Determining the optical reorder level and the optimal case pack size for the "ready-tocook vegetables" at EMTÉ stores

Transcription

1 Eindhoven University of Technology MASTER Determining the optical reorder level and the optimal case pack size for the "ready-tocook vegetables" at EMTÉ stores de Haan, K. Award date: 2015 Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Download date: 19. Jun. 2018

2 Veghel, February 2015 Determining the optimal reorder level and the optimal case pack size for the Ready-to-Cook Vegetables at EMTÉ Stores By Kim de Haan BSc Industrial Engineering (2012) Student Identity Number in partial fulfilment of the requirements for the degree of Master of Science in Operations Management & Logistics Supervisors: Ir. P. Baptist Dr. K.H. van Donselaar Dr. Z. Atan InBound Management, Sligro Food Group Eindhoven University of Technology Eindhoven University of Technology

3 TUE. School of Industrial Engineering Series Master Thesis Operations Management and Logistics Subjects headings: perishable products, reorder level, case pack size II

4 Acknowledgements This report is the result of my master thesis project in order to fulfil my master degree in the master Operations, Management & Logistics at the Eindhoven University of Technology. Therefore I would like to express my gratitude towards some people that have helped me during my thesis and during my life as a student. This project also represents the end of my life as a student. I have learned a lot during my Bachelor and Master in Eindhoven. Every course had its own challenges but I always found a way to face these challenges and conquer them. I enjoyed working in teams and project groups the most, because in my experience you can really achieve more together. I am grateful for the fact that I was given the opportunity to combine my study with professional sports, and I have always enjoyed the challenges in both the field of science and the field of professional sports. In some way they can be very similar and both fields have contributed a lot to the person I am today. I would like to thank several people who supported me throughout this project. Firstly, I would like to start with thanking my first supervisor Karel van Donselaar, for his overall support, feedback during this research project, and always being available for discussion and consultation when I needed it. He always aimed to bring out the best in me by holding me a mirror and by letting me criticize myself. Also many thanks towards my second supervisor Zumbul Atan for her overall support, feedback, and kindness throughout the project. Furthermore I would like to thank my company supervisor Patrick Baptist for his support and feedback during the project, Kees Kiestra for the opportunity to execute this project at Sligro Food Group, and my colleagues of Sligro Food group for their cooperation and friendliness this period. I am grateful to have been given this opportunity. It gave me valuable insight into the corporate world and their business way of thinking instead of the scientific way that I was taught during my studies. Last but certainly not least I would like to thank my parents, family, friends and boyfriend for their continuous support and believe in me during my studies and during this graduation project. Kim de Haan Vught, February 2015 III

5 Abstract This master thesis describes how to amount of outdating for Ready-to-Cook vegetables can be reduced at EMTÉ Supermarkets by optimizing the reorder level and the case pack sizes of these products. The reorder level and the case pack sizes are optimized through minimizing the relevant costs components. Note that an optimal reorder level for each individual SKU-store combination can be found, but that an optimal case pack can only be found for each SKU. This is due to the restriction of the supplier of this product group who does not allow different case pack sizes for the same SKU in different stores. Large costs reduction and waste reduction have been achieved. The results of this project and of the model are theoretical and no actual numbers of Sligro Food Group are mentioned. IV

6 Costs Master Thesis at Sligro Food Group, Veghel Executive Summary This report is the result of a master thesis project conducted at Sligro Food Group in Veghel, a company that encompasses food retail and foodservice companies selling directly and indirectly to the entire Dutch food and beverages market. EMTÉ Supermarkets is a part of Sligro Food Group. With 127 supermarkets EMTÉ Supermarkets has a market share of 2,7%. The project is conducted for the Ready-to-Cook vegetables, the V17 product group. Problem Definition The problem EMTÉ Supermarkets experiences at the moment is that it is unclear when to order and when not to order. To strike a balance between a certain pre-set shelf availability and no high costs of waste is difficult. EMTÉ Supermarket s aim and mission as a supermarket is to be the best Fresh Supermarket of the Netherlands. This means that the Fruits & Vegetables should be of the highest quality and that they should always be available. Therefore EMTÉ Supermarkets aims to reduce its waste and increase their service level. The objective for this project is: Reduce the total amount of waste and improve the service level of the product group Ready-to-Cook Vegetables by optimizing the reorder level and the case pack sizes at EMTÉ Supermarkets. The Trade-Off First of all the equivalence between shelf availability and the amount of waste and finding the optimal reorder level is proven. It is indicated that the costs relationship between the relevant cost components, costs of outdating, costs of lost sales, and costs of ordering can be displayed as in the figure below. Costs of Lost Sales Costs of Outdating Ordering Costs Reorder level This means that an optimal reorder level can be found at the intersection of the costs of lost sales and the costs of outdating. V

