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Page 1: Research synthesis in collaborative planning forecast and replenishment

Research synthesis incollaborative planning forecast

and replenishmentAntonio M�arcio Tavares ThomeIndustrial Engineering Department,

Pontifıcia Universidade Cat�olica do Rio de Janeiro, Rio de Janeiro,Brazil and BEMFAM, Rio de Janeiro, Brazil, and

Roberto Luis Hollmann andLuiz Felipe Roris Rodriguez Scavarda do Carmo

Industrial Engineering Department,Pontifıcia Universidade Cat�olica do Rio de Janeiro, Rio de Janeiro, Brazil

Abstract

Purpose – The purpose of this research synthesis is to gather and integrate findings on CollaborativePlanning Forecast and Replenishment (CPFR) as a business process and as a management practice;and to assemble quantitative evidence of its impact on supply chain (SC) performance.Design/methodology/approach – The researchers independently conducted a systematic reviewof 629 abstracts and 47 full-text papers. Original keywords were applied to four key electronicdatabases for operations management and information systems. Rigorous and verifiable selectioncriteria governed inter-coders reliability, review of steps and exclusion of papers. Resource anddependency-based view of the firm, contingency research and maturity models informed the analysis.Findings – There is not a single “blueprint” for CPFR. Competing models emphasize the needfor “trust and confidence” and reliable data systems. The type of products, scope, spatialdiversity and number of partners in the network are important contextual variables. Firmresources that are unique and advantages from multiple and reciprocal dependencies are powerfullevers. There is no consensus on maturity model and on required investment in data andcommunication systems.Practical implications – Practical implications are implementation related: cost-benefit analysisand simulations should precede full-scale collaboration. There is a consensus on starting CPFR smalland expanding gradually.Originality/value – This synthesis applies a rigorous review method and attempts to assemblethe dispersed literature in one study, utilizing explanatory operations management and informationsystems theories.

Keywords Collaboration, Operations management, Supply chain, Trust,Information communication technology

Paper type Research paper

1. IntroductionCollaborative Planning Forecast and Replenishment (CPFR) is a cohesive bundle ofbusiness processes whereby supply chain (SC) trading partners share information,synchronized forecasts, risks, costs and benefits with the intent of improving overallSC performance through joint planning and decision making. Accordingly, CPFRenhances customer demand visibility and matches supply and demand with a

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/0263-5577.htm

Received 13 March 2014Revised 21 April 2014

Accepted 22 April 2014

Industrial Management & DataSystems

Vol. 114 No. 6, 2014pp. 949-965

r Emerald Group Publishing Limited0263-5577

DOI 10.1108/IMDS-03-2014-0085

The authors gratefully acknowledge MCT/CNPq (Research Project No. 307996/2011-5), CAPES/DFG (BRAGECRIM Research Project No. 010/09) and CAPES/DAAD (PROBRAL).

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synchronized flow of goods from the production and delivery of raw materials to theproduction and delivery of the final product to the end consumer. According toVoluntary Inter-industry Commerce Standard (VICS)’s (2004) model, CPFR frameworkencompasses different business processes subdivided into specific steps or tasks(strategy and planning, demand and supply management, execution and analysis).From a contingency view, CPFR takes different forms, according to context (Sousa andVoss, 2008; Danese, 2011).

