The rapid evolution of modern technologies has brought tremendous opportunities to all business environments. Yet, as is applicable to any two-way street, this reality has also triggered an increase in operational risks, data loss being only one of them. Therefore, companies are constantly working on improving their processes, investing in data analysis and monitoring current trends in the industry. In the field of predictive maintenance, an essential task is properly identifying all risks associated with inefficient processes or inadequacy of internal resources.  

Identify the causes of inefficient processes  

The concept of operational efficiency encompasses practices such as the improvement of all processes in the company, whether operational or strategic. In the case of manufacturing companies, operational performance improvement in production is reinforced by optimizing all end product-related activities. This allows you to identify the causes of inefficiency:  

1. Analyze the status quo 

To correct inefficient processes, it is necessary to know exactly which operational activities are problematic for them. Only then will it be possible to improve them. Through customer feedback, employee meetings and reporting, relevant conclusions can be drawn for the company's situation. 

2. Give attention to production costs  

Every production causes unexpected costs. However, it is important to know how much resources, manpower and time each process requires. In this way, it is easier to identify the additional costs. However, the quality of production need not be compromised if cost reduction is required.    

3. Document process defects 

The analysis of each process also allows the identification of errors. They must be measured and linked so that it is clear where the changes are to take place. When an error is detected, it is important to identify the reason for it and document all related information for further control.    

4. Use technology 

Technological solutions allow to store data, integrate departments, and create reports that enable a more comprehensive analysis of the company's situation. The manual execution of an entire process means more effort and longer working hours. Therefore, technology is a partner in process improvement. 

Predictive maintenance for operational performance in Industry 4.0 

By analyzing production data, identifying patterns and making operational problems predictable, preventive maintenance is a method to avoid equipment failure. Up to now, plant managers and machine operators have always carried out scheduled maintenance work. However, in addition to the consumption of unnecessary resources and loss of productivity during machine repairs, half of all preventive maintenance measures are ineffective.     

The implementation of predictive maintenance in an industrial process involves data-capture sensors installed in the physical product or in the machine.  These ensure a data flow between the monitored plant and the central data storage. These databases are stored, processed and analyzed in IT systems or cloud. This results in algorithms that display the results and predictions in the form of dashboards. Predictive analytics algorithms are then used to gain insights into the reduction of downtime, which are then investigated using root-cause analysis software.     

Implementing the technologies to monitor equipment condition, optimize maintenance schedules and provide real-time operational risk alerts enables manufacturers to reduce service costs, maximize uptime and improve production throughput.   

Three advantages of predictive maintenance are: 

  • Reduced maintenance time: Maintenance planning and proactive repairs reduce maintenance time and lower overall maintenance costs.       
  • User-friendly machines: automated or personalized alerts, suggestions for appropriate maintenance services and a higher predictability rate are advantages of Predictive Maintenance.    
  • Competitive advantages: Branding is driven by customer experience. 

Companies implement predictive maintenance analysis, from targeted solutions to complete implementations, to increase operational efficiency throughout the production line.     

Another powerful use case for predictive maintenance is minimizing production errors and reducing scrap. Such implementations, often referred to as Quality 4.0, can predict when the number of faulty products is likely to exceed a threshold and provide the root causes of the expected failure.    

Conclusion 

Manufacturers are also using predictive factory 4.0 maintenance technology by installing sensors and security cameras in machines to predict problems throughout the factory floor. For companies implementing an integrated system for the first time, using machine learning and AI, predictive maintenance is the first step towards Factory 4.0. 

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