Master’s Thesis M.G. Pastoor 1
M ASTER
‘ S T HESIS
E XPLORING OPPORTUNITIES OF SUPPLY CHAIN
INTEGRATION IN THE GROCERY INDUSTRY TO
REDUCE TOTAL HANDLING COSTS
Master’s Thesis M.G. Pastoor 2
Master’s Thesis M.G. Pastoor 3
Exploring opportunities of supply chain integration in the grocery industry to
reduce total handling costs by
M.G. Pastoor
Keywords: Distribution Centre, Grocery industry, Handling activities, Handling costs, Handling time, Order-picking, Supermarket, Supply Chain Integration, Stacking, Warehouse.
Abstract: Until now, literature in the field of supply chain and operations management has
not covered the opportunities to reduce total handling cost for the grocery industry.
Therefore, the main objective of this study was to increase its understanding by conducting a case study in a real life distribution center (DC). This is of academic as well as managerial importance because, it increases managerial control and provides opportunities to reduce total handling costs; and contributes to the academic literature. The latter is achieved by proposing several ways of integrating the order-picking activities with handling operations in supermarkets. This is to increase supply chain efficiency and profit by reducing total handling costs. In this research it was investigated whether a redesign of the activities carried out at DCs could reduce the total handling costs in a grocery supply chain and whether this is viable in practice. In this case study, we used a real-life supermarket to investigate the effect of integrating the order-picking activities with the stacking activities in supermarkets on the stacking performance of these supermarkets. By studying this company’s stacking activities in stores and the order-picking activities at the DC by observations, interviews and experiments, this paper will propose several redesigns of the supply chain to reduce the handling costs for supply chains. The outcomes showed that decreased handling time in supermarket stores can be achieved, even when increased expenses for handling time at the DC were applied. This causes great opportunities to emerge for the entire supply chain in terms of costs and provides directions for future research.
Master’s Thesis M.G. Pastoor 4 Master’s thesis
Exploring opportunities of supply chain integration in the grocery industry to reduce total handling costs
EBM766B20
M.G. (Martin) Pastoor
Korreweg 71a, 9714 AC Groningen
m.g.pastoor@student.rug.nl / m.g.pastoor@gmail.com Tel: +31(0)6 30641456
Student number RUG: 1887491
15942 words (excluding tables, references and appendices)
April 4th, 2016
MSc. Technology & Operations Management
Supervisor & C o-assessor: dr. N.D. van Foreest - dr. W.M.C. van Wezel
University
University of Groningen, Faculty of Economics and Business Nettelbosje 2, 9747 AE
Groningen
Tel: +31(0)50 3633741
Master’s Thesis M.G. Pastoor 5
Master’s Thesis M.G. Pastoor 6
Preface
You are about to read my Master’s Thesis for the Master degree Technology & Operations (University of Groningen). The goal of this research was to explore opportunities of supply chain integration (SCI) in the grocery industry to reduce total handling costs. When I was 15, I started working in the supermarket sector, both in various DCs as in several supermarkets. During the summer, a few years ago, I worked at the DC as order- picker/reach truck driver and in the evening I worked as Team Leader in the supermarkets.
Accidentally, my own picked containers were delivered. I thought, if the orders will be picked more efficiently this will save a lot of time in the supermarkets (for me as a Team Leader). The ball started rolling…
It is very interesting to see a DC-Supermarket cooperation in this industry from two perspectives (DC vs. supermarket). During the realization of this Master’s thesis I got to know myself much better because it were periods of ups and downs. In addition, I learned to work hard, to see through and that it is not wrong to sink your teeth in something.
I would like to thank all the staff of the case company for their co-operation in this project.
Especially, I want to thank Frans Popping for explaining the Warehouse Management System (WMS). I also want to thank Resmon Roemahlewang and Coen Tinke for the interesting conversations and for delivering the right documents. Furthermore, I want to thank Hans Mittendorf for facilitating the experiment. Finally, I want to thank my supervisor, Mr. van Foreest, for his useful feedback, insights and patience and Mr. van Wezel for his constructive ideas concerning the experiment.
I hope you enjoy reading it!
Groningen, April 2016 Martin Goos Pastoor
Master’s Thesis M.G. Pastoor 7
Table of Contents
Preface ... 6
List of abbreviations ... 9
1. Introduction ... 10
2. Theoretical background ... 12
2.1 Handling costs in the grocery industry ... 13
2.2 Handling activities in the grocery industry ... 13
2.3 Characteristics of the grocery industry ... 14
2.4 Identifying KPIs regarding to the handling activities ... 15
2.5 Factors that affect KPIs ... 15
2.6 Methods to increase efficiency in general supply chains ... 16
2.7 Redesign activities at the DC ... 17
2.8 Conceptual model ... 21
3. Methodology ... 22
3.1 Research design ... 22
3.2 Data collection ... 23
3.3 Data analysis ... 23
3.4 Validity and reliability ... 24
4. Identification of handling activities in case company ... 24
4.1 Case description ... 24
4.2 Handling activities DC ... 25
4.3 Handling activities supermarket ... 26
4.4 Summary ... 27
5. Performance analysis of the case company ... 27
5.1 Case company’s performance ... 28
5.2 Cause of the current performance ... 29
5.3 Sorting activities ... 31
5.4 Potential cost savings ... 33
5.5 Summary ... 34
6. Redesign of the supply chain to improve the performance ... 34
6.1 Proposition one ... 34
6.2 Proposition two ... 36
6.3 Proposition three ... 39
6.4 Summary ... 44
Master’s Thesis M.G. Pastoor 8
7. Validation in Practice of the proposed redesigns ... 45
7.1 DC objectives ... 45
7.2 Evaluation of propositions ... 46
7.3 Summary ... 53
8. Conclusion ... 53
9. Discussion and limitations ... 54
9.1 Theoretical contribution... 54
9.2 Practical contribution ... 56
9.3 Limitations and future research directions... 56
10. References ... 58
Appendix 1 - Planning used for stacking activities in supermarkets. ... 64
Appendix 2 - Handling sub-activities... 65
Appendix 3 - Order-picking zones ... 66
Appendix 4 - Zone 17 case DC ... 67
Appendix 5 - Flow rate 2015 (case company) ... 68
Appendix 6 - Experiment ... 69
Appendix 7 - Number of shelf meters case supermarket ... 89
Master’s Thesis M.G. Pastoor 9
List of abbreviations
AHP Analytical Hierarchy Process DC Distribution Centre
KPI Key Performance Indicator RC Roll Container Station SCI Supply Chain Integration SCM Supply Chain Management SKU Stock Keeping Unit
TST Total Stacking Time
WMS Warehouse Management System
Master’s Thesis M.G. Pastoor 10
1. Introduction
A large body of literature has discussed the importance of Supply Chain Integration (SCI) between stakeholders in supply chains. However, little is mentioned concerning integration between Distribution Centres (DCs) and supermarkets in the grocery industry.