The concept emerged as an inter-industry standard designed to move beyond theshortcomings of other Supply Chain Collaboration (SCC) initiatives, such asElectronic Data Interchange (EDI), Efficient Consumer Response Movement (ECR),Vendor Managed Inventories (VMI) and Continuous Replenishment (CR) (Stanket al., 1999; Barratt and Oliveira, 2001; Seifert, 2003). CPFR captures the advantagesof such initiatives while adding the collaborative mechanism to facilitateinformation exchange in a multi-tiered SC (Cassivi, 2006). CPFR takes a morecomprehensive approach with respect to the planning of promotions, sales andorders forecast; synchronization of plans between trading partners; the making ofjoint decisions and the management of exceptions (Danese, 2011). CPFR increasesresponsiveness to changing demand patterns and provides a better coordinationalong the SC (Barratt and Oliveira, 2001). Additionally, CPFR is an exception-drivenprocess while the other SCC initiatives are more data driven and exceptions are notpart of the process (Burnette, 2010). Through exception management, tradingpartners can collaboratively review sales and order forecasts (Du et al., 2009,Burnette, 2010), and they can do so on a large scale (Du et al., 2009). It was an effortof Wal-Mart and Warner-Lambert in the mid 1990s for the Listerine line of products(Sherman, 1998). Since then, a sustained attention has been given to CPFR. In 1998,the Voluntary Inter-industry Commerce Standard (VICS) committee published thefirst guideline for implementation, reviewed in 2004 and 2010 (VICS, 1998, 2004,2010). By 2010, the VICS committee reported that over 300 large companies hadimplemented it (Lapide, 2010; Yao et al., 2013).

There are several CPFR models in the literature, with varying configurations andno systematic review available to date. Despite the growing number of publicationsin CPFR, efforts to synthesize the state of the art are still limited. As an attempt to fillthis gap, this paper provides a research synthesis aiming at assembling the dispersedliterature on the subject. The purpose of this review is twofold: to gather and integratefindings on CPFR as a business process and as a management practice; andto assemble quantitative evidences of its impact on SC performance. First, themethodology used in the research synthesis is described. Next, main findings andresults are analysed and discussed. Finally, the main conclusions and suggestions forfuture research are presented.

2. MethodologyA six-step process was used to select and retrieve papers: computerized databaseselection, identification of key words for search, criteria for exclusion of studies,manual review of selected abstracts, full-text review and review of selected referencesfrom full-text articles (Thome et al., 2012).

The databases selected were EMERALD, EBSCO, SCIENCEDIRECT and WILEY. Inaccordance with recommendations for initial research synthesis (Cooper, 2010),keywords selected were sufficiently broad to avoid artificially limiting results and stillprovided limitations to avoid undesirable results. The search keywords were

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Collaborative Planning Forecasting and Replenishment and CPFR, with no limitationsregarding publication dates.

Papers were excluded due to threats to validity for systematic reviews (Cooper,2010). Criteria for the exclusion of papers were related to the relevance for the subjectof the literature search, such as poorly defined constructs of CPFR, CPFR being usedjust as an example and not as a research topic and papers treating CPFR elements inisolation of each other (e.g. inventory management, demand forecast). An additionalcriterion for exclusion was related to the quality of original research, as papers basedon author’s opinion and anecdotic evidences of results only, papers from trade andindustry magazines not consubstantiate with empirical evidences, and paperspresenting causal relationship not based on clearly defined empirical evidences fromexplicit mathematical modelling, survey research or case studies.

The search based on the keywords returned 629 papers. The full bibliography listis available upon request. Duplicate papers and those not corresponding to the abovecriteria were excluded, resulting in 53 papers selected for full-text review. Afterfull-text reading, six papers were excluded. Thus, 47 papers were reviewed andcross-examined by three researchers. The review process was interactive and resultedin high level of agreement, with a Cohen’s k in the range of 0.87-0.99 (95 per cent CI)(Cohen, 1960).

3. Results and discussionsThe results are presented in three broad categories: study identification, literaturesearch synthesis framework and study descriptors.

3.1 Study identificationThe 47 articles included in the analysis are listed in Table I together with the number ofcitations, source and methodology.

As depicted in Table I, just one author published more than two studies on thesubject. Publications on CPFR are also recent, with the first ones appearing in the late1990s. The second column presents the number of citations of each article from GoogleScholar, after the required cleaning to avoid duplicate entries (Thome et al, 2012).In all, 52 per cent of citations concentrate on seven papers published in four leadingJournals: IJPDLM, SCMIJ, IJLM and IMDS. The third column depicts the sourceof the publications, mostly concentrated in Business Forecasting and OperationsManagement (OM) journals. The last column shows the methodology used in thestudies. Single and multiple case studies and simulations prevail, followed byconceptual models of SC collaboration. Five industry reports, five survey research andone literature review are also related in Table I.

