - € - € - € - € - € - € - integer Crew (shift) Release Decision 2≥ 2 - - - - - 2 - - - - - Reduced Capacity (Crew Release) - 4,327,200 4,327,200 4,327,200 4,327,200 4,327,200 4,327,200 4,327,200 4,327,200 4,327,200 4,327,200 4,327,200 Released Crew cost kEUR € - € (270) € (270) € (270) € (270) € (270) € (270) € (270) € (270) € (270) € (270) € (270) binary Loop 1 Crew Size Reduction Decision ------1 - - - - Loop 1 Reduced Capacity (Reduced Crew Size) ------3,429,993 3,103,327 3,429,993 3,756,659 Reduced Crew Size cost EUR € - € - € - € - € - € - € - € - € (96) € (96) € (96) € (96) binary Loop 2 Crew Size Reduction Decision ------1 - - - - Loop 2 Reduced Capacity (Reduced Crew Size) ------3,429,993 3,103,327 3,429,993 3,756,659 Reduced Crew Size cost EUR € - € - € - € - € - € - € - € - € (96) € (96) € (96) € (96) binary Loop 3 Crew Size Reduction Decision 1 ------Loop 3 Reduced Capacity (Reduced Crew Size) - 3,266,660 3,103,327 3,756,659 3,266,660 3,429,993 3,756,659 - - - - - Reduced Crew Size cost EUR € - € (96) € (96) € (96) € (96) € (96) € (96) € - € - € - € - € - Total Capacity (GUs / month) 45,615,900 32,072,140 30,252,173 37,532,041 32,072,140 33,892,107 37,532,041 37,322,100 30,462,114 27,148,846 30,462,114 33,775,382

PRODUCTION PLAN Product Family Mar-21 Apr-21 May-21 Jun-21 Jul-21 Aug-21 Sep-21 Oct-21 Nov-21 Dec-21 Jan-22 Feb-22 265 Crt ------303 Crt 4,637,428 2,083,441 5,796,685 6,272,239 3,248,381 3,292,634 1,687,149 2,717,227 2,173,453 822,288 1,900,542 2,177,108 303-11 Crt 1,173,176 1,459,361 1,221,352 3,845,909 676,369 2,039,873 2,137,877 2,643,065 1,953,838 3,877,629 - 1,633,822 312 Crt - - - 1,510,773 2,933,884 2,291,472 3,648,203 - 1,116,868 1,492,415 1,454,699 1,161,169 313 Crt ------313-4 Crt 3,956,767 5,344,758 4,135,499 6,297,923 5,605,984 8,277,297 6,900,757 9,541,949 8,144,000 6,709,919 7,166,688 6,278,818 436 Crt ------436-3/4 Crt 2,081,101 2,515,724 2,703,940 4,368,074 - 2,620,565 2,939,297 3,592,200 1,949,170 2,544,385 3,049,642 2,777,663 436-5 Crt 3,949,004 578,060 1,691,744 3,189,575 - 1,667,870 2,434,421 2,501,549 1,254,280 1,620,716 2,340,840 1,595,049 510 Crt - - - 953,271 - - 539,217 453,872 260,097 196,746 596,604 398,539 511 Crt 16,232,254 13,657,796 3,159,536 8,328,697 17,421,587 11,923,802 13,716,900 13,300,694 9,300,691 5,409,741 12,869,054 9,738,037 Fem 610 Crt ------Joy 596 Crt ------SG 556 Crt - - - 1,125,929 - - 419,264 526,823 680,736 805,551 1,084,046 790,032 SG 556-8 Crt 1,139,912 2,181,024 1,474,261 1,224,415 2,185,936 1,778,596 2,051,851 2,044,722 1,801,797 3,669,456 - 1,719,122 TOTAL PRODCUTION 33,169,643 27,820,164 20,183,018 37,116,804 32,072,140 33,892,107 36,474,935 37,322,100 28,634,928 27,148,846 30,462,114 28,269,358

