Nicolas Vandeput is a supply chain data scientist, educator, author and founder of SupChains. His work focuses on demand forecasting, inventory optimization, machine learning and practical planning analytics.
He founded SupChains in 2016 to support organizations through customized forecasting and inventory models, training and coaching. He also teaches master’s students at CentraleSupélec and contributes educational material for planners, analysts and supply-chain leaders.
His books include Data Science for Supply Chain Forecasting, Inventory Optimization: Models and Simulations and Demand Forecasting Best Practices. His published work addresses statistical and machine-learning forecasting, Forecast Value Add, forecast bias, safety stock, planner overrides and the translation of quantitative models into operational decisions.
Vandeput’s work is particularly relevant where organizations need to improve planning quality before adding more software. His approach emphasizes measurable baselines, forecast bias, Forecast Value Add and the need to determine whether manual interventions genuinely improve a statistical forecast.
The required data includes governed sales history, product and location hierarchies, promotions, lead times, service-level policies, inventory positions and override records. Business owners must define which metrics determine success, who may change planning assumptions and how model or planner performance is validated.
Lightweight Python models and analytical applications can support diagnosis, simulation and training. When recommendations begin influencing purchasing, inventory or production, organizations need version control, access rights, audit trails, manual overrides and reconciled interfaces with APS and ERP systems. Poorly governed logic can reinforce bias, inflate stock or create false confidence in forecast accuracy.
Around Nicolas Vandeput (12)
- alertsEVENT: Sunstice to host webinar on touchless demand planning and Agentic AI2026-07-09
- alertsEVENT: How I vibe-coded my Advanced Planning System2026-07-02
- newsAlain Matar and SupChains collaborate on a planner-facing forecasting application2026-06-24
- tutorialsHow to Validate an AI Demand Forecast Before Planners Use It2026-10-05
- insightsForecasting becomes more accessible with AI, but data still makes the difference2026-10-02
- insightsEvaluating agentic AI for demand forecasting requires more than forecast accuracy2026-10-01
- jobsSCM Technology Manager2026-07-20
- alertsEVENT: Intuiflow showcases Demand Driven planning at Procurement Summit Hamburg2026-06-25
- tutorials22 practical Data Science projects for Supply Chain teams2026-02-09
- tutorialsMap the supply chain IT portfolio before adding another AI application2026-10-06
- jobsDirector, Inventory Optimization and Operations Planning2026-10-05
- tutorialsHow to Compare Forecast Models Before Selecting One in AWS Supply Chain2026-10-05