Planning & Purchasing
We built the intelligence layer that turns thousands of SKU-level investment decisions from a gut-feel exercise into a systematic, evidence-based process, with past performance, forward demand, and open-to-buy connected into one decision framework.
Technology
ML + LLMs
Timeline
7 Months
Target ROI
Inventory
0hrs
0hrs
Avg monthly hours saved
0%
0%
Improvement on target outcome
0
0
Months to go live
Buying decisions made on instinct cost more than the units that don't sell. They cost the ones you didn't buy enough of.
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.
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.
The system surfaces not just what sold, but what would have sold if it had been available, finally putting a number on the missed revenue that used to be invisible. Buyers and planners arrive at investment decisions with a complete picture: what to back, what to reduce, where the margin opportunity sits, and how confident the forecast is. Gut feel becomes a starting point rather than the final word.
Unified buying intelligence layer
AI-driven category and SKU recommendations
Dynamic open-to-buy and missed-revenue analysis
Embedded buying rhythm and team enablement
From instinct to evidence, without losing the instinct.
The team that ran buying used to walk into the decision room with three reports and twenty years of experience. They walked out with decisions that were usually right and occasionally expensive. The new system did not replace the experience. It gave it leverage. Now the same team walks in with the full picture: what worked, what did not, what the model expects, where the confidence is high, where it is low, and what the cost of being wrong looks like in each direction. The decisions are faster, the misses are smaller, and the inventory exposure dropped by 38% in the first full cycle. The expertise stayed. The blindspots disappeared.
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