Production Management
We replaced manual production scheduling and intuition-based capacity planning with a machine learning forecast that drives every downstream decision, from material purchasing and supplier coordination to staffing and liquidity projections.
Technology
ML
Timeline
4 months
Target ROI
Growth
0hrs
0hrs
Avg monthly hours saved
0%
0%
Improvement on target outcome
0
0
Months to go live
Production planners are not short on talent. They are short on the analytical layer the role was always meant to have.
Production planning at scale is one of the hardest jobs in any manufacturing business. The planner is asked to predict what the market will buy, when it will buy it, what materials will be needed, and how to schedule capacity across cutting, pressing, finishing, and assembly. They do this for thousands of SKUs, across dozens of markets, against suppliers with varying lead times and a sales pipeline full of partial signals. Most do it brilliantly, on instinct, refined over years. The cost is hidden in everything that surrounds them. Forecasts get rebuilt manually every cycle. Years of order intake data sit underused in systems nobody has time to model. The high season starts earlier each year and the planner notices, but the data confirms it three months after the fact. Materials get ordered against assumptions that were already out of date. Staffing decisions get made too late. Liquidity tightens because nobody saw the inbound shape of the next quarter clearly enough.
We consolidated years of historical order intake into a single, modelling-ready dataset at SKU and article level. Machine learning detects the seasonal patterns that recur every year, and the trend shifts inside them, producing forecasts that are far more granular and reliable than any manual method can match. The forecast becomes the single demand signal that feeds every downstream decision. Sales uses it for market-by-market campaign timing. Supply uses it for semi-finished goods inventory targets and supplier forecast sharing.
Finance uses it for liquidity and inventory turnover projections. Operations uses it for staffing and capacity planning. The planner stops being the human integration layer between systems that did not talk to each other and starts being the strategic operator the role was always meant to be. Decisions get made earlier, against better signals, with the full operating picture connected behind them.
AI-supported forecasting backbone
Connected downstream planning
Supplier coordination and capacity planning
Embedded planning rhythm and team enablement
From experience alone, to experience with leverage.
The planning team had decades of experience and three working forecasts in spreadsheets that nobody fully trusted. Years of order data sat in the system, unused, while the planner adjusted manually for seasonality every cycle. The new model did not replace the experience. It gave it the analytical foundation the role had always been missing. The forecast now drives sales planning, supplier coordination, liquidity projections, staffing, and capacity decisions through a single, automated signal. The planner walks into every cycle with the full operating picture connected behind them. The instinct stayed. The blindspots disappeared.
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