Parties involved in this grocery industry, characterized by large product volumes and low margins, often have conflicting objectives, such as optimising sales versus limiting product handling times. DCs are trying to limit their product handling time by optimising their storage space allocations of the Stock Keeping Units (SKUs). In contrast, supermarkets aim to optimise their sales by developing an optimal product slot allocations design, also referred to as a planogram (Corstjens and Doyle, 1981; Urban, 1998). In addition, a clear mismatch between picking of orders in DCs and stacking of products in supermarkets can be identified. This mismatch is caused by the difference in design of order-pick lanes at the DC and the layout of the supermarket, both designed according to their own objectives (Broekmeulen, 1998).
Grocery retailers are constantly exploring for efficiency improvements in order to maximize their total supply chain profit (Saghir and Jӧnson, 2001). Scientific literature has extensively discussed ways to reduce the total operational costs in supply chains (Dong et al., 2002; Braglia and Zavanella, 2003; Valentini and Zavanella, 2003 and Bertazzi et al., 2005). These studies mainly focused on the reduction of production, inventory and/or transportation costs, but not the influence of handling costs in retail stores (Kiesmuller and Broekmeulen, 2010). This is remarkable since handling costs at the DC and retail stores comprise 75% of the total operational costs (Broekmeulen et al., 2006;
Saghir and Jönson, 2001) and are at least three times higher than inventory costs in the Dutch retail supply chain (Broekmeulen et al., 2004).
Major opportunities for efficiency improvements in the grocery industry may be achieved by redesigning the activities carried out in DCs. Shifting product handling activities from supermarkets to DCs may reduce the total time required to handle products in the DC- supermarket cooperation. It is beyond dispute that this will result in increased product handling time at DCs. However, closer integration between DCs and supermarkets can reduce the total handling time with a corresponding reduction in total handling costs. Until now, the literature has not paid attention to this opportunity to increase grocery supply
Master’s Thesis M.G. Pastoor 11 chain efficiency and profit by reducing total handling costs. It is therefore relevant to investigate whether the total handling costs in grocery supply chains can be reduced by a redesign of the activities carried out at DCs.
This case study considers a real-life grocery co-operation between a DC and a single supermarket and aims to increase profit margins by reducing total handling costs throughout the DC-Supermarket cooperation. This may be achieved by aligning the way orders are picked in DCs and the way the products are stacked in the supermarkets, thereby reducing the mismatch that was identified previously. This research contributes to the academic literature by proposing several ways of integrating the order-picking activities with handling activities in supermarkets, so to increase supply chain efficiency and profit by reducing total handling costs. It is also of managerial importance since this thesis proposes and evaluates ways to increase the cost-efficiency of grocery retail supply chains in practice. Based on literature research, observations and example calculations in the supply chain under consideration, three redesigns are proposed and evaluated in terms of their practical applicability. This could indicate ways to reduce total handling costs in the grocery industry and thus making a first step to increase the supply chain profit.
This study aims to answer the following research question:
How can we reduce the total handling costs of grocery supply chains?
In order to answer the research question, first the main drivers of the handling costs, and its underlying handling activities, in grocery supply chains need to be identified. Second, existing methods from literature need to be identified in order to improve the performance of supply chains in other sectors. These methods may be applied in the grocery industry as well. Third, the handling activities that are carried out at the case company need to be identified and the performance of these handling activities needs be analysed in order to identify opportunities to reduce the total handling costs of grocery supply chains. Based on these opportunities it was investigated how the handling activities upstream (DC) can be better integrated to the handling activities downstream (supermarket). Several redesigns and their effects on the total handling costs will be proposed and evaluated in attempt to answer to the research question. The following sub-questions are constructed to give an answer to the research question in a structured way:
Master’s Thesis M.G. Pastoor 12 1. What are the main drivers of handling costs and which handling activities can be
identified in a grocery industry case?
2. Which methods can be found in the literature to increase efficiency in a general supply chain?
3. Which handling activities can be identified at the case company and on which position are they performed?
4. Which Key Performance Indicators (KPIs) regarding handling activities can be identified and how does the case company perform regarding to these measures?
5. How could the activities at the DC be redesigned?
6. Which were the strengths and weaknesses of the proposed redesigns and what are their effects on the total handling costs?
The remainder of this paper is structured as follows. Chapter 2 provides a theoretical framework that serves as the background. Chapter 3 discusses the methodology used and chapter 4 describes the current processes and characteristics of the case DC-supermarket cooperation. Chapter 5 describes how the case DC-Supermarket cooperation currently performs and how this could be improved. Chapter 6 proposes three ways to reduce total handling costs of the DC-supermarket cooperation. Chapter 7 presents the results of the validation of the proposed redesigns of the supply chain and discusses their viability in practice. Chapter 8 gives a conclusion. Finally, chapter 9 discusses the theoretical and practical contributions, limitations and elaborates future research directions.
2. Theoretical background
This section presents theories that serve as background for exploring opportunities to reduce the total handling costs of grocery supply chains. Section 2.1 presents the handling costs in the grocery industry. Section 2.2 describes the handling activities in the grocery industry. Section 2.3 describes the characteristics of the grocery industry. Section 2.4 identifies KPIs regarding the handling activities. Section 2.5 identifies factors that affect the KPIs. Section 2.6 provides general theories about Supply Chain Management (SCM) and introduces SCI and section 2.7 presents opportunities for SCI by redesigning activities at the DC. Finally, based on the previous theories and opportunities, section 2.8 presents a conceptual model that will be used as a guide for the remainder of this paper.