7 The ROL* Model The ROL* model is based on minimizing a total costs function consisting of the costs of outdating, the costs of ordering and the costs of lost sales. The costs of outdating are the costs made when a consumer unit is outdated before it is sold and then it has to be thrown away. The costs of ordering are made when a consumer unit or a case pack is ordered. The costs of lost sales are the costs made when the customer has demand for a certain product, the product is not available and the customer decides not to postpone his buy and not to buy a similar product but walks straightaway to the competitor. If the costs of outdating can be reduced the percentage of waste will be reduced, and if the costs of lost sales can be reduced the service level will increase. Further explanation on these costs components is given in chapter 6 in the report. A lower boundary has been invented to be able to benchmark the results of the model. This lower boundary is based on the situation with the optimal reorder level and the optimal case pack size for each individual SKU-store combination. This is however unrealistic because this is not allowed from the supplier and therefore this lower boundary can be approached but never be met. The model then has been generalized so that the model can generate results with fictitious data, and that afterwards this fictitious results can be matched to the real data. It was found that the costs of the current situation were 3.720,47 per day. By only implementing the optimal reorder level for the products in the sample used in this project a cost reduction of 499,52 could be achieved. Note that this is on a yearly basis. Optimal Case Pack Size Heuristics After developing the ROL* model, the heuristics to find an optimal case pack size for each SKU were invented. Three heuristics in total were developed: 1. The first heuristic assumed that the stores in a store class are homogenous. This means that no significant differences exist among the stores in this store class. For the average demand in this store class per SKU an optimal case pack size is found. This case pack size is then implemented in the other store classes as well and then the total costs for this situation can be calculated. 2. The second heuristic also assumed homogeneity within the store classes. Via a formula obtained from literature the following multiplication should hold within every SKU in a store class: Q = x μ day m After reassessing the Q per SKU in de chosen storeclasse, the average Q in this store class is determined and this Q is implemented in the other store classes. Q being the case pack size, μ day being the average daily demand, and m being the shelf life. The optimal value for x is determined by enumeration. VI

8 3. The third heuristic is taking into account all three store classes ( Small, Medium, Big ) and Results assumes homogeneity within them. Next up all store class and the number of stores in a class and their costs are taken into account to determine the Q which find the lowest costs. So 38 small stores, 47 medium stores, and 8 big stores create a weighted sum of total costs per SKU and an optimal case pack size per SKU can be identified. The table below indicates the best result of every heuristic, which perspective gave the best result, and the current situation and the lower boundary are also included. Point of View Total Costs Costs of Outdating Costs of Lost Sales Costs of Ordering Fill Rate Expected % of Outdating P3 Discrete Ready Rate Heuristic 1 Small 1.907, ,69 234,52 165,72 0,935 0,0414 0,8624 Heuristic 2 Medium 1.813, ,31 265,12 163,82 0,9357 0,0404 0,8622 Heuristic 3 All 1.875, ,67 240,41 164,66 0,9354 0,0413 0, Current Situation All 3.720, ,30 153,38 394,79 0,8451 0,0903 0,6920 Lower Boundary All 1.661, ,56 218,43 176,62 0,931 0,0350 0,8570 One can see from the table above that Heuristic 2 Medium performs best and shows a total cost reduction of 48,74%, note that this costs reduction is especially caused by the great reduction in costs of outdating. The expected percentage of outdating is improved from 9,03% to 4,04%, and the discrete ready rate (the probability that inventory is in stock at the moment of a potential delivery) increased to 86,2%. Conclusion & Recommendations This master thesis project developed an ROL* model which determined the optimal reorder levels. Through heuristics the optimal case pack size per SKU per determined. Large savings are possible with these results. The results will be provided to Sligro Food Group and a plan for implementation is provided. The recommendations from this project are the following: Arrange the process of setting the MIN-levels centrally; Use Heuristic 2 from the All perspective, this one is easier to implement as it works from the All perspective and it was only 0,25 worse than the best heuristic solution; Be able to determine the average daily demand per SKU from your systems; Make sure that products that perish on day x are wasted on day x 1 after customer opening hours so that the service level is more reliable. VII

9 Contents Acknowledgements...III Abstract... IV Executive Summary... V Problem Definition... V The Trade-Off... V The ROL* Model... VI Optimal Case Pack Size Heuristics... VI Results... VII Conclusion & Recommendations... VII List of Figures List of Tables Definitions & Abbreviations Introduction Report Structure Sligro Food Group & EMTÉ Supermarkets Problem Introduction Chapter Summary Problem Definition Description of Current Situation Problem Definition & Analysis of Current Situation Research Questions & Scope Research Questions Scope Practical Requirements

10 2.5 Chapter Summary Literature Overview The EOQ Model & Extensions The (R, s, nq) & EWA-Policy The DoBr-Tool Automated Ordering Systems Educating the Consumers Management Literature Point of View Week Patterns Breaking Bulk Chapter Summary The Trade-Off Cost Relationship Numerical Example Chapter Summary The Data Collection The Stores & Products Data Cleaning Developing the ROL* Model The Costs Components Costs of Outdating Costs of Ordering Costs of Lost Sales The DoBr-Tool Chapter Summary

11 7. The Generalization of the ROL* Model The σ and μ Analysis Price categories The ROL* Model Division into Bins Chapter Summary The Average Optimal Case Pack Size Determine the Optimal Case Pack Size Heuristics to find Average Optimal Case Pack Size The First Heuristic The Second Heuristic The Third Heuristic Testing the Heuristics Chapter Summary Results ROL* Model Results Heuristics Results of Heuristic Results of Heuristic Results of Heuristic Total Results Conclusions Research Question Research Question Research Question Research Question

