Demand Planners have one of the toughest and thankless jobs. They are expected to deliver precise and accurate estimates of future demand every month in face of highly volatile and seemingly unknowable demand across thousands of Product/Location level planning combinations. What makes their job even more complex is inflexible and primitive planning solutions from the pre-AI era, which are only marginally better than Microsoft Excel. Demand Planners are unfairly judged either way: either because they went conservative resulting in stockouts and lost sales or because they went aggressive resulting in excess inventory. They just can’t thread the needle every month across thousands of combinations, but that’s the expectation from them.

Demand Planners need a Digital Planning Twin: an AI engine that can evaluate demand patterns at a granular Product/Location level relevant for Supply Chain decision-making (what inventory where when), learn from both historical data as well as forward-looking demand drivers to produce an Optimal Forecast which both senses and optimally shapes Demand.

Traditional approaches to Demand Forecasting are trend-extrapolation based, similar to driving a car by looking into the review mirror. This is intuitively dangerous, and now increasingly fatal given polluted history due to pandemic and other disruptions driven by wars and recessionary pressures, etc. What Demand Planners need is a forward-looking engine that can both enable demand forecasting based on relevant driver trends as well as enable an interactive demand shaping or an automated demand optimization powered by AI. Such Demand Shaping & Optimization is critical in the bi-directional orchestration of Demand and Supply in a mature IBP process. Typical IBP solutions follow an archaic sequential S&OP process of Demand Review followed by Supply Review. This approach locks a Demand statement, then tries to identify the least cost based supply response. Flaw with this cost optimization approach is that it misses out on margin optimization opportunities: through modeling changes in pricing, promotions, new product launch schedules, etc. to leverage current supply & capacity to optimize margin.