collaborative forecasting

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Collaborative forecastingand planning in supply chainsThe impact on performance in Japanese

manufacturers

Mikihisa NakanoFaculty of Management, Kyoto Sangyo University, Kyoto, Japan

Abstract

Purpose The purpose of this paper is to empirically examine the impact of internal and externalcollaborative forecasting and planning on logistics and production performance.

Design/methodology/approach To measure the degree of collaborative forecasting andplanning, the concept of collaboration is categorized into three dimensions: sharing resources,collaborative process operation, and collaborative process improvement. Based on these dimensions, asurvey of Japanese manufacturers was conducted and the analytical model is proposed to examineusing structural equation modeling.

Findings There are positive relationships between internal and external collaborative forecastingand planning. Upstream and downstream collaborative forecasting and planning are also positivelyrelated. Internal collaborative forecasting and planning has a positive effect on relative logistics andproduction performance. External collaborative forecasting and planning does not have a significanteffect on relative logistics and production performance.

Research limitations/implications This study does not clarify how firms can achieve theimprovement of forecasting and planning process. Future research should investigate the mechanismof process improvement in supply chain.

Practical implications Not only sharing resources and collaborative process operation but alsocollaborative process improvement play a crucial role in gaining sustainable competitive advantage inlogistics and production.

Originality/value This study focuses on the forecasting and planning process in supply chain andproposes new dimensions measuring the degree of collaborative forecasting and planning. By focusingon the process and using the dimensions, the relationship between supply chain collaboration andperformance are discussed concretely.

Keywords Supply chain management, Forecasting, Production planning,Operations and production management, Japan

Paper type Research paper

1. Introduction

To achieve high logistics and production performance (lower logistics andmanufacturing costs, optimal inventory levels, and better customer service), manyfirms have introduced supply chain management (SCM). To apply the concept of SCM

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0960-0035.htm

This study was supported by the research grant from Japan Logistics Society. The author wouldlike to express his appreciation to all the respondents who gave of their time to participate in thesurvey. The author is grateful for the constructive feedback and suggestions provided by twoeditors and two anonymous reviewers. The author also thank Professor Ichiro Tokutsu of KobeUniversity for his valuable comments.

IJPDLM39,2

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Received 23 June 2008Revised 26 November 2008Accepted 12 December 2008

International Journal of Physical

Distribution & LogisticsManagement

Vol. 39 No. 2, 2009

pp. 84-105

q Emerald Group Publishing Limited

0960-0035

DOI 10.1108/09600030910942377


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in practice, firms should integrate business processes in the supply chain (Bowersoxet al., 1999; Lambert, 2006). Through many empirical studies, it has been found thatsupply chain integration has a positive effect on performance (Akikawa, 2004; Ellingeret al., 2000; Frohlich and Westbrook, 2001; Gimenez and Ventura, 2003, 2005; Kim,

2006; Rodrigues et al., 2004; Rosenzweig et al., 2003; Sanders and Premus, 2005;Simatupang and Sridharan, 2005; Spekman et al., 1998; Stank et al., 1999a, b, 2001).

In general, the supply chain process includes several kinds of sub-processes, such asorder fulfillment, demand management, manufacturing flow management (Lambert,2006). These previous studies, however, tend to look at the whole supply chain process.Therefore, the relationship between supply chain integration and performance has beendiscussed ambiguously. Specifically, it has not been sufficiently confirmed whether ornot the integration of each sub-process has a significant effect on performance.

This study focuses on the forecasting and planning process, which plays animportant role in managing the supply chain effectively. The relationship betweeninternal and external integrative forecasting and planning and performance was triedto analyze. In the analysis, a survey of Japanese manufacturers was conducted and thedata were statistically examined using structural equation modeling (SEM). Thepurpose is to discuss about whether or not the integration of the forecasting andplanning process has a positive effect on logistics and production performance.

A major contribution of this study is to develop a measure of collaborative forecastingand planning. The concept of collaboration is a dimension of integration (Kahn andMentzer, 1996) and is treated as an aggregation of various elements (e.g., sharingoperational information, joint planning, joint establishment of objectives, and redesigningwork routines and processes) in previousempirical studies using SEM.These elements arecategorized into three dimensions. Based on the dimensions, the observed variablesmeasuring the latent construct for collaborative forecasting and planning are set.

This paper is organized as follows: Section 2 reviews relevant literatures; Section 3

proposes the dimensions of collaborative forecasting and planning; Section 4 presentsthe research hypotheses and the analytical model; Section 5 describes the researchmethodology; Section 6 reports the research results; Section 7 discusses some findingsfrom the results; and finally, Section 8 draws implications and describes directions forfuture research.

2. Literature reviewThe main source of competitive advantage from SCM is integration of businessprocesses within a firm and across firms (Bowersox et al., 1999; Lambert, 2006). Forintegrating business processes, inter-organizational integrative activities are veryimportant. So, many studies have empirically examined the impact of integrativeactivities on performance.

The integrative activities in supply chain are defined as interaction and collaborationby Kahn and Mentzer (1996). This definition is based on Lawrence and Lorsch (1967),which is an early study on inter-departmental integration, and the conceptualframework of inter-departmental integration in product development (Griffin andHauser, 1996; Gupta et al., 1986; Kahn, 1996). According to Kahn and Mentzer (1996,pp. 9-10), interaction represents the communication aspects associated with verbal anddocumented information exchange. Such activities are tangible and can be easilymonitored. On the other hand, collaboration is defined as the willingness of

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departments to work together. Collaborative activities are typically intangible, lesseasily regulated, difficult to sustain without joint efforts, and represent a higher level ofinterrelationship.

Using their definition, Stank et al. (1999b) and Ellinger et al. (2000) examined the

relationship between marketing/logistics integration and performance. These studiespresented that there is a strong relationship between collaboration and performance.This result implied that collaboration is more important than interaction to integratebusiness processes in a supply chain.

Extending these studies, Stank et al. (2001) proposed a new model to analyze theimpact of internal and external collaboration on performance and verified the model.Similar models were adopted in other studies (Gimenez and Ventura, 2003, 2005;Rodrigues et al., 2004; Sanders and Premus, 2005).

Furthermore, some studies on the relationship between external collaboration andperformance (Akikawa, 2004; Frohlich and Westbrook, 2001; Simatupang andSridharan, 2005) were conducted in European and Asian/Pacific countries.

From these studies, we can understand three trends:

(1) Collaborative activities have been the focus of recent studies.

