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Journal of Operations Management 30 (2012) 269–281

Contents lists available at SciVerse ScienceDirect

Journal of Operations Management

jo ur nal home page: www.elsev ier .com/ locate / jom

ustomer-facing supply chain practices—The impact of demand and distributionanagement on supply chain success

aniel Rexhausena,∗, Richard Pibernikb,c, Gernot Kaiserd

EBS Business School, Department of Supply Chain Management & Information Systems, Konrad-Adenauer-Ring 15, 65187 Wiesbaden, GermanyJulius-Maximilians University Würzburg, Chair of Logistics and Quantitative Methods, Sanderring 2, 97070 Würzburg, GermanyZaragoza Logistics Center, C/ Bari 55, Edificio Náyade 5, 50197 Zaragoza, SpainTechnical University Darmstadt, Department of Law & Economics, Hochschulstraße 1, 64289 Darmstadt, Germany

r t i c l e i n f o

rticle history:eceived 13 December 2009eceived in revised form 23 January 2012ccepted 1 February 2012vailable online 10 February 2012

eywords:upply chain performanceupply chain management practicesemand managementistribution managementurvey research

a b s t r a c t

Traditionally, distribution has been viewed as the key (physical) link between a company’s internal supplychain activities and its customers. More recently, demand management has emerged as a new dimensionat the customer interface. Although it has become increasing popular in industry, it has not yet been ana-lyzed in depth with respect to its impact on supply chain performance. Both distribution managementand demand management entail customer-facing processes and practices and that are interrelated and(may) jointly determine supply chain performance. In this paper we seek to extend the stream of researchin supply chain management by systematically investigating the impact of customer-facing supply chainpractices on supply chain performance. Specifically, the paper examines the relative impact of relevantpractices associated with demand and distribution management. To this end, we collected data from 116multi-national companies based in Europe and analyzed it using structural equation modeling techniques.Our results suggest that (i) high demand management performance has a substantial positive impact onthe overall supply chain performance, (ii) this effect is stronger than that of distribution management

performance, and (iii) there is no evidence that demand management might be an enabler for effectivedistribution management. Among the individual practices that constitute demand and distribution man-agement, adherence to the demand and distribution management processes and demand segmentationemerged as the strongest performance levers. Based upon additional in-depth interviews conducted withselected companies from our sample, we shed light on some of the most important findings that emergedfrom our survey analysis.

. Introduction

The past two decades have witnessed a fast-growing interest ofractitioners and researchers alike in supply chain management’sontribution to corporate success. Today, there is a substan-ial amount of empirical evidence on the relative importance ofraditional SCM practices and dimensions such as purchasing, man-facturing, and distribution management (Narasimhan and Das,001; Mentzer et al., 2008). One comparably new SCM dimensionhat has become increasingly popular in industry, but has not yeteen extensively analyzed in academic literature with respect to

ts impact on supply chain performance, is demand management

DeM). In its broadest sense, DeM can be interpreted as the ability of

company to understand customer demand and requirements andalance them against the capabilities of the supply chain (Lambert

∗ Corresponding author. Tel.: +49 611 7102 2100.E-mail address: daniel.rexhausen@ebs.edu (D. Rexhausen).

272-6963/$ – see front matter © 2012 Elsevier B.V. All rights reserved.oi:10.1016/j.jom.2012.02.001

© 2012 Elsevier B.V. All rights reserved.

and Cooper, 2000; Croxton et al., 2002). While traditionally, DeMhas been understood as “demand forecasting”, a number of newpractices have been identified that constitute the DeM dimension.Examples of such practices include customer and product segmen-tation as well as integrated sales and operations planning (S&OP)(Grimson and Pyke, 2007; Lapide, 2008).

Anecdotal evidence from industry demonstrates the tremen-dous impact that good DeM – or a lack of it – may have on companyperformance. Probably the most prominent case is network titanCisco Systems, who in 2001s economic downturn failed to antici-pate the decline in demand due to a lack of demand and inventoryvisibility. As a consequence, Cisco had to write off USD 2.2 billioninventory and cut staff by 18% (Byrne and Elgin, 2002). An exam-ple demonstrating the positive business impact of DeM is the flashmemory producer SanDisk. By implementing a company-wide DeM

process, SanDisk within just one year was able to increase revenuesby almost 50% with at the same time 30% more on-time deliver-ies and 20% better inventory turns (Paganini and Kenny, 2007).However, evidence of the relevance and impact of DeM is more of

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70 D. Rexhausen et al. / Journal of Ope

necdotal character and originates largely from the practitioners’omain. Lapide (2007c), for example, states that excellence in DeM

s a major competitive advantage at the customer interface andepresents the next phase in the evolution of SCM. Some academicse.g., Croxton et al., 2002) also argue that DeM is an important driverf supply chain performance. So far, however, there is no conclu-ive empirical evidence to support this claim (see Section 2.1 for

review of the relevant literature). Thus, the first objective of ouresearch is to provide empirical evidence of whether or not DeMoes indeed have such a substantial positive impact on supply chainerformance.

Traditionally, establishing a link between a company’s inter-al (physical) operations and its customers was considered toe the major objective and integral task of distribution man-gement (DiM) (Williamson et al., 1990). Thus, past researchn customer-facing processes predominantly targeted DiM;umerous contributions provide evidence of the importance ofhis dimension for supply chain performance (Stewart, 1995;unasekaran et al., 2001). However, the impact of DeM and DiM and

heir individual practices should not be studied in isolation sincehey both entail practices and customer-facing processes that arenterrelated and may jointly determine supply chain performance.uch a joint investigation is also highly relevant from a practicaloint of view: of the top 10 supply chain “disasters” that are col-

ected and published by the online platform Supply Chain Digest,s much as 7 may be attributed to failures either in DeM or in DiMGilmore, 2009). Therefore, the second objective of our research ishe simultaneous evaluation of the impact of customer-facing SCMractices related to both DeM and DiM.

Considering the typical sequence of and relationships betweenifferent planning tasks in supply chain management, we canxpect a positive relationship between the performance of DeM andiM. It is reasonable to assume that companies with more effectiveeM practices should also exhibit better performance with respect

o DiM. An interaction effect between the two may also be conceiv-ble: high performance in DeM and DiM may jointly lead to superiorupply chain performance. As we will detail in Section 2, previousesearch has not provided clear evidence of whether or not suchelationships exist. Therefore, the third objective of our research ishe analysis of a potentially existing relationship between DeM andiM.

To meet our three research objectives we conducted an empir-cal analysis. Drawing from an extensive literature review andxpert interviews, we first identify relevant practices related toeM and DiM. We then develop a conceptual model and corre-

ponding hypotheses aligned with the three research objectivesutlined above. We utilize partial least squares (PLS) to test ourypotheses on the basis of survey data that was collected from sup-ly chain professionals of 116 multi-national companies based inurope.

