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Supply Chain Use Cases for AI and Machine Learning

Here are a few supply chain problems that AI can solve:

  • Demand forecasting
  • Price forecasting
  • Anomaly detection

But those are generic problems common across many industries. Here are a few sector-specific use cases.

Supply Chain Challenges for AI

Capacity and Production Planning. The intermediate stages of planning that fall between demand forecasts for the long term, and inventory planning for the short, present problems of transparency and visibility.

Timing of consumption by location. Learning where goods will come from and where they will be distributed at their final destination can help to streamline logistics.

Sourcing, availability and material cost. Predicting shortages will be especially important to manufacturers so they can mitigate potential risks.

Production locations, lot scheduling and sizing. Distributors will need to account for size and availability of storage facilities along the distribution route.

Stress on quality control resources. Several quality control points will likely be necessary to guarantee viability. Overloading these “checkpoints” would create bottlenecks in the chain.

Spoilage probability. Many goods need to be stored in controlled temperatures and are susceptible to spoilage if deviations occur. Modeling space allocation and anticipating problems with stocking may reduce potential for wasted inventory.

Other Pathmind Wiki Posts

Chris Nicholson

Chris Nicholson is the CEO of Pathmind. He previously led communications and recruiting at the Sequoia-backed robo-advisor, FutureAdvisor, which was acquired by BlackRock. In a prior life, Chris spent a decade reporting on tech and finance for The New York Times, Businessweek and Bloomberg, among others.


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