Manufacturers need to continuously improve efficiency and productivity to stay competitive. This has always been true. Now, though, the IIoT and machine learning are helping manufacturers identify areas for improvement faster than ever before, creating a competitiveness gap between industrial manufacturing businesses that have already adopted IIoT tools and those that haven’t.
Because of the way IIoT systems with machine learning work, the gap will continue to grow. To understand why the IIoT creates a vast competitiveness advantage for manufacturers that use it now and those who implement it sooner rather than later, it’s helpful to understand the way IIoT data and advanced analytics deliver gains at different stages of deployment.
Wireless sensor networks make data visible immediately
As soon as a plant adds wireless temperature, vibration or other kinds of sensors to a piece of equipment, critical data highlights overall equipment efficiency. Continuous, real-time readings make immediate productivity and efficiency gains possible in several ways.
For example, real-time temperature readings on a storage cooler can eliminate the need for an employee to check and record temperatures several times each day. That frees the employee to focus on other tasks while maintaining safety and compliance. Plant managers can also start using sensor data right away to prevent or reduce unplanned downtime and quality control problems caused by equipment that’s operating out of range.
Equipment manufacturers who include wireless sensors on their products can immediately improve their customer service. When a customer has a problem or a question about their equipment, the customer service team can review the sensor data to troubleshoot. This may let them resolve the customer’s issue right away. If a service call is required, real-time access to equipment data can shorten the time it takes technicians to make repairs. That gets the customer back online faster and frees the technician to help the next customer sooner.
These data-generation capabilities give users an advantage right away, but competitors can quickly catch up by adding wireless IIoT sensors to their equipment. However, machine learning gives earlier adopters advantages that are harder to overcome.
Analytics deliver continuous improvements over time
The long-range benefit of IIoT systems for manufacturing is their ability to predict trends based on continuously growing data sets. By charting trends for compliance, equipment utilization, and productivity, plant managers can see where performance is improving and where it’s stagnant or declining.
For example, analytics trend graphs might show utilization trending upward for a piece of equipment during one shift but flat during other shifts. Managers can use that information to identify the barriers to better utilization in those other shifts and improve utilization overall.
Another long-term analytics advantage is the ability to switch from reactive or scheduled maintenance to predictive maintenance (PdM). PdM reduces unplanned downtime and scheduled-maintenance costs by enabling as-needed maintenance based on the equipment’s data history. For example, analysis of temperature and vibration data over time might reduce the frequency of service on a machine from every six months to every 8 to 10 months, depending on when the machine is predicted to start operating out of range.
By combining data analytics with goals in a single IIoT system, managers can stay on track to achieve the improvements they want, with real-time progress updates. Meanwhile, as the data set keeps growing, the AI can generate more precise trend projections. Because of the time required to build up this knowledge, the competitive advantage of analytics isn’t easy for later adopters to quickly match or overcome. There’s another long-term factor to consider, too—the growing power of IIoT metadata analysis.
Metadata provides the context for longer-term gains
The internet of things is producing new data at a rapid rate. By 2025, IDC projects that the annual IIoT growth rate for the industrial and automotive sectors will be 60%. Across all sectors and consumer applications, IIoT-generated data will grow at CAGR 29% during the same time, for an estimated 79 zettabytes of data by 2025. That 79-zettabyte figure represents 45% of the 175-zettabyte global datasphere total that Northrup Grumman predicts by 2025.
Even within one manufacturer’s plant or network of plants, the amount of data produced by wireless sensor networks and networked video cameras will be hard to organize and fully leverage without proper metadata practices.
When users standardize their IIoT device IDs and location descriptions, it’s possible to put those devices into a clearer context. This enables accurate comparisons across system devices and equipment sets.
For example, an IIoT system can individually analyze incoming data for three temperature sensors in different parts of a large climate-controlled storage area to monitor compliance. When the sensors are standardized and analyzed as a group, perhaps in conjunction with the two-door sensors in the same area, the system can show a clearer picture of real-time temperatures in different zones of the storage area and how door openings and closings affect temperatures.
Not only does metadata enable clearer visibility of how systems operate, but it also supports more nuanced and powerful machine learning. In other words, metadata can help industrial IIoT systems get smarter faster. That can lead to optimization and improvements that wouldn’t be possible without contextualized data. As with analytics based on individual device data, this type of competitive advantage compounds over time, which makes it hard for later adopters to catch up.
Based on the short-term impact of real-time data collection, the midrange impact of sensor data analysis and the long-term outlook for the impact of metadata analysis, one thing seems clear. Manufacturers that adopt and fully leverage IIoT sensor systems now may open a competitiveness gap that becomes impossible for lagging competitors to close.
In the News
Covered on these news sites. Click image to read more.
