OPTIMIZING THE SUPPLY CHAIN OPERATIONS OF E-SHOP WAREHOUSES

Transcription

1 OPTIMIZING THE SUPPLY CHAIN OPERATIONS OF E-SHOP WAREHOUSES Submitted by Vassilis Pergamalis A thesis Presented to the Faculty of Tilburg School of Economics and Management In Partial Fulfillment of Requirements For the Degree of Master of Information Management Under the supervision of Doctor Francesco Lelli Second Reader: Prof.dr.ir. M. (Mike) Papazoglou JULY 25, 2017 anr:

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3 ABSTRACT Recent surveys indicate that throughout the world, e-commerce activities have been growing significantly within the past decade. In order for e-shop warehouses to be capable of coping with that online demand surge, there is an increasing need for them to operate their supply chain management systems more efficiently. The order picking operation is the first candidate for improvement since it is the most labor-intensive activity in a typical warehouse and can constitute up to 75% of the total warehouse operating costs. Therefore, any underperformance in order picking can prompt to unsatisfactory service and high operational cost for the warehouse, and consequently for the whole supply chain execution. This study presents a set of best practices for order preparation for e-shops, identifies the best order picking strategies for e-shop orders and simulates the picking strategies by using original data samples and warehouse layouts in a simulation software. The challenge is to minimize the total order picking travel time needed to execute the orders, by consolidating multiple orders into batches. This travel time is then translated into the number of workers that are required to complete the order picking process. This gives the researcher the opportunity to compare various picking strategies according to the number of required workers and select the picking policy that demands the lowest workforce. The results suggest that the batch order picking policy, which combines collection and splitting of orders, is the most efficient volume order processing policy since it gives the lowest total order picking travel time and required labor force to execute the picking process. 1 P a g e

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5 CONTENTS Abstract... 1 Chapter 1: Introduction... 5 Chapter 2: Literature Review Introduction Routing Heuristics Analytical Estimation of Travel Time Chapter 3: Problem Formulation Introduction Business-to-Consumer vs Business-to-Business Warehouses Order Picking Travel Time Function Order Picking Methodologies Single order picking policy (Each order is one batch) The One-Batch Order Picking Policy Batch Order Picking Policy Chapter 4: Methodology Introduction Order Picking Data Order Lines Products Warehouse Layout Picking Cart Order Picking Time Parameters Chapter 5: Simulation and Analysis P a g e

6 5.0 Introduction Simulation Models Simulations Results Numerical Interpretation of the Models Graphical Interpretation of the Models Converting Travel Times into Labor Force Problem Generalization Chapter 6: Concluding Remarks and Future Research Chapter 7: References Acknowledgments Appendix Appendix Appendix P a g e

7 CHAPTER 1: INTRODUCTION With the development of the Internet community and the boundless possibilities the Internet provides to the single user, rapidly the online market has been able to offer a wide range of products to be purchased and delivered directly to the customers door. Various consumers decide on making web purchases for convenience, others as a result of the competitive price offered by comparing products among various e-commerce platforms. In other words, the Internet has significantly increased consumers' ability to gather information about products and prices, which has led together with other factors such as speed, cost savings and product variety, to the establishment of online purchasing as a common practice among many purchasers around the world. A recent survey indicates that throughout the world, e-commerce has grown significantly within the past decade. More specifically, according to an article published in the World Street Journal, for the first time, consumers claim they purchased more of their purchases on the web than in stores, as indicated by a yearly survey of more than 5,000 online customers by United Parcel Service Inc. The customers now made 51% of their purchases on the web compared to 48% in 2015 and 47% in 2014, as shown by a survey conducted by UPS and analytics (Stevens, 2016). The timeline (see Figure 1) displays a forecast of the number of digital buyers worldwide up to 2019, based on factual numbers from 2014 to Digital buyer is considered somebody who made at least 1 purchase from an online platform within the given year. The bar chart not only indicates that the number of digital buyers will significantly increase the next years but also that the number of digital buyers as a percentage of the world s population will experience a noticeable surge, starting from 21.67% in 2016 and reaching 27.09% in Translating those percentages into actual numbers, around 1.6 billion people made online purchases in 2016 and this number is forecast to constantly increase in the coming years. In 2019, over 2 billion people worldwide are expected to buy goods online, up from 1.46 billion global digital buyers in This exponential surge of internet purchasing has been characterized by strong consumer demands and the increasing number and type of goods available. 5 P a g e

8 Years Digital Buyers Worldwide 2019* 27.09% * 25.26% * 23.57% % % % Number of Digital Buyers Source: Statista and World Bank As a percentage of global population In billions Figure 1: Number of Digital Buyers Worldwide in billions and as a percentage of global population As a consequence, more and more business organizations responded to this channelmerging trend with the strategy of expanding their products availability via the Internet and therefore engaging in e-commerce activities directly with the customer. However, to be capable of coping with that online demand surge that will be noticed in the coming years, there is an increasing need for the e-shops to operate their supply chain management systems more efficiently and effectively. More specifically, since the number of digital buyers is expected to increase, the supply chain management system must be robustly designed and ideally controlled to achieve productivity improvements that can handle the increasing number of online customers. An effective supply chain management system can boost the customer service by ensuring that the right item arrangement and amount are delivered in a timely fashion by supporting the organization in streamlining everything from day-to-day item steams to unforeseen events. With the right methodologies and tools that supply chain management offers, businesses have the ability to properly identify and analyze issues, work on suitable solutions and determine how to effectively move goods in an emergency situation. As indicated by ELA/AT Kearney (2004), warehousing, which is a critical part of the overall supply chain operation, contributed to about 20% of the surveyed companies logistics expenses in It can therefore be concluded that warehousing forms a significant part of a firm s supply chain management system. Warehouse Management System (WMS) is a key part of supply chain and primarily aims to control the traffic and capacity of items in stock as well as 6 P a g e

9 handling related activities, such as orders, receipt, storage and picking. Generally, it is about controlling and optimizing the complex warehouse and distribution systems (Kappauf et al., 2012). One of the fundamental activities that occur in the warehouses is the order picking operation which is the process of retrieving products from storage or inventory in response to a specific customer request (De Koster et al., 2007). Order Picking is the most labor-intensive activity within warehouses with non-automated systems and is estimated to form as much as 55% of the total operational expenses within any distribution center (Goetschalckx and Ashayeri, 1989). Other studies that have been conducted estimate that the order picking operation can constitute up to 75% of the total operating costs for a typical warehouse (Coyle et al., 2016). Therefore, any underperformance in order picking can prompt to unsatisfactory service and high operational cost for the warehouse, and consequently for the whole supply chain execution (De Koster et al., 2007). Another reason why the picking function is of high importance is that all the other warehouse functions rely on the picking function and consequently affect the whole supply chain operation. All downstream workflows, such as packing, shipping and inventory control depend upon the accuracies and efficiencies achieved during the order picking operation. For example, the put-away function (the process of keeping an item in the final position of the warehouse) is important in the way that fast movers (products that are sold in a high frequency) must be placed in locations near the checking and packing zones in order to minimize the picker walking. Additionally, an effective cycle count function will lead to a high inventory location accuracy, which is a critical factor of the picking function. Conclusively, since the order picking operation has a significant influence on supply chain's productivity, it constitutes one of the warehouse management core system functionalities that needs to operate in a more effective way, being thus the first candidate for improvement. The main purpose of this research paper is to present a set of the best practices for order preparation for e-shops, to identify the best order picking strategies for e-shops orders and to simulate the picking strategies by using original data samples and warehouse layouts in a warehouse simulation software. These data have been collected from an e-shop warehouse which operates in the fashion industry in Greece. However, the methodology applicable to this study can be applied in any e-shop warehouse with similar order data characteristics. The challenge is to minimize the total order picking travel time required to execute the orders. Travel time is defined 7 P a g e