. Theoretical foundation and model development

.1. Literature review and motivation

The relationship between various SCM practices and supplyhain performance is an extremely popular research field, which aubstantial body of scholarly work has contributed to (Gunasekarannd Kobu, 2007; van der Vaart and van Donk, 2008). In the followinge review the work that has addressed the impact of customer fac-

ng processes and practices on supply chain or firm performance.

ockamy III and McCormack (2004) investigate the link betweenCM practices and supply chain performance based on the supplyhain operations reference (SCOR) model (Supply Chain Council,008). They find that those practices with the highest impact

s Management 30 (2012) 269–281

include “demand planning”, “supplier transactional collaboration”,“make planning” processes, and delivery process measurement. Intheir study, “demand planning” is interpreted as demand forecast-ing, whereas other demand planning dimensions such as definingcustomer and product priorities were combined with more generalitems such as supply chain performance measurement into a factornamed “supply chain collaborative planning”. Research examiningthe interface between a company’s internal supply chain activi-ties and its customers has predominantly focused on distribution.Stewart (1995), for instance, identifies distribution performance asthe key element of SCM excellence that drives customer satisfac-tion. Later, Gunasekaran et al. (2001) proposed four links for anintegrated supply chain and classified distribution as the link deal-ing with customers. In addition, multiple authors have studied indetail different distribution practices such as transportation/carriermanagement and warehouse/inventory management and haveargued their positive performance impacts (e.g., Williamson et al.,1990; Baker, 2008). More recently, the understanding of customer-related SCM processes and practices has been complemented byoverarching planning practices labeled as DeM. However, compa-rably little work has been conducted in this field; contributionsgenerally focus only on isolated aspects of DeM, such as forecast-ing or S&OP, and originate largely from practitioners (e.g., Lapide,2006; Mentzer, 2006). Moreover, empirical evidence of any posi-tive impact DeM as such might have on supply chain performanceis generally scarce and has foremost anecdotal character (e.g.,Bower, 2006; Milliken, 2008). Among the few academic works deal-ing with DeM more comprehensively are Croxton et al. (2002),who, as previously mentioned, provide a conceptual frameworkfor DeM, which they use to examine a company’s DeM processand its successful implementation with a focus on forecasting andsynchronizing demand and supply. Croxton et al. argue that goodDeM has a positive influence on distribution performance as wellas overall supply chain performance; they do not, however, pro-vide any empirical evidence for this claim. In another work, Zhouand Benton Jr. (2007) examine the relationship between supplychain planning practices, including aspects of DeM such as fore-casting, supply/demand balancing, and distribution performance.In contrast to the conjectures made by Croxton et al. (2002), theydo not find empirical evidence supporting a positive relationshipbetween DeM activities and distribution performance. However,their conceptualization does not consider all previously identi-fied DeM dimensions (e.g., segmentation, S&OP) and they onlytest the link between DeM and one particular dimension of sup-ply chain performance (delivery performance); their analysis doesnot address the relationship between DeM and overall supplychain performance. In the study of Lockamy III and McCormack(2004), DeM and DiM are identified among the most importantSCM planning categories; their study provides evidence for posi-tive performance effects of demand- and distribution-related SCMpractices. Lockamy III and McCormack (2004), however, primar-ily examine the forecasting element of DeM. Furthermore, theymeasure supply chain performance only as a separate self-assessedrating of each supply chain management area (plan, source, make,deliver) and not, as widely accepted, in terms of cost, service andflexibility (e.g., Beamon, 1999; Gunasekaran et al., 2004). In addi-tion, they do not investigate any interrelationships between DeMand DiM.

A number of contributions have been made with respect to indi-vidual practices that can be subsumed under the term DeM. Amongthe different works concerned with individual DeM practices,one stream dealing with the impact of forecasting on opera-

tional performance has emerged. Primarily based on modeling andsimulation methods, several authors report positive relationshipsbetween accurate forecasts and specific performance dimensions,such as inventory levels, replenishment costs and service levels

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e.g., Gardner, 1990; Chen et al., 2000). One recent contribution haseen made by Smith and Mentzer (2010), who provide empiricalvidence of a direct relationship between the utilization of forecastsor logistics decisions and logistics performance. In contrast to fore-asting, demand segmentation (constituting a specific practice ineM) has received comparably little attention in the operations and

upply chain management literature. While practitioners empha-ize the importance of customer and product segmentation formproving DeM and supply chain performance (e.g., Lapide, 2008;

ercier et al., 2010), this linkage has not yet been addressed in thecademic literature. A substantial body of scholarly work on seg-entation has emerged in the marketing domain (e.g., Hofstede

t al., 1999; Foedermayr and Diamantopoulos, 2008). However, asoedermayr and Diamantopoulos (2008) acknowledge in their lit-rature review on segmentation, most contributions are conceptualr normative, dealing with how segmentation should be done orrguing how it adds to achieving competitive advantage, ratherhan providing empirical insights. Moreover, of the few empiricaltudies they list, none analyzes segmentation practices in con-unction with other DeM practices, nor are they linked to a firm’soperational or supply chain) performance.

Another particular stream of literature that is worth mention-ng in a DeM context is dedicated to S&OP, a formalized approach tonternal integration with the primary objective to effectively bal-nce supply and demand by aligning the different internal functionsnvolved (Tohamy, 2008; Atkinson, 2009). Anecdotal evidence doc-menting the positive performance impact of S&OP is manifold and

ists, for example, reduced procurement, inventory and logisticsosts, better customer service and ultimately increased corporateerformance among the value opportunities of S&OP (e.g., Bower,006; Muzumdar and Fontanella, 2006). However, research on&OP is again rather limited and foremost conceptual. One studyorth mentioning is that of Grimson and Pyke (2007), who, based

n a literature review and some company interviews, develop aonceptual framework for S&OP, which is split into the five dimen-ions: meetings and collaboration, organization, measurements,nformation technology, and plan integration. A number of researchontributions have popularized the term “demand chain manage-ent” (DCM) (e.g., Frohlich and Westbrook, 2002; Heikkilä, 2002).CM can be considered as a set of practices for managing andoordinating the supply chain from end-customers backwards touppliers (Frohlich and Westbrook, 2002). DCM requires exten-ive up- and downstream integration between all business partnersnd a key concept in DCM is the so-called demand integration;emand integration typically relies on information technologiesincluding the Internet) and involves shared data between plan-ing and control systems (Frohlich and Westbrook, 2002). In theirtudy, Frohlich and Westbrook provide evidence of the positiveffects of demand and supply integration on (supply chain) perfor-ance. Their research is closely related to numerous publications

edicated to “supply chain integration”. Literature has shown thatxternal integration, including customer/demand-side integration,an yield significant benefits, while on the contrary, the pictures not equally clear for internal integration. As our paper focusesn customer-facing management practices of one firm and not onollaboration practices between multiple firms, integration is con-idered in the form of internal integration practices, formalized asntegrated S&OP. Although we do not account for the impact ofxternal integration, we will see later in our analysis, that our con-eption of DeM with a strong focus on internal practices explains aubstantial part of the DeM performance.

From this summary of previous work, we observe that the

cademic SCM literature is lacking a comprehensive empiricalxamination of the relevant practices associated with a firm’s DeMnd its performance impact relative to that of other customer-elated SCM functions such as DiM. Thus, in line with the research

s Management 30 (2012) 269–281 271

objectives outlined in Section 1, we want to (i) study DeM in detail,covering the most important practices and their impact on per-formance, (ii) study DiM in detail, again covering most importantpractices and their impact on performance in a comprehensivemodel of customer-facing SCM practices, and (iii) study whetherthere exists a positive relationship between DeM and DiM.

2.2. Model development

To meet our three research objectives, we propose the concep-tual model presented in Fig. 1.

In order to theoretically develop the relationships in our model,we build upon the resource-based view (RBV) of the firm. The RBVanalyzes firms based on their resources and argues that if theseresources are valuable, rare, inimitable and non-substitutable, theyconstitute the basis for competitive advantage and superior perfor-mance of a firm (Wernerfelt, 1984; Barney, 1991). Those resourcesthat can be characterized as (complex) bundles of skills, knowl-edge and processes enabling firms to advantageously deploy theirassets, are typically classified as capabilities (Day, 1994; Eisenhardtand Martin, 2000). Along the lines of this reasoning, DeM and DiMperformance in our model are realized higher-order supply chaincapabilities that (may) contribute to enhance a firm’s supply chainperformance. DeM and DiM practices, such as demand segmenta-tion and warehouse management, constitute activities that eachentail specific skills, knowledge and processes and jointly createthese higher-order capabilities. Based on this notion we developthe individual hypotheses constituting or research model.

