Skip to main content
U.S. flag

An official website of the United States government

Dot gov

Official websites use .gov
A .gov website belongs to an official government organization in the United States.


Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Model-Based Smart Manufacturing Operations Management


To develop and deploy advances in standards and test methods for operations and logistics that improve the reliability, quality, and efficiency of smart manufacturing systems.


The Model-based Smart Manufacturing Operations Management project will contribute to standards and test methods that normalize models and methods for specifying and connecting shop floor information to operations decision making and execution.  The project will focus on developing model-based approaches to design ("what control decisions?"), decision-support ("how to select the best control action?"), and execution ("how to execute selected control action?") of operations management functions, including operational control and reliability. Model-based decision-support methods combine system modeling with contextualized information to enable better access to related operations management decision-support tools, such as simulation and optimization. This project will improve the utilization of available information by synthesizing and contextualizing information from traditionally incompatible sources ("linking" data together). The result is increased manufacturing system efficiency through improved operations management capabilities leveraging linked data. 

Smart manufacturing operations management (SMOM) systems manage flows of work, resources, and information through manufacturing enterprises. The effects from these systems span across sourcing, production, and sustainment. Developing and implementing flexible, agile, and robust SMOM systems require the ability to take advantage of real-time system feedback to make dynamic control decisions close to where control is executed -- the shop floor. Achieving these capabilities is currently hindered by limited availability of contextualized integrated information, access to high-quality decision-support tools, and traditional hierarchical operations management structure. Model-based SMOM addresses these challenges with new operations management models that integrate traditionally orthogonal functions: such as production, maintenance, quality, and inventory management; their sources of data; and decision-support tools to enable richer operations decision-making and management. 

Maximizing manufacturing systems' efficiency requires optimized (smart) operations management capabilities. Model-based approaches to SMOM address design ("what control decisions?"), decision-support ("how to select the best control action?"), and execution ("how to execute selected control action?") of operations management functions, including operational control and reliability.  Many challenges faced in operations management decision-making are due to insufficient access to high-quality decision-support. These challenges involve matching correct analytical models to the required decision and availability of data as well as defining standard decision-support interfaces and interoperable decision-support (analysis) tools, such as simulation and optimization. 

Additionally, decision-support models must be fed by the most up-to-date information available about part quality, equipment reliability, process performance, and others. Dissimilar information systems (sources) collect, handle, and provide data in highly different ways – often to the point of direct incomparability or incompatibility between information sources. Even within each information regime, the myriad of data types requires specialized models to synthesize or extract pertinent knowledge that is relevant to the high-level decision planning process.  

NIST is uniquely situated to tackle this problem facing manufacturers by having the expertise and motivation to look across the multiple areas the production process -- linking information from multiple products and services that are not inherently designed to interact efficiently. Successful decision-making in this context relies on the integral parts of capturing information, contextualizing that information into knowledge, then interpreting that knowledge to develop actionable plans. Each piece of this process is necessary to ensure optimal operations management.  Integrating the many facets of smart manufacturing operations management raises the question of, "How are modeling, simulation, and systems-engineering first principles merged with IIOT data?'' Making scientific and technical contributions in Model-Based Industrial and Systems Engineering (MBISE) would help better manage operations across various phases of the lifecycle. 

There is a need for standard models, methods, and recommended practices for efficiently using information available in modern manufacturing facilities to enable intelligent operations decision-making. The goal of this project is to explore methods for processing information collected from the areas of part quality, process efficiency, and equipment reliability and methods for enabling critical operations decision-support capabilities. The two primary use cases for SMOM will focus on scheduling (operational control) and prognostics (reliability). Planning and scheduling methods optimize the matching of equipment capabilities to product requirements to best execute production. Prognostic health management uses this and other information to help ensure maintained specified levels of (required) capability and capacity.

The research plan includes three thrusts of activity:

  • Information and Linked-Data 
    • Extend MTConnect standard to support Part (Product) and Process definition and data collection
    • Develop and standardize methods for synthesizing information from related models (e.g. MTConnect part, process, and device/asset models) 
      • Dissimilar Information Merging Methods 
      • Validation Methods for Evaluating Linked Models 
  • Data Management for Reliable Operations 
    • Diagnostics for System Reliability
    • Anomaly Detection Methods for Tiered Models 
    • Discriminating Source of Anomalies Across Tiered Models 
      • For example, Process Degradation, Equipment Degradation, Operator Error, Design Flaw (in Product or Process)
    • Contribute technical findings to PHM community, including assessing and addressing current and future standards needs
  • Model-based industrial and systems engineering (MBISE) 
    • Develop reference model for production and logistics systems (DELS)    
    • Develop and standardize methods for integrating production and logistics systems models with discrete event simulation tools.
    • Contribute technical findings to OMG standards (ManTIS/SEDSIG)
    • Modeling planning, scheduling, and execution to support distributed decision-making and control (ISA-95 MOM)
    • Integrate MTConnect with ISA-95 (Operations Management) and Robotics/AGVs (Control Execution) 
    • Investigate opportunities to contribute to ISA-95 (MESA) and MANDATE (ISO)
Created December 3, 2018