10 as the time that is required for a picker to travel to the location of the Stock-keeping Unit (SKU) to pick an item. A scalar function will be created (in Chapter 3) that will take order data as input, the warehouse layout, the optimal batch size and the cart size dimensions as parameters and the travel time as output (dependent variable). This travel time will then be translated into number of workers that are required to complete the picking process. This will give the researcher the opportunity to compare various picking strategies according to the number of required workers and select the picking policy that demands the lowest number of workers (which means the most efficient volume order processing). The study will focus on enterprises that consist of only distribution centers for Businessto-Consumer transactions and no physical store. This means that in response to a customer s order that is placed on the enterprise s website, the ordered products are being delivered directly from the warehouse to the consumer without the presence of intermediaries. Consequently, the methodologies incorporated in this study do not apply to traditional Business-to-Business warehouses with non-automated systems, where the ordered products are sent first to the enterprise s physical shop and then to the purchaser. However, the only Business-to-Business case according to which this study can also be applied to, is the businesses that have adopted drop shipping as their main business model. Drop shipping is a supply chain management method in which the retailer does not keep goods in stock but instead transfers customer orders and shipment details to the wholesaler, who then ships the goods directly to the customer. Chapter two provides an analytical overview of the significant literature that has been published on the order picking process from year More than 20 articles were identified and are listed in table (1) in chronological order. They have been categorized according to whether they analyze the routing heuristics of order picking or estimate the total order picking travel time. At the end of chapter two the researcher also describes the literature gap, which gives great potential for new research papers or ideas to be created. Chapter three of this paper consists of the problem formulation which is presented both with text and scalar functions. The difference in the order picking operation between Business-to- Business and Business-to-Consumer warehouses is described at the beginning of this chapter followed by the explanation of three main order picking methodologies. Parameters, inputs and outputs of the scalar functions are also analyzed. 8 P a g e

11 Chapter four consists of the methodology which describes the investigative focus of the study and defines the research methods used to conduct it. The researcher explains from where the necessary data and information to address the research objectives and questions were collected, presented and analyzed. This chapter also identifies the main assumptions underlying the warehouse order picking problem together with an explanation of the parameters used in the scalar function described in chapter three. In general, details, reasons and justifications for the data sources, warehouse layouts, picking time parameters and analytical techniques used are given. Chapter five contains the analysis and simulation of order picking problem based on data, warehouse layouts and order picking time parameters analyzed in chapters three and four. The researcher uses a warehouse simulation software to find the optimal picking strategy that minimizes the travel time because it allows him to sample random values and to review the current operation and the effect of making changes, such as changes in storage, layouts, collection and splitting strategies, by eliminating time cost requirements associated with physical (real-life) testing. The last section of this chapter mentions examples according to which the methodology of this study can be applied to industries other than warehousing. Finally, Chapter Six presents the conclusions and discusses the results of this study. The researcher presents the optimal picking strategy and consequently the optimal number of orders to be consolidated into one batch (the batch size). Furthermore, he extends the solution to similar warehouses and presents the degree to which the optimal order picking travel time that was found by simulating the three order picking policies reduces the number of workforce within the warehouse and how those results can be translated into actual organizational benefits. 9 P a g e

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13 CHAPTER 2: LITERATURE REVIEW 2.0 Introduction Chapter two provides an analytical overview of the significant literature that has been published on the order picking process from year Extensive research has been conducted that focuses on optimizing the sequence of the picking locations within a warehouse by solving the Travelling Salesman Problem (TSP). The TSP is a classic algorithmic problem that has many similarities with that of a picker in a warehouse. As indicated by the TSP, a salesman starts from his home city (starting point) and has to travel to N cities exactly once and finish where he was at first. He is aware of the distance between each pair of the cities and wants to determine the sequence in which he has to visit the cities in such way that the aggregate traveled distance is minimized. Similarly, the picker starts at the depot (home city), where he receives a pick list, has to visit all pick locations (foreign cities) and finally has to return to the depot. The problem of a picker within a distribution center is known as the Steiner Travelling Salesman Problem (De Koster et al., 2007). Section 2.1 presents the literature that has been published regarding the routing heuristics of the order picking operation whereas section 2.2 focuses on the literature related to the analytical time estimation of the order picking process. Table (1) summarizes the literature that has been published regarding the routing heuristics and analytical estimation of the picking time in a chronological order. Publicatio n Year Title Authors Topic 1954 Solution of a large-scale traveling-salesman problem Dantzig, G., Fulkerson, R., & Johnson, S. Solved the TSP, by finding the optimal route of 49 cities within the USA 11 P a g e

14 1975 Mittlere wegzeiten beim eindimensionalen kommissionieren Kunder, R., & Gudehus, T. Derive expressions for travel time estimations for three routing heuristics in a warehouse consisting of a single block Internal layout design of a warehouse. Bassan, Y., Roll, Y., & Rosenblatt, M. J. Estimated the travel time of single and dual command cycles in multiple aisle systems 1983 Order-picking in a rectangular warehouse: a solvable case of the traveling salesman problem Ratliff, H. D., & Rosenthal, A. S. Solved the STSP with the computational effort required being linear in the number of aisles and the number of picking locations 1984 Travel-time models for automated storage/retrieval systems. Bozer, Y. A., & White, J. A. (1984) Determined the expected travel time of an automated storage/retrieval (AS/R) machine associated with each trip based on single and dual command cycles 1985 The traveling salesman problem on a graph and some related integer polyhedra Cornuéjols, G., Fonlupt, J., & Naddef, D. Extended Ratliff's algorithm with series-parallel graphs 12 P a g e

15 1991 Optimal product layout in an order picking warehouse. Jarvis, J. M., & McDowell, E. D. Provide a basis for locating product in an order picking warehouse such that average order picking time will be minimized, by relocating fast and slow moving products within the aisles Distance approximations for routing manual pickers in a warehouse Hall, R. W. Extended previous work with more advanced routing heuristics for one block warehouses 1993 Response time considerations for optimal warehouse layout design Pandit, R., & Palekar, Presented a queueing model of a rectangular warehouse with multivehicle AS/R machine to study the effect of warehouse design on time 1997 An evaluation of order picking routing policies. Petersen, C. G. Evaluated the impact of warehouse shape and pickup/drop off location and examined the interaction of the routeing policies, warehouse shape, and pickup/drop off location under different pick list sizes 13 P a g e

16 1998 Routing order pickers in a warehouse: a comparison between optimal and heuristic solutions. De Koster, R., & Van Der Poort, E. Extended Ratliff's algorithm to different conventional and modern warehouse situations that cannot be represented as seriesparallel graphs Routing policies and COI-based storage policies in picker-to-part systems. Caron, F., Marchet, G., & Perego, A. Present a model that calculates the expected travel distances for two routing techniques in a warehouse that consists of two blocks, by locating the depot between the two blocks The effect of warehouse cross aisles on order picking efficiency. Vaughan, T. S. Compared six routing methods in 80 multipleblock warehouse layouts 1999 Travel time analysis for general item location assignment in a rectangular warehouse Chew, E. P., & Tang, L. C. Present a travel time model with general item location assignment in a rectangular warehouse and use the travel time estimates to evaluate batching strategies. 14 P a g e

17 2001 Routing order pickers in a warehouse with a middle aisle. Roodbergen, K. J., & De Koster, R. Extended Ratliff's algorithm to different warehouse situations where order pickers can change aisles at the ends of every aisle and also at a cross aisle halfway along the aisles 2005 Order batching to minimize total travel time in a parallel-aisle warehouse Gademann, N., & Velde, S. Attempted to minimize the total traveling time needed to pick all items by batching multiple orders in a parallelaisle warehouse 2005 Throughput performance of automated storage/retrieval systems under stochastic demand Bozer, Y. A., & Cho, M. Presented formulas for the performance of an AS/R machine under stochastic demand 2005 Travel distance estimation in single-block ABC-storage strategy warehouses Le Duc, T., & de Koster, R. They deal with the problem of estimating the average travel distance in picker-toparts narrow-storage-aisle ABC-storage strategy warehouses. Table 1: Literature Summary of routing heuristics and analytical estimation of picking time in a chronological order. 15 P a g e