12 11. Recommendations Limitations Recommendations for Future Research References Appendix A... Error! Bookmark not defined. Store Classes & Assortments... Error! Bookmark not defined. Appendix B... Error! Bookmark not defined. Products & Stores... Error! Bookmark not defined. Appendix C... Error! Bookmark not defined. Information on Selected Stores... Error! Bookmark not defined. Appendix D... Error! Bookmark not defined. The Margin... Error! Bookmark not defined. Variables & Functions... Error! Bookmark not defined. Price Categories... Error! Bookmark not defined. Sensitivity Analysis of the Lost Sales Factor... Error! Bookmark not defined. Appendix E... Error! Bookmark not defined. Screenshot ROL* Model & Two Arrays... Error! Bookmark not defined. Appendix F... Error! Bookmark not defined. Division into Bins... Error! Bookmark not defined. Appendix G... Error! Bookmark not defined. Results ROL* Model... Error! Bookmark not defined. Appendix H... Error! Bookmark not defined. Results of Heuristics... Error! Bookmark not defined

13 List of Figures Figure 1: Location Overview Figure 2: Dynamic Reorder Level Throughout the Week Figure 3: Waste Distribution EMTÉ Supermarkets Figure 4: Cost Relationship between costs of lost sales, costs of outdating and costs of ordering Figure 5: Week Pattern EMTÉ for V17 Products Figure 6: Average Demand versus Standard Deviation Figure 7: Logarithmic Transformation of μ and σ Figure 8: ROL* Figure 9: ROL* with 90% Requirement Figure 10: Case Pack Size vs Total Weighed Costs per Product Figure 11: ROL* with Requirement of 90% & Rule of Thumb EMTÉ Figure 12: Summary of Results of Heuristic Figure 13: Distribution of Stores & Assortments... Error! Bookmark not defined. Figure 14: Distribution Shelf Life V17 Products... Error! Bookmark not defined. Figure 15: V17 Fill Rate Selection Stores & EMTÉ Overall... Error! Bookmark not defined. Figure 16: Percentage of Waste vs Fill Rate for Selection Stores & EMTÉ Overall... Error! Bookmark not defined. Figure 17: Week Patterns of the Selection Stores with Fast, Medium, and Slow Moving Products... Error! Bookmark not defined. Figure 18: Total Costs versus Reorder Level Example... Error! Bookmark not defined. Figure 19: Frequencies Low Prices... Error! Bookmark not defined. Figure 20: Frequencies High Prices... Error! Bookmark not defined. Figure 21: Sensitivity Analysis of the Price versus the Optimal Reorder Level.. Error! Bookmark not defined. Figure 22: Reorder level vs Average Daily Demand for different Lost Sales Factors (left) &Sensitivity Analysis of the Lost Sales Factor... Error! Bookmark not defined. Figure 23: Expectation of how the Average Demand versus MIN/ROL* will look... Error! Bookmark not defined. Figure 24: Screenshot of the ROL* Model... Error! Bookmark not defined. Figure 25: Screenshot of Total Results (Top) & Optimal Results (Bottom)... Error! Bookmark not defined

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15 List of Tables Table 1: Company Structure Table 2: Trade-Off Example Table 3: Coefficient of Variation Table 4: Summary of Results of Heuristic Table 5: Summary of Results of Heuristic Table 6: Summary of Results of Heuristic Table 7: Total Results of All Heuristics Table 8: Summations of Case Pack Sizes & ROLs Table 9: Products & Stores... Error! Bookmark not defined. Table 10: Seasonal Holidays... Error! Bookmark not defined. Table 11: Variables & Functions... Error! Bookmark not defined. Table 12: Results of All Heuristics... Error! Bookmark not defined

16 Definitions & Abbreviations ASO CPS EOQ FIFO LIFO μ P 2 service measure discrete P 3 service measure Q ROL ROL* σ SKU SL TU/e V17 VAT Automated Store Ordering Case Pack Size (Q) Economic Order Quantity First In First Out Last In First Out Average Demand Fill Rate Discrete Ready Rate Optimal Case Pack Size Reorder Level Optimal Reorder Level Standard Deviation (measures the amount of variation from the average) Stock Keeping Unit Shelf Life of the products Eindhoven University of Technology EMTÉ definition for the Ready-to-Cook Vegetables product group Value Added Tax - 8 -

