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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Archive for Supply Chain Planning
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 […]
Read More →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.
Read More →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.
Read More →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.
Read More →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.
Read More →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.
Read More →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.
Read More →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.
Read More →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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