Machine Monitoring: the Question is not ‘What’ or ‘How’, it’s ‘Why’!

From my decade and a half experience, machine monitoring projects fall into one of three categories: they succeed and deliver true value, or they get deployed but soon lose momentum and become background noise, or they never get launched at all.

The primary difference between success and failure of machine monitoring deployments has very little to do with ‘how’ or ‘what’ or even ‘when’ or ‘who’; it has everything to do with ‘why’.

Successful deployments always have a very clear and specific answer to the question “why do we need a machine monitoring system?”. The more general and ambiguous the answer, the greater the chance that the project will fail to deliver any meaningful value. The more clearly the answer defines the actual problems you want to solve, or the gains and improvements you want to make, the more likely your investment of energy, time and money will deliver you true, continuing value.

I have worked with over a hundred companies that have either deployed or seriously considered machine monitoring solutions. The difference between success and failure has little to do with the technology they selected, or the size of the budget (or the funding they may have received), or even the people they tasked to the team, it always comes down to solving identified problems or achieving clearly identified objectives. In other words, the “why are we doing this?” question.

Here are some examples of answers that often lead to project failure, or failure to sustain traction, or failure to deliver any actual value for the effort invested:

“We’re behind and we need to catch up and start adopting more Industry 4.0 technologies”.

“We need more data to know what we don’t know.”

“We want to start making ‘data driven decisions’”.

“We want to be more competitive internationally, and for that, we need more data.”

All of the above are indirect and non-specific answers. They lack clarity, and where clarity is missing, there is no direction or path to success.

Conversely, here are some examples of answers from companies that achieved great value from their systems, (and continue to get value):

“We want to know exactly how our quoted machining time matches our actual machining time.”

“We want to know our actual machining cost per part”.

“We want to know the exact moment when problems happen, not at the end of the month in a report”.

“We want our people to recognize the difference between just being busy and adding value (to the part and to the company). We want our people engaged, interested and involved”.

“We need objective evidence to prove our automation is meeting the production rate, and quality, our customer expects”.

Each of the above answers provides a clear understanding for everyone involved what success looks like and what the specific goal or goals are. There’s a specific outcome that is being sought and pursued. It provides more than just clarity, it provides project direction. “This is what we want to accomplish – and this is how we know that we did”.

The third category mentioned (companies that didn’t start yet) are frequently criticized and labeled as being laggards, technology hesitant, or a slow adopters, but often its because the technologies and solutions proposed for them or presented to them don’t answer their legitimate questions of “how will this system solve our problems?” or “what will this system actually do for us that has value to us?”. If the technology or solution vendor has only the generic and non specific kind of answers to the "why" question, the customer is (in my opinion) wise not to go forward until they can come up with specific and clearly identified outcomes that deliver immediate and continuing value.

Here's an approach that I have seen multiple times that has led to long term success. It starts by identifying specific immediate short term outcomes (at a very low cost, with very little effort), which then makes it easier to invest more energy and effort into bigger, important but less defined objectives (such as “data analytics” and “understanding our processes better”), which then can actually drive improved outcomes going forward (rather than simply reporting outcomes looking backwards).

In one client's case, the initial objective (their first "why") was as an “early warning system”, so that problems, when they happened, could be responded to quicker and more effectively. In another client's case, the objective was to facilitate remote support for commissioning and buy-off. And in another's, it was to objectively and empirically record productivity and quality results to satisfy the audit requirements of their customer.

In each case, the result was near instant payback, because relative to the cost, having a problem resolved within fractions of an hour or even minutes (rather than hours or days), quickly paid back the investment. Not only did the company achieve that, they quickly achieved “buy in” from everyone involved that data and the monitoring system wasn’t intended for negative or threatening reasons, but it helped everyone do their jobs better.

Based on the initial success with the client already having achieved pay back and buy-in, the machine monitoring projects were expanded to collect additional process data to allow deeper analysis of all the processes and material variables, to offer insights into how to improve the efficiency and quality of the manufacturing processes. (This became their second "why").

After doing step 2 (the deeper analytics and putting the insights into action), all of the company’s dashboards for quality and efficiency became more dynamic and meaningful (their third "why"); the dashboards became ‘leading’ indicators, continually driving improvement, rather than ‘lagging’ indicators that just reported on how they did in the previous period.

In summary, here are the most important takeaways from this post:

1)     Clearly define your ‘why’ before you consider your ‘what’, ‘how’ and ‘when’. Clearly identify, in very specific terms, what success looks like. What will it do for you? How will it provide direct value in the near term? How will you measure or empirically quantify that value? (If your goals and target outcomes are defined in vague terms, so will be your results).

2)     Start small, with something specific and immediately beneficial. An “early warning system” is a good example, as is “remote support” for complex systems, as is providing real-time proof of quality or efficiency to clients that may require it. These things deliver near immediate pay-back and are easy to do.

3)     Once you get a good data start, deeper data analytics can lead to continual process improvements. Without data and analytics to continuously deliver insights, dashboards are lagging indicators; with it, dashboards become leading indicators.

But, (from my experience) you can't go directly to step 3; steps 1 and 2 provide a proven approach for project success. Companies that I have seen succeed with machine monitoring all knew their "why" in very specific terms, and all aimed to fix, or solve, or improve something in their current manufacturing condition right out of the gate.

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For additional reading on this topic, check out the following:

Why OEE, MES and Machine Monitoring Don't Matter for Many Manufacturers

Time is Money - that's why you should measure it wisely!

Why Most Dashboards Don't Drive Change

If you are considering a machine monitoring deployment, or are struggling to get traction with one you did deploy, reach out to paul@tpi-3.ca to set up a complimentary discovery session. And be sure to follow TPI-3 for more actual experience based insights.