17 1. Introduction In this chapter the aim of the project is defined. In the first section the report structure is defined, the second section provides an introduction on the company, and the third section gives a problem introduction. 1.1 Report Structure The report starts with a description of Sligro Food Group and EMTÉ Supermarkets. After that an introduction of the problem is given, after which a problem statement can be defined. Then the research questions are stated that will be the main lead throughout the project. Furthermore literature, earlier discussed in De Haan (2014a), is summarized and additional sources are discussed. After that the trade-off between the service level and the amount of outdating is displayed and its equivalence to finding the optimal reorder level. Then the ROL* model is developed from which the optimal reorder levels and the optimal case pack sizes can be determined. Via different developed heuristics the EMTÉ overall optimal case pack size per SKU is identified. Afterwards the total costs of the current situation can be compared to the total costs of the heuristics. Finally, the conclusions and recommendations are presented. 1.2 Sligro Food Group & EMTÉ Supermarkets Sligro Food Group is a large food retailer in the Netherlands. Sligro Food Group delivers its products in three different ways to its customers: Food Retail, Foodservice (Cash & Carry), and Food Delivery Service. Table 1 displays the company structure (SligroFoodGroup, 2015). Table 1: Company Structure Central Distribution Center and Head Office in Veghel Food Retail Foodservice Cash & Carry Foodservice Delivery service EMTÉ Sligro Sligro/Van Hoeckel 127 supermarkets and franchise supermarkets Large and small scale hospitality, recreation, catering, gas stations, wholesale, institutional. 2 Distribution Centers Nationwide Network of 47 selfservice wholesale shops Nationwide Network of 9 delivery services. EMTÉ Supermarkets exists 50 years in 2015 so is already an experienced player in the Dutch retail market. Since 2002, the Brabantic company EMTÉ is part of Sligro Food Group. They owned 12 stores when they joined Sligro Food Group and within 3 years the total number of stores increased to 18, including 3 stores in Zeeland. This was a special moment, because this was the first step outside of their original county, North-Brabant. After a few acquisitions the number of stores went up fast. In 2006 with the takeover of - 9 -

18 Edah-stores the number of stores went up to 82. Through acquisitions of Sanders, Golff and Spar the number of EMTÉ supermarkets reached the current number of 127 stores. Fresh is the top priority of EMTÉ supermarkets. Fresh steak, grilled chicken, and ground beef is made every day by their traditional butchers, and at the bread department fresh bread, apple pie, and sausage rolls are baked in their ovens. EMTÉ has grown into a professional supermarket chain with more than employees and various awards. They won awards like the Best Cheese Department, the Best Meat Department, and the Best Supermarket in Fresh Produce in 2011, The Customer Friendliest Supermarket of the Netherlands in 2012, the Best Meat Department and the Best Bread Department in 2013, and the Best Cheese Department in (SligroFoodGroup, 2015). At the moment of writing, the grocery supply chain of EMTÉ Supermarkets consists out of 98 EMTÉ stores and 29 franchisers. Figure 1 displays the locations of the EMTÉ Stores, the location of the Fruits & Vegetables Supplier, and the locations of the two EMTÉ Distribution centres (SligroFoodGroup, 2015). Locations of EMTÉ Stores Location of Fruits & Vegetables Supplier Location of EMTÉ Distribution Centers Figure 1: Location Overview 1.3 Problem Introduction In June 2014 the first meeting with the company Sligro Food Group took place in Veghel with the director of operations, Kees Kiestra, inbound manager and from that moment on company supervisor, Patrick Baptist, and TU/e professor Karel van Donselaar. Current problems within the company were discussed and it was decided that focus should be on defining and investigating the trade-off between the shelf availability

19 and the total amount of waste within the supermarket chain EMTÉ. In addition their current reorder levels and current case pack sizes should be reviewed for the Ready-to-Cook Vegetables product category, taking into account that reorder levels can be unique for each store-product combination and that case pack sizes can only be unique per product and not for each store-product combination. 1.4 Chapter Summary This chapter gave an introduction to the company and the purpose of this Master Thesis. The next chapter will dive deeper in the current situation and problems of Sligro Food Group and EMTÉ Supermarkets. 2. Problem Definition In this chapter the problem within EMTÉ Supermarkets is discussed more elaborately. In the first section background information is given on the current ordering process and ordering system, the current way of determining the reorder levels, and the case pack sizes of the products to be ordered. The second section provides an analysis of the current situation. The third section will discuss the research questions to be answered. The fourth section will discuss the practical requirements of the project. Section five and six will discuss the scope of the project. The last section will summarize the chapter. 2.1 Description of Current Situation When managing a supermarket several aspects should be taken into account, including the ordering process to replenish the shelves. The ordering process can be classified in various ways, depending on the review policy the store is holding onto. EMTÉ supermarkets has determined MIN and MAX levels per product per supermarket. The current inventory position consists of the current inventory in stock and the inventory in transit. The current automated ordering system controls the inventory position based on four factors: - The demand expectation; - MIN: a certain pre-set minimum; - Current Inventory and inventory in transit. The ordering system contains a fourth factor: the Max level. This number represents that maximum amount of consumer unit which can be stacked in the shelf. This factor is however not a restriction when placing an order. In this ordering systems two incentives can create an order. The first one is that the inventory position drops below the MIN level and the demand expectation (including safety stock) is lower than the MIN level. Then the ordering system orders up to the MIN level and then the MIN level can be seen as their

