Is Industry 4.0 confusing the heck out of you?

The future of maintenance: A practical guide to Industry 4.0

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Industry 4.0 is everywhere in the maintenance community. It’s front and centre in blog posts, conference sessions and meeting rooms. Yet, talk of it is often full of buzzwords and short on real solutions. So today, we’re cutting through the jargon and the grandiose promises to uncover what exactly Industry 4.0 is, how it relates to maintenance, and how you should be thinking about it now to be ready for the future.

What is Industry 4.0?

Industry 4.0 is a new way of manufacturing goods. The way companies produce things has evolved over time as manufacturers seek to get the most out of their equipment and people. And each big shift in manufacturing has been marked by huge technological change as businesses look to advanced tech to help them meet bigger productivity goals. There have been four such eras, from Industry 1.0 to Industry 4.0.

Industry 1.0

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Mechanization through water and steam power.

Industry 2.0

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Mass production and assembly lines using electricity.

Industry 3.0

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Digitalization of the manufacturing process with computers.

Industry 4.0

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Automation using smart systems, data and machine learning.

While Industry 3.0 introduced the manufacturing world to the power of computers, Industry 4.0 uses those advancements as a foundation for even greater innovation. Now, computers are connecting and communicating with one another to make complex decisions. Advanced systems and technologies, combined with greater access to data, are making Industry 4.0 possible, ushering in a period of greater efficiency and less waste.

Understanding Industry 4.0 buzzwords

Buzzwords are annoying. They’re phrases that don’t mean much to your day-to-day life. If you’re going to walk the walk when it comes to Industry 4.0, it’s important to understand what all these terms actually mean and why they matter.

Artificial intelligence (AI)

The definition of artificial intelligence is a moving target. More generally, AI is when a computer gains the ability to think and reason like a human and, in doing so, is capable of doing uniquely human tasks, such as speech recognition or decision-making.

The way this definition translates to the real world is also constantly changing. A calculator was once considered AI, since math was something only the human brain could perform. Today, we have digital assistants, like Siri or Alexa, or generative design programs that solve complex engineering problems in manufacturing.

Machine learning (ML)

Machine learning is teaching a computer to learn on its own by finding patterns in a large amount of data and making conclusions based on these patterns. It’s a quicker way to parse information and uncover new insights that can be used to improve processes.

It’s like how Netflix learns from all the previous movies and tv shows you have watched, and uses this knowledge to suggest more viewing material, or how doctors can introduce a computer program to a series of x-ray images and corresponding symptoms so they can find common patterns and better diagnose illness or injury.

Don’t worry if you’re a little puzzled about the difference between AI and machine learning. Although the two are very similar, there are key differences. AI is a culmination of different technologies to help computers achieve a higher level of thinking and reasoning. Machine learning is one of these technologies with a singular program and a specific goal.

In this way, AI is like a bridge and machine learning is one of its pillars. Another pillar might be the Internet of Things or Big Data. All these technologies come together to bridge the gap between what’s possible for humans and what’s possible for computers.

Industrial Internet of Things (IIoT)

The Industrial IoT connects machines, data, and people. First, it takes a network of industrial devices, like sensors and maintenance software, and allows them to share information with each other. This provides a platform to track, collect, exchange, access, and analyze large chunks of data more efficiently. The insights obtained from this data are then used to improve manufacturing procedures.

Instead of collecting data from several sources separately and trying to connect the dots, IIoT does this for you. Imagine all the assets and software programs in your facility speaking to each other, sharing information and spitting out numbers that give you deeper insight into your operation. This is the power of IIoT.

The spotlight usually shines brightest on your maintenance team when something goes wrong…This backwards mindset is set to change with Industry 4.0.

Big Data

Big Data describes the huge amount of information we’re able to collect, analyze and use to find trends and associations in the way we live. Big Data is often characterized by the way data is used, its ability to determine cause and effect, and its implications for decision-making.

For example, analyzing the health records of thousands of people with the same diet might tell you that certain food makes people more prone to heart disease. With this knowledge, people can choose to stop eating this food to be healthier.

The conclusions made from Big Data comes from large sample sizes and are therefore more accurate and more valuable. When manufacturers are armed with insights from Big Data, they can identify the root causes of inefficiency and waste to reduce costs and streamline processes.

How maintenance is tapping into Industry 4.0

There are many ways asset management will be changed by the dawn of Industry 4.0, from how technicians complete day-to-day tasks, to the way plant managers set up their facilities. Here are three of the biggest ways maintenance intersects with Industry 4.0.

Predictive maintenance

Predictive maintenance (PdM) allows facilities to predict when equipment failure might occur and take action to prevent it. Ideally, PdM keeps maintenance frequency low while reducing time spent on unplanned maintenance and preventive maintenance. The benefits of this approach are almost too many to mention, including less downtime, improved safety, increased production, and more. The tools and technology of Industry 4.0 have turned PdM from an abstract concept into a practical solution.

One way this is happening is with smart sensors. These machine sensors can detect a change in the way assets are operating, like if a part is vibrating at higher-than-normal-speeds. The sensors can be connected with maintenance software, like a CMMS, and relay this message to the software so it can schedule maintenance. The software will notify technicians of the newly scheduled task on their mobile devices.