18 2.1 Routing Heuristics Dantzig and Fulkerson (1954) were one of the first who came up with an algorithm that can solve the TSP by showing that a certain tour of 49 cities, one in each of the 48 at that time existing states and Washington DC, has the shortest road distance. Ratliff and Rosenthal (1983) found then a mathematical linear algorithm in the number of aisles and the number of picking locations that can solve the Steiner Travelling Salesman Problem (STSP). Cornuéjols et al. (1985) extended the algorithm of Ratliff and Rosenthal (1983) by giving an efficient mathematical formula that can solve the STSP in series-parallel graphs. De Koster and Van der Poort (1998) also extended the well-known polynomial algorithm of Ratliff and Rosenthal that considered warehouses with a central depot and give a new algorithm for both conventional and modern warehouses that can determine the shortest route in warehouses with decentralized depositing. Decentralized depositing means that the picker can deposit picked items at the head of every aisle. Roodbergen and De Koster (2001) propose an algorithm that can be applied to different warehouse situations that cannot be represented as series-parallel graphs. More specifically, they present an algorithm that can find the shortest order picking tour in the type of warehouses, where pickers can change aisles at the end of each aisle and also at a cross aisle halfway along the aisles, with the maximum of three cross aisles. All these algorithms that solve the STSP are using heuristics (De Koster et al., 2007). This occurs due to the following 3 reasons. Firstly, an optimal algorithm is not available for every layout (De Koster et al., 2007). Secondly, Gademann and Van de Velde (2005) who attempted to minimize the total traveling time needed to pick all items by batching multiple orders in a parallelaisle warehouse, claim that optimal routes may seem irrational to the pickers who then, as a result, may veer off the predetermined routes. Thirdly, a standard optimal algorithm cannot consider aisle congestion, in contrast to heuristic methods which can avoid (or at least to reduce) the aisle congestion (De Koster et al., 2007). Hall (1993), Petersen (1997) and Roodbergen (2001) distinguish several heuristic methods for routing order picking in single-block warehouses. All above-mentioned algorithms were initially designed for single-block warehouses, though, they can be used for multiple-block warehouses with some modifications. More specifically, Vaughan and Petersen (1999) designed various techniques particularly intended for multiple-block warehouses by comparing six routing methods (optimal, largest gap, S-shape, aisle- 16 P a g e

19 by-aisle and combined), in 80 warehouse layouts, with the number of aisles varying between 7 and 15, the number of cross aisle between 2 and 11 and the pick-list size between 10 and Analytical Estimation of Travel Time However, estimating the travel time that a picker requires to collect the items that are listed on a customer s order is also part of the research on optimal routing. Using techniques from statistics and operations research, many attempts have been made on estimating how much time it takes to collect all items of an order. Bozer and White (1984) determined the expected travel time of an automated storage/retrieval (AS/R) machine associated with each order trip based on single and dual command cycles. Bassan et al. (1983) estimated the travel time of single and dual command cycles in multiple aisle systems and Pandit and Palekar (1993) presented a queueing model of a rectangular warehouse with multivehicle AS/R machine and studied the effect of warehouse design on time. An interesting addition came from Bozer and Cho (2005) who presented analytical formulas for the performance of an AS/R machine under the case where storage and retrieval requests arrive randomly. Few research papers have been conducted regarding the travel time for order picking in systems with multiple aisles and multiple picks per route. Kunder and Gudehus (1975) derive expressions for travel time estimations for three routing heuristics in a warehouse consisting of a single block. Hall (1993) extended this work with more advanced routing heuristics for one block warehouses and expands on previous work, in which optimization algorithms were developed, by deriving equations which relate route length to warehouse attributes. An important assumption that is made in both publications (Hall s and Kunder s) is that the pick locations are distributed randomly over the order picking area according to a uniform distribution. De Koster and Van der Poort (1998) and De Koster et al. (1998) use simulations to compare different kinds of picking methodologies (S-shape and largest gap) for single-block random storage warehouses. They conclude that the S-shape provides routes which are, on average, between 7% and 33% longer than the optimum solutions. Jarvis and McDowell (1991) provide a basis for locating a product in an order picking warehouse such that average order picking time will be minimized, by relocating fast and slowmoving products within the aisles. Le-Duc and De Koster (2005) develop similar travel time estimates in narrow-storage-aisle ABC-storage strategy warehouses. Chew and Tang (1999) 17 P a g e

20 present a travel time model with general item location assignment in a rectangular warehouse and use the travel time estimates to evaluate batching strategies. Finally, Caron et al. (1998) present a model that calculates the expected travel distances for two routing techniques in a warehouse that consists of two blocks, by locating the depot between the two blocks. So far, the literature has focused on solving problems that Business-to-Business warehouses face, which means a large number of order lines with big quantities per order line and has ignored the other dimension (section 3.1 explains this in more detail). This study attempts to reduce the total warehouse order picking travel time (and so achieve high-volume order processing operations for e-shop warehouses) by combining several orders in the warehouse. More specifically, travel distance can be reduced by consolidating small orders into batches. Order batching is an essential operation of order processing in which a number of customers orders are grouped into batches (Chen et al., 2005). The reduced total travel time that will result from this process will then be translated into workforce within the warehouse. A significant difference between this study and past papers is that most of the above-listed studies focus on solving the STSP, whereas this study assumes that the optimal route is always selected in response to a specific customer request. In other words, the algorithm of the Steiner travelling salesman is not solved throughout this study, instead, it is presumed as standard from past research. An additional difference is that the researcher of this study does not focus on relocating the items to achieve a minimal travelling distance, in contrast to a number of articles listed in table (1). More specifically, storage locations are not a parameter of the scalar function but are generated randomly via the simulation software. The next chapter focuses on a deeper problem formulation of the study and explains the problem also from a mathematical perspective. 18 P a g e

21 CHAPTER 3: PROBLEM FORMULATION 3.0 Introduction Chapter three consists of the problem formulation underlying the order picking problem which is presented both with text and mathematical functions. The difference in the order picking operation between Business-to-Business and Business-to-Consumer warehouses is described in the first section of this chapter followed by a detailed explanation of three fundamental order picking methodologies that will be analyzed throughout this study. A scalar function will be created that will take the order data as input, the warehouse layout, the optimal batch size and the cart size dimensions as parameters and the travel time as output (dependent variable). This travel time will then be converted into number of workers that are required to complete the order picking process. This will give the researcher the opportunity to compare various picking strategies according to the number of required workers and select the picking policy that demands the lowest number of workers that can execute all customer orders on any given day (which means the most efficient volume order processing). 3.1 Business-to-Consumer vs Business-to-Business Warehouses A customer order consists of one or more order lines and each order line refers to a unique product or stock keeping unit (SKU) in a certain quantity. Due to the significant growth of online Business-to-Consumer purchases within the past decade, e-shop Business-to-Consumer warehouses receive a large number of customer orders on a daily basis with each of them containing only a few order lines with few units (in most cases 1 order line with 1 unit), since most customers are purchasing a small number of items intended for personal use and not for reselling. In other words, the number of customer orders is very large, but each customer order contains only a few order lines, and each order line contains only a few number of items (see Table 2). This leads to an increased order picking activity compared to traditional Business-to-Business orders, where the warehouse receives a significantly smaller number of orders with a higher number of order lines, since businesses order items in much flarger quantities than consumers. This increased picking activity within the e-shop warehouses needs to be optimized in a more efficient way. 19 P a g e

22 Business-to-Business Warehouses Business-to-Consumer (eshop) Warehouses Number of Customer Orders Small Large Number of Order Lines per Customer Order Large Small Quantity of each SKU Large Small Table 2: Order Comparison between Business-to-Business and Business-to-Consumer warehouses In traditional Business-to-Business warehouses the picker moves within the warehouse to collect all the products that are listed on one customer order and transfers them to the depot for packing and shipping. As soon as he finishes with this order he proceeds with the next one. This process is repeated until all orders have been processed on a certain day. Herewith, every order is executed separately and none of them is grouped or batched with other orders. However, this methodology, although simple, can hardly be applied in Business-to-Consumer warehouses where the pickers receive a significantly larger number of orders because the traveling distance and order picking time (and consequently the total operational warehouse costs) would have been much higher. It is important to note that businesses that have adopted drop shipping as their core business model form an exception. Drop shipping is a supply chain management method in which the retailer does not keep goods in stock but instead transfers customer orders and shipment details to the wholesaler, who then ships the goods directly to the customer. This means that the order traffic and characteristics are almost identical to Business-to-Consumer warehouses. Conclusively, this study focuses on Business-to-Consumer warehouses and the only Business-to-Business case according to which this study can also be applied to, is the businesses that have adopted drop shipping as their core business model. The ultimate goal is to find an optimal number of orders to be collected in one order picking tour so as minimize travel time. After a batch of items is collected, it is brought to a secondary (or supporting) racking area where the individual items are consolidated to form the orders. In such a system, the splitting is conducted by walking along the secondary aisle and separating the batch of orders. This process of collecting multiple items and then splitting the batch into the individual 20 P a g e

