Digitalization of manufacturing and connecting operations to the IIoT has clear benefits, but the manufacturing industry has resisted this for a number of reasons. Chief among them are a lack of urgency and technical personnel, as well as the extra investment and shutdown costs that come with installing new equipment. Furthermore, concern about factory workers resisting digitalization and potentially causing disruption of a smooth-running factory can stop all discussion of change. It seems safer to continue as usual than to forge ahead. However, in the wake of this pandemic, companies are realizing that they can no longer continue as is. Unmanned operations are fast becoming a necessity to ensure production continues and to stay competitive. While unmanned operation is an obvious benefit of digitalization, it is not the only benefit.

Beyond dealing with present obstacles, digitalization provides a wealth of real-time data that can be combined with a manufacturing execution system (MES) to improve production efficiency. For example, low utilization (UT) results in a long production cycle, a low order fill rate, and high overtime costs. In the past, due to insufficient information, manufacturing companies would struggle to pinpoint issues, let alone resolve them. Through the IIoT, data can be extracted from equipment and then analyzed to discover operational bottlenecks. These insights can then be presented visually to managers, helping them optimize production.

Right Data = Right Insights: The Power of Being Well-informed

We have seen an example of this ourselves from working with our customers. We worked with a manufacturer of engine parts that used the IIoT to collect electric current signal data from equipment to determine their run time. Initially, they were facing critical issues with low UT, which demanded a thorough investigation. Upon examination, we found that their MES was tied to the manual clock-in systems that tracked employee attendance. The manual system allowed night staff to earn overtime without having to work. Meanwhile, the MES interpreted the clocked-in hours as the machine’s available operating hours. This led to inconsistent data, with the MES data incorrectly showing longer run times. IIoT allowed operations managers to better understand accumulated production through accurate data from the equipment itself, provided daily. Their night shift UT improved and allowed high-level supervisors to regularly track weekly and monthly improvements. After a year and a half of testing, their equipment run time increased from 70% to 82-85%. Not only was the production cycle improved, but staff overtime had also been greatly reduced.

1% Yield = 1% Profit Margin: AI Turns Today’s Data into Tomorrow’s Profits

IIoT’s wide range of applications in factories can solve various operational problems by using collected data in different ways. One of these applications is using AI on collected data to enable predictive maintenance. Adding sensors to IIoT-connected machines allows data to be sent to the cloud in real time. The back-end AI platform can then analyze this collected data on vibration, temperature, rotation speeds, and electric current to establish a control standard and check for variations. Predictive maintenance can then be performed before deviations get out of hand. When measurements of a machine’s cutting tool’s current frequency are too high, tooling damage can be analyzed. This analysis can trigger early replacement of potentially damaged tooling, reducing unexpected downtime or accidents. This can in turn improve production yields and reduce equipment maintenance costs. Other common applications include materials management, production planning, and scheduling optimization. By using the IIoT to analyze data and make predictions, companies can act preemptively, effectively turning simple data into real profits.

In our experiences with cases throughout the globe, we have seen many companies use the IIoT to improve their production efficiency. Now, with the push for unmanned factories, applications of the IIoT in factories will become wider and more diverse.

To learn more about predictive maintenance, please visit our microsite.