Supply Chain Risk Management
using Machine Learning
Supply Chain Risk Management using Machine Learning
Overview
Our Customer is a multi-national power and auto component company with multi-billion sales looking to transform their supply chain using analytics and machine learning. One particular challenge facing this client was the lack of transparency and predictability of the receipt of manufacturing components, which resulted in significant supply chain risk.
Challenge
The project requirements consisted of 2 primary elements. First, the supply chain risk assessment is to be automated so that the solution and its adoption are efficient and scalable. In terms of certainty of delivery, the client had an existing manual process utilizing public sources to stay aware of potential delivery impacts such as weather patterns, current news, and political developments. The requirement of the project was to replace this manual process with a data-driven automated solution.
Finally, the project required a risk assessment mechanism capable of assigning a risk score for both likelihood and confidence.
Technologies Utilized
Data Used
- Inventory details
- Material code, Cost, Vendor, Usage for last three years
For Supply chain risk management:
- News articles
- SEC fillings
- Tweets
Inventory prediction accuracy
that was not possible in the past
Inventory prediction accuracy that was not possible in the past
Solution
Upon completion, the solution provided the inventory prediction accuracy the client required, which was not possible in the past. In addition, the solution was designed to predict the effects of such events such as COVID by using state-of-the-art Natural Language Processing (NLP) algorithms.
