One of the things a small manufacturing company that wants to continue to grow and prosper should be concerned with is efficiency. Microsoft’s Bill Gates said “The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.”
Consider those poor maintenance strategies can substantially reduce a plant’s productive capacity. As machine parts are taken offline for servicing, many organizations face the challenge of weighing lost production time against the risks of breakdowns. Deloitte tells us that poor maintenance strategies can reduce a plant’s overall productive capacity between 5 and 20 percent and that unplanned downtime costs industrial manufacturers an estimated $50 billion each year.
So it’s necessary to work towards efficiency in all manufacturing processes including how you fix your machines. Simply stated, it’s a good idea to adopt a sound maintenance strategy.
Reliability-centered maintenance (RCM) is just such a corporate-level maintenance strategy that is implemented to optimize the maintenance program of a company or facility. The final result of an RCM program is the implementation of a specific maintenance strategy on each of the assets of the facility.
RELAX. This blog isn’t going to go deep into topics like Failure Mode and Effect Analysis (FMEA), Production Schedule Adherence, Mean Time Between Failures, and such. There are literally hundreds of proclaimed KPIs that can help us to measure and control our manufacturing, reliability and maintenance performance, but it is our desire to keep things simple, yet smart.
4 Methods to our Maintenance
There are four basic models for conducting maintenance on the machines that produce our products and the products themselves—like copy machines, nuclear reactors, and jet engines—that are out in the field providing a service, and our emphasis will be on maintaining these using Predictive Maintenance.
“So how often should a machine be taken offline to be serviced?”
That is the cost-saving question. This predicament forces most manufacturers into a trade-off situation where they had to choose
between maximizing the useful life of a part at the risk of machine downtime—run-to-failure, where assets are deliberately allowed to operate until they break down—or attempting to maximize uptime through early replacement of potentially good parts using time-based preventive maintenance, which has been demonstrated to be ineffective for most equipment components.
Oftentimes, maximum utilization of tooling or machine components can be achieved by running them until they fail. But this can lead to catastrophic machine damage as parts begin to vibrate, overheat, and break.
Reinforcing the old adage “pay me now or pay me later.” And, while run-to-failure (RTF) may be an acceptable approach for some assets, it still needs to be understood that unplanned downtime is almost always more expensive and time consuming to correct. Conversely, you might consider more frequent replacement of parts and servicing of equipment. But this can not only increase replacement costs over time, but it can also increase planned downtime and disruption to operations.
Spare-parts management presents a similar challenge that can feel like a constant balancing act. With limited budgets, maintenance professionals must evaluate which parts they’ll need and when to procure them. If the spare is not on hand or on order when it’s needed, the downtime of an asset can be anywhere from days to weeks—or even months—while waiting for the replacement part. This typically leads to the buildup of spares inventory, which not only ties up working capital but also increases the risk of excess and obsolescence that erodes the bottom line.—Deloitte University Press
Reactive Maintenance (Run to Failure or Breakdown Maintenance)
Run to failure maintenance is an acceptable strategy for equipment that is of minimal importance to operations or has low cost. Take, for example, a $1000 copy machine, whose lifetime value can be extended by 10% by servicing it every 4 months.
Equipment designated as run-to-failure are fixed in the event of a breakdown (by repair, restoration or parts replacement) until it is more feasible to simply order a replacement equipment.
Preventative Maintenance (scheduled)
This strategy is employed by most companies. Preventive maintenance consists of assets being taken offline, inspected at periodic, predetermined intervals and repaired if necessary. Although it’s a relatively easy strategy to set up and execute, it can prove quite costly in the long run as a majority of the time these inspections result in an inspection pass.
Manufacturers should lend serious attention to the efficiency of these schedules. Consider an annual review of a schedule’s effectiveness in raising overall equipment effectiveness by preventing breakdowns and see if the schedule can be lengthened or swapped out for predictive maintenance is ideal.
Condition Based Maintenance (CBM)
This is a maintenance strategy that monitors the actual condition of the asset to decide what maintenance needs to be done. CBM dictates that maintenance should only be performed when certain indicators show signs of decreasing performance or upcoming failure. Checking a machine for these indicators may include non-invasive measurements, visual inspection, performance data and scheduled tests.
