Fortifying Supply Chain resilience with Predictive Analytics

The article emphasises the critical importance of resilient supply chains in the face of various disruptive events. It highlights the significant challenges many companies face in achieving supply chain resilience and discusses how emerging technologies like Al, ML, and lot offer promising solutions.

- Selvakumaran Mannappan,

If recent history has taught us anything, it is the undisputed need to build resilient supply chains that can withstand unforeseen disruptions. Whether it was the 2011 Japan tsunami that severely impacted global supply chains (particularly in the automotive and electronics industries), the 2017 NotPetya ransomware attack that highlighted vulnerabilities of interconnected systems, the COVID-19 pandemic that brought global production to a standstill, or the more recent geo-political challenges in East Europe and the Middle East, the emphasis on shaking up enterprise supply chain strategies and making them robust and adaptable is more critical now than ever.

And yet, Gartner predicts that “95% of companies will have failed to enable end-to-end resiliency in their supply chains by 2026," primarily due to a failure to manage uncertainty in their supply chains.

Having said that, advancements across technologies like Artificial Intelligence and Machine Learning (AI/ML) and the Internet of Things (IoT) are today being leveraged for proactive insights on supply and demand, enhancing inventory visibility, identifying risks preemptively, and ensuring organisational strength amid global supply chain complexities. Predictive analytics is instrumental in supply chain management, offering a strategic advantage in demand forecasting, logistics optimisation, warehouse efficiency, risk assessment, and supplier management. Applying AI and IoT principles further enhances these capabilities by enabling inventory monitoring in real-time and proactively identifying potential disruptions.

How is AI-driven Predictive Analytics changing the game?