The manufacturing industry is going through rapid technological advancements and transformations driven by next-gen technologies like artificial intelligence, machine learning, IoT, and business intelligence. Besides, the widespread adoption of the fourth industrial revolution (Industry 4.0) has disrupted the manufacturing sector, driving the development of highly automated and interconnected smart factories.
Amongst the key innovations transforming the manufacturing industry, predictive maintenance (PdM) is a crucial element that helps forecast potential equipment failures. Leveraging predictive analytics solutions in manufacturing allows manufacturers to take early, proactive measures and prevent major breakdowns that cause hefty financial losses. According to IoT Analytics, 58% of manufacturers worldwide have either implemented predictive maintenance in manufacturing, or they are planning to implement it soon. This clearly indicates the growing popularity of predictive maintenance in the manufacturing industry.
In this blog post, we shed light on the significance of predictive maintenance in manufacturing while covering broad aspects of a smart manufacturing strategy.
What is Predictive Maintenance?
Predictive maintenance (PdM) is the process of monitoring the health and performance of various machines and manufacturing equipment to identify potential points of failure. It incorporates a series of interconnected devices, sensors, AI algorithms, and real-time data analytics to make highly accurate forecasts about various machines and equipment associated with smart manufacturing. By providing real-time alerts about equipment health conditions, it allows manufacturing companies to schedule maintenance and timely prevent issues that lead to a system breakdown. This, in turn, helps increase the lifespan of machines and other manufacturing equipment, thus mitigating downtime and driving significant cost savings.
An application with a predictive maintenance (PdM) feature identifies potential equipment failures using the following analysis:
- Equipment usage data to track wear and tear over time
- Monitoring temperature variations using thermal imaging
- Identifying mechanical issues using vibration analysis
- Performing oil analyses to check the quality of machine lubricants
By integrating Industrial Internet of Things (IIoT) devices, predictive maintenance in manufacturing becomes even more effective. This enables enterprises to keep close tabs on the health and performance of their manufacturing equipment.
How Predictive Maintenance Works?
Predictive maintenance uses IoT sensors and machine learning algorithms to monitor equipment performance and predict potential failures with high accuracy. This helps automate maintenance schedules, enabling enterprises to prevent unplanned downtime, increase machine lifespan and optimize repair schedules. In other words, predictive maintenance provides a proactive approach to equipment monitoring and scheduling, rather than a reactive one. This means it focuses on solving a problem before it materializes rather than the other way round. The flowchart below provides a clear illustration of how predictive maintenance in manufacturing works:
The Key Components
Given below are the most crucial components of predictive maintenance in manufacturing that clearly highlight its workflow and significance in the industry.
Data Collection
When it comes to predictive maintenance, the first step is to collect data through sensors installed on various manufacturing equipment. The sensors can be of many types i.e temperature, pressure, vibration and they collect data accordingly. Furthermore, this data is fed into a central database to facilitate continuous monitoring of manufacturing equipment.
Data Processing and Analysis
The data extracted from sensors is further processed to reveal trends that may point to early stages of machine failure. Here, it incorporates predictive algorithms that are powered by machine learning. Since machine learning models are trained to comprehend both typical and atypical behavior, they are able to anticipate problems long before they arise.
Prediction and Action
The system makes highly accurate predictions basis the analysis. This helps anticipate future events like the approximate time up to which a machine is going to last or when it may require regular maintenance.
Continuous Improvement
Continuous improvement is one of the crucial aspects of predictive maintenance. As it continuously monitors equipment health and performance, it fosters continuous improvement and facilitates regular maintenance.
The Impact on the Manufacturing Industry
Implementing predictive maintenance in manufacturing may have a profound effect for manufacturers, enabling them to enhance operational efficiency and drive significant cost savings. Let us delve into the crucial factors and the key features of predictive maintenance that are disrupting the manufacturing industry.
1. Reduced Downtime
Unplanned downtime is one of the major challenges faced by a large number of manufacturers worldwide. Traditional maintenance strategies often lead to unnecessary expenses or unexpected machine failures. That said, Predictive Maintenance minimizes unplanned downtime by forecasting potential failures with sheer accuracy. According to Deloitte, adopting predictive maintenance practices may reduce unplanned downtime by up to 50 percent. As a result, manufacturers are able to schedule repairs or part replacements during non-peak times, keeping production lines running smoothly.
