Old Notes: What the hell is EAM

This is a post based on some now years old notes I have on different things that I've learned along the way. Some are pretty basic, but I didn't know much about them at the time of taking the notes, so I figure some other people may not either, and may find this sort of thing useful. If you do, leave a comment below and I can expand with some more recent information.

There are a bunch of enterprise software systems that don't really eek out into the mainstream consumer markets (as opposed to consumer technologies which do tend to eventually eek into enterprise markets). One such example, that maybe you've heard of or not is Enterprise Asset Management, or EAM.

EAM as a broad concept is the process of managing an asset's entire lifecycle. An asset in this context may be a manufacturing plant, an individual device, a building, a vehicle, a ship, or any of a number of things. An asset lifecycle in this context crosses many business functions that otherwise may not share common tracking systems.  The value of an EAM system is therefore to give a broad, cross-functional, single source of truth for information about an asset.

Over the course of an asset's lifecycle it may be:

  1. designed or selected
  2. constructed or installed
  3. commissioned
  4. used (operated)
  5. maintained
  6. overhauled
  7. decommissioned
  8. replaced

So each of those major steps may have some data either sourced from or useful to:

  1. Finance / accounting
  2. HR
  3. Engineering
  4. Operations
  5. Maintenance
  6. Supply chain

or any of a number of business groups.

The global EAM market is currently around 3.5 billion / yr, and is growing at 11.9% CAGR (source).

Large vendors of EAM systems include:


Will has a background in Mechanical Engineering from Auburn, but mostly just writes software now. He was the first employee at Predikto, and is currently building out the premiere platform for predictive maintenance in heavy industry there as Chief Scientist. When not working on that, he is generally working on something related to python, data science or cycling.

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