INVENTORY

Product Family Mar-21 Apr-21 May-21 Jun-21 Jul-21 Aug-21 Sep-21 Oct-21 Nov-21 Dec-21 Jan-22 Feb-22

265 Crt 318,526 318,526 318,526 318,526 318,526 318,526 318,526 318,526 318,526 318,526 318,526 318,526 303 Crt 3,226,423 3,034,208 4,975,151 6,427,080 5,450,964 4,925,970 3,778,925 3,659,804 3,333,289 2,341,170 2,389,697 2,686,356 303-11 Crt 1,610,062 1,753,956 1,676,230 3,155,508 2,158,804 2,365,452 2,573,331 2,938,814 2,795,801 4,709,295 2,653,124 2,521,733 312 Crt 13,242,740 13,175,701 8,662,647 5,813,383 4,928,037 4,067,329 4,782,583 2,694,413 2,177,875 2,067,769 1,984,489 1,850,387 313 Crt 3,524,794 3,524,794 3,524,794 3,524,794 3,524,794 3,044,968 2,636,539 2,265,940 1,907,061 1,641,283 1,461,287 1,298,419 313-4 Crt 5,679,157 6,299,380 5,878,805 8,220,210 7,789,405 9,051,663 9,306,907 10,619,074 10,721,756 9,820,662 9,570,338 9,323,033 436 Crt 3,529,764 3,529,764 3,529,764 3,529,764 3,529,764 3,322,829 3,135,743 3,035,464 2,930,314 2,874,932 2,806,952 2,728,105 436-3/4 Crt 1,644,672 2,377,369 2,862,710 5,533,477 3,117,452 3,232,685 3,526,847 4,010,730 3,405,657 3,352,137 3,606,636 3,755,470 436-5 Crt 2,582,463 1,806,013 1,970,568 3,621,105 2,040,059 2,088,974 2,584,797 2,865,547 2,354,187 2,239,382 2,580,407 2,456,150 510 Crt 1,392,095 1,392,095 1,140,746 1,670,936 1,161,187 654,190 681,947 639,898 514,283 400,579 561,794 564,901 511 Crt 14,645,230 16,173,157 10,891,658 10,983,060 16,310,745 15,906,787 16,927,821 17,030,150 15,663,779 11,943,983 13,979,175 13,951,301 Fem 610 Crt 185,056 185,056 185,056 185,056 185,056 163,926 138,363 111,510 86,503 68,956 58,976 52,976 Joy 596 Crt 217,411 217,411 217,411 217,411 217,411 217,411 217,411 217,411 217,411 213,642 177,428 145,585 SG 556 Crt 614,726 546,009 409,122 1,455,158 1,097,921 618,547 593,035 630,906 749,509 876,090 1,104,302 1,114,314 SG 556-8 Crt 1,527,760 2,119,305 2,024,545 1,856,548 2,277,456 2,285,099 2,478,257 2,548,157 2,485,688 4,491,934 2,530,667 2,499,876 TOTAL STOCK LEVEL 53,940,880 56,452,745 48,267,732 56,512,016 54,107,581 52,264,357 53,681,032 53,586,345 49,661,639 47,360,341 45,783,797 45,267,132 3.4 What-if Scenarios

Following the model’s attempted optimization of total supply chain costs based on current constraints, several what-if scenarios were simulated. The main purpose of running these scenarios was to identify the realm of possibilities available for company XYZ to reduce their supply chain costs further, beyond the baseline state that can be achieved based on existing constraints. Moreover, the additional scenarios can highlight to XYZ the cost of tightening one of the current constraints, as adding further restrictions to an existing constraint usually results in a worse solution (higher cost in this case).