CBM has some benefits. For starters, because it works while the equipment is in service, it doesn’t interrupt equipment operation. It can help ensure equipment reliability and worker safety, and also reduce failure rates and unscheduled downtimes. Moreover, because maintenance activities are scheduled ahead of time, CBM tends to be less costly than preventive maintenance.
But CBM is not without its drawbacks. The tools used to monitor equipment for CBM can be expensive to install. Employees must be trained to use CBM technology effectively, which can cost time and money. Furthermore, the sensors employed might not work in harsher operating environments and can have trouble detecting fatigue damage.
“Unless you’re confident that every part of your manufacturing operation is performing at peak efficiency, then there’s enormous value in exploring the world of predictive maintenance for IoT.”
—Wael Elrifai/Contributing Writer at Internet of
Predictive Maintenance
Predictive is the result of the confluence of improved sensor technology which is more affordable, remote data analysis capabilities, the Internet of Things (IoT), broader connectivity, sophisticated big data analysis, a decreased cost of storage, and improved Bluetooth, Wi-Fi and cloud technology. The machines talk and we listen.
What are the sensors on these machines measuring and telling us? Operational temperatures, heat, light, strain, flow, vibrations, pressure, speed, loads, humidity, moisture, electrical fields, sound, power consumption, fuel consumption, oil levels, and a plethora of elements. Powered by predictive analytics, these solutions detect even minor anomalies and failure patterns to determine the assets and operational processes that are at the greatest risk of problems or failure.
Typically, monitoring equipment is linked to a Computerized Maintenance Management System (CMMS), and generates work orders based on the monitoring sensor device. Instead of making broad-based maintenance decisions based upon historical data from similar devices, a real-time data-driven predictive maintenance approach minimizes unplanned downtime, reduces man-hours spent on maintenance, provides more insight as to the performance and potential issues arising with the machine, and improves employee and factory efficiency. The result is that organizations can prevent unplanned downtime by having fine-tuned visibility into their operations and the ability to automatically sense warning signs that indicate equipment failure or reduced performance.
By connecting equipment, organizations can capture massive volumes of data from sensors and other connected devices, so they can not only cut unplanned downtime and its associated costs, but also create new operational efficiencies, exploit new opportunities in supply chain optimization, and accelerate their overall digital transformation strategies.
Fortune Magazine reports that predictive maintenance will be “one of the first killer applications for the Industrial Internet of Things.” According to Deloitte, the benefits of Predictive Maintenance are significant.
Predictive Maintenance benefits include:
- Material cost savings (5-10 percent in operations and MRO material spend)
- Reduced inventory carrying costs
- Increased equipment uptime and availability (10-20 percent)
- Reduced maintenance planning time (20-50 percent)
- Reduced overall maintenance costs (5-10 percent)
- Improved HS&E compliance
- Less time spent on brute-force information extraction and validation
- More time spent on data-driven problem solving
- Clear linkages to initiatives, performance, and accountability
- More confidence in data and information leading to ownership of decisions
(Source: Deloitte)
Predictive Maintenance in Action
ROLLS-ROYCE—Jet Engines
Besides making exceptional luxury automobiles, Rolls-Royce makes some of the most highly sophisticated jet engines in the world. But it isn’t just Rolls-Royce’s premium engines that airlines care about. Approximately 20 years ago, Rolls-Royce went from manufacturing and selling engines to extending comprehensive maintenance services to the airlines that use their engines. The company employs a “power by the hour” model in which airline customers pay based on engine flying hours. The responsibility for engine reliability and maintenance rests with Rolls-Royce, which analyzes engine data to manage customers’ engine maintenance and maximize aircraft availability.
To better serve its customers and maintain its more than 13,000 commercial aircraft engines around the world, Rolls-Royce applied the predictive analytics capabilities to access data that helped them reduce fuel consumption, minimize maintenance costs, and improve the customer experience.
