integrative retail logistics: an exploratory study

Oper Manag Res (2013) 6:2–18DOI 10.1007/s12063-012-0075-9

Integrative retail logistics: An exploratory study

Heinrich Kuhn · Michael G. Sternbeck

Received: 17 February 2012 / Revised: 7 May 2012 / Accepted: 18 December 2012 / Published online: 29 January 2013© Springer Science+Business Media New York 2013

Abstract Grocery retail companies have gone through atransformational change in the past by heavily investing indistribution centers of their own and by expanding theirlogistics activities. As a result, many retailers are now inthe process of better adjusting their logistics operations totheir specific requirements against the backdrop of rais-ing pressure in a highly competitive environment. In thislight, we provide an exploratory study based on semi-structured face-to-face interviews with 28 leading Europeangrocery retailers. First we examine the current strategicdesigns of grocery retailers’ internal logistics networks.Next, we shift our focus to the resulting interdependenciesin tactical supply chain planning between instore opera-tions and upstream logistics processes. We have identi-fied five interdependent planning issues: order packagingunit, store delivery pattern, store replenishment lead time,store delivery arrival times and arrival time windows, aswell as roll-cage sequencing and loading carriers. Eachof these mid-term planning interdependencies is evaluatedwith regard to implications in the stores, in transportationand in the distribution centers. The mid-term operationsplanning issues in the grocery retail industry consideredin this paper have remained practically unexplored up tonow. The outcome of this empirical research study there-fore has substantial relevance for future retail researchand practice.

Keywords Grocery retailing · Retail supply networks ·Retail supply chain planning · Retail operations

H. Kuhn · M. G. Sternbeck (�)Catholic University of Eichstaett-Ingolstadt, Ingolstadt, Germanye-mail: [email protected]

H. Kuhne-mail: [email protected]

1 Introduction

The stationary retail environment is becoming increasinglycompetitive due to ever rising consumer requirements andmarket consolidation. One factor is that consumers alwaysexpect well-stocked shelves with products that are guar-anteed fresh, clear aisles in the supermarkets and storeemployees who have time to answer their questions. Theseservice elements contribute to the store’s atmosphere, whichis considered a key driver of customer loyalty (Molinaet al. 2009). On the other hand, competition in the gro-cery retail sector is growing due to market consolidation.1

This in turn leads to better purchasing conditions for theretail companies, which can as a result reinvest in lowersales prices. This however has the knock-on effect ofintensifying competition.

These conditions and developments raise the pres-sure on logistics—one of the core activities of groceryretail companies today—to operate efficiently. This focus onoperational efficiency is supported by the fact that averagelogistics costs in the retail sector are higher than for man-ufacturing companies (Van der Vlist 2007, p. 3). Retailershave continuously assumed more of the logistics operationsfor which the manufacturers had traditionally been respon-sible (Fernie et al. 2000, 2010). Nowadays grocery retailersusually operate distribution centers (DCs) and thereforehave their own vertically integrated logistics network tomanage. After such radical changes in the assignmentof logistics tasks within the fast-moving consumer goods

1For example, in Switzerland the two largest retailers hold amarket share of around 67 % in the food retail market (The EconomistIntelligence Unit 2010); in Austria, the three largest grocery retail-ers have a market share of 78.5 % (Trautrims et al. 2010, pp. 70–71);after two takeovers, the German drugstore market is dominated by onlythree companies operating nationwide.

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Integrative retail logistics: An exploratory study 3

(FMCG) supply chain, many retail companies are currentlyin the process of better adjusting their logistics system to therequirements of the market without modifying the systemat its core.

In spite of these transformational changes, retail researchlacks a comprehensive view on retail logistics networks asthey have evolved over time, and on the interdependentlogistics planning problems (Hubner et al. 2010). In partic-ular, logistics store processes, which account for the largestpool of operating costs in the internal supply chain, are stillnot properly integrated in retail supply chain planning. Theobjectives of this exploratory research are therefore twofold,reflected in the research questions (RQ):

RQ1. How are grocery retail logistics networks and theirassociated product flows designed?

RQ2. Which interdependent decisions have to be madeon a tactical level to manage network efficiencyand capacity, and what are the implications for thedifferent subsystems of the network?

The findings of this paper are based on an exploratoryempirical investigation. We have conducted in-depth inter-views with operations managers of 28 German, Austrian,and Swiss-based grocery retailers. The experts describedthe design of their store and logistics networks, and illus-trated interdependencies in operations planning from theirperspective. Many interviewees emphasized that it is a chal-lenging task to balance the requirements of the differentsubsystems to improve operations performance and keep upwith the growing competition (Kuhn and Sternbeck 2011).

Related to the first research question, we investigated thecurrent designs of grocery logistics networks. We categorize

the network types explored based on applied distributionstages and the implementation of internal product flow con-solidation. As most of the retailers operate both centraland regional distribution centers, we asked the managersabout the factors they take into account when assigningstock keeping units (SKUs). We demonstrate that togetherwith further planning variables, these differing physicalflow structures lead to varying supply chain segments withdivergent characteristics.

The second purpose of this paper is to investigate themain interdependencies in tactical operations planning.From a functional perspective, the internal grocery retailsupply chains examined can be divided into three logisticssubsystems: distribution center, transportation, and store(see Fig. 1). As every subsystem with its own working andplanning mechanisms is dependent on the requirements ofthe other systems, the result is a complex interrelated struc-ture, which has to be taken into consideration in operationsplanning. Our interviews identified five elements of tacti-cal supply chain planning that significantly affect more thanone logistics subsystem:

1. Order packaging unit, i.e., the number of consumerunits that are combined to one order and distributionunit for supplying the individual stores

2. Store delivery pattern, i.e., the number of store deliver-ies per period and the specific days of delivery

3. Store replenishment lead time, i.e., the time betweenstore order and delivery

4. Store delivery arrival times and arrival time windows,i.e., the scheduled time of day or time window for storedelivery

Fig. 1 The part of thestationary FMCG supply chainfocused on in this paper

IndustryRetailer‘s distribution

center Retail storeIndustry supplier

ConsumerDistribution center

Transportation Store

Retail system

4 H. Kuhn, M.G. Sternbeck

5. Roll-cage sequencing and loading carriers, i.e., theintegration of the store layout into packing processesand the loading aids selected for store delivery

The remainder of this paper is organized as follows.Section 2 presents an overview of relevant literature. Wethen discuss the methodology employed and outline thecompanies and interviewees who participated in our studyin Section 3. Section 4 categorizes the logistics networksof the participating companies according to the first objec-tive of our research. We discuss the allocation of SKUsto distribution stages and the segmentation of the grocerylogistics networks. Section 5 addresses the second objec-tive of this paper. Interrelated planning issues between thethree logistics subsystems—distribution center, transporta-tion and store—are discussed in detail. Section 6 concludesthe paper with research contributions, perspectives and ideasfor future research.

2 Related literature

Retail distribution systems of supermarket chains alteredover time. Fernie and Sparks (2004) describe these changesas “supply chain transformation.” While in the past retail-ers were “the passive recipients of products,” today theymanage their own logistics network “as channel captains”(Fernie and Sparks 2004, p. 9). In the last 20–30 years gro-cery retailers have built up distribution centers and nowchannel a large proportion of their product flows throughtheir own warehouses (Fernie et al. 2010, p. 895). Fernieet al. (2010) see this process as virtually complete in foodretailing in the UK. An interesting question is thereforewhat network configurations are the current result of thatprocess. De Koster and Neuteboom (2000) present a com-parison of seven supermarket chains in the Netherlands andprovide detailed analyses of network configurations of thecompanies investigated. However, there is little structuredinsight into the configuration of logistics networks operatedby grocery retailers.

