Archive for Supply Chain Planning

Clorox Drives Value by Implementing Value Chain Segmentation

The idea behind supply chain segmentation is that not all customers or consumers should be served with a one size fits all supply chain. Clorox has shown that implementing this strategy can drive great value. But it is not easy.

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What is Integrated Supply Chain Planning?

Mike Reibsamen – the Director of Integrated Supply Chain – at Berry Global, spoke about Berry Global’s supply chain transformation at the Council of Supply Chain Management Professionals (CSCMP) annual conference in early October. Mr. Reibsamen’s primary point was that companies will get much better payback from their supply chain projects if they don’t do them in an ad hoc manner. A company should have a comprehensive end-to-end vision of what they want to accomplish, […]

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The Halloween Supply Chain

Halloween is a mere two weeks away, and the shopping season is in full swing. And by “in full swing,” I mean that consumers are spending big bucks on all the Halloween essentials. According to the National Retail Federation’s (NRF) annual survey conducted by Prosper Insights & Analytics, Halloween spending is expected to exceed $9 billion. With more than 175 million people planning on celebrating the holiday this year, that equates to an average spend […]

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A Perfect Marriage: Demand Modeling and Supply Chain Design

Demand modeling has emerged as a key decisioning tool in supply chain design. Standard forecasting tools can fall short if they can’t take into consideration external causal factors that drive demand like weather and economic and political indicators, or help predict demand 5-10 years out when making long-term strategic decisions.

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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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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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