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
- insightsModern Demand Planning Requires Model Selection, External Signals and Governance2026-06-24
- newsPigment Introduces Graphite Architecture for Scalable, Governed Planning2026-06-05
- alertsEVENT: ASCM and IBF Best of the Best S&OP Conference returns to Chicago in June 20262026-06-04
- newsVekia reinforces automatic replenishment as a concrete AI planning use case for retail2026-06-03
- jobsSCM Technology Manager2026-07-20
- alertsEVENT: Intuiflow showcases Demand Driven planning at Procurement Summit Hamburg2026-06-25
- insightsUpdating planning parameters is a practical first step for supply-chain AI2026-06-23
- insightsTillamook turns supply-chain planning into a growth engine2026-06-22
- insightsAI readiness is a planning-process question before it is a model question2026-06-21