If considered from a functional perspective, a retail logis-tics network of this kind can be divided into the subsystemsdistribution center, transportation and store (Sternbeck andKuhn 2010, p. 1020). Although operational costs in the sub-system store are higher than in transportation or warehous-ing, only little attention has been paid to these processesand their connection with upstream activities. Referringto the objectives of this study, instore operations are ofparticular interest as a comprehensive supply chainperspective is applied.

There is a slowly growing body of literature address-ing instore logistics. Nachtmann et al. (2010), Ramanet al. (2001) refer to two execution problems: inaccurate

inventory records, i.e., a difference between physical inven-tory and system data, and misplaced SKUs in stores, i.e.,the problem that items are not in the right place on thesales floor and are hard to find in the backroom, even wheninventory records are accurate. Raman et al. (2001) figureout that store processes and the increasing variety of prod-ucts are among the factors responsible for misplaced SKUsin the stores as most stores have deficits in managing thebackroom and promptly refilling shelves. Concerning inac-curate inventory records, picking accuracy in distributioncenters is also of fundamental importance. However, Ramanet al. (2001) see instore execution as “the missing link inretail operations.”

Motivated by high on-shelf-availability requirements andhigh inventory carrying and handling costs in the store,Kotzab and Teller (2005) addressed instore logistics pro-cesses, which they call a “neuralgic business area” (Kotzaband Teller 2005, p. 604). They closed the gap in retail sup-ply chain analyses by developing and testing an explorativematerials flow model of the final yards in the store. Theauthors point out that the overall aim of instore logistics isefficiency, and identified four instore problem areas: knowl-edge of cost and service levels, standardization, qualifiedpersonnel, and store design. Besides this, they recognizedthree aspects of upstream processes and the stores that areinterdependent. Late deliveries and product quality (i.e., thedelivery of damaged products) affect store level executionas well as the concept of roll-cage sequencing (i.e., the sort-ing of products on loading carriers oriented to the storelayout). Trautrims et al. (2010) build on the model offeredby Kotzab and Teller (2005) and provide two case stud-ies. However, the clear focus lies on instore processes—theservices from upstream supply chain activities are treatedas a given.

Van Zelst et al. (2009) provide the cost structure of oneEuropean retail chain. According to this, instore operationalcosts account for 45 %, transportation for 22 % and ware-housing for 33 % of total operational costs of the internalpart of the retail supply chain. They studied instore handlingoperations intensively by differentiating between the fillingof shelves with single consumer units or with bundles (e.g.,trays) containing several units. The authors claim that futureresearch is needed to focus on the impact of case pack quan-tities on a store level, but also on picking operations in theretail warehouses (Van Zelst et al. 2009, p. 629).

In the literature stream addressing the problem of retailout-of-stocks, instore operations are discussed in the lightof the finding that the outlet is the stage in the supplychain that is responsible for the largest proportion of theresulting out-of-stock rate (Corsten and Gruen 2003, p. 614;Fernie and Grant 2008; McKinnon et al. 2007). However,McKinnon et al. (2007), Taylor and Fawcett (2001),Trautrims et al. (2009) remark that logistics studies have


so far always stopped at the ramp of the outlet: the finalyards of the supply chain are ignored. Researchers con-duct root cause analyses and case studies to examine severalstages of the retail and consumer goods supply chain, andpartially touch on interdependencies between logistics sub-systems. Fernie and Grant (2008) provide an extract ofa case study of one UK-based grocery retailer in whichstore operations needed to be integrated with supply chainimprovements. The reduction of lead times and synchro-nization of transport schedules and store processes areparticularly mentioned. Corsten and Gruen (2003) claimthat “reducing out-of-stock requires initiatives that cutacross functional boundaries which can require a funda-mental rethinking of retailer processes” (Corsten and Gruen2003, p. 615). However, a structural approach to exploreor identify interdependencies in the retail supply chainwith the aim of better integrating instore logistics systemin supply chain planning has not so far emerged. Thisdeficit is the starting point of this study. The paper aimsto shed light on both the grocery logistics networks andthe resulting planning interdependencies when integratinginstore operations.

3 Methodology and description of interviewees

Retail research has not so far provided deep insights intogrocery logistics networks, and lacks an approach for study-ing planning interdependencies between instore operationsand upstream supply chain activities. The goal of thisresearch was therefore to broadly explore structures, pro-cesses and interrelations in grocery retail logistics. To dothis, we selected an open and flexible research design basedupon personal communication.

During this study, semi-structured face-to-face in-depthinterviews were conducted and accompanied by a short,semi-standardized face-to-face questionnaire, both based onexploratory techniques of analysis. The information gath-ered in this way contributes to enhancing operations man-agement research as it provides insights into state of the artof grocery retailing and the complex interrelations betweendifferent logistics subsystems (Flynn et al. 1990, p. 251).The order of the key questions in the interview guidewas not prescribed, and interposed questions were gen-erally allowed (Lindlof and Taylor 2011, pp. 200–201).The interview guide was tested and refined in one initialpretest interview. After this we spent a lot of time in thefield between 2009 and 2010, as recommended by severalauthors (e.g., DeHoratius and Rabinovich 2011; Fawcettand Waller 2011; Meredith 1998; Schmenner et al. 2009;Singhal et al. 2008). Each interview took around 80 minon average. All interviews were conducted by the sametwo interviewers. Field notes were written during and

Table 1 Geographical distribution of the companies interviewed

Country # of interviews Percentage of sales

volume covered by

participating companies

Germany 21 57 % of the sales of the Top 30

national grocery retailers

Austria 5 69 % of the sales of the Top 10


Switzerland 2 48 % of the sales of the Top 10


immediately after the interviews. Additionally, the inter-views were complemented by a short, semi-standardizedquestionnaire that allowed systematic inquiry into associ-ated data.The questions asked were identical and in the sameorder, some of the answer options were predefined, whileothers were open.

The company sample was derived based on the 2009industry rankings of the market research companiesTradeDimensions and Planet Retail, which use annual salesas a ranking criterion. We contacted the top 30 gro-cery retailers (including drugstores) in Germany, the top10 in Austria and Switzerland respectively, as well astwo specialty store chains operating in the grocery sec-tor. We chose the largest retailers as they were likelyto exhibit the structures and processes from which inter-dependent planning problems arise. In total, we con-ducted 28 face-to-face in-depth interviews. This corre-sponds to a participation rate of 54 %. This high responserate demonstrates the importance attributed to the topicby practitioners.

The companies interviewed are located in Germany,Austria and Switzerland. The geographical distribution ofthe retailers that took part in this study can be foundin Table 1. Interviewees at the retailers were generallyoperations managers, but in several cases people fromthe sales department or from the stores also joined theinterview session.

An overview of our main discussion partners is shownin Table 2. In many cases, the companies interviewed

Table 2 Overview of interviewees

Interviewee’s position # of interviewees

in the company

Member of the management board 6

Head of department 18

Senior manager 4


operate several different retail formats. Table 3 describesthe different formats and presents the number of compa-nies interviewed that operate the specific store type. Insum, the 28 interviewees represent 51 company-formatcombinations.

As in all qualitative research projects, representative-ness is neither the goal nor the result of our approach.

Table 3 Overview of retail formats operated by companies inter-viewed in this study

Retail store Profile # of companies

format operating this

format

Hypermarket � 5000 m2 self-service stores 11

offering a complete food and

nonfood assortment comparable

to department stores

Superstore 1000–5000 m2 self-service 13

stores offering a broad

assortment in the food

and nonfood categories

Supermarket 100–900 m2 self-service 14

stores offering an

assortment mainly

in food categories

Discount store 500–1500 m2 self-service 7

stores operating according

to the hard discount

principle, i.e., limited

breadth of assortment,

low prices, and some

products in the shelves

in case packs

Drugstore 300–1400 m2 self-service 4

stores offering a focused

assortment in the categories

of home and personal care,

cosmetics, non-pharmacy-

only over-the-counter

pharmaceuticals, baby

food, baby care and

photography

Specialty store 400–1100 m2 specialized 2

self-service stores offering

a deep but narrow assortment;

in our case only in selected

grocery categories

Source The Nielsen Company GmbH (2010, p. 15), Levy and Weitz(2009, pp. 41–51), Ahlert et al. (2006, p. 290–291), Zentes et al. (2007,pp. 13–17)

This exploratory study serves as groundwork for furtherresearch in the field of grocery retailing, e.g., to derivehypotheses to be tested via quantitative methods or mod-eling and optimization approaches. Incorporating the realworld data of this study in other research projects helpsto improve the relevance of business research aspired to(Ellram 1996, p. 97; Flynn et al. 1990, p. 251; Holmstromand Romme 2012).

