An analytic infrastructure for harvesting big data to enhance supply chain performance

Yuanzhu Zhan and Kim Hua Tan

European Journal of Operational Research, 2020, vol. 281, issue 3, 559-574

Abstract: Big data has already received a tremendous amount of attention from managers in every industry, policy and decision makers in governments, and researchers in many different areas. However, the current big data analytics have conspicuous limitations, especially when dealing with information silos. In this paper, we synthesise existing researches on big data analytics and propose an integrated infrastructure for breaking down the information silos, in order to enhance supply chain performance. The analytic infrastructure effectively leverages rich big data sources (i.e. databases, social media, mobile and sensor data) and quantifies the related information using various big data analytics. The information generated can be used to identify a required competence set (which refers to a collection of skills and knowledge used for specific problem solving) and to provide roadmaps to firms and managers in generating actionable supply chain strategies, facilitating collaboration between departments, and generating fact-based operational decisions. We showcase the usefulness of the analytic infrastructure by conducting a case study in a world-leading company that produces sports equipment. The results indicate that it enabled managers: (a) to integrate information silos in big data analytics to serve as inputs for new product ideas; (b) to capture and interrelate different competence sets to provide an integrated perspective of the firm's operations capabilities; and (c) to generate a visual decision path that facilitated decision making regarding how to expand competence sets to support new product development.

Keywords: Decision support systems; Big data; Analytic infrastructure; Competence set; Deduction graph (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:281:y:2020:i:3:p:559-574

DOI: 10.1016/j.ejor.2018.09.018

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