Today, each Rolls Royce engine has thousands of sensors that can product terabytes of data on long haul flights. Rolls Royce is able to analyze data remotely in order to deliver actionable insights to pilots and to the airlines about engine performance and operational efficiencies. Advanced analytics helps airlines to optimize fuel economy, anticipate maintenance needs, and avoid costly downtime and delays. Worldwide, flight delays and disruptions cost the airline industry millions of dollars every year. A single unscheduled disruption and its residual effects to the fleet and the passengers can cost an airline up to a million dollars a day. Even a small reduction in “aircraft on ground” (AOG) time can translate into a significant amount of money, so airlines are always looking for ways to improve the efficiency of maintenance activities. With early notice, the Rolls Royce team can proactively have parts at the right place and time reducing inventory costs and maximizing availability.
Further, if an issue occurs in flight, the cabin crew can report it immediately to ground operations. Rolls-Royce will access data from similar issues in the past and compare this information against technical guidelines including necessary materials and fixing time. Maintenance technicians fix the issue on the ground and enter their actions into the system to add to their product knowledge database.
“The market and the customer need have become much broader as aircraft and engines have gotten more talkative and the scope of our services has increased. There are terabytes of data coming from large aircraft fleets, with gigabytes per hour—rather than kilobytes—to process and analyze,” says Nick Farrant, Senior Vice President, Rolls-Royce. “Just managing all this data is driving us into different areas, but it also gives us opportunities to solve different problems through machine learning and analytics. We can use data and insight in new ways to refine our customers’ operations to add more value to them and allow them to do more with less.”
For example, aircraft and engine components, such as a fuel pump, often have a “soft life”—the point at which it is recommended to remove it for maintenance based on its time in operation. By analyzing detailed data from each specific pump and comparing it to data models and other pumps in the fleet, it is possible to provide an alert that indicates that a specific pump might not be performing well and should be replaced sooner than its soft life. Conversely, if a pump is close to its soft life, but monitoring and analytics show that the performance is normal, a decision could be made to defer until a later, routine maintenance window. Moving to an approach based on components’ actual condition could potentially add up to tremendous savings across a fleet by minimizing the disruption and cost of maintenance. “We see emerging digital technologies and robust prognostic analytics allowing us to work with customers to realize these types of opportunities,” Farrant says.
Rolls-Royce foresees that by gaining access to wider sets of operational data, it will be able to offer more valuable services to airline customers.
SANDVIK COROMANT—Metal Cutting
Sandvik Coromant, headquartered in Sandviken, Sweeden, is a worldwide supplier of cutting and metal working tools, and services the metal cutting industry. Advances in composite materials and the benefits of sensors and other IoT technologies have prompted many manufacturers to retool for the realities of “Industry 4.0”.
Sandvik Coromant collects and analyzes data from sensors embedded in all of the tools across the shop floor, monitoring every aspect of their performance, as well as the existence of any bottlenecks in the overall supply chain or manufacturing. Then, the system takes that analysis and makes recommendations on how to optimize the manufacturing process, and creates a predictive maintenance schedule that’s designed to help avoid unscheduled shutdowns.
We have developed the new predictive analytics manufacturing solution connects an in-house shop floor control tool that collects all the information, such as machine data, tool data, and using Machine Learning algorithms to optimize the process in real-time and set up predictive maintenance schedules and set alarms so that we can know when to take a machine offline before a failure occurs. In the end, our customers will be able to make quicker and better-informed decisions to become more profitable
–Nevzat Ertan, Chief Architect & Sr Manager/Sandvik Coromant.
With this technology, Sandvik Coromant has digitized its deep expertise and provides services that help customers make more informed decisions, and more easily calculate the financial return on a new machining tool. That translates to additional revenue, happier customers and greater flexibility in how its technical experts connect with customers.
Final Thoughts
Predictive Maintenance in tomorrow’s smart factories is the inevitable future. Maintenance strategy and processes are typically among the core elements for any successful manufacturing organization. Without the foundational building blocks of the process and people in place, investment in technology is not likely to yield the desired results. All of the sensors and smart devices in the world are useless unless the managers and support personnel know what to do with that information.