(2) The target of collaboration has been widened from within a firm to across firmsor both within a firm and across firms.

(3) Examination of the impact on performance has been performed in variouscountries.

Through these studies, we have obtained a consensus about the importance of internaland/or external collaboration in supply chain regardless of the country.

These studies except for Simatupang and Sridharan (2005), however, treat theconcept of collaboration as an aggregation of many elements and do not definethe dimensions of collaboration. Moreover, Simatupang and Sridharans (2005)

three dimensions of external collaboration, that is:

(1) information sharing;

(2) decision synchronization; and

(3) incentive alignment.

Are not defined from the viewpoint of integrating business processes, which is themain source of competitive advantage in supply chain. Therefore, we cannot yetunderstand how firms should collaborate internally and/or externally to integratesupply chain processes. To answer this question, we need to categorize the concept ofcollaboration into some dimensions regarding process integration in supply chain.

3. Three dimensions of collaborative forecasting and planningThis study focuses on collaborative activities in the forecasting and planning process,which is one of the business processes in supply chain and is called demand managementprocess by Lambert (2006). The reason is as follows. Forecasting and planningplays an important role in every major functional area of business management(Ballou, 2004, p. 287; Makridakis and Wheelwright, 1977, p. 24; Mentzer and Moon, 2005,p. 11). This process is also one of the main competencies/capabilities essential tointernal and external integration in supply chain (Bowersox et al., 1999, pp. 22-6;

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Sanders and Ritzman, 2004, p. 514). In addition, this process is one area in whichcollaboration is taking place in supply chain (McCarthy and Golicic, 2002, p. 433).

Recently, some case studies on collaborative forecasting and planning (Barratt,2004; McCarthy and Golicic, 2002; Smaros, 2003) and some surveys about the

implementation (Barratt and Oliveira, 2001; Skjoett-Larsen et al., 2003; Stank et al.,1999a) were conducted in various countries (Denmark, UK, and the USA). However,there are few studies that empirically examined the relationship between both internaland external collaborative forecasting and planning and performance.

The forecasting and planning process in supply chain is often called sales andoperations planning (S&OP). Figure 1 shows the S&OP process of a manufacturer.This figure is modified from Figure 1.4 in Mentzer and Moon (2005, p. 11). As thisfigure represents, there are two kinds of collaborative activities:

(1) sharing resources; and

(2) collaborative process operation.

In the S&OP process. Sharing resources is to share standardized information(e.g., forecast, shipment, inventory, production, and purchasing data) and customizedinformation (e.g., factors of demand fluctuation, and operational resources andconstraints). Collaborative process operation is to connect forecast and plan based on aschedule established in advance and to reexamine activities to adjust deviations fromforecast and plan when contingencies arise. Referring to March and Simon (1958, p. 160),we can call the former coordination by plan and the latter coordination by feedback.

The purpose of these activities is to execute forecasting and planning in the S&OPprocess periodically. Through these activities, firms can integrate the S&OPoperational process monthly, weekly, and daily.

In addition, firms, especially in a rapidly changing environment, should refine theS&OP process. Firms need to add new processes (e.g., getting new data for forecasting,

such as actual demand, and sharing tacit domain knowledge), eliminate unnecessaryprocesses (e.g., ordering automatically and non-duplicate forecasting), and redesignorganizational roles and responsibilities (e.g., defining responsibility for inventory andrules on returned goods).

These activities are called process innovation (Davenport, 1993). Process innovation,also known as business process reengineering (Hammer and Champy, 1993), businessprocess redesign (Davenport and Short, 1990; Martinsons, 1995; Stoddard and

Jarvenpaa, 1995) or business process change (Guha et al., 1997; Kettinger and Grover,1995), is essential to gain sustainable competitive advantage. In particular, theimplementation of process innovation is successful through continuous improvement(Harrison and Pratt, 1993, p. 11), evolutionary change (Jarvenpaa and Stoddard, 1998,pp. 25-26), or a series of incremental steps (Dennis et al., 2003, p. 39).

In-depth interviews were conducted with 22 Japanese manufacturers in food,beverage, cosmetic/household, and electrical/electronic equipment industries from April2003 to May 2007. Through the interviews, it was found that three manufacturers:

(1) Calbee Foods Corporation, which is a food manufacturer.

(2) Kao Corporation, which is a cosmetic and household manufacturer.

(3) Pokka Corporation, which is a beverage manufacturer, have collaborativeprocesses to refine the S&OP process.


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Figure 1.S&OP process

Inter-departmental

collaborativeprocess

operation

Downstream

collaborative

processoperation

Upstream

collaborative

process

operation

Channel

Partners

Salesand

Marketing

department

Production,

Logistics,etc.

department

Suppliers

SalesFore

cast

DemandP

lan

SalesForecast

DemandPlan

Sales

Forecast

Dem

andPlan

Adjuste

d

SalesFore

cast

DemandP

lan

Adjusted

SalesForecast

DemandPlan

Ad

justed

Sales

Forecast

Dem

andPlan

CapacityPlan

OperationalPlan

CapacityPlan

O

perationalPlan

CapacityPlan

OperationalPlan

Adjusted

CapacityPlan

O

perationalPlan

Adjusted

CapacityPlan

OperationalPlan

Adjusted

CapacityPlan

OperationalPlan

DownstreamCollaboration

Upstream

Collaboration

Interdepartmental

Collaboration

organization

collaborativeprocessoperation

resourcesflow

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These firms have frequently discussed operational problems and solutions, forexample, what the causes of forecasting errors were, why the demand side could notcoordinate the errors with the supply side, why they could not share some data forforecasting and planning, and how they should manage inventory levels, through

cross-functional meetings within the firms, internal meetings involving suppliers, andexternal meetings with channel partners. These firms have continued suchcollaborative meetings since the end of the 1990s or the beginning of the 2000s andgained high logistics and production performance.

Therefore, firms in an unstable environment should continuously modify the S&OPprocess to adapt to market uncertainty. We call the activities collaborative processimprovement, which means collaborative activities for improving the S&OP process.Namely, collaborative process improvement is to redesign and implement theforecasting and planning process collaboratively and continuously.

Through the above discussion, we can categorize the concept of collaboration intothree dimensions:

(1) sharing resources;

(2) collaborative process operation; and

(3) collaborative process improvement.

Using these dimensions, the observed variables adopted in previous empirical studieswere classified, as Table I shows.