Master’s Thesis M.G. Pastoor 13 2.1 Handling costs in the grocery industry
The handling activities are very important in the total operational costs made in a supply chain that includes a retailer’s DC and a retail store (Figure 1) (Broekmeulen et al., 2006).
Saghir and Jönson (2001) stated that handling activities and therefore costs at the end of the supply chain dominate and that 75% of the handling costs of the retail chain occur in the retail store. Saghir and Jönson (2001) concluded that a reduction of one second of handling time in a supermarket store would represent a cost reduction of five million euro in the Swedish grocery industry.
Figure 1: Distribution of total operational costs made in a typical supply chain in the grocery industry (adopted from van Zelst et. al 2009)
2.2 Handling activities in the grocery industry
A colli is a packaging unit in which similar products are packed. Zone stacking is used in the supermarket of the case company because clerks are responsible for stacking the incoming collis for a specific stacking zone in the supermarket (Appendix 1). A stacking zone is an aisle in a supermarket in which different product categories are located. Zone stacking assumes that when the containers are delivered to the store the collis in containers are already sorted per stacking zone (van Zelst, 2009). So when we refer to sorting in this paper, we mean that the collis are sorted per stacking zone.
2.2.1 Stacking activities
Shelf stacking represents the daily process of manually refilling the shelves with products from new incoming deliveries. For most retailers, handling activities are labour-intensive and are often very costly. Handling activities in a supermarket are carried out by clerks who are stacking the product shelves. The stacking method used in supermarkets is called
‘zone stacking’. This means that each clerk is assigned to a specific zone in the supermarket, in which he has to stack the collis. This is to reduce the travel time in supermarkets to keep the handling time in supermarkets as low as possible (van Zelst et
Master’s Thesis M.G. Pastoor 14 al., 2009). Van Zelst et al. (2009) investigated that we can distinguish between several handling activities of the shelf stacking process in supermarkets which are the following sub-activities:
1. Grab and unpack the case pack
2. Search for the assigned location on the shelf 3. Travel to the shelf
4. Prepare the location on shelf for stacking
5. Check the shelf life of the inventory on the shelf 6. Put the new inventory on the shelf
7. Put the old inventory back on the shelf 8. Dispose the waste
9. Other (helping customers, customer is blocking the aisle etc.) Appendix 2 describes these activities in more detail.
2.2.2 Order-picking activities
De Koster et al. (2007:7) stated that order-picking is the most labour-intensive operation in warehouses with manual systems and defined order-picking as “the process of retrieving products from storage (or buffer areas) in response to a specific customer request’’. Order-picking is the most important handling activity at DCs. More about the design of an order-pick system will be described in section 2.7.
2.3 Characteristics of the grocery industry
An order line is a unique product or SKU that needs to be picked by an order-picker in a certain quantity (de Koster et al., 2007). The grocery industry is characterised by customer orders consisting of large amounts of order lines and fixed departure times of trucks (de Koster et al., 2007). For the sake of order control, customer orders are split into multiple pick-orders.
A clear mismatch between the ways orders are picked in DCs and the ways the products are stacked in the supermarkets can be identified. This mismatch is caused by the divergence in design of order-pick lanes at the DC and the layout of the supermarkets, both designed according to their own objectives (Broekmeulen, 1998). DCs are trying to limit their product handling time by optimising their storage space allocations of the SKUs. In contrast, retail stores aim to optimise their sales by developing an optimal
Master’s Thesis M.G. Pastoor 15 product slot allocations design, also referred to as a planogram (Corstjens and Doyle, 1981; Urban, 1998).
2.4 Identifying KPIs regarding to the handling activities
Saghir and Jönson (2001) indicated the importance of the handling activities in the retail store (Section 2.1). A KPI identified regarding handling activities in retail stores is the Total Stacking Time (TST) per order line (Curseu et al., 2009). The TST is a KPI, which measures the time to stack one individual SKU (Section 2.2.1). By measuring the TST, Curseu et al. (2009) were able to identify the factors that drive the shelf stacking time.
2.5 Factors that affect KPIs
Several factors which affects the TST can be identified in literature. These factors are described below in order to identify causes and effects of time variation during the handling activities in supermarket stores.
The first factor that can affect the TST identified in literature is the weight or type of packaging. Van Zelst et al. (2009) stated that when the number of different types of packages is increased, more time is required for stacking. Van Zelst et al. (2009) concluded that the TST for different product categories varied between 35.47 seconds (products for personal care) and 80.86 seconds (coffee milk).
The second factor is the average number of customers (environment) who are present in the store during stacking (Curseu et al., 2009). This affects the amount of customer interruptions and customers that are blocking the aisle.
The third factor is the personal characteristics of the workers should not be neglected and include the motivation and experience of the clerks (Van Zelst, 2009). When the employees have more experience and/or motivation this may help to improve the their working pace (Van Zelst, 2009).
The fourth factor, the impact of supervision on the productivity is discussed broadly.
Haenisch (2012) investigated which factors affect the productivity of government workers and found that poor supervision negatively affects the productivity of workers and leads to decreased employee effort (Hinkin and Tracey, 2000). Omisore (2014) ascertained factors that affect employees’ productivity and stated that when supervisors are uninvolved or unavailable, employees have no one to turn to for direction or guidance which contribute to low productivity. These findings can be explained by the Hawthorne
Master’s Thesis M.G. Pastoor 16 effect. The Hawthorne effect states that attention to employees has a substantial impact on productivity (Levitt and List, 2009) because people have the tendency to work harder and perform better when they are aware that they are being observed.
Finally, other factors that affect the TST are the inventory replenishment rule, assortment and the amount of shelf space. In the grocery industry the Last In First Out (LIFO) and First In First Out (FIFO) replenishment rules are mostly used in the literature (Broekmeulen and van Donselaar, 2007). In order to the assortment, most grocery retailers focus on products that are delivered in pre-packed form but presented to the final consumer in individual units. This is typical for a large part of the assortment of most retailers (Curseu et al., 2009). Logically the amount of shelf space differs per situation.
