RESILIENT SUPPLY CHAIN DESIGN USING PREDICTIVE ANALYTICS TO MITIGATE DISRUPTIONS AND ENHANCE OPERATIONAL CONTINUITY PERFORMANCE
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2022
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| _version_ | 1866901390492696576 |
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| author | Grace Omitoyin1* and Adeyinka Towobola1 and Stephen Olayemi2 |
| author_facet | Grace Omitoyin1* and Adeyinka Towobola1 and Stephen Olayemi2 |
| contents | <p><a href="https://ijetrm.com/issues/files/Dec-2022-20-1766202893-DEC202218.pdf">Resilient </a>supply chain design has become a strategic imperative as organizations face increasing exposure to<br>systemic disruptions arising from geopolitical instability, pandemics, climate-related events, cyber threats, and<br>demand volatility. Traditional supply chain models, which rely heavily on static planning assumptions and<br>historical averages, have proven insufficient in anticipating and absorbing shocks that propagate rapidly across<br>interconnected global networks. This study examines the role of predictive analytics as a foundational capability<br>for building resilient supply chains capable of maintaining operational continuity under uncertainty. From a broad<br>perspective, the paper situates supply chain resilience within contemporary risk management and operations<br>strategy literature, emphasizing the shift from reactive disruption response toward proactive, data-driven<br>anticipation and mitigation. The analysis then narrows to explore how predictive analytics leveraging machine<br>learning, advanced forecasting, and real-time data integration enables organizations to detect early disruption<br>signals, assess cascading risk impacts, and dynamically reconfigure sourcing, inventory, and distribution<br>decisions. Predictive models enhance visibility across multi-tier supply networks, support scenario-based stress<br>testing, and inform pre-emptive interventions that reduce downtime and performance degradation. The study<br>further highlights how analytics-driven resilience supports continuity by aligning demand sensing, capacity<br>planning, and logistics execution with evolving risk profiles. By integrating predictive analytics into supply chain<br>design, firms can transition from efficiency-dominated optimization toward balanced architectures that prioritize<br>adaptability, redundancy, and rapid recovery without excessive cost penalties. The paper concludes that resilient<br>supply chain performance is increasingly contingent on the systematic deployment of predictive analytics as a<br>decision-support layer embedded within governance, planning, and execution processes. Such integration<br>strengthens operational continuity, enhances responsiveness to disruptions, and positions organizations to sustain<br>competitive advantage in volatile operating environments.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17994154 |
| institution | Zenodo |
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| publishDate | 2022 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | RESILIENT SUPPLY CHAIN DESIGN USING PREDICTIVE ANALYTICS TO MITIGATE DISRUPTIONS AND ENHANCE OPERATIONAL CONTINUITY PERFORMANCE Grace Omitoyin1* and Adeyinka Towobola1 and Stephen Olayemi2 Resilient supply chains; predictive analytics; disruption mitigation; operational continuity; supply chain risk management; data-driven decision-making <p><a href="https://ijetrm.com/issues/files/Dec-2022-20-1766202893-DEC202218.pdf">Resilient </a>supply chain design has become a strategic imperative as organizations face increasing exposure to<br>systemic disruptions arising from geopolitical instability, pandemics, climate-related events, cyber threats, and<br>demand volatility. Traditional supply chain models, which rely heavily on static planning assumptions and<br>historical averages, have proven insufficient in anticipating and absorbing shocks that propagate rapidly across<br>interconnected global networks. This study examines the role of predictive analytics as a foundational capability<br>for building resilient supply chains capable of maintaining operational continuity under uncertainty. From a broad<br>perspective, the paper situates supply chain resilience within contemporary risk management and operations<br>strategy literature, emphasizing the shift from reactive disruption response toward proactive, data-driven<br>anticipation and mitigation. The analysis then narrows to explore how predictive analytics leveraging machine<br>learning, advanced forecasting, and real-time data integration enables organizations to detect early disruption<br>signals, assess cascading risk impacts, and dynamically reconfigure sourcing, inventory, and distribution<br>decisions. Predictive models enhance visibility across multi-tier supply networks, support scenario-based stress<br>testing, and inform pre-emptive interventions that reduce downtime and performance degradation. The study<br>further highlights how analytics-driven resilience supports continuity by aligning demand sensing, capacity<br>planning, and logistics execution with evolving risk profiles. By integrating predictive analytics into supply chain<br>design, firms can transition from efficiency-dominated optimization toward balanced architectures that prioritize<br>adaptability, redundancy, and rapid recovery without excessive cost penalties. The paper concludes that resilient<br>supply chain performance is increasingly contingent on the systematic deployment of predictive analytics as a<br>decision-support layer embedded within governance, planning, and execution processes. Such integration<br>strengthens operational continuity, enhances responsiveness to disruptions, and positions organizations to sustain<br>competitive advantage in volatile operating environments.</p> |
| title | RESILIENT SUPPLY CHAIN DESIGN USING PREDICTIVE ANALYTICS TO MITIGATE DISRUPTIONS AND ENHANCE OPERATIONAL CONTINUITY PERFORMANCE |
| topic | Resilient supply chains; predictive analytics; disruption mitigation; operational continuity; supply chain risk management; data-driven decision-making |
| url | https://doi.org/10.5281/zenodo.17994154 |