How Six Companies Use AI For Demand Planning

Article from MHI Solutions Magazine

If you’ve ever been frustrated because your favorite brand of ketchup is sold-out at the grocery store or had to postpone a home improvement project because some of the key materials were out-of-stock in your region, you’re not alone. Retailers, manufacturers and distributors all share your aggravation. Indeed, they’re likely even more frustrated, considering they have teams dedicated to forecasting, allocation and replenishment planning.

Historically, these processes have all been manual, time-consuming, error prone and reactive—instead of proactive. Planners making phone calls to suppliers and stores, inputting data into spreadsheets, looking at what happened in the past, analyzing statistics and relying on intuition was the way of this world for decades. But with the increasing availability of affordable cloud-based computing power, data aggregators and advanced artificial intelligence (AI) algorithms and machine learning (ML) technologies, the world of demand planning has experienced a seismic shift.

McKinsey researchers found that AI-enhanced supply chain management significantly improves forecasting accuracy while simultaneously increasing granularity and optimizing stock replenishment. The firm notes AI can cut forecasting errors by 20% to 50%. his boost in predictive accuracy can cut lost sales due to stock-outs by up to 65%, while safety stock inventory can be reduced by as much as 50%.

Where applying AI to demand planning truly adds value is in the ability to factor in external influencers that impact demand, said Knut Alicke, a partner in McKinsey’s Stuttgart, Germany office…

Read the full article in MHI Solutions Magazine