Retail & Distribution
Data Utilization Cloud Migration
Store-level demand forecasting reduced fresh-food waste by 22%
A demand forecasting platform combined point-of-sale and weather data. Recommended order quantities standardized a process that previously depended on individual experience.
| Client | Company C, a food supermarket operator |
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| Scale | 128 stores |
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| Industry | Retail & Distribution |
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| Project period | January-October 2025 (10 months) |
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| Business Challenge | Data Utilization / Cloud Migration |
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Challenges
- Fresh-food ordering depended on individual experience, with waste rates varying by almost threefold between stores.
- Point-of-sale data remained inside individual store systems, preventing analysis across locations at headquarters.
- With only two analysts, building the platform entirely in-house was not practical.
Our Approach
- We built a platform that consolidated daily point-of-sale data from every store and combined it with weather, calendar and promotional information.
- Category-level demand models displayed recommended quantities on ordering terminals.
- Rather than automatically placing orders, the system retained final decisions with store staff to support understanding and adoption.
- Headquarters dashboards showed differences between forecasts and actual results for each store.
Results
Fresh-food waste
22% reduction
Ordering workload
5.5 hours saved per store each week
Variation in waste rates between stores
41% improvement in standard deviation
"It worked because the recommendations were understandable and usable for staff, rather than imposed on them." - Merchandising Division
Figures are measured results from the client's environment and do not guarantee equivalent outcomes elsewhere. Information reflects the project at completion.