4 Logistics network architectures

In this section we provide an overview of the delivery modesapplied and the corresponding logistics network designsof our retailer sample. In the course of our analysis itbecame clear that deliveries via retail distribution centersare used for the majority of delivery quantities, with atendency to rise still further. Most of the companies there-fore operate a network consisting of central and regionaldistribution centers.

4.1 Delivery modes

Each of the companies interviewed applies more than onedelivery mode to bridge the gap between supplier and store.We distinguish between direct-to-store delivery, delivery viacross-docking in its pure form, with synchronized inboundand outbound flows, and delivery via retail distribution cen-ters, in which stock is kept and store order picking alsotakes place. In our sample, 82 % of the quantities deliv-ered to the stores are assigned to this latter delivery mode.However, at 92 %, discounters have a significantly higherproportion of quantities listed and picked in their ware-houses, and also strictly avoid cross-docking. Note thatEuropean “hard” discounters should not be confused with“big box” discount stores, which are operated (for example)by Walmart, with a reputation for cross-docking (Arnoldand Fernie 2000; Zentes et al. 2007). 60 % of all inter-viewees plan to increase their proportion of deliveries viaretail distribution centers, underlined by the fact that at thetime of the interviews, 25 % of all companies had plansto construct new DCs. Figure 2 shows the extent to whichthe various delivery modes are applied per retail format inour sample.

4.2 Logistics network designs

It became apparent that delivery via retail distribution centeris the dominant mode for delivering products to stores today,and this is likely to become even more important in future.That is why in the following sections we only focus on theparts of the retail logistics networks that are associated withthis delivery mode.


Flo

w o

f qua

ntity

in th

e ou

tlets

(%

)

Direct-to-store delivery

21%

5%8%

Delivery via cross-docking

3%6%

0%

Delivery via distribution center

76%

89%92%

Full-line formats

Drugstore

Discounter

Fig. 2 Average proportions of delivery modes applied per retailformat

4.2.1 Categorization of retail logistics networks

In spite of numerous particularities, the network structuresof the retailers interviewed can be categorized into fourphenotypes (see Fig. 3):

– Network type 1: One central distribution stage withone DC

– Network type 2: One regional distribution stage withseveral DCs

– Network type 3: Multiple distribution stages withoutinternal consolidation

– Network type 4: Multiple distribution stages with inter-nal consolidation

One central distribution stage Seven companies in oursample operate just one central distribution center for theirentire network of outlets. These are partly companies oper-ating in a limited geographical area. However, retailersoperating nationwide also have this type of internal logis-tics network. Outlet deliveries are always carried out directfrom the warehouse, without transshipment operations.

One regional distribution stage Three companies (onlydiscounters) operate numerous regional distribution cen-ters, but do not have a central warehouse for all out-lets. In the distribution centers the identical products arelisted and shipped from there to the stores without anytransshipment operation.

Multiple distribution stages without internal consolidationThree companies have designed an internal network, eachconsisting of one central warehouse for all outlets and twoor more regional distribution centers for an allocated sub-set of stores. All of them exclusively list SKUs in onetype of warehouse (central or regional). In general, thereare no supply connections between the central distributioncenter and the regional warehouses. Each stage is serveddirectly by the manufacturers. Downstream, the companiesdo not consolidate flows between their central and regionaldistribution centers. The stores are supplied direct by thevarious warehouses. Typically, these are companies withcomparatively large outlets, e.g., hypermarkets, which han-dle large volumes per shipment. This means the higherbundling potential from consolidating the product flowscannot compensate for the longer distances via an internalconsolidation point.

Fig. 3 Categorization of retaillogistics networks

Network type 4: Multiple distribution stages with internal consolidation

Network type 3: Multiple distribution stages without internal consolidation

Network type 2: One regional distribution stage with several DCs

Network type 1: One central distribution stage with one DC

Outlet

Central distribution

center

Regional distribution

center

Internal consolidation

point

SupplierOutlet

Central distribution

center

Regional distribution

center

Supplier

OutletCentral

distributioncenter

Supplier OutletRegional

distributioncenter

Supplier


Multiple distribution stages with internal consolidation Fif-teen companies and thus over half of the retailers inter-viewed operate a network consisting of central and regionaldistribution centers with internal consolidation. One com-pany even has not just a central warehouse but also two dif-ferent stages of regional distribution centers. Outlet orders,which contain SKUs from the central distribution stage, arealways picked store-specifically in the central warehouse,and the resulting loading units are distributed via internalconsolidation points. These internal cross-docking pointsprovide the option to bundle product flows between the cen-tral and regional distribution centers on the expensive lastfew miles to the stores. The regional distribution centers aresimultaneously used as internal consolidation points in thevast majority of cases. Only two retailers in our sample useseparate internal cross-docking points for consolidation, atwhich no stock is kept.

4.2.2 Allocation of SKUs to different distribution stages

Retail companies operating a network of Type 3 or Type 4face the inventory deployment decision, i.e., the problemof where to allocate SKUs along the different distributionstages (Shapiro and Wagner 2009). We asked the inter-viewees which factors they take into consideration whensolving this assignment problem. The aspects mentioned inthe answers can be summarized in five points: rate of SKUturnover, freshness requirements, value density of the SKU,error of SKU demand forecast, and sourcing conditions.

Rate of SKU turnover The rate of SKU turnover was theaspect mentioned most frequently. The higher the rate ofSKU turnover, the higher the order quantities. This in turnmeans that it is comparatively less important to bundleproduct flow better by routing via the central warehouse.The reduction of distances when using regional distributioncenters gets more important as order quantities increase.Moreover, given the high degree of freight space utiliza-tion by large order quantities, higher packaging density ofsingle-item pallets in primary distribution in comparison toorder-picking pallets in secondary distribution has a higherimpact when routing via regional warehouses.

Freshness requirements Highly frequent produce deliveriesrequire short lead times and therefore short distances. Thisis the reason why nearly all of the companies interviewedselect a regional distribution stage for critical perishables.

Value density of the SKU The criterion “value density of theSKU” is used by one retailer to allocate the products to dif-ferent distribution stages. The criterion is calculated as theratio between product value and physical product volume.The higher the value density, the higher the compensation

effects in inventory holding when the products are assignedto the central distribution center. In addition, the secondarytransportation costs are less important.

Error of SKU demand forecast As one decision criterion forallocation, retailers partly referred to the demand forecasterror for SKUs. The higher the demand forecast error, thehigher the need for safety stocks. Supply chain safety stocksare smaller overall when the product is listed in the centralwarehouse than when in several regional warehouses due tocompensation effects. Seasonal and promotional productsare often mentioned. The process of phasing out at the endof a promotion or season is easier from the central distri-bution center, as the concept of geographical postponementis applied.

Sourcing conditions In many interviews the sourcing condi-tions that had been arranged with suppliers were mentioned.The bundled order calls via the central distribution centerare compared with the fragmented calls when listing theSKU in the regional distribution centers.

4.3 Segmentation of the retail supply chain

Section 4.2.1 outlined and categorized the retail distributionnetworks of the participating companies. In addition to thesedifferences in the structure of the physical flow of prod-ucts, companies use planning variables to control the flow ofgoods. Our interviews show that these are the store deliverypatterns and replenishment lead times in grocery retailingin particular. All companies interviewed fix these variableson a tactical level and pass the values down to the executionlevel as parameters.

