Create a More Efficient Process and More Qualified Products

By using sensors and machine learning algorithms, predictive maintenance can predict when equipment, such as mixers, bin blenders, blister packaging machines, sterilizers and autoclaves and dryers is likely to fail and schedule maintenance before problems occur. This proactive approach helps ensure that equipment is available when needed, which is crucial in fast-paced pharmaceutical production.

  • Tablet presses
  • Capsule fillers
  • Mixers (blenders, granulators, etc.)
  • Encapsulation machines
  • Filling machines (liquid, powder, etc.)
  • Packaging equipment (bottling, blister packaging, etc.)
  • Labeling machines
  • Capping machines
  • Palletizing and packaging robots
  • Compression machines
  • Pumps (peristaltic, centrifugal, etc.)
  • Centrifuges (laboratory, industrial, etc.)

The primary cause of downtime for 60% is due to equipment failure in pharmaceutical.

Safeguard The Future of Healthcare by Ensuring Reliable and Efficient Production of Life-Saving Drugs

Monitor the condition of pharmaceutical complex equipment in real-time, identify potential issues early, and schedule maintenance before problems occur. This proactive approach helps to reduce downtime, extend the life of equipment, improve overall efficiency, and in ensuring that the equipment is operating safely and in compliance with industry regulations, ensuring that the end product is safe for human consumption.

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Create a Reliability Centered Maintenance Culture

Reliability Centered Maintenance (RCM) is an approach that aims to maximize the reliability and availability of equipment by identifying the functions that are critical to the operation of the system, and then developing and implementing a maintenance strategy to ensure that these functions are performed reliably. It helps in identifying and eliminating unnecessary maintenance tasks, thus improving efficiency and productivity.

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