As promised in my last blog post, today I want to explore what insights you can gain from only a few data points within the new AEM AEMP Telematics Standard. This blog post is inspired by a session held by Tim Truex at the AEMP Asset Symposium 2014 in Memphis, TN. During the session, groups were given three or four data points and asked what conclusions they can draw using only this limited amount of information.

With the new standard you might be tempted to think that a new machine or asset should support as many data points as possible. However, I already touched briefly on the fact that the new standard can provide almost too much information. For example, imagine that a machine provides only the following three data points: Last known position, Cumulative Power Take Off Hours, Is Engine Running.

This is hypothetical as I would not expect that any machine would provide these specific data points in a real-life setting. But with this information alone, we can reason our way to a significant amount of valuable conclusions:

• Obviously knowing the current position and whether or not the engine is running is already a valuable piece of information for a broad spectrum of situations. For example, it allows you to check whether a rented machine is used (engine on) outside the rental agreement.
• Current positions over time allow you to create a movement profile. Circumstantially, it might be in your interest to check whether a machine stayed within its job site or was removed from the site unauthorized.
• Based on a movement profile, speed estimates are possible. To determine a more dependable and accurate speed estimate, you may decide to use only the data recorded when the engine was running. This is beneficial in making sure that health & safety regulations (like speed limits on a job site) are followed.
• If the last known position is not moving and the engine is running, the asset might be in idle mode and is burning unnecessary fuel (at least for machines that are productive when they are moving; like pavers for instance.)
• If you are able to determine when your machine is in idle mode, you are also able to calculate the operating hours, which can be highly interesting.
• Even the case that the engine is turned off but the machine sends ongoing (non-moving) current positions can be of interest. This could reveal for example, instances where the ignition is left on during longer periods of time, draining the machine’s battery.
• The cumulative power take off hours can be contextualized when put in comparison with similar equipment. If the take off hours of a machine are significantly higher when compared to a similar machine, this machine might be an excess capacity.
• If the cumulative power take off hours increase significantly when a machine is brought on-site, this particular machine might not be used very often for the project and could potentially be more productive on another job site.

This is only the beginning of a long list of proofs that you can write when exploring the relationship dynamics of just three pieces of information. Feel free to post any conclusions you may have found!

Hopefully I’ve effectively illustrated how you can gain valuable insights on your fleet with the new AEM AEMP Telematics Standard. Then again, I hope you I’ve shed light on the fact that there is a risk of the new standard being able to provide almost too much information. One of the key messages I took home from the AEMP Asset Symposium is that you need a clear understanding of the aspects you want to know about a fleet, otherwise you just get overwhelmed by all the available data.

In my next blog post, I want to finish this mini-series with some thoughts on who owns the data that is created during the construction process and uploaded to the Internet.