All equipment fails at some point if it’s not properly maintained. That’s why daily production schedules rely on maintenance that is precisely timed. Typical maintenance programs have waste and unnecessary downtime. Predictive maintenance is a new approach that can increase operational reliability with or without new technology.
In this series, we’ve discussed strategic methods for optimizing management and scheduling to increase capacity. The last piece of the puzzle for a holistically optimized system is developing a strategic maintenance strategy. Our latest e-guide, Increasing Operational Reliability Through Predictive Methods, discusses the significance of adopting predictive maintenance tools to maximize operational efficiency.
In this blog, we’ll discuss how to implement predictive maintenance tools in a manufacturing maintenance strategy and how to foster a culture of growth that supports change all the way up the ladder.
Unplanned Downtime: What’s It Really Costing Your Plant?
Unplanned downtime is often (incorrectly) considered to be a necessary evil. Equipment failure is the source of 42% of unplanned downtime in industrial manufacturing — and it’s avoidable. Don’t take our word for it, just ask your workforce.
88% of employees say their organization could do more to prevent downtime, with 81% saying digital tools could eliminate unplanned downtime.
Frontline workers often find themselves in the position of metaphorically having to watch a train crash in slow motion. They know unplanned downtime is on the horizon, but aren’t in a position to stop it. Unplanned downtime causes friction between frontline workers, management, and customers — and can cost organizations millions.
The average plant experiences 800 hours of downtime per year. This results in financial losses due to lost productivity, emergency equipment repairs, overtime and expedited shipping costs to meet customer agreements, and a loss of business if customer relationships are severely impacted.
And the bigger they are, the harder they fall.
Unplanned downtime costs Fortune Global 500 companies $129 million per facility, nearly $1.5 trillion globally — up 65% from the year prior.
It’s time enterprises started listening to their employees and taking the steps needed to prevent unnecessary losses from unplanned downtime. A strategic maintenance strategy that implements predictive maintenance tools is the only way forward for manufacturers who want to stay competitive in a quickly evolving field.
Building A Bomb-proof Maintenance Strategy
There are three types of maintenance: reactive, preventative, and predictive — and it may surprise you to hear it’s not a matter of which is “best”. The fact is that manufacturers need competency in all three to safeguard their plants, their machines, and their people.
In an ideal world, reactive maintenance is the category that people want to avoid as much as possible, but always be prepared to initiate at a moment’s notice. Reactive maintenance occurs only when problems surface. Since they’re unexpected, they can cause costly delays, repairs, and damaged customer relationships. That being said, seasoned manufacturing leaders know they must expect the unexpected, and have a contingency plan for reactive maintenance needs.
The next two types of maintenance, preventative and predictive, are essential to limiting the need for reactive maintenance as much as possible.
Preventative maintenance is routine, scheduled repairs, often cyclical in nature and calculated based on (at best) equipment manufacturer-recommended service timelines or (at worst) arbitrary plant timelines (e.g. annual appraisals).
In theory, preventative maintenance should “prevent” the need for reactive maintenance. However, preventative maintenance schedules are based on generalizations, and therefore do not take into account the needs of an individual plant — let alone a plant’s individual machines. This is where predictive maintenance becomes essential to avoid unscheduled downtime.
Predictive maintenance customizes maintenance needs with hard data. The level of sophistication in predictive maintenance techniques varies, but it involves collecting and calculating data like equipment usage or measured values, such as vibration levels and temperature.
How much of a difference does data make on downtime when compared to preventative maintenance techniques? A lot.
Data on Data:
While manufacturers who rely on predictive and preventative maintenance report 52.7% less unplanned downtime and 78.5% fewer defects than those who focus on reactive maintenance, manufacturers who practice predictive maintenance see 18.5% less unplanned downtime and 87.3% fewer defects than those who rely on preventative maintenance.
There are different ways for how to implement predictive maintenance strategies, but the most modernized predictive maintenance tools include Internet of Things (IoT) technologies.
IoT technologies use sensors to connect devices (“things”) to software over a data network (the public “internet” or a private network). These web-enabled smart devices are often interconnected with other smart devices or directly to software for analysis, and are increasingly mechanical devices not historically thought of as part of the tech industry — such as manufacturing equipment. IoT in manufacturing is a major component of Industry 4.0, also known as the Fourth Industrial Revolution.
Industry 4.0 is the next-generation manufacturing industry, comprised of state-of-the-art IoT technologies, integrated analytics, and enhanced robotics.
