Big data and supply chain resilience: role of decision-making technology

Abstract

PurposeAs global supply chains continue to develop, uncertainty grows and supply chains are frequently threatened with disruption. Although big data technology is being used to improve supply chain resilience, big data technology's role in human–machine collaboration is shifting between “supporters” and “substitutes.” However, big data technology's applicability in supply chain management is unclear. Choosing appropriate big data technology based on the enterprise's internal and external environments is important.Design/methodology/approachThis study built a three-factor structural model of the factors “management support,” “big data technology adoption” and “supply chain resilience”. Big data technology adoption was divided into big data-assisted decision-making technology (ADT) and big data intelligent decision-making technology (IDT). A survey was conducted on more than 260 employees from supply chain departments in Chinese companies. The data were analyzed through structural equation modeling using Analyze of Moment Structures (AMOS) software.FindingsThe study's empirical results revealed that adopting both ADT and IDT improved supply chain resilience. The effects of both types of big data were significant in low-dynamic environments, but the effect of IDT on supply chain resilience was insignificant under high-dynamic environments. The authors also found that government support had an insignificantly effect on IDT adoption but significantly boosted ADT adoption, whereas management support factors promoted both ADT and IDT adoption.Originality/valueBy introducing two types of big data technology from the perspectives of the roles in human–machine collaborative decision-making, the research results provide a theoretical basis and management implications for enterprises to reduce the supply chain risk of enterprises.

Developing an effective data-led strategy: managing the enablers

“…Findings emanating from their study show that BD technology capability only has a direct effect on resource bricolage, while BD management capability has a stronger effect on resource optimisation than that on resource bricolage. Investigating BD-assisted decision-making technology and BD intelligent decision-making technology, Liu et al. (2023b) demonstrate how these technologies improve supply chain resilience.…”

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“…The adoption of big data in SCM necessitates significant organizational and cultural shifts. Change management considerations are crucial to facilitate the transition towards a data-driven decision-making environment (Liu et al, 2023). Organizations need to align their culture with data-driven decision-making by fostering a collaborative and transparent organizational culture that supports the integration of big data analytics into SCM processes (Porter, 2019).…”

“…The use of big data technology allows enterprises to monitor and visualize information flow through supply chains in real time, enabling them to make informed decisions and improve supply chain resilience (Liu et al, 2023;Akindote et al, 2023). Furthermore, the utilization of big data in supply chain management can optimize logistics and distribution, taking into account transport costs, inventory, and customer demand, thereby improving overall supply chain network performance (Golabek et al, 2021;Wu & Shi, 2017).…”

The relationship between sustainability and resilience in supply chains: an empirical study on the Egyptian manufacturing firms.

“…Ali et al (2022) included ability to respond quickly, moving to new desirable state after disruption and learning from change in the variable measurement tool. Haq et al (2023) andLiu et al (2023) focused on ability to cope with change and the adaptation and flexibility to any disruptive event. Also, Hamidu et al (2023) measured SCR by including collaboration with supply chain partners, recovery to a desirable state and learning from change.…”

“…Moreover, many of these studies were focus on developed economies (Ciasullo et al, 2022;Liu et al, 2023;Yang & Wang, 2023;Yuan & Li, 2022) with limited focus on developing countries (Haq & Aslam, 2023;Jabbarzadeh et al, 2018;Kumar & Anbanandam, 2020).…”