2.2.1. The impact of demand managementAccording to the existing literature, DeM can be interpreted as a

firm’s capability to understand customers’ demand and require-ments and balance them against the capabilities of the supplychain (Lambert and Cooper, 2000; Croxton et al., 2002). Literature(both academic and from the practitioners domain) discusses dif-ferent practices that, if effectively implemented, may create thiscapability, i.e. determine how well a company is able to balancecustomers’ demand and requirements with the capabilities of thesupply chain. These practices include demand forecasting, segmen-tation, S&OP, and DeM adherence. In order to be able to effectivelybalance demand and supply, a company needs to know as accurateas possible what the future demand of their customers will be. Thisis the fundamental task of a firm’s demand forecasting and requiresa clear-cut and regularly modified methodology that processes allavailable historic and current demand information from internaland external sources (Croxton et al., 2002; Mentzer, 2006). Hav-ing accurate and reliable forecasts decreases uncertainty and thushelps firms to optimize their inventory levels and replenishmentpolicies, and to increase service levels (e.g., Gardner, 1990; Chenet al., 2000). In this sense, effective forecasting practices constitutea resource that contributes to a firm’s DeM performance.

H1. The extent to which effective forecasting practices are imple-mented is positively related with DeM performance

Demand segmentation bundles practices related to the iden-tification of key (groups of) customers and product-specificrequirements (Lambert and Cooper, 2000; Childerhouse et al.,2002). Thus, it plays a critical role in a firm’s DeM, as each firm hasits individual customer base and product offering. By segmentingon the one hand their customers into disjoint groups characterizedby different product and service requirements, and on the otherhand their portfolio according to the supply chain characteristics

and requirements of their products (e.g., make-to-stock vs. make-to-order), firms accumulate unique knowledge and skills to betterunderstand the current and future demand and the capacities tosatisfy this demand (e.g., Lambert and Cooper, 2000; Lapide, 2008).

272 D. Rexhausen et al. / Journal of Operations Management 30 (2012) 269–281

performanceSC

Forecasting Segmentation S&OPDeM

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n line with this reasoning, we consider demand segmentation a keyesource enabling the enhancement of a firm’s DeM performance.

2. The level of demand segmentation is positively related witheM performance

To ensure the efficiency of flows throughout the entire sup-ly chain, firms seek to synchronize customer demand with theupply chain’s purchasing, production, and distribution as well asverarching functions such as controlling. This activity is com-only referred to as integrated S&OP (Croxton et al., 2002). Through

ross-functional processes, regular information exchange and jointecision making, S&OP creates alignment between the different

nternal functions (Grimson and Pyke, 2007; Milliken, 2008) ands such unique knowledge and skills to surface and tackle cross-unctional issues with impact on inventory, cost and customerervice (e.g., Bower, 2006; Muzumdar and Fontanella, 2006). Asuch, S&OP practices add to a firm’s capability of effectively bal-ncing supply and demand (Tohamy, 2008; Atkinson, 2009).

3. The extent to which integrated S&OP is implemented is pos-tively related with DeM performance

Having the aforementioned practices in place might just note enough for a firm to achieve superior DeM performance. Oneay argue, for instance, that a highly complex forecasting algo-

ithm that is neither well understood nor rigorously applied maye far less effective than a quite simple one that is clear to everyone

n the organization and strictly followed. Therefore, we hypothe-ize that the level of adherence to the DeM processes, i.e., they areell-defined, implemented, and strictly complied with, plays an

mportant role for a firm to develop superior DeM capabilities.

4. The level of adherence to the DeM processes is positivelyelated with DeM performance

We positioned DeM performance as a realized capability tonderstand customers’ demand and effectively balance it with thewn supply. Knowing what customers exactly want and aligningupply chain capabilities, may enable firms to plan more accuratelynd with less uncertainty, which may result in improved order ful-llment in terms of quantity, speed and quality, lower inventory

evels and obsolescence and increased flexibility. Hence, we arguehat superior DeM performance helps firms to improve their overallupply chain performance (e.g., Lockamy III and McCormack, 2004;

apide, 2006).

5. DeM performance is positively related with supply chain per-ormance

ent adherence

uation model.

2.2.2. The impact of distribution managementDiM is commonly interpreted as a firm’s capability to ensure

reliable and efficient flow and storage of goods in order to meetcustomers’ requirements (Bowersox et al., 2007, p. 22; Frankelet al., 2008). In this sense, three important practices can be dis-tinguished that create this capability: warehouse management andtransportation management (Larson et al., 2007), which both focuson operations related to efficient storage and handling of goodsand materials, and DiM adherence. Warehouse management com-monly bundles practices related to the efficient storage, handlingand picking of raw materials, work in progress, and finished goodsinventories (Bowersox et al., 2007, p. 22; Frankel et al., 2008).By optimizing warehouse structures in terms of size, locations,resources, technology and automation, firms may improve theirdistribution capabilities significantly. A high degree of automa-tion supported by state-of-the-art information technology may, forinstance, increase internal process efficiency and reliability throughless manual labor and errors, ultimately resulting in lower cost andinventory levels as well as better delivery service and higher flex-ibility (e.g., Baker, 2008; de Koster and Balk, 2008). In this sense,effective warehouse management practices constitute a resourcethat improves a firm’s DiM performance.

H6. The extent to which effective warehouse management isimplemented is positively related with DiM performance

To ensure the efficient flow of materials and goods from the sup-pliers via different internal facilities to the customers, firms seek tooptimize their transportation networks. This activity is commonlyreferred to as transportation management. Holistically optimiz-ing transport routes and schedules and bundling transports toachieve economies of scales may increase utilization and decreasetransportation cost significantly without sacrificing customer ser-vice (e.g., Thomas and Griffin, 1996; Gunasekaran et al., 2001). Inthis sense, effective transportation management practices add to afirm’s capability of ensuring efficient flow of goods.

H7. The extent to which effective transportation management isimplemented is positively related with DiM performance

Again, having certain DiM practices in place might not be suf-ficient. One may argue, for instance, that the latest warehouseautomation technology, if not fully understood or leveraged, maybe far less effective than a more traditional and less automated onethat is clear and consequently applied by everyone. Therefore, we

hypothesize that the level of adherence to the DiM processes, i.e.,they are well-defined, implemented, and strictly complied with,plays an important role for a firm to develop superior DiM capabil-ities.

rations Management 30 (2012) 269–281 273

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Table 1Demographics of respondents.

Percentage of respondents

Position within the firmDirector 38.8Head of department 25.0Vice president 15.5CxO 4.3Others 16.4

Annual revenue (2008)EUR 5 billion and above 12.9EUR 1 billion to less than EUR 5 billion 22.4EUR 500 million to less than EUR 1 billion 11.2EUR 250 million to less than EUR 500 million 15.5EUR 100 million to less than EUR 250 million 12.1Less than EUR 100 million 16.4N/A 9.5

Industry sectorManufacturing 71.6

Consumer products 19.8Raw materials and chemical products 16.4Machinery 11.2Pharmaceutical and healthcare products 8.6Hightech 6.0Electronic and other/electrical equipment 5.2Automotive 3.4

D. Rexhausen et al. / Journal of Ope

8. The level of adherence to the DiM processes is positivelyelated with DiM performance

We characterized DiM performance as a realized capability tonsure a reliable and efficient flow and storage of goods. Throughptimized transportation and warehousing across the entire logis-ics network, e.g., better utilization and higher automation, thisapability should aid firms to lower overall supply chain cost,ncrease speed and flexibility and improve customer service inerms of more ‘on time in full’ deliveries (e.g., Gunasekaran et al.,001; Rodrigues et al., 2004). Hence, superior DiM performancehould improve a firm’s overall supply chain performance.