23 orders should be repeated until all orders are executed. What needs to be found is the optimal number of those repeats (number of batches) and consequently the optimal number of orders to be consolidated into one batch (the batch size) that minimize the total order picking travel time within the warehouse. Travel time is defined as the time that is required for a picker to travel to the location of the SKU to pick an item. In section 3.2 and 3.3 this problem is expressed with scalar functions. 3.2 Order Picking Travel Time Function The reason why this study aims to minimize the travel time is that travel is the most dominant order-picking time component in a usual non-automated warehouse as shown in Figure (2). Although various case studies have shown that also activities other than travel may substantially contribute to order-picking time (Dekker et al. 2004, De Koster et al. 1999a), travel is the dominant component. According to Bartholdi and Hackman (2005), travel time is waste. It costs labor hours but does not add value. It is, therefore, a first candidate for improvement. Figure 2: Typical distribution of picker s time (Tompkins et al. 2003) As previously discussed, this study is going to analyze the Business-to-Consumer order picking problem from a two-dimensional perspective. More specifically, since the proposed (of this study) order picking process consists of the function of collecting the batch of items that are listed on the customer orders and the splitting of the batch items with respect to each order, the total order picking travel time is equal to the sum of the order collection travel time and the order splitting travel time (see equation 1). The splitting travel time occurs from the travel distance that 21 P a g e

24 the picker is required to travel from walking along the secondary racking area to split the batch into the individual orders so that they can be packed and shipped afterwards. Equation (1) is a scalar valued function, which means that it is a function that takes one or more values but returns a single value. T (D p, w, c) represents the total order picking travel time which consist of the travel time of collecting the items TC (D p, w, c) and the travel time of splitting the items TS (D p, w, c) after they have been collected. The order data will be used as input in the function; the warehouse layout, the optimal batch size and the cart size dimensions as parameters; and the travel time as output. All those inputs and parameters will be analyzed in the next chapter. T (D p, w, c) = TC (D p, w, c) + TS (D p, w, c) with p P (1) popt = arg min T (D p, w, c) with p P (2) Where D: Data Orders (input) P: represents the set of all possible batch strategies p: represents the size of the batch (parameter) w: warehouse layout (parameter) c: cart size (parameter) From equation (1) it can be noticed that many different combinations of order collection and splitting can be made. The goal is to find the optimal number of orders to be consolidated into one batch (the batch size) that minimizes T (D p, w, c) as shown in equation (2). The travel time will then be translated into labor force by dividing T (D p, w, c) with the number of working hours per day as shown in equation (3). Section (3.2) will evaluate different combinations of the order picking function by analyzing three picking policies. L = TC (D p,w,c) + TS (D p,w,c) W = T (D p,w,c) W (3) Where L: Labor force W: Number of working hours per day 22 P a g e

25 Before we proceed with the policy explanation, two observations need to be made. Firstly, in all three cases that will be examined, it is assumed that the most optimal route is always selected for each order picking tour by using the Steiner Travelling Salesman algorithm that has been solved by various research papers as indicated in chapter two. In other words, the algorithm of the Steiner travelling salesman is not solved throughout this study, instead it is presumed from past research. Appendix (1) includes more detailed information regarding the design of the optimal picking route. Additionally, five other main routing methods are presented in Appendix (1) and compared with the optimal one. Secondly, another crucial point is that the researcher of this study does not use as a parameter the positioning of the items. This occurs because items in the fashion industry are constantly changing position within the warehouse, due to seasonality. Thus, the item locations will be generated randomly via the simulation software each time an order simulation is executed. 3.3 Order Picking Methodologies Three diverse types of picking strategies that can be applied to e-shop distribution centers will be evaluated. Then, the most optimal picking strategy for the e-shops orders will be identified among the three by comparing the outputs of the scalar function. The first two cases that will be examined are the extreme cases where TC (D p, w, c) or TS (D p, w, c) receive their maximum values while the third will examine various levels of combinations of TC (D p, w, c) and TS (D p, w, c). Figure 3: Illustration of Order Picking Operation 23 P a g e

26 3.3.1 Single order picking policy (Each order is one batch) The first extreme order picking scenario that will be evaluated is the baseline model; the model that is currently used among e-shop Business-to-Consumer transactions. According to this model, each customer order will be executed separately without batching multiple orders together. For example, in figure (3) each of the black squares may represent a separate customer order within the warehouse, and each of them will be collected and brought to the depot for packing and shipping, then the process continues with the next order. This procedure is repeated until all orders are executed. So, according to this scenario, the picker executes the orders one by one (one order per picking tour) and no simultaneous activity of order processing is involved. This way of picking is often referred as the single order picking policy (or discrete or pick-by-order) (De Koster et al., 2007). TC (D p, w) is max (4) TS (D p, w, c) is zero (5) T (D p, w) = TC (D p, w) (6) Since there is no splitting activity involved in this policy, the splitting travel time will be 0 as shown in equation (5). However, TC (D p, w, c) will take its maximum value, since no other policy exists that involves a higher order collection distance travelling within the warehouse (see equation 4). This signifies that the total order picking travel time will be equal to travel time of collecting the individual items (see equation 6). A noteworthy observation is that this policy does not require any collection carrier which means that the cart size does not constitute a parameter of the function. In conclusion, in scenario 1 the order collection effort is high (maximum value), while the order splitting effort is 0 (minimum value) The One-Batch Order Picking Policy The second extreme order picking scenario that will be evaluated is the One-Batch grouping policy. Here, all items of all customer orders are grouped and collected with a collection carrier in one tour and placed then on a secondary (or supporting) racking area on which all items are split to form the individual customer orders. An important assumption that needs to be made is that the racking area should be large enough to fit all the orders that are collected in a certain batch. This way of picking is often referred as the One-batch order picking policy. In Figure (3), 24 P a g e

27 the empty rack on the right is used for splitting the batch into the orders that is brought from the black squares. The exact layout for the splitting process will be analyzed in chapters four and five. TC (D p, w, c) is min (7) TS (D p, w, c) is max (8) T (D p, w, c) = TC (D p, w, c) + TS (D p, w, c) (9) Subject to V(P) c (10) Where V(P): Total volume of items of one batch c: Capacity of the available Collecting Carrier Since all items of all customer orders are picked in one phase, TC (D p, w, c) takes its minimum value (see equation 7), where TS (D p, w, c) takes its maximum value (see equation 8). The total order travel time is equal to the sum of the order collection travel time and the order splitting travel time (see equation 9). However, this scenario may not be realistic due to the limited number of items that the collection carrier can take. More specifically, if the number of orders that need to be collected on a certain day is high, it may not be possible to collect all the items in just One-batch. So, an important limitation of this formula is that the batching volume should be small enough to fit in the collecting carrier (see equation 10). This signifies that the maximum number of orders that fit into the collection carrier will always be collected and brought to the splitting rack and this process shall repeat by performing multiples routes until all orders have been transferred to the splitting station. As a consequence, the splitting process will not start until the total daily order volume has been brought to the splitting rack Batch Order Picking Policy The third order picking policy is called batch order picking. More specifically, when orders lines per order are small, there is a potential for reducing the total travel picking time by picking multiple customer orders in one single picking tour, which are placed then on a secondary (or supporting) racking area on which all items are split to form the individual customer orders. The difference between the second and third picking policy is that in the second all items are collected in one phase (minimum number of batches), whereas in the third, the process of collecting and splitting the size is repeated multiple times until all orders have been executed. 25 P a g e

28 Min of TC (D p, w, c) < TC (D p, w, c) < Max of TC (D p, w, c) (11) Min of TS (D p, w, c) < TS (D p, w, c) < Max of TS (D p, w, c) (12) T (D p, w, c) = TC (D p, w, c) + TS (D p, w, c) (13) Subject to V(P) c (14) with P P According to this picking policy, TC (D p, w, c) receives a lower value than in the single order picking policy but a higher value than in the One-batch order picking policy (see equation 11), whereas TS (D p, w, c) takes a higher value than in single order picking policy but a lower value than in the One-batch order picking policy (see equation 12). This occurs because different levels of combinations of TC (D p, w, c) and TS (D p, w, c) are examined in contrast to the first two scenarios where the researcher examines only the two extreme cases where TC (D p, w, c) or TS (D p, w, c) receive their maximum values. As in the second scenario, here we must take again into consideration the limitation of this formula which is that the batching volume should be small enough to fit in the collecting carrier (see equation 14). 26 P a g e

29 CHAPTER 4: METHODOLOGY 4.0 Introduction This chapter describes the investigative focus of the study and defines the research methods used to conduct it. The methodology used is a research framework encompassing quantitative methods and measures. The researcher explains from where the necessary data and information to address the research objectives and questions were collected, presented and analyzed. This chapter also identifies the main assumptions and parameters of the scalar function (see equation 1) underlying the warehouse order picking problem. Reasons and justifications for the data sources, warehouse layouts, picking time parameters and analytical techniques used are given. All those data, layouts and parameters described in this chapter will be used in the warehouse simulation model in the next chapter. 4.1 Order Picking Data The order picking data is the only input in the scalar function and is addressed as D (see equation 1). More specifically, the order data contains order lines that have been collected from an e-shop warehouse which operates in the fashion industry in Greece. The total number of customer orders that contain those order lines is All orders that will be examined were collected in 60 consequent weekdays (12 weeks) from 05/09/2016 to 27/11/2016. A few number of the order lines in the dataset had missing values such as product-ids or dates. All those incomplete data that had at least one missing value among the order number, order date, product- ID, quantity or order day have been removed from the dataset and will not be used as input to the simulation model. Table (3) represents a sample of the order data structure. Order Number Order Data Product-ID Quantity Order day Monday Monday Monday Wednesday Table 3: Sample of Order Data Structure 27 P a g e

