Supply Chain Risk Management in the Era of Big Data

Abstract

The trend of big data implies novel opportunities and challenges for improving supply chain management. In particular, supply chain risk management can largely benefit from big data technologies and analytic methods for collecting, analyzing, and monitoring both supply chain internal data and environmental data. Due to the increasing complexity, particular attention must not only be put on the processing and analysis of data, but also on the interaction between big data information systems and users. In this paper, we analyze the role of big data in supply chains and present a novel framework of a supply chain risk management system for improving supply chain planning and supply chain risk management under stochastic environments by using big data technologies and analytics. The process-oriented framework serves as a guideline to integrate and analyze big data as well as to implement a respective supply chain risk management system. As such, this paper provides a novel direction of utilizing big data in supply chain risk management.

Supply chain risk management and artificial intelligence: state of the art and future research directions

“…The most recent studies in this category use Big Data Analytics for various SCRM tasks. Generic SCRM frameworks based on Big Data are proposed in Fan, Heilig, and Voss (2015) and He et al (2015), based on monitoring data both within and external to the supply chain. The case of fleet management is explored in Mani et al (2017) with vehicle tracking systems employed to identify social and environmental risks (e.g.…”

“…Newer techniques and recent advances in more established ones have yet to be exploited in the context of SCRM. The more recent studies of He et al (2015) and Fan, Heilig, and Voss (2015) have provided conceptual SCRM frameworks that rely on Machine Learning techniques and Big Data but have not proceeded to implementing, applying and evaluating the frameworks. To the best of the authors' knowledge, the only recent studies that implement learning algorithms in the context of SCRM are Zage, Glass, and Colbaugh (2013), Garvey, Carnovale, and Yeniyurt (2015) and Ojha et al (2018).…”

Predicting supply chain risks using machine learning: The trade-off between performance and interpretability

“…Recently, there has been an AI resurgence due to the availability of increased computing power and large amounts of data, as well as the success of approaches within the broad area of machine learning. This has also led to SCRM researchers considering the potential of AI techniques in relation to tasks such as risk identification, prediction, assessment and response [6,7,8,9]. However, research is still at early stages, proposing either purely theoretical frameworks that have not been implemented and applied in real-world case studies [6,7], or ad-hoc solutions that are only applicable within the confines of a particular case study [8,9].…”

“…The potential of applying big data analytics and machine learning techniques for SCRM has recently been considered in literature. Fan et al [6] investigate potential big data sources related to supply chains and then propose an SCRM framework that relies on the availability of such data. The framework relies on analysing and monitoring supply chain data to detect emerging risks, maintain relevant risk reports and use these to initiate suitable actions such as replanning the supply chain.…”

Big Data Analytics and Its Applications in Supply Chain Management

“…Statistical analysis, simulation, optimization, and techniques are used to supply chain decision making [19].…”