2.6 Methods to increase efficiency in general supply chains 2.6.1 SCM
Retail management literature does not seem to recognize the importance of the supply chain (van der Vlist, 2007). Fernie et al. (2000) stated that literature about retail management ignore the supply chain. This is surprising because the key to success in retailing is the ability to meet customers’ needs and coordinate the logistics to get products to the shelf as efficiently as possible. For this reason, we can argue that SCM is of great importance for a grocery supply chain. Mentzer et al., (2001:18) defined SCM as ‘’the systemic, strategic coordination of the traditional business functions and the tactics across these business functions within a particular company and across businesses within the supply chain, for the purposes of improving the long-term performance of the individual companies and the supply chain as a whole.’’ Regarding this definition, a conceptual model about how SCM works is showed in Figure 2. A supply chain can be pictured as a pipeline. Flows of products, services, financial resources, the information associated with these flows, and the informational flows of demand and forecasts etc. can be recognized in Figure 2 (Mentzer et al., 2001). Business functions such as marketing, sales, research and development, forecasting, production, procurement, logistics, information technology, finance, and customer service manage these flows from the suppliers through the customers in order to ultimately provide value and satisfy the customer (Mentzer et al., 2001). To achieve lower costs, increased customer value and competitive advantage it is necessary to coordinate and integrate these flows intensively (De Vries and Huijsman, 2011; Skjott-Larsen et al., 2007).
Master’s Thesis M.G. Pastoor 17
Figure 2: Conceptual model of SCM (from Mentzer et al., 2001)
2.6.2 SCI
An opportunity to reduce operational costs in supply chains is SCI. Flynn et al. (2010:177) defined SCI as ‘’the degree to which a manufacturer strategically collaborates with its supply chain partners and collaboratively manages intra- and inter-organization processes’’. To reduce the operational costs of supply chains in the grocery industry, a SCI focus is needed because it is important that the DC and the supermarkets integrate processes to improve the performance (decrease the total costs) of the holistic supply chain (JrJung et al., 2010). When DCs and retailers collaborate this may improve the performance of the supply chain (Simatupang and Sridharan, 2005; Singh and Power, 2009). Research has shown that collaborative and aligned operations between suppliers and retailers will improve replenishment, forecasting, and planning in terms of total costs for the entire supply chain (JrJung et al., 2010).
2.7 Redesign activities at the DC
Factors that influence the way in which customer orders are picked at DCs are partly hard to change. These include the chosen storage systems (racks), the layout, the order-picking system. However, parts that are more easily changeable, such as the storage strategy (the storage location determination), the sequence by which items are collected from storage locations (routing strategy) and the possible clustering of customer orders in a single order-picking route (batching) (de Koster and van der Poort, 1998). The extensive literature review by de Koster et al. (2007) on typical decision problems in design and
Master’s Thesis M.G. Pastoor 18 control of manual order-picking processes will be used to indicate which opportunities are available for closer SCI in grocery supply chains. The aim is to align the order-picking activities to the stacking activities in stores. De Koster et al. (2007) described the following objectives of warehouse design and optimization which may be important:
minimising the average travel distance
minimising the total cost
minimise the overall throughput time (e.g. to complete a batch of orders)
maximise the use of space
maximise the use of equipment
maximise the use of labour
maximise the accessibility to all items
In the following sections, typical order-picking systems, lay-outs of order-picking systems, storage strategies and possible routing methods will be discussed. For an extensive description of storage systems (racks), the reader is referred to de Koster et al.
(2007) because these seems not of great importance for this research.
2.7.1 Order-pick design
Decisions in the design of an order-picking system involve the processes of clustering and scheduling customer orders, assigning stock on locations to order lines, releasing orders to the floor, picking the articles from storage locations and the disposal distribution of the picked articles (de Koster et al., 2007). Many different order-picking system types can be found in DCs, which can be distinguished based on human or automated handling (Figure 3, de Koster et al. (2007)).
In the grocery industry, humans are employed to pick the orders in the majority of warehouses. Therefore, the most common order-picking methods employed here include picker-to-part, put system and parts-to-picker (Figure 3). In a picker-to-parts system, the order-picker walks or drives along the aisles to pick the items (de Koster et al., 2007). In a put system, items have to be retrieved first. When the items are retrieved, they are offered to order-picker who distributes them over customer orders (de Koster et al., 2007). In a parts-to-picker system, automated storage and retrieval systems retrieve items from storage locations and transport them to a depot where the order-picker can pick the
Master’s Thesis M.G. Pastoor 19 required number of items. For a more extensive description of order-picking methods the reader is referred to de Koster et al. (2007).
2.7.2 Order-picking lay-out
De Koster et al. (2007) stated that in designing the layout for order-picking, two main sub- problems arise: the layout of the facility containing the order-picking system (facility layout problem) and the layout within the order-picking system (internal lay-out design).
The facility layout problem concerns with deciding where to locate various departments (receiving, picking, storage, sorting, and shipping, etc.) to minimise handling costs (de Koster et al., 2007). The internal lay-out design problem concerns the determination of the number of blocks, and the number, length and width of aisles in each block of a picking area (de Koster et al., 2007). The most important objective of an appropriate warehouse lay-out is to minimise the total travel distance.
2.7.3 Storage assignment methods
De Koster et al (2007:10) defined a storage assignment method as ‘’a set of rules which can be used to assign products to storage locations’’ and described several storage assignment methods (Table 1). For an extensive description of storage assignment methods the reader is referred to de Koster et al. (2007).
Figure 3: Distinction in order-picking methods (from de Koster et al. 2007)
Master’s Thesis M.G. Pastoor 20
Storage assignment method
Description
Random storage Every incoming pallet is assigned a location in the warehouse that is selected randomly from all eligible empty locations with equal probability.
Closest open location storage
The first empty location that is encountered by the employee will be used to store the pallet.
Dedicated storage Storing each pallet at a fixed location.
Full turnover storage
Distributing pallets over the storage area according to their turnover.
Class based storage A combination of above described methods.
Table 1: Storage assignment methods
2.7.3.1 Family grouping
De Koster et al. (2007) mentioned that the storage assignment methods (Table 1) did not entail possible relations between products. When customers buy certain product often in combination with another product (for example pasta and pasta sauce) it may be interesting to locate these products close to each other at the DC. This is called family grouping and can be used in combination with the storage assignment methods described in Table 1.
