Supply Chain Optimization

Target Audience

Supply chain and operations teams in manufacturing and retail, logistics teams

Challenge

Managing inventory for millions of SKUs across 4,700 stores and distribution centers while anticipating demand fluctuations, seasonal patterns, and unexpected disruptions like hurricanes. Traditional inventory management couldn't adapt quickly enough to changing conditions or provide granular predictions by geographic zone.

Solution Approach

Walmart built proprietary AI/ML engines that analyze historical sales data, online searches, page views, macroeconomic trends, weather patterns, and local demographics. The system uses an AI built entirely in-house to forecast demand across multiple time horizons.

Value Add

After starting to deploy their AI solutions, Walmart's employee productivity increased 15%, as they report. Their Route Optimization AI has helped avoid 30 million unnecessary miles of driving. During Hurricane Ian, AI systems automatically rerouted shipments.

How to Apply This

Supply chain use cases are highly complex due to their many dependencies and influential factors. However, they often pose the chance to save much money with only slight improvements. Therefore, the best approaches for your supply chain challenges can be plentisome. Besides using third-party tools, you can create own solutions that anylyzes your proprietary data - like it was done in this use case. While this approach requires that a good level of data management is existing in your company, it offers you the possibility to turn your unique data into a unique asset that puts you ahead of the competition. It all starts with the questions: what are your biggest challenges in your supply chains, and what is your proprietary data that you could leverage?

Need guidance on how to best leverage your data for supply chain processes? Reach out to me and book a free 30-minute feasibility call.