The observed variables on sharing resources are adopted by seven authors. Theobserved variables on collaborative process operation are also used by six authors.These dimensions are recognized as collaborative activities by many authors. Theobserved variables on collaborative process improvement, however, are utilized byonly four authors. Moreover, only two authors (Gimenez and Ventura, and Rodrigueset al.) adopt the observed variables on the dimension in both internal and external

supply chain. Consequently, we can understand that the importance of collaborativeprocess improvement is not sufficiently noticed.

Based on this recognition, using these dimensions, this study empirically examinesthe impact of collaborative forecasting and planning in both internal and externalsupply chain on performance.

4. Hypotheses4.1. Relationship between internal collaboration and external collaborationStank et al. (2001) and Gimenez and Ventura (2003, 2005) presented a positiverelationship between internal collaboration and external collaboration. Regarding thecorrelation in the S&OP process, we can explain as follows.

If a manufacturer shares resources among departments and operates the forecastingand planning process collaboratively, the manufacturer can provide accurate forecastsand plans to suppliers and channel partners, and jointly operate the forecasting andplanning processes with them. Similarly, through a high degree of external resourcessharing and operational coordination, the manufacturer can operate the forecastingand planning process using accurate forecasts and plans.

Moreover, to adapt to high market uncertainty, firms should change businessprocesses not only within firms but also across firms, as proposed by Champys (2002)X-engineering concept. In other words, manufacturers need to improve internal and


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Authors

Internal/externalLatentvariab

les

Dimension

Observedvariables

FrohlichandWestbrook

(2001)

External

Integration

Sharingresources

Accesstoplanningsystems;Sha

ring

productionplans;JointEDIaccess/networks;

Knowledgeofinventorymix/levels;Common

logisticalequipment/containers;Commonuse

ofthird-partylogistics

Others

Packagingcustomization;Delive

ry

frequencies

Stanketal.(2001)

Internal

Collaboration

Sharingresources

Integrateddatabase;Sharingoperational

information;Sharingbothstandardizedand

customizedinformation;Objectivefeedback

toemployeesregardingintegratedlogistics

performance

Collaborativeprocess

operation

Compensation,incentive,andrew

ardsystems

External

Collaboration

Sharingresources

Sharingoperationalinformation


operation

Integratingoperations;Arrangementsthat

operateunderprinciplesofsharedrewards

andrisks;Operationalflexibility


improvement

Developingperformancemeasur

es;

Benchmarkingbestpractices/processes

GimenezandVentura(20

03,

2005)

Internal

Integration

Sharingresources

Sharedideas,information,ando

ther

resources


operation

Jointplanningtoanticipateand

resolve

operativeproblems


improvement

Jointestablishmentofobjectives;Joint

developmentoftheresponsibilities

understanding;Jointdecisionsaboutwaysto

improvecostefficiencies

Others

Informalteamwork;Established

teamwork

(continued)

Table I.Literature on therelationship betweencollaborative activities insupply chain andperformance

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Authors


les

Dimension

Observedvariables

External

Integration

Sharingresources

Sharedinformation


operation

Jointplanningtoanticipateand

resolve

operativeproblems


improvement

Jointdevelopmentoflogisticspr

ocesses;

Establishedworkteamforthe

implementationanddevelopmen

tofCRPor

otherECRpractice;Jointestablishmentof

objectives;Jointdevelopmentof

the

responsibilitiesunderstanding:joint

decisionsaboutwavstoimnrovecost

efficiencies

Others

Informalteamwork

Akikawa(2004)

External

Coordination

Sharingresources

Informationsharing


operation

Planningintegration

Rodriguesetal.(2004)

Internal

Integratedop

erations

Sharingresources



operation

Cross-functionalworkteamformanaging

day-to-dayoperations


improvement

Redesigningworkroutinesandprocesses;

Shiftingfrommanagingfunction

sto

managingprocesses

External

Integratedop

erations

Sharingresources



operation

Operationalflexibility;Integratin

goperations


improvement

Initiativestostandardizesupply

chain

practicesandoperations

(continued)

Table I.


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Authors


les

Dimension

Observedvariables

SandersandPremus

(2005)

Internal

Collaboration

Sharingresources

Integrateddatabase;Sharingoperations

information


improvement

Cross-functionalcollaborationin

strategic

planning

External

Collaboration

Sharingresources

Sharingoperationsinformation;

Sharing

cross-functionalprocesses;Sharingcost

information


operation

Engagingincollaborativeplanning

SimatupangandSridharan

(2005)

External

Informationsharing

Sharingresources

Promotionalevents;Demandforecast;POS

data;Pricechanges;Inventory-holdingcosts;

On-handinventorylevels;Inventorypolicy;

Supplydisruptions;Orderstatus

ororder

tracking;DeliverySchedules

Decision

synchronization


operation

Jointplanonproductassortment,

promotionalevents;Jointdevelopmentof

demandforecasts;Jointresolutiononforecast

exceptions,orderexceptions;Consultationon

pricingpolicy;Jointdecisiononavailability

level,inventoryrequirements,op

timalorder

quantity

Incentivealig

nment


operation

Jointfrequentshopperprogramm

es;Shared

savingonreducedinventorycos

ts;Delivery

guaranteeforapeakdemand;A

llowancefor

productdefects;Subsidiesforretailprice

markdowns;Agreementsonorderchanges

Table I.

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external forecasting and planning process simultaneously. Thus, we can expect thatthere will be a positive relationship between internal collaboration and externalcollaboration in the forecasting and planning process.

Gimenez and Ventura (2005), however, found that the significance of the

relationship between internal collaboration and external collaboration depended onthe strength of the relationship with external firms. So, this study focuses on therelationship with main suppliers and channel partners, and the following hypothesesare proposed:

H1a. The higher the degree of internal collaborative forecasting and planning, thehigher the degree of collaborative forecasting and planning with mainsuppliers.

H1b. The higher the degree of internal collaborative forecasting and planning, thehigher the degree of collaborative forecasting and planning with mainwholesalers/retailers.

4.2. Relationship between collaboration with suppliers and collaboration with channelpartnersPrevious studies have not sufficiently examined the relationship between collaborationwith suppliers and collaboration with channel partners. This is because upstreamintegration and downstream integration tend to be separated in research (Frohlich andWestbrook, 2001, p. 186; Vickery et al., 2003, p. 524).

To propose a hypothesis on this relationship, we refer to Forza (1996). Forza presented ahypothesis that the companies which seek the benefits of upstream interaction/integration will also seek the benefits of greater downstream interaction/integration (orvice versa) (p. 42). This hypothesis was statistically supported.

