5 Steps to Using Data to Simplify the Chaos of Managing Fleet Service Events

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Michael-Riemer-HeadshotIn this special guest feature, Michael Riemer, VP Product and Channel Marketing at Decisiv, discusses 5 steps for the commercial asset service industry to turn lots of raw, unfiltered data from the service event management process into reliable, actionable big data and ultimately a competitive advantage. Michael Riemer is the Vice President of Products and Channel Marketing for Decisiv, a provider of Service Relationship Management (SRM) solutions for the trucking, transportation and construction industries.  He is a recognized commercial fleet industry thought leader and has authored dozens of articles covering fleet maintenance, regulatory compliance, utilization and availability.

Traditionally, commercial asset maintenance has been fraught with headaches. Constant phone calls, voice mails, faxes, and other manual processes introduce costly inefficiencies across the service supply chain. In the trucking business, for example, the average breakdown time for an asset is four days, of which less than 10% of the time is spent turning a wrench. The remainder is spent pushing paper, searching for warranty information, navigating siloed systems and applications, and chasing authorization and accountability.

The commercial asset service ecosystem, whether it’s transportation, construction, mining, agriculture, or power generation, requires good communication and collaboration and in-context access to:

  • the right people (fleets, OEMs, service providers),
  • the right information (diagnostics, service history, maintenance schedule/status, service bulletins/recalls),
  • at the right place and time (service location, roadside repair, fleet depot, etc.).

To be effective, this challenging matrix of many inputs and many outputs must all be funneled into a single, closed loop process stream.

Rather than a brute force approach to managing this process, technology can be leveraged to optimize communications, eliminate manual data entry, and shorten the length of service events. In this approach, actionable, intelligent and in-context data is used to ensure that every step in the process, and each service supply constituent, is working together to achieve the ultimate goal of asset availability, at the lowest possible cost.

For a service process that has not changed much since the 1990s, status quo isn’t good enough anymore. To improve decision-making and reduce costs, commercial asset owners are looking to leverage data from across the service supply chain to enable real-time status and collaboration with stakeholders, while dramatically improving asset uptime. But data alone isn’t enough. Only in-context, quality and reliable data can help simplify the chaos into one shared view. This is where the concept of service relationship management (SRM) comes into play.

SRM strips away the complexities of many-to-many relationships and the chaos that plagues this process to provide access to the right information at the right time. No playing phone tag, no paper shuffling or faxing, no constant fire drills. With data-powered business intelligence (BI) insights, teams can detect problems earlier in the repair process, apply appropriate course corrections during a service event, and drive continuous improvement of the process through tools that measure successes and failures.

Here are five steps to turning lots of raw, unfiltered data from your service event management process into reliable, actionable big data and ultimately a competitive advantage

1. Start by defining the business problem or question you are trying to solve

To achieve effective BI insights, start by defining the business decisions it will drive rather than requesting information for information’s sake. For example, if your goal is to realize cost efficiencies, rather than asking for a report into the average repair cost of your assets, consider that a report identifying your five most expensive assets might be more insightful and actionable.

2. Identify the data elements and information sources required

Next, identify the specific data elements that you need to capture in a consistent and reliable way to inform your BI-driven decisions. Going back to trucking, this data could include information on the life service history, diagnostic data, or use of industry standard reporting metrics (VMRS) to identify the complaint, cause and corrective actions

3. Define a standard operating procedure (SOP) to ensure the right data is captured

Not all data is equal. Consider this example. A repair on an asset is logged as a “brake job.” That doesn’t tell you much. What was done? Were brake pads replaced? Was there a diagnostic fault code? Where in the process is the optimal time to capture the required data? Spend some time reengineering your processes and technologies and define a SOP to ensure that you’re capturing the right information for reporting purposes.

4. Utilize real-time notifications for event-based issues

Real-time notifications during a repair provide the opportunity to intervene with a course correction. Instead of a repair job taking 4 days to complete, could a shorter downtime be achieved if you had the real-time insight to intervene and trigger a course correction during that repair window? Identifying key risk thresholds around your key data elements can help alert you before a small problem becomes a big one.

5. Define a standard set of reports based on your target KPIs

While it may seem obvious to do, many people forget to measure success and compare progress against a baseline. Downtime, preventative maintenance currency (including number of breakdowns between PMs), frequency and cost of operations based on VMRS codes are a good place to start.

With the right technology, such as SRM, transportation managers and other commercial asset owners have the opportunity to dynamically link the assets, people, process, technology and content to improve their service and repair management.  The result is a host of business benefits including enhanced decision-making, transparency and accountability, ecosystem-wide data sharing, and real-time alerts and after-the-fact data analysis. Last, but by no means least, it also makes people’s working lives simpler, more rewarding, and more productive.


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