Demand forecasting with AI: Saint-Gobain Sekurit case study

Saint-Gobain Sekurit, the automotive glass replacement division of the €46.6 billion multinational Saint-Gobain, was facing a challenge familiar to many manufacturing and distribution companies: fragmented ERP systems, manual spreadsheets, and inaccurate demand forecasts that led to excess inventory and service level gaps. By implementing Flowlity's AI-powered demand planning solution across its end-to-end supply chain, Sekurit achieved a 15% improvement in forecast accuracy at SKU level, reduced inventory by 9.25%, and raised product availability from 95.8% to 97.2%.

This demand forecasting case study explores how one of the world's largest automotive glass distributors transformed its supply chain operations using artificial intelligence, and the measurable ROI that followed.

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About Saint-Gobain Sekurit: a global leader in automotive glass replacement

Saint-Gobain is the world leader in sustainable construction, with 161,000 employees across 80 countries and over 1,100 production sites worldwide. Founded 360 years ago, the company generated €46.6 billion in revenue in 2024.

Within this global group, Saint-Gobain Sekurit AGR (Automotive Glass Replacement) operates a specialized supply chain for the production and distribution of replacement automotive glass. The division manages a complex network that includes three dedicated manufacturing plants in Europe, one central distribution hub serving Europe and export markets, and 30 local distribution centers. Sekurit's product catalog contains over 10,000 glass references — 4,000 windshields, 1,500 rear windows, and 5,000 side windows — with an annual volume of 2.5 million pieces. More than 95% of products are manufactured in Europe.

The end customers are garages and auto glass fitting centers, served through a multi-tier supply chain that flows from suppliers through plants, a central warehouse, and distribution centers before reaching the final client.

The scale of the challenge

Managing demand planning for 10,000+ SKUs across 30 distribution centers, with seasonal fluctuations and varying lead times from European factories, requires more than spreadsheets and basic ERP logic. Sekurit's supply chain team recognized that the company needed a fundamentally different approach to demand forecasting — one powered by AI rather than spreadsheets.

The challenge: why traditional demand planning was failing

Before implementing Flowlity, Saint-Gobain Sekurit's supply chain planning process was hampered by several critical issues that are common across manufacturing and spare parts organizations.

Fragmented systems with no single source of truth

The division operated with multiple different ERP systems across its network of plants and distribution centers. Each site had its own data logic, making it nearly impossible to consolidate demand signals into a coherent forecast. Supply chain planners spent more time reconciling data between systems than actually analyzing demand patterns.

Over-reliance on manual spreadsheets

Demand planning was done through cumbersome manual spreadsheets — a familiar pain point for supply chain teams worldwide. These spreadsheets were error-prone and impossible to scale across 10,000+ references.

Inaccurate macro-level sales forecasts

The forecasting approach relied on macro-level sales estimates rather than granular, SKU-level predictions. This top-down method failed to capture the nuances of demand at the individual product and location level, leading to systematic forecast errors that cascaded through the entire supply chain.

No anticipation and limited capacity readiness

Without accurate demand signals, the supply chain team had no anticipation capability. They couldn't prepare for seasonal peaks, react to shifts in the product portfolio, or align production capacity with actual market needs.

Critical service level gaps

The combined effect of these issues was a service level that left significant room for improvement. Stockouts were occurring more frequently than acceptable, and the seasonality of the automotive glass market amplified forecasting errors.

The solution: how Flowlity transformed demand forecasting at Sekurit

Saint-Gobain Sekurit was looking for a single digital tool to replace the patchwork of ERPs and spreadsheets. The objectives were clear: digitalize the supply chain, improve forecast reliability, optimize stock health, increase product availability, and ultimately improve the client experience. The company chose Flowlity's AI-powered platform to address all five goals within a single, integrated solution covering the end-to-end supply chain — from plants and the central warehouse to distribution centers.

End-to-end supply chain integration

Flowlity was deployed progressively across Sekurit's entire supply chain, not just at one node. The implementation covered three layers.

At the distribution center level, the solution handles sales forecasting, inventory management, and purchase forecasting for each of the 30 local centers. At the central warehouse level, it adds collaborative planning capabilities on top of the core forecasting and inventory optimization modules. At the plant level, the solution incorporates production forecasting, production forecasting, and stock management.

Each layer feeds into the next — better demand forecasts at distribution centers improve replenishment orders to the central hub, which in turn enables more accurate production planning at the factories. This cascading approach means that every step in the implementation adds value to the next.

