Question: What is “the world’s most common and safest mode of transportation?”
Wait for it…
Elevators have long been the most common and safest form of transportation.
Answer: Elevators. Those ubiquitous mobile cubicles found in office buildings all over the planet. Elevators are big business, and the manufacturing and servicing these machines is no easy venture. German-based Thyssen Krupp, one of the world’s leading elevator companies, has turned to a disruptive approach to machine maintenance to keep the trains—i.e. elevators–running on time.
As Thyssen Krupp tells it, “Worldwide, more than 12 million elevators make seven billion trips and move over one billion people every day. Yet every year, maintenance needs render elevators unavailable for a total of 190 million hours.”
The deck is stacked against efficiently maintaining that kind of fleet; or at least it used to be.
With the advent of IoT (Internet of Things) machine sensor technology, machine learning, and cloud-based asset maintenance software, digital prescriptive maintenance can be conducted as easily as the touch of a button.
Thyssen Krupp employs predictive machine maintenance to dramatically increase elevator availability by reducing out-of-service situations through real-time diagnostics. They are able to predict maintenance issues before they occur, and alert elevator engineers by flagging the need to replace components and systems before the end of their lifecycle.
Thyssen Krupp believes that the growing requirement of high-speed and energy-efficient elevators in hotels, hospitals, parking buildings, commercial, residential, and industrial sector will be boosting demand for IoT in elevators market.
Other manufacturers are taking to predictive maintenance to manage the machines running their factory floors and the machines their customers rely on. Sensing end-user demand, manufacturers are focusing on the development of smart products with interactive touch screen panels, intuitive technology, and cloud-predictive maintenance. Customers are looking for manufactured products that are highly efficient, effective, and engaging. The demand is going up for smart products.
Historically, manufacturers have practiced preventative maintenance.
First, it’s important to distinguish between predictive and preventative maintenance. Preventative maintenance, a.k.a reactive maintenance, breakdown maintenance or run-to-failure, is a maintenance practice that seeks to decrease the likelihood of a machine’s failure through the performance of regular maintenance. However, predictive maintenance relies on data to determine a machine’s likelihood of failure before that failure occurs. This allows manufacturers to move from a repair and replace model to a predict and fix maintenance model using predictive analysis.
The good news is, machine monitoring costs less than you think (see our June blog). Rather than having to alter or rebuild existing infrastructures, bolt-on monitoring solutions like our ShiftWorx Platform are bolt-on, making them extremely simple to incorporate on the shop-floor. Machine monitoring solutions can help manufacturers save on production costs, helping pay off the system in days rather than months and years. Once switched on, machine monitoring solutions instantly start paying themselves off. Learn more.
The Rewards
The main objectives or rewards for manufacturers to move to a predictive maintenance model are about improving production efficiency and improving maintenance efficiency. The cost savings can be enormous.
A recent McKinsey Global Institute report as one of the most valuable applications of the Internet of Things (IoT) on the factory floor. The report, The Internet of Things: Mapping the Value Beyond the Hype, calculated that predictive maintenance manufacturers’ savings would total $240 to $630 billion in 2025.
Predictive maintenance in factories could reduce maintenance cost by 10 to 40 percent by fostering better maintenance, according to McKinsey. It also reduces downtime by 50 percent and lowers equipment and capital investment by 3 to 5 percent by extending machine life.
A report by Deloitte University Press, Industry 4.0 and manufacturing ecosystems provides examples in which, for companies like Schneider Electric and Caterpillar, predictive maintenance and understanding root cause of failures can offer millions of dollars in potential savings along with far fewer days of equipment downtime.
The McKinsey study calculated that predictive maintenance manufacturers’ savings would total $240 to $630 billion in 2025. Predictive maintenance in factories could reduce maintenance cost by 10 to 40 percent by fostering better maintenance, according to McKinsey.
GE Transportation is moving toward self-aware locomotives and digitalization of the entire rail operation system. Sensors mounted on railcars enable operators to receive real-time notifications about the condition of key railcar components, as well as broader risk events related to broken wheels, hot bearings, and handbrake application. Using predictive maintenance, GE Transportation is applying the technology to help extend the life of locomotives, reduce fuel consumption, decrease emissions, boost velocity and improve operations.
Locomotive technology has come a long way since the invention of the steam engine.
And we come full circle…
Thyssen Krupp competitor, Otis Elevators, “the world’s largest manufacturer and maintainer of people-moving products”–elevators, escalators and moving walkways—is using smart sensor technology in its “Otis ONE” digital platform that monitors and gathers data from more than 300,000 connected units to create predictive insights and a more proactive service solution for their customers. This allows Otis teams to stay ahead of potential issues – keeping equipment running and passengers moving safely and reliably. In the event when service is required, OTISLINE customer care can proactively contact the customer and service professionals to arrive on site with the information and parts needed to enable a faster return to service.
Predictive maintenance in manufacturing is becoming the norm, not the exception
Autonomous operations in manufacturing may be futuristic in the eyes of some but your business can start moving towards operational intelligence. For example, ask yourself, how do factory analytics impact your business and what software will work with your current manufacturing execution system (MES) to give you the data that is critical to your business. Do you have intelligent software solutions in place to help manage your maintenance and service operations to make them more efficient?
For more on how technology can help you improve your own maintenance needs and open services-based offerings for your customers while enabling cost savings and productivity gains throughout your organization, get in touch with us.
Step 1: Establish an empirical baseline to truly understand the critical “value-adding” activities on your plant floor. This is done simply by connecting your machines to FreePoint’s 
Step 2: Engage your people in the process of improvement by using 
Step 3: Connect your live, empirical machine data, and your fully engaged people, to your schedule and workflow plan dynamically – in real-time. Most plants use a combination of accounting systems, whiteboards, even excel spreadsheets, to plan and then report on the performance of their productivity. Some may have an ERP system deployed and some may even be considering an MES system. The problem with most ERP and MES system deployments is that it represents a single, very large bet, that will take a very long time to pay out if it pays out at all. FreePoint’s approach is to use what was gained (knowledge, insight, plus early financial payback) from the first 2 stages, tying them together in a process using visualization tools that line up with your plant’s current effective processes. Placing a bet on a system that takes years to fully implement and requires your manufacturing processes to be modified for the system to work is not just a bad bet, it’s often a losing bet.
If you are still reading, you are probably wondering how long it should take to hit this trifecta. The first step should take 30 days and should result in a payback 30 days later. The second step should take 30 days, and it too should result in a payback in 30 days. The third step should take about 120 days, but it can take as long as you like, because at that point, you will already have great empirical data, engaged and motivated people, and your system can be built to match your improved, and continually improving, manufacturing processes.
