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

Abstract : Purpose 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/approach The 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. Findings The 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/value This 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.
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https://hal-audencia.archives-ouvertes.fr/hal-03766121
Contributor : Leonardo Marques Connect in order to contact the contributor
Submitted on : Wednesday, August 31, 2022 - 5:47:39 PM
Last modification on : Thursday, September 1, 2022 - 3:19:40 AM

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Leonardo de Assis Santos, Leonardo Marques. Big data analytics for supply chain risk management: research opportunities at process crossroads. Business Process Management Journal, 2022, 28 (4), pp.1117-1145. ⟨10.1108/BPMJ-01-2022-0012⟩. ⟨hal-03766121⟩

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