Combining the allocation of products to central orregional warehouses with fixing of further planning vari-ables results in subsets of the assortment with an identicalflow through the retail network. The companies use thehigher homogeneity to better balance service requirementson the one hand and cost savings on the other. This allowsthe retailers to create several internal supply chain seg-ments with different specific characteristics (in this context,see Aitken et al. 2005; Fisher 1997; Lovell et al. 2005;Mason-Jones et al. 2000; Naylor et al. 1999).

There are highly reactive segments with high store deliv-ery frequencies and short store replenishment lead times,e.g., for critical perishables. But there are also segmentswith longer lead times and lower frequencies in order torealize efficiency gains through bundling over time andquantity, e.g., slow-moving ambient products. This meansthere are as many supply chain segments as there are com-binations of different physical flow types and different fixedplanning variables. One retailer from our sample runs 40different supply chain segments according to this definition,


which was the maximum. Figure 4 exemplifies retail sup-ply chain segmentation with the help of the example of oneretailer operating several hundred stores.

4.4 Partitioning of the supply chaininto functional subsystems

The product flow through such retail networks can bedivided into functional subsystems, in which different logis-tics transformation processes take place. In the case ofdominant delivery via retail distribution centers, the flow ofmerchandise passes through the functional subsystems dis-tribution center, transportation and store. To explore therelational weight of the different subsystems, the partic-ipants in this study were asked about the distribution ofoperational costs to these three areas. On average, 28 %of operational costs are incurred in the warehousing area,24 % in the (secondary) transportation subsystem, and 48 %in the store. Similar results are achieved by Evans andSimons (2000) and Van Zelst et al. (2009). Although instore

operational costs are the biggest cost pool, only few retail-ers apply activity-based costing for this part of the supplychain. In most cases operations managers are not respon-sible for instore logistics, as it is considered part of thesales domain. Therefore, costs related to these activitiesare not recognized as logistics operations. However, somecompanies have deep knowlege of their cost structures inthe stores. They have sometimes built up organizationalunits as departments or project organizations to intensifythe integrative perspective on the supply chain. For trans-portation and warehousing, detailed cost information isregularly available.

Compared to classical logistics functions of transporta-tion and warehousing, instore logistics plays a minor rolein publications about retail logistics. As shown in the liter-ature review above, only a slow-growing body of literatureon that topic can be found. The interdependent efficiencydrivers have to become apparent in order to better connectthe domains of store operations, transportation, and ware-housing. These efficiency drivers should later be influenced

Fig. 4 Supply chainsegmentation: exampleof one grocery retailer


such as to balance the requirements of the different domains.The second aim of this study is therefore to explorethe process and planning interdependencies between thesubsystems as a basis for further logistics planning andresearch approaches.

5 Interdependencies in tactical product flow planning

One of the main goals of this study was to analyze the inter-dependencies in the tactical flow control of merchandisethrough the internal retail network. We focus the plan-ning interdependencies between different logistics subsys-tems with the knowledge that integrative approaches “mustovercome the specialization of functional areas” (Olivaand Watson 2011, p. 434) and effective lateral commu-nication mechanisms have to be installed (Germain et al.2008, p. 558). This study therefore serves as groundworkfor collaboratively managing intra-organizational processes,which can be seen as one dimension of supply chain inte-gration (Flynn et al. 2010, p. 59).

The interdependencies between distribution center, trans-portation system and store received attention primarilyduring the interviews conducted. The integration of storerequirements in tactical operations planning was a partic-ularly central question, since this is still an open topicin internal supply network planning. That is why weapply a shelf-back perspective in the following analyses—motivated by the concept of “line-back planning” in theautomotive industry. The particular relevance of this aspectis supported by the statement of one interviewee: “Thebiggest levers for the costly instore processes can be foundin activities taking place in the upstream part of oursupply chain.” Going through the interview transcripts,the aspects mentioned most frequently that have effectsacross the subsystem boundaries can be summarized infive broad points, and will be investigated in more detailin the following:

1. Order packaging unit2. Store delivery pattern3. Store replenishment lead time4. Store delivery arrival times and arrival time windows5. Roll-cage sequencing and loading carriers

5.1 Order packaging unit

The selection of the order packaging unit per SKU, whichis equivalent to the smallest possible order quantity, wasconsidered an important planning issue in numerous inter-views. The order packaging unit defines the granularity ofpossible order sizes, since the store orders have to be aninteger multiple of this quantity.

While in the literature, case pack quantity effects aretreated partly as exogenous for retailers (e.g., Ketzenberget al. 2000; Waller et al. 2008, 2010), our interview part-ners explained that case packs are sometimes broken up inthe retail DCs in order to use a smaller packaging unit asorder and distribution unit for the stores, e.g., subpackagesor single customer units. The extent to which case packs arebroken up per retail format type in our sample is shown inFig. 5. Of course, decisions may also involve the revereseapproach. Multiple case packs can be combined to one orderpackaging unit in order to realize economies of scale. Sevenmanagers of the companies interviewed explained that theyoffer a small proportion of the assortment in different orderpackaging units for their stores. They often install auto-matic rounding mechanisms. For example, if a store needsmore than 80 % of the next bigger packaging unit, the orderquantity will be adjusted automatically.

Store implications The order packaging unit of a producthas great implications for its average and maximum inven-tory in the store. The most common inventory policy inretail industries is a periodic review system with fixed packsizes. The replenishment system is organized as follows.At each review moment, the inventory position is checkedand compared with a dynamic reorder level. An order isonly created if the inventory position is below the reorderlevel. In this case, the smallest integer multiple of the fixedpack size is ordered that will raise the inventory position toor above the dynamic reorder level (Broekmeulen and vanDonselaar 2009; Wensing 2011). All of the 28 companiesinterviewed use this policy for the normal assortment. 46 %of the companies interviewed use automated store order-ing systems for non-promotional dry groceries, which arebased on this replenishment system. 70 % of the companiesoperating manually plan to introduce an automated system.However, using this policy may lead to a significant over-shoot above the reorder level, since order sizes have to be an

8%

63%

4%

Ave

rage

pro

port

ion

of p

rodu

cts

unpa

cked

in

ret

ail D

Cs

(% o

f lis

ted

SK

Us

in D

C)

Full-line supermarket

DrugstoreDiscounter

Fig. 5 Average proportion of products unpacked in retail DCs


integer multiple of the order packaging unit. If shelf capac-ity is limited, the overshoot may have to be stored in thebackroom of the store. So the order packaging unit selectedhas an influence on instore stock levels and the degree towhich the shelf can be filled directly from the DC palletwithout additional handling effort, i.e., storing the productin the backroom and restocking the shelf later. 75 % of thecompanies interviewed aim to use the backroom as littleas possible for storing products intermediately. DeHoratiusand Raman (2008) as well as DeHoratius and Ton (2009)show that higher inventory levels have a negative impacton store performance due to greater inventory record inac-curacy and a greater percentage of products that are in thebackroom but not available on the shelves. Of course, theseeffects will rarely occur if enough shelf capacity is avail-able, as is the case with the discounters. One representativeof a discount chain explained that in high seasons severalcase packs were simply put on top of each other on the shelf.So in this case the shelf capacity restriction is not as hardas it is in full-line markets, where single customer units arepresented on the shelves. A smaller order packaging unitwould increase shelf filling frequency and therefore resultin additional costs.

Transportation implications Some of the intervieweesargued that the selection of order packaging units alsoaffects transportation. Generally, the heterogeneity ofunpacked packaging units makes it difficult to achieve highpackaging density in the picking processes. The lower thepackaging density, the more pallets have to be distributed,which increases the freight space required.

