Two Approaches the Industrial Internet of Things Transforms Data Into Valuables for Manufacturing

Modernizing manufacturing and linking operations to the IIoT offers clear advantages, yet the manufacturing sector has shown reluctance for various reasons. Primarily, challenges include a lack of immediacy and technical expertise, as well as additional investment and downtime expenses associated with installing new machinery. Moreover, apprehensions regarding factory workers resisting digitization and causing potential disruptions in a well-functioning factory can halt any conversation about change. It may seem more prudent to maintain the status quo rather than forge ahead. Nonetheless, in light of recent events, companies are recognizing that they can no longer persist unchanged. Unmanned operations are swiftly becoming a necessity to sustain production and stay competitive. While unmanned operation is a conspicuous benefit of digitalization, it is not the lone advantage.

Alongside addressing current impediments, digitalization offers a plethora of real-time data that can be merged with a manufacturing execution system (MES) to enhance production efficiency. For instance, low utilization (UT) can lead to extended production cycles, low order fulfillment rates, and high overtime expenditures. Previously, due to inadequate information, manufacturing companies struggled to pinpoint issues, let alone rectify them. Through the IIoT, data can be extracted from machinery and subsequently analyzed to pinpoint operational constraints. These insights can then be visually presented to managers, aiding them in streamlining production.

Accurate Data = Relevant Insights: The Influence of Being Well-informed

We have personally witnessed an illustration of this from collaborating with our clients. We partnered with an engine parts manufacturer who utilized the IIoT to gather electrical current signal data from equipment to assess their operational hours. Initially, they encountered critical challenges with low UT, necessitating a comprehensive review. Upon investigation, we discovered that their MES was integrated with manual time-tracking systems that monitored staff attendance. The manual system allowed night shift employees to accrue overtime without actually working. Concurrently, the MES misinterpreted the clocked hours as the machinery’s operational hours. This resulted in inconsistent data, with the MES figures inaccurately showing extended operational durations. IIoT empowered operations managers to gain deeper insights into cumulative production using precise equipment data, provided daily. Their night shift UT showed improvement, enabling senior supervisors to monitor ongoing weekly and monthly enhancements. After a year and a half of experimentation, their machinery operational hours surged from 70% to 82-85%. Not only was the production cycle enhanced, but employee overtime was also significantly reduced.

1% Output = Boosted Profit Margin: AI Transforms Present-day Data into Future Gains

The extensive scope of IIoT applications within factories can resolve numerous operational challenges by leveraging assorted analytical approaches on gathered data. One of these applications involves utilizing AI on collected data to enable predictive maintenance. Integrating sensors with IIoT-linked machines allows instantaneous transmission of data to the cloud. Subsequently, the backend AI system can scrutinize these data on vibration, temperature, rotation speeds, and electrical currents to determine an operational norm and detect deviations. Predictive maintenance can then be carried out before anomalies escalate. For example, when assessments reveal elevated current frequencies in a machine’s cutting tool, potential tooling damage can be assessed. This evaluation can prompt the early replacement of potentially damaged tools, mitigating unexpected downtimes or accidents. Consequently, this can boost production yields and curtail equipment maintenance expenses. Other prevalent applications encompass material management, production planning, and optimized scheduling. By utilizing the IIoT for data analysis and forecasting, companies can proactively make decisions, effectively converting raw data into tangible profits.

Based on our extensive global engagements, we have observed numerous companies leverage the IIoT to enhance their production efficiency. With the trend towards unmanned factories gaining momentum, the applications of the IIoT in factories will continue diversifying and expanding.

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