Archive for Machine Learning

Machine Learning in Today’s Warehouse Management Systems (WMS)

While speaking with a number of WMS product managers, I began to notice that machine learning was called out as a focal point for WMS product development efforts. In general, machine learning is a hot topic in the world of supply chain technologies. Just last week, Chris Cunnane wrote about machine learning for transportation execution. And Steve Banker recently wrote  about Vecna Robots use of machine learning to improve its vision system. Naturally, I wanted […]

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Machine Learning Limitations: The Need for a Clear Measure of Success

Demand planning is a good application for machine learning because the measure of success – the forecast accuracy – is clear. To learn, an application needs a clear measure of success. Having a clear measure of success sounds easy. But often, defining success in not easy. Consider a situation where a manufacturer learns of a shortage of a key component.  Customers have already been promised products that depend upon that key production input.  The supply […]

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Applying Machine Learning to Supply Planning is Tough

Machine learning has been successfully applied to demand planning, but leading suppliers of supply chain planning are beginning to work on using machine learning to improve supply planning.  But architecturally and culturally, this is a much tougher problem than machine learning applied to demand planning.

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Artificial Intelligence in Demand Planning

There is an arms race to incorporate artificial intelligence in demand planning solutions. Many new data sources, features, and tools are being explored. A new demand modeling tool has been introduced which will make it easier to analyze new data sources to see if they can be used to improve forecasts.

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The Peril of Circular Reasoning in Machine Learning and Forecasting

Machine learning engines can take data on forecast accuracy and use that data to automatically improve the forecast model. However, three is a rub. Lost sales is a key piece of data on the accuracy of the forecast, but lost sales is generated with a demand forecast. This is circular reasoning. But that does not mean this analysis is without value.

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The Arms Race to Leverage Machine Learning in Supply Chain Planning

Artificial intelligence (AI) is hot.  Over $4 billion in venture capital has been invested in AI firms just in the US. But supply chain planning software companies, with their cadre of operations research Ph.Ds who have been modeling complex problems for decades, may be better poised to solve many complex business problems than the hot new Silicon Valley firms.

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Machine Learning in the Digital Supply Chain isn’t New

Machine learning has become hot this year. Supply chain software suppliers are investing in improving their software’s capabilities by using machine learning. But machine learning in supply chain software is not new.

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A Practical AI Platform? What a Concept!

I’ve been briefed by several providers of AI platforms. I’m left uneasy by these briefings. It is as if Artificial Intelligence is like a magic wand, wave it and your problems disappear. But Teknowlogi appears to have a practical AI Platform, the first I’ve come across.

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Machine Learning with Data Overload

Just because we have a massive junkyard of data in a data lake does not mean we have the right data to answer key supply chain questions like: “How do I improve production rate, reduce maintenance costs, or improve product quality in my factory”. Machine learning with neural networks is a promising technology for extracting useful models for data, but typically needs very specific data that is not likely to be found in a data lake.

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CSX’s IoT Enabled Machine Learning Journey

The Internet of Things (IoT) and machine learning are both getting a great deal of attention. Many pundits say there is more substance than reality to this coverage. But CSX has shown that one area where the ROI of IoT is clear, is in the area of predictive and prescriptive maintenance.

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