30 4.1.1 Order Lines Table (4) shows the percentage (see column 2) and daily average number of orders per order line (see column 3) regardless of the weekday. More specifically, from the second column of Table (4) it can be noticed that the majority of the total number of orders (39.95%) contain only one order line which can be explained in that the customers are usually purchasing a small number of items (one order line) intended for personal use. On the contrary, only 4 orders per day in average, which accounts for 0.39% of the total number of orders contained exactly 5 order lines and this percentage is subject to decrease as the number of order lines per order increases (the more order lines per order, the less the percentage). Columns 3 and 4 of Table (4) show the average number of orders and order lines respectively. More specifically, the average number of orders per day that has been placed is 1050 orders, containing in average a total of 1966 order lines. Figure (4) is a graphical representation of table (4) and indicates the total number of orders containing from one to five order lines. It is important to note that a small number of orders included a larger than usual number of order lines (in some cases bigger than 20). Those special orders that were placed by groups of people were excluded from the dataset, since they contribute to less than 0.01% of the total number of orders. Hence, they comprise a rare order operation within the e-shop warehouse and do not occur on a daily e-shop warehouse routine. Only orders with up to five order lines were kept as input for the warehouse simulation model which account for 99.9% of the daily operational warehouse routine. Order Lines per Order % Orders Orders OrderLines % % % % % 4 20 Total % 1,050 1,966 Table 4: Number of order lines per order per day 28 P a g e

31 Number of Orders Total number of Orders 30,000 25,000 20,000 15,000 10,000 5, Number of Order Lines per Order Figure 4: Total number of orders lines per order in the examined time-period Table (5) shows the average number of order lines that are executed throughout each weekday in the selected period, in contrast to table (4) which doesn t take into consideration the weekday. It can be noticed that a slightly higher number of orders are executed on Monday compared to the rest of the weekdays. This can be explained due to the weekends at which the warehouse does not operate, so the customers orders placed on weekend are executed on the next working day which is Monday Products Weekday Orders Order Lines Quantity Monday 1,013 2,025 2,134 Tuesday 984 1,968 2,072 Wednesday 971 1,942 2,037 Thursday 966 1,932 2,024 Friday 972 1,944 2,038 Average 981 1,962 2,061 Table 5: Average number of order lines and quantity for each weekday The total number of Products (SKUs) of the e-shop warehouse within 2016 (from ) was However, during the selected period of 12 weeks (from 05/09/2016 to 27/11/2016) only SKUs appeared in the individual orders. The main reason for that is seasonality and variability which may be caused by numerous factors, such as weather, trend, fashion, vacation, and holidays and thus affecting customer's purchasing behavior. More 29 P a g e

32 specifically, it can be assumed that since the period that is examined in this study is from September to November, mostly seasonal (autumn) products were purchased by the customers. Figure (5) shows the results of ABC Analysis (Pareto analysis) that has been conducted, showing the relationship between SKUs and order lines. Here, the results deviate slightly from the rule, according to which 20 percent of the SKUs should account for 80 percent of the total quantity. Instead, it was calculated that the demand for the SKUs is based on a distribution so that 20 percent of the SKUs account for 69 percent of the total quantity sold. Similar ABC analysis has been also conducted with the number of order lines instead of order quantity, showing that 20 percent of the SKUs account for 68 percent of the total number of items (see Figure 6). Since the ratio [Quantity/Order Line] is close to 1, both ABC Analysis are very similar. Figure 5: Pareto Analysis with Order Quantity 30 P a g e

33 Figure 6: Pareto Analysis with Order Lines 4.2 Warehouse Layout The warehouse layout (see Figure 7 and 8) that will be used in the simulation model is one of the three parameters of the scalar function and is addressed in equation (1) as w. The shape of the warehouse is a rectangle that has a length of 41 meters and a width of 35.5 meters, which accounts for a total area of square meters. It has 15 picking aisles with front and back crossaisles and each picking tour begins and ends at the depot (see yellow box in Figure 8) which is located right next to the warehouse entrance. Each cross-aisle has a width of 1.20 meters and consists of 16 rack bays that are equally divided on the left and right of the central aisle. As far as the rack bays are concerned, each of them has a height of 2.30 meters, a length of 2.10 meters and a depth of half a meter containing 5 storage locations on each of the 4 levels reaching a total of 20 storage places per bay (see Figure 8). Consequently, the total number of storage locations per cross aisle is 320, which are equally divided (160) on each side of the central aisle. This accounts for a total number of 9600 storage places within the warehouse. It can be observed that the total number of storage locations within the warehouse which is 9600, is smaller than total number of SKUs in 2016, which is However, this does not mean that there is lack of storage locations because most products in the fashion industry are seasonal and are only available for sale from 4 to 6 months within the year. Figure (7) shows the dimensions of a halfway cross-aisle, consisting of 8 rack bays. 31 P a g e

34 Figure 7: Half Cross-aisle Dimensions Figure 8: Horizontal floor plan of warehouse layout 32 P a g e

35 Figure 9: Vertical floor plan of warehouse layout Several assumptions will be made regarding the layout and pickers characteristics that will be used in the simulation model. Firstly, the picking aisles are two-sided and are wide enough for two-way travel. However, it is also assumed that the aisles of the warehouse are narrow enough (1.20 meters) to allow the picker to retrieve products from both sides of the aisle without changing position. The picker should be tall enough to be able to reach items from the fourth level of any rack bay, which has a height of at least 1.80 meters. Secondly, each SKU can be assigned to more than one storage locations if the quantity of an SKU doesn t fit in its predefined storage place. However, one storage location allows up to four different SKUs to be stored. Additionally, every storage location has the same size. Finally, it is assumed that picking locations are distributed randomly over the order picking area according to a uniform distribution. 4.3 Picking Cart The collection carrier that will be used in the simulation model is one of the three parameters and is addressed as c in the scalar function (see equation 1). More specifically, in this paper, it is assumed that those carts which are only used in the second and third picking policies (sections and 3.2.3) have no separate compartments. This means that splitting will be 33 P a g e

36 conducted on the secondary racking area before order shipments, so individual order integrity is not maintained on the picking cart. The number of capacity of each of those picking carts will be 100 order lines which is approximately just over 100 items and on average 54 randomly selected customer orders. 4.4 Order Picking Time Parameters Several assumptions are made in the simulation models regarding the pickers time parameters which include collection time, picking time, order preparation time walking speed etc. A summary of all those picking time parameters are listed in Figure (10). The first assumption that is made is that each working shift in the warehouse lasts eight hours of which seven hours are working time and the one hour is for break or worker personal uses. Secondly, a picker s average walking speed of 0.7 meters per second (2.52 kilometers per hour) is assumed to be constant throughout the whole simulation process. As far as the collection time parameter is concerned, each picker will have 10 seconds before he starts collecting an order, to read the list and orientate himself within the warehouse in order to locate the storage places that are listed on the customer order. Additionally, the fetching time per order line is also constant and assumed to be 2.5 seconds per order line. However, a strong assumption that is made is that the number of SKUs is independent of the picking time per SKU, because the ratio of [Quantity / Order Line] is close to 1. In other words, since the number of items per order line is very small (in most cases one item), it is assumed that the fetching time per order line is constant, regardless of the number of items per order line that is picked. For example, the same amount of time is required for a picker to fetch one item or three items of the same storage location. Lastly, as already mentioned in the previous section, picking is completed manually using a picking cart with a capacity of 100 SKUs. The required time to place the items into the cart is assumed to be 2.5 seconds per order line. On the other hand, two crucial time order parameters will also be added in the simulation model. More specifically, the required time for the picker to prepare a new order and get a picking cart is assumed to be 10 seconds. Finally, the picker will have an additional time of 10 seconds to finish the order and sign the list. All those parameters discussed in this chapter will be used in the simulation model that will be run in the next chapter. 34 P a g e

