Equipment Data

Historical and real-time equipment data collection isn’t close to exhausting its industrial potential. In 2020, the commercial telematics industry was worth $28.4 billion and analysts expect it to grow a further 21.9% each year until 2030.

Throwing money at telematics is easy, but getting value from such investments requires close attention. There are several ways to improve your understanding of this data as it comes in to ensure it results in productive, profitable, and concrete action.

Define Needs and Benchmarks

Defining the organization’s needs and its current level of success is step one for capitalizing on advanced equipment data collection tools. Stakeholders must ask themselves:

  • Where are our data-gathering blindspots? Which processes are a “black box” currently?
  • How does the company measure success in those processes?
  • Which metrics — if measured — would help stakeholders understand current productivity in real-world terms? Efficiency? Cost-effectiveness? Future forecasts?
  • Where does the company currently fall short based on its definition of success? Which kinds of data would help identify these opportunities?

If you throw money at telematics platforms without asking yourself these questions, you’ll end up with a product that doesn’t do what you need or provides more functionality than you bargained for. Define your mission first.

Build Process Ownership Into the Culture

When a company invests in telematics, Internet of Things (IoT) devices and other cyber-physical investments, there are several common pitfalls to avoid. On one hand, complex organizations might find themselves with data and processes siloed away, unable to interface meaningfully with one another and failing to provide value. On the other hand, with so many processes now generating and transmitting data, the flow becomes ungovernable and the sheer volume of data becomes overwhelming to the point of decision paralysis.

The solution is to build a culture with integral process ownership, understand which data points are most mission-critical to their subject-matter area and how best to measure them. They’ll be able to provide recommendations for new technologies and process improvements over time. Compared to a top-down decision-making approach, the bottom up cultural focus should yield more relevant observations and money more wisely spent.

Explain the “Why” (And Make the “How” Easy)

Communication is another closely related cultural element worth emphasizing due to its impact on data quality and comprehension. For equipment data to deliver value, it has to exist. Furthermore, it must exist in an understandable and actionable format. It should be consistent and organized.

Consider one of the industry’s biggest and most essential parts fleet management. Servicing fleet trucks is a task that benefits from and contributes to an organization’s collection of equipment data. Like most other tasks in manufacturing and logistics, working from an incomplete dataset could cause trouble for you.

Service technicians should receive comprehensive training on the data gathered during service intervals. Beyond that, they should understand its value and why collecting it is not just busy work. Consider the difference between the typical set of data points gathered during your average truck service appointment —

  • Tire pressure
  • Depth of tread
  • Vehicle mileage
  • Tire position

and compare it to the more robust set of data points below:

  • Tire pressure
  • Depth of tread
  • Vehicle mileage
  • Tire position
  • Brand of tire
  • Series (if applicable)
  • Retreaded? (Y/N)
  • # of retreads
  • The party that conducted the last service
  • DOT code(s)
  • Reason for removal or service

Which of these two will provide more valuable insights over the truck’s life span? Which is more likely to result in meaningful cost-saving and enterprise-planning insights? Help your people see the bigger picture and give them the tools to be consistent and appropriately detailed as they gather data in the field or on the floor.

Understand How Maintenance Impacts Equipment Data Collection

Automation and enhanced maintenance are two of the most significant opportunities brought by Industry 4.0 and the Industrial Internet of Things. Combining the two by investing in predictive equipment maintenance platforms is beneficial on more levels than you may know.

To start with the obvious, advanced machine telematics with predictive maintenance can reduce or eliminate unexpected downtime. It is a cost-saving investment you will watch pay off in longer machine life spans. Active monitoring and timely interventions via IoT sensors can recapture a substantial portion of that material and financial waste.

Taking machine maintenance more seriously through telematics and predictive IoT platforms or any form of proactive maintenance results in more consistent performance across your vehicle fleet, material-handling equipment or manufacturing environment. In turn, consistent performance means more accurate and actionable equipment data.

Keeping water flowing smoothly also keeps the data flow smooth and provides organizations with the bigger-picture perspective they need to achieve ongoing improvements. Digital twins perfectly capture one possible endgame for manufacturers, fleet managers, and supply-chain operators. With telematics data streaming in from throughout the network, companies can build digital counterparts of their entire operation a further step toward deeper data integration.

Get a Demo and Ask Questions

Some final words of advice for capitalizing on equipment data successfully are to get a demo, ask the right questions and make use of any extended trial periods your future technology partners may offer. Beware of generalities and vague statements. If you have read this far, you should have a better idea of what to measure, what you want to improve and how to spot a product or service that does not support your use case.


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