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 list of the Top 4 Uses Cases for GenAI in Supply Planning.

Supply Chain executives and Supply Planning leads will face the following challenges when adopting GenAI in supply planning:

  1. S&OP & GenAI: Poor data certainty already undermines your monthly S&OP review. Is that stock number correct? Can we believe that service KPI? At least pre-GenAI you could chase down the owner of the number to explain themselves. Try doing that with an algo and a chat interface. Now extend that to all the planning processes your organization has and the problem becomes clear. Deployed incorrectly, GenAI will further muddy your data challenges. Explainability remains a core challenge in all AI applications, including GenAI.  
  2. Oversimplification. GenAI is excellent at taking a very large amount of data and simplifying into easily understood paragraphs. That’s great for the Cliff Notes on Economic Theory. But Planning is different. The detail is vital and can make or break the right decision. The nuance and context is vital and, if lost, will significantly compromise the value of the plan. GenAI will help summarize what’s happening, and will help planners to kick off workflows, but ultimately it will still need experienced planners and managers to make the right decisions.
  3. Access to Talent: At this stage in the adoption curve it appears that the best GenAI talent is mostly found in the largest management consulting and strategy firms. The types of skill sets to run localized models within a manufacturer will require an understanding of Prompt Engineering and model tuning. Enterprise and mid-market F&B tech teams will need the capability to support the prompts and prompt engineering necessary for GenAI. This is going to take time to develop and is akin to the data scientist era, hard to find and often hard to get real value. Ultimately it means that bigger companies will get value from GenAI much earlier than smaller companies and will therefore increase their competitive gap.  
  4. Manufacturer Specific Tuning: Effective implementation requires training GenAI on supply chain semantics and tuning it on a per-customer basis. Think about the level of hygiene required around process documents, planning rule books, S&OP decisions and reports. All of these need to be accurate and have sufficient history to tune your specific LMM. This will be a major roadblock to value in the early stages.  

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 executives and operators can overcome the initial challenges. 

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