Can artificial intelligence (AI) make supply chains more agile? The short answer is yes. For a fuller explanation, let’s first consider where supply chains stand today. With deep roots in supply chain, I’ve been through many cycles of innovation, deployment and inertia. These 25-plus years of entrepreneurial, management consultant, and operational and IT experience have helped me to assess the transformational potential of AI/ML within a deployable reality. Take the vision of a centralized, cross-functional, supply chain “brain.” While compelling, it remains largely aspirational. Two years ago, for instance, a survey of global supply chain leaders found that close to three-quarters of respondents relied on spreadsheets for supply chain planning. More than half still used an application that is approaching end-of-life (EOL). Though many companies are modernizing their supply chain IT, impediments and technical debt remain at large. This manifests in redundant or out-of-date enterprise resource planning tools, like the one alluded to above, and also disconnected supply chain processes and point solutions that were once quick fixes and now need more work. It sometimes takes a crisis to bring technical debt to the surface: You receive the EOL notice from your vendor. Or a legacy software application needs work, but no one uses that code anymore. Or your developers are too busy with workarounds to deploy anything new. Patchwork complexity can make supply chains brittle and difficult to refresh. We can use AI to identify burdensome technical debt. Tools such as generative AI can help further by extracting and reading outdated code, as well as by fixing and refactoring it. Reducing debt can free up resources you may need to undertake a supply chain platform upgrade.
Full opinion : From Debt To Agility: AI-Enabled Supply Chain.