3.2 A synthesis frameworkThe framework depicted in Figure 1 is an aide to assemble and organize the review.It is based on an original framework proposed by Thome et al. (2012), expanded withinformation from explanatory theories of resource-based view (RBV) and resourcedependent theory (RDT) (Ramanathan and Gunasekaran, 2014), maturity models(Larsen et al., 2003) and contingency research (Danese, 2011) applied to CPFR.The structure of the framework embraces all the constitutive elements required todescribe individual CPFR elements, their relationships and impact upon performance.The adapted framework adds the dimension of SCC to the original Thome et al. (2012)’sfirm-centred framework. It also adds the vertical functional role of CPFR in bridging

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Reference No. of citations Source Methodology

Sherman (1998) 55 JMTP Conceptual modelStank et al. (1999) 145 SCMIJ SurveyBarratt and Oliveira (2001) 327 IJPDLM SurveyHolmstrom et al. (2002) 124 SCMIJ Conceptual modelMcCarthy and Golicic (2002) 155 IJPDLM Case study, multipleEsper and Williams (2003) 116 TJ Conceptual modelFliedner (2003) 167 IMDS Conceptual modelLarsen et al. (2003) 218 IJPDLM SurveyAttaran (2004) 20 IM Industry reportDanese et al. (2004) 64 JPSM Case study, multipleCaridi et al. (2005) 54 IJPR SimulationIreland (2005) 7 JBF Industry reportSimatupang and Sridharan (2005) 137 IJLM Conceptual modelCaridi et al. (2006) 20 JEIM SimulationCassivi (2006) 89 SCMIJ SurveyDanese (2006a) 3 SCFIJ Conceptual modelDanese (2006b) 46 IJPR Case study, multipleThron et al. (2006) 18 IJPDLM SimulationAttaran and Attaran (2007) 78 BPMJ Conceptual modelChang et al. (2007) 24 SCMIJ SimulationChen et al. (2007) 32 I&M SimulationDanese (2007) 70 IJOPM Case study, multipleSmaros (2007) 56 JOM Case study, singleThron et al. (2007) 17 IJLM SimulationChang and Wang (2008) 14 IJAMT Case study, singleD’Aubeterre et al. (2008) 19 JAIS Case study, singleDerrouiche et al. (2008) 30 IJCIM Conceptual modelGhosh and Fedorowicz (2008) 46 BPMJ Case study, singlePoler et al. (2008) 33 JMTM SimulationSari (2008a) 21 IMDS SimulationSari (2008b) 65 IJPE SimulationBuyukozkan et al. (2009) 5 WASET SimulationDu et al. (2009) 19 SCMIJ Case study, multipleBaumann (2010) 3 JBF Conceptual modelBurnette (2010) 1 JBF Industry reportChoi and Sethi (2010) 45 IJPE Literature reviewHvolby and Trienekens (2010) 23 CI Conceptual modelLapide (2010) 1 JBF Industry reportShu et al. (2010) 0 IJITDM Conceptual modelSmith et al. (2010) 6 JBF Industry reportYuan et al. (2010) 7 RCIM Case study, singleDanese (2011) 15 IJPR Case study, multipleBuyukozkan and Vardaloglu (2012) 9 ESA SimulationAudy et al. (2012) 19 ITOR Case study, multipleYao et al. (2013) 0 JOM Case study, singleRamanathan (2014) 1 ESA SimulationRamanathan and Gunasekaran (2014) 9 IJPE Survey

Notes: BPMJ, Business Process Management Journal; CI, Computers in Industry; ESA, Expert Systems withApplications; I&M, Information & Management; IJAMT, International Journal of Advanced Manufacturing Technology;IJCIM, International Journal of Computer Integrated Manufacturing; IJITDM, International Journal of InformationTechnology & Decision Making; IJLM, International Journal of Logistics Management; IJOPM, International Journal ofOperations & Production Management; IJPDLM, International Journal of Physical Distribution & LogisticsManagement; IJPE, International Journal of Production Economics; IJPR, International Journal of Production Research;IM, Industrial Management; IMDS, Industrial Management & Data Systems; JAIS, Journal of the Association forInformation Systems; JBF, Journal of Business Forecasting; JEIM, Journal of Enterprise Information Management;JMTM, Journal of Manufacturing Technology Management; JMTP, Journal of Marketing Theory & Practice; JOM,Journal of Operations Management; JPSM, Journal of Purchasing and Supply Management; RCIM, Robotics andComputer-Integrated Manufacturing; SCFIJ, Supply Chain Forum: an International Journal; SCMIJ, Supply ChainManagement: An International Journal; TJ, Transportation Journal; WASET, World Academy of Science, Engineering& Technology. No. of citations obtained in 11 January 2014

