Predictive maintenance: Fixing potential hazards before they happen

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  Quantumrun Foresight
• August 24, 2022

Insight summary

Predictive maintenance (PM), using artificial intelligence (AI) and Internet of Things (IoT) technology, is transforming how industries maintain and operate equipment, reducing downtime and enhancing efficiency. This strategy not only saves costs and improves product reliability for manufacturers but also boosts safety and compliance with labor laws. Additionally, predictive maintenance is shaping future labor market demands, regulatory policies, and environmental sustainability through smarter resource use and waste reduction.

Predictive maintenance context

Maintenance and reliability professionals have long struggled to balance maximizing asset availability and minimizing downtime. Fortunately, the late 2010s introduced advances in PM strategies that have provided new options for keeping machines running efficiently.

At its core, PM is a system that uses AI and machine learning (ML) algorithms to create models of how equipment behaves. These models can then predict when a particular component is likely to fail, allowing for proactive maintenance and repairs. IoT technology is also crucial to making predictive maintenance work effectively. By constantly monitoring the performance of individual machines and components, sensors can provide real-time data that can be used to improve the accuracy of maintenance predictions. This functionality is essential because, according to consultancy firm Deloitte, a factory/plant’s output rate can be reduced up to 20 percent when there are no proper maintenance strategies in place.

PM utilizes data from various sources (described below) to predict failures enabling Industry 4.0 manufacturers to monitor their operations in real-time. This ability allows factories to become “smart factories” where decisions are made autonomously and proactively. The main factor that PM manages is the entropy (the state of deterioration over time) of equipment, considering the model, manufacturing year, and the average period of usability. Effectively managing equipment deterioration is also why companies must have reliable and updated datasets that can correctly inform PM algorithms of the equipment’s origin and the brands’ known historical issues.

Disruptive impact

Predictive maintenance systems integrate sensors, enterprise resource planning systems, computerized maintenance management systems, and production data to forecast potential equipment failures. This foresight minimizes disruptions in the workplace by addressing issues before they escalate into costly repairs or downtime. For industrial manufacturers, this approach translates into significant financial savings by reducing unplanned downtime. Beyond cost savings, predictive maintenance enhances operational efficiency, enabling managers to strategically schedule maintenance tasks to minimize impact on production schedules. 

For equipment manufacturers, analyzing how their products perform and identifying factors leading to equipment failure can avoid costly product recalls and service issues. This proactive stance not only saves significant amounts in refunds but also protects the company's brand from damage associated with faulty products. Additionally, manufacturers gain valuable insights into product performance under various conditions, enabling them to refine their designs.

Predictive maintenance is also a key driver in enhancing worker safety and regulatory compliance. Well-maintained equipment is less likely to malfunction, reducing the risk of workplace accidents and ensuring a safer environment for employees. This aspect of PM aligns with compliance with labor laws and safety regulations, a critical consideration for businesses in all sectors. Moreover, the insights gained from PM can inform better design and manufacturing practices, leading to inherently safer and more reliable equipment. 

Implications of predictive maintenance

Wider implications of predictive maintenance may include: 

• Factories forming specialized teams for maintenance strategy, utilizing predictive maintenance tools for enhanced efficiency and reduced equipment failure rates.


• The automation of maintenance processes, encompassing tool testing, performance tracking, and immediate detection of malfunctions, leading to streamlined operations.


• Public transportation and electricity providers integrating predictive maintenance into their systems, ensuring consistent and reliable service to the community.


• Equipment manufacturers incorporating predictive maintenance technology in product testing phases, resulting in higher quality and more reliable products entering the market.


• Data analysis enabling equipment vendors to monitor the performance of their entire product range, leading to improved product design and customer satisfaction.


• Autonomous vehicles equipped with PM technology, alerting owners of potential issues, reducing road accidents and enhancing passenger safety.


• Enhanced employment opportunities in data analysis and maintenance strategy, reflecting a shift in labor market demands towards more specialized technical skills.


• Governments implementing policies to regulate data usage in PM, ensuring privacy and security.


• Increased consumer confidence in products and services due to the reliability and safety improvements brought by PM.


• Environmental benefits stemming from efficient resource use and waste reduction, as PM enables longer equipment lifespans and less frequent replacements.

Questions to consider

• Have you interacted with any PM technology in your home or workplace? 


• How else can PM create a safer society?