9. DiM performance is positively related with supply chain man-gement performance

.2.3. Interrelationships between demand and distributionanagement

Considering the typical sequence of and relationships amongifferent planning practices in supply chain management, we canxpect that companies with more effective DeM practices shouldot only exhibit better DeM performance but also better perfor-ance with respect to their DiM. A systematic DeM approach, for

nstance, that provides an accurate prediction of which customerseed to be delivered with which quantities of products within aertain time, also serves as a basis for more efficient logistics andistribution (Croxton et al., 2002; Taylor, 2006). Thus, we arguehat clear customer segmentation, clear-cut and executed forecast-ng processes as well as integrated and aligned S&OP do not onlyonstitute key resources to enhance a firm’s DeM performance, buthould also provide DiM with a robust and reliable informationasis for effectively managing its activities. Hence, we formulatehe last hypothesis of our model:

10. DeM performance is positively related with DiM perfor-ance

. Research design and methodology

.1. Survey instrument

In order to design and validate an appropriate survey instru-ent, we undertook an extensive review of the literature to identify

cales used in past research. Newly created scales were based uponhe literature and Churchill’s (1979) paradigm of developing effec-ive measures for theoretical constructs.

Prior to data collection, the initial survey instrument was pre-ested for content validity. A panel of 5 researchers familiar withhe constructs employed and 22 practitioner experts were askedo critique the questionnaire as regards structure, clarity, ambigu-ty, appropriateness, and completeness. Having reviewed the fewomments (mainly wording), the survey instrument was modifiedccordingly.

.2. Data collection

A combination of mail and online survey was used for dataollection. The target sample frame consisted of members ofhe Bundesvereinigung Logistik (BVL)1 drawn from multi-national

rms based in Europe from various economic sectors and coverednder the International Standard of Industrial Classification of Allconomic Activities (ISIC) codes between 15 and 52. These include

1 BVL is one of Europe’s largest professional logistics and supply chain manage-ent associations.

Wholesale and retail trade 19.0Others 9.4

manufacturing of consumer products, raw materials and chemi-cal products, machinery, pharmaceutical and healthcare products,high-tech, electronic and other electrical equipment and automo-tive products as well as retail and wholesale trade. The title of therespondents targeted was typically director/head of supply chainmanagement or logistics. In an effort to maximize the response rate,a modified version of Dillman’s (1978) total design method was fol-lowed. All mailings, including a personalized cover letter and thesurvey, were sent via first-class mail. In order to make the submis-sion as convenient as possible, participants were offered severaloptions for returning the questionnaire (online, via mail, or viafax). Three weeks after the initial mailing, personalized remindere-mails were sent to all potential participants. Those who did notrespond within six to eight weeks after the initial mailing, receiveda reminder telephone call. Of the 817 surveys mailed, 136 werereturned due to address errors, or because the contact person wasno longer with the firm or in the department. This reduced the sam-ple size to 681, of which 116 responses were received, resulting in aresponse rate of 17.0%, in line with response rates of other surveystargeting members of professional organizations (van der Vaart andvan Donk, 2008). Respondents were, for the most part, middle andsenior level supply chain executives within their organization. Asshown in Table 1, the responding firms represented a wide range ofindustries. Also, firm revenues were well represented, with aboutone third of all respondents reporting revenues in excess of EUR 1billion, while approximately another third represented firms withrevenues of less than EUR 250 million.

To test for the existence of a potential non-response bias, theresponses of early responders were compared to late respondersto detect any statistical differences (Armstrong and Overton, 1977;Lambert and Harrington, 1990). The results did not point towardsa non-response bias in our sample.

3.3. Measures

All ten constructs introduced in Section 2 constitute latent vari-

ables requiring indirect measurement (Churchill, 1979; Bagozziand Phillips, 1982). As the constructs in our study are managementpractices and performances, which, from their inherent mean-ing, reflect (i.e., cause) their indicators, they were specified

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o be reflective (Diamantopoulos and Winklhofer, 2001;iamantopoulos et al., 2008). All indicators were selectedased on an extensive literature review as well as evidence fromractitioners. The individual items which were used to measurehe constructs in our research model are described in Appendix A.n Appendix B, we also list the references that were used for theevelopment of the measures.

As it is generally very difficult to obtain objective data on a firm’sperational and financial issues (Narasimhan and Das, 2001), exist-ng SCM survey research primarily relies on subjective measures oferformance (van der Vaart and van Donk, 2008). Following thispproach like many other SCM researchers (e.g., Tan et al., 2002;utry et al., 2005), this survey also relied on executives’ perceptionsf their firms’ performance in the different dimensions. In line withast practice, the construct supply chain performance is opera-ionalized by items indicating a firm’s competitive position in termsf supply chain cost, service level and flexibility (Beamon, 1999;unasekaran et al., 2004). A 5-point Likert scale ranging between

strongly agree” and “strongly disagree” was used to measure thetems. As it is common practice to assess supply chain performancey relating it to the performance of major competitors (van deraart and van Donk, 2008), the supply chain performance itemsere measured using a 5-point Likert scale ranging between “much

etter” and “much worse” compared with the (best) competitors.he questionnaire is included in Appendix A.

As one respondent from each firm provided the data for ourtudy, concerns of common method variance may be raised. There-ore, we conducted Harman’s single-factor test (Podsakoff andrgan, 1986), the most widely used method to evaluate the possi-ility of common method variance (Podsakoff et al., 2003). We didot find any general factor that accounted for the majority of theariance in these variables (10 factors emerged in the exploratoryactor analysis: the variance explained by the first and second fac-ors were 12% and 11% respectively; the last factor explained 5% ofhe variance). Therefore, we conclude that common method vari-nce is not a problem in our study (Podsakoff and Organ, 1986).

.4. Analytical procedure

The partial least squares (PLS) structural equation model-ng technique was applied to test our research model. PLS is aomponents-based approach to structural modeling and has lowerample size requirements than traditional covariance-based tech-iques such as LISREL (Chin et al., 2003; Braunscheidel and Suresh,009). In particular, PLS avoids the problems inherent in small sam-le sizes, does not require normally distributed data and providesore conservative estimates of the individual path coefficients

ompared with covariance-based techniques (Chin, 1998; Henselert al., 2009), which constitute the major reasons why PLS is appliedo analyze the data in our sample. Several software packages sup-ort PLS; we used SmartPLS version 2.0 (Ringle et al., 2005).

Indicator reliability was tested using a bootstrapping procedureith 500 randomized samples taken from the original sample and

f original cardinality (Henseler et al., 2009). As shown in Table 2, allstimates of the outer loadings exceed the recommended minimumalue of .7 and exhibit sufficient t-values. When testing for indicatoreliability, convergent validity is also assessed, as loadings greaterhan .7 imply that the indicators share more variance with theirespective constructs than with the error variances (Chin, 1998).o assess construct reliability, Cronbach’s alpha value (˛) and com-osite reliability (CR) were determined. As depicted in Table 2, the

for the constructs are all above the suggested cut-off value of .7

Cronbach, 1951; Litwin, 1995). Similar results were observed forhe CR values, which were all greater than .8 and as such above theuggested cut-off value of .6 (Bagozzi and Yi, 1988; Henseler et al.,009). Convergent validity was assessed using the average variance

s Management 30 (2012) 269–281

extracted (AVE). As depicted in Table 2, the AVE is in all cases abovethe recommended value of .5 (Fornell and Larcker, 1981; Henseleret al., 2009). AVE was also used to evaluate discriminant validity.Table 3 indicates the correlations between the latent variables andthe square roots of AVE on the diagonal. As the square root of AVE isin each case greater than the correlation among the latent variablescores with respect to its corresponding row and column values,we can conclude that none of the constructs shares more variancewith another construct than with its own indicators, thus exhibit-ing sufficient levels of discriminant validity (Fornell and Larcker,1981; Henseler et al., 2009). Content validity does not have a for-mal statistical test, but is augmented in this study by a thoroughfoundation of the model (see Section 2) based on the relevant lit-erature and the review of the survey instrument by a panel of bothacademic and practitioner experts, as pointed out in Section 3.1.To assess the structural model’s prediction relevance, we applieda blindfolding procedure with an omission distance of 5 (Henseleret al., 2009). All resulting Q2 values are larger than zero, indicatingsufficient predictive power of the structural model (Stone, 1974;Geisser, 1975).