The author has found a similar phenomenon through a case study of Calbee Foods

Corporation, which is introduced above (see Section 3) and is a best practice firm ofSCM. At Calbee, the sales department started external collaborative planning calledplanning meeting with main retailers. Through the reports by the sales department atinternal cross-functional meeting called bukai, the production and logisticsdepartments recognized the effect. Next, the production and logistics departmentsstarted planning meetings with their main material suppliers and physical distributionservice suppliers.

For this reason, we expect that there will be positive relationship between upstreamcollaborative forecasting and planning and downstream collaborative forecasting andplanning, and a second hypothesis is proposed:

H2. The higher the degree of collaborative forecasting and planning with mainsuppliers, the higher the degree of collaborative forecasting and planning

with main wholesalers/retailers.

4.3. Relationship between internal collaboration and performanceIn general, the higher the degree of internal collaboration, the more superior theabsolute performance, as measured against a pre-determined target. However,the relative performance, as measured against performance of competitors, is notalways improved. This is because internal collaboration does not lead to competitiveadvantage if most firms perform a high degree of internal collaboration. In fact,


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Stank et al. (2001) presented a significant relationship between internal collaborationand relative performance. In contrast, Gimenez and Ventura (2003) did not find thesignificance. As Gimenez and Ventura (2003, p. 85) indicated, this difference dependson the sample demographic (e.g., industry and country).

In Japan, some manufacturers started to introduce SCP (Supply Chain Planning)software in the latter half of the 1990s. Simultaneously, these firms reformed theorganizational structure and process (e.g., establishing SCM committee, setting upforecasting department, reassigning responsibility for inventory, and rebuildingforecasting and planning process).

A survey of Japanese manufacturers was conducted with 275 members firms of JapanInstitute of Logistics Society (JILS) in November 2003 (68 effective responses) (Nakano,2005). As a result, it was found that only 14 firms (20.6 percent) used information systemsfor both forecasting and planning, and the functional departments (e.g., production andlogistics) planed based on a one-number forecast calculated by the forecasting departmentor decided through consensus meeting called jukyu-chousei-kaigi or seihan-kaigi. Theresult implies that there are a few manufacturers advanced in internal collaborativeforecasting and planning, but the degree of internal collaborative forecasting and planningis low at most Japanese manufacturers.

Therefore, it is expected that internal collaborative forecasting and planning willlead to highly competitive performance, and a third hypothesis is offered:

H3. Internal collaborative forecasting and planning has a positive effect onrelative performance.

4.4. Relationship between external collaboration and performanceThe strength of the relationship between external collaboration and relativeperformance also depends on the sample demographic.

On external collaboration, it is well known that Japanese automotive manufacturers(e.g., Toyota, Nissan, and Honda) have very strong partnerships with their suppliers.As Gimenez and Ventura (2003, p. 85) mentioned, in this industry, externalcollaboration does not lead to a competitive advantage because it is a prerequisite tosurvive, and almost all companies have implemented it.

However, firms in other industries are not similar to automotive manufactures.Akikawa (2004) conducted a survey of Japanese manufacturers aimed at 700 firms inMarch 2002 (70 effective responses). His study showed that the ratio of firms that shareinformation and integrate planning with suppliers and customers was 20-30 percent.The result implies that most Japanese manufacturers have not sufficiently collaboratedwith main suppliers and customers on the forecasting and planning process.

Recently, there are some initiatives on external collaborative forecasting and

planning: Collaborative Planning, Forecasting, and Replenishment by AEON (majorgeneral merchandise store, GMS) and grocery manufacturers; continuousreplenishment program (CRP) by Heiwado (local GMS) and grocery manufacturers;vendor managed inventory (VMI) by Asahi Breweries (beer manufacturer) andwholesalers; SCM under the leadership of retailer called demand chain management byYamada Denki (electronics retailer) and electronics manufacturers; electronicprocurement system called Spirits by Sony; partnership with suppliers called WP2(World production partners) by World (apparel firm). These initiatives imply that some

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advanced manufacturers have a high degree of external collaborative forecasting andplanning.

So, it is expected that collaborative forecasting and planning with main suppliersand customers will lead to very competitive performance, and fourth hypotheses are

proposed:

H4a. Collaborative forecasting and planning with main suppliers has a positiveeffect on relative performance.

H4b. Collaborative forecasting and planning with main wholesalers/retailers has apositive effect on relative performance.

Hypothesized relationships are represented in Figure 2. In the following section, we callrelative performance simply performance for convenience.

5. Method5.1. DataData were collected through a mail survey sent to Japanese manufacturers. The surveyinstrument was initially pre-tested at meetings with four managers of SCM or logisticsdepartment and one consultant. The individuals were asked to review readability andambiguity of expressions in the questionnaire. Their suggestions for rewording werereflected in the final survey instrument.

The questionnaire was mailed to 256 manufacturers among members of JILS inJanuary 2005. The respondents were managers of SCM or logistics department. Afterremoving incomplete responses, the total number of effective responses was 65, whichrepresented a response rate of 25.4 percent. While the sample size is relatively small,there are precedents for using SEM with similar sample sizes (Boyd and Fulk, 1996;Gimenez and Ventura, 2003, 2005; Larsson and Finkelstein, 1999; McGrath et al., 1996;

Miller et al., 1997; Singh, 1986; Walker and Poppo, 1991; Walker and Weber, 1987). Thesample demographic is shown in Table II.

Figure 2.Conceptual model

Internal Collaborative

forecasting and planning

Collaborative


with main Suppliers

Collaborative forecasting

and planning with

main Customers

Logistics and

Production

Performance

H1a

H1b

H2

H3

H4a

H4b


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Based on the procedure described by Armstrong and Overton (1977), an analysis ofnon-response bias was conducted. Concretely, the responses were numbered sequentiallyin the order they were received, and compared mean scores of the first quartile assumed tobe most motivated to participate with those of the last quartile assumed to be most similarto non-respondents to all variables. As a result, a significant difference (at p , 0.05) inmeans was not found. Hence, there was no evidence of response bias.

5.2. MeasuresThe hypothesized model illustrated in Figure 2 was tested using SEM. The model iscomposed of four constructs:

(1) internal collaborative forecasting and planning;

(2) collaborative forecasting and planning with main suppliers;

(3) collaborative forecasting and planning with main customers (wholesalers/retailers); and

(4) logistics and production performance.

These constructs are latent variables that cannot be observed directly. Accordingly,observed variables listed in Table III were used.

