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How to Break Free from the Dependence on Intuition and Unique Skills in Maintenance Work

The field tends to have a shortage of staffing and a reliance on unique skills, and this often leads to a dependence on the intuition, experience, and unique skills of the individual maintenance technician.

Maintenance work involves responding to breakdowns in factory equipment. This field tends to have a shortage of staffing and a reliance on unique skills, and this often leads to a dependence on the intuition, experience, and unique skills of the individual maintenance technician. Even so, when thinking about boosting the competitiveness of factories and improving their productivity in the years ahead, an important keyword has become “standardization” for enabling a wider range of people to be involved in maintenance work, rather than relying on the intuition and unique skills of certain select people. That is how it is explained by Kenichi Furutani, a consultant at Japan Excel-Management Consulting (JEMCO), who is known as an expert in enhancing factory performance.

workWe talked to him about how to break free from the dependence of maintenance work on intuition and unique skills, and how IoT should be used to achieve this.

Why does maintenance work tend to become narrowly focused so that it becomes virtually inaccessible to most people?

Mr. Furutani has provided consulting services to many companies, primarily in the manufacturing industry. I think that you may be looking at a wide range of issues from upstream management to on-site operations, but is maintenance work such as inspection and repair of equipment often the key to making improvements there?

I feel it is quite significant. However, it is a rare case when a company comes to us from the beginning with a request to improve its maintenance work. Rather, in many cases, maintenance is the result of discussions and pursuit of what the factory should be and what it should do to increase its competitiveness and productivity. It is only when you think deeply about the future and the essence of the factory that you realize the challenges facing maintenance work. It is expected that high-mix low-volume production and individual production where required quantities are produced on an as-needed basis will increase in the future. As the manufacturing process becomes more complex, the equipment also becomes more complex and sophisticated. When that happens, the key to competitiveness will be maintaining this equipment and achieving stable operations. If we think about the state of our factories over the long term, looking several years into the future, we need to redefine the position of maintenance for the company as a whole In general, the area of maintenance has been the most difficult to address, and it is also an area that is often put off. That’s why it is often left in a poor situation and requires considerable effort when reforms are finally implemented.

Why is maintenance so hard to address and often put off?

The key issue for many factories is to maintain daily production and operations, and so they inevitably prioritize their work for this purpose. Even if we perform the necessary maintenance work every day, the maintenance work performed when a problem occurs is not needed until something goes wrong. In these types of situations, maintenance work is often put off and there are not enough maintenance technicians; as a result, there are many factories where only a limited number of technicians oversee the maintenance of the entire factory.

Nevertheless, maintenance technicians are required to have a great deal of knowledge, experience, and expertise. They must know the structure and characteristics of all types of factory equipment and be able to solve problems quickly. However, if only a few people oversee maintenance work, the knowledge and expertise accumulated in the process of the work will not be shared. As a result, the work of maintenance technicians develops into a state of limited accessibility. This is what we call the dependence on intuition and unique skills in maintenance work.

What exactly are the problems caused by the dependence on intuition and unique skills?

For example, large-scale replacement of factory equipment is not done very often; therefore, many factories continue to use machines that are quite old for many years.

In such factories, it is not uncommon for drawings and manuals for equipment to be lost, and in some cases, authorized parts are no longer available for replacement. In these cases, the maintenance technicians add their own parts or make repairs whenever a failure occurs. When this happens, the original specifications of the machine will not be known to anyone other than the maintenance technician, and operation of the machine will depend on the intuition and unique skills of the technician, which will make the limited accessibility even worse. This is extremely problematic.

When production problems such as short stoppages occur frequently due to equipment trouble, we sometimes take measures to return the equipment to its initial state. In these types of situations, if there are no drawings and only a few technicians know the true state of the machine, this will entail a tremendous amount of work. This is especially true if the maintenance technician has already retired or left the work site.

Consequently, the machine becomes a black box that is accessible only to select maintenance technicians.

As I mentioned earlier, few companies provide much available staff or resources for maintenance. In these situations, failure to share maintenance expertise will be detrimental to the operation of the plant. The number of tasks that can be done only by the maintenance technicians increases, and they always have their hands full, only repairing machines after they have broken down. In other words, it’s like trying desperately to bring failures, a negative state, back to zero, and it never turns into a positive in terms of productivity. To increase the productivity of a factory, the operating rate must be increased. To achieve this, equipment breakdowns must be prevented, and maintenance work should play a role in this process.

So instead of reducing the negative, it is important to prevent breakdowns so that a negative situation does not occur in the first place?

That’s right. Maintenance can be divided into three main types. Reactive maintenance (Corrective maintenance/ Breakdown maintenance) is the process of dealing with problems after they have occurred. Preventive maintenance is used to prevent problems before they occur by periodically performing prescribed maintenance tasks. Predictive maintenance is the process of recognizing problems before they occur and preventing them from happening in the first place. There is nothing better than being able to perform preventive and predictive maintenance rather than reactive maintenance. However, when a state of limited accessibility becomes more prevalent in the maintenance work, it is easy to get caught up in reactive maintenance work, which in turn makes it difficult to boost equipment operation, thus creating a vicious cycle.

