Abstract
This paper presents the development of a model based decision support system with a case study on solving the supplier selection problem in a chemical processing industry. For the evaluation and selection of supplier, the analytical hierarchy process (AHP) and grey relational analysis (GRA) were used. The intention of the study is to propose an appropriate platform for process industries in selecting suppliers, which was tested with an electroplating industry during the course of development. The sensitivity analysis was performed in order to improve the robustness of the results with regard to the relative importance of the evaluation criteria and the parameters of the evaluation process. Finally, a practical implementation study was carried out to reveal the procedure of the proposed system and identify the suitable supplier with detailed discussions about the benefits and limitations.
Highlights
Supplier selection is one of a critical decision in supply chain management in both manufacturing and process industries
The developed decision support systems (DSS) is tested with the case study in an Electroplating industry to select the best supplier for its raw materials
The nature of electroplating industry selected for this study is involved in nickel coating and chrome plating for auto components
Summary
Supplier selection is one of a critical decision in supply chain management in both manufacturing and process industries. Hybrid approach applications Yang and Chen (2006) proposed an integrated model by combining AHP and GRA to evaluate and select the supplier for a notebook computer manufacturer. Montazer et al (2009) developed an expert decision support system (EDSS) using a fuzzy version of ELECTRE III method for ranking the alternatives based on the experts’ knowledge This EDSS was applied to a vendor selection process in an Iranian oil industry. This system aims to develop appropriate decision strategies for supplier evaluation and selection in a process industry. The various decision making activities and provide necessary decision support based on built in analytical tools which are used in this proposed DSS namely, AHP and GRA. The interfaces available in this developed DSS are scheduled reports, questions/answers and menu driven input/output
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