The what-if scenarios can be viewed as a form of sensitivity analysis, where the model’s robustness can be tested, as well as the possible variations in site B operations. To determine which constraints were to be tested, several working sessions were conducted with teams in XYZ, to ensure that the constraint modifications were realistic and acceptable. The following constraints were modified:

- Inventory target (Days Forward Coverage “DFC”) - Maximum overtime frequency per month - Minimum contract duration for new crew - Minimum lead time to hire new crew

These constraints were selected as they contribute the most to the two main SC costs: labor and inventory. Next, the variation range of these constraints was determined, based on a balance between significant yet plausible change, yielding 20 distinct simulation scenarios (summarized in Table 8). Table 8 Summary of the 20 distinct simulated scenarios

Scenario Inventory target Maximum overtime Minimum contract Minimum hiring LT [#] [DFC] [# shifts/month] [months] [months]

1 20 12 6 2 2 30 12 6 2 3 40 12 6 2 4 50 12 6 2 5 60 12 6 2 6 20 24 6 2 7 30 24 6 2 8 40 24 6 2 9 50 24 6 2 10 60 24 6 2 11 20 12 4 2 12 30 12 4 2 13 40 12 4 2 14 50 12 4 2 15 60 12 4 2 16 20 12 6 1 17 30 12 6 1 18 40 12 6 1 19 50 12 6 1 20 60 12 6 1

DFC was varied from 20 to 60 days since the standard historical target coverage has been between 25-40 days, and a minimum of 20 days was estimated by XYZ planners as necessary to fulfill the committed service level. A maximum of 60 days was set since any coverage beyond would entail an extremely low- response supply chain form an innovation planning perspective. Regarding overtime, only one different value was assessed during the simulated scenarios compared to the current standard of 12 shifts per month, as 24 shifts is the absolute maximum number of monthly overtime shifts that can be achieved, embodying the “significant change” principle.

For the minimum contract duration, which had a standard value of six months, the different value assessed was four months. This value was chosen mainly since hiring a new crew required a training of one month for safety/quality aspects (included in the contract), so it seemed unrealistic to assess the model using a constraint that only benefits from a newly hired crew for two months after one full month of training. Finally, the lead time to hire a new crew was pushed to the minimum, like the overtime constraint, where it was brought down from two months to one month (covering only the training period). Chapter 4 Results and Discussion

The main purpose of the project was to deliver suggestions to optimize the production planning strategy for site B at company XYZ, which could result in reduced supply chain costs. In addition, simulation of several what-if scenarios targeted generating multiple opportunities for XYZ to optimize their supply chain costs beyond what is currently permissible under the existing constraints.

4.1 Results

To assess the model results objectively and accurately, the model output was compared with the “as-is” state, the state that would normally occur without any consideration of the model suggestions.

This as-is state mainly comprises:

- Current crewing pattern for the different production lines - Current headcount per line - Opening stock per product family based on actual stock on-ground - Production as recommended by the S&OP cycle - Inventory as an outcome of stock and production - Same cost drivers and cost rates as used in the model - Quantification of SC costs using S&OP decision points and model cost structure

Comparison of the as-is state with the optimized model output (the baseline) was done on multiple fronts: production volumes, inventory and ultimately costs (total and unit costs). The main cost elements included in the comparison are depicted in Figure 12.

Figure 12 Overview of cost elements in as-is vs. baseline optimized comparison

Logistics Manufacturing Holding Total SC Costs

Handling Labor Cost of Excluding pallets Holding (in and out) (fixed) Capital

Storage Labor Including (standard) (variable) Holding

Extra Maint. & Total storage Repair (k EUR) (rework)

Depr. and Unit Cost Transport Utilities (EUR / U)

The model created suggestions that could result in the minimal total supply chain costs for site B, while adhering to all constraints. Model parameters included staffing decisions such as overtime shifts, crew release and size change, as well as other parameters pertaining to production and inventory levels. Company XYZ’s standard S&OP cycle (known as “DPI”) generates proposals for production and inventory but does not provide guidance relating to the staffing plan. In other words, S&OP does not show how to fulfill the production plan with the lowest cost. In this sense, the optimization model can exceed the standard S&OP outcome, as it dives into operational details that attempt total cost optimization. General, estimated results of the comparison between the model suggestions and S&OP outcome show that:

- The model’s annual production is 21% less than S&OP outcome - The resultant inventory in the model is around 32% less than S&OP outcome - The total SC costs in the model are around 15% less than the S&OP-induced costs

For details, see Table 9.

