Browsing by Author "Papadopoulos, Thanos"

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    Critical analysis of the impact of big data analytics on supply chain operations
    (Taylor & Francis, 2022-05-16) Hasan, Ruaa; Kamal, Muhammad Mustafa; Daowd, Ahmad; Eldabi, Tillal; Koliousis, Ioannis; Papadopoulos, Thanos
    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.
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    Data-driven digital transformation for emergency situations: the case of the UK retail sector
    (Elsevier, 2022-09-02) Papanagnou, Christos; Seiler, Andreas; Spanaki, Konstantina; Papadopoulos, Thanos; Bourlakis, Michael
    The study explores data-driven Digital Transformation (DT) for emergency situations. By adopting a dynamic capability view, we draw on the predictive practices and Big Data (BD) capabilities applied in the UK retail sector and how such capabilities support and align the supply chain resilience in emergency situations. We explore the views of major stakeholders on the proactive use of BD capabilities of UK grocery retail stores and the associated predictive analytics tools and practices. The contribution lies within the literature streams of data-driven DT by investigating the role of BD capabilities and analytical practices in preparing supply and demand for emergency situations. The study focuses on the predictive way retail firms, such as grocery stores, could proactively prepare for emergency situations (e.g., pandemic crises). The retail industry can adjust the risks of failure to the SC activities and prepare through the insight gained from well-designed predictive data-driven DT strategies. The paper also proposes and ends with future research directions.