Distribution center implications The selection of orderpackaging unit highly influences DC operations. Thesmaller the order packaging unit, the more picks in theDC are necessary in order to realize the same output.This results in higher picking costs. Moreover, the deci-sion for a smaller packaging unit could imply that theproduct has to be picked with another picking technol-ogy and distributed with different loading aids. This inturn means that DC capacity and product allocation haveto be taken into consideration when deciding on the orderpackaging units. On the other hand, defining the minimumorder quantity as several case packs may lead to addi-tional productivity gains in the DC since double pickingis very efficient.

5.2 Store delivery pattern

The store delivery pattern determines the number of deliv-eries and specific days of delivery for a given deliverycycle (e.g., one week, two weeks, etc.). The delivery pattern

therefore has to be distinguished from the delivery fre-quency (e.g., twice a week). For example, there are 15possibilities for a delivery frequency twice in a six-day

week[(6

2

) = 15].

Our interviews show that deriving the delivery patternshas a crucial impact on processes and operational effi-ciency in all three logistics subsystems. In addition, severalinterviewees reported that decision making is conductedsequentially. First, delivery frequencies are fixed accordingto a predefined decision scheme. Second, the specific daysof delivery are determined.

Generally, the retailers use differing concepts to spec-ify their delivery frequencies (see Fig. 6). 7 % operate adaily delivery strategy: the entire assortment is delivered toall stores daily. This means it is not necessary to decideon delivery frequencies and patterns. 4 % of the retailersoffer different delivery frequencies for their stores, but withidentical frequencies for the entire assortment. 18 % of theretailers differentiate between product groups, and supplyall their stores using the same product-group-specific fre-quencies. By far the majority of the companies interviewed(71 %) differentiate both between stores and between prod-uct groups. This results in store- and product-group-specificdelivery frequencies. This is definitely the most complexplanning situation.

When offering store- and product-group-specific deliv-ery frequencies, the question arises as to how the relatedcompanies specify these frequencies. Where the assort-ment perspective is concerned, the criteria mentioned werefreshness requirements, best-before dates, mean sales vol-umes and the quality of forecasts. The store-specific cri-teria are the sales volume of the outlet, productivityper square yard, size of the backroom, and the goodsreceiving capacity. 70 % of the companies adjust their

yes no

yes

no

Differentiation between products

Diff

eren

tiatio

n be

twee

nou

tlets

Product-group andoutlet-specific

delivery frequencies

Outlet-specificdelivery frequencies

(same deliveryfrequency for theentire assortment)

Product-group-specific delivery

frequency(same delivery

frequencies for all outlets)

The same deliveryfrequency for the

entire assortment forall outlets

71% 4%

7%18%

Fig. 6 Dimensions for determining delivery frequencies


delivery frequencies over time, while the remaining 30 %establish key dates for monitoring and adjustment. It isparticularly common for companies operating stores inholiday areas to alter delivery frequency according tothe season.

Concerning determination of specific days for delivery,the companies interviewed partly apply a fixed set of deliv-ery patterns that can be selected. One third of the retailersconstrain the possible delivery schemes to reduce complex-ity and enhance their ability to monitor their store network.Where the other two-thirds are concerned, all kinds of deliv-ery patterns can be selected in principle. However, severalmanagers mentioned that they prefer order patterns withequidistant intervals between the deliveries. The transporta-tion department is generally responsible for specifying thedelivery days. While route planning IT support is widelyused in the short term, only a few retailers mentioned thatthey use specialized methods to generate their mid-termmaster route plan.

In all the interviews it became clear that the selection ofstore delivery frequencies and the resulting store deliverypatterns is of high significance for all three logistics subsys-tems, since the overall efficiency and capacity required arehighly influenced by these decisions.

Store implications From a handling perspective, severalmanagers explained that the main objective of instore oper-ations is to fill the shelves direct from DC pallets withoutinconvenient temporary storage in the backroom. At thesame time, shelf filling frequency should be minimized.The store delivery pattern thus substantially influencesutilization of the backroom and the number of refillingoperations as it determines the review intervals. Applyingshorter review periods leads to smaller order sizes, anda larger proportion of the amount delivered fits directlyonto the shelves. Needless to say, the length of the reviewperiod has a direct implication on the amount of safetystock required in the store (Tempelmeier 2006). Thisimplies that if the review period is shorter, there is—onaverage—more shelf space available for cycle inventory.This effect is naturally more evident for products with highforecast errors.

Besides these effects related to shelf filling efficiency,the delivery pattern also influences personnel schedulingin the store, and is subject to capacity restrictions. Forexample, one manager explained that from a store per-spective, delivery patterns should be determined such thatthe mean number of pallets per day does not exceed thegoods receiving and handling capacity of the store. 72 %of the companies interviewed fill their shelves completelyusing their own personnel, without employing staff frominstore service providers. Nevertheless, 86 % of the retail-ers have employees whose only task is to fill the shelves. In

addition to the aspects mentioned so far, the weekly sea-sonality in grocery sales volumes (see Fig. 7) has a majorinfluence on decisions.

Store employees face the problem that, by assumingmultiple review periods a week, the receiving volumesvary. The maximum amount results from the review period,which includes the sales-intensive days near the weekend.The problem is managed in different ways. One man-ager explained that his company aims to offer employeesevenly distributed working volumes, which is achieved viainterlocking delivery patterns for different product groups.Several interviewees argued that for instore logistics thehigh receiving volumes before the weekend are better thanintermediately storing the products in the backroom whenshelf capacity is limited.

Transportation implications Determining store deliverypatterns also influences the efficiency of the transportationsystem. Twenty-two of the 28 retailers interviewed believethat in future transportation will influence their competitiveposition more than in the past. Transportation is cost effi-cient when capacity utilization of the vehicles is maximizedand expensive ramp dockings are minimized. Both factorsare affected by the delivery patterns selected.

The efficiency of the transportation system is dependenton the criterion transport volume to store per delivery. Thehigher the delivery frequency, the smaller and more frag-mented the resulting transportation lot sizes. In addition, thetotal number of stops at the stores increases as the frequencyof deliveries increases.

Moreover, balancing transportation capacity is high-lighted as a challenging goal since 50 % of the companiesinterviewed distribute more than half of the unit loads withtheir own vehicles. The required freight volume per day andregion is defined by the predetermined delivery pattern of

+60.7%

Saturday

21.7%

Friday

19.6%

Thursday

16.4%

Wednesday

14.3%

Tuesday

13.5%

Monday

14.5%

Fig. 7 Weekly seasonality of consumer packaged goods sales inGermany. Source Nielsen household panel Germany 2009, in: TheNielsen Company GmbH (2010)


the individual stores. From a transportation perspective, thedelivery patterns should therefore be fixed such that capac-ity utilization is well-balanced to maintain a basic workloadand minimize the use of expensive additional vehicles.

Transportation planning is also very affected by weeklydemand seasonality. One interviewee complained that theirown vehicles go unused on Wednesday, while on Thurs-day additional vehicles are needed from third-party serviceproviders. From the viewpoint of transportation, the orderpatterns of the individual stores should be interlocked suchthat the impact of weekly seasonality on capacity utiliza-tion is kept to a minimum. For example, one logisticianexplained that they try to harmonize delivery patterns suchthat the volume is spread over the week as equally as pos-sible while also ensuring that the major volume per dayflows in the same geographical region. In contrast withGerman companies, decision makers in Austria andSwitzerland repeatedly mentioned that they plan to sig-nificantly enlarge their rail shipments. Large volumes persource-destination link are required to achieve this goalefficiently.

Distribution center implications DC operations productiv-ity and capacity usage is also highly influenced by storedelivery patterns. Picking productivity is driven by ordersizes and order structures, which are a result of the orderpatterns selected. Nearly 90 % of the companies interviewedoperate picking systems with static staging, which impliesthat the picker has to move through the aisles to pick theproducts. As in most cases only one store order is picked atthe same time per picker, the order size is relevant becausetravel distance in the commissioning area is distributed tothe number of products on the picking list. The higher thedensity of picking positions and the more multiple pickingper product, the higher the picking productivity, which ismostly measured in picks per time unit.