37 Figure 10: Summary of Order Picking Time Parameters 35 P a g e

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39 CHAPTER 5: SIMULATION AND ANALYSIS 5.0 Introduction This chapter contains warehouse simulations that have been conducted by using models, layouts and parameters that have been discussed in the previous chapters. To answer the research question, which is how to improve the e-shop warehouse efficiency by finding the optimal picking strategy that minimizes the travel time of the order collection and splitting activity, a warehouse simulation software is used. A warehouse simulation is defined as a computer-based modeling technique of a real warehousing system. The researcher uses a simulation software to conduct the experiments because it empowers warehouses to analyze, explore and experiment with its warehousing processes in a virtual environment. In that way, time and cost requirements associated with physical (real-life) testing can be eliminated. Additionally, warehouse simulations allow the researcher of this study to sample random values and to review the current operation and the effect of making changes, such as changes in storage, layouts, collection and splitting strategies. Even human workforce can rapidly be adjusted within the simulation software, allowing the researcher to decide on how to fully utilize the warehouse equipment and maximize effectiveness. The simulation software that is used in this study is ALA The first section of this chapter analyzes the simulation models whereas the second section presents the simulation results pertinent to travel times of each policy with numerical and graphical representation. The third section translates the travel time estimations into labor force and the last section mentions cases according to which the methodology of used in this study can be applied to industries other than warehousing. 5.1 Simulation Models This section contains simulation models and characteristics of the various order picking policies that were described in chapter three. Table (6) contains information regarding the batch size and the number of order lines and items of each order picking policy. Model I (sub-chapter 3.2.1) and VI (sub-chapter 3.2.2) refer to the two extreme order picking scenarios, which are the single and One-batch order picking policy respectively, whereas models II, III, IV and V refer to the batch order picking policy (sub-chapter 3.2.3) and examine various levels of combination of collection and splitting strategies. 37 P a g e

40 Before we proceed with the model analysis, four observations need to be made. Firstly, the length of the splitting area that is used in each model is variable and depends on the number of orders that each batch contains. More specifically, each model requires a different number of slots in the splitting area, since the batch size varies from model to model. For instance, model V requires a larger splitting area than model III, since one batch in model V contains in average 108 orders that need to be split in in contrast to 27 orders in model III. Secondly, the number of orders that a batch contains should always be less or equal than the number of splitting rack slots so that the splitting process can take place. It is important to note that model I does not require any splitting activity so there is no need to create a separate splitting area within the warehouse for the single order picking policy. Thirdly, one picking tour always starts and ends at the depot. Fourthly, as far as the batch order picking policy is concerned, four models are analyzed. Reasons and justifications for selecting those four specific models are given in the next section. For detailed information regarding the process of creating those six simulation models with ALA 2000, please refer to Appendix 2. The process of collection and splitting sequence can be made clearer with the following example: Let s assume that the warehouse chooses to operate with model IV and has to execute 1050 orders in the examined day. The product of the batch size and the picking tours give the maximum number of orders that each model can handle. Model IV can handle at most 54 x 20 = 1080 orders per day. Since the total number of orders of one day is 1050, 19 picking rounds with a batch size of 54 orders and 1 round (the last one) with a batch size of x 54 = 24 orders will be each collected and then split separately. After each batch is split, the picker brings the orders to depot for packing and shipping. On the contrary, if the company operates with model VI, the process will be as following: The picker starts from the depot and picks the first 100 order lines, which is the maximum number of orders that can fit in the collection carrier, and brings them to the splitting area. At that point, he positions the items in the splitting rack locations but does not conduct any splitting nor returns to the depot. Instead he continues to pick the next 100 order lines and brings them to the splitting area. The splitting is conducted only after all items are brought to the splitting area, after which the picker returns to the depot. As mentioned before, the splitting travel time occurs from the travel distance that the picker is required to travel from walking along the secondary racking area to split the batch into the individual orders so that they can be packed and shipped afterwards. 38 P a g e

41 MODEL I: Model Strategy Batch Size Picking Tours # Order Lines # Items Model I Single Order Model II ⅛ x Cart Size Model III ½ x Cart Size Model IV 1 x Cart Size Model V 2 x Cart Size Model VI All Orders Table 6: Order Picking Summary of six models The simulation model I refers to the single order picking policy and is the model where the picker executes the orders one by one (one order per picking tour) and no simultaneous activity of order processing is involved. According to this policy, each order is collected separately and brought to the packing and shipping station, then the process continues with the next order. This procedure is repeated in average 1050 times per day until all orders are executed. For this case, there is no need for an extra splitting station. Consequently, the batch size of each order picking tour is only one order, containing in average 1.85 order lines. MODEL II: This is the first of the four batch order picking policy models and groups 7 orders together in a single batch. Since the average number of orders per day is 1050, the total daily collection workload consists of approximately 150 order batches (picking tours). Therefore, after the collection procedure of each batch, a splitting process is necessary to form the individual orders. A rack with 720 centimeters length and six layers is necessary for the splitting process so that the total number of slots in the splitting area which is 10 is larger than the number of orders that each batch contains, which is 7. Figure (11) indicates the splitting rack dimensions of this model that fulfill this criterion. 39 P a g e

42 Figure 11: Splitting Rack Dimensions of Model II MODEL III: This model refers to the batch order picking policy and groups 27 orders together in a single batch. Since the average number of orders per day is 1050, the total daily collection workload consists of approximately 39 order batches (picking tours). Therefore, after the collection procedure of each batch, a splitting process is necessary to form the individual orders. A rack with 2.02 meters length and six layers is necessary for the splitting process so that the total number of slots in the splitting area which is 30 is larger than the number of orders that each batch contains, which is 27. Figure (12) indicates the splitting rack dimensions of this model that fulfill this criterion. Figure 12: Splitting Rack Dimensions of Model III MODEL IV: This model refers to the batch order picking policy and groups 54 orders together in a single batch. Since the average number of orders per day is 1050, the total daily collection workload consists of approximately 20 order batches (picking tours). Therefore, after the collection procedure of each batch, a splitting process is necessary to form the individual orders. A rack with 40 P a g e

43 4.03 meters length and six layers is necessary for the splitting process so that the total number of slots in the splitting area (which is 60) is larger than the number of orders that each batch contains (which is 54). Figure (13) indicates the splitting rack dimensions of this model that fulfill this criterion. Figure 13: Splitting Rack Dimensions of Model IV MODEL V: This is the last batch order picking model and groups 108 orders together in a single batch. That means one batch consists of two full carts. After completion of the collection of the two-carts batch, the splitting process is executed. Since there are in average 1050 orders per day that need to be executed, the total daily collection workload consists of approximately of 10 order batches (picking tours). After collection of each batch, a splitting process is necessary to form the individual orders. A rack with 8.05 meters length and six layers is necessary for the splitting process. Figure (14) indicates the splitting rack dimensions of this model so that the total number of slots in the splitting area (which is 120) is greater than the number of orders that each batch contains (which is 108). 41 P a g e

44 Figure 14: Splitting Rack Dimensions of Model V MODEL VI: Model VI groups all orders together in a single batch. That means this single Batch consists of 20 full carts. The splitting process can only start when this single batch, which contains the total amount of daily order lines, has been brought to the splitting station. Two racks of meters length and six layers are necessary for the splitting process (see Figure 15). The racks are placed in a back-to-back arrangement and the operator has to run around the splitting block for the orders separation (see Figure 16). Figure 15: Splitting Rack Dimensions of Model VI 42 P a g e

45 5.2 Simulations Results Numerical Interpretation of the Models Figure 16: Back-to-back Arrangement of splitting rack in model VI This section presents the simulation results with a numerical interpretation. Table (7) depicts the collection, splitting and total travel times in hours of each of the six models mentioned in the previous section. As already discussed in chapter three, the total order picking travel time is equal to the sum of the order collection travel time and the order splitting travel time (see equation 1), which means that the sixth column of table (7) is equal to the sum of the fourth and fifth. Strategy Batch Size Picking Tours TC (D p, w, c) TS (D p, w, c) T (D p, w, c) Single (MODEL I) ⅛ Cart Size (MODEL II) /2 Cart Size (MODEL III) Cart Size (MODEL IV) Cart Size (MODEL V) All Orders (MODEL VI) Table 7: Order Picking Results of the six models From Table (7) it can be concluded that the two extreme order picking scenarios which are models I and V are the least efficient in terms of travel time. The results indicate that the single order picking policy requires the highest number of picking hours to execute all orders on any given day followed by the One-batch order picking policy, which requires 4 working hours less. The batch order picking policy which refers to the models II, III, IV and V gives a considerable lower travel time output, however, the results vary only slightly. The simulation experiments indicated that the optimal number of orders to be consolidated into one batch (the batch size) that minimizes T (D p, w, c) is 54. So, it can be observed that the optimal strategy is to combine both collection and splitting by performing 20 picking tours each containing 54 orders. Conclusively, 43 P a g e