3.5. Results of analysis

The results from the evaluation of the structural model areshown in Fig. 2 and reported in Tables 4 and 5. According to Chin(1998), the R2 values of the endogenous latent variables DeM per-formance (R2 = .60) and DiM performance (R2 = .59) are substantial,while the R2 value of supply chain performance (R2 = .20) is weakto moderate from a statistical point of view. In our specific con-text, however, an R2 of .20 can be considered quite substantial,because there are other SCM practices (e.g., purchasing or man-ufacturing management), which certainly impact the supply chainperformance, but are not included in our model.

The significance of the relationships among the latent variableswas tested using the associated t-statistics obtained from PLS boot-strapping. As can be seen from the results reported in Table 4, sevenof the ten hypotheses can be confirmed, of which H2, H4, H5 andH8 are significant at the .01 level, H6 and H9 are significant at the.05 level and H3 (S&OP) is significant only at the .10 level.

In Table 5 we report effect sizes f2 and the Q2 values for theStone–Geisser criterion for the structural model’s latent variables.The effect size f2 describes the increase in R2 relative to the pro-portion of variance of the endogenous construct that remainsunexplained (Cohen, 1988). According to Cohen (1988), for theendogenous latent variable DeM performance, the f2 of demandsegmentation and DeM adherence signify relatively large effects;for DiM performance, only the variable DiM adherence exhibits alarge effect. In the case of overall supply chain performance, theeffect sizes of DeM and DiM performance range between smalland medium, with that of DeM performance being almost twiceas large as that of DiM performance. Table 5 also reports the latentvariable scores and respective 95% confidence intervals, of whichnon-overlapping intervals allow ranking of constructs in terms ofhigher maturity/level of implementation. From there, we observethat the constructs associated with DiM (performance and adher-ence) exhibit substantially higher latent variable scores than thoseassociated with DeM (performance, adherence and segmentation).

Overall, the results of our analysis indicate a good model fit withsubstantial effects and predictive power.

4. Discussion of results

Our model and the corresponding analyses contribute to andextend a growing research stream documenting the impact of dif-ferent SCM practices on supply chain performance by (i) providing a

D. Rexhausen et al. / Journal of Operations Management 30 (2012) 269–281 275

Table 2Overview of indicators and measures of reliability and validity.

Constructs and indicators Outer loadings 95% Confidence interval

Point estimation t-Value Lower bound Upper bound

Demand forecasting ( ̨ = .78, AVE = .69, CR = .87)DeF1 Clear-cut forecasting process .795 10.56 .784 .794DeF2 Regular measurement and modification .815 14.40 .792 .806DeF3 Monitoring and acting upon accuracy .880 29.76 .877 .883

Demand segmentation ( ̨ = .71, � = .549, AVE = .77, CR = .87)DeS1 Customer segmentation .863 21.07 .855 .863DeS2 Product segmentation .896 38.73 .896 .900

Sales & operations planning ( ̨ = .74, AVE = .65; CR = .85)SOP1 Fully integrated S&OP processes .869 31.92 .867 .872SOP2 Organization follows S&OP processes .840 21.11 .831 .837SOP3 Decision makers participate in S&OP meetings .705 9.85 .695 .707

Demand management adherence ( ̨ = .81, AVE = .72, CR = .89)DeP1 Monitoring and acting upon demand management KPIs .765 14.76 .757 .766DeP2 Clear process definition .887 43.31 .885 .889DeP3 Strict process execution .890 51.45 .889 .892

Demand management performance ( ̨ = .78, � = .638, AVE = .82; CR = .90)PDe1 Achieving desired performance .903 45.31 .900 .904PDe2 Meeting operational business needs .907 48.49 .905 .909

Warehouse management ( ̨ = .77, AVE = .68, CR = .87)WM1 Achieving desired performance of facilities and processes .797 20.54 .793 .800WM2 Sufficient warehouse automization .887 30.73 .880 .885WM3 state-of-the-art WMS .793 18.07 .787 .795

Transportation management ( ̨ = .72, � = .568, AVE = .78, CR = .87)TM1 Optimization of transport modes and routes .836 13.64 .829 .839TM2 Optimization of scheduling and routing .924 44.77 .923 .927

Distribution management adherence ( ̨ = .79, AVE = .70, CR = .88)DiP1 Monitoring and acting upon distribution management KPIs .698 10.64 .691 .703DiP2 Clear process definition .921 70.53 .919 .922DiP3 Strict process execution .882 40.66 .882 .885

Distribution management performance ( ̨ = .71, � = .556, AVE = .76, CR = .76)PDi1 Achieving desired performance .848 17.50 .840 .848PDi2 Meeting operational business needs .901 47.33 .904 .904

Supply chain performance ( ̨ = .73, AVE = .65, CR = .85)PSC1 Supply chain cost .744 9.92 .732 .745PSC2 Supply chain service level .878 25.32 .872 .878PSC3 Supply chain flexibility .797 12.89 .784 .795

˛ .�

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– Cronbach’s alpha; AVE – average variance explained; CR – composite reliability–inter-item correlation for 2-item construct.

omprehensive model of customer-facing SCM practices, integrat-ng relevant practices associated with both DeM and DiM; and (ii)y demonstrating that DeM has an even stronger positive impactn supply chain performance compared with DiM. In particular, ourndings provide evidence supporting statements by practitionerss regards the positive impact of DeM on supply chain performancee.g., Lapide, 2006, 2007a). Although we expected a direct positiveelationship between DeM and DiM, we did not find any empiricalvidence supporting the existence of such a relationship. This is in

ine with the results of Zhou and Benton Jr. (2007) who also did notnd empirical support for a direct relationship between DeM andiM. To further explore this finding, we provide additional insights

rom our survey data as well as from interviews with participants

able 3orrelations between constructs.

Construct DeF DeS SOP

Demand forecasting (DeF) .83Demand segmentation (DeS) .36 .88Sales & operations planning (SOP) .47 .42 .81Demand management adherence (DeP) .52 .39 .51

Demand management performance (PDe) .50 .58 .52

Distribution management adherence (DiP) .33 .36 .44

Warehouse management (WM) .31 .36 .26

Transportation management (TM) .39 .23 .32

Distribution management performance (PDi) .33 .31 .38

Supply chain performance (PSC) .32 .36 .21

quare root of AVE on diagonal in bold face.

on why this may be the case and why this might change over time,as DeM practices mature.

In general, our results provide evidence that DeM and DiM per-formance strongly impact a supply chain’s overall performance. Weobserve that, in our sample of companies, DeM performance has astronger impact on the overall supply chain performance (b = .28) ascompared with the performance of DiM (b = .24; confidence inter-vals not overlapping). This is in line with the latent variable scoresdisplayed in Table 5. On average, companies perceive their distri-

bution performance to be higher (mean of 3.97) with less variability(standard deviation of .69) than their DeM performance (mean of3.27; standard deviation of .91). These results reflect the fact that,while DeM is a comparatively new dimension that has received

DeP PDe DiP WM TM PDi PSC

.85

.68 .91

.60 .50 .84

.43 .34 .47 .82

.35 .32 .53 .40 .88

.49 .46 .73 .54 .48 .87

.31 .39 .26 .36 .34 .37 .81

276 D. Rexhausen et al. / Journal of Operations Management 30 (2012) 269–281

performanceSC

Forecasting Segmentation S&OPDeM

adherence

performanceDiM

performanceDeM

Transport

management

Warehouse

management

DiM

adherence

0.09 0.32*** 0.46***0.10*

0.09

0.23** 0.08 0.53***

0.28***

0.24**

R² = 0.60

R² = 0.20

R² = 0.59

*** Significant at 0.01 level, ** Significant at 0.05 level, * Significant at 0.10 level

Fig. 2. Complete model of customer-facing SCM practices and performance.

