Forecasting Demand and Cutting Perishable Waste

01Overview

Two problems decide margin in food and beverage: predicting perishable demand accurately and moving product through the cold chain before it spoils. Datagonomix targets both with machine learning. We model demand at SKU, store, and day granularity and wire those forecasts directly into replenishment, pricing, and cold-chain operations. This lets us treat shelf-life risk as a managed variable rather than an unavoidable loss.

02The Challenge

Perishability makes forecast error and supply-chain friction expensive, and the scale of loss in the sector is well documented:

• The world wasted 1.05 billion metric tons of food in 2022. That figure represents about 19% of all food available at the retail, food-service, and household levels (UNEP Food Waste Index Report, 2024).
• A further 13.2% of food is lost between harvest and retail, with highly perishable produce, dairy, and meat hit hardest (FAO / UNEP, 2024).
• Food loss and waste generate an estimated 8-10% of global greenhouse-gas emissions, which makes waste a carbon and compliance liability, not just a cost (UNEP, 2024).
• Confusion over date labels (“Use By” vs. “Best Before”) drives an estimated 4.3 million tons of U.S. food waste each year and costs households and businesses more than $21 billion. It remains one of the leading consumer-level causes of avoidable waste (ReFED, 2024).

03The Transformation

Datagonomix builds probabilistic demand-forecasting models at the SKU-store-day level, fusing POS history, promotions, seasonality, local weather, and events to capture the short-horizon swings that drive fresh-category demand. These forecasts feed a supply-chain optimization layer that sizes orders against each product’s remaining shelf life, so replenishment matches perishability instead of a static reorder point. IoT sensor streams from refrigeration and transport enable predictive maintenance that heads off cold-chain failures before they spoil inventory. Pricing and markdown frameworks trigger dynamic discounts as expiry approaches, recovering margin on at-risk stock. The models integrate with existing ERP and CRM systems, and financial ML quantifies the waste, carbon, and working-capital impact of each decision.

04Expected Outcomes

• SKU-store-day forecast accuracy improved 20-40% over legacy statistical baselines.
• Perishable spoilage and fresh-category shrink reduced 25-40% through shelf-life-aware ordering and dynamic markdowns.
• Excess and safety-stock inventory cut 20-30% while maintaining or improving on-shelf availability.
• Service levels and perfect-order fulfillment lifted roughly 2%, with materially fewer stockouts on fast-moving fresh items.
• Cold-chain unplanned downtime reduced 10-20% via IoT-fed predictive maintenance, preventing spoilage events at the source.

05Lessons Learned

• Forecast at SKU-store-day granularity: aggregated forecasts smooth over exactly the perishable-demand spikes that create spoilage and stockouts.
• External signals such as weather, local events, and promotions drive short-horizon fresh demand more than historical averages, so they must be first-class model inputs.
• Waste is as much a data-quality and operations problem as a modeling one. Date-label discipline, cold-chain telemetry, and accurate receiving and shrink capture gate any model’s real-world gains.
• Dynamic markdown pricing only recovers margin when tied to live expiry and inventory data, not scheduled discount calendars.