Aug 8, 2018
Predictive supply chain optimization relies on heavily prepared data. The purpose of this data is twofold: first, the historical supply chain data is used to build the forecasting models, second, the data describing the supply chain's current state is used to drive the optimization of the decisions.
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Aug 1, 2018
New products do not have a sales history that can be represented as a time-series. As a result, time-series forecasting models don't work for new products. Forecasting demand for new products requires alternative forecasting models capable of leveraging data, such as product attributes, that do not come as a time-series.
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Jul 25, 2018
A good Supply Chain Management (SCM) practice includes a healthy dose of conservatism as the cost of failure tends to be high. Yet, rejecting all change is not an option in a world where innovation drives laggard companies out of business.
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Jul 12, 2018
Managing supply chains and optimizing them is particularly challenging from a software perspective. The 'Software Frankensteinisation' refers to the technological decay that plagues entreprise software when faced with its own evolution over multiple decades.
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Jul 5, 2018
Optimizing supply chains relies on having insights about the future. Classic forecasts dismiss uncertainty entirely, and assume that the forecast is perfectly known. In contrast, probabilistic forecasts embrace uncertainty, and reflect that supply chain optimization should remain robust when faced with unexpected events.
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Jun 27, 2018
For a supply chain management practice to be performant, managers need to have access to the position of every single asset. Unlike classic electronic inventory management, Internet-of-Things (IoT) offers the possibility to gain real-time visibility on all assets, vehicles included.
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Jun 20, 2018
Properly preparing the data is a requirement to achieve success for any data-driven initiative. When considering supply chain challenges, data preparation is difficult because it involves complex enterprise systems that have not been designed with data science in mind.
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Jun 6, 2018
In this episode we talk about this major buzzword and its application to supply chains.
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May 31, 2018
Forecasting promotional demand is necessary in order to allocate the correct amount of stock. However, time-series forecasting models are typically not a good fit to address pricing-related demand patterns. More complex machine learning forecasting models are needed to properly take into account past promotions, and to reflect the upcoming impact of those that are planned.
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May 23, 2018
Supply Chain Management (SCM) systems feature complex user interfaces. Among them, demand forecasting subsytems are not only complex but complicated as well. Better user inferfaces are needed to tackle this complexity.
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May 9, 2018
Modern supply chains are complex, and the most direct answer to complexity is a 'specialization of labor'. Unfortunately, this approach results in 'silos' that fail at delivering decisions that maximize returns for the company.
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May 2, 2018
Supply chain challenges are frequently quantitative and data driven. This makes them a good fit for a data science practice. However, understanding the business is a frequently overlooked aspect of the data science practice in supply chain.
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