Three latent variables on inter-organizational collaborative forecasting andplanning are measured using six observed variables: two variables on sharingresources (sharing standardized information and sharing customized information), twovariables on collaborative process operation (coordination by plan and coordination by

feedback), and two variables on collaborative process improvement (collaborativeprocess redesign and continuous process improvement).

In some previous studies, performance was measured using an index of customerservice level (Ellinger et al., 2000; Gimenez and Ventura, 2003, 2005; Stank et al., 1999;Stank et al., 2001). However, Narasimhan and Das (2001) and Sanders and Premus (2005)described that successful firms engage in the simultaneous pursuit of multipleperformance objectives. These studies treated performance as a composite constructencompassing cost, product quality, delivery speed, and new product introduction time.

Industry sub-sector Frequency Percentage

1. Food 13 20.02. Beverage 10 15.4

3. Apparel 1 1.54. Chemical 6 9.25. Cosmetic & Household 5 7.76. Pharmaceutical 3 4.67. Ceramic 3 4.68. Nonferrous metal 1 1.59. Metal 5 7.7

10. Machinery 1 1.511. Electrical/Electronic equipment 8 12.312. Automotive 5 7.713. Other 4 6.2

65 100.0Table II.Sample demographic

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In the same way, this study treats performance as a composite construct encompassinglogistics cost, manufacturing cost, final product inventory level, order fill rate, deliveryspeed, and delivery times. This study calls it logistics and production performance.

For inter-organizational collaborative forecasting and planning items, respondentswere asked to indicate their agreement based on a five-point scale between 1 strongly

Mean SD

Internal collaborative forecasting and planning (IC)IC1 Internal sharing standardized

information 3.26 1.02IC2 Internal sharing customized

information 3.40 0.93IC3 Internal coordination by plan 3.54 0.95IC4 Internal coordination by feedback 3.69 0.58IC5 Internal collaborative process

redesign 3.32 0.79IC6 Internal continuous process

improvement 3.37 0.74Collaborative forecasting and planning with main Suppliers (CS)CS1 Sharing standardized information

with main suppliers 3.34 0.91CS2 Sharing customized information

with main suppliers 3.45 0.81CS3 Coordination by plan with mainsuppliers 3.35 0.87

CS4 Coordination by feedback withmain suppliers 3.58 0.73

CS5 Collaborative process redesignwith main suppliers 3.20 0.79

CS6 Continuous process improvementwith main suppliers 3.31 0.79

Collaborative forecasting and planning with main Customers (CC)CC1 Sharing standardized information

with main customers 3.22 0.89CC2 Sharing customized information

with main customers 3.23 0.84

CC3 Coordination by plan with maincustomers 3.05 0.89

CC4 Coordination by feedback withmain customers 3.31 0.86

CC5 Collaborative process redesignwith main customers 3.08 0.89

CC6 Continuous process improvementwith main customers 3.08 0.87

Logistics and Production Performance (LPP)LPP1 Logistics cost 3.45 0.77LPP2 Manufacturing cost 3.35 0.72LPP3 Final product inventory level 3.20 0.90LPP4 Order fill rate 3.57 0.75LPP5 Delivery speed 3.51 0.59

LPP6 Delivery times 3.38 0.78

Table III.

Questionnaire items


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disagree and 5 strongly agree. For logistics and production performance items,respondents were asked to indicate their perceptions based on a five-point scale between1 much worse than competitors and 5 much better than competitors.

6. Results6.1. Reliability and validityThe test for reliability of each construct was conducted using Cronbachs coefficient a.Table IV reports that the values are greater than 0.70, which is a criterion that isgenerally accepted as indicating reliability (Garver and Mentzer, 1999).

Next, convergent validity of each latent variable was evaluated using confirmatoryfactor analysis via AMOS 6.0 software. Table V shows multiple fit indices,standardized coefficients, and t-values.

Because no single statistic is considered superior regarding assessment, a review ofmultiple fit indices is desirable. x-square values for four latent variables are significant(at p , 0.05). The ratios of x-square value to the degree of freedom are within the

recommended range (x2/df% 3) (Hair et al., 1998). Goodness of fit index (GFI),comparative fit index (CFI), and incremental fit index (IFI) had values exceeding the0.90 cutoff (Hu and Bentler, 1995). Root mean square residual (RMR) is small. Also,standardized coefficients are statistically significant at p , 0.05 level. These resultsprovide the evidence of convergent validity.

Furthermore, following the recommendation of Garver and Mentzer (1999),discriminant validity was evaluated through a confidence interval test. If theconfidence interval ( two standard errors) around the correlation estimate between twofactors does not include1.0, the result provides the evidence that two factors are different(Anderson and Gerbing, 1988). In the correlation estimates between latent variables,none of the confidence intervals contained 1.0, demonstrating discriminant validity.

6.2. Hypotheses test resultsThe model was tested using SEM via AMOS 6.0 software. Maximum likelihoodmethod was used to estimate parameters. Some correlations were assumed to improvegoodness-of-fit. These correlations were assumed among similar kinds ofinter-organizational collaborative activities. Figure 3 shows the result.

Multiple fit indices were used to evaluate goodness-of-fit of the model. x-squarevalue is significant (at p , 0.05). But the ratio of x-square value to the degree offreedom is within the recommended range (x2/df% 3) (Hair et al., 1998). RMR isslightly high value. GFI is below the recommended criteria (GFI $ 0.90) (Hu andBentler, 1995). Instead, CFI, IFI, and Non-Normed Fit Index had values exceeding the0.90 cutoff (Hu and Bentler, 1995). The result of these alternative indices provides the

evidence of the overall validity of the hypothesized model.

Constructs Cronbach a

IC 0.829CS 0.910CC 0.904LPP 0.780

Table IV.Test for reliability

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Each hypothesis was assessed by reviewing the significance, magnitude, and direction

of each parameter coefficient. The results show that internal collaborative forecasting

and planning and collaborative forecasting and planning with main suppliers or

customers are strongly and positively correlated; this result supports H1a and H1b.Similarly, collaborative forecasting and planning with main suppliers and

collaborative forecasting and planning with main customers are strongly and

positively correlated; this supports H2.

A causal relationship between internal collaborative forecasting and planning andlogistics and production performance is statistically significant. So, H3 is supported.

