Comparing the factors that influence the adoption of CPFR by retailers and suppliers

• DOI: https://doi.org/10.1108/IJLM-10-2014-0168
• Date: 14 November 2016
• Pages: 931-946
• Published date: 14 November 2016
• Author: Hsin-Pin Fu
• Subject Matter: Management science & operations,Logistics

Comparing the factors that

influence the adoption of CPFR

by retailers and suppliers

Hsin-Pin Fu

National Kaohsiung First University of Science and Technology,

Kaohsiung, Taiwan

Abstract

Purpose –The purpose of this paper is to compare the factors that influenced the adoption of a

Collaborative Planning, Forecasting, and Replenishment (CPFR) model by a retailer that is a lifestyle

accessories chain, and a supplier that is a food manufacturer.

Design/methodology/approach –The study started with the construction of a questionnaire based

on the factors of the theoretical framework of a Technology-Organization-Environment that were

discovered through a literature review and distribution of the questionnaire to experts. The fuzzy

analytical hierarchy process method was used to determine the weights (importance) of these factors.

Findings –The study found that among the top ten factors, two are the same for both the retailer and

the supplier –“partner trust and communication,”and “support of top managers”–while the other

factors differ. In addition, it was found that the supplier pays more attention to the environmental

issues, while the retailer focuses on organizational issues when introducing a CPFR model.

Practical implications –This study offers five practical implications for the successful adoption of a

CPFR model: first, top management support and trusting relationship building are important factors

for both the retailer and the supplier; second, adoption of CPFR is a management issue, rather than a

technological issue; third, retailers lead more successfully; fourth, a careful selection of collaborative

partners is essential; and finally, an innovative model should be constructed.

Originality/value –The results can serve as a reference to help enterprises to better allocate their

resources, according to the weights of the important adoption factors when they are formulating their

strategies for the adoption of CPFR. Decisions based upon this guideline can increase the success rate

of CPFR adoptions and can ensure better allocation of resources.

Keywords FAHP, MCDM, Adoption factors, CPFR

Paper type Case study

1. Research background

The retail industry everywhere has entered an age of intense competition, and firms need

to adopt complicated business procedures in order to ensure their competitiveness. The

challenges faced by the retail industry include those related to demand forecasts, an

efficient replenishment system, and an effective marketing plan. Supply chain collaboration

is a complex and dynamic phenomenon; however, it is a struggle to both explain existing

“collaborative”behavior and provide prescriptions for leveraging collaboration to achieve

differential supply chain performance (Fawcett et al., 2010). Sandberg (2007) also presented

a broad overview concerning logistics collaboration that covers many of the most

important supply chain management issues. The main challenge is that the sharing of

information in business-to-business relationships involves integrated value chains (Kaipia

and Hartiala, 2006; Bartlett et al., 2007). In order to address these challenges, the Voluntary

Inter-industry Commerce Solutions Association (VICS) developed the Collaborative The International Journal of

Logistics Management

Vol. 27 No. 3, 2016

pp. 931-946

©Emerald Group Publis hing Limited

0957-4093

DOI 10.1108/IJLM-10-2014-0168

Received 7 October 2014

Revised 27 October 2014

Accepted 25 October 2015

The current issue and full text archive of this journal is available on Emerald Insight at:

www.emeraldinsight.com/0957-4093.htm

The author thanks the Ministry of Science and Technology (MOST) of the Executive Yuan,

Taiwan, R.O.C. for supporting this research (Research Grant No. NSC 102-2410-H-327-033).

931

Adoption of

CPFR by

retailers and

suppliers

Planning, Forecasting, and Replenishment (CPFR) model in 1998 to provide retailers and

suppliers with a cooperative approach to supply chain forecasting (Voluntary Interindustry

Commerce Standards Association (VICS), 2012). The CPFR model proposed by VICS in

1998 is a process model with three phases and nine steps:

•Phase 1: Collaborative Planning (including Step 1: establishing a collaborative

relationship, and Step 2: creating a joint business plan).

•Phase 2: Collaborative Forecasting (including Step 3: creating the sales forecast,

Step 4: identifying exceptions for the sales forecast, Step 5: resolving/

collaborating on the exception items of the sales forecast, Step 6: creating order

forecasts, Step 7: identifying exceptions for the order forecast, and Step 8:

resolving/collaborating on the exception items of the order forecast).

•Phase 3: Collaborative Replenishment (including Step 9: generating the order).

Pilot and production implementations of CPFR over the years have yielded many

insights. A joint committee of VICS and the Efficient Consumer Response organization

revised the guidelines slightly in 2001 in order to incorporate the global requirements

sanctioned by the Global Commerce Initiative. In 2004, the VICS-CPFR committee

developed a major revision of the CPFR

@

model (Table I) (VICS, 2012).

Since CPFR was proposed by VICS in 1998 (VICS, 2012), many studies of applications

of CPFR have been carried out, such as those that examined Procter and Gamble (P&G)

(Williams, 1999), the Whirlpool Corporation (Sagar, 2003), and Sears and Michelin

(Steermann, 2003). These studies all indicated that CPFR can enhance operational

performance and the accuracy of sales forecasting, and can reduce inventory costs. Except

for these case studies (Sagar, 2003; Steermann, 2003; Danese, 2007; Kaipia and Hartiala,

2006), most of the other previous studies of CPFR that were related to the impact factors

used the method of multiple regression analysis (Barratt and Oliveira, 2001; Rawwas et al.,

2008; Sanders, 2007; Wang et al., 2005). However, the significant factors affecting the

adoption of a CPFR model that were obtained were presented in unstructured frameworks

and were “unweighted”. This has resulted in two gaps in the research:

(1) Although the beta value in multiple regression analysis can indicate the relative

weights of the factors, its value is obtained indirectly, through testing. Also,

due to the measurement errors of independent variables and dependent

variables, prediction errors can occur between real dependent value and

predicted dependent value, and can result in collinearity problems between the

independent variables. Sometimes, the beta value could be a minus value

(Hair et al., 2009), and as a result, it is difficult to measure the importance of

the resulting value of the variable. For this reason, few studies have determined

the weights of the factors by using their beta value.

(2) Factor selection, which involves more than one assessment criterion or goal, can

assist enterprises in decision making and in executing their strategies by

providing them with a clear positioning strategy (Molla and Licker, 2005).

Factor selection is a multi-criteria decision-making (MCDM) task and a MCDM

tool can be used to select a set of available solutions based on multiple criteria.

Thus, the MCDM tool can determine the better solutions by providing complete

and systematic evaluation criteria (Chen and Cheng, 2009). However, few

previous studies have used an MCDM tool to compare the factors of CPFR

adoption by retailers and suppliers.

932

IJLM

27,3