In addition, the capacity requirement is also influencedby the store order patterns applied. However, the interdepen-dence is not as great as for the transportation system becausethe store destination has no relevance for DC operations.The sum of the single store order quantities corresponds toincoming picking orders. This defines the necessary pickingcapacity. Several managers reported that a certain imbalancein picking volumes cannot be eliminated by interlockingdelivery patterns. To get around this problem, 48 % ofthe companies employ more than 5 % temporary workerson average.

5.3 Store replenishment lead time

The store replenishment lead time also affects how effi-ciently the different subsystems can be operated. The retail-ers predefine the replenishment lead times on a tactical

planning level. However, the empirical study shows that thereplenishment lead times for ambient products (i.e., prod-ucts that do not need a chilled or frozen environment) varysignificantly between the participating retailers (see Fig. 8).The lead times are remarkably shorter for produce and dairyproducts: more than 90 % of the companies provide leadtimes of 24 h or less for those products. Due to the conflictof objectives between the different subsystems, managers’answers to the question as to how store replenishment leadtimes will evolve are diverse.

Store implications The shorter the replenishment leadtimes, the better the store can react to short-term demandfluctuation. This leads to lower safety stock since the riskperiod decreases. More shelf space can therefore be used forthe cycle inventory. Of course, the impact greatly dependson forecast accuracy.

Transportation implications Whether the transportationsystem is affected by lead time decisions or not dependson how the available time span is distributed between ware-house and transport operations. Generally, the longer inadvance the transportation department or an external ser-vice provider receives a transportation order, the easier itis to manage capacity. For example, prices for additionalvehicles on the transportation spot market rise substan-tially as lead times decrease. Moreover, the retail companiesoperating central and regional DCs with an internal consol-idation point can benefit from longer lead times as there aremore bundling opportunities during a longer time span. Forinstance, loading units that would not fit in the last shut-tle can be loaded into the next regular one. Transportationfrequency between central and regional DC can be reducedas a result, and the utilization of transportation capacityincreased.

Distribution center implications The DC benefits fromlonger lead times. However, this means the warehouse has

0

10

20

30

40

50

]48; 72]

14%

36%

[12; 24] ]24; 36]

43%

7%

]36; 48]Num

ber

of c

ompa

nies

inte

rvie

wed

(%

)

ambient assortment (hours, classified)

Fig. 8 Store replenishment lead times for the ambient assortment


to be equipped with sufficient buffer space for inventoryin transit, for example, by using swap trailers on whichready-picked pallets can be loaded. Managers explainedthat the reason for this is the opportunity of load leveling,which means that peaks resulting mainly from seasonalityeffects can be moderated and therefore capacity usage canbe smoothed. This is due to the time buffer and the resultingadvantage that store orders can be produced earlier if thereis free picking capacity available. Bringing forward orderpicking thus has the positive effect of reducing upcomingproduction peaks.

5.4 Store delivery arrival times and arrival time windows

Store operations are highly influenced by the accuracyof the announced arrival time of deliveries. However,all the other subsystems are also affected by this issue,even though their objectives are different. The DC andthe transportation system prefer large delivery time win-dows. The store, however, prefers a scheduled arrival timethat is according to its wishes, and fulfilled as accuratelyas possible.

Twenty-five of the 28 retailers interviewed have agreedupon specific self-implied arrival time windows for theirdeliveries at the stores (Quak and de Koster 2007). Thedistribution of the sizes of the announced time windowsof truck arrivals for ambient deliveries is shown in Fig. 9.Since the aims of the subsystems are different in respectto this question, it is interesting to know who determinesthe truck arrival times at the store. In our sample, 30 % ofthe companies allow their stores to set their favorite arrivaltimes, sometimes with time-dependent transportation rates.The remaining 70 % derive their shipment arrival timesfrom master route planning with respect to store-specificdelivery restrictions.

0

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20

30

40 37%

]0; 0,5]

13%

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)

Size of standard store delivery time window for the ambient assortment (hours, classified)

]2; 3]

17%

]1; 2]

33%

]0,5; 1]

Fig. 9 Applied standard arrival time windows of store delivery for theambient assortment

Store implications Store operations are highly influencedby truck arrival times and the possible size of the timewindow. An ideal situation occurs when the arrival timecan be scheduled by the store itself. Deliveries can thenbe connected with instore operations. Scheduled arrivaltimes are particularly relevant when the arrival process isclosely linked to shelf filling. A store manager explainedthat this is primarily done when storage space in the outletis very small. In this case a broad time window com-plicates the scheduling of shelf-filling staff. Needless tosay, the uncertainty of arrival times has to be reflectedin inventory planning. The broader the time window, thelonger the potential lead time, which is relevant for safetystock calculations.

Transportation implications Clearly, freely selectabledelivery times and broader time windows lead to greaterdegrees of freedom in vehicle route planning, whichresults in more bundling opportunities and therefore lowertransportation costs. Some stores are subject to specialrestrictions. For example, only limited delivery times areallowed for stores in pedestrian areas, or there are time-access restrictions to urban areas in general (Quak andde Koster 2007). One interviewee explained that stores inresidential areas may not be supplied during the night andtherefore cannot offer a very broad time window whilethe store is closed. If delivery during the night is possible,the truck driver has access to a special part of the storagearea in the store where he/she deposits the loading units.In the morning the shelf filling staff can start workingimmediately, and do not have to risk waiting until thedelivery arrives.

Distribution center implications For distribution centers,store delivery times and time windows are only relevant ifpicking and delivery processes are tightly connected, with-out buffer times or intermediate storage. In this case DCoperations also benefit from higher degrees of freedom inthe delivery process.

5.5 Roll-cage sequencing and loading carriers

The concept of “roll-cage sequencing” includes provid-ing loaded carriers that are packed in the sequence of thestore layout. A prerequisite for applying this concept isthat the layout of a man-to-goods picking area in the DCis organized accordingly. The roll-cage sequencing con-cept leads to a dilemma when considering the total supplychain, i.e., picking efficiency in the DC vs. direct shelffilling in the store (Kotzab and Teller 2005, p. 603). How-ever, 85 % of the companies interviewed with static stagingin their DCs try to adapt their bin allocation to an idealstore layout.


Another aspect often referred to by the interviewees as aninterdependent problem is the selection of loading carriers.It was mainly reported that a decision has to be made on thetype of loading carrier, i.e., between roll cages or pallets.

Store implications The stores greatly benefit from apply-ing the roll-cage sequencing concept. The loading carrierscan be placed directly in front of the shelf without anytime-consuming presorting processes in the backroom. Withrespect to the type of loading carrier used, the managersinterviewed noticed that the stores largely prefer roll cagesas these can be rolled to the shelf without further handlingequipment. The handling of pallets requires a pallet car-riage, which needs additional floor space in the backroomand is more difficult to handle.

Transportation implications While the sorting of productson the pallets has no influence on transportation, the selec-tion of loading carriers has a big impact. First, roll cagescannot be arranged in a pile, which has the effect that thereverse volume back to the DCs is as high as in down-stream distribution. Second, the capacity of roll cages inrelation to their footprint is lower, which results in highertransportation costs.

Distribution center implications Applying the concept ofroll-cage sequencing reduces the degrees of freedom withrespect to bin allocation in the DC. Instead of assigningproducts to picking locations with the objective of maximiz-ing picking efficiency, an ideal store layout determines theassignment strategy, which results in comparatively higherpicking costs. In DCs, the problem is therefore to find afeasible assignment that balances store layout orientationand stackability. Some interviewees explained that they reg-ularly reorganize product assignment to picking places tocorrespond to the store layout as well as possible due to themany changes in the listed assortment.

