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CMS Evaluation Flowchart

Sim-PROCESD can be used to evaluate and compare condition monitoring systems along side various control or maintenance policies

Credit: NIST

SimPROCESD is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines with finite buffers. It also provides functionality for modeling the degradation and maintenance of machines in these systems.

In addition to modeling the behavior of existing systems, SimPROCESD is also intended to help with optimizing those systems by simulating various changes to them and reviewing the results. For instance, users may be interested in evaluating alternative maintenance policies for a particular system.

The software is available for public use through a publicly available GitHub repository. Any user may create a fork (copy) of the repository to freely experiment (e.g., class extensions to model complex processes) with the code without affecting the original source code.

NOTE: SimPROCESD project is in early development and may receive updates that are not backwards compatible.


Current published version:

Our user guide, including API, is available at:



SimPROCESD is designed to be a flexible, open source tool to support a broad range of analysis and planning for process manufacturing.

The major elements allow for production planning, maintenance policy evaluation, and condition monitoring systems evaluation.
Indicators such as part quality, equipment health, resource use, and others can be tracked and gauged in any arbitrary metric or KPI relevant to the user.
Tracking many standard metrics, such as net production profit, or part quality are demonstrated in our Examples Page:

Relevant Publications:

  1. Sharp, Michael; Dadfarnia, Mehdi; Sprock, Timothy; Thomas, Douglas; Procedural guide for system-level impact evaluation of industrial artificial intelligence-driven technologies: Application to risk-based investment analysis for condition monitoring systems in manufacturing, Journal of Manufacturing Science and Engineering, 144, 7, 071008, 2022, American Society of Mechanical Engineers
  2. Dadfarnia, Mehdi; Sharp, Michael; Key Elements to Contextualize AI-Driven Condition Monitoring Systems towards Their Risk-Based Evaluation, 2022 5th International Conference on Artificial Intelligence for Industries (AI4I), 38-41, 2022, IEEE
  3. Mehdi Dadfarnia, Michael Sharp, Serghei Drozdov and Jeffrey Herrmann. A Simulation-Based Approach to Assess Condition Monitoring-Enabled Maintenance in Manufacturing. 7th International Conference on System Reliability and Safety (ICSRS). November 22-24, 2023. Bologna, Italy. Co-sponsored by the IEEE Reliability Society (Italy Chapter). 
  4. Mehdi Dadfarnia, Michael Sharp. Key Elements to Contextualize AI-Driven Condition Monitoring Systems towards Their Risk-Based Evaluation. 2022 5th IEEE International Artificial Intelligence for Industries (AI4I), Laguna Hills, CA, US. 2022/10/11


Created November 1, 2023