Table I.Publications, numberof citations, sourceand methodology

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business and corporate strategic plans of individual firms with joint SC operations.CPFR results feedback to inputs. Important contextual variables emanated from thecontingency theory were added, such as number of SC partners, product-characteristicsand SC goals (Danese, 2011). This model is consistent with Simatupang and Sridharan’s(2004) conceptual model, showing an outcomes cell revised from the original frameworkthat now comprise shared SC processes, with a feedback loop to actual performance.Changes from the original framework had better portray the specific results expectedfrom CPFR. It equally contemplates the evolutionary approach embedded in CPFRmaturity models (Larsen et al., 2003) with the inclusion of the level of collaborationin the meetings and collaborations cell, which was absent from Thome et al.’s (2012)original framework. For the RBVand RDT of the firm, companies engage in CPFRprocesses with inimitable and unique resources to gain competitive advantages(Ramanathan and Gunasekaranf, 2014). SC shared strategies, the definition of thelevel of collaboration, use of resources/inputs, as well as resources and informationsharing are essential aspects of RBV and RDB theories integrated into this revisedframework.

3.3 Study descriptorsThe following sub-sections present a review of research and key findings from theframework.

3.3.1 Context. There are reports of CPFR implementation in different contexts.Collaboration varies in scope and configuration according to contextual variables, amongwhich market dynamics (demand uncertainty/unpredictability), goals (responsiveness vsefficiency), product diversity (same or different products) and number of partners (spatialcomplexity) seems to be the most relevant (Danese, 2011). Countries of implementationvary, with most studies conducted in Europe and USA, but Canada, India, Mexico,Philippines, Taiwan and the Middle East are represented as well. Industries covered werefood, apparel, general merchandize retail, transportation, healthcare, automotive,mechanical equipment, agriculture, pharmaceutical, computers and packaging. Someauthors argue that CPFR methodology is applicable to any industry (Fliedner, 2003;Ireland, 2005), while others contempt that its applicability is highly dependent on context(Danese, 2011). Product characteristics are also viewed as enablers in CPFR, such as:highly differentiated or branded products (Larsen et al., 2003; Attaran, 2004; Attaranand Attaran, 2007; Danese, 2007); products with short life cycles (Chen et al., 2007;Sari, 2008b; Yuan et al., 2010); high elasticity of demand related to product promotions(Danese, 2011); innovative products (Fliedner, 2003); high-volume/high-value products(Stank et al., 1999; Ghosh and Fedorowicz, 2008).

Regarding product aggregation, CPFR implementation is reported for single oras many as 100 plus stock keeping units (SKUs) (D’Aubeterre et al., 2008) and notat the aggregate level of families of products; the number of SKUs is quoted as animpediment for a successful implementation (Fliedner, 2003). In most successful pilots,only few products were included (Chang and Wang, 2008).

CPFR planning horizon is also variable. Smaros (2007) provide a typical planninghorizon for CPFR, in a single case study from the European grocery sector. It variesfrom one to four months for planning; two weeks to one month for forecasting and oneday to one week for replenishment, differing for retailers and for suppliers due todifferent planning horizons and product aggregation levels.

CPFR can be equally effective under different manufacturing strategies: make-to-stock (Chang and Wang, 2008); make-to-order and make-to-stock (Danese et al., 2004);

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make-to-order, make-to-stock and assemble-to-order (Danese, 2007, 2011); however,generalization to buy-to-order or engineering-to-order is not warranted (Danese, 2007).

