Advances in the digitization of asset management has expanded its reach from a horizontal focus to include multiple verticals and industry-specific capabilities.
Digital Transformation Goes Vertical
Thanks largely to the widespread expansion of digital transformation initiatives, including the use of sensors, IoT devices, telemetry information, and predictive capabilities, fleet operations’ efficiency and productivity are on the rise.
Progressive end users and suppliers are spearheading a drive toward deeper vertical functionality in asset management. Ongoing need for awareness and proactive analyses of potential problems is a top priority. Nowhere is this more evident than in fleet management and asset management of rolling stock, which have unique needs and challenges.
Challenges abound for many trucking and transportation operators. For most operators, a key point to consider is the fact that asset reliability and uptime enable customer satisfaction in a world of just-in-time supply chains. Customers can encompass various stakeholders, including end users and drivers. Mobile assets can also be difficult to repair on the road or to find suitable replacements when road failures occur, which compounds the problem.
United Road’s Approach to Improved Asset Reliability
The United Road family of companies is an industry leader in providing vehicle and heavy-haul transportation services. Adoption of advanced technology is core to delivering value for customers and reliable assets for drivers. The company is an industry leader in the delivery of finished vehicle logistics, ranging from passenger vehicles to heavy-haul transportation equipment. Approximately four million units are shipped annually, across 90 locations, with information fully integrated across their entire supply chain.
United Road COO Jason Walker recently shared his insight on achieving customer satisfaction with reliability and predictive maintenance at a recent Enterprise Asset Management session at the ARC Advisory Group Industry Forum. (Note: Click here to see Jason Walker’s complete presentation from the 2021 ARC Forum: https://youtu.be/ZvQmQcVhYm0
Jason noted that for United Road, maximizing vehicle availability is essential to their success. He added that the use of predictive maintenance, predictive analytics, and machine learning (ML) are becoming the foundation from which maximum vehicle reliability and uptime are achieved.
Such capabilities are key to a successful fleet operation, as unplanned downtime is unacceptable to both customers and drivers. In addition, there is often a 20-30 percent cost premium to have work done by vendors. The fact that there are no car-hauler-specific tractors available in the commercial truck rental market poses additional challenges.
Some of the main issues that Jason shared were centered around predictive maintenance, asset utilization, and asset – and driver -- utilization and uptime. To this point, Jason cites two primary, underlying pain points that challenge the industry and United Roads in particular: personal shortages and unplanned downtime. At the core of these challenges are the following key pain points:
- Pain Point #1: Driver and Technician Shortages
- Backfilling an open driver position can cost carriers about $4,000 on average. To make matters worse, the average driver stays within the same company for less than a year.
- Repair shops lose an average of $1,200 per day for every unfilled technician role.
- Technicians are retiring at high rates, exacerbating shortages.
- Pain Point #2: Unplanned Downtime
- Unplanned downtime is a main pain point, affecting customers, drivers, and the company’s bottom line.
- It leads to swamped repair shops and underpaid drivers.
- Fleets are addressing unplanned downtime by prioritizing vehicle maintenance and reliability, which is increasingly aided by predictive maintenance, predictive analytics, and machine learning.
Emerging Partnership with Uptake Enhances Predictive Maintenance Capabilities, Customer Service
With a heightened interest and emphasis on advanced predictive maintenance, United Road has partnered with Uptake to improve fleet reliability, efficiency, and productivity. The Uptake Fleet solution is an industrial analytics application that enables proactive maintenance optimization. United Road’s decision to pilot the Uptake Fleet solution is delivering impressive benefits. The pilot has progressed so well that United Road is in the process of expanding its deployment even further.
Uptake Fleet allows United Road maintenance personnel to sift through any “noise” in the vehicle data coming from the trucks’ smart components. The application enables operational excellence through features such as failure prediction and automated alerts.
This has dramatically changed how United Road evaluates and assesses maintenance. Previously, it was overwhelming to be inundated with too many (and often erroneous) fault codes.
The use of pre-built data science models are equipping United Road fleet managers and technicians with maintenance insights to conduct proactive maintenance, prevent unexpected roadside breakdowns, and increase driver efficiency and utilization. This new approach is often based on the logic that if condition “A” is happening, and “B” is happening, then condition “C “is likely to occur at some point, even if there are no fault codes yet occurring on the vehicle.
Here is an example of the power of such predictive maintenance initiatives. Nitrogen Oxide (NOx) sensor failures on diesel engines can now typically be predicted about 21 days in advance of an impending failure with the Uptake Fleet system, and the company can plan accordingly. Sometimes, Uptake Fleet triggers warning well before lights in the cab could alert drivers. In initial pilots on-road breakdown, events have declined by about 26 percent, and maintenance cost savings of almost $3,400 per vehicle have been achieved. These results have held constant as United Road has expanded their deployment of Uptake Fleet across its entire fleet of trucks, continuing to see an average 400 percent return on investment.
While the use of predictive maintenance in asset management has been around for years, its use beyond reading sensor data is a relatively new concept. Much of this is due to the power of analytics – and more recently, machine learning – to glean and infer information from seemingly unrelated sources.
These concepts are now coming into their own in the management of modern vehicles. Aided by similar advances in engine sensors and truck and trailer telematics, information on the current and likely future operation of major components is now possible. These are powerful capabilities that can help optimize fleet operations and maximize equipment efficiency, productivity, and uptime.
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Keywords: Enterprise Asset Management, Fleet Management, EAM, Analytics, Predictive Maintenance, Predictive Analytics, Machine Learning, ML Reliability, Artificial Intelligence, AI, ARC Advisory Group.