6 Discussion and conclusions

During the last 20 years, grocery retail companies have gonethrough a radical change by establishing and operating theirown DCs and by expanding their logistics activities. Asa result, nowadays they have to manage an internal sup-ply chain composed of stores, consolidation points, regionalDCs, central DCs and the necessary transportation systemto link these nodes of the network. However, a comprehen-sive perspective on existing retail logistics networks is stillmissing. In addition, the full potential of cost savings fromintegrated supply chain planning has not yet been tapped.To close these gaps from a retail practice perspective, anexploratory study was conducted based on semi-structured

face-to-face interviews with 28 leading European groceryretailers. To our knowledge, this study is one of the mostextensive ever conducted in European grocery logistics.This paper has presented the main results of the study.

The analyses of the logistics networks show that thegrocery retailers have widely built up differentiated supplychain concepts by combining different physical flow typesthat are operated in various manners. The internal networksconsisting of the different domains, i.e., stores, transporta-tion links and DCs, generate internal interdependent mid-term operations planning problems, that are relatively newfor retailers.

The second contribution of our research lies in theexploratory identification and description of five interre-lated mid-term planning issues that are of major relevancefor retail operations managers: (1) order packaging unit,(2) store delivery pattern, (3) store replenishment lead time,(4) store delivery arrival time and arrival time window, aswell as (5) roll-cage sequencing and loading carriers.

A major question is (1) whether and to what extent it isadvantageous to unpack or bundle case packs to create orderpackaging units for the stores. The chosen store deliverypatterns (2) have a major influence on whether it is possi-ble to balance utilization of the DCs and the transportationsystem. In addition, this issue greatly influences to whatextent the shelf filling process in the stores is efficient. Thestore replenishment lead time (3) is seen by the intervie-wees as another issue affecting these two key operationalcharacteristics. The arrival times and arrival time windowsof the store deliveries (4) also represent a conflict of inter-est between the stores and their transportation system. Lastbut not least (5), the retail managers strive to manage theconflict between stores and DCs of selecting appropriateloading carriers and the question of whether to sort themaccording to the store layout or not.

This study verified that a comprehensive perspective onsupply chain operations is of particular importance in sta-tionary retailing since the objectives of the instore logisticssystem and upstream logistics activities are partially in con-flict with one another. It is also very hard to automate a largeshare of handling processes, primarily in the stores. Thisbranch of industry is therefore extremely labor intensive,and this will continue to be the case.

A main deficit in retail practice and research is there-fore the fact that instore handling operations are not wellembedded in planning procedures on higher planning lev-els, even though instore handling operations contributeto nearly half of the total operational costs of groceryretailers. It is remarkable that our interview partners fre-quently mentioned the inefficiencies in instore handlingoperations and the additional handling costs incurred as aresult. Nevertheless, these relevant aspects of instore logis-tics have rarely been considered in literature until now


(Van Zelst et al. 2009). The large body of literature is relatedto the demand side in retailing, or to inventory holdingissues, neglecting instore handling operations.

Less than 40 % of the companies in our sample havebuilt up organizational units to develop a comprehensiveperspective with the aim of integrating instore logistics andupstream operations, e.g., the department or project organi-zation. Several interviewees argued that a better connectionbetween the subsystems would lead to greater operationalefficiency, so these companies handle the related taskswith top priority. Some retailers are still working on theimprovement of mid-term supply chain planning as theseimprovements promise to have a noticeable impact on thesubordinate execution level.

A limitation of this study is of course the geographicalarea in which the companies interviewed are located. Asthere are structural differences within and across Europe,the results of our study are valid for the countries studiedor for countries with similar structures, respectively (e.g.,see Perkins 2001). The main feature of the grocery retaillandscape investigated is control over a distribution sys-tem consisting of retail DCs, retail-managed transportationlinks, and stores. An interesting follow-up to this studycould therefore be a comparative analysis of grocery logis-tics networks in different regions of the world. It seemsparticularly interesting to compare regions with compara-ble states of economic development with a focus on theinfluences that drive grocery logistics network design, e.g.,a comparative analysis between Western European coun-tries and the US. A further limitation of this study can beseen in narrowing the focus to grocery retailing. The ques-tion as to whether parts of the findings of our study arevalid for other retail sectors could be the leading topic ofanother follow-up.

In this exploratory study we provide insights into sup-ply chain planning problems in the grocery retail industry.Future research could be built on the empirical findings ofour study to better adjust prospective approaches to practi-cal structures and needs. Large parts of this paper may beused as an agenda for future research.

First, the insights of this study could be used to examineprocess interrelations via quantitative methods. For exam-ple, instore cost patterns as a function of different supplycharacteristics (e.g., delivery frequencies) need to be quan-tified, as well as picking costs depending on differentstore order structures. An empirical measurement approachbased on quantitative methods that retailers can apply isrelevant to incorporate the behavior of the different subsys-tems into integrative decision support systems. Second, thisstudy provides a basis for developing integrative approachesbased on optimization models. There are research deficitsin the development of decision support systems to over-come the complex interdependent planning problems by

incorporating the efficiency drivers identified. Sometimesthe interviewees complain about the lack of methodologicalsupport, as available software helps to administer prod-uct flow but appropriate optimization methods are avail-able only in rare cases. For example, retail research lacksapproaches to determine order packaging units that incorpo-rate shelf capacity and thus reflect instore handling efforts.One approach could be to integrate handling operations ininventory decision rules applied to grocery retailing. Wherethe second interdependency investigated in this paper is con-cerned, store delivery patterns, the majority of intervieweesexplained that they either apply simple rules of thumb ormainly focus on transportation subsystem, with the resultthat instore and DC operations are largely neglected. Moreanalytical approaches appear promising against the back-drop of the complex interdependencies described in thisstudy. The same applies to the determination of store replen-ishment lead times in the context of grocery retailing.Analytical models that integrate the associated effect of loadleveling seem to be an appropriate instrument to improvegrocery supply chains. Furthermore, this study shows thatthere is a practical need for a methodology to comparativelyassess the trade-off between store delivery time windowsand necessary storage space in the outlets. Additionally, alack of decision support has been recognized for assigningproducts to static picking places in the DC incorporatingstackability and instore handling.

A further additional research challenge is to consider thelinks between demand-motivated category management andsupply-oriented operations management issues. The inter-dependencies described in this study gain an additionaldimension from that perspective. Category planning, whichcomprises assortment decisions and the configuration ofshelf planograms, impacts shelf capacity per SKU, whichis in turn seen as an efficiency driver in the interdependentoperations planning problems discussed in this study. A bal-ance of interests will certainly influence how retail chainsand stores are designed and operated in the future.

Acknowledgments We are deeply grateful to all the managers andemployees of the 28 retailers who assisted us with the interviewsdiscussed in this paper. They contributed to an intensive exchangebetween academia and those involved in day-to-day operations. Wewould also like to acknowledge the financial support provided by theGerman retail and FMCG Foundation “Goldener Zuckerhut.” More-over, we very much appreciate the comments and suggestions receivedfrom two anonymous reviewers, which significantly improvedthis paper.

