Data Science in Supply Chain Management - Predictive Analytics

Data Science as you all know is the science which we apply on data.


From Raw Materials to Consumer vast amounts of data is generated, which is valuable and can generate beautiful insights.

But the major problem is, companies do not know how to capture and leverage the data.

Supply chain management is a vast domain, which in particular need the help of data scientists. Every day, 1000's of trucks hit roads to reach their delivery targets. If I as a manager can track each truck, driver driving patterns, it helps me to provide better service to customers.

Data in this digital age, can be captured by sensors. If following data can be captured, it could generate millions of savings:

  • Longitude and latitude, geo location
  • Speed
  • Halts of truck
  • Drunk(Yes or No)
  • Tyre air pressure
All the above details can be well captured using sensors, collection using Apache flume or chukwa, and HBase Hadoop for storage.

Using the data collected following batch analysis can be done:
  • Average speed of each truck on a given day
  • Max speed of each truck on a given day
  • Driver drunk or not
  • Number of halts 
  • Average speed on High Ways
  • Average speed in crowded areas
  • Distance from delivery point
  • Average speed in accident prone areas
  • Driver driving patterns in a given day
  • Average Fuel usage in a given day
  • Number of on-time deliveries
  • Number of delays in delivery
  • Average speed in curved roads
  • Speed during accidents
  • Number of red signals crossed-
Using the data for real time analysis, following key value added services can be provided:
  • If driver is 100 kms away from delivery point, 2 hours is left for ontime delivery, now using the historical data, if a data science model could give the best route with minimum traffic, it will ensure on time delivery.
  • If driver is travelling at high speeds even in public roads, immediate message can be sent to driver to slow down.
  • If it a festive season, and some roads are completely blocked which can be seen from historical data, then data science model will avoid that route and suggest all possible routes.
  • If sensor detects driver is drunk, driver can be asked to stop immediately.
  • If driver driving patterns are very different and very bad, it means there is some thing wrong.
  • Fuel savings is one best thing that can save millions.
  • Every thing can be brought under control with right models and machine learning algorithms.
Finally i would like to conclude with Data Science, journey plan for each driver can be generated automatically, from journey start to end time, every point can be deeply analysed and summarized, with predictive analytics performance of each driver can be predicted and right driver for each delivery can be assigned.

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