Widespread Predictive Maintenance Implementation Will Take Longer Than We All Think

by | Jan 7, 2019 | Growth

“AN OUNCE OF PREVENTION IS WORTH A POUND OF CURE”

When Ben Franklin first uttered these words, he referred to fire safety and prevention. Today, these words are equally applicable to machine maintenance and uptime. If true, why isn’t Predictive Maintenance (PdM) being deployed everywhere?

While there are several reasons that we all can list, like cost, reliability, accuracy, and supportability, there are a few obstacles that may not be obvious:
  • Conflicts with established procedures
  • Interconnected manufacturing lines cause unique challenges
  • Shortage of skilled and qualified plant maintenance personnel

Each of these is discussed below.

CONFLICTS WITH ESTABLISHED PROCEDURES

Many products have well-defined inspection schedules. Think schedules are based on easily measured intervals such as time, miles, or hours.

  • Automob: Think about aviation, automobiles, and railroad rolling stock. Safety and emissions inspections occur every year in the same month, while oil change intervals are still based on mileage.
  • Aircraft structural parts are inspected after a fixed number of flying hours or takeoff/landing cycles.
  • Train wheels and trucks are inspected after a fixed number of rolling miles.
Assume aircraft structural integrity was continuously monitored, and the system detected an issue. The plane operator would schedule an inspection and repair as soon as a tiny crack or sign of fatigue was detected. All good, right? What if the following month, the historical metrics indicated that a complete inspection was needed and the airplane would have to be out of service for several days? While the operator would argue like crazy that the review was redundant, the authorities that required the complete inspection would dig their heels in, and today, they would win!

I believe the solution to remove this type of potential conflict will take several years and require each manufacturer to demonstrate that their predictive maintenance system was accurate enough to detect all visibly noticeable flaws without taking the equipment out of service. The medical device industry has done this, and PdM systems are now being widely used in imaging systems.

INTERCONNECTED MANUFACTURING LINES CAUSE UNIQUE CHALLENGES

Most interconnected manufacturing lines include commercial off-the-shelf (COTS) and custom-designed modules. The COTS modules may come from different manufacturers and consist of robots, conveyors, and machine tools. Custom-designed modules may come from external or internal design and manufacturing businesses. The COTS equipment will probably have robust PdM capabilities long before the custom equipment. Think of the burden this puts on the maintenance crew.

Because a mature PdM system will present the maintenance team with one or more probable failure timeframes at different statistically significant intervals, the team will have to continuously decide when to stop a line for an unscheduled repair. What else, if anything, will they do during the shutdown?
Here are two simple examples for a line with two COTS and two custom-made modules that assume that any repair and any PM would take approximately the same time. If there were a significant time difference, then all bets are off:
  1. One of the two COTS modules is predicted to fail within four weeks with a 95% confidence level (this is as sure a bet as you can make). The second COTS module has no failures predicted in the foreseeable future. The two custom modules have a scheduled preventative maintenance check in six weeks. In this case, the team would repair the COTS as soon as parts were available, but definitely within four weeks when the production schedule could accommodate the downtime. The two PMs would be conducted at the same time,
  2. One of the COTS will fail within four weeks, the other within eight weeks, and the two custom modules have a scheduled PM in 16 weeks. What do you do to minimize the number and duration of line shutdowns? Not so easy.

Even in these straightforward scenarios, you can see how many possible situations are available, especially until all modules have access to a well-developed predictive maintenance system. The complexity of these potential outcomes may cause some plants to purchase PdM equipment equipped with appropriate sensors, communications equipment, and access to remote AI-powered software, but not enabled until a total is automated at the same time.

SHORTAGE OF SKILLED AND QUALIFIED PLANT MAINTENANCE PERSONNEL

Today, most plant maintenance professionals are skilled at commissioning, troubleshooting, repairing, and testing the equipment they support. They also work closely with material planners to ensure spare parts are available.

In the new world of predictive maintenance, these people’s jobs will change in two very different ways:

1. Skills related specifically to PdM

As my friend Titos Anastassacos of Si2 Partners told me:
“Facilities engineers and managers will have to understand the principles of modeling and how machine learning algorithms work (at least their philosophy).  Additionally, they will need to understand what data they have, in what form it is, and what they can do with it -so as to also decide what to collect.  Culturally, they will have to learn to work with the computer, trust (eventually) what the computer is telling them, and find ways to continuously make improvements.  Finally they will have to learn to articulate technical issues in a way that data scientists can use their observations and corrective actions to turn into predictive algorithms.”

2. Skills required because of the workplace changes resulting from PdM

Facilities engineers and managers must also understand statistics, finance, purchasing, and vendor communications. They will do less hands-on troubleshooting and more explaining to plant managers why they elected the maintenance course they recommend. Their total compensation may be tied into the same metrics as the plant manager or line supervisor’s.

As you can see, there are several reasons to hold off on implementation until all related production equipment has PdM.

UNMET EXPECTATIONS ARE NOT UNIQUE TO PREDICTIVE MAINTENANCE

With new technology, everything takes longer and costs more than everyone thinks. And gaining widespread acceptance for Predictive Maintenance (PdM) is no exception.

Here is some data to demonstrate this phenomenon concerning Internet-connected devices:
  • In 2012, IBM projected 1 trillion IoT devices by 2015
  • In 2017, CISCO launched 50 billion IoT devices by 2020
  • In 2018, GSMA launched 25 billion IoT devices by 2025
  • In 2018, CISCO launched 14.6 billion IoT devices By 2022
While the target years keep changing, the forecasted number of IoT-connected devices is declining. The decline is partly due to technical difficulties like battery capability and platform availability. On the other hand, commercialization is also limited by a lack of compelling use cases and user acceptance.
And several months ago, AP (Associated Press) published an article that started with the following:
Jeff Bezos boldly predicted five years ago that drones would be carrying Amazon packages to people’s doorsteps by now. Amazon customers are still waiting. And it’s unclear when, if ever, this particular order by the company’s founder and CEO will arrive.

As you can see, although widespread implementation of new technologies usually takes longer than we expect, the results will be worth the wait.

RECOMMENDATIONS

The journey to full PdM implementation in a large facility will be a technical, cultural, and economic challenge. You have two choices:

  1. Please wait until it is plugged and play
  2. Start now
If you elect to wait, you will miss out on all the learning experiences that will make you a much better use than if you wait. Your facilities team will grow their skills as needed, making it easier to keep them motivated and with a positive mental attitude. And you will generate an ROI at every step of the journey.

 

If you start soon, you can play a role in co-creating PdM solutions with your crucial equipment suppliers. Your needs will be included in products you will buy in the future. You will benefit from being an early adopter because of your new supplier relationships. You will also be able to attract the best and brightest people who will then be willing to focus their creativity on solving your problems over the long haul. I recommend that you heed the advice provided by Francis of Assisi:

“Start by doing what’s necessary; then do what’s possible; and suddenly you are doing the impossible.”

About Middlesex Consulting

Middlesex Consulting is an experienced team of professionals whose primary goal is to help capital equipment companies create more value for their clients and stakeholders. We continue to provide superior solutions to meet our clients’ needs by focusing on our strengths in Services, Manufacturing, Customer Experience, and Engineering. If you want to learn more about how we can help your organization maximize your IoT investments, please get in touch with us or check out some of our free articles and white papers here. 

This post first appeared on January 4, 2019, on Si2 Service in Industry Hub.