Machine learning for supply chain - Lecture 4.4

00:21 Introduction
01:53 From forecasting to learning
05:32 Machine learning 101
09:51 The story so far
11:49 My predictions for today
13:54 Accurate on data we don’t have 1/4
16:30 Accurate on data we don’t have 2/4
20:03 Accurate on data we don’t have 3/4
25:11 Accurate on data we don’t have 4/4
31:49 Glory to the template matcher
35:36 A deepness in the learning 1/4
39:11 A deepness in the learning 2/4
44:27 A deepness in the learning 3/4
47:29 A deepness in the learning 4/4
51:59 Go big or go home
56:45 Beyond the loss 1/2
01:00:17 Beyond the loss 2/2
01:04:22 Beyond the label
01:10:24 Beyond the observation
01:14:43 Conclusion
01:16:36 4.4 Machine learning for supply chain - Questions?


Forecasts are irreducible in supply chain as every decision (purchasing, producing, stocking, etc.) reflect an anticipation of future events. Statistical learning and machine learning have largely superseded the classic ‘forecasting’ field, both from a theoretical and from a practical perspective. We will attempt to understand what a data-driven anticipation of the future even means from a modern ‘learning’ perspective.


  • A theory of the learnable, L. G. Valiant, November 1984
  • Support-vector networks, Corinna Cortes, Vladimir Vapnik, September 1995
  • Random Forests, Leo Breiman, October 2001
  • LightGBM: A Highly Efficient Gradient Boosting Decision Tree, Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu, 2017
  • Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, last revised December 2017
  • Deep Double Descent: Where Bigger Models and More Data Hurt, Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever, December 2019
  • Analyzing and Improving the Image Quality of StyleGAN, Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila, last revised March 2020
  • Generative Adversarial Networks, Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, June 2014
  • Unsupervised Machine Translation Using Monolingual Corpora Only, Guillaume Lample, Alexis Conneau, Ludovic Denoyer, Marc’Aurelio Ranzato, last revised April 2018
  • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, last revised May 2019
  • A Gentle Introduction to Graph Neural Networks, Benjamin Sanchez-Lengeling, Emily Reif, Adam Pearce, Alexander B. Wiltschko, September 2021