Evolution of modern manufacturing: more data
Today’s manufacturing industry looks vastly different than it did decades ago and rapid technological change has reshaped what’s possible. As smartphones have replaced clipboards, the day-to-day work of manufacturing has evolved to include Big Data and real-time automation.
The workforce is changing, too, and companies have their sights on very different goals. So how did manufacturing get to where it is today? And what does tomorrow hold?
The data revolution
Traditionally, manufacturing companies struggled with incomplete and manual processes. A few high-value assets garnered all the attention. Measurements never made it into the system of record, or the data collection required specific and expensive expertise.
With the Industrial Internet of Things (IIoT), data collection and data analysis have been transformed. What would have required hours of expert analysis can be done by a computer in a fraction of the time. Automation gives us more data with less effort, gives us context for the data and turns the data into actionable insights.
In a reactive environment, failures required teams to call in experts. That much-needed expertise drove up maintenance costs and extended downtime. With the introduction of smart tools, maintenance teams can handle the initial screening themselves, resolving many common equipment problems prior to failure and avoiding shutdown and external fees.
Because of the advancements of Industry 4.0 and smart technology, operators and technicians are doing more in-house work. As tools have become smarter, they’ve also become multifunctional and less expensive. As electronics continue to evolve, the possibilities for industrial test and measurement will keep expanding.
From run-to-failure to reliability
While the concept of reliability has been around for 30 or 40 years, smart technology has made it attainable for businesses of every size. Many manufacturing companies have been operating run-to-failure maintenance for decades. But more companies are seeing a better path forward and embracing that being competitive means adopting new ideas. This means retraining personnel and using data insights to gain an edge.
Reliability used to be considered a nice-to-have concept. Maintenance teams and management would say, “Reliability would be great if we had the resources or time.” The practice seemed limited to big companies where either downtime or assets were very expensive — top-tier machinery that would require a million-dollar fix needed sensors and telemetry to keep it healthy. Today, even small companies are adopting a reliability strategy to stay competitive. A plant that runs smoothly and efficiently is more profitable and better able to recruit new talent.
Changing demographics, experience
As team members with decades of specialized experience have retired, many manufacturing companies have had to deal with a loss of knowledge. It can be difficult to hire replacements at a similar level of expertise. The resulting skills gap poses a significant obstacle to the adoption of predictive maintenance practices. The good news for innovative companies is new hires are often more computer savvy. Digital natives are quick to learn automated data gathering, data manipulation, dashboard creation and real-time analysis.
In the past, maintenance teams focused on troubleshooting and scheduled maintenance rounds. When an asset failed, the goal was to get it back up and running as soon as possible. Today, the goal is to get ahead of potential issues and predict impending faults. This means team members have to be more cross-functional and interdependent. There is more to this type of work than replacing bearings, seals, couplings and belts based on a calendar cadence. The digital transformation has made it possible to find the root cause early and keep machines running better and longer. Teams can reduce spending by monitoring conditional change and getting the full life out of each part.
The transition from 100% reactive maintenance to planned maintenance or condition-based maintenance (CBM) doesn’t happen overnight. Successful companies start by using small pilot programs and grow by proving success on a small scale to get buy-in based on results. The right tools integrated into a user-friendly system can track and trend data to detect problems before they cause downtime. By maximizing uptime and production, maintenance has become a strategic entity that impacts profitability and adds value to a manufacturing business.
While IIoT and Industry 4.0 are widely-known concepts, they have not been widely adopted. However, data connectivity in physical devices that can communicate with each other, and be remotely monitored, has clear business value. Work processes can become more efficient, data entry errors are minimized or eliminated, and uptime increases while failures decrease.
Data properly collected and leveraged provides critical insights and makes it possible to act at the most convenient times. Tools such as computerized maintenance management software (CMMS) eliminate the data silos that hampered reliability in the past by giving teams all needed information at their fingertips.
Embracing efficiency can help companies can set themselves up for long-term success. Some companies had ignored costs such as energy use or environmental impact because they were focused on the bottom line or the amount of product going out the door.
More companies understand those are real costs; energy used is money spent and policies that result in detrimental environmental impacts cause real harm. As electricity and fuel costs increase, it makes sense for manufacturing companies to be good stewards of all the resources they use. Paying attention to more than the bottom line creates business value.
As technology continues to improve, the manufacturing industry will continue evolving to tackle and solve new problems.
KEYWORDS: IIoT, data analytics, smart maintenance
Data is often gathered seldom turned into actionable insights.
Information enables predictive maintenance instead of wasteful scheduled maintenance or run-to-fail scenarios.
Pilot projects can help prove Industry 4.0 and IIoT benefits.
What information could help your processes flow more smoothly?