Supply Chain Lectures

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May 10, 2023
supply chain lectures

Pricing Optimization for the Automotive Aftermarket - Lecture 6.2

The balance of supply and demand very much depends on prices. Thus, pricing optimization belongs to the realm of supply chain, at least to a sizeable extent. We will present a series of techniques to optimize the prices of a fictitious automotive aftermarket company. Through this example, we will see the danger associated with abstract lines of reasoning that fail to see the proper context. Knowing what ought to be optimized is more important than the fine print of the optimization itself.

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Mar 8, 2023
supply chain lectures

The Supply Chain Scientist - Lecture 7.3

At the core of a Quantitative Supply Chain initiative, there is the Supply Chain Scientist (SCS) who executes the data preparation, the economic modeling and the KPI reporting. The smart automation of the supply chain decisions is the end-product of the work done by the SCS. The SCS takes ownership of the generated decisions. The SCS delivers human intelligence magnified through machine processing power.

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Jan 11, 2023
supply chain lectures

Lead-time forecasting - Lecture 5.3

Lead times are a fundamental facet of most supply chain situations. Lead times can and should be forecast just like demand. Probabilistic forecasting models, dedicated to lead times, can be used. A series of techniques are presented to craft probabilistic lead time forecasts for supply chain purposes. Composing those forecasts, lead time and demand, is a cornerstone of predictive modeling in supply chain.

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May 12, 2022
supply chain lectures

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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