Table 4Path coefficients and R2 of structural model.

Constructs and indicators Path coefficients 95% Confidence interval Hypotheses

Point estimate t-Value Lower bound Upper bound

PDe (R2 = .599)DeF .094 1.21 .087 .101 H1 RejectedDeS .323 4.88 .318 .330 H2 SupportedSOP .103 1.46 .104 .117 H3 SupportedDeP .456 5.40 .444 .458 H4 Supported

PDi (R2 = .589)WM .233 2.47 .217 .234 H6 SupportedTM .079 1.13 .075 .087 H7 RejectedDiP .531 6.53 .530 .545 H8 SupportedPDe .087 .98 .084 .100 H10 Rejected

PSC (R2 = .199)PDe .281 2.65 .271 .290 H5 SupportedPDi .241 2.43 .240 .257 H9 Supported

Table 5Effect size, prediction relevance and latent variable scores.

Q2 f2 Latent variable scores

PDe PDi PSC Mean STDEV 95% Confidence interval

Lower bound Upper bound

DeF .392 .01 3.67 .87 3.51 3.84DeS .224 .20 3.44 .98 3.25 3.62SOP .324 .02 3.25 .88 3.09 3.42DeP .459 .32 3.44 .80 3.29 3.59PDe .470 .01 .07 3.27 .91 3.10 3.44DiP .419 .38 3.78 .76 3.63 3.92WM .316 .10 3.44 .92 3.27 3.62TM .266 .01 3.36 .89 3.19 3.53

Q

lllis

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PDi .442 .05

2 calculated with omission distance of 5.

ess attention, DiM is relatively more mature and maturity variesess across companies. Moreover, the results also suggest that aarger potential for improvement lies in the area of DeM, and thatmprovements in this area will have a stronger overall impact onupply chain performance compared with DiM.

The general managerial implication of this is that supply chainanagers should broaden their view of customer-facing SCM prac-

ices, rather than merely focusing on optimizing the (physical)istribution of goods. Instead, they should seek to improve theireM, which was shown in our study to be less mature and to be areater lever for supply chain success.

3.97 .69 3.84 4.10

4.1. DeM practices and performance

Out of the four DeM dimensions included in our model, twoexhibit significant positive effects and emerge as the strongestdrivers of performance in DeM: DeM adherence (b = .35; f2 = .21)and demand segmentation (b = .35; f2 = .19). Hence, applying dif-ferent fulfillment policies for different customer segments and

products, and adhering to clearly defined and strictly implementedDeM processes appear to be the key to successful DeM. This pro-vides empirical evidence for the often encountered “managerialwisdom” that it is important to define the right supply chain for

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product (e.g., Fisher, 1997), which requires obtaining an under-tanding of different customer needs and product requirementse.g., in terms of responsiveness and service levels), while, athe same time, defining and implementing fulfillment processesccordingly (e.g., Hall and Johnson, 2009).

Although S&OP is presumed to be very important in linking theustomer facing activities to internal activities such as productionlanning and inventory management (e.g., Lapide, 2007b; Milliken,008), we only find a weak effect of S&OP implementation on DeMerformance (b = .10; f2 = .02) which is significant only on a 10%

evel. Based on the results displayed in Table 5, we observe thathe overall implementation of S&OP is, with a mean of 3.25, least

ature compared with other practices. This is further supportedy the fact that only 13% of the surveyed companies reported thathey “strongly agree” when asked whether they have implementedully integrated S&OP processes, while only 7% “strongly agree”hat they rigorously follow these processes. The consistently lowevels of implementation of S&OP may be one explanation for theact that we did not find strong and significant evidence for a rela-ionship between S&OP and DeM performance. In order to furtherxplore this finding, we conducted personal interviews with theey informants of two companies from our sample. We ranked theompanies according to their supply chain performance constructcores and split this ranking into a top half, i.e., those 50% of com-anies with the highest construct scores, and a bottom half, i.e.,hose with the lowest construct scores. One of the intervieweesas drawn from the top half of participants (company A) and onerawn from the bottom half (company B). Company A is one ofhe leading European consumer goods manufacturers of foods andersonal care products. The interview partner of company A wasupply Chain Manager and responsible for manufacturing plantsnd distribution centers across Europe. Company B is a leading pro-ucer of pharmaceuticals. The interview partner of company B washe Head of Global Supply Chain Management. Also, we conducted

third interview with the Head of the Supply Chain Managementractice of a large consulting company (consultant) who was partf the expert panel that reviewed our survey instrument. All threenterview partners were confronted with specific findings of ournalysis and were asked to comment on these, based on their expe-ience within their own company (A and B) and across differentompanies (consultant). Regarding the (negligible) effect of S&OPn our sample, the respondent from company A stated that his com-any correctly defined and implemented an S&OP process alreadyome time ago, but until today did not see the full benefits real-zed. He identified the underlying cause in the complex naturef S&OP: company A’s S&OP process integrated decision makersrom the different departments and corporate functions involvedn supply chain management. Due to the cross-functional characterf S&OP, conflicts of interest between the participants frequentlyrose. As most of them were on an equal level in the corporateierarchy, there was no natural process owner with clear manage-ial authority to resolve these conflicts. This situation either led tolliance building and suboptimal decisions in favor of the major-ty, or it took relatively long to achieve a compromise solution. Inhe respondent’s view, both effects prevented the S&OP processrom unfolding its full benefits. The interview partner from com-any B shared a similar view with us: formally, company B hadlso defined and implemented an S&OP process, but in practice,ue to the high complexity, the process was not properly exe-uted. Production planning was, for instance, not (fully) alignedith the demand forecast, there was no data transparency across

he different units, production maintained a dominant role in the

upply chain, conflicts were not escalated to the executive levelnd company B finally failed to “reap the fruits of effective S&OP.”urther support stems from the interview with the consultant.is comments on the insignificant effect of S&OP in our model

s Management 30 (2012) 269–281 277

highlighted two particular aspects. First, he confirmed the argu-ment previously brought forward by companies A and B: the highlevel of complexity and cross-functional coordination led to manycompanies struggling with their S&OP initiatives and preventedthem from implementing a successful S&OP process. Moreover, theconsultant pointed out that S&OP was a comparably recent inno-vation and as such “one of the most wide-spread buzzwords inSCM” in the sense that many companies purported to have S&OP,but in fact they did not. Instead in most companies, S&OP in itstrue sense was still in its infancy or in other cases even had yetto find its way into the supply chain organization. However, healso stated that it still needed more time and experience with theprocess in order to see a broad substantial effect of S&OP mate-rialize. Altogether, this suggests that a major reason for the lowperformance effect of S&OP in our model might be found in the com-parably complex nature of S&OP. While segmentation, for example,requires less cross-functional coordination, S&OP calls for coor-dination of multiple stakeholders and functional divisions withdiffering responsibilities and incentives involved in supply chainmanagement, i.e., sales, marketing, manufacturing, and sourcing(Milliken, 2008). Also, the presumed performance effect of S&OPin our model might have been diluted by (i) a potentially existingmisconception about what fully implementing S&OP in practicereally means and (ii) an insufficient number of companies thatalready achieved satisfactory maturity levels in their S&OP pro-cesses to draw conclusions. Hence, our findings only imply thatS&OP does not (yet) have a direct and substantial impact on DeMperformance and thus should be the subject of further research –especially as higher maturity levels and better integration of S&OPacross different functions are reached. From a managerial stand-point, this implies that it is important for supply chain managersto understand the cross-functional nature of S&OP, cope with itscomplexity and implement the S&OP process in order to realize itspotential benefits. Naturally, we would then expect S&OP to play animportant role in efforts aimed at achieving superior performance.

