Nicolas Vandeput

Nicolas Vandeput

Brussels, Brussels Region, Belgium
43K followers 500+ connections

Join to follow

About

I help supply chain leaders to reduce their forecast errors by 20 to 35% and inventory…

Articles by Nicolas

See all articles

Contributions

Join now to see all contributions

Activity

Experience

  • SupChains Graphic

    SupChains

  • -

  • -

    Paris, Île-de-France, France

  • -

    Paris, Île-de-France, France

  • -

    Paris, Île-de-France, France

  • -

    Bruxelles

  • -

    Brussels Area, Belgium

  • -

    Région de Bruxelles, Belgique

  • -

    Région de Bruxelles, Belgique

  • -

    Vilvoorde

  • -

    Belgium

Education

  • CentraleSupélec Graphic

    Ecole Centrale Paris

    -

  • -

  • -

    Consist of 5 online classes from the MIT MBA in supply chain.
    Equivalent to one semester of the on-site MBA.

Licenses & Certifications

Join now to see all certifications

Publications

  • Demand Forecasting Best Practices

    Manning

    Demand Forecasting Best Practices reveals forecasting tools, metrics, models, and stakeholder management techniques for managing your demand planning process efficiently and effectively. Everything you learn has been proven and tested in a live business environment. Discover author Nicolas Vandeput’s original five step framework for demand planning excellence and learn how to tailor it to your own company’s needs. Illustrations and real-world examples make each concept easy to understand and…

    Demand Forecasting Best Practices reveals forecasting tools, metrics, models, and stakeholder management techniques for managing your demand planning process efficiently and effectively. Everything you learn has been proven and tested in a live business environment. Discover author Nicolas Vandeput’s original five step framework for demand planning excellence and learn how to tailor it to your own company’s needs. Illustrations and real-world examples make each concept easy to understand and easy to follow. You’ll soon be delivering accurate predictions that are driving major business value.

    See publication

  • Data Science for Supply Chain Forecasting

    De Gruyter

    Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning, must be applied to supply chains to achieve excellence in demand forecasting.

    This second edition adds more than 45 percent extra content with four new chapters, including an introduction to neural networks and the…

    Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning, must be applied to supply chains to achieve excellence in demand forecasting.

    This second edition adds more than 45 percent extra content with four new chapters, including an introduction to neural networks and the forecast value added framework. Part I focuses on traditional statistical forecasting models, Part II on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both (demand) forecasting models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves.
    This hands-on book, covering the entire range of forecasting--from the basics all the way to leading-edge models--will benefit supply chain practitioners, demand planners, forecasters, and analysts looking to go the extra mile with demand forecasting.

    See publication

  • Inventory Optimization Models and Simulations

    De Gruyter

    Inventory Optimization argues that mathematical inventory models can only take us so far with supply chain management. In order to optimize inventory policies, we have to use probabilistic simulations. The book explains how to implement these models and simulations step-by-step, starting from simple deterministic ones to complex multi-echelon optimization.

    The first two parts of the book discuss classical mathematical models, their limitations and assumptions, and a quick but effective…

    Inventory Optimization argues that mathematical inventory models can only take us so far with supply chain management. In order to optimize inventory policies, we have to use probabilistic simulations. The book explains how to implement these models and simulations step-by-step, starting from simple deterministic ones to complex multi-echelon optimization.

    The first two parts of the book discuss classical mathematical models, their limitations and assumptions, and a quick but effective introduction to Python is provided. Part 3 contains more advanced models that will allow you to optimize your profits, estimate your lost sales and use advanced demand distributions. It also provides an explanation of how you can optimize a multi-echelon supply chain based on a simple—yet powerful—framework. Part 4 discusses inventory optimization thanks to simulations under custom discrete demand probability functions.

    Inventory managers, demand planners and academics interested in gaining cost-effective solutions will benefit from the "do-it-yourself" examples and Python programs included in each chapter.

    See publication

  • Data Science for Supply Chain Forecast

    Data Science for Supply Chain Forecast is a book for practitioners focusing on data science and machine learning; it demonstrates how both are closely interlinked in order to create an advanced forecast for supply chain. As one will discover in this book, artificial intelligence (AI) & machine learning (ML) are not simply a question of coding skills. Using data science in order to solve a problem requires a scientific mindset more than coding skills. The story behind these models is one of…

    Data Science for Supply Chain Forecast is a book for practitioners focusing on data science and machine learning; it demonstrates how both are closely interlinked in order to create an advanced forecast for supply chain. As one will discover in this book, artificial intelligence (AI) & machine learning (ML) are not simply a question of coding skills. Using data science in order to solve a problem requires a scientific mindset more than coding skills. The story behind these models is one of experimentation, of observation and of constant questioning; a true scientific method must be applied to supply chain.

    See publication

Languages

  • Français

    Native or bilingual proficiency

  • Anglais

    Professional working proficiency

View Nicolas’ full profile

  • See who you know in common
  • Get introduced
  • Contact Nicolas directly
Join to view full profile

Other similar profiles

Explore more posts

Explore collaborative articles

We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.

Explore More

Others named Nicolas Vandeput in Belgium

4 others named Nicolas Vandeput in Belgium are on LinkedIn

See others named Nicolas Vandeput

Add new skills with these courses

See all courses