I held a podcast this week with Supply Chain Management Review on the topic of AI in Planning.

We discussed the structural complexities facing demand and supply planning functions today and the imminent role of AI and machine learning.

You can view the podcast below:

Key takeaways

Businesses across all sectors are pressuring supply chains to become a competitive weapon. As such, the demand for Batch of 1, as I Want It, When I want It, is becoming increasingly real.

This results in major structural headwinds for S&OP and planning. Companies will increasingly be caught in a “performance trap”, throwing ever more headcount and investment just to stay in the same place in terms of service, inventory cost and speed (see figure): 

planning-complexities2-e1551220215961.png

A.I. and machine learning (ML) technology can help address the fundamental structural complexities in planning. Firstly, by enabling faster and better demand prediction based on disparate data. Secondly, through automating mundane and labor intensive activities. Thirdly, by injecting real time decision twins to drive trade-offs and enabling a leaner planning organization. (see figure below

AI planning solutions

Many organizations today are starting to pilot and experiment with A.I./ML across their planning organization – often in point solution mode such as using ML for predictive demand forecasting or using RPA to automate mundane work flow tasks

However, A.I./ML by itself is not sufficient, as supply chain organizations need to go from simply digitizing existing processes and work flow to reinventing the E2E model to enable more flexible “sensing and pivoting. For example how would S&OP activate daily or even hourly for cross-functional decision trade-offs? Also, what are the new feedback loops between production planning to demand forecasting? etc.

We discussed several steps in getting started with AI in Supply Chain Planning –  including articulating a future vision of what S&OP model looks like, and starting to get a handle on integrating disparate data for training and scaling solutions, as well as developing a fail fast MvP (minimum viable product) mentality.

Link to the original SCMR podcast here

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