Using AHP and TOPSIS approaches in customer-driven product design process
Computers in Industry
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Review: A state-of the-art survey of TOPSIS applications
Expert Systems with Applications: An International Journal
A method for the selection of customized equipment suppliers
Expert Systems with Applications: An International Journal
Analysing network uncertainty for industrial product-service delivery: A hybrid fuzzy approach
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Selection of Concrete Production Facility Location Integrating Fuzzy AHP with TOPSIS Method
International Journal of Productivity Management and Assessment Technologies
Student satisfaction evaluation based on AHP-TOPSIS method
International Journal of Computer Applications in Technology
Hi-index | 12.06 |
This paper aims to develop a benchmarking framework that evaluates the cold chain performance of a company, reveals its strengths and weaknesses and finally identifies and prioritizes potential alternatives for continuous improvement. A Delphi-AHP-TOPSIS based methodology has divided the whole benchmarking into three stages. The first stage is Delphi method, where identification, synthesis and prioritization of key performance factors and sub-factors are done and a novel consistent measurement scale is developed. The second stage is Analytic Hierarchy Process (AHP) based cold chain performance evaluation of a selected company against its competitors, so as to observe cold chain performance of individual factors and sub-factors, as well as overall performance index. And, the third stage is Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based assessment of possible alternatives for the continuous improvement of the company's cold chain performance. Finally a demonstration of proposed methodology in a retail industry is presented for better understanding. The proposed framework can assist managers to comprehend the present strengths and weaknesses of their cold. They can identify good practices from the market leader and can benchmark them for improving weaknesses keeping in view the current operational conditions and strategies of the company. This framework also facilitates the decision makers to better understand the complex relationships of the relevant cold chain performance factors in decision-making.