H‍ow a national food distributor faced growing pressure and in the end reduced repacking and waste.

Overview

‍A national food distributor selling perishable goods to retailers across the U.S. faced growing pressure to balance demand uncertainty with operational efficiency. With dozens of packing locations, hundreds of SKUs, and tight lead times dictated by packaging and retailer requirements, the company was constantly reacting instead of anticipating.Oxrow.ai was engaged to solve what their teams had long accepted as “just the way the business runs.” The result: a data-driven demand forecasting platform that provided real-time visibility, reduced waste, and increased margin — transforming reactive logistics into proactive planning.

‍The Challenge

‍The company’s operational pain points were clear:

• Repacking inefficiency: Branded and unbranded SKUs couldn’t be substituted easily, forcing costly last-minute repack operations.
• Cold storage waste: Frequent repacks meant product was left outside temperature-controlled environments longer than acceptable, increasing spoilage.
• Customer attrition risk: Inconsistent fill rates eroded trust and drove retailers toward alternative suppliers.

These issues all pointed to a single root cause: fragmented data and limited forecasting visibility. The operations group didn’t know what was in inventory, what was moving, or what was coming next — and that lack of context drove daily fire drills.

‍The Root Cause

‍Oxrow.ai’s discovery process identified three systemic gaps:

1. Siloed Inventory Data
2. Cold storage inventory data wasn’t unified, making it nearly impossible for teams to locate specific SKUs quickly or track product age in real time.
3. Short Demand Horizons
4. Retailer demand was visible only a week in advance, while certain SKUs required 2–3 weeks of lead time to produce and package.
5. Disconnected Systems
6. ERP, production scheduling, and warehouse systems weren’t communicating — meaning decision-makers were working from partial truths.

Solution

‍Oxrow.ai implemented its Profit Optimization Platform to unify data and deliver real-time operational visibility. Within weeks, management could see what was previously hidden: SKU-level inventory, velocity, and forecast variance, all in one dashboard.

‍Key Capabilities Deployed:

• Unified Data Layer: Oxrow connected historical inventory, order, and SKU data into a single model, eliminating blind spots across storage and distribution sites.
• AI-Driven Forecasting: Oxrow’s forecasting engine analyzed five years of SKU data to produce weekly demand projections by retailer and region.
• Dynamic Service Levels: Operations teams used forecast confidence scores to set differentiated service levels — prioritizing key accounts without overcommitting inventory.
• Interactive Dashboards: Forecasts and real-time performance metrics were published to a centralized Oxrow dashboard, eliminating static reports and spreadsheets.

The Impact

‍The transformation was immediate and measurable:

• Reduced Repacking by 40%
• Clear SKU visibility and earlier demand signals meant packing teams could plan efficiently — cutting labor-intensive repacks almost in half.
• Waste Reduction of 25%
• Less time out of cold storage and smarter production scheduling significantly reduced spoilage.
• Improved Retailer Confidence
• Fill rate accuracy improved, strengthening customer trust and positioning the distributor as a reliable partner during demand surges.
• Faster Decisions, Higher Profitability
• Leadership could see forecast accuracy, production timing, and inventory positions in one place — turning every meeting into a strategic discussion instead of a firefight.

‍The client initially engaged Oxrow to “fix a visibility problem.” They stayed because Oxrow delivered a platform that did far more — it aligned operations, finance, and supply chain under a single, profit-driven lens.

‍Oxrow.ai wasn’t just the right tool for the job — it became the backbone of how the company uses their data.

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