Table 9 Detailed cost comparison of model recommendations vs. S&OP outcome (numbers have been changed)

Model S&OP Difference Cost Element Total k EURO Unit cost Total k EURO Unit cost Total k EURO Unit cost Standard storage € 158 € 0.0004 € 236 € 0.0005 € 78 € 0.0001 Rework (FWIP-SWFIP) € 244 € 0.0007 € 608 € 0.0013 € 364 € 0.0006 Handling cost (in & out) € 49 € 0.0001 € 56 € 0.0001 € 7 € (0.0000) Transportation € 179 € 0.0005 € 227 € 0.0005 € 48 € - Total Logistics € 630 € 0.0017 € 1,127 € 0.0024 € 496 € 0.0007 Holding (Cost of Capital) € 2,675 € 0.0072 € 4,000 € 0.0085 € 1,325 € 0.0013 Labor € 31,867 € 0.0855 € 42,436 € 0.0899 € 10,569 € 0.0044 M&R € 6,301 € 0.0169 € 7,912 € 0.0168 € 1,610 € (0.0001) Depreciation, Utilities, other € 38,877 € 0.1043 € 38,877 € 0.0824 € - € (0.0220) Manufacturing € 77,045 € 0.2068 € 89,224 € 0.1891 € 12,179 € (0.0177) Total SC € 80,351 € 0.2157 € 94,351 € 0.1999 € 14,000 € (0.0157) Total (excl. cost of capital) € 77,676 € 0.2085 € 90,351 € 0.1915 € 12,675 € (0.0170) Overall, the model suggested that significant annual savings (up to 7M Euros) can be attained by optimizing site B staffing strategy and aligning it with the required production plan, while adhering to the constraints imposed. In terms of unit cost, the model suggestions result in a unit cost that is 8% higher than the S&OP unit cost, mainly driven by the massive volumes in the S&OP recommendation. As shown in Table 9, the two main contributors to the difference between the model and S&OP costs are:

• Labor Costs, which are completely dependent on the labor strategy and changes • M&R and Holding Costs, which increase significantly as production and inventory levels rise

4.2 Discussion

20 additional scenarios were simulated, and their results were compared to the baseline model (a summary of the scenario groups and subsequent scenarios is shown in Figure 13). In contrast to the model vs. S&OP comparison done earlier, the purpose of comparing the scenarios with the baseline was to attempt to quantify the financial gains that could be realized if specific constraints were relaxed. It is important to note that in constrained optimization models, there are mainly two types of constraints: binding and non-binding (slack) constraints. Binding constraints can be viewed as constraints that the model is only barely adhering to, which means that relaxing these constraints should improve the model results (lower costs). In contrast, slack constraints are those which are not currently limiting the model results and can be seen as ones the model is easily meeting. It is important to note that relaxing slack constraints will have no impact on the model objective function (total SC costs). Figure 13 Framework of scenario groups and subsequent scenarios

Identifying the type of each constraint and differentiating between binding and slack constraints is the key purpose of the “what-if” scenario analysis, since it suggests to company XYZ key areas to focus on. Relaxing just one or two of the existing constraints might yield significant benefits, instead of being distracted with several constraints. A summary of the 20 scenario results is shown in Table 10, highlighting key comparative elements such as annual production, average inventory, and absolute costs.

