A novel hierarchical fuzzy inference system for supplier selection and performance improvement in the oil & gas industry



Linha de Pesquisa
Administração e Economia de Negócios

Amir Homayoun Sarfaraz, Amir Karbassi Yazdi, Peter Wanke, Elaheh Ashtari Nezhad, Raheleh Sadat Hosseini



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Journal of Decision Systems, Vol. ahead-of-print No. ahead-of-print. Abstract: Evaluation of suppliers is essential to increasing competitive power, customer satisfaction, and profitability. Oil and gas companies can use this research to evaluate suppliers and map the potential path forward for future collaborations. Six supply chain managers in Iran designed HFIS for the oil and gas industry. Shannon Entropy was used to determine the relative weights of suppliers concerning overall uncertainty because the Oil and Gas industry uses many unstructured Key Performance Indicators (KPIs). Using Matlab Toolbox FIS, a future cooperation roadmap was developed. Experts suggested future collaboration with certain suppliers based on the HFIS results. The future cooperation strategy proposed by the framework is highly in line with their expectations. FIS results indicate that the proposed can help select the most appropriate suppliers for cooperation while providing a roadmap for weaker suppliers to improve their performance.

Keywords: Fuzzy inference system (FIS), supplier selection, Shannon entropy, Kraljic portfolio purchasing model, oil & gas (O&G) industry.

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