How AI and IIoT in predictive maintenance reduce factory downtime costs

This article provides a comprehensive overview of the importance and practical approach to using the Industrial Internet of Things (IIoT) for predictive maintenance operations in factories. Moreover, how this approach can reduce the time taken to investigate equipment issues by up to 93%

For factories, it can also be difficult to secure capital investment budgets to purchase new equipment. It is one of the reasons why companies are increasingly looking for ways to extend the life of their existing assets, in which maintenance plays an important part. It emphasises how this approach is crucial for reducing downtime costs and extending the life of existing equipment.

Understanding Maintenance in Factories

Hiroshi Nishiyuki, Head of Mitsubishi Electric’s FA Systems Design and Engineering, draws an analogy between regular car maintenance and factory equipment maintenance. Just as cars require regular servicing and part replacements to prevent breakdowns, factory equipment needs regular checks and maintenance. This is vital to avoid unplanned downtime that can disrupt the entire production plan. Users often classify factory maintenance into either preventive maintenance or corrective maintenance. Yet, there is another form of maintenance becoming popular.

The Shift to Predictive Maintenance

With economic constraints making it difficult to secure budgets for new equipment, the focus is on extending the lifespan of existing assets. This is where predictive maintenance comes into play, using technologies such as IIoT and AI. Sensors and AI collect data and predict equipment wear before failures occur, allowing for timely interventions. This approach not only prevents unexpected breakdowns but also optimises component replacement cycles, thus saving costs and improving efficiency.

The Role of IIoT and AI in Maintenance

A survey by the Japan Institute of Plant Maintenance highlighted the growing interest in leveraging technologies like IIoT and AI for facility management and predictive maintenance. Mitsubishi Electric’s Total Maintenance Solutions, as explained by Nishiyuki, combine IIoT data collection with AI analysis for efficient, predictive diagnostics. Furthermore, this data-driven approach helps in pre-empting issues and effectively addressing equipment failures through corrective maintenance.

Mitsubishi Electric’s solutions

Mitsubishi Electric offers solutions like the MELSEC iQ-R System Recorder. This device links PLC data with video recordings for quick root cause analysis of equipment malfunctions. This technology has significantly reduced the time needed for investigating equipment issues by as much as 93%.

The broader context of predictive maintenance

Predictive maintenance, while requiring investment, is as important as cybersecurity measures in terms of its necessity. As factories become more connected and adopt IIoT-driven solutions, cybersecurity becomes crucial. Although their ROI may not be immediately visible, they are essential for risk management and business continuity planning.


Nishiyuki underscores the ongoing transformations in the manufacturing industry and the role of Mitsubishi Electric in supporting these changes. He advocates companies to engage with trusted partners like Mitsubishi Electric for finding the most suitable solutions for their factories. The overarching message is that embracing digital transformation, particularly through predictive maintenance using IIoT and AI, is not just a technological upgrade but a strategic move to enhance efficiency, reduce costs, and ensure the longevity of factory equipment.

For the complete article, visit the Mitsubishi Electric website.

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