Tactical simulation capabilities

One of the most powerful features for Sekurit's planning team is the tactical simulation module. Planners can adjust buffer levels by distribution center and product tag, then simulate the impact on stock levels and stockout days before committing to changes. For example, when preparing for the high season or adjusting for a client portfolio change, the team can model scenarios like reducing buffer from 95% to 70% and instantly see the projected impact on inventory quantities, coverage days, and rupture risk.

This simulation capability replaced the old approach of making inventory policy changes blindly and hoping for the best.

Smart alerting and supply chain automation

Flowlity's cockpit provides automated alerts for outliers and shortages, allowing planners to focus their attention where it matters most. Instead of reviewing thousands of SKUs manually, the team can quickly identify products with abnormal demand patterns, incoming stockout risks, or new product launches that need attention. A dedicated new product module helps manage the critical introduction phase when no historical data exists — powered by Flowlity's AI agents.

Results: a proven ROI that keeps improving over time

The results of this AI demand forecasting case study at Saint-Gobain Sekurit have been measured rigorously — and they continue to improve as the system learns from more data.

Forecast accuracy: +15% improvement at SKU level

The most fundamental improvement was in forecast accuracy at the SKU level, which increased by 15%. This is a significant achievement given the complexity of forecasting demand for 10,000+ glass references across 30 distribution points. More accurate forecasts cascade into better decisions across the entire supply chain — from purchasing and production to inventory allocation.

Product availability: service level from 95.8% to 97.2%

Product availability improved from 95.8% to 97.2%, a 1.4 percentage point increase that represents a substantial reduction in stockout events across thousands of SKUs. Critically, the seasonality effect was tamed — the availability curve became much more stable throughout the year, eliminating the deep seasonal dips that previously disrupted service to garages and fitting centers.

Inventory optimization: stock reduced by 9.25%

While service levels were rising, inventory levels were simultaneously declining by 9.25%. This is the hallmark of effective demand planning — not simply building more stock to improve availability, but intelligently positioning the right inventory in the right place at the right time. Inventory turnover improved accordingly, freeing up working capital and warehouse space.

Operational KPI development

Beyond the headline metrics, Sekurit developed a comprehensive set of operational KPIs powered by Flowlity data. Upstream indicators include OTIF (on-time in-full), planning adherence, lead time tracking, and frozen zone compliance. Downstream indicators cover availability and forecast quality. Stock health indicators track coverage, obsolescence, and overall inventory condition. The team is also working on end-to-end supply chain cost measurement — a natural next step once demand planning accuracy and inventory efficiency are under control.

Watch the full story: Saint-Gobain Sekurit & Flowlity webinar

For a deeper dive into this AI demand forecasting case study, watch the full webinar replay where the Saint-Gobain Sekurit supply chain team shares their experience implementing Flowlity and the lessons learned along the way.

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Key takeaways for supply chain leaders

This demand planning case study from Saint-Gobain Sekurit offers several lessons for supply chain leaders evaluating AI-driven forecasting solutions.

Start with the data foundation

Before any AI model can add value, you need clean, consolidated data. Sekurit's first challenge was unifying data from multiple ERPs into a single reliable pipeline.

Think end-to-end, implement progressively

Flowlity was deployed across distribution centers, the central warehouse, and manufacturing plants — but not all at once. The progressive rollout meant that each phase delivered value while building the foundation for the next. This approach reduces implementation risk while maintaining momentum.

Demand planning is not just about the forecast

The real value of AI demand forecasting comes from what you do with it. Sekurit's gains in inventory reduction and service level improvement came not just from better forecasts, but from the tactical simulation tools, automated alerting, and collaborative planning processes that Flowlity enabled around those forecasts.

Measure what matters, continuously

The development of comprehensive operational KPIs — from OTIF and planning adherence to stock healthiness and obsolescence tracking — shows that AI demand planning is not a one-time project but an ongoing capability that improves with measurement and refinement. A structured S&OP process helps ensure these metrics translate into strategic decisions.

About Flowlity

Flowlity is an AI-powered supply chain planning platform that helps manufacturers and distributors optimize demand forecasting, inventory management, and supplier collaboration. Using probabilistic forecasting and machine learning, Flowlity enables companies to reduce inventory while improving service levels. The platform is deployed across industries including automotive, chemicals, FMCG, and industrial distribution.

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