Risks of data-driven technologies in sustainable supply chain management

Abstract

PurposeWith 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/approachA 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.FindingsResults 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/valueThe 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.

Safety risk and operational efficiency on logistic service providers’ sustainable coal supply chain management

“…The result of this research aligns with the study by Kusi-Sarpong et al (2021), who gives a better understanding and control of the nature of risk inherent in the supply chain. This research is reinforced by Reinerth et al (2019), Mukhsin andSuryanto (2022), andOzkan-Ozen et al (2023), concluding that sustainable supply chain management must include the concept of security. This research also aligns with Goerlandt and Pulsifer (2022) and Thanu et al (2022), concluding that applying safety risk will improve environmental performance by placing standards on environmental issues.…”

Addressing Cybersecurity Challenges in Sustainable Supply Chain Management: A Review of Current Practices and Future Directions

“…Through a hybrid multi-criteria decision-making model, the study identifies the economic dimension of sustainable SCM as particularly vulnerable to these risks. The findings highlight the critical need for organizations to address cybersecurity risks as part of their sustainability strategies, emphasizing the interconnectedness of data security, privacy, and the technological infrastructure within sustainable supply chains (Ozkan-Ozen et al, 2023). In synthesizing these perspectives, it becomes clear that the critical intersection of cybersecurity and sustainable supply chain management is defined by a complex interplay of technological, organizational, and environmental factors.…”

“…The authors call for future research to focus on developing robust strategies to reduce the frequency and impact of these attacks, thereby enhancing the resilience of supply chains against cybersecurity threats (Kumar & Mallipeddi, 2022). Ozkan-Ozen et al (2023) explore the risks associated with data-driven technologies in sustainable SCM, noting that the digitalization and reliance on data for decision-making have made supply chains more competitive but also more vulnerable. The study identifies data privacy, trust, data availability, information sharing, and traceability as key areas of concern.…”

“…Their work underscores the importance of cybersecurity in ensuring the resilience and sustainability of supply chains in the face of evolving cyber threats (Kumar & Mallipeddi, 2022). Ozkan-Ozen et al (2023) focus on the risks associated with data-driven technologies in sustainable supply chain management. They argue that the digitalization and data-centric approaches essential for achieving sustainability also expose supply chains to new kinds of risks, including those related to data privacy, trust, and information sharing.…”

Research on Credit Risk Prediction Method of Blockchain Applied to Supply Chain Finance

“…Currently, supply chain finance credit risk prediction and evaluation methods include grey prediction model [8], linear regression model [9], support vector regression [10], machine learning methods [10], deep learning methods [11], etc. Literature [12] applies blockchain technology to the supply chain smart contract aspect and proposes a credit risk prediction method for supply chain finance based on grey theory; Literature [13] researches the method of combining blockchain technology with the actual needs of enterprises based on the underlying technology of Bitcoin and using the transparency of the transaction information; Literature [14] researches the supply chain architecture based on the blockchain technology, and proposes the supply chain process optimization method; Literature [15] proposed a financial credit risk prediction method based on improved machine learning method through the perspective of global supply chain product security and challenges; Literature [16] proposed blockchain-based encryption technology and studied the evaluation and analysis method of the corresponding technology; Literature [17] predicted the supply chain financial credit risk by analyzing the supply chain financial credit risk influencing factors and adopting the artificial neural network method to predict the supply chain finance credit risk and responds to enterprise demand in real time. According to the analysis of the above literature, the existing credit risk prediction methods of supply chain finance have the following defects:…”