The causal relationship between collaborative forecasting and planning with main

suppliers or customers, and logistics and production performance are not statistically

significant; this does not support H4a and H4b.Consequently, only internal collaborative forecasting and planning has a significant

effect on logistics and production performance. External collaborative forecasting and

planning does not significantly affect logistics and production performance.

Constructs x2 df x2/df p-value GFI RMR CFI IFIIC 22.851 9 2.539 0.007 0.909 0.046 0.908 0.912CS 18.470 9 2.052 0.030 0.913 0.031 0.960 0.961cc 15.854 9 1.762 0.070 0.923 0.029 0.971 0.971LPP 12.535 9 1.393 0.185 0.945 0.031 0.958 0.961

Items Standardized coefficient t-valueIC1 0.550 4.471 *

IC2 0.739 6.501 *

IC3 0.805 7.323 *

IC4 0.480 3.814 *

IC5 0.682 5.834 *

IC6 0.804 7.305 *

CS1 0.813 7.720 *

CS2 0.756 6.938 *

CS3 0.851 8.282 *

CS4 0.732 6.629 *

CS5 0.730 6.605 *

CS6 0.876 8.685*

CC1 0.743 6.638 *

CC2 0.675 5.826 *

CC3 0.908 9.036 *

CC4 0.676 5.839 *

CC5 0.844 8.029 *

CC6 0.879 8.574 *

LPP1 0.496 3.793 *

LPP2 0.594 4.672 *

LPP3 0.654 5.252 *

LPP4 0.745 6.171 *

LPP5 0.629 5.010 *

LPP6 0.559 4.349 *

Note:*

p , 0.01

Table V.Test for convergent

validity


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7. DiscussionThe supports for H1a and H1b suggest that there are positive relationships betweeninternal collaborative forecasting and planning and external (upstream/downstream)collaborative forecasting and planning. This result is the same as the results ofGimenez and Ventura (2003, 2005) and Stank et al. (2001).

The correlation coefficient between internal collaborative forecasting and planningand collaborative forecasting and planning with main suppliers is larger than onebetween internal collaborative forecasting and planning and collaborative forecastingand planning with main customers. As further analysis, a significance test for thedifference between the two coefficients was conducted. As a result, null hypothesis thattwo coefficients are equal was not supported. This result means that the correlationwith upstream firms is stronger than that with downstream firms.

This phenomenon may depend on the manufacturers position in an externalrelationship. A manufacturer trades with suppliers as a buyer and with customers as aseller. In general, a buyer can require resources sharing, coordination, and processimprovement of partners more easily than a seller. It is considered that the difference ofposition in an external relationship influences the strength of the correlation.

The support for H2 suggests that there is positive relationship between upstream

collaborative forecasting and planning and downstream collaborative forecastingand planning. As introduced above (see Section 4), at Calbee, external collaborativeplanning called planning meeting was conducted by the sales department at first, andafter that, the production and logistics departments started the meetings with theirsuppliers. It is considered that the internal cross-functional meeting called bukaiplayedan important role in mediating the dissemination of the planning meeting. It isspeculated that the production and logistics departments learned how to operate theplanning meeting through the reports by the sales department in bukai. With regard to

Figure 3.Hypotheses test results

H1a**

H1b**

H2**

H3*

H4a

H4b

2 = 312.82

df = 239p = 0.00

2/df = 1.31

GFI = 0.744RMR = 0.057CFI = 0.918IFI = 0.921NNFI = 0.905

0.746

(2.184)

0.174(0.616)

0.009

(0.053)

0.835

(16.249)

0.612

(6.698)

0.580

(6.216)

Hypothesis

Parameter estimate

(t-value)**p-value < 0.01* p-value < 0.05

Internal Collaborative


Collaborative


with main Suppliers

Collaborative forecasting

and planning with

main Customers

Logistics and

Production

Performance

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the mechanism through which internal organizational learning leads to externalcollaboration, we will need more research in the future.

The support for H3 is the same as the results of Sanders and Premus (2005) andStank et al. (2001). This result means that internal collaborative forecasting and

planning leads to competitive advantage of logistics and production in Japanesemanufacturers. This is similar to the phenomenon found through the in-depthinterviews, which is described in Section 3. Three firms (Calbee Foods Corporation,Kao Corporation, and Pokka Corporation) have maintained the performance of somemeasures (e.g., order fill rate and manufacturing cost) and improved the performance ofother measures (e.g., safety inventory level and logistics cost), or improved theperformance of these measures simultaneously. In contrast, other firms in the sameindustries have faced a trade-off in performance or got a worse performance.The difference between the former and the latter is whether or not firms continuouslyimprove their forecasting and planning process to adapt to market uncertainty.

Therefore, the author is convinced that not only sharing resources and collaborativeprocess operation but also collaborative process improvement play a crucial role in

gaining sustainable competitive advantage in logistics and production.This conclusion, however, applies only to internal collaborative forecasting and

planning because H4a and H4b are not supported. The external collaborativeforecasting and planning in Japanese advanced manufactures may be in the phase ofpilot test and a necessary but not sufficient condition for performance improvementsimilar to European firms (Barratt, 2004; Smaros, 2003). Future research is needed toexamine how external collaborative forecasting and planning influences logistics andproduction performance.

8. Implications and future researchMany researchers and practitioners agree that collaborative activities have a veryimportant role in integrating the supply chain process. In this study, the concept ofcollaboration is defined into three dimensions:

(1) sharing resources;

(2) collaborative process operation; and

(3) collaborative process improvement.

And statistically analyzed the impact of internal and external collaborative forecastingand planning on the performance of Japanese manufacturers. As a result, it was foundthat the combination of these three activities on internal collaborative forecasting andplanning can lead to superior logistics and production performance.

This result implies that we need to pay attention to collaborative processimprovement, which has not been sufficiently recognized the importance in previousempirical studies, as well as sharing resources and collaborative process operation,which have been treated by many researchers. This study, however, does not clarifyhow firms can achieve the improvement of forecasting and planning process.

Regarding capabilities to conduct such a dynamic process improvement, which arecalled dynamic capabilities, there are many discussions on dynamic resource-basedview in strategic management theory (Eisenhardt and Martin, 2000; Teece and Pisano,1994; Teece et al., 1997; Zollo and Winer, 2002). In the academic area of SCM, SupplyChain 2000 framework proposed by Bowersox et al. (1999) shows typical


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competencies/capabilities of supply chain integration, but they treat the static aspect ofcompetencies/capabilities. Except for a dynamic resource-based framework ofcompetition by Olavarrieta and Ellinger (1997, p. 577), there are few studies ondynamic capabilities in supply chain.