46 the percentage of the travel time reduction from moving from model I and VI to model IV is 60.87% and 57.3% respectively. However, it can fairly be concluded that other optimal solutions with a batch size close to 54 orders may also exist. Although, the order picking policy that groups more than 54 orders in a single batch required a slightly higher total travel time to execute the daily orders, since one additional collection-cart is required to transfer the one extra order, the order picking policy with a batch size of orders gave about the same results with model IV. In fact, sometimes, the simulation experiments indicated as the optimal batch size not model IV, but policies with batch sizes of orders. This can be explained in that the simulation software is generating the item positioning randomly each time a new simulation experiment is run. Models II, III and V are shown in this study to indicate the minimal differences in travel time between the different variations of the batch order picking policy. Conclusively, although the efficiency improvement from moving from the two extreme order picking scenarios (model I and VI) to batch order picking (model II, III, IV and V) is huge, the simulation experiments indicated that it is almost insignificant which close-to-the-optimal batch size will be chosen, hence the optimal picking strategy that minimizes the total travel has a batch size of approximately 50 orders (around 100 order lines) Graphical Interpretation of the Models This section presents the simulation results with a graphical interpretation. The following graphs depict the results of table (7). Figure (17) shows the relationship between the three travel times and the batch size. In figure (18), the x-axis represents a logarithmic interpretation of the batch size in relation to the three travel times, having a base of 10. In both cases, the optimal batch size is the one at which the total travel time function which is indicated by the gray line, receives its lowest value. An important observation that can be made is that the time function is convex and flatten enough in its minimum value so that changes around the optimal value only slightly change the optimal value of the time function. 44 P a g e

47 Hours Hours Time Function [N -hrs] Batch Size Collection (hrs) Splitting (hrs) Picking (hrs) Figure 17: Relationship between the three travel times and the batch size Time Function [ log(n) - hours] Logarithmic Batch Size Collection (hrs) Splitting (hrs) Picking (hrs) Figure 18: Logarithmic interpretation of the batch size in relation to the three travel times 5.3 Converting Travel Times into Labor Force In this section, the total travel times of each of the six models will be translated into labor force by dividing T (D p, w, c) by the number of working hours per day as shown in equation (3). As already discussed, each working shift in the warehouse lasts eight hours of which seven hours are working time and one hour is for break or worker personal uses. Consequently, the denominator of equation (3) will be 7 hours whereas the nominator varies from model to model and is taken from the last column of table (7). For instance, the required number of operators in model IV is 2.7 which is found by dividing 18.7 by P a g e

48 Translating travel times into labor force gives the researcher the opportunity to compare various picking strategies according to the number of required workers and select the picking policy that requires the lowest number of labor force (which means the most efficient volume order processing). The following table depicts the number of operators (pickers) in relation to the picking strategy. It is important to note that the number of operators needs always to be rounded up to the following integer to obtain the final number of required pickers. Model Strategy # Operators Model I Single Order Model II ⅛ Cart Size Model III 1/2 Cart Size Model IV Cart Size Model V 2 Cart Size Model VI All Orders Table 8: Number of operators in relation to the picking strategy. Since the travel time is directly related to the workforce, the efficiency ranking has remained unchanged compared to the ranking with respect to travel times discussed in the previous section. This means that the two extreme order picking scenarios which are models I and VI are, as expected, again the least efficient in terms of labor force. The results indicate that both the single order picking policy and the One-batch order picking policy require the highest number of workers (7 workers) to execute all orders on any given day. The batch order picking policy which refers to the models II, III, IV and V gives a significantly lower number of required labor force, however, the results do not vary among those models since the number of workers needs always to be rounded up to the following integer. At the moment, the fashion e-shop warehouse operates with the single order picking policy, thus our simulation experiments show that the total number of required workers should be 7. However, they use only 6 workers and this can be explained in the following way: The pickers sometimes try to occasionally take a small number of orders in the range of 2 to 5 and empirically attempt to collect these orders simultaneously. This explains the slight deviation from our first model which requires 7 operators and not 6. By moving to a batching strategy of approximately 50 orders per picking tour the warehouse managers can afford to reduce the number of operators from 6 to 3, which means they are going to have 50% reduction in the number of operators in the customer order picking function 46 P a g e

49 without any loss in operational efficiency. This 50% excess in workers can be repositioned in other warehouse operations, and thus improve the overall operational efficiency without hiring new labor force or purchasing new equipment. 5.4 Problem Generalization This section gives a generalization of the problems formulation based on the order picking problem. Three examples are given according to which the methodology used in this study can be applied to industries other than warehousing. The use of this simulation methodology can provide a near optimal solution for these examples. Problem Formulation Let J be a job to be performed and G the wished goal of this Job and R a set of restrictions. The cost (time, money, effort, etc.) of performing the job J is CJ. Suppose we can split the job in a finite set of sub sequencing tasks T = {T1, T2,..., Tk} which overall perform the same goal G while keeping the restrictions R. Let CT = CT1+CT2+ + CTk be the cost of performing all tasks of T. Let T be the set of all possible T which achieve or outperformed the goal G. We define T* as the optimal strategy if: CT*< CT for every T in T and CT* < CJ Examples of Optimization Problems Transportation of Cargos: Instead of executing individual transportation tasks, cargos can be grouped and then transported in a central hub. These groups will then be rearranged in new groups and finally redistributed to the final targets. Food Delivery: Group delivery tasks from various restaurants in a central depot and then create new delivery group tasks to the final consumers. Manufacturing: Group products of common spare parts, distribute them to the plants, assemble them into final products and finally ship them to the end customers. 47 P a g e

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51 CHAPTER 6: CONCLUDING REMARKS AND FUTURE RESEARCH The main purpose of this study was to present a set of best practices for order preparation for e-shops, to identify the best order picking strategies for e-shop orders and to simulate picking strategies by using original data samples and warehouse layouts in a warehouse simulation software. The challenge was to minimize the total order picking travel time required to execute the orders. So far, the literature has focused on solving problems that Business-to-Business warehouses face, which means a low number of orders containing a large number of order lines with big quantities per order line and has ignored the other dimension of many orders with few order lines and quantities. This study attempts to reduce the total warehouse order picking travel time (and so achieve high-volume order processing operations for e-shop warehouses) by combining several orders into batches within the warehouse. The simulations, as expected gave different total order picking travel times for each picking policy that was examined. These travel times were then translated into number of workers that are required to complete the order picking process. This gave the researcher the opportunity to compare various picking strategies according to the number of required workers and select the picking policy that requires the lowest number of labor force (which means the most efficient volume order processing). The order data, that have been used as input in the simulation software contained order lines that have been collected from an e-shop warehouse which operates in the fashion industry in Greece and were collected in 60 consequent weekdays (12 weeks) from 05/09/2016 to 27/11/2016. Additionally, existing warehouse layouts, order picking time parameters and collection carriers have been used in the simulation software. From the simulation experiments of the warehouse design environments, the researcher verifies that he can find an optimal order picking policy for warehouse design that gives the best performance in terms of travel time minimization. More specifically, the optimal picking policy is not the one of the two extreme picking policies where TC (D p, w, c) or TS (D p, w, c) receive their maximum values. Instead, it was found that the batch order picking, which is a picking policy that combines order collection and splitting, gives the lowest travel time and consequently the 49 P a g e

52 lowest number of required workers to complete the picking process. Conclusively, it has the largest impact on reducing total fulfillment time when small order sizes are common as it occurs with e- shop orders. The fundamental organizational benefit of this study is that the 50% excess in workers in the customer order picking function can be repositioned in other warehouse operations, and thus improve the overall operational efficiency without hiring new staff or purchasing new equiptment. However, the researcher concluded that although an optimal picking policy can be identified, there is no optimal batch size that gives for every simulation experiment the lowest total travel time. In fact, sometimes, the simulation experiments indicated as the optimal batch size not model IV, but policies with batch sizes close the batch size of model IV. This can be explained in that the simulation software is generating the item positioning randomly each time a new simulation experiment is run. Although the efficiency improvement from moving from the two extreme order picking scenarios (model I and VI) to batch order picking (model II, III, IV and V) is severe, the simulation experiments indicated that it is almost insignificant which close-to-theoptimal batch size will be chosen. Graphically speaking, the time function is convex and flatten enough in its minimum value, so that changes around the optimal value only slightly change the optimal value of the time function. Although these simulation experiments have been specifically designed for items that belong to the fashion industry, the results are also applicable to other industries with similar order data characteristics. For instance, the same simulation experiments could be conducted to industries that have a relatively high number of SKUs and a relatively small size of items (micropicking) such as the pharmacy warehouses, book warehouses or spare parts electronics warehouses and similar results shall be expected. Conclusively, the optimization techniques presented in this study do not apply to warehouses with a low number of SKUs since the total travel time from collecting and splitting the orders would have been much higher. Additionally, an attempt was made to apply the methodology of this study to industries other than warehousing. The work presented provides a methodology based on average overall order picking travel time as an evaluation index to the industry. This study can be used as a reference for the warehouse design or improvement of the warehouse planning. Therefore, it is hoped that this article can be a practical and useful guideline to the industry in the design and planning of an order picking system in an e-shop warehouse system. 50 P a g e

