Big data-driven supply chain performance measurement system: a review and framework for implementation

Sachin S. Kamble, Angappa Gunasekaran

Research output: Contribution to journalReview articlepeer-review

44 Scopus citations

Abstract

Performance measures and metrics (PMM) is identified to be an essential aspect of managing diverse supply chains. The PMM improves the firm’s performance by providing open and transparent communication between the various stakeholders of an organisation. The literature suggests that big data analytics has a positive impact on the supply chain and firm performance. Presently, the literature lack studies that recognise the PMM relevant to big data-driven supply chain (BDDSC). The present study is based on a comprehensive review of 66 papers published with the primary objective to identify the various PMMs used to evaluate the BDDSC. The findings suggest that the PMMs applicable to BDDSC can be classified into two non-mutually exclusive categories. The first category represents 24 performance measures used to evaluate the performance of the big data analytics capability and the second category represents 130 measures used for assessing the performance of BDDSC processes. The study also reports the emergence of new performance measures based on increasing use of predictive and social analytics in BDDSC. Based on the results of the study a framework on BDDSC performance measurement system is proposed which will guide the managers to have a robust performance measurement system in their organisation.

Original languageEnglish (US)
Pages (from-to)65-86
Number of pages22
JournalInternational Journal of Production Research
Volume58
Issue number1
DOIs
StatePublished - Jan 2 2020

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this