Planning & Purchasing
FOUNDATION
S&OP
TECHNOLOGY
Machine Learning
DELIVERY
6-8 months
(Overview)
Buying and planning teams across most growing businesses are making investment decisions across thousands of SKUs and dozens of categories with limited visibility into what has actually worked. Sellthrough lives in one report, margin in another, demand signals in a third, and open-to-buy in a spreadsheet that gets emailed around twice a season. Decisions get made in the room they were always made in, by the people who have always made them, against the same gut feel that has always been the final word. The cost shows up twice. Once in the units that sit in the warehouse longer than they should. And again, more quietly, in the units the business never bought enough of, where the shelf went empty and the demand walked away. Neither cost shows up cleanly in any single report. Both compound every season.
(Our Approach)
We connected historical sellthrough, margin performance, channel behaviour, and forward demand signals into a unified planning intelligence layer. Machine learning analyses performance across every category and SKU simultaneously, identifying where investment has historically generated the strongest return and where capital has been quietly misallocated. Open-to-buy is calculated dynamically against real demand signals rather than static plan assumptions. Category-level recommendations are grounded in both past performance and forward opportunity.
(Key Findings)
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Time saving on planning and purchasing (% per employee)
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Inventory value requirement % reduction
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Last implementation in months