3.3.2 Inputs. The study descriptors of inputs are presented in the Appendix. Mostinputs to the CPFR process are related to demand factors, with a larger concentrationon aspects pertaining to sales, marketing and forecasts. Levels and policies prevailamong operational inputs related to inventory. Source/delivery variables are lessfrequent in the CPFR literature, although some authors added transportation to orderfulfilment as a necessary formal step (Esper and Williams, 2003). Financial data areabsent in most cases, appearing as generic financial data and flows (Caridi et al., 2006)or gross margin (Simatupang and Sridharan, 2005).

3.3.3 Structure and processes. Meetings and collaboration, organization andinformation and communications technology (ICT) are discussed in this section.

3.4 Meetings and collaborationParticipants collaborating during meetings inside the firm and among firms varyaccording to the level of maturity of the CPFR process and the SC configuration(Larsen et al., 2003; Danese, 2011). Maturity models are inspired from CapabilityMaturity Model proposed by the Software Engineering Institute at Carnegie MellonUniversity (Paulk et al., 1993). From the least to the most advanced stage, maturitymodels consist of multiple evolutionary and successive stages in the advancement ofbusiness processes. According to Larsen et al. (2003), CPFR maturity model can besubdivided into basic, developed and advanced. In basic CPFR only few partners andprocesses are involved (e.g. exchange of stock level data for order planning) and it isdriven by the need to lower transactional costs. In developed CPFR, there is increasedintegration in several areas driven by the desire to make delivery faster and moreprecise, enhancing service level and customers responsiveness. Under advanced CPFR,planning and decision making are synchronized including production planning,promotions, marketing and new products launching, in a relationship that is RBV andaiming at long-term mutual learning. Companies enter basic CPFR-like agreementsdue to its low transactional costs, move to a network perspective under developedCPFR and into a mutually beneficial long-term RBV exchange under advanced CPFR.Some authors advocate that CPFR collaboration should start with transactionalinformation sharing and evolve to more mature models gradually (Barratt andOliveira, 2001; ECR Europe, 2001; Larsen et al., 2003; Seifert, 2003; Danese, 2007).

Participant companies can be downstream or upstream dyad, one-to-many ormany-to-one networks (e.g. a supplier-manufacturer-retailer network) (Danese, 2007).For Buyukozkan et al. (2009) and Buyukozkan and Vardaloglu (2012) collaborationshould start with a small number of strategic customers and suppliers. For Daneseet al. (2004); Danese (2006b), the depth of the collaboration defines the type of “liaisondevices”, ranging from liaison agents to task forces to “integrating managers” withformal authority. Several authors emphasize the need for cross-functional coordinationamong: retailers, sales persons, regional managers (Chang and Wang, 2008);purchasing, manufacturing, logistics, marketing and R&D (Buyukozkan et al., 2009;Buyukozkan and Vardaloglu, 2012); merchandizing, purchasing and distributionat the retailer and sales, planning/forecast and logistics personnel at the supplier(Simatupang and Sridharan, 2005); sales, marketing, product/brand management,demand planning (Baumann, 2010). Several authors also quote the importance of topmanagement support (Attaran and Attaran, 2007; Chen et al., 2007; Smith et al., 2010;Buyukozkan and Vardaloglu, 2012; Ramanathan, 2014). In VICS’s (2010) issued

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guidelines for linking CPFR to Sales and Operations Planning (S&OP), emphasizingthe role of ICT process (Baumann, 2010; Smith et al., 2010).

The relationships in CPFR guidelines are governed by a front-end agreement onsupplying and ordering, with shared risks and profits (VICS, 2010; ECR Europe, 2001).In addition to commit resources (Buyukozkan et al., 2009), several authors emphasizethe need to reduce gaming and to develop trust and confidence among partners (e.g.Choi and Sethi, 2010; Yuan et al., 2010; Buyukozkan and Vardaloglu, 2012). Trust isviewed by most as a long-term endeavour (e.g. Attaran and Attaran, 2007; Buyukozkanet al., 2009; Buyukozkan and Vardaloglu, 2012).

