Jan 11, 2023
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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Nov 2, 2022
Stuttgart is a fictitious automotive aftermarket company. They operate a network of stores delivering car repairs, car parts and car accessories. In the early 2010s, Stuttgart also started two ecommerce channels, one to buy and sell car parts, and one to buy and sell used cars.
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Sep 14, 2022
We seek a numerical recipe to drive an entire class of mundane decisions, such as stock replenishments. Automation is essential to make supply chain a capitalistic endeavor. However, it carries substantial risks of doing damage at scale if the numerical recipe is defective.
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Jul 6, 2022
Conducting a successful predictive optimization of a supply chain is a mix of soft and hard problems Unfortunately, it is not possible to take those aspects apart. The soft and hard facets are deeply entangled. Usually, this entanglement collides frontally with the division of work as defined by the organigram of the company.
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May 12, 2022
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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Mar 2, 2022
A forecast is said to be probabilistic, instead of deterministic, if it contains a set of probabilities associated with all possible future outcomes, instead of pinpointing one particular outcome as “the” forecast.
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Feb 2, 2022
Differentiable Programming (DP) is a generative paradigm used to engineer a broad class of statistical models, which happen to be excellently suited for predictive supply chain challenges.
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Jan 5, 2022
In 2020, a team at Lokad achieved No5 over 909 competing teams at the M5, a worldwide forecasting competition. However, at the SKU aggregation level, those forecasts landed No1. Demand forecasting is of primary importance for supply chain.
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Dec 15, 2021
Cybercrime is on the rise. Ransomware is a booming business. Due to their physically distributed nature, supply chains are particularly exposed. Moreover, ambient complexity is a fertile ground for computer security woes.
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Nov 24, 2021
Taming complexity and chaos is the cornerstone of software engineering. Considering that supply chains are both complex and chaotic, it shouldn’t come as too much of a surprise that most of the enterprise software woes faced by supply chains boil down to bad software engineering.
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Nov 3, 2021
The majority of supply chains are still run through spreadsheets (i.e. Excel), while enterprise systems have been in place for one, two, sometimes three, decades - supposedly to replace them. Indeed, spreadsheets offer accessible programmatic expressiveness, while those systems generally do not.
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Oct 13, 2021
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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