Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Big data analytics for supply chain risk management: research opportunities at process crossroads

Big data analytics for supply chain risk management: research opportunities at process crossroads The purpose of this study is to map current knowledge on big data analytics (BDA) for supply chain risk management (SCRM) while providing future research needs.Design/methodology/approachThe research team systematically reviewed 53 articles published between 2015 and 2021 and further contrasted the synthesis of these articles with four in-depth interviews with BDA startups that provider solutions for SCRM.FindingsThe analysis is framed in three perspectives. First, supply chain visibility – i.e. the number of tiers in the solutions; second, BDA analytical approach – descriptive, prescriptive or predictive approaches; third, the SCRM processes from risk monitoring to risk optimization. The study underlines that the forefront of innovation lies in multi-tiered, multi-directional solutions based on prescriptive BDA to support risk response and optimization (SCRM). In addition, we show that research on these innovations is scant, thus offering an important avenue for future studies.Originality/valueThis study makes relevant contributions to the field. We offer a theoretical framework that highlights the key relationships between supply chain visibility, BDA approaches and SCRM processes. Despite being at forefront of the innovation frontier, startups are still an under-explored agent. In times of major disruptions such as COVID-19 and the emergence of a plethora of new technologies that reshape businesses dynamically, future studies should map the key role of such actors to the advancement of SCRM. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Business Process Management Journal Emerald Publishing

Big data analytics for supply chain risk management: research opportunities at process crossroads

Loading next page...
 
/lp/emerald-publishing/big-data-analytics-for-supply-chain-risk-management-research-KU22pxDoyy

References (100)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1463-7154
DOI
10.1108/bpmj-01-2022-0012
Publisher site
See Article on Publisher Site

Abstract

The purpose of this study is to map current knowledge on big data analytics (BDA) for supply chain risk management (SCRM) while providing future research needs.Design/methodology/approachThe research team systematically reviewed 53 articles published between 2015 and 2021 and further contrasted the synthesis of these articles with four in-depth interviews with BDA startups that provider solutions for SCRM.FindingsThe analysis is framed in three perspectives. First, supply chain visibility – i.e. the number of tiers in the solutions; second, BDA analytical approach – descriptive, prescriptive or predictive approaches; third, the SCRM processes from risk monitoring to risk optimization. The study underlines that the forefront of innovation lies in multi-tiered, multi-directional solutions based on prescriptive BDA to support risk response and optimization (SCRM). In addition, we show that research on these innovations is scant, thus offering an important avenue for future studies.Originality/valueThis study makes relevant contributions to the field. We offer a theoretical framework that highlights the key relationships between supply chain visibility, BDA approaches and SCRM processes. Despite being at forefront of the innovation frontier, startups are still an under-explored agent. In times of major disruptions such as COVID-19 and the emergence of a plethora of new technologies that reshape businesses dynamically, future studies should map the key role of such actors to the advancement of SCRM.

Journal

Business Process Management JournalEmerald Publishing

Published: Aug 22, 2022

Keywords: Big data analytics; Supply chain risk management; Startups; Literature review

There are no references for this article.