Meeting regularity varies from one network to another, as for example: jointbusiness plans every semester in Network B and every year in Network C (Daneseet al., 2004); yearly promotional plan reviewed every three months in Network H andyearly joint promotional plan reviewed within a fixed schedule every week – salesforecasts on Fridays, exceptions management on Mondays, order forecasts onTuesdays and order forecasts exception management on Wednesdays in NetworkI (Danese, 2011). For Smith et al. (2010), CPFR meetings regularity should parallelinternal S&OP meetings.

3.5 OrganizationOrganizational readiness (adequate technological capacity, educated employees,financial sufficiency and willingness and organizational culture to collaborate withtrading partners) is a key success factor (Larsen et al., 2003; Buyukozkan et al., 2009;Du et al., 2009; Burnette, 2010; Buyukozkan and Vardaloglu, 2012). Lack of internalintegration (Smaros, 2007), of collaborative forecasting training (Attaran and Attaran,2007; Chen et al., 2007) and of a flexible organizational structure (Attaran andAttaran, 2007) are quoted as organizational impediments.

There is no consensus about the required steps and the agenda for CPFR. VICS(2004) reviewed the 1998 model and changed it from a linear presentation with ninesteps to a cyclic model with four collaborative processes, subdivided in two steps each:strategy and planning (collaboration arrangement, joint business plan); demand andsupply management (sales forecasting, order planning/forecasting); execution (ordergeneration, order fulfilment); and analysis (exception management, performanceassessment). Fliedner (2003) proposes a five-step agenda: creation of a front-endagreement; joint business planning; development of demand forecast; sharing forecast;and inventory replenishment.

The majority of CPFR structure and processes are based on VICS model (Barrattand Oliveira, 2001; Danese 2006b, Smith et al., 2010). However, the VICS frameworkmet with criticisms based on rigidity of steps, costs and complexity (ECR Europe, 2001;McCarthy and Golicic, 2002; Larsen et al., 2003; Smaros, 2007; Du et al., 2009;Danese, 2011). Tenants of maturity models argued that VICS framework are seldomimplemented as such and steps should be viewed as a modular approach to SCcollaboration rather than a “slavish step-by-step” blueprint (ECR Europe, 2001; Larsenet al., 2003; Seifert, 2003). McCarthy and Golicic (2002) criticize VICS model for beingtoo detailed and comprehensive and advocate for practices that require less investmentin human or technological resources. Du et al. (2009) consider the VICS’s model toocomplicated to implement and propose a new model for agricultural products.Simatupang and Sridharan (2005) propose an augmented CPFR, explicitly addingincentive alignment to the model. Chang et al. (2007) propose an augmented CPFRmodel including an application service provider. Chang and Wang (2008) apply

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Six Sigma methodology to CPFR. VICS (2010), Baumann (2010) and Smith et al. (2010)integrate CPFR and S&OP into what they call Integrated Business Planning.

3.6 Information and communication technologyAppropriate ICT is deemed necessary in all steps of the process. ECR Europe (2002)emphasize that simple technologies can be used such as fax, spreadsheets of sales,e-mails on orders and forecast, as well as more complex ICT tools as EDI, web portals,synchronized joint forecasting and simulation. Costs increase with increased levelsof ICT sophistication. Caridi et al. (2005, 2006) propose two CPFR models withautonomous agents with different levels of “intelligence” and compare with traditionalCPFR model. They find that CPFR models with intelligent agents have better resultsthan the traditional CPFR. Thron et al. (2006) and Ramanathan (2014) argue thatsimulation analysis can be conducted prior to implementation, avoiding the pitfallsand costs of unsuccessful CPFR projects.

3.6.1 Metrics, outcomes and results. Metrics and results of CPFR are presented in theAppendix and commented in this section.

