Big data in manufacturing
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The document describes a big data analytics solution developed for a leading spare part manufacturing company facing $500 million in revenue loss due to fraudulent warranty claims. The solution, built with Golang, Python, and Hadoop, analyzes 2 PB of structured and unstructured data to identify patterns in product complaints and vehicle breakdowns. As a result, 937 fraudulent vendors were identified, claims against poor quality products decreased by 50%, and the company saved $250 million in false claims, significantly improving profit margins.
Big data in manufacturing
- 2. Client Overview • The Client is a leading Spare Part Manufacturing Company for Commercial Vehicles and Trucks. • The Company loses revenue worth $500 Million to Fraud warranty claimants. Client is looking for a Big Data analytics solution that will determine: • Which Product comes back with repeated complaints. • On which particular routes vehicles break down most number of times.
- 3. Business Solution • We developed a Big Data analytics platform using Golang, Python, and Hadoop to pull data from Teradata. • The Platform can produce Historical data analysis and Predictive Analytics based on The Hadoop Data storage. • The client has a Data volume turnover of 2PB annually in the form of Structured as well as Unstructured Data.
- 4. Analytics provided Historical Data Analytics • Clients who have made false claims • Reasons for breakdown of vehicles • Which product reported most complains Predictive Analytics • Which spare part or product will report most number of breakdowns • Which route will cause most breakdowns
- 5. Business Benefit • 937 Vendors were found to be making False Claims • Total claims against poor quality products have reduced by 50%, and profit margin has increased by $250 million • $ 250 Million was saved from being paid to false claimants • There was a Cost Saving of 50%