Supply Chain Management Blogs by Members
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Since the pandemic, it became evident that supply chain is not only the nervous system for any organization and evidently, one of the most looked upon area in operational excellence since then. The pandemic pushed us to talk about resiliency in supply chain from 2020, and the Suez Canal incident in 2021 proved it right. Now that we are past the pandemic, and AI capabilities have already knocked on the door, it’s time we discussed how we can leverage and create a meaningful impact in our supply chain and be resilient.

AI in its essence, is a fascinating and multi faced field that has captured the imagination of technologists, philosophers and the public alike. It’s the capability of computer system to perform tasks that typically require human intelligence. These tasks range from visual perception, speech recognition, decision making, language translation. But what makes AI truly remarkable its ability to not only mimic human intelligence but to learn and adapt over time.

SAP has already enabled AI copilot for most of the cloud applications, but what does it really mean? Joule is a natural language, generative AI co-pilot, that can be accessed throughout your SAP business applications. There are already use cases for Joule copilot integration with SAP Build Process automation to allow customers to generate automation and workflows using natural language request in addition to the standard no code visual editor, but there is a segment in supply chain that is still being discovered as we embark upon this journey.

We see SAP’s vision of bringing a copilot to make work easier for users by taking away the repetitive work and this blog talks about the different use cases where we see AI being impactful in logistics under the supply chain umbrella, particularly SAP S/4 Transportation Management

  1. Joule Co-Pilot in Transportation Cockpit
    • SAP Joule brings a spark to the interaction of user, with transportation cockpit . One could easily think of various options in which Joule can compliment the transportation planners who are responsible for planning transportation demand, find cheapest carrier, see if there is an alternate route available and ask “What if” questions.
    •         Text to action
      • Transportation planners, as of today, are clicking their way to perform any action. With Joule, the transportation planners can give commands such as “Plan all open demand that has Good Issue date in next 2 days” or “Plan all freight units originating from Peachtree City”. The data model would be trained to use these commands, query the dataset and perform the planning action.
  2.         Freight Rating
    • One of the most critical data points in logistics is the historic freight spent by carrier, by lane, by product and so on. With the power of AI, now we can understand historic freight spend trends, combine it with seasonality, peak season volumes, carrier performance and ask Joule in future what can be the potential freight rate for a lane for next year. This will fuel the procurement part of the freight negotiations and provide transportation planners with current costs of carriers without having to click multiple buttons. This could then be offered to the customer service agents, who sometimes need to know the cost of freight for the lane on which the customer is ordering, plus it can even add a mark up based on trends.
  3.        What-If Simulation
    • A white gap that has existed so far in logistics has been a what-if simulation scenario. What if my product was 200LB instead of 100LB, how would this impact my routing and freight spend, or, what if these orders were shipped on an LTL than FTL, would there be any savings. We can see this gap being filled by Joule that is specifically trained on such models.
    • Even with master data, a lot of time, one must change the master data to run a simulation. Ideally Joule could change the master data, transiently, for simulations, without impacting the core master data in database. This would be “huge”! This empowers the business to understand the impact of changes to master data, even before committing to the changes.
  4.         Logs
    • Another white space that exists today is the user interaction with system, when it comes to understanding why a certain action resulted in certain ways, or, what happened to my order since I last planned it. Change documents have been a in-effective way for a user to decipher what happened to the order, applications logs are not that explanatory and ideally the business gets in touch with internal IT teams to help. This would take a drastic turn, if we enable the concept where Joule could read all your change documents, fields, values, and provide you a summary of it. Even more, it potentially could give you an impact analysis of that change.


This is an exciting era to be in to witness the next revolution! We are already exploring these, plus many more use cases across our customers, to bring to reality the next-gen experience within SAP S/4 Digital Supply Chain suite. If you have any use cases, please share it with us and we can collaborate to innovate!

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