I held a podcast this week with Supply Chain Management Review on the topic of Artificial Intelligence in Planning.  We discussed the structural complexities facing demand and supply planning organizations today and the imminent role of AI and machine learning.

You can view the podcast below:

Some key takeaways:

Businesses across all sectors are pressuring supply chains to become a competitive weapon – Batch of 1, as I Want It, When I want It, Where I want It

This results in major structural headwinds for planning and S&OP – 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 complexities

A.I. and machine learning (ML) as a technology enabler can help address the fundamental structural complexities facing end to end planning :  faster and better demand prediction based on disparate data; automating mundane and labor intensive activities; real time decision twins to drive trade-offs; and enabling a leaner more integrative planning organization, to name just a few (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 – supply chain organizations need to go from simply digitizing existing processes and work flow to reinventing the supply chain planning 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? What are the new feedback loops between production planning to demand forecasting? etc.

We discussed several steps in getting started –  including articulating a future vision of what S&OP model looks like, starting to get a handle on integrating disparate data source for training and scaling A.I./ML solutions and developing a fail fast MvP (minimum viable product) mentality

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Link to the original SCMR podcast here

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