20 reorder level. However the second incentive does not uses the MIN level to create an order but the demand expectation. Whenever the demand expectation is higher than the MIN level and the current inventory position, the ordering system will order up to the demand expectation. Therefore the following situations may occur: 1. The current inventory position is less than the MIN level and the demand expectation is smaller than the MIN level. The automated ordering system orders up to the MIN level. 2. The current inventory position is more than the MIN level, and the demand expectation is larger than the current inventory position. The automated ordering system orders up to the demand expectation. 3. The current inventory position is more than the MIN level and the demand expectation is larger than the MAX level. The automated ordering system orders up to the demand expectation (Note that the MAX level is not a restriction in the ordering system). 4. The current inventory position is larger than the MIN level and the demand expectation is lower than the current inventory position. The automated ordering system does nothing. One can state that when the demand expectation is always lower than the MIN level, the MIN level can be seen as their reorder level and that their ordering policy is equal to the (R, s, nq)-policy mentioned in de Haan (2014a). The background of this policy will be repeated in the literature part of this report. Broekmeulen & Van Donselaar (2009) mention that the reorder level s t is set for each day of the week because it has to deal with a week pattern in demand. This is not the case within Sligro Food Group, the MIN level will not change throughout the week. However, when the demand expectation is higher than the MIN level the demand expectation takes over. In general the demand in supermarkets follows a week pattern, this week pattern will also be visible in their ordering pattern. Figure 2 provides an example of how their reorder level can fluctuate throughout a week. Note that the MIN level is set to 6, that the y-axis displays the demand expectation throughout the week, and the x-axis the week days. In the beginning of the week the MIN level is leading but closer to the weekend the demand expectation will take over. Their reorder level can be written as follows: Max(MIN Level, Demand Expectation). This enables EMTÉ to take into account the week pattern in the ordering process

21 Figure 2: Dynamic Reorder Level Throughout the Week Note that their system does not take into account the perishability of products when necessary. Most of the time the MAX level represents the maximum capacity of the shelf, however a store can adjust this MAX level themselves. In addition, for example for the product Coca Cola the shelf capacity is structurally too small because demand for this product is so high. The warning for this product, given before you will exceed the maximum capacity of the shelf, is ignored and therefore the store accepts this product in the backroom, where inventory is stored which cannot be stacked in the shelf immediately after delivery. However, in the case of perishable products the MAX level is not ignored most of the time by the ordering clerk. However the MAX level still is no hard restriction. Due to the perishability of these products it is not beneficial to stack them in the backroom. For determining the MIN level, guidelines are provided from the Sligro Food Group headquarters. Initially, when a product enters the assortment the MIN level should be set to three times the number of facings (the number of identical products/skus on a shelf turned out towards the customer), if a planogram for this article is available. Another guideline is that the MIN level should be equal to the sales of the sum of an average Monday, Tuesday, and Wednesday. Otherwise the default values for the MIN and MAX levels are set to 2 and 88, respectively. Afterwards the supermarket managers will adjust this level and base their MIN level on the cumulated weekly sales of the product. The guidelines provided from headquarters state that these MIN and MAX levels should be reassessed every month. Determining the MIN levels can however differ per product group. For the perishable products delivered by Smeding, the general guideline to determine the MIN level is to sum up the average sales of the Monday, Tuesday, and Wednesday of the last six weeks. So the MIN level will be different for each article in each store. The reorder level of EMTÉ Supermarkets can be defined as the maximum of the MIN level and the demand expectation. Within EMTÉ Supermarket it is determined whether a product can be ordered per unit or per case. The amount of units in a case differs per product. Ordering a case can cause a lot of waste if demand is not high enough to sell the whole case within the expiration date. On the other hand, ordering per unit can cause a lot of handling costs because then a clerk in the distribution centre has

22 to pick, in worst case, one unit of a product and then the supermarket clerk has to stack one unit of the product, putting the newest unit in the back of the shelf. One can imagine if the clerk can stack more than one unit at a time the stacking and picking time per unit will decrease Note that this ordering per consumer unit is not allowed at the supplier of the Ready-to-Cook Vegetables, there only ordering per case pack is allowed.. The ordering process of fresh products is more complex because of an extra variable, the expiration date in comparison with the ordering process of non-perishable products. However, all product types have to take into account the variable shelf capacity, because when the optimal order size is larger than the shelf capacity and the clerk is not able to stack the whole order in one replenishment, extra handling costs will emerge because the products that do not fit the shelf have to be stored in the backroom. Finding an optimal order size per product, for perishables and non-perishables, could greatly decrease the total supply chain costs. At the moment in the Netherlands, the registered waste per supermarkets varies from 1 to 3% of the purchase value (RetailNews, 2014). For EMTÉ Supermarkets this waste can be subdivided in the categories mentioned in Figure 3. The waste of the Fruits & Vegetables group contributes significantly to the total amount of waste of 1 to 3%. Figure 3: Waste Distribution EMTÉ Supermarkets

