Machine Learning applications can help in forecasting and mitigating organizational risks, and there are several applications relative to an organizational supply chain that are technically viable and proven to deliver value and early successes.
Supply Chain resilience is a subject that’s been getting a great deal of attention in the light of the various global challenges that businesses are facing, such as raw materials shortages, the ongoing COVID-19 pandemic, geo-political and economic struggles, labor shortages, and transportation issues. It’s not surprising, therefore, that there’s a keen focus on improving and optimizing supply chain resilience as organizations seek to predict, address, and minimize the potential impact of disruptions as much as possible, keep operations running smoothly and maintain competitive advantages. Machine Learning (ML) applications can help in forecasting and mitigating organizational risks, and there are several applications relative to an organizational supply chain that are technically viable and proven to deliver value and early successes.
In December 2020, Gartner published a study called Artificial Intelligence Use-Case Prism for supply chain which talks about top AI/ML applications in the supply chain space that can be easily implemented and helps derive maximum value. One of the main applications that the Gartner called out is a simple implementation and has a rapid ROI is “end-to-end risk management.” Risk management is a discipline that does not have a fixed formula to deliver the desired results. So, implementing ML can take the manual effort and anomalies out of the equation and provide a platform for better business decisions. In this article we look at seven areas that procurement leaders can focus on immediately that can help to support supply chain resilience:
In closing, the applications of ML for supply chain are both numerous and versatile. Those that we have touched upon here are just a few of the ways that ML can kickstart the drive to maximize value through improvements in forecasting and mitigating risks. ML applications learn from the data they process and become better at predictions as they analyze more and more data to extract historical trends that drive informed outputs. These solutions are SaaS-based and easy to implement across organizational records systems and are upgradable and extendible. With a typical implementation period of just 3-6 months, and initial actionable results being generated within a few weeks of implementation, cost of ownership can be optimized relatively quickly.