In a recent visit to Honda in Japan, we heard first-hand the extent of the possibilities that predictive maintenance and connected vehicles can bring to both the makers and users of automobiles.
From a user standpoint, electric vehicles (EV) have a clear advantage of low maintenance and reduced energy costs. However, concerns of battery life were preventing users from making the switch to EV. With the connectivity and operations data that these cars provide, the user can receive regular status updates on his or her own battery’s life. Such capability goes an extra mile – pun intended – and more in easing concerns of being stranded midway from returning on an extended trip due to your battery not charging.
From a maker’s point of view, engineering departments now have access to continuous data from the field. Not only battery life can be correlated to usage patterns at an individual level, but also aggregate analysis can be done to understand how anticipated consumer usage patterns are matching actuals. Further, battery lots or battery components showing early signs of premature wear can be traced through the supply chain. Corrective action can then take place before the issue becomes a larger warranty liability.
IBM Predictive Maintenance and Quality (PMQ) brings together both analytics and data integration in a single software product offering. IBM SPSS software (predictive analytics) and IBM Cognos software (business intelligence) provide the necessary analytics, while PMQ unique foundation code leverages IBM data tools from Websphere, AIM, and Information Management orchestrate the data movement for this predictive maintenance application. PMQ connects to the enterprise systems of record (telematics data, manufacturing data, operations data) and to their system of engagement (design tools, manufacturing dashboards, maintenance systems).
In this example, both the asset user and asset maker are benefiting from PMQ’s analysis. Here PMQ is being used for a number of use cases to predictively monitor battery health for the user, provide warranty analytics to the maker, deliver part quality analysis to manufacturing, and engineering guidance to design.
Let me know what business problems you are looking to address with this accessible and integrate-able analytics capability. Meanwhile, please visit the PMQ webpage to learn more about this solution.
Olivier Jouve, Director, Analytic Solutions with
Ishan Sehgal, Program Director of PMQ
near Tokyo, Japan.
By Ishan Sehgal, Program Director, IBM Predictive Maintenance and Quality Product Management
Follow Ishan on Twitter @ishansehgal