Accordingly, we must clarify the dynamic aspect of how firms improve forecastingand planning process. To investigate the mechanism of process improvement in supplychain, qualitative research is more effective. We can explore the mechanism bystudying some cases of best practice firms in detail, comparing the cases, andanalyzing the similarities and differences. The research will present more concreterequirements on the improvement of forecasting and planning process for academicsand practitioners.

Finally, the findings of this study derived from survey data on Japanese firms arequite restrictive. Thus, it is difficult to generalize to other regions like North American,European, and other Asian countries. By conducting an international cross-sectionalstudy, we can analyze the impact of collaborative forecasting and planning onperformance and make comparisons among countries.

References

Akikawa, T. (2004), Sapurai chein manejiment ni kansuru jissyou kenkyu (An Empirical Study onSupply Chain Management), Pureadesu Syuppan, Osaka (in Japanese).

Anderson, J.C. and Gerbing, D.W. (1988), Structural equation modeling in practice: a review andrecommended two-step approach, Psychological Bulletin, Vol. 103 No. 3, pp. 411-23.

Armstrong, J.S. and Overton, T.S. (1977), Estimating nonresponse bias in mail surveys, Journalof Marketing Research, Vol. 14 No. 3, pp. 396-402.

Ballou, R.H. (2004), Business Logistics/Supply Chain Management, 5th ed., Prentice-Hall,Englewood Cliffs, NJ.

Barratt, M. (2004), Unveiling enablers and inhibitors of collaborative planning,The International Journal of Logistics Management, Vol. 15 No. 1, pp. 73-90.

Barratt, M. and Oliveira, A. (2001), Exploring the experiences of collaborative planninginitiatives, International Journal of Physical Distribution & Logistics Management, Vol. 31No. 4, pp. 266-89.

Bowersox, D.J., Closs, D.J. and Stank, T.P. (1999), 21st Century Logistics: Making Supply ChainIntegration a Reality, Council of Logistics Management, Oak Brook, IL.

Boyd, B.K. and Fulk, J . (1996), Executive scanning and perceived uncertainty:a multidimensional model, Journal of Management, Vol. 22 No. 1, pp. 1-21.

Champy, J. (2002), X-engineering the Corporation: Reinvent Your Business in the Digital Age,Hodder & Stoughton, Sevenoaks.

Davenport, T.H. (1993), Process Innovation: Reengineering Work Through Information

Technology, Harvard Business School Press, Boston, MA.

Davenport, T.H. and Short, J.E. (1990), The new industrial engineering: information technologyand business process redesign, Sloan Management Review, Vol. 31 No. 4, pp. 11-27.

Dennis, A.R., Carte, T.A. and Kelly, G.G. (2003), Breaking the rules: success and failure ingroupware-supported business process reengineering, Decision Support Systems, Vol. 36No. 1, pp. 31-47.

Eisenhardt, K.M. and Martin, J.A. (2000), Dynamic capabilities: what are they?, StrategicManagement Journal, Vol. 21, pp. 1105-21.

IJPDLM39,2

102


20/22

Ellinger, A.E., Daugherty, P.J. and Keller, S.B. (2000), The relationship between

marketing/logistics interdepartmental integration and performance in US manufacturing

firms: an empirical study, Journal of Business Logistics, Vol. 21 No. 1, pp. 1-22.

Forza, C. (1996), Achieving superior operating performance from integrated pipeline

management: an empirical study, International Journal of Physical Distribution &Logistics Management, Vol. 26 No. 9, pp. 36-63.

Frohlich, M.T. and Westbrook, R. (2001), Arcs of integration: an international study of supply

chain strategies, Journal of Operations Management, Vol. 19, pp. 185-200.

Garver, M.S. and Mentzer, J.T. (1999), Logistics research methods: employing structural

equation modeling to test for construct validity, Journal of Business Logistics, Vol. 20No. 1, pp. 33-57.

Gimenez, C. and Ventura, E. (2003), Supply chain management as a competitive advantage in

the Spanish grocery sector, The International Journal of Logistics Management, Vol. 14No. 1, pp. 77-88.

Gimenez, C. and Ventura, E. (2005), Logistics-production, logistics-marketing and external

integration: their impact on performance, International Journal of Operations &Production Management, Vol. 25 No. 1, pp. 20-38.

Griffin, A. and Hauser, J.R. (1996), Integrating R&D and marketing: a review and analysis of the

literature, Journal of Product Innovation Management, Vol. 13, pp. 191-215.

Guha, S., Grover, V., Kettinger, W.J. and Teng, J.T.C. (1997), Business process change and

organizational performance: exploring an antecedent model, Journal of ManagementInformation Systems, Vol. 14 No. 1, pp. 119-54.

Gupta, A.K., Raj, S.P. and Wilemon, D. (1986), A model for studying R&D-marketing interface

in the product innovation process, Journal of Marketing, April, pp. 7-17.

Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998), Multivariate Data Analysis,5th ed., Prentice-Hall, Englewood Cliffs, NJ.

Hammer, M. and Champy, J. (1993), Reengineering the Corporation: A Manifesto for BusinessRevolution, Harper Business, New York, NY.

Harrison, D.B. and Pratt, M.D. (1993), A methodology for reengineering businesses, PlanningReview, March/April, pp. 6-11.

Hu, L. and Bentler, P.M. (1995), Evaluating model fit, in Hoyle, R.H. (Ed.), Structural EquationModeling: Concepts,Issues, and Applications, Sage publications, Thousand Oaks, CA,pp. 76-99.

Jarvenpaa, S.L. and Stoddard, D.B. (1998), Business process redesign: radical and evolutionary

change, Journal of Business Research, Vol. 41 No. 1, pp. 15-27.

Kahn, K.B. (1996), Interdepartmental integration: a definition with implications for product

development performance,Journal of Product Innovation Management, Vol. 13, pp. 137-51.

Kahn, K.B. and Mentzer, J.T. (1996), Logistics and interdepartmental integration, International

Journal of Physical Distribution & Logistics Management, Vol. 26 No. 8, pp. 6-14.Kettinger, W.J. and Grover, V. (1995), Special section: toward a theory of business process

change management,Journal of Management Information Systems, Vol. 12 No. 1, pp. 9-30.

Kim, S.W. (2006), Effects of supply chain management practices, integration and competition

capability on performance, Supply Chain Management: An International Journal, Vol. 11No. 3, pp. 241-8.

