Selecting an appropriate partner is a vital and strategic decision-making process in any supply chain collaboration initiative. The purpose of this paper is to introduce and explore the key factors considered by manufacturers in the selection of an appropriate retailer(s) for collaboration and collaborative planning, forecasting and replenishment (CPFR) implementation and the relationships between these factors.
A comprehensive literature review and experts’ views are applied to identify the main retailer selection and evaluation factors for CPFR implementation. A fuzzy decision-making trial and evaluation laboratory approach is then used to rank and analysis the interaction among identified factors. The findings are finally evaluated using a case study from a high-tech industry.
The most important partner selection factors comprising of five dimensions and 24 factors are introduced. Of the identified criteria, three factors: manufacturer’s familiarity with the retailer, workforce skills and training and customer service orientation and capability have been identified as critical when selecting retailers for CPFR implementation. The technological capabilities dimensions are identified as the only net cause dimension which affects all other dimensions and its importance and role in simplifying and enhancing the speed and flexibility of CPFR implementation.
The paper identifies practical retailer selection factors for CPFR implementation and the causal relationships between factors. Developed retailer selection dimensions and criteria will assist manufacturers and retailers in understanding the role these factors play in CPFR implementation. This will also assist in appropriate retailer(s) selection by manufacturers.
This paper contributes to the literature on CPFR and tackles the important issue of selecting appropriate partners by developing retailer selection dimensions and criteria in CPFR implementation.
Panaihfar, F., Heavey, C. and Byrne, P. (2015), "Developing retailer selection factors for collaborative planning, forecasting and replenishment", Industrial Management & Data Systems, Vol. 115 No. 7, pp. 1292-1324. https://doi.org/10.1108/IMDS-01-2015-0009Download as .RIS
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