23 2.2 Problem Definition & Analysis of Current Situation The problem EMTÉ Supermarkets experiences at the moment is that it is unclear when to order and when not to order. To strike a balance between a certain pre-set shelf availability and no high costs of waste is difficult. At a grocery store a certain shelf availability is desired (for example 98%), but because of uncertain demand either waste or out-of-stocks will occurs. The grocery store can decide to order an extra case pack to guarantee the shelf availability, but then risks a higher amount of outdating, or the grocery store can decide to not order the extra case pack to ensure a lower amount of outdating, but then risks out-of-stocks. As one could see in Figure 3, the highest amount of waste is caused by the Fruits & Vegetables department. Of the waste caused by the Fruits & Vegetables department a total of 42% is caused by the prepacked Fruits & Vegetables. EMTÉ Supermarket s aim and mission as a supermarket is to be the best Fresh Supermarket of the Netherlands. This means that the Fruits & Vegetables should be of the highest quality and that they should be always available. The problem with wanting to reach a high shelf availability of perishable products is the expiration dates of these products. If you do not sell the whole batch you will have to throw away the perished units and this is costly. To lower these costs there must not be too many units of products on the shelves and all products must be sold. The problem with this situation is that it may end into out-of-stocks when demand is higher than the forecasted demand, as we deal with uncertain demand. One can state that if you have a very accurate forecasting system one can lower the out-of-stocks and the total amount of waste. The current forecasting system of EMTÉ Supermarkets is based on a Moving Average of the last 6 weeks (excluding promotion weeks). Research of LMS students of the TU/e pointed out that not much improvements could be achieved concerning this forecasting system because the demand forecast is fluctuating a lot. Improvements should be sought in parameter settings, for example case pack sizes and MIN levels (LMS-StudentsTU/e, 2014). The first step should be mapping the trade-off between the shelf availability and the total amount of waste. EMTÉ Supermarkets state that this trade-off is always there, however the hypothesis is that this can be covered by finding an optimal reorder level based on the minimum costs and a pre-set restriction on the minimum required service level. With the mapping of this trade-off and finding optimal reorder levels the case pack size will be seen as fixed for that moment. The second step will be reassessing the current case pack sizes. The products taken into account in this research are delivered by Smeding. Smeding has a unique case pack size per product and EMTÉ stores can only order this quantity or a multiple of this quantity. But one can imagine that small stores benefit more

24 from smaller case pack sizes than big stores, because the big stores experience higher demand in general. The supplier is willing to modify the case pack sizes but if so, always for the entire EMTÉ chain, they will not provide personalized case pack sizes per EMTÉ store. The goal therefore is, to find the optimal case pack size (Q ) per SKU. In current literature the case pack sizes have already been optimized via a mathematical model (Van de Ven, 2014), but now a different method is used to come up with optimal case pack sizes. The point of view is now set to store classes. Case pack sizes can be optimal for the stores in store class Small but not necessarily optimal for the stores in store class Medium. So by finding the optimal case pack size (Q ) via these store perspectives a new way of approaching this problem is developed. Different heuristics need to be developed to find out the best way of approaching this problem. A lower boundary has to be created, further explanation on this later on in the report, to be able to compare the results of the heuristics to the value of a lower boundary. This way the performance of the heuristics can be evaluated towards the ideal situation, the lower boundary. Another difference with Van de Ven s research is that this research is carried out for all non-franchise EMTÉ stores so the solution is broad and will be directly applicable. Van de Ven (2014) only took into account three stores. In addition the heuristics developed in this project are handson and easy to apply for future new products. 2.3 Research Questions & Scope In this section the problem definition is translated into two research questions and some sub-questions. These questions will be leading throughout the project. Further on the scope is defined Research Questions The objective carried out at EMTÉ Supermarkets is the following: Objective Reduce the total amount of waste and improve the service level of the product group Ready-to-Cook Vegetables by optimizing the reorder level and the case pack sizes at EMTÉ Supermarkets. The corresponding research questions and sub-questions are the following: Research Questions & Sub-Questions 1. How can we proof the equivalence between adjusting the reorder level to the optimal value and using the trade-off between shelf availability and the total amount of waste to decide whether to order or not to order?

25 2. What heuristic can assist in finding an optimal reorder level (ROL*) for the products in the product category Ready-to-Cook Vegetables without having to simulate all products individually? a. How to deal with the week pattern in demand? b. Which variables have to be incorporated in this heuristic? c. How can the costs of waste, the costs of lost sales, and the costs of ordering be determined? d. What are the costs of the current situation in comparison with the costs of the improved situation? e. How to incorporate the requirement of a minimum service level of 90% on SKU level? 3. How can the optimal case pack size (Q ) be determined? a. What is the proportion in the different store classes? b. What heuristics can be invented to determine the optimal case pack sizes (Q )? c. What are the costs, the advantages, and disadvantages of the invented heuristics? Which heuristic is best? 4. How can the developed methods and solutions be implemented in the company? Scope Throughout this project the product category Ready-to-Cook Vegetables will be used to execute the calculations. Within the EMTÉ systems this category is referred to as the V17 group. This product group also has an ordering number, namely the V10 number. Due to reasons only known by the company some products only have a V10 number and are therefore excluded from the sample used in this project because it was not possible to retrieve the data from the company systems for these V10 numbers. The percentage of the Ready-to-Cook Vegetables V17 products included in this project is 78,35%.These products can be ordered and delivered to the supermarkets every day with an exception for Sunday. The lead time is 1 day and the review period is 1 day. The lead time of 1 day is verified by checking the ordering times and the delivery times. All stores are delivered within 24 hours after placing the order, of course with the exception of ordering on Saturday, this delivery is delivered on Monday as Sunday is not an order and deliver day. For this project however, the assumption is made that the lead time and review period are always 1 day as the effect on the outcomes are expected to be minor as this is only 1/7 th part of the week. It might be possible that sometimes deliveries are late or incomplete. This is not taken into account and the assumption is made that all deliveries are correct and in time. These products are delivered directly by an external supplier, Smeding. The EMTÉ distribution centre is not used for these products. Take into account that this project is carried out for EMTÉ Supermarkets and Sligro Food Group and that the possible extra handling costs of

