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Nicolas Vandeput is a supply chain data scientist specializing in demand forecasting and inventory optimization.
Data science in SCM 4:56. Small data 6:09. Historical sales/stockouts/marketing expenses data 6:40. Importance of data culture 9:11. Challenge of developing in house machine learning expertise 11:32. Statistical models for determining demand patterns 12:12. Open source, Python and R 14:47. Forecasting models 17:24. Building your own machine learning model 18:08. Overfit 18:49. Underfit 22:10. Selecting KPIs 23:04. Mean supply error 24:48. Bias 25:01. Forecast added value 26:02. Code used in production 30:15. Process of machine learning 30:38. Unsupervised learning 33:52. Norwegian car sales35:35. Off-the-shelf solutions vs having models built 38:22. What are outliers in the COVID-19 pandemic 42:21? Inventory optimization 45:24. ROI from machine learning 47:57.
"Data Science for Supply Chain Forecasting" now available here: https://lnkd.in/dBm56q4
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Nicolas Vandeput is a supply chain data scientist specializing in demand forecasting and inventory optimization.
Data science in SCM 4:56. Small data 6:09. Historical sales/stockouts/marketing expenses data 6:40. Importance of data culture 9:11. Challenge of developing in house machine learning expertise 11:32. Statistical models for determining demand patterns 12:12. Open source, Python and R 14:47. Forecasting models 17:24. Building your own machine learning model 18:08. Overfit 18:49. Underfit 22:10. Selecting KPIs 23:04. Mean supply error 24:48. Bias 25:01. Forecast added value 26:02. Code used in production 30:15. Process of machine learning 30:38. Unsupervised learning 33:52. Norwegian car sales35:35. Off-the-shelf solutions vs having models built 38:22. What are outliers in the COVID-19 pandemic 42:21? Inventory optimization 45:24. ROI from machine learning 47:57.
"Data Science for Supply Chain Forecasting" now available here: https://lnkd.in/dBm56q4