Demand Forecasting — Engine & Power Solutions

The Opportunity

A leading engine and power solutions manufacturer faced a forecasting challenge shaped by three compounding forces: volatile end-market demand driven by infrastructure cycles and seasonal agricultural patterns, extended material lead times requiring procurement commitments months in advance, and intensifying competition from both domestic and international players. The existing forecasting approach — a combination of sales-team estimates and simple moving averages — consistently produced errors large enough to cause costly stockouts on high-margin product lines while simultaneously building excess inventory on slower-moving variants. Production planning was reactive, procurement was hedged rather than precise, and customer delivery commitments were increasingly at risk. The mandate was clear: build a forecasting system accurate enough to simultaneously reduce stockouts, cut excess inventory, and restore delivery reliability.

• 01Multi-product portfolio spanning engines, gensets, and power solutions — each with distinct demand seasonality, lead times, and margin profiles.
• 02Procurement commitments required 3–6 months in advance — amplifying the cost of forecast errors on both stockout and overstock outcomes.
• 03Existing sales-estimate-based forecasts consistently diverging from actuals — eroding confidence in production planning and customer delivery commitments.
• 04Competitive pressure requiring tighter inventory turns without sacrificing order fulfilment rates.

The Solution

We ran a rigorous model benchmarking exercise — evaluating ARIMA (classical statistical), LSTM (deep learning sequential), and ML ensemble methods against MAPE, RMSE, and MAD across the full product portfolio. DeepAR+ emerged as the clear winner: its ability to learn complex seasonal patterns, capture cross-product demand correlations, and produce probabilistic forecasts (confidence intervals rather than point estimates) made it uniquely suited to the client's multi-product, multi-season environment. The model was deployed on AWS SageMaker for scalability, with automated retraining pipelines ensuring the model continuously learns from the latest demand signals without manual intervention.

• 01DeepAR+ deployed on AWS for scalability and seamless integration.
• 02Monthly forecasts feed production planning and procurement.
• 03A Power BI dashboard gives planners and ops teams real-time visibility into accuracy and inventory projections.
• 04Probabilistic forecasts providing confidence intervals — enabling procurement to plan for best-case and worst-case demand scenarios rather than single-point estimates.
• 05Automated retraining pipeline on AWS SageMaker — model continuously learns from latest actuals without manual intervention.
• 06Cross-product correlation captured — demand signals from related product lines improve forecast accuracy across the portfolio.

The Impact

More accurate demand signals fundamentally changed the relationship between procurement, production, and sales. Inventory holding costs dropped measurably as procurement moved from hedging against uncertainty to ordering against high-confidence forecasts. Production planning shifted from reactive schedule adjustments to forward-looking capacity allocation. The forecasting system became a trusted input to the S&OP process rather than a starting point for debate — and the roadmap for continuous improvement was clear: integrate marketing campaign data, distributor sell-through signals, and customer-submitted forecasts to build a perpetually learning planning engine.

• 0117% reduction in inventory holding costs — direct outcome of more accurate demand signals.
• 02Production planning shifted from reactive schedule adjustments to forward-looking, forecast-driven capacity allocation.
• 03Procurement lead-time utilization improved — orders placed with higher confidence earlier in the planning cycle.
• 04S&OP process elevated — forecasts trusted as a strategic input rather than a starting point for debate.
• 05Roadmap established for continuous improvement: integration of marketing campaign data, distributor sell-through, and customer-submitted forecasts.