26 the supplier Smeding are not taken into account. The reason that these costs are not taken into account is that the focus is on EMTÉ Supermarkets and that turnover has to be increased in this part of the company. Sligro Food Group has a 49% share in Smeding so they should have the power to partially control the way of working of Smeding. The Retail department of Sligro Food Group, EMTÉ Supermarkets wants to increase the profit internally, so it is the intention to increase this profit and reduce the waste in this specific department of the company and not to increase the hidden profit within Smeding as this is also income for Sligro Food Group. EMTÉ Supermarkets operates with tree types of assortments within this product group: a small assortment, a medium assortment, and a large assortment. The small assortment contains 109 SKUs, the medium assortment is based on the small assortment and elaborated with 27 number of products, and the large assortment is based on the medium assortment elaborated with 34 number of products. Figure 13 in Appendix A contains information on the distribution in store classes and assortments within these classes. A conclusion is that the store class does not necessarily tell which assortment the store has. All three assortment types are taken into account in this project. Unfortunately some products with only a V10 number will be missing from this sample. To start up the research and determine the components for the general ROL* model three stores accompanied by 15 products each have been selected. The store sample contains a small, a medium, and a large store based on the amount of turn over. Appendix B contains Table 9 which contains a list of selected products and stores. Table 10 in Appendix B contains the openings hours during holidays of the selected stores, this table will be used while cleaning the data. Appendix B also contains Figure 14 which displays the shelf life distribution among the V17 products. As can be seen more than 70% has a shelf life of 5 days, and around 25% has a shelf life of 3 and 4 days. This supports the later made decisions to model shelf life 3 to 7 days. Further on in the project the medium store data is used to determine the time used for ordering, filling, maintenance and quality control, and receive and control goods. The selected products will be used as input data to determine the standard deviation from the average demand, chapter 7.1. After developing the general ROL* model the data of all 93 non-franchise EMTÉ stores is used to determine the costs of the current situation. Afterwards, with help of the ROL* model, the optimal reorder level and the optimal case pack sizes can be determined for all products of the V17 in 93 non-franchise EMTÉ stores. Note that in total there are 96 non-franchise EMTÉ stores but 3 of these stores were excluded due to the fact that some of them switched from being a non-franchise to being a franchise, vice versa, or an opening of close of a store during the year

27 2.4 Practical Requirements Besides the research questions it is important to take into account some practical requirements from the company. This because when working on a Master Thesis you can lose yourself into the theory behind the project, and in the end you execute the project for a company that only can use the project if the results are practically relevant as well. Three practical requirements can be formulated for Sligro Food Group: 1. Simplicity; 2. Operational limitations; 3. Data availability. The first requirement is Simplicity. This is an important requirement as it is important that the concepts and ideas developed from the project can be understandable and implementable for Sligro Food Group. One should never lose the importance of the practical relevance for the company. Making sure it is practical relevant for the company, one should take into account as well the operational limitations, as the theoretical solution may be brilliant if the systems of Sligro Food Group cannot process it, it is practically useless and not implementable. Finally, the data that is needed for the project is gathered from the systems of Sligro Food Group. It is therefore important to take into consideration the data availability. 2.5 Chapter Summary This chapter gave an introduction to the current situation and the current problems. The research questions and their sub-questions are presented and they will be leading throughout the project. The next chapter will give an overview of literature that will support this thesis. 3. Literature Overview In this chapter the literature earlier discussed in De Haan (2014a) will be briefly summarized and additional sources will be discussed. In de Haan (2014a) several inventory models have been discussed: the basic EOQ model (Silver, Pyke, & Peterson, 1998), the EOQ Model for perishable products (Nahmias, 2011), the (R, s, nq)-policy (Silver, Pyke, & Peterson, 1998) and the elaboration of this last mentioned policy, the EWA-policy (Broekmeulen & van Donselaar, 2009), which takes into account the expected outdating when making the replenishment decision. 3.1 The EOQ Model & Extensions The EOQ model is one of the oldest and classical production scheduling model, and is mostly used when minimizing total inventory costs and ordering costs. In the EOQ-model the economic order quantity (EOQ)

