Predictive Maintenance in Manufacturing

Many enterprises today utilize preventative maintenance programs based on schedules to determine when equipment should be serviced. There is an opportunity for asset-heavy industries to improve their efficiencies and costs using a more proactive approach to maintenance systems such as one based on artificial intelligence or actual equipment condition. While these new methodologies can provide numerous benefits, many manufacturers lack the resources to effectively select, implement, and operate these new systems. This project will develop a comprehensive playbook for designing and implementing a predictive maintenance solution for manufacturing equipment and share the ROI, OEE, and other key metrics.

About the Project

MxD is releasing a Request for Proposal (RFP) to fund the development of a comprehensive playbook for designing and implementing a predictive maintenance solution for manufacturing equipment and share Return of Investment (ROI), Overall equipment effectiveness (OEE), and other key metrics.

A core value of Industry 4.0 is that factory equipment will become smarter in how it is monitored and maintained through the use of IoT (Industrial Internet of Things) and intelligent sensors, raising the bar on asset performance requirements. Many enterprises today utilize schedule-based maintenance programs which are based on a statistical analysis of historical data such as mean time between failures to decide when equipment should be serviced or worse, run to failure.

There is an opportunity for asset-heavy industries to improve their efficiencies and costs using a more proactive maintenance system such as either condition-based or artificial intelligence-based. Condition-based maintenance uses actual asset conditions to decide when maintenance will be needed (i.e., turbidity in lubrication oil, motor amperage). Artificial intelligence-based algorithms use real-time asset conditions to predict maintenance needs(i.e., pressure changes at pump exit).

These new methodologies can help provide foresight to operations leaders on when equipment will need to be serviced, preventing costly downtime due to failures or operating efficiencies. While these new methodologies can provide numerous benefits, many manufacturers lack the resources to effectively select, implement, and operate these new systems. The goal of this project is not to develop a new predictive algorithm, but rather to provide a practical roadmap for manufacturers to following the adoption of these innovative technologies.

RFP responses are due on or before
Thursday, February 24, 2022, 5:00 p.m. CT

Proposals must be submitted by teams, and MxD membership is not required for submission but will be required prior to project award. To facilitate project teaming, MxD will compile and disseminate contact information from parties interested in Team Formation during the first month of the proposal period.

If you are interested in submitting your contact info to this distribution list, please email projects@mxdusa.org.

For questions or more information, contact projects@mxdusa.org.

Note: All project work must be performed in the United States.

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