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Predictive maintenance and the smart factory

Connecting machines to reliability professionals

For maintenance and reliability professionals, the goal is always the same: to maximize asset availability. Predictive maintenance can help.

Maximizing utilization

How often should a machine be taken offline to be serviced? Traditionally, this dilemma has forced most maintenance organizations into a trade-off situation: Do they choose to maximize the useful life of a part at the risk of machine downtime (run-to-failure)? Or do they attempt to maximize uptime through early replacement of potentially good parts (time-based preventive maintenance)? (Which has been demonstrated to be ineffective for most equipment components.)

Often, 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. And while run-to-failure 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, 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 isn't 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.

Leveraging the power of the smart factory

Predictive maintenance (PdM) aims to break these tradeoffs by empowering companies to maximize the useful life of their parts while avoiding unplanned downtime and minimizing planned downtime. With the advent of Industry 4.0 for manufacturing, companies are able to leverage new technologies, such as the Internet of Things, in order to monitor and gain deeper insight into their operations in real time, turning a typical manufacturing facility into a smart factory. Simply put, a smart factory is one equipped with technology that enables machine-to-machine (M2M) and machine-to-human (M2H) communication in tandem with analytical and cognitive technologies so that decisions are made correctly and on time. More about the smart factory concept can be seen in this discussion on The rise of the digital supply network.

PdM (which has been around for many years now) utilizes data from various sources, such as critical equipment sensors, enterprise resource planning (ERP) systems, computerized maintenance management systems (CMMS), and production data. Smart factory management systems couple this data with advanced prediction models and analytical tools to predict failures and address them proactively. Additionally, over time, new machine-learning technology can increase the accuracy of the predictive algorithms, leading to even better performance.

Explore Deloitte’s Experiential Learning Environments

The Smart Factory @ Wichita is one of several of Deloitte's global immersive experiences designed to accelerate digital transformation—a network that includes the Digital Factory @ Dusseldorf and the Digital Factory @ Sheffield, established in collaboration with the University of Sheffield Advanced Manufacturing Research Centre.

Getting to predictive maintenance

Maintenance organizations across industries are, by design or default, at different stages of maturity. Some may be running scheduled maintenance checks based on estimates or original equipment manufacturer (OEM) recommendations. Others may utilize statistics-based programs individually tailored to each fixed asset.

There are steps to take on the journey toward reliability optimization, beginning with some of the basics of preventive maintenance and reliability-centered maintenance, while simultaneously piloting PdM with one or two well-suited assets. Prime assets for one of these pilots should be highly integral to operations and must fail with some regularity in order to create baseline predictive algorithms.

Now, the idea of PdM sounds enticing. But how does it work? Many of the technologies that make up a smart factory are not necessarily new, but they have become much more affordable, robust, advanced, and integrated for business use. Computing, storage, and network bandwidth are all now available at fractions of the cost compared to just 20 years ago, making piloting and scaling financially feasible.

Building the foundation

Maintenance strategy and processes are core elements for any successful maintenance organization. And it's important to note that while technology is a key enabler, it's only one key pillar for success. Without the fundamental building blocks in place, investment in technology will likely never yield the desired results.

It should also be understood that not all companies require the same level of reliability from their assets. A good place to start is to assess your organization's mission requirements and maintenance program maturity.

Smart factories and PdM are the future. And the options are endless. But it can be overwhelming to determine what your next step should be or how to drive value through investments in maintenance optimization. Learn more about Deloitte's Supply Chain and Manufacturing Operations practice to see how we can help you get started.

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