An integrated BWM and PIV approach for vendor selection methodology for predictive maintenance 4.0 in chemical fertilizer industry

The chemical fertilizer industry is progressing towards digitalization and implementing value added data driven methodologies to enhance the process control and reliable critical equipment operation. Companies are hiring Predictive maintenance (PDM) vendors to digitalize the real time predictive maintenance of high-speed critical rotating machines. This article presents an integrated MCDM approach of Best worst Method (BWM) and Proximity Index Value (PIV) methods in selection of PDM vendors. Selection criteria has been framed from literature review, the weight of the criteria is determined by BWM, and the ultimate ranking of the vendors is based upon the overall proximity value, obtained from PIV. The proposed model is illustrated with a real case of online vibrating continuous monitoring system vendor selection in fertilizer company. finally, rank reversal issue in PDM vendors was performed to prevent the rank reversal phenomenon. The outcome of BWM highlights that vendor industry experience, appropriate modelling technique, IT infra structure integration and model ownership are a favourable criterion for selection of PDM vendors and whereas cost factor is least important. Based on PIV method, ranking of vendor has been done and rank reversal is minimised. This integrated model is extremely adaptable and provides effective insights in the selection of PDM vendor offering.