53 CHAPTER 7: REFERENCES Bartholdi, J. J., & Hackman, S. T. (2008). Warehouse & Distribution Science: Release 0.89 (p. 13). Supply Chain and Logistics Institute. Bassan, Y., Roll, Y., & Rosenblatt, M. J. (1980). Internal layout design of a warehouse. AIIE Transactions, 12(4), Bozer, Y. A., & Cho, M. (2005). Throughput performance of automated storage/retrieval systems under stochastic demand. IIE Transactions, 37(4), Caron, F., Marchet, G., & Perego, A. (1998). Routing policies and COI-based storage policies in picker-topart systems. International Journal of Production Research, 36(3), Chen, M. C., & Wu, H. P. (2005). An association-based clustering approach to order batching considering customer demand patterns. Omega, 33(4), Chew, E. P., & Tang, L. C. (1999). Travel time analysis for general item location assignment in a rectangular warehouse. European Journal of Operational Research, 112(3), Cornuéjols, G., Fonlupt, J., & Naddef, D. (1985). The traveling salesman problem on a graph and some related integer polyhedra. Mathematical programming, 33(1), Coyle, J. J., Langley, C. J., Novack, R. A., & Gibson, B. (2016). Supply chain management: a logistics perspective. Nelson Education. Dantzig, G., Fulkerson, R., & Johnson, S. (1954). Solution of a large-scale traveling-salesman problem. Journal of the operations research society of America, 2(4), De Koster, R., & Van Der Poort, E. (1998). Routing orderpickers in a warehouse: a comparison between optimal and heuristic solutions. IIE transactions, 30(5), De Koster, R., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182(2), Dekker, R., De Koster, M. B. M., Roodbergen, K. J., & Van Kalleveen, H. (2004). Improving order-picking response time at Ankor's warehouse. Interfaces, 34(4), Gademann, N., & Velde, S. (2005). Order batching to minimize total travel time in a parallel-aisle warehouse. IIE transactions, 37(1), Hall, R. W. (1993). Distance approximations for routing manual pickers in a warehouse. IIE transactions, 25(4), P a g e

54 Jarvis, J. M., & McDowell, E. D. (1991). Optimal product layout in an order picking warehouse. IIE transactions, 23(1), Kappauf, J., Lauterbach, B., & Koch, M. (2012). Logistic Core Operations with SAP: Inventory Management, Warehousing, Transportation, and Compliance. Springer Science & Business Media. Kunder, R., & Gudehus, T. (1975). Mittlere wegzeiten beim eindimensionalen kommissionieren. Zeitschrift für Operations Research, 19(2), B53-B72. Le Duc, T., & de Koster, R. (2005). Travel distance estimation in single-block ABC-storage strategy warehouses. In Distribution Logistics (pp ). Springer Berlin Heidelberg. Pandit, R., & Palekar, U. S. (1993). Response time considerations for optimal warehouse layout design. TRANSACTIONS-AMERICAN SOCIETY OF MECHANICAL ENGINEERS JOURNAL OF ENGINEERING FOR INDUSTRY, 115, Petersen, C. G. (1997). An evaluation of order picking routeing policies. International Journal of Operations & Production Management, 17(11), Ratliff, H. D., & Rosenthal, A. S. (1983). Order-picking in a rectangular warehouse: a solvable case of the traveling salesman problem. Operations Research, 31(3), Roodbergen, K. J., & De Koster, R. (2001). Routing order pickers in a warehouse with a middle aisle. European Journal of Operational Research, 133(1), Stevens, Laura. "Survey Shows Rapid Growth In Online Shopping". The Wall Street Journal. N.p., Web. 24 Feb Tompkins, J. A., White, J. A., Bozer, Y. A., & Tanchoco, J. M. A. (2010). Facilities planning. John Wiley & Sons. Vaughan, T. S. (1999). The effect of warehouse cross aisles on order picking efficiency. International Journal of Production Research, 37(4), P a g e

55 ACKNOWLEDGMENTS This research was partially supported by Veltion S.A. I personally would like to take the opportunity to thank Antonis Gonos, the director from the Research and Development Department of Veltion who provided me with the order data, parameters and warehouse layouts that greatly assisted progress of this research. Additionally, I would like to thank my supervisor, Doctor Francesco Lelli, for the patient guidance, encouragement and advice he has provided throughout this project. 53 P a g e

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57 APPENDIX Appendix 1 As already mentioned, this study assumes that the most optimal route (minimum travel time) is always selected for each order picking tour by using the Steiner Travelling Salesman algorithm that has been solved by various research papers as indicated in chapter two. However, this appendix gives a brief overview of the six main routing methods (including the most optimal one) that are commonly used according to Roodbergen and de Koster. Those can be seen in Figure (19) one and are: S-shape (or traversal), Return, Mid-point, Largest-Gap, Combined and Optimal. Figure 19: Six main routing methods for a single-block warehouse (Roodbergen and de Koster, 2001) The simplest routing method is the S-shape (or traversal) method, according to which the pickers move within the warehouse in the form of the letter S. More specifically, any aisle 55 P a g e

58 containing at least one item to be picked is crossed entirely whereas aisles without picks are not entered. After the last visited aisle, the picker returns back to the starting location. Another relatively simple routing method is the return, where the picker enters and exits each aisle from the same side. Again here, only aisles that contain items to be picked are entered. The mid-point method divides the warehouse horizontally or vertically into two sections. Items in the one half are picked from the first aisle and items in the second half are picked from the second aisle. The picker crosses the second half by either the last or the first aisle to be visited. Hall estimated that the mid-point method gives a substantially lower travel time than the S-shape method when the number of items to be picked in each aisle is relatively small (on average one items per aisle). The largest gap method has many similarities with the midpoint method. In particularly, the picker visits an aisle until he reaches the point at which 2 items within the same aisle have the largest gap, instead of the mid-point. It is obvious that the largest gab method performs better than the mid-point method, since the only difference is that the mid-point is substituted by the largest gab between two adjacent picks, so the travel distance per picking tour decreases. In the case that the largest gap is between two adjacent items, the picker returns from both ends of the aisle. Therefore, the largest gap is defined as the distance that the picker is not crossing within a specific aisle. Lastly, according to the combined and optimal method, aisles with items to be picked are either crossed entirely or entered and left from the same end. Despite that, for every entered aisle, dynamic simulation programs test the travel time and make the routing choice (see Roodbergen and De Koster 2001a). 56 P a g e

59 Appendix 2 This appendix indicates the steps that have been followed to create the six warehouse simulation models. The following figures represent some screenshots of the simulation software ALA ) Setup Warehouse Layout a) Setup Rack bay (dimensions, layers, etc) b) Define Number of picking Locations per layer c) Setup Rack element composed of Rack bays d) Setup Aisles between racking elements e) Setup Rack blocks of Rack elements f) Define Front and Back aisle of block g) Arrange blocks to form the warehouse layout 2) Creation of aisles between racks 3) Creation of path network which the racking elements 4) Creation of working areas (starting area, ending area, carrier area) and connection them to the aisle path network 5) Setup of the collecting (or splitting) jobs 6) Setup of Job Flow (see Figure 22) 7) Setup of Simulation models running parameters (see Figure 10) 8) Setup of Report templates 9) Run Simulation model (see Figure 20 and 21) 10) Analyze and evaluate Simulation results 11) Make Fixes and correct errors 12) Re-run Simulation model and evaluate results 57 P a g e

60 Figure 20: Designing a warehouse simulation with ALA 2000 Figure 21: Running a Warehouse simulation with ALA P a g e

61 Figure 22: Flow Definition Screen 59 P a g e