3.7 OutcomesFrom a paradigmatic standing point, the outcomes from CPFR are collaborative plansthat synchronize forecasts, based on which the production and replenishmentprocesses take place (Larsen et al., 2003), as SC partners’ joint decisions (Barratt andOliveira, 2001) on sales, promotions, production, purchasing and product development(Larsen et al., 2003; Attaran, 2004; Attaran and Attaran, 2007; Danese, 2007;Sari, 2008b; Yao et al., 2013; Ramanathan and Gunasekaran, 2014). A single demandprojection is created, generating a unique and mutually agreed forecast (Larsen et al.,2003; Ireland, 2005; Danese, 2006b, 2007; Chang et al., 2007; Chen et al., 2007;Yao et al., 2013; Ramanathan, 2014). Based on this forecast, production and deliveryin response to market demand are synchronized; and collaborative inventoryreplenishment is developed (Sherman, 1998; Larsen et al., 2003; Danese, 2007; Yao et al.,2013). These outcomes are the means to achieve results and are based on inputs andmetrics enumerated in the Appendix.

3.8 Metrics and resultsMetrics and results are mainly measured by market-related variables, such as sales,service levels and time-to-market. Quotations of results related to the goal of SCresponsiveness (144 quotes) slightly outnumber quotations of results reported forefficiency (67 for finance and 70 for operations). The fact that financial indicators areabsent from metrics and less represented as a result of the process in the Appendix isnot a surprise, due to its under representation among the inputs to CPFR.

Three studies submitted CPFR processes to formal test of hypothesis related to itsresults. Stank et al. (1999) test operational results. The authors analyse a sample of 98USA manufacturing and retailing firms, finding univariate positive associationsbetween high levels of implementation of CPFR and: operational changes; enhancedinformation capabilities. However, the authors find a “very weak” association betweenCPFR and the effectiveness of operational results. Yao et al. (2013) submit CPFR to testoperational results as well. They used a transactional database of nine products of aphone company and a major retailer in the USA, concluding that CPFR learning curvesand the sequencing of product launching impact upon forecast errors and inventorylevels. Ramanathan and Gunasekaran (2014) apply structural equation modelling and

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confirmatory factor analysis to test operational and market-related results in a sampleof 150 companies (wholesalers, distributors, retailers and private customers) belongingto the network of a large textile industry in India. The authors find a positive impact ofcollaborative planning and collaborative execution on the success of collaboration andon future collaboration in the SC.

4. ConclusionThe research synthesis in CPFR allowed the review and classification of 629 abstractsand 47 full papers. Despite the growing volume of publications in the subject, the fieldis still recent and evolving, with a large majority of conceptual papers, case studyresearch and simulations that are exploratory and aiming at understanding CPFRmechanisms and impact upon SC performance. No systematic statistical inference andtest of hypothesis were found but in two survey-based studies and one transactionaldatabase research, meeting with mixed evidences of CPFR effects on SC performance.The research synthesis framework evidenced the relevance of contextual variables, theon-going debate about the appropriate structure and processes of CPFR in light ofcontingency research and maturity models and the paucity of empirical researchshowing CPFR results.

Regarding structure and processes, proponents of maturity models criticize theVICS’ models, arguing that at different development stages CPFR configurationswould differ (Larsen et al., 2003). The maturity model is a valid explanation to the factthat different CPFR configurations might exist but one of its drawbacks is that it fallsshort in explaining the influence of the environment and context. In addition, it mightmislead to the expectation that with time all networks converge to the advanced stageof CPFR (Danese, 2011). RBV and RDT theories might explain why companies preferto limit collaboration even when their relationships are mature and the context isfavourable to full collaboration. RDT supports the dependency of SC members; inparticular, SC partners seeking high performance will tend to depend on each otherand to collaborate for long-term results (Ramanathan and Gunasekaran, 2014).

The theoretical models of SC collaboration open important venues for practitionersand researchers.