References

Ahlert D, Blut M, Evanschitzky H (2006) Current status and futureevolution of retail formats. In: Krafft M, Mantrala MK (eds)Retailing in the 21st century. Springer, Berlin, pp 289–308


Aitken J, Childerhouse P, Christopher M, Towill DR (2005) Design-ing and managing multiple pipelines. J Bus Logist 26(2):73–95

Arnold SJ, Fernie J (2000) Wal-Mart in Europe: prospects for the UK.Int Mark Rev 17(4–5):416

Broekmeulen RACM, van Donselaar K (2009) A heuristic to man-age perishable inventory with batch ordering, positive lead-times, and time-varying demand. Comput Oper Res 36(11):3013–3018

Corsten DS, Gruen TW (2003) Desperately seeking shelf availability:an examination of the extent, the causes, and the efforts to addressretail out-of-stocks. Int J Retail Distrib Manag 31(12):605–617

De Koster R, Neuteboom J (2000) The logistics of supermarketchains: a comparison of seven chains in the Netherlands. Elsevier,Doetinchem

DeHoratius N, Rabinovich E (2011) Field research in operations andsupply chain management. J Oper Manag 29(5):371–375

DeHoratius N, Raman A (2008) Inventory record inaccuracy: anempirical analysis. Manag Sci 54(4):627–641

DeHoratius N, Ton Z (2009) The role of execution in managing prod-uct availability. In: Agrawal N, Smith SA (eds) Retail supply chainmanagement. Springer, New York, pp 53–77

Ellram LM (1996) The use of the case study method in logisticsresearch. J Bus Logist 17(2):93–138

Evans B, Simons D (2000) Lean delivery road map: how to make ithappen. Logist Transp Focus 2(9):30–39

Fawcett SE, Waller MA (2011) Making sense out of chaos: why theoryis relevant to supply chain research. J Bus Logist 32(1):1–5

Fernie J, Grant DB (2008) On-shelf availability: the case of a UKgrocery retailer. Int J Logist Manag 19(3):293–308

Fernie J, Sparks L (2004) Retail logistics: changes and challenges. In:Fernie J, Sparks L (eds) Logistics and retail management. KoganPage, London, pp 3–37

Fernie J, Pfab F, Marchant C (2000) Retail grocery logistics in the UK.Int J Logist Manag 11(2):83–90

Fernie J, Sparks L, McKinnon AC (2010) Retail logistics in the UK:past, present and future. Int J Retail Distrib Manag 38(11/12):894–914

Fisher ML (1997) What is the right supply chain for your product? Asimple framework can help you figure out the answer. Harv BusRev 75(2):105–116

Flynn BB, Sakakibara S, Schroeder RG, Bates KA, Flynn EJ(1990) Empirical research methods in operations management.J Oper Manag 9(2):250–284

Flynn BB, Huo B, Zhao X (2010) The impact of supply chain integra-tion on performance: a contingency and configuration approach.J Oper Manag 28(1):58–71

Germain R, Claycomb C, Droge C (2008) Supply chain variability,organizational structure, and performance: the moderating effectof demand unpredictability. J Oper Manag 26(5):557–570

Holmstrom J, Romme AGL (2012) Guest editorial: five steps towardsexploring the future of operations management. Oper Manag Res5(1–2):37–42

Hubner A, Kuhn H, Sternbeck MG (2010) Demand and supply chainplanning in grocery retail: an operations planning framework.http://ssrn.com/abstract=1635752

Ketzenberg M, Metters R, Vargas V (2000) Inventory policy for denseretail outlets. J Oper Manag 18(3):303–316

Kotzab H, Teller C (2005) Development and empirical test of a groceryretail instore logistics model. Br Food J 107(8):594–605

Kuhn H, Sternbeck M (2011) Logistik im Lebensmittelhandel.Ingolstadt, Catholic University Eichstaett-Ingolstadt, Forschungs-bericht der Wirtschaftswissenschaftlichen Fakultat

Levy M, Weitz BA (2009) Retailing management, 7th edn. McGraw-Hill Irwin, Boston, Mass

Lindlof TR, Taylor BC (2011) Qualitative communication researchmethods, 3rd edn. Sage, Los Angeles

Lovell A, Saw R, Stimson J (2005) Product value-density: managingdiversity through supply chain segmentation. Int J Logist Manag16(1):142–158

Mason-Jones R, Naylor B, Towill DR (2000) Lean, agile or leagile?Matching your supply chain to the marketplace. Int J Prod Res38(17):4061–4070

McKinnon AC, Mendes D, Nababteh M (2007) In-store logistics: ananalysis of on-shelf availability and stockout responses for threeproduct groups. Int J Logist: Res Appl 10(3):251–268

Meredith J (1998) Building operations management theory throughcase and field research. J Oper Manag 16(4):441–454

Molina A, Martın VJ, Santos J, Aranda E (2009) Consumer serviceand loyalty in Spanish grocery store retailing: an empirical study.Int J Consum Stud 33(4):477–485

Nachtmann H, Waller MA, Rieske DW (2010) The impact of point-of-sale data inaccuracy and inventory record data errors. J Bus Logist31(1):149–158

Naylor B, Naim MM, Berry D (1999) Leagility: integrating the leanand agile manufacturing paradigms in the total supply chain. Int JProd Econ 62(1-2):107–118

Oliva R, Watson N (2011) Cross-functional alignment in supply chainplanning: a case study of sales and operations planning. J OperManag 29(5):434–448

Perkins B (2001) The European retail grocery market overview. BrFood J 103(10):744–748

Quak HJ, de Koster MBM (2007) Exploring retailers’ sensitivity tolocal sustainability policies. J Oper Manag 25(6):1103–1122

Raman A, DeHoratius N, Ton Z (2001) Execution: the missing link inretail operations. Calif Manag Rev 43(3):136–152

Schmenner RW, van Wassenhove L, Ketokivi M, Heyl J, Lusch RF(2009) Too much theory, not enough understanding. J Oper Manag27(5):339–343

Shapiro JF, Wagner SN (2009) Strategic inventory optimization. J BusLogist 30(2):161–173

Singhal V, Flynn BB, Ward PT, Roth AV, Gaur V (2008) Editorial:empirical elephants - why multiple methods are essential to qual-ity research in operations and supply chain management. J OperManag 26(3):345–348

Sternbeck M, Kuhn H (2010) Differenzierte Logistik durch ein seg-mentiertes Netzwerk im filialisierten Lebensmitteleinzelhandel.In: Schonberger R, Elbert R (eds) Dimensionen der Logistik.Gabler, Wiesbaden, pp 1009–1038

Taylor JC, Fawcett SE (2001) Retail on-shelf performance of adver-tised items: an assessment of supply chain effectiveness at thepoint of purchase. J Bus Logist 22(1):73–89

Tempelmeier H (2006) Inventory management in supply networks:problems, models, solutions. Books on Demand, Norderstedt

The Economist Intelligence Unit (2010) Switzerland: consumer goodsand retail report, London

The Nielsen Company GmbH (2010) Universen 2010 Deutschland.Handel - Verbraucher - Werbung. http://www.de.nielsen.com/pubs/documents/Universen 2010 2.pdf

Trautrims A, Grant DB, Fernie J, Harrisson T (2009) Optimizing on-shelf-availability for customer service and profit. J Bus Logist30(2):231–247

Trautrims A, Grant DB, Schnedlitz P (2010) In-store logisticsprocesses in Austrian retail companies. European Retail Res25(1):63–84

Van der Vlist P (2007) Synchronizing the retail supply chain: syn-chroniseer de retail supply chain, ERIM Ph.D. series research in

http://ssrn.com/abstract=1635752

http://www.de.nielsen.com/pubs/documents/Universen_2010_2.pdf

http://www.de.nielsen.com/pubs/documents/Universen_2010_2.pdf


management, vol 110. Erasmus Research Inst. of Management(ERIM), Rotterdam

Waller M, Williams B, Tangari A, Burton S (2010) Marketing atthe retail shelf: an examination of moderating effects of logis-tics on SKU market share. J Acad Market Sci 38(1):105–117

Waller MA, Tangari AH, Williams BD (2008) Case pack quantity’seffect on retail market share: an examination of the backroomlogistics effect and the store-level fill rate effect. Int J Phys DistribLogist Manag 38(6):436–451

Wensing T (2011) Periodic review inventory systems: performanceanalysis and optimization of inventory systems within supplychains, lecture notes in economics and mathematical systems,vol 651. Springer, Berlin, Heidelberg

Van Zelst S, van Donselaar K, van Woensel T, Broekmeulen R,Fransoo J (2009) Logistics drivers for shelf stacking in groceryretail stores: potential for efficiency improvement. Int J Prod Econ121(2):620–632

Zentes J, Morschett D, Schramm-Klein H (2007) Strategic retailmanagement: text and international cases. Gabler, Wiesbaden

integrative retail logistics: an exploratory study

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