Risks of data-driven technologies in sustainable supply chain management

Abstract

Purpose: With the rapid change that has taken place with digitalization and data-driven approaches in supply chains business environment become more competitive and reaching sustainability in supply chains become even more challenging. In order to manage supply chains properly in terms of considering environmental social and economic impacts organizations need to deal with huge amount of data and improve organizations' data management skills. From this view increased number of stakeholders and dynamic environment reveal the importance of data-driven technologies in sustainable supply chains. This complex structure results in new kind of risks caused by data-driven technologies. Therefore the aim of the study to analyze potential risks related to data privacy trust data availability information sharing and traceability i.e. in sustainable supply chains. Design/methodology/approach: A hybrid multi-criteria decision-making (MCDM) model which is the integration of step-wise weight assessment ratio analysis (SWARA) and TOmada de Decisao Interativa Multicriterio (TODIM) methods is going to be used to prioritize potential risks and reveal the most critical sustainability dimension that is affected from these risks. Findings: Results showed that economic dimension of the sustainable supply chain management (SSCM) is the most critical concept while evaluating risks caused by data-driven technologies. On the other hand risk of data security risk of data privacy and weakness of information technology systems and infrastructure are revealed as the most important risks that organizations should consider. Originality/value: The contribution of the study is expected to guide policymakers and practitioners in terms of defining potential risks causes by data-driven technologies in sustainable supply chains. In future studies solutions can be suggested based on these risks for achieving sustainability in all stages of the supply chain causes by data-driven technologies. © 2023 Elsevier B.V. All rights reserved.