IoT as a methodology to break free from dependence on intuition and unique skills

workingHow can we break away from this dependence on intuition and unique skills, share expertise, and standardize maintenance work so that more people can be involved in maintenance?

First, it is necessary to thoroughly identify the work processes of maintenance. In other words, you can say that intuition and unique skills are tacit knowledge that depends largely on the knowledge and experience of the individual. In other words, this is knowledge that is not clearly verbalized, but that only maintenance technicians comprehend. And so, this tacit knowledge must be converted into formal knowledge that other people can understand. For example, there are many ways to document the maintenance process, such as creating a procedure manual, or keeping a record of every step of the maintenance process with photos and notes. What is important is where tacit knowledge occurs in the process of maintenance work, and where dependence on intuition and unique skills occurs. It’s about figuring this out.

And so, the foundation for everything is verbalizing the maintenance work and knowing what tacit knowledge is?

That’s right. However, this is not an easy task either. Verbalizing maintenance expertise is not a straightforward task. There are many cases where a maintenance technician writes something down thinking that others will understand it, but it is not conveyed to people unfamiliar with maintenance work, or the technician himself or herself is not aware of what tacit knowledge is in the first place.

When I provide consulting services, I strongly advise that when creating procedure manuals and other documents, they should be written in a basic and straightforward way that anyone can understand. It is vital to identify work processes in detail and reliably so that any person can reproduce them by following the written instructions. Intuition and unique skills are not found in poor-quality manuals and procedures, and it is extremely important to provide visualization using words and illustrations.

This is a process that requires a lot of perseverance to implement.

One thing that I am focusing on in breaking from the dependence on intuition and unique skills is the use of IoT technology. I believe that IoT has the potential to not only eliminate the limited accessibility of maintenance, but also to dramatically raise the level of maintenance work by enabling predictive maintenance.

For example, let’s say you have a sensor attached to a machine to acquire vibration data. It then compares the data with that of past failures and sounds an alarm when the vibration is close to that of the failure. Of course, data analysis is not quite so simple, but this is a simple example of IoT, which is exactly what predictive maintenance should be. However, preventive maintenance is also effective in preventing breakdowns, but in some cases, it may make the work of maintenance technicians busier because they must check and inspect the machine periodically regardless of its condition. In situations where there are few people to devote to maintenance, we must consider the negative effects that sometimes occur.

Then, predictive maintenance is the most efficient way to predict and prevent failures, and IoT is the best way to support this?

IoT uses data to visualize what used to depend on the intuition and unique skills of maintenance technicians. It will also lead to the improvement of the quality of maintenance work. Of course, it is as important as ever to develop human resources, such as by training high-level maintenance technicians. I think it would be ideal if we could use IoT as a complementary solution and train human resources at the same time.

The dependence on intuition and unique skills leads to limited accessibility of maintenance work. I realized that for factories to be competitive in the future, it is important to change the tacit knowledge of intuition and unique skills into formal knowledge to achieve a higher level of maintenance. And the key to this is the use of IoT.

The key to the success of IoT in maintenance is to start collecting data as soon as possible.

We asked about the potential for IoT-based maintenance work. Are companies and factories increasingly implementing IoT now? The use of IoT is a frontier area in the field of maintenance, and more factories are implementing it. There are also growing numbers of IoT tools themselves that support maintenance. For example, a tool to monitor vibration by attaching sensors to the drive parts of factory equipment, such as motors, is an easy example. In addition, there are systems that not only measure vibrations, but also perform frequency decomposition and other data processing, and if an abnormality is detected, the computer will sound an alarm. These systems are for the purpose of predictive maintenance.

There is also a more rudimentary form of IoT. For example, pieces of equipment are linked to each other through communication so that if a problem occurs in one area, the equipment in the next process will automatically stop. This is classified as reactive maintenance, where a failure is dealt with after it has occurred, but since the failure can be detected quickly, the impact on subsequent processes can be minimized. If you notice a failure 10 seconds too late, it could also cause a machine to fail further down the line. Another example would be a machine with an arm that grabs a certain part, and if for some reason it fails to grab the part, the line automatically stops. Another common example is preventive maintenance when a part that should not flow through the line passes through, the line is stopped to prevent accidents and breakdowns because the part could damage equipment further down the line.

Is it possible that the use of IoT will allow for more standardization of work that used to rely on the intuition and unique skills of maintenance technicians?

Yes, it is. I think there is enormous potential, for preventive and predictive maintenance where equipment data is monitored. It is also important for companies and factories that have no previous experience in implementing IoT to start small and be flexible in their implementation.

There may be cases where suddenly implementing IoT in the entire factory will not work. Therefore, it is advisable to first try to implement IoT in a limited way, such as by implementing it partially or setting up model lines or model equipment. Of course, it is assumed that you have a picture of what implementation in the entire factory should look like, but it is wise to start with a partial, small start and then expand the scale of implementation once you have gained the expertise.

If you start small, you will be able to implement IoT quickly and easily.