Controlling costs

The bottom line is the biggest priority for many organizations, regardless of how technology changes. The shift to Industry 4.0 can help maintenance teams save money, with inventory management being one area ripe with potential.

New systems give facilities the means to improve the ordering process and slash the number of resources it takes to keep fully stocked. A leaner, more efficient inventory system equals increased reliability and fewer costs. 3D printing, in particular, is already transforming the maintenance supply chain by allowing spare parts to be printed on-site and on-demand. Not only are shipping costs reduced, but the ability to make parts for highly stressed, production-critical assets helps minimize costly, unplanned downtime.

Proving the value of maintenance

Let’s face it, the spotlight usually shines brightest on your maintenance team when something goes wrong. You are either blamed for the problem or glorified for saving the day using a reactive approach. This way of thinking devalues maintenance best practices and rewards poor performance.

This backwards mindset is set to change with Industry 4.0. The newest tools and methods are capable of measuring maintenance activities in minute detail and determining how each action affects a business. For example, you can collect data on failure patterns and preventive maintenance using machine sensors and maintenance software. When combined with data from production and financial software, these metrics can tell you the impact of maintenance, from the floor to the balance sheet. Now you can prove the link between better maintenance and lower costs.

Industry 4.0 is a big change, and change is never easy. That’s why implementing new technology starts with people.

Maintenance and Industry 4.0: Possibility or pipe dream?

Industry 4.0 is like a five-star resort on a beautiful island. Everyone wants to go, but getting there takes a lot of time, money and effort. Integrating elements of Industry 4.0 into your maintenance operation is not a matter of buying new technology and flicking a few switches. You have to take the time to put all the right tools, processes and systems in place. Luckily, there are many steps your maintenance team can take right now to move toward Industry 4.0.

1. Master preventive maintenance

There are a lot of complex systems used in Industry 4.0, both for technology and people. Without a solid foundation, these systems could quickly crumble while leaving you no closer to your maintenance goals. The habits, processes and tools of a well-built preventive maintenance program increase the chances of successfully transitioning to Industry 4.0.

There are eight steps to creating a solid preventive maintenance program, which we’ve outlined in this blog post. They cover everything from defining your goals to getting the right technology and measuring success. This process will help you fine-tune your maintenance practices while giving you the know-how to build and implement a strategy for Industry 4.0 with fewer hiccups.

Summary, steps to building an effective preventive maintenance program

2. Focus on collecting good-quality data

Data is the cornerstone of Industry 4.0. Advanced technology can’t do its job without detailed, accurate information. Having lots of high-quality data makes it easier to use the systems of Industry 4.0 to their full potential. The time is now to start building that inventory of intelligence.

There are two main ingredients to the kind of asset data you should be collecting — quantity and quality. Be as detailed and consistent as possible when building asset histories. Don’t just document what the problem was and when it was fixed, but also how it was fixed, what parts were required, how long it took, and more. Create naming conventions for your assets to establish standardization throughout your organization. Take it a step further by running reports to establish KPIs, like mean time between failure. Freeing data from the confines of paper and making it digital will ensure the information isn’t just accessible, but also accurate. Digital files allow you to easily audit inputs, find gaps, fix mistakes and gather it all in one place.

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3. Create a reliability culture

Industry 4.0 is a big change, and change is never easy. That’s why implementing new technology starts with people. Not only do you have to do an exceptional job at training and organizing maintenance staff, but you must also prepare them for the changes that will come with new systems and processes. The best time to start building this culture of continuous improvement at your operation is now.

The first item on your list should be to establish guiding principles to support maintenance best practices. Creating an asset management policy helps you outline goals and expectations so everyone is working in the same direction. The next step is to create official processes for everything from work orders to purchasing. This reinforces good habits and ensures data integrity. It’s also important to prioritize good communication to breed trust and accountability. Lastly, recognize and reward those who embrace change so staff are motivated to learn a new way of doing things.

4. Start small with predictive maintenance

Running is great exercise, but you wouldn’t do a marathon if you’ve never even gone for a jog. Implementing predictive maintenance follows the same logic. You have to take smaller steps to take full advantage of PdM. An incremental approach allows your organization to slowly transition into Industry 4.0 and learn from any mistakes along the way.

One way to introduce a predictive maintenance program into your operation in small doses is to use condition-based maintenance. Condition-based maintenance (CBM) helps you create an early warning system for predicting failure with real-time asset data. Mastering CBM teaches you to integrate technology, data collection, and change management into your maintenance practices — all things you’ll need for a full-blown predictive maintenance strategy. Start small by choosing one or two assets, learn how to run an effective condition-based monitoring program, and test your strategy. Once you’ve ironed out the process, start spreading CBM around your facility. This will give you a great foundation for advanced predictive maintenance.

The bottom line: Unlocking the potential of Industry 4.0 in maintenance

Unlocking the true potential of Industry 4.0 won’t come all at once for your maintenance team. The goal isn’t to simply purchase a bunch of new technology, but to use these tools to build a better maintenance organization. Adopting a new way of planning, completing and measuring maintenance is a journey paved with dozens of incremental improvements. Focusing on small, practical changes to your maintenance strategy may take time, but it will help empower your team and unlock the awesome potential of Industry 4.0.

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