Accurate and timely updates on manufacturing equipment health is a game-changer for predictive maintenance strategies. Sensors continuously record real-time measurements such as vibration, temperature, pressure, humidity, current, and more, then can use this data to automatically generate reports, prioritize maintenance needs of individual machines throughout the plant, and even automatically schedule maintenance.
Who Is Reacting?
Given the potential fallout from unplanned downtime, it’s no surprise that predictive maintenance is making waves and creating a general shift towards a more proactive approach to maintenance in manufacturing.
71% of manufacturers want to utilize machine data within the next one to three years. The most common technologies being adopted by manufacturers are predictive maintenance and analytics solutions (42%) and sensors for machine health monitoring (35%).
With a CAGR growing at a rate of 21.7%, the manufacturing predictive analytics market size is predicted to surge to $2.5 billion by 2026.
The coming years will be a race for manufacturers to discover how to implement predictive maintenance tools before falling behind their competitors.
What’s Stopping Some Manufacturers From Adopting Predictive Maintenance Tools?
With progressively increasing value of predictive maintenance in manufacturing systems, what’s stopping some enterprises from adopting predictive maintenance tools?
#1 The psychology of comfort zones
Manufacturers today are standing on the shoulders of giants. The legacy of proven productivity methods is psychologically difficult to undermine on an individual scale, let alone implement enterprise-wide changes. There is risk associated with any system-wide change, risk that can affect not just the company’s bottom line, but its entire workforce.
However, there is more risk in failing to adapt to a rapidly changing, competitive landscape. As businesses across industries shift towards data-driven strategies, businesses that fail to capitalize on modern solutions will inevitably fall behind.
If the rate of change on the outside exceeds the rate of change on the inside, the end is near.
- Jack Welch, former Chairman and CEO of General Electric
The solution to stepping out of an enterprise-level comfort zone lies in a thorough understanding of the tools and methods available, an assessment of current practices, and the correct implementation of new methodologies that keep pace with the evolution of the manufacturing industry.
A comfort zone is a disease in the context of business leadership because it stunts growth, defeats innovation, and blunts leadership skills.
- John Mattone Global, executive and leadership coaching company
#2 Technological complexity makes it difficult to develop a business case to present to stakeholders.
Even if you intuitively understand that data is the way of the future for businesses, it can be difficult to quantify an ROI for stakeholders.
Predictive maintenance tools vary in complexity. Winning buy-in from leadership can prove challenging, especially when knowingly confronting comfort zones via legacy systems.
To determine ROI and win buy-in, leadership must provide hard numbers that are easy for stakeholders to understand, ideally visualized in graphics for at-a-glance understanding. Here are some factors to take into consideration when calculating the ROI of predictive maintenance tools:
- How much unplanned downtime has your plant experienced in recent years?
- What were the causes? (Odds are, it’s equipment failure.)
- How much did the unplanned downtime cost your company? (Be careful to evaluate both the direct effects and the indirect “domino effects” of unplanned downtime.)
- What information would have prevented these cases of unplanned downtime?
- How much would the proposed predictive maintenance tools cost to implement?
Examples of related, demonstrated ROIs for other companies within the industry can be helpful, too.
An IoT analytics survey observed that predictive maintenance implementations yielded a positive ROI for 83% of companies, with 45% reporting amortization in less than a year.
Executives expect investments in digital engineering technologies, such as IoT and production plants wired for Industry 4.0, to rise at a CAGR of 19% between 2022 and 2026 — nearly double the pace of all engineering research and development spending for 2022 to 2026.
Leverage predictive maintenance tools to boost operational reliability.
Learning how to implement predictive maintenance tools is critical for manufacturers to move with the times and keep up with the shift toward Industry 4.0. To gain buy-in from stakeholders and reap the benefits of predictive maintenance, leadership must create a culture shift that steps beyond traditional manufacturing practices and paints a clear picture of the ROI of predictive maintenance tools.
Learn more about how to leverage predictive maintenance in our e-guide, Increasing Operational Reliability Through Predictive Methods.
About the author: Michael Parent is a management consultant and Principal of Michael Parent Consulting Services. He is a Lean Six Sigma Master Black Belt and was named a 40 Under 40 "Rising Star" by the American Society for Quality. Throughout his career, Michael has coached executives through strategic problem solving and operational excellence, and has led continuous improvement projects in a myriad of manufacturing sub-industries.