Industrial Internet of Things (IIoT)

Introduction to Production Process Modeling and Optimization 306

This class introduces the concepts of process modeling and optimization in smart manufacturing. Basic process modeling involves using various types of static and dynamic models to evaluate current production processes and test the impact of process changes. Organizations can use smart manufacturing technology to enhance process modeling and process analysis for a variety of optimization purposes. Types of process optimization include bottleneck analysis, downtime prevention, and quality optimization.

Understanding the range of options for process analysis and the smart tools available to enhance process optimization can help organizations improve operations and cost efficiency by eliminating process constraints, exploiting new opportunities, and testing process changes before they are implemented. After taking this course, users will be able to identify different strategies for achieving production process optimization.

  • Difficulty Advanced

  • Format Online

  • Number of Lessons 10

  • Language English


Or fill out this form and a specialist will contact you shortly

Course Outline
  • Decision Modeling for Production Process Optimization
  • Optimization as a Key Technique in Smart Manufacturing
  • Types of Optimization Analysis
  • Additional Optimization Analysis Examples
  • Review: Process Optimization Basics
  • Production Process Analysis Methods
  • Static Process Analysis Techniques
  • Dynamic Process Analysis Techniques
  • Process Simulation
  • Final Review: Process Optimization Techniques
  • Define production process optimization and decision modeling.
  • Describe optimization as a key smart manufacturing technique.
  • Describe types of optimization analysis.
  • Describe types of optimization analysis.
  • Distinguish between static and dynamic analysis models.
  • Describe common static process analysis techniques.
  • Describe common dynamic process analysis techniques.
  • Describe simulation for static and dynamic process models.
Vocabulary Term


A logical and mathematical expression that models a process or action. Algorithms are coded into a computer program that forms the rules by which a machine learning model processes data inputs and produces outputs.

artificial intelligence

AI. A computer program with algorithms that enable a machine or computer to imitate intelligent human behavior. Artificial intelligence allows machines to perform a process with autonomy.


The relationship between one or more variables or actions that frequently occur together. Associations in purchasing patterns reveal how certain products are often purchased together.

Big Data

A valuable collection of information from the devices or assets in an operation. Big Data can be analyzed to reveal patterns and make calculations.


A point of congestion during the production process. Bottlenecks limit the flow of production.

bottleneck analysis

The practice of evaluating a process to expose areas of congestion. Bottleneck analysis can be enhanced with digital tools like cloud-based platforms and other smart tools.


The process of switching a machine from one part setup or process to another. Changeover activities may include cleaning, disassembly, reassembly, and other setup activities.

computer vision

A machine learning capability that enables an artificial intelligence to recognize visual patterns through a camera or on a screen. Computer vision can help manufacturers automate visual inspection tasks for quality control.


Any limitation in a production process. A constraint in a production process is typically any production bottleneck that limits or hinders production.


The extent of the interdependence between variable quantities. Correlation analysis involves establishing a relationship or connection between two or more variables.

data inputs

A digital value or variable entered into a digital model or function. One or more data inputs are used in process modeling and optimization to predict process outcomes.

data output

A calculated result or action produced by a computing device after processing data inputs. Data outputs are evaluated when training machine learning models to determine the accuracy and integrity of data inputs.

decision modeling

A systematic approach to making decisions about process adjustments after evaluating the relationships among parameters, variables, and outcomes and the impact that different decisions about process parameters have on the outcomes. Decision modeling maps out decisions in visual flowcharts and can be enhanced using digital modeling tools.

differential equations

A mathematical formula used to represent the impact of changing environmental conditions, such as laws of physics or chemistry, on a process. Differential equations include one or more environmental functions or conditions, such as laws of physics or chemistry, as well as the derivatives that result from each condition.

digital twin

A virtual representation of a product or process that is synchronized to the current conditions of the physical product or process via real-time collected data. A digital twin evolves with the product or process throughout its lifecycle.

downtime prevention

The practice of evaluating production processes and employee tasks to expose areas of increased idle time. Downtime prevention typically involves eliminating production process bottlenecks or correcting flaws in the process.

dynamic process analysis

A strategy used in optimization analysis that evaluates process variables that fluctuate over a period of time. Dynamic process analysis is often used to make predictions based on historical data and current trends.

empirical process modeling

A dynamic process modeling technique that uses basic mathematical equations to represent relationships between process variables to analyze a process. Empirical process modeling can help manufacturers predict the impacts of different process changes.

historical data

Digital information that has been collected and stored over time. Historical data includes various data types and can be used to train digital optimization models.