4.2. DiM practices and performance

Of the three dimensions of DiM included in our model, twoexhibit significant positive effects on DiM performance: DiM adher-ence (b = .53) and warehouse management (b = .23). It is interestingto observe that in this dimension, adherence also proves to bea strong driver of performance – in this case even by far thestrongest (f2 = .38). This finding empirically confirms the impor-tance of business process management in an SCM context: effectivemanagement of processes – in terms of defining, improving, and,most importantly, adhering to the right processes – strongly con-tribute to improving operational performance and efficiency (e.g.,Benner and Tushman, 2003). Although the importance of processadherence is hardly surprising, it is interesting to observe that it stillhas a dominant effect on DiM’s performance relative to other prac-tices. The extent to which processes in distribution are successfullymanaged and adhered to still varies significantly across compa-nies, providing an explanation for much of the variation in DiM’sperformance. The effect of warehouse management also turned outto be significant, albeit moderate, compared with DiM adherence(f2 = .10). Recalling the individual items that were used to oper-ationalize this construct, we can conjecture that optimization ofwarehouse facilities and processes coupled with sufficient levelsof automation have a positive impact on distribution performance.Although transportation management is a frequent subject of dis-cussions in SCM and accounts for a substantial share of overall

supply chain cost (Thomas and Griffin, 1996; Gunasekaran et al.,2001), we do not find any effect on DiM performance in our model(b = .08; f2 = .01). This may be a manifestation of good transportationmanagement increasingly becoming a commodity for both high

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78 D. Rexhausen et al. / Journal of Ope

nd low performing supply chains. Another potential reason forhe negligible effect might be that companies no longer have fullransparency of their transportation, as it is frequently outsourcedTrunick, 2008; Dean, 2009). Looking at our sample, we find sup-ort for this argument: more than 70% of the participants state thathey have outsourced a substantial share of their transportationctivities, of which more than half have outsourced 75% or more.

Overall, we observe that the traditional practices of DiM arelready relatively mature and, thus, do not appear to be strongevers for achieving competitive advantage. Since we find that evenompanies with comparably moderate supply chain performancere quite advanced in managing their distribution, supply chainanagers should view DiM more as a pre-condition and not as a

ever for superior supply chain performance.Revisiting the performance effects we observed for both the DiM

nd DeM practices, another interesting managerial implication sur-aces: ranked by strength, they provide managers with a “priorityist” that indicates where to concentrate activities and resourcesrst in order to achieve substantial improvements quickly. In thisense, strong adherence to the DeM and DiM processes consti-utes the most impactful lever for generating superior supply chainerformance. Considering the relative importance of the differentractices, our results suggest that it is more important to have well-anaged processes that are strongly adhered to than to pursue

igher levels of sophistication in individual practices.

.3. The (missing) link between DeM and DiM

As mentioned in the above, it would be reasonable to considerood DeM as an enabler that enhances DiM performance. However,ur data does not provide statistical evidence to support this con-ecture. The influence of DeM performance on DiM performance is

eak (b = .09) and not statistically significant (see Table 4). More-ver, the coefficient of correlation between the two constructsf .46 is comparably moderate (see Table 3). This implies thatompanies that excel in DiM do not necessarily exhibit high DeMerformance and vice versa. To shed some more light on this find-

ng, we separately analyzed the top and bottom 25% of our sample inerms of supply chain performance. We observe that the top groupas higher average construct scores for DeM/DiM performance3.75/4.33) than the bottom group (2.70/3.58) as well as lowertandard deviations (.80/.52 vs. .97/.74), resulting in substantiallyifferent correlations between the two performance constructscoefficient of correlation of .55 for top 25% vs. .19 for bottom5%). From these results we conjecture that the top performers

ndeed tend to show good distribution performance in conjunctionith good performance in DeM, while this relationship is diluted

n our model by the low performing group. Naturally, we cannotnfer causality from the correlation of the construct scores and were therefore unable to conclude that high DeM performance isn enabler for high DiM performance. We do, however, observehat some companies in our sample are able to achieve high lev-ls of performance in both dimensions. In light of these results, annteraction effect between DeM and DiM performance could alsoe conceivable. We tested for the existence of such an interactionffect in a competing model. The results, however, did not provideny statistical evidence in favor of an interaction effect.

In the above mentioned interviews with companies A, B and theonsultant, we also asked our interview partners to comment on themissing) link between DeM and DiM performance. In general, allhree were not surprised to see an only weak and insignificant rela-ionship in our model. All three respondents attributed the missing

ink between DeM and DiM performance to the fact that DeM wasust one factor – and not the most important one – influencingistribution performance. Instead, the efficiency of production (orrocurement in case of retailers/wholesalers) was argued to be a

s Management 30 (2012) 269–281

much more important factor for the delivery of goods and thus themoderator between DeM and DiM. For instance, if there was notenough short-term capacity (or supply of goods) available or pro-duction management was not flexible enough, DeM could be highlyefficient; however, its effect would be limited by the production (orsupply) capabilities. In such situations the company would observelow DiM performance despite a well-performing DeM. This argu-ment is closely related to what was previously stated about S&OP: ifthe individual functions in SCM (especially DeM, production man-agement, DiM) are not aligned, the company will neither achievethe potential benefits of S&OP nor will good DeM translate intohigher levels of DiM performance. Thus, the potential explanationfor the missing link between DeM and DiM performance may bethat DeM and DiM are not closely integrated within SCM, withouta seamless flow of information and close synchronization of inter-related decisions (Lapide, 2007b). This synchronization is the majortask of S&OP (Croxton et al., 2002), which, in our sample, exhibitson average the lowest score among all latent variables (mean of3.25, see Table 5). On the one hand, we observe that the compara-bly weak effect of S&OP on performance (see Section 4.1) and themissing link between DeM and DiM performance may have verysimilar roots. On the other hand, it is also reasonable to assumethat both (non-) findings are closely related: a well-defined andimplemented S&OP process would, among other things, synchro-nize demand, production and distribution management activities.Hence, low levels of cross-functional process integration (reflectedby our results regarding S&OP) provide a potential explanation ofthe missing link between DeM and DiM performance. Based onthese results, we should be careful about drawing quick conclu-sions about the absence of joint effects. Our data only capturesthe current status for our sample companies and, as previouslymentioned, there is substantial correlation between DeM and DiMperformance for the top performing companies. Thus, as such man-agement practices become more established (as they are in DiM)and better integrated, we may very well observe stronger effects.

5. Conclusion and further research

In an era of globalized competition, effective management ofthe customer interface is key to sustainable supply chain success.A review of the relevant literature shows that the most recentapproach to customer-facing SCM practices entails both traditionalDiM and DeM practices, but still lacks sufficient empirical evidenceregarding the impact of the two dimensions and their key practices.Based on these premises, this paper endeavored to measure theinfluence of DeM and DiM practices on supply chain performance.A structural equation model was developed based on an extensiveliterature review and insights from practitioners. Based on surveydata obtained from 116 supply chain professionals, our analysisprovides evidence that efficient DeM and DiM positively impactthe performance of a firm’s supply chain. While the direct effectof DeM is stronger compared with that of DiM, we could not con-firm any substantial influence of DeM on DiM performance. Amongthe individual practices that constitute DeM and DiM, adherenceto DeM and DiM processes and demand segmentation emerged asthe major performance levers. The effects of other practices suchas warehouse management or S&OP turned out to be moderate atbest.

