Archive for Supply Chain Planning

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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Supply Chain Planning in the Chemical Industry: Complexities Abound

Supply chain planning in the chemical industry is difficult because of the complexities associated with this industry. Optimization depends on models. The models for the chemical industry tend to be much more complex and detailed than in most other industries.

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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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Benchmarking: A Key Ingredient to Campbell Soup’s Ability to Greatly Improve their Forecasting

Campbell Soup’s forecast accuracy had gradually deteriorated. The company engaged in advanced benchmarking as part of a holistic strategy to improve our results by strengthening their processes, people, and tools. The result? Forecast accuracy better than the industry average.

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Robust Benchmarking of the Demand Management Process is Possible

Robust benchmarking is difficult. It is now possible to do robust benchmarking of the demand management process. That is much harder to do than you might think.

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3M Commits to Digitization

For 3M, a digital supply chain is about reducing friction, both internally and with partners and customers. And according to their Sr. VP of SCM, “Friction occurs at the connection points.” Digitization provides the information that allows different parties to get to a point where they can more efficiently work with each other.

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Optimizing the Panama Canal’s Operations

The Panama Canal is quite the engineering feat, described by some as the eighth wonder of the world. It is especially interesting to those in supply chain and logistics, as it is a critical resource in support of today’s vast international trade. It is also a complex operation subject to high levels of traffic with numerous constraints that can impact many company’s supply chain schedule. Modern supply chain planning applications model complex planning and scheduling […]

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