Abstract

۱٫ Introduction

۲٫ Literature review: conceptualising BDA and SCO

۳٫ Research methodology

۴٫ Empirical findings

۵٫ Proposed framework

۶٫ Discussion and implications

۷٫ Conclusions

Disclosure statement

Notes on contributors

References

Abstract

     Undoubtedly, due to the increasingly competitive pressures and the stride of varying demands, volatility and disturbance have become the standard in today’s global markets. The spread of Covid-19 is a prime example of that. Supply chain managers are urged to rethink their competitive strategies to make use of Big Data Analytics (BDA), due to the increasing uncertainty in both demand and supply side, the competition among the supply chain partners and the need to identify ways to offer personalised products and services. With many supply chain executives recognising the need of ‘improving with data’, supply chain businesses need to equip themselves with sophisticated BDA methods/techniques to create valuable insights from big data, thus, enhancing the decision-making process and optimising the efficiency of Supply Chain Operations (SCO). This paper proposes the building blocks of a theoretical framework for understanding the impact of BDA on SCO. The framework is based on a Systematic Literature Review (SLR) on BDA and SCO, underpinned by Task-Technology-Fit theory and Institutional Theory. The paper contributes to the literature by building a platform for future work on investigating factors driving and inhibiting BDA impact on SCO.

Introduction

     Over the past decade, the manufacturing sector has been in the midst of a fourth wave of technological advancement (Rüßmann et al. 2015). During this, a plethora of manufacturing and supply chain businesses have transformed their operations into intelligent/smart manufacturing. This is by adopting a variety of innovative technologies such as autonomous robots, simulations, horizontal and vertical systems integration, internet of things [IoT], cybersecurity, cloud services, additive manufacturing, and most importantly, big data analytics (Wamba et al. 2020). Several noticeable researchers and practitioners have recognised the significance and applicability of Industry 4.0 (I_4.0) for operations, logistics and production management at large (Sivarajah et al. 2017; Rahman et al. 2022). However, relatively little is known about the impact of BDA on SCO – particularly focussing on the five dimensions of SCO: demand planning, production and manufacturing, logistics, procurement, and inventory. Among the extant research studies published, a few shed light on the link between IoT and SCO (e.g. Ben-Daya, Hassini, and Bahroun 2019) and the impact of additive manufacturing on SCO, processes and performances (e.g. Li, Tang, et al. 2017).

Conclusions

     Arguably, SCO and BDA can enable the dynamic capabilities of firms, allowing decision makers to enhance the corporate or company abilities or to better sense emerging opportunities and threats. This paper presents the past trends and current state of BDA research published specifically in the context of SCOs and its respective dimensions. We proposed a conceptual framework based on two research questions: Investigating the benefits of implementing BDA methods/techniques to achieve better decision-making for SCO? and Investigating the benefits of implementing BDA methods/techniques in achieving optimisation of SCOs? The continuing interest and the use of BDA specifies that in future research studies academics, researchers and practitioners may focus on the factors driving and inhibiting BDA to further propose robust solutions to the service related problems. The intention in conducting this detailed investigation was to provide a useful and yet usable source of information for future researchers. The discussion also highlighted several research limitations and future directions for BDA applications within the SCO research area to catalyse the research development of the topic.