Food Producer
Reduction in lost sales from stock-outs
Reduction in raw-material waste from over-production
About
The producer needed digital muscle to support a three-fold store expansion. Stock-outs ran at 7%, over-production at 12%, and finance closed the books ten days late — masking the root causes behind all three. We built a single source of truth in Azure Synapse, causal-factor demand forecasts, an MILP batch-sizing optimiser, and real-time executive alerts.
Industry
Food Producer
Company size
200 – 500 employees
Founded
2011
The Company
A food producer scaling faster than its data infrastructure
The producer was preparing for a three-fold expansion of its retail store footprint, a growth plan that would multiply the complexity of demand planning, batch sizing, and financial reporting well beyond what its existing manual processes could support.
Stock-outs were running at 7% and over-production at 12% simultaneously — a sign that demand signals weren't reaching production planning cleanly, and that the business had no reliable way to diagnose which of several possible causes was driving either number.
The challenge
Books closing ten days late masked the real causes
Finance closed the books roughly ten days after month-end, which meant that by the time leadership saw a clear financial picture of stock-out or over-production losses, the operational decisions that caused them were already three weeks old and impossible to trace back to a specific root cause.
Committing to a three-fold store expansion on top of this reporting lag risked compounding every existing inefficiency by roughly three times, rather than fixing them before scale made them harder to unwind.
The Solution
A single source of truth ahead of the expansion
We built an Azure Synapse data platform to create a single, unified source of truth across sales, production, and finance. Prophet forecasting models incorporating causal demand factors — promotions, seasonality, local events — replaced trend-only forecasting, and a Mixed Integer Linear Programming optimiser sized production batches against that improved demand signal.
Tableau CRM alerts were configured to flag executives to developing issues in near real time rather than waiting for the monthly close, closing the ten-day lag that had been masking root causes.
The Results
Lost sales down 25%, capacity freed for the expansion
Lost sales from stock-outs fell 25%, and raw-material waste from over-production fell 15%, as the causal-factor forecast and MILP batch sizing brought demand and production planning into alignment for the first time. The faster, more granular reporting gave finance visibility close enough to real time to catch issues while they were still fixable.
The infrastructure was explicitly built to scale to the three-fold store expansion, giving leadership confidence to proceed with growth on a foundation that had already proven it could catch and correct problems quickly rather than one that would have been overwhelmed by the added complexity.
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