As applications in generative AI continue to rise in popularity thanks to programs like ChatGPT, supply chain industry leaders are beginning to explore how this technology can potentially help accelerate ideation and support decision-making processes in the supply chain. In this episode of our Big Ideas in Supply Chain podcast, Mike Watson, faculty member at Northwestern University and Polly Mitchell-Guthrie, VP, Industry Outreach & Thought Leadership at Kinaxis, explore how generative AI can refine data, impact productivity, and empower practitioners to develop more efficient supply chains.
Generative AI applications such as ChatGPT are waking people up to the potential of AI, and some C-level executives are wondering how it could augment the way supply chain practitioners can solve complex issues requiring ideation and decision-making. Mike Watson, faculty member at Northwestern University and Polly Mitchell-Guthrie, VP, Industry Outreach & Thought Leadership at Kinaxis, speculate how ChatGPT could refine data, impact productivity, and empower practitioners to develop more efficient supply chains.
Mike says the recent popularity of ChatGPT has piqued the interest of many C-level executives who are now starting to think seriously about AI and the value it can add in terms of increasing business and individual resource productivity. [5:12]
While many people fear job loss when it comes to adopting AI, Mike feels optimistic that this new technology will assist rather than replace many roles in the industry. For example, a supply chain planner or data analyst might not be able to code, but with the help of Generative AI, they may be able to produce a code that can automate certain tasks. [10:32]
Mike states there is a lot of potential for generative AI within the supply chain including tasks like automation and the possibility of creating a ChatGPT based application focused solely on supply chain solutions. However, Polly points out that AI lacks context, collaboration, and conscience, which all prevent it from making complex, informed decisions – which still require a human judgement. [17:21]