A retail chain operating 120+ store locations managed inventory forecasting through a combination of spreadsheets and regional manager intuition. The result was predictable: chronic stockouts on fast-moving items during demand spikes, and overstock of slow-moving inventory tying up working capital across the network.
Previous attempts to introduce forecasting software had stalled at the pilot stage — the data infrastructure feeding those tools was inconsistent across regions, and store managers had little trust in "black box" recommendations they couldn't understand or override when local knowledge suggested otherwise.
Before discussing any model or algorithm, we assessed the actual data situation: what was being captured consistently, what wasn't, and why previous forecasting attempts had failed to gain adoption. The honest finding was that the problem was as much organisational as technical.
Within two quarters of full rollout, the chain saw a substantial reduction in stockouts on high-demand items, alongside measurably reduced overstock carrying costs. Store manager adoption remained high specifically because the system was designed to be understood and overridden, not simply trusted blindly.
Related Service
Data Modernization & Analytics →We'll give you an honest assessment of where a modern data platform will and won't add real value.