Digital Transformation in Manufacturing: How Predictive Maintenance Could Be a Game Changer
Rising competition. Global pricing pressure. Volatile taxation. The manufacturing industry has a constant list of troubles to take care of. Above all this comes the unexpected and uninvited equipment breakdowns, which throws the spanner into the otherwise smooth manufacturing chain.
Traditionally, manufacturers had to take equipment off their production hours to diagnose these emergency performance issues. This was not only affecting productivity, but was also inefficient in finding the right issue that was causing the trouble.
According to WSJ. Custom Studios & Emerson, “unplanned downtime costs industrial manufacturers an estimated $50 billion annually.”
It is here that predictive maintenance makes its entry as a problem-solver. With the massive amount of data that the manufacturing industry churns out on a daily basis, data-driven predictive maintenance is a natural occurrence.
The larger technology that will propel predictive maintenance as an enabler for digitized manufacturing would be digital transformation. Digital Transformation in manufacturing would provide manufacturers with access to modern technologies that would help turn data into valuable insights. This digitized way of manufacturing is popularly referred to as Manufacturing 4.0.
Manufacturing 4.0: The Fourth Industrial Revolution At Play
From a bird’s eye view, Manufacturing 4.0 represents an ecosystem of interconnected machinery that works smarter than their isolated ancestors. Technologies like the Internet of Things, Cloud connectivity, Artificial Intelligence & Machine Learning, Cognitive Computing are among some technologies that are pushing the possibilities of “smart factory” environments.
Manufacturing 4.0 will provide several economic benefits – predictive maintenance being the tip of an iceberg.
Manufacturers have been long since translating manual records to digitized records and then interpreting them for decision making, particularly to identify parts nearing their end of life that need immediate replacement. This method of predictive maintenance was largely reactive in nature than proactive. Reactive maintenance fails to address the actual issue that could prevent the downtime from recurring frequently.
For example, recurring breakdowns from an equipment might be due to a loose component that needs to be replaced. Fixing the breakdown each time instead of replacing the component itself could lead to higher maintenance costs and more downtimes.
To reduce the damage of reactive maintenance, manufacturers resorted to planned preventive maintenance. But, planned preventive maintenance often replaces parts even before they have extinguished their useful life. This practice is also uneconomical since there is a chance that healthy equipment parts, which still have a long useful life, may get discarded or written off.
Real-time data from factory floors will help manufacturers take proactive measures without running into downtimes or sacrificing healthy equipment parts.
Closing the Physical-to-Digital Data Gap
McKinsey estimates that in the oil and gas industry, 99 percent of data is discarded even before decision-makers have a chance to use it. The data discarding happens primarily because of its historical nature, which doesn’t hold relevance to current and future operations.
But, real-time data can close that gap by giving manufacturers a pulse of the equipment’s health on an as and is basis. For instance, data from the factory-floor is captured using IoT sensors. The data so collected is then analyzed by predictive maintenance systems using Artificial Intelligence, Machine Learning or other cognitive systems.
The results or predictions are then acted upon to fine tune the equipment performance, like repairing or refining a component without replacing the entire equipment. This process, as Deloitte calls the physical-to-digital-to-physical loop, helps manufacturers take proactive action using data rather than just be informed by it.
The highlight of this process is that a cluster of manufacturing equipment can be managed and monitored by factory personnel from a remote location. This saves time and cost otherwise incurred for engaging individual personnel for each equipment.
Boeing Spreads Wings With Digital Transformation
Boeing, the aviation pioneer, is a spearheading digital transformation in the manufacturing space. The airplane maker has already earmarked dedicated Engineering and Technology Center for its digital transformation initiative.
Boeing is using IoT-driven blockchains to manage its operations – right from asset management to customer servicing. According to Robert Rencher, Senior Systems Engineer with Boeing, “We’ll be able to apply predictive analytics to anticipate when there’s going to a problem, and either mitigating the problem or preventing it from happening through technological means.”
Boeing is showing the way for manufacturers how to achieve reduction of outages and unplanned maintenance by embracing predictive maintenance.
And it has already delivered benefits for the company. The realized benefits include:
- Forecasting maintenance events
- Fine tuning the production plans
- Extending the life cycle of machinery
- Customizing component-wise maintenance schedules
Predictive Maintenance As A Growth Opportunity
Digital Transformation is slowly changing the face of manufacturing. Automation and data-driven decision making is becoming commonplace. According to this IDC Manufacturing Insights blog, by 2019, 75% of large manufacturers will upgrade their operations and operating models with IoT and analytics-based situational awareness to reduce risk and increase speed time to market.
Data would form the focal point of all these activities. Predictive maintenance would then become an inevitable norm. Predictive maintenance will empower manufacturers to plan and pursue their production plans with better accuracy. As Boeing has achieved with predictive maintenance, manufacturers would be able to reduce machine downtimes and unplanned maintenance activities to a great extent. It will lead to seamless production chains that will boost overall profitability.