Contextual variables from the synthesis framework deserve additional research andshould constitute the next step in improving our knowledge about CPFR configurations.Maturity models for CPFR can assist in classifying collaboration under different SCconfigurations. Danese’s exploratory, theory-building contingency hypothesis could bevalidated with different industries and countries, as well as with larger samples (Daneseet al., 2004; Danese, 2006b, 2007, 2011). Three suggestions are made to improve uponcontingency research in CPFR: to identify and expand upon existing contextual variablesand contingency models; to validate and verify the generalization of existing models; andto apply survey research techniques for statistical validity and representativeness.Other important research areas emerge from the limitations of maturity models and ofcontingency theory, as well. While the first can help explaining how SC collaborationevolves, the later inform under which conditions it might happen. However, none of themdeals with the fact that companies may voluntarily choose not to collaborate, even whentheir relationships are mature and the context is favourable. RBV and RDT come handyunder such circumstances (Ramanathan and Gunasekaran, 2014). Other theories shouldalso be explored and applied to the understanding of SC collaboration. Examples are theexternal/institutional limitations emanated from governments, corporate policies, tradeunions, etc., as informed by institutional theory (Sousa and Voss, 2008; Danese, 2011).

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The synthesis framework can assist practitioners to make use of very detailedimplementation guidelines for CPFR (VICS, 1998, 2004, 2010; ECR Europe, 2001).However, critical reviews of implementation steps are also of immediate use. Maturitymodels can be used as a checklist for implementation. CPFR maturity model andcontingency research demonstrates that under certain circumstances, basiccollaboration might fit the needs of SC partners at a lower cost. Furthermore, theinvestment costs for the collaboration, in particular for ICT and organizational changesshould be carefully outweighed against the expected benefits (Stank et al., 1999;Danese, 2011). Another important finding from CPFR research of relevance tomanagement lies in the distinction between ICT and organizational changes. It iscautioned that misled and expensive investments in ICT would not result in theabsence of the required organizational changes related to a culture of collaboration,trust and teamwork within the firm and between firms in the SC (Danese, 2006b, 2007,2011; VICS, 2010; Baumann, 2010; Smith et al., 2010). The contingency approach toCPFR demonstrates that there is not such a general rule as a CPFR model with specificand rigid steps that would fit all companies, sectors and countries, regardless ofcontext and environments.

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Appendix

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About the authors

Dr Antonio M�arcio Tavares Thome obtained his Doctoral and Master Degrees in IndustrialEngineering Department at PUC-Rio (Pontifıcia Universidade Cat�olica do Rio de Janeiro).He graduated at the Political Sciences Institute of Bordeaux, France and obtained a MasterDegree in Demography at Sorbonne-Nouvelle, University of Paris I. His interests in the fieldof engineering and organizational sciences are sales and operations planning, SC management,inventory control and operations research. He is the Head of the Evaluation and StatisticsDepartment at BEMFAM – Family Welfare in Brazil. Currently he collaborates with the CatholicUniversity of Portugal (Porto) as Research Affiliate. He was a Postdoctoral Fellow and VisitingResearcher at the University of Munster (Germany) and was formerly with WestinghouseElectric Corporation, The Population Council and Cambridge Consulting Corporation. He haspublished in international journals as Population Studies, Population et Societe, Studies in Family

Planning, Industrial Management and Data Systems, International Journal of Production

Economics, International Journal of Production and Productivity Management and International

Journal of Production Research. Dr Antonio M�arcio Tavares Thome is the corresponding authorand can be contacted at: [email protected]

Roberto Luis Hollmann is an Engineer at Petrobras. He obtained his undergraduate degree atUNIVATES and his Master Degree in Industrial Engineering at PUC-Rio. His research interestsinclude operations management, mainly S&OP and CPFR.

Dr Luiz Felipe Roris Rodriguez Scavarda do Carmo is an Associate Professor of the IndustrialEngineering Department of PUC-Rio (Pontifıcia Universidade Cat�olica do Rio de Janeiro).He obtained his undergraduate, Master, and Doctoral Degrees in Industrial Engineering atPUC-Rio. During 2000/2002 he joined the German Fraunhofer Institute for ManufacturingEngineering and Automation and in 2009 he was a Visiting Research Professor at the ViennaUniversity of Technology. His research interests include supply chain flexibility, supply chainrisk management, product variety management and S&OP. Currently his is a researcher withgrant by the Brazilian National Research and Development Centre (CNPq). He has publishedin journals as International Journal of Operations & Production Management, International

Journal of Production Economics, International Journal of Production Research, and Interfaces

and Bioresource Technology.

To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints

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