Plant Simulation Training

Lesson 1 • Creating and Saving Projects

Guides students through creating a new model file, setting project properties, and saving work. Establishes good file management habits from the start.

Lesson 2 • Running a Pre-Built Simulation

Students execute an existing model to observe simulation behavior before building their own. Connects theoretical concepts to visible simulation output.

Lesson 3 • Discrete-Event Simulation Fundamentals

Covers the logic of discrete-event simulation and its application to manufacturing systems. Establishes conceptual grounding before any software interaction.

Lesson 1 • Observing and Measuring Throughput

Students run the basic model and read throughput, cycle time, and utilization statistics. Connects model configuration to quantitative performance output.

Lesson 2 • Connecting Objects with Connectors

Teaches how to link flow objects using connectors and set flow direction. Proper connections are prerequisite to any valid simulation run.

Lesson 3 • Debugging Common Build Errors

Addresses frequent mistakes in object placement and connection that prevent model execution. Students learn systematic error-checking before advancing to complex models.

Lesson 4 • Core Flow Objects and Their Roles

Introduces sources, drains, buffers, and single-process stations. Students understand each object's function within a material flow chain.

Lesson 1 • Resource Object Types

Introduces workers, transporters, and resource pools available in Plant Simulation. Students distinguish when each resource type is appropriate for a given process.

Lesson 2 • Shift Calendars and Working Hours

Covers shift schedule creation and assignment to resources and machines. Shift patterns directly determine when resources are available during simulation.

Lesson 3 • Modeling Downtime and Failures

Introduces machine failure distributions and repair time modeling for realistic availability. Downtime modeling is essential for accurate capacity planning.

Lesson 1 • SimTalk Language Basics

Covers SimTalk syntax, data types, variables, and basic operators. Provides the minimum language knowledge needed to write functional control methods.

Lesson 2 • Accessing Object Attributes via Code

Teaches how to read and write object attributes programmatically using SimTalk paths. Enables dynamic model behavior driven by runtime conditions.

Lesson 3 • Writing and Triggering Methods

Covers method creation, parameter passing, and event-based triggering on simulation objects. Methods are the primary mechanism for custom simulation logic.

Lesson 4 • Debugging SimTalk Code

Introduces the SimTalk debugger, breakpoints, and watch variables for code inspection. Debugging skills reduce development time and prevent logic errors.

Lesson 1 • Hierarchical Model Structuring

Teaches use of frames and sub-models to organize complex layouts into manageable units. Hierarchy improves model readability and reuse across projects.

Lesson 2 • Parallel and Branching Flow Paths

Covers split and merge objects for modeling parallel processing and alternative routing. Branching logic is fundamental to multi-product and redundant-line models.

Lesson 3 • Conveyor and Transport Systems

Models conveyor belts, accumulating conveyors, and automated guided vehicles. Transport system accuracy directly affects buffer sizing and cycle time results.

Lesson 4 • Assembly and Disassembly Processes

Teaches assembly station setup requiring multiple part types before processing begins. Models reflect real joining and kitting operations in manufacturing.

Lesson 1 • Comparing Alternative Scenarios

Teaches structured comparison of two or more model configurations using statistical tests. Scenario comparison is the core decision-support output of simulation studies.

Lesson 2 • Built-In Statistics and Charts

Introduces Plant Simulation's statistics objects, Gantt charts, and throughput diagrams. Visual output tools accelerate identification of performance patterns.

Lesson 3 • Warm-Up Period and Steady State

Explains transient bias in simulation output and methods for determining warm-up length. Removing warm-up data is essential for valid steady-state performance estimates.

Lesson 4 • Replication and Confidence Intervals

Covers running multiple replications and computing confidence intervals for key metrics. Replications quantify output variability caused by stochastic inputs.

Lesson 1 • Documenting Optimization Studies

Covers recording experiment configurations, results, and recommendations in a structured report. Proper documentation ensures reproducibility and supports stakeholder decisions.

Lesson 2 • Sensitivity Analysis Techniques

Teaches one-at-a-time and global sensitivity analysis to rank input parameter influence. Sensitivity analysis guides data collection priorities before full optimization.

Lesson 3 • Genetic Algorithm Optimization

Applies Plant Simulation's built-in genetic algorithm to multi-parameter optimization problems. Students configure fitness functions and population settings for practical problems.

Lesson 4 • Design of Experiments Principles

Introduces factorial and fractional factorial designs for efficient parameter exploration. DOE reduces the number of runs needed to characterize system behavior.

Lesson 1 • Model Verification and Validation

Teaches structured verification of model logic and validation against real system performance. A validated model earns stakeholder trust and supports reliable decisions.

Lesson 2 • Scenario Development and Analysis

Students design and run improvement scenarios based on project objectives and constraints. Scenario analysis translates simulation capability into actionable business insights.

Lesson 3 • Final Report and Presentation

Guides students in structuring a simulation study report and presenting findings to stakeholders. Professional communication of results is as important as technical accuracy.

Lesson 4 • Project Scoping and Data Collection

Defines study objectives, system boundaries, and required input data for a simulation project. Clear scoping prevents model over-complexity and misaligned deliverables.