2. Increased Equipment Lifespan
Predictive maintenance facilitates continuous monitoring of machinery and manufacturing equipment, constantly checking their health conditions through thermal imaging, vibration analyses, and temperature sensing. This significantly improves the maintenance process and increases the equipment lifespan by addressing technical issues before they escalate. As reported by IBM, predictive maintenance enables enterprises to extend the lifespan of their machines and equipment by 20-40 percent.
3. Cost Savings
By anticipating system issues or equipment failures, PdM enables enterprises to prevent major breakdowns, thus saving costs and mitigating financial losses. Besides, it reduces the frequency of unnecessary maintenance checks and part replacements to drive significant time and cost savings. According to the US Department of Energy, implementing predictive maintenance may help enterprises reduce maintenance costs by up to 30 percent. Similarly, a report by McKinsey & Company indicates that manufacturing companies that use predictive maintenance may reduce their maintenance costs by up to 40 percent.
4. Improved Safety and Risk Management
Unexpected equipment failures may lead to accidents, mishaps, and unsafe working conditions, especially in manufacturing and automotive industries. By monitoring equipment health, performance, and identifying system issues, it ensures timely maintenance and contributes to improved workplace safety and better risk management. Besides, predictive models may help keep operators informed about potential safety hazards, thus contributing to a safer work environment.
5. Better Resource Allocation
With predictive maintenance, manufacturing companies may optimize their maintenance schedules and manage resources accordingly. Besides, maintenance teams may focus on the main problem areas rather than investing time and energy on routine audits and inspections, thus saving time and costs. This, in turn, leads to better resource management and improved workforce productivity.
Also, read Predictive Analytics and Its Impact on the Manufacturing Industry
Challenges With Predictive Maintenance
Despite the apparent benefits of predictive maintenance, there are many challenges involved with the implementation process. Given below are major challenges associated with the implementation of predictive maintenance solutions.
- Initial Upfront Costs: While opting for a PdM system, you are going to invest in various high-end equipment like sensors, BI tools, robotics, and more. Besides, you need to set up an advanced IoT infrastructure that indicates there are generally higher upfront costs involved. However, the long-term benefits you get are well worth the investment you are going to make.
- Data Quality Issues: Understandably, PdM systems require enormous volumes of data in order to provide accurate insights. That being said, data quality issues are quite frequent and impact the accuracy of these PdM systems and their forecasts.
- Compatibility Issues: A large number of manufacturers still rely on legacy systems to majorly run their operations. However, many legacy applications may not be compatible with modern-day PdM technologies. This is where technical expertise is required and, in some cases, legacy software modernization is the only option.
- Skilled Workforce: Making the right use of predictive maintenance required technical expertise in data analysis, machine learning, and system integration. Therefore, it may be a challenging task to find skilled resources to extract maximum value from PdM systems.
The Future of Predictive Maintenance in Manufacturing
With rapid technological advancements, predictive maintenance is poised to become a crucial element to maintain consistency and efficiency across manufacturing processes. According to Boston Consulting Group (BCG), artificial intelligence is likely to play a major role in enhancing the core capabilities of predictive maintenance models. Besides, advanced techniques like anomaly detection and the use of unsupervised learning models are growing in importance. These models can identify issues without requiring extensive historical failure data, making them scalable and adaptable across different machines
The accuracy of Predictive Maintenance systems increases as more data is fed, making predictive maintenance a self-sustaining technology. Besides, new developments and innovations in the field of AI, IoT, and edge computing will continue to enhance PdM capabilities, enabling enterprises to save maintenance costs, enhance profitability, and foster efficient maintenance drives. Nevertheless, with manufacturing processes becoming more connected and data-driven, the demand for predictive maintenance will continue to increase, presenting a rapidly evolving industrial ecosystem. These trends indicate that predictive maintenance will continue to evolve, reducing costs and improving operational efficiency in the manufacturing industry.
