What are the new requirements for OEMs today?
OEMs are transforming their product and service offerings with data-driven, intelligent products. They are also looking for new ways of monetizing data. It’s no longer enough to just manufacture and sell mission-critical assets. Customers are looking for the next generation of ownership and servicing models. They are requiring zero
In order to meet these requirements, OEMs have invested in IoT technologies to monitor their equipment and try to produce meaningful business outcomes. This has remained a challenge for many organizations, and much of the data being produced is underutilized.
What are the gaps in current IoT Solutions?
Traditional IoT solutions are underutilizing the data because for the most part they are oriented towards upstream analytics and performance monitoring dashboards.
Why Is Predii Necessary?
Changing business models and customer expectations are only one half of the Repair Problem. The equipment is getting more complex while the workforce is aging, and there aren’t enough new hires picking up the tribal knowledge from the technicians with years of experience (Millennials are already the largest segment of the American workforce). The failures are now mostly electronic, requiring deeper expertise and time to diagnose. Further complicating this, industrial trends have shifted responsibilities for the technicians who are performing the repairs. They are now widely expected to be generalists, instead of specialists.
What do you do if no one person has all of the knowledge? We built a system that gleans the useful information from an entire workforce at the same time, giving one person access to the knowledge that comes from millions of man-hours. We bring the expertise to the technician, equipping them with advanced diagnostics, repair solutions
This knowledge can then be built into the business, democratized for anyone who might benefit from it. Our intelligence is used by field dispatchers, quality engineers, servicing executives, data scientists; in competitor analysis, in pricing strategy, and – of course – on the repair floor.
How Do We Solve It?
Regardless of the industry, repair scenarios all have this in common: a complaint is registered, a cause is diagnosed, and a correction is administered. This information is recorded in service orders – the notes that accompany any repair job, where the technician records what was happening and what they did about it.
Reading a single service order gives one a (usually) easily comprehensible recap of the specific issue and its correction. But what about reading 50 million service orders, and issuing corrections intelligently at scale? Repair IntelligenceTM solves this issue using highly-targeted proprietary machine learning and AI techniques — including custom Natural-Language Processing (NLP) algorithms.
We are able to ingest real-time data from sensors and historical data from service orders, correlating them to know exactly what is happening presently and what solutions were administered in the past. Then, to close the loop, we integrate OEM manuals, providing step-by-step instructions on those solutions, immediately – no more leafing through thousand-page manuals.
Our purpose-built AI platform is completely adaptable, configurable, and scalable. It is currently deployed in specialized equipment and automotive industries, processing billions of data points every month. Our laser focus on repair and maintenance means that we can deploy AI-driven solutions to your servicing rapidly and accurately.
