Supply Chain Lectures


May 12, 2022

Retail stock allocation with probabilistic forecasts - Lecture 6.1

Supply chain decisions require risk-adjusted economic assessments. Converting probabilistic forecasts into economic assessments is nontrivial and require dedicated tooling. However, the resulting economic prioritization, illustrated by stock allocations, proves itself more powerful than traditional techniques. We start with the retail stock allocation challenge. In a 2-echelon network that includes both a distribution center (DC) and multiple stores, we need to decide how to allocate the stock of the DC to the stores, knowing that all stores compete for the same stock.

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Oct 13, 2021

Supply chain persona: San Jose, homeware ecommerce - Lecture 3.3

San Jose is a fictitious ecommerce that distributes a variety of home furnishing and accessories. They operate their own online marketplace. Their private brand competes with external brands, both internally and externally. In order to remain competitive with larger and lower priced actors, San Jose’s supply chain attempts to deliver a high quality of service that takes many forms, well beyond the timely delivery of the goods ordered.

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Sep 22, 2021

Machine learning for supply chain - Lecture 4.4

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 Statistical learning and machine learning have largely superseded the classic ‘forecasting’ field, both from a theoretical and from a practical perspective.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.

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Aug 25, 2021

Mathematical optimization for supply chain - Lecture 4.3

Mathematical optimization is the process of minimizing a mathematical function. Nearly all the modern statistical learning techniques - i.e. forecasting if we adopt a supply chain perspective - rely on mathematical optimization at their core. Moreover, once the forecasts are established, identifying the most profitable decisions also happen to rely, at its core, on mathematical optimization. Supply chain problems frequently involve many variables. They are also usually stochastic in nature. Mathematical optimization is a cornerstone of a modern supply chain practice.

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