2.7.4 Routing
The objective of routing policies is to deliberately sequence the items on the pick list to ensure a good route through the warehouse (de Koster et al., 2007). Several methods for routing order-pickers in warehouses are proposed in literature (Table 2) (Hall, 1993;
Petersen, 1997; Roodbergen, 2001).
Routing heuristic
Description
S-shape Any aisle containing at least one pick is traversed entirely. Aisles without pick are not entered. From the last visited aisle, the order-picker returns to the depot.
Return method
Order-picker enters and leaves each aisle from the same end and only aisles with picks are visited.
Mid-point This method divides the warehouse into two areas. Picks in the front half are accessed from the front cross-aisle and picks in the back half are accesses from the back cross aisle. The order-picker traverses the back half by either the last or the first aisle to be visited.
Largest gap
Similar to the midpoint strategy except that an order-picker enters an as far as the largest gap within an aisle, instead of the midpoint.
Combined Aisles with picks are either entirely traversed or entered and left at the same end.
Optimal The optimal routing heuristic is a combination of above described heuristics.
Table 2: Routing heuristics
Master’s Thesis M.G. Pastoor 21 2.7.5 Batching
De Koster et al. (2007:15) defined order batching as ‘’the method of grouping a set of orders into a number of sub-sets, each of which can then be retrieved by a single picking tour’’. A pick tour is a route that an order-picker has to travel at the DC to pick all collis of an order. In a colli multiple products of an unique item are packed together. Sorting the items per order can happen during picking (‘sortwhile-pick’) or afterwards (‘pick-and- sort’), the latter often via a sorting machine (de Koster and van der Poort, 1998). Orders are batched based on storage locations of other orders (proximity batching) or arrival (time window batching). For an extensive review about order batching, the reader is referred to de Koster et al. (2007).
2.8 Conceptual model
In order to answer sub-question one and two, in this chapter the main drivers of handling costs and the handling activities have been identified in a grocery industry case. In addition, methods to increase efficiency in general supply chains have been identified in the literature.
The degree of integration between the order-picking activities at the DC and the stacking activities in supermarkets affects the total handling costs. When these activities can be performed in a more efficient way, this will affect the total handling costs in a positive way. Logically, shifting product handling activities from supermarkets to DCs may reduce the total time required to handle products in supermarkets (1 (+), see Figure 4). It is beyond dispute that this will cause increased product handling time at DCs (2 (-), see Figure 4), which means increase handling costs for the supply chain (3 (-), see Figure 4). However, closer integration between DCs and supermarkets may reduce the total handling time required within the DC-Supermarket cooperation, which can cause a reduction in total handling costs. This can be substantiated by Saghir and Jönson (2001) who stated that handling activities, and therefore costs, at the end of the supply chain dominate so that reduction of handling time in retail stores can result in great cost savings for the grocery supply chain (4 (++), see Figure 4). This conceptual model will be used as a guide to explore ways in which the DC can be redesigned.
Master’s Thesis M.G. Pastoor 22
Figure 4: Conceptual model
3. Methodology
3.1 Research design
The aim of this research is to indicate ways in which the total handling costs of supply chains in the grocery industry can be reduced by proposing a redesign of activities carried out at a DC and closer integration of DCs and supermarkets. In order to investigate these opportunities, we use a real-life DC-supermarket cooperation as a case example. By studying this cooperation’s stacking activities in supermarket and the order-picking activities at the DC by observations, interviews and experiments, this paper will propose several redesigns at DCs to reduce the total handling costs.
Due to the explorative nature of the research question, an explorative single-case study was conducted. Case studies have consistently been one of the most powerful research methods in operations management (Voss et al., 2002) and this method is appropriate for completely new, exploratory investigations (Meredith, 1998). Moreover, case studies can lead to new and creative insights (Voss et al., 2002), which are required to satisfy the aim of the research question. Because multiple means of data collection enhances triangulation, validity of this case study is ensured (Voss et al., 2002). Nevertheless, several challenges in conducting case study research must be noted: “it is time consuming, it needs skilled interviewers, care is needed in drawing generalizable conclusions from a limited set of cases and in ensuring rigorous research (Voss et al., 2002:195).”
Master’s Thesis M.G. Pastoor 23 3.2 Data collection
First, data were collected by a single researcher, working for the case company both as an order-picker at the DC and as a Team Leader in the supermarket. This provided valuable knowledge and understanding of the activities carried out in the DC as well as in the supermarket. Second, this provided access to documents and computer files. Thirdly, different information sources were used during this research, including quantitative measurements, interviews, observations, and informal conversations. Using different information sources enhanced data triangulation (Karsson, 2009). In order to acquire more detailed insight in the case company and identifying potential problems, open-ended interviews were conducted with stakeholders. The stakeholders in this paper are the Production Planner (DC), Supply Chain Manager (DC), Production Managers (DC), Team Leaders (supermarket), Supermarket Manager (supermarket) and the Department Manager (supermarket). In order to get more insight in the information gathered from the interviews, observations and informal conversations gathered during working hours provided extra insights. Findings obtained from observations and informal conversations were noted on a clipboard for later use. Moreover, a lot of secondary data about processes and performance were available on the intranet of both the supermarket and the DC. To answer sub-question 6, three redesigns were presented to production stakeholders (DC) and afterwards individual open-ended interviews about these redesigns were conducted to evaluate the practical ability of the proposed designs. In addition, the pros and cons that arose from the literature, quantitative measurements, interviews, observations, and informal conversations were also taken into account.
3.3 Data analysis
Important findings of the first interviews were summarized and discussed individually at a later time for verification. The findings of the interviews were combined with the quantitative measurements, direct observations and informal conversations. From the findings, solely key points were noted in order to reduce the amount of data. From these key points three ways to redesign the activities carried out in DCs were proposed to the stakeholders of the DC. The outcomes of the evaluation and pros and cons that arose from literature research, observations and example calculations for the redesigns are assessed on positive, negative or neutral and for each of the redesigns depicted in a matrix for clear overview.
Master’s Thesis M.G. Pastoor 24 3.4 Validity and reliability
When doing case research, four main quality criteria have to be considered: construct validity, internal validity, external validity and reliability (Yin, 2009). Construct validity is the extent to which correct operational measures for the concepts being studied are established (Karlsson, 2009). In this research the construct validity will be guaranteed by using multiple sources (personal execution of the work, quantitative measurements, interviews, observations, and informal conversations). Internal validity describes to what extent the research findings provide information about causality (Pelham and Blanton, 2007). In this research the internal validity will be guaranteed by using pattern matching.
