Our Top 4 Challenges for using GenAI in Supply Planning

There is a lot of noise out there around GenAI, transformers and LLMs. A lot of vendors are selling tools with fuzzy use cases attached. 

Here at Replan, we’ve been working in advanced algorithms, data science and AI for almost 15 years and here’s our take on the challenges manufacturers face in implementing this emerging technology. 

Don’t miss our note on the Top 4 Challenges for using GenAI in Supply Planning.

  1. Which flavor of GenAI for your Supply Chain: Manufacturers can choose one of 3 paths to GenAI. Many vendors will embed generalized pre-trained models into their SAAS applications that users will engage by chatbot. You can work with a consulting firm or a vendor to train your own model. Or finally you can find and test hyper specialized pre-trained models addressing specific use cases. Navigating this first step will require careful analysis of the pros and cons of the IT and operational implications of any one route. 
  2. Building Trust with Planners: Any localized model implementation requires extensive training and can often get confused, leading to “hallucinations” where it generates plausible but incorrect answers. This poses a significant risk, especially for non-technical planners. It’s crucial to ensure GenAI can admit when it doesn’t know an answer. Many questions will emerge in the setup and deployment phase of GenAI and vendors will need to assist IT teams with roll out and adoption. 
  3. Budgets, Integrations and Roadmaps: Manufacturing IT Teams are typically responsible for large chunks of Software Real Estate, the ERP, WMS, TMS and Advanced Planning systems. Incumbents and Challenger Vendors in each of these areas are rapidly introducing GenAI solutions and approaches. How does the CIO and CTO knit all this together into a cohesive and effective IT landscape? What does it mean for the Global ERP harmonization project you are supposed to be starting next year?
  4. Stability and Efficiency of LLMs: The stability of large language models (LLMs) is a concern, with diminishing returns on efficiency improvements. LLMs are plateauing, making incremental gains hard to achieve. The focus now is on making GPTs more specific to datasets, training them explicitly on individual datasets, and tuning them on a per-customer basis. IT Teams will need to work with operational colleagues to identify the right sources of structured and unstructured training materials for localized implementations. 

As the industry thinks about the transformative potential of Gen AI, manufacturers stand poised to unlock greater levels of operational optimization and resilience. The future of supply chain management might be driven by the revolutionary capabilities of GenAI, provided vendors and IT teams can navigate the roll out and ongoing support of these new tools.

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