When conceptualizing and implementing a predictive maintenance project, it can be hard to grasp the entire chain of people and technology needed for success. In this post I’ll try to break down all the people and teams needed for this type of project, and also provide insight into how these people work together.

One mistaken assumption I see people make with predictive maintenance programs is that the cost savings of many lower value projects will add up to a big number, so it’s worth putting in the effort. But sometimes saving a million dollars isn’t worth it if it costs you more than that to implement it. In this post we’ll look at four types of programs and understand why some of them aren’t worth doing because they have a negative return-on-investment (ROI)

In my previous post, we looked at how stakeholders and organizational structures influence decisions about what data is collected and exposed for use. If you haven’t seen machine data before, that post might have felt a little abstract and some things might have felt contradictory (e.g. how does one look at the events before a failure without any sensor data?). For people who prefer concrete details and real examples, this post is for you.

This post will be the first of two that discusses how organizational dynamics, stakeholder incentives, and the goals of the business drive decisions about what data is collected and why. What is discussed is relevant to companies trying to do predictive maintenance on industrial and commercial machines, but it applies to other industries as well.