An integrated fuzzy approach for prioritizing supply chain complexity drivers of an Indian mining equipment manufacturer

Abstract

The complexities in present day supply chain are numerous and are evolving due to globalization, customisation, innovation, flexibility, sustainability and uncertainties. The growing supply chain complexity results in negative consequences on cost, customer service and reputation. Managing supply chain complexity without compromising the profitability is a challenging task. Supply chain complexity (SCC) management involves identifying, prioritizing, measuring, analysing and controlling/eliminating the drivers of complexity. The SCC drivers denote number and variety of suppliers, customers, products, processes and uncertainties which are highly interdependent. Firms need to prioritize the drivers in order to manage and simplify SCC. Models and methods to prioritize the complexity drivers considering their interdependence are limited in literature. Prioritizing the complexity drivers requires a subjective approach and it is a multi criteria decision making (MCDM) problem. In this research, at first a fuzzy ISM (Fuzzy Interpretive Structural Modelling) is used to establish the interdependence of SCC drivers. A fuzzy AHP (Analytic Hierarchy Process) and fuzzy PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) are then used to quantify and prioritize the complexity drivers considering the strength of interdependence obtained using the fuzzy ISM. A case example of a mining equipment manufacturer located in India is presented to illustrate the proposed approach. From the results it is identified that unreliability of suppliers, forecast inaccuracy, lack of visibility /information sharing and number/variety of processes are the significant drivers.