Implementation as soon as possible is also extremely important. That’s because IoT-based maintenance will not be able to reach its full potential unless more data is collected and accumulated. If IoT is used for reactive maintenance to notify that equipment has stopped, it can provide immediate results, but for preventive or predictive maintenance, the obtained data must be analysed, and technical evaluation must be added.

The number of IoT maintenance tools is increasing, and more factories are adopting them, but in fact, there are many areas where the detailed methodology has not been completely established, such as where sensors can be put to be more effective, what types of data must be obtained to predict failure, and how obtained data should be analysed. No one yet knows the optimal solution for IoT-based maintenance. This is where it becomes important to collect as much data as possible and discuss internally the best way to use this data. To have an accurate fact-based discussion, it is important to have as much data as possible to use as raw material.

So starting small is effective for collecting as much data as possible. That’s right. I don’t think critical failures of equipment happen that often. It is important, then, to collect data early on to understand exactly what kind of behaviour will lead to what kind of failure, and what the trends are.

Moreover, as I mentioned earlier, no one yet has the best answer to the question of what sensors should be installed on what equipment and what data should be collected to predict failures. Consequently, this means that the companies and factories that quickly gain this knowledge will have a competitive advantage. Starting small is also an effective way to develop your company in the future. Another important thing to do then is to analyse the data you have collected.

Will the future of maintenance be a data-based approach in addition to a hands-on approach?technology

Data analysis is an area that has not been a part of maintenance work in the past.

And yet, it is a key feature of IoT-based maintenance work. IoT may bring about a change from the traditional hands-on approach of maintenance work that relied on intuition and unique skills to a hands-on approach that relies on data. This change will also greatly enhance the quality of maintenance. In other words, in the past, maintenance work has been handled by a limited number of on-site people, but now a new way of conducting maintenance will be required that involves people like data scientists who can analyse data in detail.

In order to achieve this, it will be important to acquire personnel who can accurately analyse the collected data, and it will also be important to determine how this staff can work with the on-site maintenance technicians. To establish this type of internal collaboration, those at the top must also reaffirm the importance of maintenance and redefine its value for the entire company.

It seems that the task of analysing the data obtained from IoT will be just as important as implementing IoT.

Even if you use IoT to store plenty of data, if you can’t make use of it, it’s useless. For example, even if you obtain the vibration data of a motor, it may not be enough to understand the difference in vibration before and after the failure. The clear differences that are identifiable the moment they are seen can be recognized even by maintenance technicians checking visually.

While monitoring vibrations, you can try to decompose their frequencies, or isolate the various elements in detail. In this way, how accurately can we establish the fact that a certain behaviour is a sign of failure? The ability to analyse data is required. Of course, it is also essential to refer to the knowledge and expertise of the maintenance technicians who have a wealth of actual hands-on knowledge. In other words, IoT is hardware, but the software part, that is, how to analyse the data obtained from it and what kind of knowledge to establish, will also be extremely important. The implementation of IoT is not the end of the story, but rather the beginning of a more practical analysis that compares the collected data with the knowledge and insights gained in daily maintenance work. This is also connected to the suggestion that we should start small and implement the system as soon as possible to store as much data as possible.

Lastly, how should we train maintenance technicians in terms of human resources as we move forward with the implementation of IoT?

Of course, it is also necessary to pass on the skills of maintenance technicians at the same time. It’s never about IoT or people or about choosing one over the other. As more jobs are becoming remote due to the coronavirus, maintenance work is no exception. The traditional way of passing on knowledge where senior maintenance technicians work together with younger technicians at the site may be replaced by remote instruction and education. It was already a struggle for technicians to pass on tacit knowledge, but with remote instruction and education, it could take even longer than before to train maintenance technicians. There is a limit to how much you can teach remotely, and there are many detailed operations and nuances that are difficult to understand without being taught on site. This will make it more difficult than ever to pass on the skills of maintenance technicians, and there will be a need to devote resources to training them.

While it is important to collect and analyse data, the actual work of maintenance beyond that is left to human hands. Therefore, the training of maintenance technicians who can perform hands-on work on site will continue to be of high importance in the future.

This is where the process of identifying and formalizing each of the maintenance work tasks becomes important. This is a basic task that is the foundation for everything, both in implementing IoT and in training maintenance technicians. When considering the implementation of IoT, this will help you to think about which parts of the identified tasks should be included in IoT and which data should be captured to be effective. In human resource development, by verbalizing each task, the knowledge can be taught in a way that is easier to understand. In order to succeed in both the implementation of IoT and the training of maintenance technicians, it is important to start with this kind of solid foundation.

To learn more on the technologies and solutions in Predictive Maintenance – Condition Monitoring for factories and buildings, you may check out our Content Hub:

Wireless Sensing Solution (https://solution.murata.com/en-global/service/wirelesssensor/)

E-Book: How to How to navigate digitization of the manufacturing sector (https://go.murata.com/SFANavigating-Digitization-in-Manufacturing.html)

Survey (https://go.murata.com/SFA-India-Survey2021. html )

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This article is provided by Murata Manufacturing Co., Ltd.

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