This means that empirical data that is derived will constantly be compared to theory (Eisenhardt, 1989). The external validity is the extent to which the results are generalizable to other situations. Because of the short time span, the external validity for this research cannot be completely guaranteed because only the case company will be studied.
Reliability is the extent to which a study’s operations can be repeated, with the same results (Yin, 2009). A case study database will be used to ensure reliability (Voss et al., 2002).
4. Identification of handling activities in case company
This chapter will give a description of the case company and will identify current processes and characteristics of the case supply chain. According to Figure 1, the handling activities in a DC-retail store supply chain are the most important activities that affect the total operational costs. Therefore the handling activities at the case DC and supermarket will be identified.
In this chapter the following sub-question will be answered:
3. Which handling activities can be identified at the case company and on which position are they performed?
4.1 Case description
This research is performed at Jumbo Supermarkten (Jumbo) located in the Netherlands.
Jumbo has grown rapidly in recent years. The last major acquisition was that of C1000 in 2012, making Jumbo the second largest supermarket in the Netherlands. In 2014, Jumbo had a turnover of €4,823 million and a market share of 14%. At this time, Jumbo consists of 500 supermarkets in the Netherlands with an average floor space of 1600 square meters
Master’s Thesis M.G. Pastoor 25 each. The assortment consists of 32.000 different articles and Jumbo employs more than 30,000 people. Jumbo's headquarter is located in Veghel. In addition, Jumbo has six DCs in Beilen, Breda, Den Bosch, Elst, Veghel and Woerden. DC Beilen and supermarket Hoogeveen are included in this case study. DC Beilen supplies in total 142 supermarkets.
4.2 Handling activities DC
At the DC an order-pick-truck which fits 4 containers and a voice-picking system are used for order-picking.
The order-picking activities can be divided into the following main-activities with corresponding sub-activities described from the order-picker’s perspective:
Preparing a route
1. Request order via the voice-picking system.
2. Retrieve required number of containers at the roll container station (RC) and put the containers on the order-pick truck.
3. Pick up shipping labels at the printer, for each container two labels.
4. Drive with order-pick-truck to first pick location.
Picking the collis
5. Voice-picking system instructs from which location in a zone and how many collis from that location need to be picked.
6. After the last picked colli the shipping labels need to be attached to the containers.
Transport containers to dock.
7. Driving to the dock (described on the shipping label) assigned to a specific supermarket.
8. Confirm via voice-picking system the number of containers used.
Then the cycle starts all over again (Figure 5).
Master’s Thesis M.G. Pastoor 26
Figure 5: Order-pick cycle
4.2.1 Order-pick layout
The DC can be identified as a DC that only employs humans according to the criteria in chapter 2. It is a picker-to-parts system because the order-picker walks or drives along the aisles to pick items (de Koster et al., 2007). The DC is divided into zones out of which the picker does not pass to other zones, which is called synchronised zoning (de Koster et al., 2007). Zoning is used because products with the same size and weight can easily be stacked in containers and containers are very stable (data from interviews stakeholders).
Because of this the amount of broken collis during transport to the supermarkets will be minimized. The order-picking method described as wave-picking means that pick-orders for a common destination are released simultaneously for picking in multiple warehouse areas (de Koster et al., 2007). The batch-size of a wave is 5 which means that orders for 5 supermarkets are released simultaneously. The S-shaped routing method is used which means that any aisle containing at least one pick is traversed entirely (except potentially the last visited aisle) and aisles without picks are not entered (de Koster et al., 2007).
4.3 Handling activities supermarket
In this section the handling activities of the case supermarket will be described. The handling activities can be divided sorting and stacking activities.
4.3.1 Sorting activities
The stacking method used in supermarkets is called “zone stacking’’ (Section 2.3). The collis on the delivered containers are sorted by the Team Leaders per stacking zone in the
Picking the collis
Transporting containers to
dock Preparing a
route
Master’s Thesis M.G. Pastoor 27 supermarket, with each aisle of the supermarket functioning as a stacking zone (Appendix 1).
4.3.2 Stacking activities
The stacking activities in the supermarket are carried out as described by van Zelst et al.
(2009) in section 2.2.1. For a more extensive description of the sub-activities of the stacking activities the reader is referred to Appendix 2.
4.4 Summary
To answer sub-question three, the handling activities and its location in the supply chain have been identified Figure 6. The handling activities can be categorized in order-picking, sorting and stacking activities. “Start’’ can be seen as a supermarket order that is placed and “end’’ as a supermarket order that is stacked on the shelves.
5. Performance analysis of the case company
To gain insight in the current performance of the case company, the current handling activities in the case supermarket were analysed. Regarding the outcomes of this analysis, improvements can be made. Analysis of the current handling activities in the supermarket demonstrated that the performance can be improved to reduce the total handling costs. In order to give a structured analysis of the performance of the supply chain, the following sub-question have been taken into account:
Figure 6: Handling activitites in supply chain
Master’s Thesis M.G. Pastoor 28 4. Which Key Performance Indicators (KPIs) regarding handling activities can be identified and how does the case company perform regarding these measures?
5.1 Case company’s performance
Because the handling costs in supermarkets are the most important cost factor in retail grocery supply chains (chapter 2), interviews with stakeholders (supermarket) were conducted to identify problems. The following KPIs used in the case supermarket are identified and will be used to investigate the identified problems:
Norm time; the maximum given time from when the first colli enters the shop floor till the last colli of the delivery is stacked on the shelve (Curseu et al., 2009) (used to evaluate the stacking time).
Stacking time; the measured time in which the first colli enters the shopfloor, till the last colli of the delivery is stacked on the shelve (Curseu et al., 2009), part of the handling time.
Sorting time; the measured time in which all the collis are sorted per aisle, part of the handling time.
Handling time; stacking time + sorting time
In section 2.4 the TST has been identified. The KPI ‘’stacking time’’ used in supermarkets is comparable to the TST. The difference is that the TST measures the stacking time of one order line and the stacking time used in supermarket measures the stacking time of all the delivered order lines that need to be stacked on a stacking evening. The stakeholders (supermarket) confirmed that the norm time is a viable norm to evaluate the performance of the clerks because an it was created by an external company and validated to be viable.