Table 10 Summary of result of 20 scenarios including the baseline and the S&OP outcome

DFC Max. OT Min. contract Min. leadtime Production Production Inventory Scenario Mfg cost Logistics cost Holding cost Total cost target /month duration to hire (annual) (monthly avg) (monthly avg) BASELINE 40 12 6 2 372,566,154 31,047,180 60,502,057 € 38,523 € 313 € 1,333 € 40,169 1 20 12 6 2 352,204,394 29,350,366 46,514,917 € 37,783 € 271 € 1,026 € 39,079 2 30 12 6 2 362,385,275 30,198,773 56,620,869 € 38,148 € 305 € 1,248 € 39,702 3 40 12 6 2 372,566,154 31,047,180 60,502,057 € 38,523 € 313 € 1,333 € 40,169 4 50 12 6 2 382,747,036 31,895,586 71,137,211 € 38,897 € 366 € 1,567 € 40,830 5 59 12 6 2 391,400,785 32,616,732 75,581,013 € 39,255 € 379 € 1,664 € 41,298 S&OP S&OP 12 6 2 471,919,556 39,326,630 89,570,014 € 44,612 € 563 € 2,000 € 47,176 6 20 24 6 2 352,204,396 29,350,366 46,214,417 € 37,783 € 268 € 1,019 € 39,070 7 30 24 6 2 362,385,275 30,198,773 56,540,537 € 38,148 € 305 € 1,246 € 39,700 8 40 24 6 2 372,566,154 31,047,180 60,502,057 € 38,523 € 313 € 1,333 € 40,169 9 50 24 6 2 382,747,036 31,895,586 71,137,211 € 38,897 € 366 € 1,567 € 40,830 10 59 24 6 2 391,400,784 32,616,732 75,490,863 € 39,255 € 379 € 1,662 € 41,296 S&OP S&OP 12 6 2 471,919,556 39,326,630 89,570,014 € 44,612 € 563 € 2,000 € 47,176 11 20 12 4 2 352,204,395 29,350,366 47,988,918 € 37,764 € 271 € 1,058 € 39,094 12 30 12 4 2 362,385,275 30,198,773 55,426,450 € 38,120 € 321 € 1,221 € 39,663 13 40 12 4 2 372,566,155 31,047,180 60,517,056 € 38,495 € 341 € 1,333 € 40,168 14 50 12 4 2 382,747,036 31,895,586 67,666,655 € 38,870 € 354 € 1,490 € 40,713 15 59 12 4 2 391,400,783 32,616,732 76,626,754 € 39,236 € 396 € 1,687 € 41,319 S&OP S&OP 12 6 2 471,919,556 39,326,630 89,570,014 € 44,612 € 563 € 2,000 € 47,176 16 20 12 6 1 352,204,396 29,350,366 46,094,217 € 37,783 € 268 € 1,017 € 39,067 17 30 12 6 1 362,385,275 30,198,773 56,690,787 € 38,148 € 307 € 1,250 € 39,704 18 40 12 6 1 372,566,154 31,047,180 60,592,207 € 38,523 € 313 € 1,335 € 40,171 19 50 12 6 1 382,747,036 31,895,586 71,137,211 € 38,897 € 366 € 1,567 € 40,830 20 59 12 6 1 391,400,784 32,616,732 75,556,606 € 39,255 € 379 € 1,663 € 41,297 S&OP S&OP 12 6 2 471,919,556 39,326,630 89,570,014 € 44,612 € 563 € 2,000 € 47,176 For each scenario, we observed the variation in model suggestions and ultimately in the corresponding costs, to fully comprehend the significance of each of the four constraints. When computing the costs in each scenario, absolute and unit costs were calculated, since unit costs are an important metric for XYZ. In scenario group 1, it is observed that with variation of DFC from 10 to 59 (and to the S&OP outcome), significant increases are incurred in absolute costs, with reduction in unit costs. This absolute cost increase is mainly triggered by the need for increased inventory, which in turn leads to an increase in production volumes and all costs associated with manufacturing and logistics (also results in a lower unit cost).

