Top 10 use cases for Machine Learning in Supply Chain:

Supply chains across the world are adopting Machine Learning to improve their processes, reduce costs and risk, and increase revenue. Here are 10 ways that you can leverage the power of ML in your supply chain. 

  1. Demand ForecastingLet AI remove the guesswork in forecasting and avoid supply chain surprises. Leverage AI to manage complex and unpredictable fluctuations in demand volumes.
  2. Supply Forecasting – Based on supplier commitments and lead times, the bills of material and PO’s data can be structured and accurate predictions can be made in for supply forecasts. Balance your demand and transform your business needs to span the entire value chain. 
  3. Text Analytics –  Data can be cleansed with text analytics to drive better decisions. Text analytics can be implemented with supply data, partner data, or shipment data to derive better insights from the supply chain.
  4. Price Planning – Leverage ML to optimize the increase or decrease in product prices based on demand trends, product lifecycles, as well as stacking products with the competition.
  5. Inventory Planning – Automatically raise POs with suppliers based on shortages or future demand shortages by predicting both demand and supply to make sure you have the right products at the right time but are not overspending for excess inventory. 
  6. Inventory Price Balancing – ML can recommend products that are in excess and automatically reduce prices to clear inventory accordingly. ML uses historical data like past buying patterns to recommend products based on inventory positions. 
  7. Stock Analytics – Based on multiple structured and unstructured datasets, machines can now predict the cause of out of stock items or when those items will run out of stock more accurately than ever before so that you can plan shipments and delivery accordingly. 
  8. Exception Analytics – Stock-outs at every level in the supply chain can be predicted. Understanding the root cause of stockouts and predicting accurate demand trends with better lead times from suppliers to reduce stock-outs.
  9. Component Level Analytics – Plan your supply on a component level with dynamic replenishment based on raw material planning. 
  10. Production Planning – Leverage IoT sensors and production automation mechanics to increase/decrease products and increase quality based on real-time customer feedback.

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