The Team Leaders indicated that the norm times of the stacking activities are not met. The supermarket KPIs will be observed to confirm these statements. Observation is a useful method to check how much time is spent on various activities by the clerks (Schmuck, 1997). The stacking activities are observed during 3 weeks on different days in the case supermarket (Table 3).
Master’s Thesis M.G. Pastoor 29
Observed day Number of collis Norm time Stacking time Sorting time
Friday week 47 2548 36:48:00 38:00:00 2:30:00
Wednesday week 47 524 7:26:00 8:45:00 1:00:00
Tuesday week 48 1831 24:32:00 27:15:00 2:00:00
Friday week 48 3325 41:22:00 42:30:00 3:30:00
Tuesday week 49 1575 26:31:00 28:00:00 1:45:00
Friday week 49 3002 40:07:00 43:10:00 3:10:00
Table 3: Outcomes observation stacking activities
Indeed, the observations confirm that the norm times are not met. It also clarified that sorting takes 2 hours and 19 minutes on average. The Team Leaders are working from 17:00–21:00 which means that on average half of their working hours are spent on sorting the collis in containers per stacking zone (Figure 7). In the next section the cause of the performance will be identified.
Figure 7: Performance analysis
5.2 Cause of the current performance
In section 2.5 several factors which affect the TST are described. To identify a substantiated cause for the stacking times exceeding the norm times (time variation), these factors will be discussed:
First, the weight or type of packaging. This factor is already included in the norm time.
To understate this, the norm times of two different product groups used by the case company are compared. The norm time for stacking 1 colli soft drinks is 40 seconds (1:28/132 colli, Appendix 1) and the norm time for stacking 1 colli of beer is 22 seconds (0:46/128 colli, Appendix 1). Because of the fact that the weight or type of packaging is
Master’s Thesis M.G. Pastoor 30 already included in the norm time this will not be seen as a cause for the fact that the stacking time is exceeding the norm time.
Second, the average number of customers (environment) which are present in the supermarket during stacking (Curseu et al., 2009). This factor is already included in the norm time and is mentioned on the planning (‘’gebasseerd op vullen in de avond’’, Appendix 1).
Third, the effect of the worker. Because of time constraints it is assumed that the clerks have the capacities and are motivated and are able to stack the shelves within the norm time.
Fourth, the impact of supervision on the productivity. Regarding the observations in the supermarket products on containers delivered are not sorted per stacking zone as in zone stacking is assumed (Section 2.2.1). Delivered containers need to be sorted per stacking zone by the Team Leader after delivery. The Team Leaders indicated that the sorting activities are time consuming and during the sorting activities the Team Leaders have no overview on the stacking activities of the clerks. This was confirmed by observations. The Team Leaders indicated that when supervisors are uninvolved and unavailable during the first stacking hour (startup) a lot of productivity is lost because (social) interaction is occurring when retrieving necessary tools. When tools are not available clerks have no one to turn to for direction or guidance. This effect is known from literature (Section 2.5) and will therefore not be further investigated.
It is also broadly investigated in the literature (Section 2.5) that productivity is enhanced when Team Leaders are present because the clerks are aware they are being observed.
There is a match in patterns between the observed data (less supervision because of the sorting activities) and the theory (less supervision affects productivity in a negative way) and therefore it can be assumed that the degree of supervision is a sufficient explanation for the fact that the stacking time is exceeding the norm time.
Finally, other factors that affect the TST are inventory replenishment rule, assortment and the amount of shelf space. As mentioned by the stakeholders (supermarket) it can be assumed that these factors are included in the norm time because these factors have not changed since creation of norm time by the external company.
Master’s Thesis M.G. Pastoor 31 5.3 Sorting activities
The identified cause of the current performance is that products in containers delivered are not sorted per stacking zone as in zone stacking is assumed. Team Leaders need to sort the containers per stacking zone after delivery. This is at the expense of the supervision in the supermarket and can be seen as a substantiated cause for the lacking performance.
To validate that sorting is necessary, the different product categories in a zone at the DC will be compared to the stacking zones of the supermarket. The different zones at the DC can be seen in floor map in Appendix 3. To identify the different stacking zones in the case supermarket the researcher walked through the supermarket and wrote down on a clipboard, the product categories in an aisle. The stacking zones are shown in Table 4. The stacking zones in the case supermarket will be compared to zone 17 (aisle 142 – 155) at the DC (Appendix 4) because the wine, canned vegetables, candies and chocolate are the most time consuming product categories in order to sort (stakeholders, supermarket).
Appendix 4 shows which product groups are meant with the numbers. For the case supermarket the products that are part of zone 17 at the DC are part of 6 different stacking zones at the case supermarket (Table 4).
I. Cleaners, detergents, laundry detergents (Stacking zone 4) II. Pastas, rice, international, ethno (Stacking zone 8)
III. Soup, canned fruit/vegetables (Stacking zone 7 & 8) IV. Wine (Stacking zone 7)
V. Pet food ( Stacking zone 4)
VI. Baking agent, eggs, UHT milk (Stacking zone 1 & 2) VII. Candy and chocolate (Stacking zone 3)
In addition, the researcher observed (by visiting) also the stacking zones in other supermarkets that are supplied by the case DC (Table 4). Regarding these observations it can be concluded that the planograms of the supermarkets differ.
