OWS Foods

OWS Foods faced persistent challenges with demand forecasting accuracy that cascaded into scheduling inefficiencies across its production operations. In food and beverage manufacturing, where shelf life is finite and ingredient lead times are tight, inaccurate demand signals force planners into reactive mode — over-producing to avoid stockouts or under-scheduling and missing fill rates. Manual forecasting processes, disconnected from real-time sales and inventory data, left schedulers working from stale inputs. The result was excess inventory, unnecessary changeovers, and wasted capacity — all carrying direct cost implications in a margin-sensitive industry.

OWS Foods partnered with Rockwell Automation to implement an automated production scheduling solution with integrated demand forecasting analytics, likely leveraging Rockwell's Plex MES platform and its Finite Scheduler module, which is purpose-built for food and beverage environments. The system connected demand signals — including historical sales data and forward-looking forecast inputs — directly into the scheduling engine, replacing manual handoffs with automated data flows. Rather than planners reconciling spreadsheets, the platform continuously updated production schedules based on current demand projections. This integration between forecasting analytics and finite scheduling logic allowed OWS Foods to align production sequences more tightly with actual market demand, reducing the guesswork that had driven previous scheduling decisions.

The implementation delivered a 25% improvement in demand forecast accuracy, a significant gain that directly improves scheduling quality and reduces waste across OWS Foods' production operations. More accurate forecasts mean fewer emergency schedule changes, more stable production runs, and better alignment between what is produced and what is actually needed.

• 25% improvement in demand forecast accuracy
• Reduced scheduling inefficiencies driven by stale or inaccurate demand inputs
• More reliable production planning, supporting improved inventory management
• Planners shifted from reactive firefighting toward forward-looking schedule management

• Demand forecasting accuracy is a prerequisite for effective production scheduling — fixing the data input improves every downstream decision.
• Automating the connection between demand signals and scheduling systems removes latency that manual processes introduce.
• Food and beverage manufacturers benefit from purpose-built scheduling tools that account for perishability, changeover costs, and finite capacity simultaneously.
• Integration with existing sales and inventory data is critical; the value comes from real-time signal flow, not a standalone analytics tool.
• Even a 25% forecasting improvement has compounding effects on inventory, waste, and labor utilization.