As the Fourth Industrial Revolution (4IR or Industry 4.0) marches forward, the manufacturing industry continuously evolves with technology. The 4IR technologies have enabled manufacturers to generate large quantities of data from various sources. This has led to the digital transformation in manufacturing being driven by data technologies.
Data can be used directly to monitor and perform analytics on production processes, machinery, and conditions, enabling preventive actions. Manufacturers have already been employing preventive techniques to improve production performance and safety, and technology adds to these efforts. However, manufacturing can utilize more advanced data technologies like machine learning and AI to enable predictive analytics.
Predictive analytics can help identify and mitigate costly events, such as machinery failures and equipment breakdowns. This ensures a continuous and predictable production process by enabling timely maintenance of machinery and equipment. However, performing predictive analytics using traditional analysis methods is challenging. Thus, automated and autonomous analytics are essential to achieve scalable and deep analytics capabilities for real-time monitoring and control of production.
Manufacturers have been using monitoring technology for over a decade to enable ‘condition-based’ maintenance and operations. This technology helps reduce costs by minimizing losses after an event occurs. On the other hand, predictive analytics aims to predict events and take preventive measures, thereby saving costs associated with downtime and failures.
In manufacturing, machinery, and equipment represent significant expenses due to regular maintenance, repairs, and the frequent need for replacement after failure. Predictive maintenance overcomes these challenges by utilizing predictive models and real-time data to anticipate future behavior and take corrective actions. It effectively prevents unplanned downtime and extends the lifespan of machines and equipment. While predictive analytics can be used in various contexts, maintenance is the major advantage for manufacturers. The return on investment (ROI) of predictive maintenance is significantly higher in the manufacturing industry compared to other sectors.
Predictive maintenance brings about a significant shift in how production problems are handled. Traditionally, reactive management styles kept production moving. However, in today’s competitive world, reactive management is neither relevant nor efficient. Predictive maintenance and other management activities are based on a proactive approach to eliminating breakdowns and failures before they occur. It combines data-driven learning with reactive management to transition towards proactive, predictive management.
Predictive analytics is not limited to preventive strategies but is also useful for predicting other trends to optimize overall production and business performance. Predictive maintenance extends beyond plant machinery and equipment; it can also be applied to on-field machines such as vehicles, transportation containers, inventory, and materials, among others. As a result, all aspects of the production cycle can be covered with predictive analytics, shifting the overall management towards a proactive approach.
Predictive analytics is deeply rooted in modern data technologies and requires strong, centralized, and democratized data capture. The technology requirements for predictive management include:
- Centralized data management systems such as SLK’s DataHub platform and master data management systems.
- Integration of machine learning or AI to test and improve predictive models.
- Installation of IoT devices and sensors on machinery and equipment to capture and monitor operational data.
- Integration with other systems to provide necessary data for analytics.
Predictive analytics can save up to 12% in extra costs compared to preventive maintenance and over 40% compared to reactive management. Despite this potential, 80% of manufacturing plants still rely on preventive maintenance techniques (upkeep). This points to a significant gap between manufacturing goals and the current state of the industry. Additionally, it highlights the competitive advantage for manufacturers who adopt predictive maintenance early. Today, predictive technology provides a competitive advantage, but in the future, it will become the industry norm.