Smart maintenance architecture – data-driven approach to smarter maintenance

Increasing levels of capability in predictive or smart maintenance does not necessarily remove the need for reactive/time-based maintenance. A blend of approaches can be beneficial and complementary. Investment will almost certainly be required to implement smart maintenance at any level and sponsors may decide to target its deployment only to specific assets/components/lines.  The benefits of smart maintenance may be measured in a range of ways, from better safety and increased capacity to reduced unplanned downtime and reduced time for preventive maintenance. 

 

This paper describes use cases and their technical answers for implementing predictive maintenance solutions in the biopharmaceutical manufacturing environment. It extends the foundational material in Smart maintenance: digital evolution for biopharma manufacturing to support biopharma manufacturing companies in their digital journey in two key areas: 

  1. Sharing best practices and industry’s collective data to build and use smart maintenance, which includes predictive models and analytics.
  2. Sharing architectural best practices and use cases to access and leverage data for smart maintenance.

These address the key challenges related to data identification, access, integration, advanced smart maintenance analytics, GMP environment, leveraging proprietary systems, cloud technology, etc. 

The use cases, together with the experiences of BioPhorum members, show that implementing smart maintenance at any level creates the potential for improving capacity, avoiding unplanned downtime, making cost savings, and increasing energy efficiency.  

Attached Files

File
Smart maintenance architecture data driven approach to smart maintenance January 2023.pdf
Preview
  • Version
  • Download 68
  • File Size 3.04 MB
  • File Count 1
  • Create Date 24th January 2023
  • DOI https://doi.org/10.46220/2023IT001
  • Last Updated