Scenario group 2 included the same variation to DFC target, as well as relaxing the maximum overtime constraint. Comparing scenario group 2 results to group 1, minimal differences were observed at the same DFC target. The main reason behind the differences leaning towards insignificance is that the model suggested almost the same production and inventory levels. The only difference between groups 1 and 2 is that in group 2, the overtime constraint was slightly more relaxed, allowing for an extremely minor enhancement in labor costs, which reflected on total costs. A very similar trend can be observed when comparing scenario group 3 to scenario group 1. However, in the case of scenario group 3, the relaxed labor constraint was contract duration, which had a slightly better impact on labor costs. Finally, when comparing scenario group 4, which features a reduction in hiring lead time, to group 1, almost no differences were observed. The reason behind observing no differences is simple: there was no need for additional crew hiring in the model, and therefore no impact of changing the hiring lead time. Taking a more holistic view, since there is an abundance of capacity with the current staffing structure, there was no need for additional crew hiring and thus no benefit of relaxing the crew hiring lead time constraint.

Figure 14 show the estimated variation of absolute and unit costs for scenario group 1, while figure 15 presents the comparison from a group-to-group perspective.

Figure 14 Scenario group 1: variation in DFC against the corresponding total and unit costs

Figure 15 Comparison between different scenario groups for different DFC targets

As shown by the comparative figures above, it can be inferred that the DFC constraint is the most significant one that requires focus by company XYZ. While changing contract duration (scenario group 3) provides more cost reduction than changing maximum overtime (scenario group 2), neither constraint generates enough cost reduction to justify further analysis or investment of company resources.

Figure 16 highlights a summary of the findings regarding the significance/importance of the different constraints and encapsulates the financial benefits of relaxing each constraint.

Figure 16 Comprehensive comparison of scenario group outcomes and financial benefits

Chapter 5 Conclusion

Production planning can be a key enabler in fulfilling a firm’s operations strategy if it finds the right tradeoff between flexibility and efficiency. In this capstone, we explored the different types of production planning strategies, highlighting the pros and cons of each, as well as their practicality. The main driver of this capstone project was to address the production planning strategy of company XYZ’s site B and suggest changes that could lead to total end-to-end supply chain cost optimization. One of the most important preliminary steps was establishing an understanding of the site’s cost structure, followed by detailed mapping of the various costs across the supply chain.

The next step was establishing the model framework: an objective function, inputs, constraints, and outputs (known as decision variables). In establishing constraints, it is critical to find the right balance between have a model that can be as realistic as possible without being overly complex and therefore unsolvable. The model created, a Mixed Integer Linear Programming one, was able to yield suggestions to company XYZ regarding their production planning strategy and how to optimize supply chain costs. In addition, several what-if scenarios were simulated, to assess the model sensitivity and gauge the financial impact of changing different constraints. The purpose of the what-if scenarios was mainly to identify the binding and slack constraints, thereby suggesting to company XYZ where they could focus their efforts.

Further improvements to the model can include expanding the time horizon to the full span of aggregate production planning (18 months). The model can also be adjusted to cater to different requirements in other sites/companies, such as creating product-specific constraints or considering additional cost elements in the analysis. In addition, the model can be adopted to include several manufacturing sites, several objective functions and can be expanded to tackle elements relating to safety stock calculation and its inter-link with customer service level. Some noteworthy concepts emerged in the course of this research, especially surrounding the nature of labor costs and constraints, and these may warrant study in subsequent projects or research.

While this capstone addressed production planning strategy at a specific site within a specific company, the concepts adopted are not new, and the methodology could essentially be implemented in any site in a variety of industries. In summary, while the model suggests that in this specific case, significant cost savings could be generated across the supply chain, it is essential that the underlying message be recognized; companies must challenge the status quo and target true global optimization to minimize total E2E supply chain costs, instead of settling for marginal improvements driven by local optimization.

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