IoT and Machine learning for the Industrial Aftermarket Parts and Services Business
The original equipment manufacturers (OEMS)s are facing head-winds in today’s environment with uncertainties on raw material price increases and new tariffs for importing and exporting. The Aftermarket Services business becomes an area of focus for many CEOs of MEM companies. The advantage of this business is:
• Stable revenue: from existing install-base
• High margins than sales of new equipment
According to a McKensey analysis across 30 industries, the average earrings-before-interest-and-taxes margin for aftermarket services was 25 percent, compared to 10 percent for new equipment.
The new digital innovation fueled by IoT and Machine Learning allows OEMs to create new business models and services around the provision of parts, repair, maintenances to their customers in personalized approach similar to what consumers have grew accustomed to in eCommerce, with Amazon setting the gold standards.
HAND has partnered with a Silicon Valley pioneer in industrial IoT and machine learning, Lecida to offer a comprehensive solution for OEMs. HAND leverages its 20+ years of experience in the enterprise software service business:
• Leading global enterprise software solutions provider
• Deep ERP systems (Oracle/SAP) integration capabilities
• Extensive experience with manufacturing customers
• Proprietary IP software solutions in MES, SRM
Our solutions provide three levels of support for the new aftermarket services business:
Depending on where the OEM is in the journey to IoT/ML, HAND can provide our solution at the appropriate level to allow OEM to start the digital transformation for the service business and evolve the model over time. In the long run, we envision a model with equipment owners, OEMs, their parts suppliers are connected digitally so that potential unscheduled downtime can be predicted, and maintenance can be scheduled proactively along with the right parts being shipped directly from the parts suppliers. Demand can be better forecasted and shared based on real usage across the install-base customers.