Automotive S&OP Boosts Forecast Accuracy and Delivers £100M+ Impact

Context

A global automotive manufacturer operating in multiple markets sought to improve sales forecasting accuracy and planning stability across its vehicle lines to optimise inventory, supply chain management, resilience and decision quality. Manufacturing constraints and volatile market conditions had undermined the company’s Sales & Operations Planning (S&OP) process, making it difficult to align production plans with true customer demand and to allocate inventory efficiently across channels and regions.

Challenges

The business faced several interrelated challenges. A wide variety of demand signals — retail deliveries, wholesale shipments, order books, online interest indicators and independent industry forecasts — existed in different formats and cadences, requiring alignment to a common data model to be useful. External events such as launches, promotions and holidays produced fluctuations in typical sales patterns that were difficult to identify and quantify, and supply chain and production constraints often meant observed sales reflected available supply rather than unconstrained demand. Additionally, certain vehicle lines were recently introduced and therefore lacked sufficient historical sales data, complicating model training and confidence in projections. Sales forecasts produced at vehicle-line and market levels also needed to reconcile with global planning figures to support consolidated S&OP decisions.

Implementation

The team built a robust Data Engineering pipeline that ingested and processed a wide variety of data sources and created a reusable data product aligned to a unified demand data model. Sources integrated into the pipeline included retail point-of-sale, wholesale shipments, order book data, Google Trends consumer interest signals, and independent forward-looking auto industry data and insights. Data normalization and timestamp alignment ensured that supply-side and demand-side signals could be compared on a like-for-like basis.

Our Fractional Head of AI implemented an “event intelligence” layer fed by a global event calendar capturing product launches, model refreshes, promotions, holiday periods and other market events. This layer detected and quantified shocks to typical patterns, enabling models to distinguish event-driven volume from baseline demand. For vehicle lines with limited history, the team relied on analog or look-alike forecasting, matching recently introduced models to comparable past launches in the same segment to create credible priors on adoption curves.

Hierarchical forecasting solutions were implemented to reconcile granular forecasts (by vehicle line and market) with mid- and top-level aggregates used in global planning. The hierarchical approach combined bottom-up signals where robust data existed with top-down constraints where supply information or strategic targets drove limits, producing self-consistent forecasts across levels. The pipeline also included methods to identify periods when sales were supply-constrained and to use independent forward-looking indicators to estimate latent, unconstrained demand. Outputs from the data product and forecasting layers were integrated into the S&OP cadence, providing planners with transparent forecast drivers, event-attribution and scenario comparisons.

Results

Implementation of the unified demand model, event intelligence and hierarchical forecasting delivered a significant increase in forecast accuracy and materially improved planning stability across markets. The solution enabled clearer separation of supply constraints from true demand signals, supported credible forecasting for newly launched vehicle lines using look-alike analogs, and ensured reconciliation of forecasts from line-level to global figures. As a result, the company realised more effective inventory allocation, reduced mismatch between supply and demand, and improved resilience in the face of shocks.

Through adoption in the Sales & Operations Planning process, the initiative generated over £100m in quantified impact by optimising inventory levels, improving production planning, and raising the quality of commercial decisions. Planners reported faster identification of event-driven variances, reduced emergency rework of production plans, and greater confidence when sizing safety stock or shifting allocations across markets. Overall, the program strengthened the manufacturer’s ability to translate diverse demand signals into actionable, reconciled forecasts that supported better operational and strategic outcomes.