The results of our study raise a number of interesting questionsand reveal a need for further research in several areas. In partic-ular, there is a clear need to examine more closely relationships

that managerial intuition would predict to have positive perfor-mance effects, but for which our data did not provide sufficientstatistical support. In-depth case studies could, for example, helpto better understand why such an important task as S&OP does

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D. Rexhausen et al. / Journal of Ope

ot (yet) unfold its full potential or which practices are importantntecedents of successful S&OP. In addition, further research shouldnvestigate and identify other SCM dimensions impacted by DeM,nd why good DeM performance does not appear to enhance DiMerformance directly, and there, in particular, whether this mighte attributable to the existence of functional silos or productionanagement performance being the moderator of this relationship.Other starting points for further research arise from limitations

egarding methodology and scope. First, the sample populationas drawn from the members of the German logistics associa-

ion BVL. Although this sample covered a wide range of firms inerms of industry, size, and geography, we cannot claim that theesults of this research can be generalized without restrictions.hus, future research may need to include an even broader pop-lation to validate our results. Moreover, the analyses performed

n this study were based on a self-assessment of the participants.bjective data could complement our study in future research.inally, the scope of our study is limited to DeM and DiM prac-ices. Future research could embed this into a broader model thatlso includes purchasing and production management practicesn order to investigate the magnitude of and interrelationshipsetween these SCM practices. Despite these limitations, this studyaves the way for researchers and practitioners to more fully under-tand the positive impact of customer-facing SCM practices onupply chain performance.

ppendix A. Questionnaire

Demand segmentationDeS1 Customer segmentation We have an up-to-date

segmentation of ourcustomers according totheir service-levelrequirements

DeS2 Product segmentation We have an up-to-datesegmentation of ourproducts according to theirspecific supply chainrequirements

Demand forecastingDeF1 Clear-cut forecasting

processWe have implemented aclear-cut forecastingprocess

DeF2 Regular measurement andmodification

We regularly measure andmodify our forecastingprocess

DeF3 Monitoring and actingupon accuracy

We consequently monitorforecast accuracy and actupon inaccuracies

Sales & operations planningSOP1 Fully integrated S&OP

processesWe have fully integratedprocesses for end-to-endsales and operationsplanning that are alignedwith customerrequirements

SOP2 Organization follows S&OPprocesses

Our organisation rigorouslyfollows the defined salesand operations planningprocesses

SOP3 Decision makersparticipate in S&OPmeetings

The decision makersregularly participate in thesales and operationsplanning meetings

DeM adherenceDeP1 Monitoring and acting We regularly monitor and

upon demand KPIs act upon DeM KPIsDeP2 Clear process definition Our DeM processes are

clearly definedDeP3 Strict process execution Our DeM processes are

100% executed andfollowed by our staff

s Management 30 (2012) 269–281 279

Appendix A. (Continued. )

DeM performancePDe1 Achieving desired

performanceOur DeM delivers the desiredperformance within oursupply chain

PDe2 Meeting operationalbusiness needs

Our DeM meets theoperational needs of ourbusiness

Warehouse managementWM1 Achieving desired

performance of facilitiesand processes

Our warehousing facilitiesand processes are designedto deliver the desiredperformance

WM2 Sufficient warehouseautomization

Our warehousing processesare sufficiently automated.

WM state-of-the-art WMS We use a state-of-the-artWarehouse ManagementSystem (WMS)

Transportation managementTM1 Optimization of transport

modes and routesWe have optimised thetransportation modes andcorresponding routes withinour given distributionnetwork

TM2 Optimization of schedulingand routing

We have optimisedtransportation schedulingand routing across ourdistribution network

DiM adherenceDiP1 Consequent measurement We regularly monitor and act

upon DiM KPIsDiP2 Clear process definition Our DiM processes are

clearly definedDiP3 Strict process execution Our DiM processes are 100%

executed and followed byour staff

DiM performancePDi1 Achieving desired

performanceOur DiM delivers the desiredperformance within oursupply chain

PDi2 Meeting operationalbusiness needs

Our DiM meets theoperational needs of ourbusiness

Supply chain performancePSC1 Supply chain cost How would you rank your

supply chain costperformance relative to yourbest competitors?

PSC2 Supply chain service level How would you rank yoursupply chain service levelperformance relative to yourbest competitors?

PSC3 Supply chain flexibility How would you rank yoursupply chain flexibilityrelative to your bestcompetitors?

Appendix B. Overview of indicators and references

Indicators References

Demand segmentationDeS1 Customer segmentation Lambert and Cooper (2000),

Lockamy III and McCormack(2004), Lapide (2008)

DeS2 Product segmentation Lambert and Cooper (2000),Lockamy III and McCormack(2004), Lapide (2008)

DeF1 Clear-cut forecastingprocess

Lockamy III and McCormack(2004), Croxton et al. (2002),Mentzer (2006)

DeF2 Regular measurement andmodification

Lockamy III and McCormack(2004), Croxton et al. (2002),Mentzer (2006)

2 ration

A

R

A

80 D. Rexhausen et al. / Journal of Ope

ppendix B. (Continued. )

DeF3 Monitoring and actingupon accuracy

Lockamy III and McCormack(2004), Croxton et al. (2002),Mentzer (2006)

SOP1 Fully integrated S&OPprocesses

Grimson and Pyke (2007),Pagell (2004), Lapide (2007b),Milliken (2008)

SOP2 Organization follows S&OPprocesses

Grimson and Pyke (2007),Pagell (2004), Lapide (2007b,2007c)

SOP3 Decision makersparticipate in S&OPmeetings

Grimson and Pyke (2007),Milliken (2008)

DeM adherenceDeP1 Monitoring and acting

upon KPIsCroxton et al. (2002), Germainet al. (2008)

DeP2 Clear process definition Croxton et al. (2002), Germainet al. (2008), Lambert et al.(2005)

DeP3 Strict process execution Croxton et al. (2002), Germainet al. (2008), Lambert et al.(2005)

DeM performancePDe1 Achieving desired

performanceAutry et al. (2005), Lockamy IIIand McCormack (2004)

PDe2 Meeting operationalbusiness needs

Autry et al. (2005), Lockamy IIIand McCormack (2004)

DiM adherenceDiP1 Consequent measurement Germain et al. (2008),

Gunasekaran et al. (2004),Oliver et al. (1994)

DiP2 Clear process definition Germain et al. (2008),Gunasekaran et al. (2004),Lambert et al. (2005), Oliveret al. (1994)

DiP3 Strict process execution Germain et al. (2008),Gunasekaran et al. (2004),Lambert et al. (2005), Oliveret al. (1994)

Warehouse managementWM1 Achieving desired

performance of facilitiesand processes

de Koster and Balk (2008),Gunasekaran et al. (2001)

WM2 Sufficient warehouseautomization

de Koster and Balk (2008), Levy(1997), Oliver et al. (1994)

WM3 state-of-the-art WMS de Koster and Balk (2008), Levy(1997)

Transportation managementTM1 Optimization of transport

modes and routesGunasekaran et al. (2001),Rushton and Oxley (1991),Thomas and Griffin (1996)

TM2 Optimization of schedulingand routing

Gunasekaran et al. (2001),Rushton and Oxley (1991),Thomas and Griffin (1996)

DiM performancePDi1 Achieving desired

performanceAutry et al. (2005), Lockamy IIIand McCormack (2004)

PDi2 Meeting operationalbusiness needs

Autry et al. (2005), Lockamy IIIand McCormack (2004)

Supply chain performancePSC1 Supply chain cost Beamon (1999), Gunasekaran

et al. (2004), Gunasekaran et al.(2001), Ho et al. (2002),Narasimhan and Das (2001)

PSC2 Supply chain service level Beamon (1999), Gunasekaranet al. (2004), Gunasekaran et al.(2001), Ho et al. (2002),Narasimhan and Das (2001)

PSC3 Supply chain flexibility Beamon (1999), Gunasekaranet al. (2004), Gunasekaran et al.(2001), Ho et al. (2002),Narasimhan and Das (2001)

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