First introduced in 2006, the concept of Industry 4.0—an initiative to digitize the manufacturing process to produce high-quality products faster and at the lowest possible cost—holds much promise for the future of manufacturing. But as more and more industrial automation components have become IIoT-friendly, a significant return on investment can be realized today for a minimal investment in Industry 4.0 technology.
Industry 4.0, IIoT (Industrial Internet of Things), Smart Manufacturing, and Factory of the Future are concepts that generate lots of buzz these days, promising efficiencies that, until very recently, were beyond the imagination of most manufacturers. Although the data-analytic and equipment upgrades required to implement Industry 4.0 technologies come with a cost, affordable options are becoming available, allowing more small- to mid-size manufacturers to get into the game. Companies that implement Industry 4.0 technologies sooner than others will have a distinct competitive advantage over their competition, by:
- Decreasing unplanned downtime and increasing machine availability using predictive maintenance programs.
- Improving overall quality and customer satisfaction while reducing costs associated with quality management systems.
Make the Move to Predictive Maintenance (PdM)
Your business can realize substantial savings by using IIoT diagnostic technology to cut maintenance costs and reduce downtime through predictive maintenance. As more and more equipment manufacturers integrate IIoT functionality into their components, it’s getting easier to find affordable options to implement a predictive maintenance program. Sensors and controls, as well as data storage and processing via cloud technologies, have become less expensive, allowing manufacturers of all sizes to tap into the promise of predictive maintenance.
Why switch to a predictive maintenance model? Keep in mind that all components degrade over time to the point where they will need to either be repaired or replaced. One could wait until a part reaches its end of life before replacing it—run it until it breaks—but this reactive maintenance approach results in unplanned downtime, is inefficient, and is not cost-effective in the long run. Preventive maintenance programs, which are time-based, may seem like the right approach since parts and equipment are maintained or replaced on a regular schedule, but this schedule is usually based on worse-case predictions regarding a component’s lifetime. It may not take into account the equipment’s age or actual usage data. As a result, with preventive maintenance, parts are often replaced before they have reached their end-of-life state. Although unplanned downtime for maintenance is reduced, overall downtime may increase due to the performance of maintenance that may be unnecessary. And catastrophic equipment failures are still a possibility since the actual condition of the system is not being monitored.
programs are based on the equipment’s actual condition, not a set maintenance schedule. Equipment degradation is measured using sensor data indicating conditions such as temperature, humidity, pressure, noise, vibration, and other predictors of upcoming failure. Sensor data is connected to cloud-based programs and becomes “smart data” when analyzed against other datasets, which might include:
- Equipment stats, such as make, model, and configuration
- Existing engineering data, such as expected performance models based on manufacturers’ testing data, End-of-Life (EoL) information, or other historical data
- Usage data, such as the number of starts, cycle times, or load statistics
- Maintenance data, such as service frequency or part replacement history
Upgrading existing machines and production platforms to implement an Industry 4.0 predictive maintenance program can pay off significantly in large facilities, which can have thousands of maintenance points. This modernization does not need to happen overnight; affordable incremental upgrades can be made over time. For example, during scheduled maintenance or periodic inspection, small sensors can be mounted on equipment. Adding additional sensors over time means more data points will be monitored, increasing the overall digital intelligence of the system. Connecting this sensor data to the cloud can now be achieved without any additional engineering or programming costs using configurable solutions such as Phoenix Contact’s IoT-Gateway
or Bosch Rexroth’s IoT Gateway Rack
Smart data can be used to determine if equipment is likely to fail within a specific timeframe or to predict its remaining life. Smart data is also used to trigger alerts indicating unusual equipment behavior. Mobile devices can receive these alerts on a 24/7 basis, and system settings can be adjusted and optimized remotely to maximize performance instantly.
Improved equipment condition insights garnered from smart data increase the longevity of equipment. Since equipment receives service when required, unscheduled downtime decreases, and production is more reliable. Machine availability also improves due to the performance of maintenance only when needed. Another plus for OEMs and integrators—remote monitoring can open up a new revenue stream from Maintenance-as-a-Service (MaaS) programs they can provide for their customers.
Use IIoT Data to Improve Quality
Companies are now able to significantly improve their manufacturing processes by using smart data to increase quality and reduce waste. Connected machinery and products generate vast amounts of data, which is good news for quality management personnel who previously had to rely upon time-intensive, manual processes to gain insights from production trends and other key performance indicators. These manual processes are not only costly, but it may take weeks or longer to analyze data accurately and implement corrections. Industry 4.0 technologies aggregate cloud metadata with sensor data relating to raw materials, the working environment, output, waste, and other metrics. This real-time smart data can be used to trigger an alarm to notify quality-control personnel as well as machine operators and plant managers who can take immediate action to correct quality issues.
A manufacturing plant with smart, connected equipment can also continually analyze data to make process efficiency improvements. A core tenet of continuous improvement is reducing all forms of waste, and IIoT data can be evaluated to increase throughput and cut down on scrap and rework. Error-proofing methods can be enhanced, and monitored production processes can be adapted to become leaner. Since most quality problems stem from human error, it makes sense to implement Industry 4.0 technologies to lessen operator errors by eliminating non-value-added steps or reducing the number of decisions operators need to make. Production data is accurately recorded and can be accessed via unique serial numbers for full traceability, making quality consistently high.
In addition to connected manufacturing equipment, companies are also producing connected products, which add usage metrics into the mix of data available to quality management personnel. By embedding smart sensors into the products they sell, manufacturers are gaining new insights regarding product performance “where the rubber meets the road” and can implement ways to improve reliability and overall customer satisfaction. This emerging technology promises to be a game-changer, uncovering new opportunities for product improvement or new product development.
While almost every manufacturer can benefit tremendously from these new technologies, not all companies are poised to make the significant investment necessary to implement an advanced smart manufacturing strategy all at once. An approach that makes the most sense involves making initial expenditures in areas that have the most significant and most immediate payoff and investing the resulting savings into additional upgrades down the road. Rather than doing it yourself, it pays to consult with an expert who can identify and prioritize the actions that will bring the most value to your business. Create small, proof-of-concept projects around those to gain new insights into your production equipment and processes. You’ll soon be improving productivity, quality, and efficiency by basing business decisions on the smart data generated by Industry 4.0 technologies.
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