Hitachi and Penske are raising the bar for predictive fleet maintenance with their Proactive Diagnostics solution. Learn how Hitachi and Penske partnered to explore how data-driven insights using AI and ML can make fleet maintenance and repair more responsive, reduce vehicle downtime, save driver time, and improve the customer experience.

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How Penske and Hitachi Solve Problems Before They Arise
Predictive Maintenance for Enterprise Fleet: A Proactive Diagnostic Solution

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Contents
1
• The Challenge:
Penske wanted to transition from reactive to more proactive data-driven fleet maintenance and respond
in real time to customer challenges, including service interruptions and unplanned downtime.
• The Solution:
Harnessing Hitachi’s expertise in digital technologies, Hitachi and Penske worked together to co-create a
Proactive Diagnostics capability to predict future potential failures.
• The Result:
Penske customers are better equipped to meet their scheduling commitments and delivery deadlines, and
they are more able to prevent disruptions to their end customers’ businesses.
• Conclusion

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The Challenge
Innovative solutions such as the Guided
Repair model, developed by Hitachi in
collaboration with Penske, which operates
and maintains one of the largest and most
diverse fleet of trucks in North America, help
fleet maintenance technicians respond as
quickly as possible to get vehicles back up
and running.
While the Guided Repair solution was the
fundamental first step toward reducing
downtime and elevating the customer
experience, the next logical step was for
Penske to predict problems before they occur.
Penske's Connected Fleet technology was already
tracking maintenance and repair issues identified by
fault codes that occur during the life of a vehicle.
These fault codes showed patterns over time, but
analysis of this data in real time to predict impending
failures remained a challenge.
The biggest challenge was optimizing the use of this
data and moving from reactive to proactive fleet
maintenance to respond in real time to customer
problems , including service disruptions and
unplanned downtime.

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The Solution : Data-Driven Proactive Diagnostics
By leveraging Hitachi's advanced expertise in artificial
intelligence and machine learning , the partners
expanded their existing model into a predictive model
called Proactive Diagnostics.
Now Penske can detect vehicles with impending
mechanical problems and repair them before they fail.
With its enhanced capacity for data analysis, Proactive
Diagnostics enables Penske to compare fault codes
and search for patterns that signal a potential failure.
This enabled the company to notify its customer and
get the fleet in for a scheduled repair before the
failure occurred on the road, avoiding any sudden
unplanned downtime.

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The Result : Driving Sustainability with Predictive Maintenance
Proactive Diagnostics gives Penske technicians
the ability to use Connected Fleet data to
process fault code information virtually in real-
time applications to reduce downtime,
manage maintenance costs and increase
operational efficiency. This service is currently
used in over 100,000+ vehicles in the Penske
fleet.1
With greater confidence in the information
now available, Penske can act decisively to
ensure their customers' schedules are not
disrupted, reducing stress and frustration not
only for drivers, but also for everyone
depending on those schedules across the
entire supply chain.
1As of October, 2022

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Conclusion
Penske’s early-adopter mindset helped make it possible for the partners to co-create, adapt and solve fleet
maintenance problems with a solution that reduces downtime, manages expenses and increases operating efficiency.
The Proactive Diagnostics solution is an
example of how the long-standing
collaboration between Hitachi and Penske is
raising the bar on innovation and leveraging
digital technologies to pave the path for a
green mobility future.
Read the full case study here: https://social-innovation.hitachi/en-us/case_studies/proactive-diagnostics-for-
enterprise-fleet-maintenance-and-repair/

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