Information technologies impact on SC risk management
| Digital twins are virtual representations of physical objects, processes, or systems that enable simulation, monitoring, and optimisation. In supply chain risk management, they offer advantages such as heightened visibility for faster risk detection, scenario analysis for assessing risk impacts, data-driven insights for resilience, and enhanced collaboration among stakeholders | Ivanov and Dolgui (2019), Wong et al. (2022) |
| Big data serves as an asset in supply chain risk management by offering insights into supplier performance, customer behaviour, and potential cost-saving avenues. Through the utilisation of big data analytics, organisations can aptly recognise and address risks, enhance operational transparency, and allocate resources with precision. To conclude, big data stands as a strong resource enabling companies to streamline and optimise their supply chain management processes | Darvazeh et al. (2020), Ivanov and Dolgui (2021), Bui et al. (2021), Sheng et al. (2021) |
| Information sharing plays a pivotal role in the management of supply chain risks. Through the exchange of information, organisations can bolster their readiness to tackle potential risks or disruptions, cooperate with their supply chain collaborators in pinpointing and alleviating risks, and cultivate a comprehensive grasp of the risks within their purview. In summary, proficient information sharing stands as a crucial element in fortifying supply chain risk management | Zhu et al. (2020), Kabadayi et al. (2020), Zhu et al. (2020), Birkel and Hartmann (2020) |
| loT enhances supply chain risk management through real-time visibility and data analysis. IoT devices deployed in the supply chain collect data on goods' location and condition, aiding risk identification and mitigation. For example, tracking goods can anticipate delays, while sensor data on trucks helps prevent breakdowns. IoT fosters collaboration among supply chain partners by sharing data, enabling better risk understanding and contingency planning | Sharma et al. (2020), Venkatesh et al. (2020) |
Digital twin | Digital twins are virtual representations of physical objects, processes, or systems that enable simulation, monitoring, and optimisation. In supply chain risk management, they offer advantages such as heightened visibility for faster risk detection, scenario analysis for assessing risk impacts, data-driven insights for resilience, and enhanced collaboration among stakeholders | |
Big data | Big data serves as an asset in supply chain risk management by offering insights into supplier performance, customer behaviour, and potential cost-saving avenues. Through the utilisation of big data analytics, organisations can aptly recognise and address risks, enhance operational transparency, and allocate resources with precision. To conclude, big data stands as a strong resource enabling companies to streamline and optimise their supply chain management processes | |
Information sharing | Information sharing plays a pivotal role in the management of supply chain risks. Through the exchange of information, organisations can bolster their readiness to tackle potential risks or disruptions, cooperate with their supply chain collaborators in pinpointing and alleviating risks, and cultivate a comprehensive grasp of the risks within their purview. In summary, proficient information sharing stands as a crucial element in fortifying supply chain risk management | |
IoT | loT enhances supply chain risk management through real-time visibility and data analysis. IoT devices deployed in the supply chain collect data on goods' location and condition, aiding risk identification and mitigation. For example, tracking goods can anticipate delays, while sensor data on trucks helps prevent breakdowns. IoT fosters collaboration among supply chain partners by sharing data, enabling better risk understanding and contingency planning |
Source(s): Authors' own creation