Google’s Machine Learning for your supply chain. Join our workshop.

Use Cases

Machine learning is a buzzword in the technology world right now, and for good reason: It represents a major step forward in how computers can learn. We specialise in Machine Learning in the Supply Chain industry.

A Machine learning algorithm is given a set of data that teaches the system. The data is used to solve a problem. For example, you want to understand how many customers placed an order of a certain product A in the last 3 months.

In the above example I ask the computer how many recipients/customers have reordered from me. It checks for years of data and returns me the value in seconds.

Every customer it identifies – correctly or incorrectly – gets added to the teaching set of data, and the Machine gets smarter and better at completing it’s task overtime.

It’s in effect learning, by itself !

Top use cases for Machine Learning in Supply Chain:-

  1. Machine Learning in Forecasting Demand – forecasting demand for the future, forecasting the declining and end of life of a product on a sale channel and the growth of a new product introduction
  2. Machine Learning in Supply Forecasting – based on supplier commitments and lead time – Bills of material and PO data can be structured and accurate predictions can be made in supply forecast.
  3. Machine Learning in Text Analytics – This mainly is due to data cleansing to drive better master data. Text analytics can be implemented with supply data, partner data, shipment data to derive better insights from the supply chain.
  4. Machine Learning in Price Planning – increase / decrease the price based on demand trends, product lifecycle and stacking product with competition.
  5. Machine Learning in Inventory Planning – automatically raise POs with suppliers based on shortages or future demand shortages.
  6. Machine Learning recommendations in drop shipment business – recommend products that are in excess and automatically reduce price to clear inventory. Based on past buying patters by customers in the past recommend products based on inventory position.
  7. Machine Learning in Stock Analytics – based on multiple structured and unstructured data the machine can now predict why we ran out of stock or when we will run out of stock accurately.
  8. Machine Learning in Exception Analytics – Stock outs at every level in the supply chain. Retail customers particularly feel the need of the machine to study root cause of stock outs and predict accurate demand trends with better lead times from suppliers to reduce stock outs.
  9. Machine Learning in Component Level Analytics – planning supply on a component level with dynamic replenishment based on raw material planning has become a reality.
  10. Machine Learning in Production Planning – Using sensors and production automation mechanics to increase / decrease products + increase quality based on realtime customer feedback.
  11. Machine Learning in understanding customer intelligence – understanding of price discounts phasing, buying patters and growth opportunities.