Supply chain excellence is a core element of a firm’s competitive strategy. While supply chain agility is widely acknowledged by managers as a key requirement, experiences during the global pandemic and with other recent geopolitical events have cemented the importance of supply chain resilience as well. We define agility as the ability to respond quickly and effectively to disruptions, and resilience as the ability to rapidly recover from unanticipated disruptive events through longer term adjustments. Unfortunately, many companies have failed to develop strategies that can deliver agility and resilience. In particular, the most promising enablers of these capabilities, i.e., artificial intelligence and machine learning, cloud computing, and big data, have fallen short of their promise. A key challenge for executives, therefore, is how to leverage these capabilities in their supply chain planning process to achieve resilient and agile supply chains.
In this talk, we explain the reasons why the application of machine learning has fallen short of delivering success in the context of supply chain planning. We describe Optimal Machine Learning, (OML), a new paradigm that we have developed, that leverages the capabilities of advanced technology to overcome these challenges. We describe how OML can be used to optimize the use of available data to generate resource allocation decisions. We illustrate the power of this new approach for supply chain planning by reporting on successful implementations in complex global supply chain environments. We then discuss how the OML framework can be used to guide efforts to design effective strategies for supply chain resilience and agility that mitigate geopolitical risks through application of detailed scenario planning.
About the speaker
Dr. Morris A. Cohen is the Panasonic Professor Emeritus of Manufacturing and Logistics in the Operations Information and Decisions Department of The Wharton School of the University of Pennsylvania. He is also the CEO of AD3 Analytics LLC.