28 is optimized to get the minimal total inventory costs and ordering costs. The basic EOQ model has a number of assumptions stated in Silver, Pyke, & Peterson (1998) and summed up in de Haan (2014a). The model in Silver et al. (1998) looks as follows: Q or EOQ = 2Kλ h (1) With Q = the optimal value for the order quantity K λ h = the fixed costs of placing new orders = the demand rate = the holding cost per unit time This model however does not take into account the perishability of a product. When a product is perishable, the product is subject to decay and will wither over time (de Haan, 2014a). Nahmias (2011) writes that when demand is known with certainty, the problem of managing perishables is straightforward for the most part. Considering the basic EOQ model, the optimal time between orders is T = Q λ = 2K. Suppose that λh a product has a lifetime of m, all deliveries are assumed to be of fresh units only, then only two cases are possible: (1) T m and (2) T > m. In the first case the optimal policy stays the same, since all products are consumed by demand before they expire. However, in the second case, if Q is ordered at the beginning of a cycle, positive inventory will remain at the time m. This inventory (Q λm) is then outdated, must be disposed, and a new order has to be placed. 3.2 The (R, s, nq) & EWA-Policy In the article of Broekmeulen & Van Donselaar (2009) a comparison is made between the base policy, earlier presented in Silver at al. (1998) and the newly introduced EWA policy. The base policy is an (R, s, nq)-policy (Silver, Pyke, & Peterson, 1998) in which an order is only placed when the inventory position IP t, the sum of inventory on hand in the store and the inventory in transit, is strictly below the reorder level s t. When an order is placed n t case packs with size Q are ordered, which brings back the inventory position above the reorder level s t, but strictly less than (s t + Q). The reorder level s t is set for each day of the week because it has to deal with a week pattern in demand (Broekmeulen & van Donselaar, 2009). According to Silver et al. (1998) the reorder level can be set as follows:

29 t+l+r s t = SS + E[D i ] i=t+1 (2) t+l+r With SS as the safety stock and i=t+1 E[D i ] as the expected demand during the lead time plus a review period. If IP t is defined as the inventory position at day t just before an order is placed, then n t can be determined as follows Note that x rounds up to the nearest integer. If IP t < s then n t = s t IP t Q (3) After discussing the base policy of Silver et al. (1998), now the EWA policy can be discussed. In the EWA policy the inventory position IP t is first corrected for the estimated amount of outdating and an order is placed if the corrected inventory position drops below the reorder level s t. The value of n t is determined by Broekmeulen & Van Donselaar (2009) as follows: t+l+r 1 t+l+r 1 If IP t i=t+1 Ô i < s then n t = s t IP t + i=t+1 Ô i Q (4) Broekmeulen & Van Donselaar (2009) note that the outdating on the (L + R)th day does not affect the ability to meet demand during that day, since the outdating take place at the end of a day. They state that the estimated amount of outdating is the only difference between the EWA policy and the base (R, s, nq)- policy. Their conclusions after simulation experiments were that costs would be significantly lower when using the EWA policy in comparison to the base (R, s, nq) policy. The withdrawal methods FIFO (First in First Out) and LIFO (Last In First Out) both showed a greater benefit for the EWA policy. 3.3 The DoBr-Tool From the EWA policy a tool was developed (Van Donselaar & Broekmeulen, 2012), the DoBr tool, which generates the exact results for the (R, s, nq)-policy for among others several key performance indicators, the expected amount outdating, and approximations for the (R, s, S)-policy. The tool is developed to handle many situations found in practice. The tool generates exact results for situations with stationary demand and backordering. In other situations with non-stationary demand and lost sales, the results are based on approximations. To reach for example a high fill rate, the safety stock or other parameters can be adjusted to reach the desirable fill rate. For an example and more elaborate explanation of the key performance indicator, look into De Haan (2014a)

30 As one can see in De Haan (2014a) this tool can be a help in calculating costs. For example, when you want to calculate total costs of a perishable product in a supermarket you need the following costs components: 1. Handling costs at Fresh Supplier or Distribution Centre (depending on distribution methods and whether the supplier is (partially) owned by the supermarket chain; 2. Handling costs at the stores of the supermarket chain; 3. Costs for amount of outdating of the product; 4. Penalty costs for not meeting the pre-set fill rate. The third and the fourth component can be calculate with the help of the DoBr-tool. The tool will calculate the expected amount of outdating so that this amount only needs to be multiplied with the costs per unit outdating. The tool calculates the fill rate with which a dummy variable can make sure that costs (determined per product group, because with some product groups it is worse to not meet the fill rate than others) will be paid if the pre-set fill rate is not met (de Haan, 2014a). The approximations for the fill rate and the relative outdating enable the trade-off of what you want to achieve. If one aims for a high fill rate, with a lot of products in stock, one can imagine that the number of outdated products is higher as well. Vice versa, if one aims for a low amount of outdating the fill rate most probably will be lower. The DoBr-tool is a significant help in this trade-off (Van Donselaar & Broekmeulen, 2014). 3.4 Automated Ordering Systems Furthermore de Haan (2014a) discussed the literature concerning the automated ordering systems and the importance of educating consumers concerning the reduction of waste. Supermarket managers do not always follow the advice of the ASO system if they have reasons not to follow it (Van Donselaar, Van Woensel, Broekmeulen, & Fransoo, 2005), for example when dealing with perishable products. If your inventory is perishing the next day but the ASO system does not take this into account the supermarket manager may correct for this phenomenon and place an order for this products while the ASO system may not advice this. According to Van Donselaar et al. (2007) supermarket managers experience the following incentives not to follow the ASO system. Supermarket managers may advance orders from peak days to preceding non-peak days in order to compensate for incentive misalignment and inadequacy of the automated ordering system. Incentive misalignment exists because supermarket managers are assessed on revenues, and not on inventory holding costs, whereas the automated replenishment system aims to minimize inventory holding costs. For the same reason, being rewarded on total revenues, they may try to