This new integrated solution analyzes big data from multiple static and streaming sources to make informed decisions by generating predictive statistical models to predict equipment failure conditions and create alerts. The alerts are then displayed on an employee or manager's tablet, smart phone or browser with recommended corrective actions that should be taken through interactive tools that perform root cause analytics and process improvements.This new offering is intended to help clients in the Automotive, Electronics, Aerospace, Defense, Manufacturing, Mining, Transportation, Telecommunications and Energy and Utilities industries. For example:
- In the City of Cambridge, Ontario, the transportation and public works department found itself less able to complete inspections and preventive maintenance because its resources were increasingly being used to respond to emergency repair calls. The city established a division devoted to asset management and with the help of IBM analytics they are evolving from a break-fix mode of operation moving toward more proactive, industry based practices to boost the quality of city services and address infrastructure sustainability.
- A global auto manufacturer was looking to improve its production quality. With IBM analytics the company was able to use real-time data to monitor the production quality and more quickly identify and resolve issues. This resulted in a reduced the defect rate by 50 percent in 16 weeks in the production of their cylinder heads and increased customer satisfaction.
- Another global manufacturing company was looking to more quickly detect part defects. Through IBM analytics the company implemented an early detection model and is now saving $130M on warranty costs per year.
- A regional utility company needed to maintain an aging infrastructure. By implementing IBM predictive analytics technology into their supply chain processes they can now detect potential problems before they occur, and have seen 20 percent productivity gains for service trucks and up to 20 percent reduction of fuel costs due to fewer truck rolls.