The drivers and benefits of IIoT
The Industrial Internet of Things (IIoT) encompasses digital transformation for manufacturing and is sometimes referred by the term Industry 4.0, or the 4th Industrial Revolution. Nevertheless, the main goal remains the same: integrate disparate systems and data into one large, connected ecosystem to increase productivity and drive innovation by leveraging advanced digital technologies. These are the three main drivers that have allowed this transformation to grow in the past few years:
- Data acquisition – the capability to capture large amounts of real-time data in an accurate and consistent method through sensors.
- Networking and connectivity – the ability to connect sensors and devices and send large amounts of data over a certain range of distance.
- Cloud analytics – the use of cloud-based software to provide expertise in data analytics and data storage.
These drivers can help drive automation and productivity improvements in manufacturing by leveraging a whole portfolio of digital technologies. The focus of IIoT is to bring data to the forefront and share this data across different platforms. Studies show that implementing IIoT works: a recent study by the MPI group reports that 72% of manufacturers saw increases in productivity and 69% saw increased profitability over the past year after implementing IIoT technology.
There are three main benefits of the adoption of IIoT on production lines that can give managers a competitive advantage:
1. Production efficiency
It’s hard to know how much money is being wasted when organizations operate a line without collecting data or by collecting data manually. With the IIoT, more accurate and consistent data can be collected automatically as the line operates. You’ll be able to gain a better understanding of the production rate, line speed, cycle time, and asset utilization.
2. Better quality products
Improvements in line efficiency can increase the quality of products. The IIoT can provide insight into machines before they break down and when quality is not meeting specification. This helps prevent two of the biggest problems a line faces: waste and poor quality. By improving line inefficiencies and identifying areas or equipment that aren’t functioning properly, less scrap products are likely to come off the production line.
3. Improved uptime
Through sensor data, equipment usage, environmental conditions, application, and throughput can be collected. With this data, an analytics-based maintenance model can be created to provide descriptive, predictive, and prescriptive information that can be utilized to improve uptime. It’s rare that a one-size fits all maintenance schedule works for every organization. A maintenance model that empowers an operator to be proactive instead of reactive and can increase line performance, lower waste, and improve production costs.
