A framework for investigating optimization of service parts performance with big data

Abstract

As national economies continue to evolve across the globe, businesses are increasing their capacity to not only generate new products and deliver them to customers, but also to increase levels of after-sales service. One major component of after-sale service involves service parts management. However, service parts businesses are typically seen as add-ons to existing business models, and are not well integrated with primary businesses. Consequently, many service parts operations are managed using ad-hoc practices that are often subordinated to primary businesses. Early research in this area has been instrumental in assisting organizations to begin optimizing some aspects of service parts management. However, performance goals for service parts management are often ill-defined. Further, because these service parts businesses are often subordinated to primary businesses within a firm, the use of newer big data applications to help manage these processes is almost completely absent. Herein, we develop a framework that seeks to define service parts performance goals for the purpose of outlining where scholars and practitioners can further examine where, how, and why big data applications can be employed to enhance service parts management performance.

Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice

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Big Data in operations and supply chain management: a systematic literature review and future research agenda

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Data supply chain (DSC): research synthesis and future directions

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