hybrid process modeling

A process modeling technique that incorporates empirical or physics-based equations into a machine learning model. Hybrid process modeling leverages the ability of machine learning tools to analyze large amounts of data.

improvement targets

A desirable process goal or outcome determined through process analysis. Improvement targets are determined based on insights gained from existing process data.

machine learning

ML. The process that enables a digital system to analyze data in order to build predictive models and make decisions autonomously. Machine learning systematically solves problems using highly complex algorithms.

optimization analysis

The practice of constantly evaluating a process in order to expose flaws or areas of potential improvement. Optimization analysis may employ a variety of tools to achieve a variety of optimization goals or targets.

physics-based process modeling

A dynamic process modeling technique that uses mathematical equations to represent relationships between process variables in conjunction with differential equations that represent environmental factors. Physics-based process modeling techniques incorporate physical factors like temperature, chemical properties, and other variables to test their impact on a particular process.

predictive analytics

The use of data to predict future events. Predictive analytics allows manufacturers to make informed decisions to address things like future supply needs, machine maintenance, and customer demands.

process analysis

The practice of evaluating a process in order to gain a greater understanding of variables in the process. Process analysis may employ a variety of data collection and analysis tools to help discover process flaws and identify areas for improvement.

process variables

Parameters in production or other processes that, when changed, can alter the process outcome. Process variables often influence one another and are affected by environmental factors.

product mix

A production strategy that aims to optimize the variety and quantity of products manufactured at a given time. Product mix strategies use smart tools to analyze data such as purchasing patterns.

production process optimization

The practice of constantly measuring the effectiveness of production processes and striving to make continuous process improvements. Production process optimization in smart manufacturing is driven by the collection and analysis of data.

proximity sensor

A device that uses an electronic sensing field to detect the presence of an object. Proximity sensors allow for safe human-robot collaboration.

quality control

QC. A system of managing quality by inspecting finished products to make sure they meet specifications. Quality control relies on error detection and correction.

real-time data

Digital information captured from a data source in the present moment. Real-time data monitoring and analysis tools often detect a problem in a production process before personnel can recognize the issue.

reinforcement machine learning

A process in which an artificial intelligence (AI) learns the best actions to achieve a goal through trial and error. Reinforcement machine learning enables an AI to learn details about inputs and produce correct outputs autonomously.

resource management

The strategic process of organizing, allocating, and tracking production assets. Resource management involves managing production materials, personnel, budgeted funds, and other assets.


A function or command that executes a program or software task. Simulation software uses a run function to initiate iterations of a simulation based on variables and parameters set by an operator.


Any material or part discarded because it does not meet product specifications. Scrap increases waste for the manufacturer.


An imitation of a production process that is designed to predict aspects of the system's behavior. Simulation may be done using physical modeling or a specialized computer program.

simulation software

A computer program that models or simulates the operation of a production process or system. Simulation software tests the efficiency of a process and the effects of potential process changes.

smart manufacturing

The information-driven, event-driven, efficient, and collaborative orchestration of business processes along with physical and digital processes within plants, factories, and across the entire value chain. Smart manufacturing uses the Industrial Internet of Things to connect devices and operations.

static process analysis

A strategy used in optimization analysis that evaluates a process based on process variables that do not fluctuate. Static process analysis is typically used to visualize general process steps in order to gain insights and make informed decisions.

supervised machine learning

A process in which a human operator labels data inputs for a machine learning model and defines the desired outputs the model should produce. Supervised machine learning typically requires extensive human labor in order to prepare and label datasets.

supply chain

A complex network of companies and suppliers that produce and distribute a product. A supply chain consists of a company, its suppliers, its distributors, and its customers.


Capable of being maintained without any negative impact on the environment. Sustainable manufacturing involves using green manufacturing processes that conserve energy and resources and are non-polluting, economically sound, and safe for all.

theory of constraints

TOC. A process analysis methodology that aims to expose and eliminate process bottlenecks. The theory of constraints method can be used as a continuous optimization strategy for improving production process efficiency.

thermal sensors

A type of sensing device that measures temperature values. Thermal sensors may be used in production machines or in the production environment to detect hazardous operating temperatures.

unsupervised machine learning

A process in which a machine learning model processes and analyzes unlabeled data and produces outputs without human interaction. Unsupervised machine learning models help discover patterns in data that humans may not recognize.

value stream mapping

VSM. The process of creating a visual layout of all the processes required to make a product. Value stream mapping helps determine which parts of a process add value and which do not.


The series of activities that are necessary to complete a task. Production workflows include the sequence of industrial, administrative, and other processes through which a product passes from initiation to completion.