Forecast error down 30%. Inventory down 20%.
We build demand forecasting and inventory planning models for manufacturers, distributors and retailers.
What we can do for your team
−30%forecast error
A demand forecasting model
One machine-learning engine across your whole portfolio, benchmarked against your current process. We start with a proof of concept; after that it runs as a service or under licence.
The forecasting model−20%inventory
An inventory policy optimizer
Simulation instead of the safety-stock formula, tuned per item. Inventory down by up to 20% at the same service level.
The inventory model
Training, coaching and workshops
Live online sessions, two hours at a time, for your planners, or coaching for the people who lead them.
The coursesThe SupChains App
Planners review and enrich forecasts. Leadership tracks Forecast Value Added across all dimensions.
The AppWhy they trust us
The forecast engine created by SupChains allowed our demand planning team to improve our forecasting accuracy by 20 points in a matter of months, an objective we have had for a few years.
Sesh AddankiCOO, VantageIn 19 weeks, we improved inventory turns by 26% and reduced shortages by 65% while increasing our sales.
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Thanks to SupChains and RitterDragon, Marzam could implement a new demand and inventory planning process. In 19 weeks, we improved inventory turns by 26% and reduced shortages by 65% while increasing our sales.
Hector DoportoVP Supply Chain Planning, MarzamWe successfully moved away from legacy forecast accuracy metrics and implemented Forecast Value Added framework across all business units.
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Working with Nicolas Vandeput and the SupChains Team has started to fundamentally change how we approach demand forecasting at Qnity. After reading his excellent and approachable Demand Forecasting Best Practices book and completing the first wave of training, we successfully moved away from legacy forecast accuracy metrics and implemented Forecast Value Added framework across all business units. That change immediately surfaced issues and behaviors we had not previously been able to see and started to genuinely improve decision‑making.
We are now actively applying Nicolas’ framework to make demand more explicit and decision‑ready, have completed an initial ML forecasting proof of concept, and are running a second wave of training while piloting SupChains’ forecast‑as‑a‑service.
Matti VarheenmaaGlobal Supply Chain Manager, Qnity ElectronicsHe focuses on what can make the difference, not wasting time on buzzwords that do not bring any relevant improvement.
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Nicolas makes sure he understands well the business context, so any solution is properly applied when it makes sense. He focuses on what can make the difference, not wasting time on buzzwords that do not bring any relevant improvement. This has been my experience working with him, where he delivered both a tailored inventory optimization model and a machine-learning-based forecasting model for Bridgestone Europe, which resulted in considerable improvements from our previous projects.
Jon San AndresDirector Center of Excellence Supply Chain Planning, BridgestoneThe SupChains Way
We developed our own way of doing demand and inventory planning. We stopped using MAPE, ABC-XYZ classes and the textbook safety stock formula. We rely on machine learning, Forecast Value Added, insight-driven forecasts and inventory simulations. Want to learn more? Download it for free.
- 13 practices
- 16 articles
- 7 infographics
- 1 AI-ready file
Forecasting Engine4 practices
Run one global, bulletproof machine-learning enginenot statistical models tuned SKU by SKU
Planners3 practices
Enrich only on real insightno reviewing top products one by one, no tweaking models, no gut feeling
Measuring success4 practices
Track the value added by every step (FVA)not one final accuracy number
Inventory Engine2 practices
Tune inventory targets by simulation on real demand and forecastsnot the textbook formula, not DDMRP
About Nicolas
I founded SupChains in 2016 to help supply chain leaders achieve demand & supply planning excellence, by delivering customized models, training and coaching their teams.
I am passionate about education and teach Master’s students at CentraleSupélec in Paris, and I have been a guest speaker at various universities worldwide.
Data Science for Supply Chain Forecasting
Inventory Optimization: Models and Simulations
Demand Forecasting Best PracticesLearn on your own
Latest articles
- How Demand Planners Should (Not) Collaborate with Finance
- Should You Forecast Demand per Customer? (Usually Not)
- Outgrowing the Safety Stock Formula
Conferences & webinars
How to make an efficient demand planning process
- How Machine Learning impacts Demand Planning
- Machine Learning for Demand Forecasting in Supply Chains
How can we help?
A few lines about your products, your data and your planning goals are enough to start the conversation.