Lambert, D.M. (Ed.) (2006), Supply Chain Management: Processes, Partnerships, Performance,Supply Chain Management Institute, Sarasota, FL.


planning

103


21/22

Larsson, R. and Finkelstein, S. (1999), Integrating strategic, organizational, and human resourceperspectives on mergers and acquisitions: a case survey of synergy realization,Organization Science, Vol. 10 No. 1, pp. 1-26.

Lawrence, P.R. and Lorsch, J.W. (1967), Organization and Environment: Managing

Differentiation and Integration, Harvard University Press, Boston, MA.Makridakis, S. and Wheelwright, S.C. (1977), Forecasting: issues and challenges for marketing

management, Journal of Marketing, October, pp. 24-38.

March, J.G. and Simon, H.A. (1958), Organizations, Wiley, New York, NY.

Martinsons, M.G. (1995), Radical process innovation using information technology: the theory,the practice and the future of reengineering, International Journal of Information

Management, Vol. 15 No. 4, pp. 253-69.

McCarthy, T.M. and Golicic, S.L. (2002), Implementing collaborative forecasting to improvesupply chain performance, International Journal of Physical Distribution & Logistics


McGrath, R.G., Tsai, M., Venkataraman, S. and MacMillan, I.C. (1996), Innovation, competitiveadvantage and rent: a model and test, Management Science, Vol. 42 No. 3, pp. 389-403.

Mentzer, J.T. and Moon, M.A. (2005), Sales Forecasting Management, 2nd ed., Sage, ThousandOaks, CA.

Miller, D., Droge, C. and Vickery, S. (1997), Celebrating the essential: the impact ofperformance on the functional favoritism of CEOs in two contexts, Journal of


Nakano, M. (2005), Juyou yosoku niyoru bumonkan keikaku tougou (Inter-departmentalplanning integration by demand forecasting), Business Insight, Vol. 12 No. 4, pp. 110-26(in Japanese).

Narasimhan, R. and Das, A. (2001), The impact of purchasing integration and practices onmanufacturing performance, Journal of Operations Management, Vol. 19, pp. 593-609.

Olavarrieta, S. and Ellinger, A.E. (1997), Resource-based theory and strategic logistics

research, International Journal of Physical Distribution & Logistics Management, Vol. 27Nos 9/10, pp. 559-87.

Rodrigues, A.M., Stank, T.P. and Lynch, D.F. (2004), Linking strategy, structure, process, andperformance in integrated logistics, Journal of Business Logistics, Vol. 25 No. 2, pp. 65-94.

Rosenzweig, E.D., Roth, A.V. and Dean, J.W. Jr (2003), The influence of an integration strategyon competitive capabilities and business performance: an exploratory study of consumerproducts manufacturers, Journal of Operations Management, Vol. 21, pp. 437-56.

Sanders, N.R. and Premus, R. (2005), Modeling the relationship between firm IT capability,collaboration, and performance, Journal of Business Logistics, Vol. 26 No. 1, pp. 1-23.

Sanders, N.R. and Ritzman, L.P. (2004), Integrating judgemental and quantitative forecasts:methodologies for pooling marketing and operations information, International Journal ofOperations & Production Management, Vol. 24 No. 5, pp. 514-29.

Simatupang, T.M. and Sridharan, R. (2005), The collaboration index: a measure for supply chaincollaboration, International Journal of Physical Distribution & Logistics Management,Vol. 35 No. 1, pp. 44-62.

Singh, J.V. (1986), Performance, slack, and risk taking in organizational decision making,Academy of Management Journal, Vol. 29 No. 3, pp. 562-85.

Skjoett-Larsen, T., Thernoe, C. and Andresen, C. (2003), Supply chain collaboration: theoreticalperspectives and empirical evidence, International Journal of Physical Distribution &

Logistics Management, Vol. 33 No. 6, pp. 531-49.

IJPDLM39,2

104


22/22

Smaros, J. (2003), Collaborative forecasting: a selection of practical approaches, InternationalJournal of Logistics: Research and Applications, Vol. 6 No. 4, pp. 245-58.

Spekman, R.E., Kamauff, J.W. Jr and Myhr, N. (1998), An empirical investigation into supplychain management: a perspective on partnerships, International Journal of Physical

Distribution & Logistics Management, Vol. 28 No. 8, pp. 630-50.Stank, T.P., Daugherty, P.J. and Autry, C.W. (1999a), Collaborative planning: supporting

automatic replenishment programs, Supply Chain Management: An International Journal,Vol. 4 No. 2, pp. 75-85.

Stank, T.P., Daugherty, P.J. and Ellinger, A.E. (1999b), Marketing/logistics integration and firmperformance, The International Journal of Logistics Management, Vol. 10 No. 1, pp. 11-24.

Stank, T.P., Keller, S.B. and Daugherty, P.J. (2001), Supply chain collaboration and logisticsservice performance, Journal of Business Logistics, Vol. 22 No. 1, pp. 29-48.

Stoddard, D.B. and Jarvenpaa, S.L. (1995), Business process redesign: tactics for managingradical change, Journal of Management Information Systems, Vol. 12 No. 1, pp. 81-107.

Teece, D.J. and Pisano, G. (1994), The dynamic capabilities of firms: an introduction, Industrialand Corporate Change, Vol. 3 No. 3, pp. 537-56.

Teece, D.J., Pisano, G. and Shuen, A. (1997), Dynamic capabilities and strategic management,Strategic Management Journal, Vol. 18 No. 7, pp. 509-33.

Vickery, S.K., Jayaram, J., Droge, C. and Calantone, R. (2003), The effect of an integrative supplychain strategy on customer service and financial performance: an analysis of direct versusindirect relationships, Journal of Operations Management, Vol. 21, pp. 523-39.

Walker, G. and Poppo, L. (1991), Profit centers, single-source suppliers, and transaction costs,Administrative Science Quarterly, Vol. 36, pp. 66-87.

Walker, G. and Weber, D. (1987), Supplier competition, uncertainty, and make-or-buydecisions, Academy of Management Journal, Vol. 30 No. 3, pp. 589-96.

Zollo, M. and Winer, S.G. (2002), Deliberate learning and evolution of dynamic capabilities,Organization Science, Vol. 13 No. 3, pp. 339-51.

About the authorMikihisa Nakano is an Associate Professor of Management at Kyoto Sangyo University.He received his PhD from Kobe University. His research interests focus on the mechanismof internal and external integration in supply chain and the impact on performance.Mikihisa Nakano can be contacted at: [email protected]


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