The Basics                                                 

Since the inception of supply chain planning some 25 years ago, the discipline has broken through many barriers. Only recently, though, has technology advanced to make possible the creation of precision models with timely data and sufficient accuracy across all aspects of the supply chain network: from raw material production to the ultimate customer delivery. 

The current conundrum is rooted in the tremendous complexity of end-to-end supply chain data, which has exceeded the capacity of traditional planning and management solutions. Now, planners are moving beyond a reliance on spreadsheets and enterprise resource planning (ERP) software to autonomous systems that must have accurate, real-time data.  

Artificial intelligence (AI) and machine learning (ML) make it possible to digest the massive amounts of data needed to develop truly dynamic models. Still, challenges remain in the form of limited staff, legacy processes and ingrained attitudes. There is a growing recognition that only through robust collaboration across traditional boundaries will the potential benefits of autonomous supply chains be realized. 

The Future  

Supply chain planners will be focusing on greater resilience in the future. This is true partly because autonomous operations make that possible, and in part because business leaders have learned through some difficult times — most notably COVID 19 — that resilience is an essential competitive advantage. 

The path toward a more resilient future isn’t simply evident. To find it, planners must move from creating periodic annual or quarterly plans to monthly, then weekly, and finally to a dynamic and continuous process. Along the way, manual processes will naturally give way to more automation.  

An important element will be the evolution of tools for supply chain planning similar to generative AI like today’s ChatGPT. As models learn to communicate in natural language, it will be progressively easier to match technology’s capabilities with human guidance. For now, it is still important to be technologically astute. Over time there will be a greater need for humans to trust and rely on autonomous technology as the foundation for envisioning ever-better supply chain processes. 

Planners, freed from managing the technology, will focus on matching specific optimization technologies to specific circumstances. With strong autonomous technologies at their disposal, humans can determine when and how to deploy autonomous systems to solve challenges. Third-party vendors offer global data for a wide range of supply chain impacts, from weather to political situations to market conditions. AI and ML will churn through this complex data and reveal patterns that influence supply chain performance that had been difficult or impossible to see previously. 

Find appropriate technology partners who can help integrate AI and ML with a well-defined business case. Developing first steps will depend on the available data, systems, staff competency and external resources. With those constraints understood, set goals that can be measured. Scale the initial activity and set a pace that minimizes risk until lessons are accumulated to move toward broader process changes. 

Well-planned, executed, and measured initial steps will improve confidence and gain support from the C-suite. Then doors will open to significant changes that enable more resilient supply chains to turn future fantasies into tomorrow’s reality. 

Resource Link: https://discover.3ds.com/unleashing-supply-chain-resilience