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Your Ultimate Guide to Predictive Maintenance in Manufacturing

Predictive Maintenance in Manufacturing
Digital Transformation

Your Ultimate Guide to Predictive Maintenance in Manufacturing

A prime reason that contributes to no value but growing costs in Manufacturing is machine downtime. According to an Aberdeen report, the average cost of unplanned equipment downtime is $260,000 per hour. Unplanned downtime plays a critical role in the production and profit of any manufacturing company. 

Common reasons that contribute to machine downtime are

  • Ambiguous Working Instructions
  • Improper Manufacturing Equipment Maintenance
  • Human Error
  • Lack of Gathering Downtime Feedback
  • Changeovers
  • Cleaning
  • Tool changes
  • Early shutdowns
  • Personal breaks
  • Excessive machine set-up time
  • Unplanned machine maintenance
  • On-machine press checks
  • Machine operator errors

Here are some of the setbacks manufacturing industries will face due to unplanned downtime. 

All of the above factors contribute to increased TCO and reduced value. This is the reason why top manufacturing heads have started to embrace predictive maintenance systems to improve OEE and reduce overhead maintenance costs and production delays. 

What is predictive maintenance

In general, the predictive maintenance systems help manufacturing companies with condition-monitoring tools to track the performance of any equipment/machine in idle, normal and peak performances. The data garnered from machines operating at different conditions will help manufacturers plan for maintenance in the near future to prevent sudden failure or downtime. 

How predictive maintenance works in manufacturing

Condition-based monitoring is all about collecting real-time data from machines and performing this manual is extremely hard for many humans. With an IoT device and sensors attached to a machine, garnering the real-time data for every single movement of the machine is seamless. 

Here are some of the critical parameters captured in real-time by sensors.

  • Vibration
  • Temperature
  • Pressure
  • Chemical content
  • Liquid/solid levels

The data collected will be pushed to a Cloud from where it is fed into an AI/ML enabled system to analyze and process data for predicting the future maintenance problems that could arise from that machine. The data processed will be pushed to a maintenance specialist to make plans to negotiate downtime problems. 

Benefits of using predictive maintenance for manufacturing

  • Capture condition-based real-time data collection accurately
  • Foresee & predict machine downtime early
  • Higher transparency 
  • Reduced product delays
  • Improved planned production rate 
  • Lower maintenance costs
  • Minimize machine failures
  • Reduced downtime for repairs
  • Increased machine efficiency
  • Improved operator safety
  • Verify machine’s performance post-maintenance
  • Increased overall profits

Predictive maintenance use cases for manufacturing 

Mostly predictive maintenance is used in machines where humans can’t intervene and monitor data. 

There are a lot of potential use cases for predictive maintenance. I have highlighted a few for the reader’s reference. 

Predictive maintenance for Glass Manufacturing 

The glass industry involves tons of data but lower analytics to track the performance. Two common challenges that impact this industry is lack of data leading to increased scrap rate and unstable fault density. Both can reduce the overall production and quality of the material. With a predictive maintenance system in place, capturing the real-time data during every second of the glass manufacturing is possible which gives massive control over overall yield and improvements. 

Predictive maintenance for Chemical Manufacturing 

In chemical industry production the challenges never cease. Maintaining quality production and maximizing output within health, safety, environmental and quality goals is the key. There are many variables: the quality and variability of raw materials and the control over critical parameters like processing equipment durability, waste management, etc. A predictive maintenance system will ensure complete control over the complete chemical manufacturing, employee safety and frees up maintenance managers to focus more on the external maintenance programs. 

Predictive maintenance in Tyre Manufacturing

Mixing the right combination of different polymer compounds is critical for maintaining the tyre quality. Drop in the percentage can lead to potential production loss and poor batch quality. In addition, measuring pressures in tires is equally challenging as well. With a predictive maintenance system, mixing right polymers and checking the pressure of tyre becomes seamless. Also, the system plays a critical role in improving the product rate on the floor. 

The manufacturing industry has started to leverage the power of predictive analytics and companies that have successfully implemented such systems are seeing improving productivity and RoI. The industry is changing, are you ready for the transformation? Get started now.