Master’s Thesis M.G. Pastoor 32
Supermarket
Stacking zone
Hoogeveen Beilen Zuidwolde Groningen
(Wilhelminakade)
Groningen (Oosterstraat)
1 Coffee, Tea, Sugar, Eggs, Cookies, Cookies pre-packaged, Snacks, Baking agents
International, Pastas, Rice, Sours , Canned fish
International, Pastas, Rice, Soup, Canned meat
Coffee, Tea, Sugar, Cookies, Cookies pre-packaged, Snacks, Gluten free
Gluten free, Cereals, Bread substitutes
2 Soft drinks, UHT milk Canned vegetables, Sauces/sours, Eggs, Baking agents
Canned vegetables , Canned fish, Sauces/sours
UHT milk, Cereals, Canned meat, Canned fish, Baking agents
Coffee, Tea, Sugar, Cookies, Cookies pre- packaged, Snacks, Baking agents
3 Candies, Chocolate,
Bread substitutes, Spreads, Cereals
Beer, Nuts, Toasts, Canned meat
Chips, Wine, Candies, Chocolate
Eggs, Spreads, Bread substitutes
Pastas, International, Rice, Soup, Canned fish, Canned meat, Canned fruit/vegetables
4 Pet food, Cleaners, Detergent, Laundry detergent
Chips, Wine, Candies, Chocolate
Soft drinks, Beer Pet food, Non-food, Papers Nuts, Sauces/sours, Wine, Pet food, Non- food, Papers, Detergent, Laundry detergent
5 Ladies bandage,
Personal hygiene, Baby food, Baby care, Baby diapers, Non-food, Papers
Soft drinks Papers, Ladies bandage, Personal Hygiene, Non- food
Ladies bandage, personal hygiene, Baby food, Baby care, Baby diapers, Cleaners, Detergent, Laundry detergent
Candy’s, Chocolate, Soft drinks, Eggs
6 Beer, Chips, Nuts,
Toasts
Ladies bandage, Personal hygiene, Baby food, Baby care, Baby diapers, Gluten free
Pet food,
Cleaners, Detergent, Laundry detergent
Soft drinks, Candies, Chocolate
Chips
7 Canned fruit/vegetables, Wine, Sauces, Sours, Canned fish
Cleaners, Detergent, Laundry detergent, Non-food, Papers, Pet food
Coffee, Tea, Sugar, Cookies, Cookies pre- packaged
Beer, Chips, Wine, Nuts, Toasts
Beer
8 International, Pastas, Rice, Soup, Canned meat, Soup
Coffee, Tea, Sugar, Eggs, Cookies,
Cookies pre-
packaged , Snacks
Eggs, Baking agents, UHT milk, Bread substitutes, Cereals
International, Pastas, Rice, Canned fruits/vegetables, Sauces/sours, Soup
Baby food, Baby care, Baby diapers, Ladies bandage, Personal hygiene
9 Gluten free, Oils UHT milk, Bread
substitutes
Bread substitutes, Snacks, Gluten free
UHT milk
10 Cereals, Spreads
Table 4: Stacking zones
Master’s Thesis M.G. Pastoor 33 5.4 Potential cost savings
Suppose that the collis on containers will be delivered sorted per stacking zone as in zone stacking is assumed (van Zelst, 2009). Regarding section 5.2, it is plausible that the norm times will be met because the Team Leaders can fully focus on managing the clerks and the productivity of the clerks will increase because of the increased supervision. The potential cost savings of meeting the norm times compared to the current situation can be seen in Table 5 and is in total €263.000 per year.
Description Value
Average extra stacking time per colli (sec) 3,0 Number of collis delivered to supermarkets (2015) 37.500.000 Total extra stacking time (hours) 32.000 Cost per hour of a clerk (22 year) € 8,22 Total costs of extra stacking time € 263.00
Table 5: Potential cost savings stacking
This is calculated as follows: the total extra stacking time compared to the norm time of the observed days is 10 hours and 54 minutes and the total number of collis stacked is 12805 (Table 6).
Observed day Number of collis Norm time Stacking time Extra stacking time used compared to norm time
Friday week 47 2548 36:48:00 38:00:00 1:12:00
Wednesday week 47 524 7:26:00 8:45:00 1:19:00
Tuesday week 48 1831 24:32:00 27:15:00 2:43:00
Friday week 48 3325 41:22:00 42:30:00 1:08:00
Tuesday week 49 1575 26:31:00 28:00:00 1:29:00
Friday week 49 3002 40:07:00 43:10:00 3:03:00
12805 10:54:00
Table 6: Extra stacking time used compared to norm time
The average extra stacking time per colli can be calculated and is rounded 3 seconds (10:54:00/12805). Over 2015, in total 37.500.000 (rounded) collis from the case DC to the supermarkets (142 supermarkets) are delivered and stacked (Appendix 5). It is assumed that all the delivered collis are stacked. The total extra stacking time of these collis (hours) in 2015 is 32.000 (rounded) hours (Table 6). The average age of a clerk is determined on 22 years (€8,22/hour1). When the total extra stacking time (hours) of all the 142 supermarkets will be multiplied by the wage of an overage clerk (€8,22/hour) the total costs of extra stacking time will be obtained and is €263.000 (Table 5). So when the norm
1 http://www.betaaldwerk.nl/banen-in-beeld/detailhandel/4-vakkenvuller-of-vulploegmedewerker
Master’s Thesis M.G. Pastoor 34 times will be met, the potential cost savings for the 142 supermarkets supplied by the case DC are €263.000 per year.
5.5 Summary
In order to answer sub-question four, the KPIs regarding handling activities have been identified and the performance regarding to this measures has been analysed. The norm time, stacking time and the sorting time of the handling activities in supermarkets are identified as the KPIs regarding the handling activities in supermarkets. The stacking time is always exceeding the norm time, which is assumed to be due the sorting activities. The DC does not deliver collis on containers that are already sorted per stacking zone of the supermarket and therefore the containers need to be sorted per stacking zone. When the DC is able to deliver containers to supermarkets as assumed in zone stacking, this can save the case company €263.000 per year resulting in a reduction of the total handling costs.
6. Redesign of the supply chain to improve the performance
In this chapter three ways to reduce the total handling costs of the supply chain will be proposed. The first proposition will move the sorting activities from supermarkets to DC, while the second and third will investigate how the total handling costs of the supply chain can be reduced even further. The following sub-question will be answered:
5. How could the activities at the DC be redesigned?
6.1 Proposition one
In this section the first redesign will be proposed.
6.1.1 Opportunity 1; to reduce the total handling costs of the supply chain
To reduce the total handling costs of the supply chain, containers need to be delivered to the supermarkets already sorted per stacking zone. When containers do not need to be sorted anymore by the Team Leaders in supermarkets, the Team Leaders can fully supervise the clerks during stacking in the supermarket. According to chapter 5, more supervision will improve the stacking times of the clerks.
6.1.2 Solution
To improve the stacking time of the clerks the sorting activities need to be moved from supermarket to DC to improve the supervision over the clerks.
