Manufacturing Systems Modelling and Discrete-Event Simulation Training Course

Course Introduction

The Manufacturing Systems Modelling and Discrete-Event Simulation Training Course is designed to provide professionals with advanced knowledge and practical capabilities in developing, analyzing, and optimizing complex manufacturing systems. Modern industries increasingly rely on simulation-based decision-making to improve productivity, reduce operational risks, optimize resources, and evaluate future production strategies. This course equips participants with the methodologies required to model manufacturing processes and simulate real-world operational scenarios.

This comprehensive training program explores the principles of manufacturing systems modelling, discrete-event simulation techniques, process representation, system analysis, and performance optimization. Participants will gain a deep understanding of how manufacturing systems behave under different operating conditions and how simulation models can support better engineering decisions. The course integrates industrial engineering concepts with advanced computational approaches for improving manufacturing performance.

Participants will develop practical expertise in simulation model development, production flow analysis, capacity evaluation, bottleneck identification, scheduling optimization, and resource utilization improvement. The program demonstrates how discrete-event simulation can be applied to manufacturing lines, assembly systems, logistics networks, warehouses, and complex industrial operations. Real-world case studies provide valuable insights into successful simulation applications across different manufacturing sectors.

The course also addresses emerging technologies transforming manufacturing simulation, including digital twins, artificial intelligence, machine learning, Industry 4.0, industrial Internet of Things, virtual manufacturing, and smart factory platforms. Participants will understand how advanced simulation technologies enable predictive analysis, real-time optimization, improved production planning, and more effective decision-making in modern industrial environments.

Through practical workshops, modelling exercises, simulation development activities, and industrial examples, participants will develop the ability to create accurate manufacturing models, evaluate alternative production scenarios, analyze system performance, and recommend optimization strategies. The course emphasizes practical engineering applications that improve efficiency, flexibility, reliability, and competitiveness in manufacturing operations.

Upon successful completion of the course, participants will possess advanced skills required to apply manufacturing systems modelling and discrete-event simulation techniques effectively. They will be prepared to support production improvement initiatives, evaluate investment decisions, optimize operational processes, and contribute to the development of intelligent, efficient, and data-driven manufacturing systems.

Duration

10 days

Who Should Attend

• Manufacturing Engineers

• Industrial Engineers

• Production Engineers

• Operations Managers

• Plant Managers

• Process Improvement Specialists

• Simulation Engineers

• Systems Engineers

• Automation Engineers

• Production Planning Professionals

• Supply Chain Specialists

• Lean Manufacturing Professionals

• Digital Manufacturing Specialists

• Data Analysts

• Research and Development Engineers

• Manufacturing Consultants

• Quality Improvement Professionals

• Process Optimization Specialists

• Engineering Project Managers

• Professionals involved in manufacturing system improvement projects

Course Objectives

• Develop advanced knowledge of manufacturing systems modelling principles and discrete-event simulation methodologies for industrial applications.

• Understand how simulation models represent complex manufacturing processes, workflows, resources, and operational interactions.

• Learn techniques for collecting, analyzing, and preparing manufacturing data for accurate simulation model development.

• Master discrete-event simulation approaches for evaluating production performance and identifying improvement opportunities.

• Apply simulation techniques to optimize production capacity, resource allocation, scheduling, and manufacturing efficiency.

• Develop expertise in identifying bottlenecks, reducing operational constraints, and improving manufacturing system performance.

• Understand digital twin concepts and their integration with manufacturing simulation and real-time operational systems.

• Learn how artificial intelligence and machine learning enhance simulation accuracy and predictive manufacturing analysis.

• Examine emerging Industry 4.0 technologies supporting smart manufacturing simulation and intelligent decision-making.

• Strengthen skills in validating simulation models and interpreting simulation results for engineering decisions.

• Understand simulation-based approaches for evaluating facility changes, process improvements, and investment strategies.

• Build professional capabilities required to manage manufacturing modelling projects and implement optimization solutions.

Comprehensive Course Outline

Module 1: Fundamentals of Manufacturing Systems Modelling

• Introduction to manufacturing system concepts and modelling methodologies

• Role of modelling in improving industrial decision-making processes

• Types of manufacturing models and their engineering applications

• Challenges associated with modelling complex production environments

Module 2: Principles of Discrete-Event Simulation

• Fundamentals of discrete-event simulation and system representation

• Events, states, resources, and process logic in simulation models

• Applications of discrete-event simulation in manufacturing industries

• Advantages and limitations of simulation-based analysis approaches

Module 3: Manufacturing Data Collection and Analysis

• Methods for collecting reliable manufacturing system data

• Data preparation techniques for simulation model development

• Statistical analysis of production system performance information

• Improving simulation accuracy through effective data management

Module 4: Simulation Model Development Techniques

• Building conceptual models for manufacturing system analysis

• Translating real processes into simulation environments

• Developing logical relationships between system components

• Verification and validation methods for simulation models

Module 5: Production System Modelling Applications

• Modelling assembly lines and manufacturing workflows

• Simulation of batch production and continuous manufacturing systems

• Analysing production variability and operational performance

• Improving manufacturing processes through simulation studies

Module 6: Resource and Capacity Optimization

• Simulation approaches for evaluating production capacity

• Optimizing equipment, labor, and resource utilization

• Identifying capacity limitations through simulation analysis

• Improving throughput using resource optimization strategies

Module 7: Bottleneck Identification and Process Improvement

• Detecting production bottlenecks using simulation techniques

• Analysing constraints affecting manufacturing performance

• Testing improvement strategies through virtual experiments

• Reducing production delays through optimized process design

Module 8: Scheduling and Production Planning Simulation

• Simulation of manufacturing scheduling strategies

• Evaluating production sequencing and workflow alternatives

• Improving delivery performance through simulation analysis

• Supporting production planning with predictive modelling

Module 9: Supply Chain and Logistics Simulation

• Modelling manufacturing supply chain interactions

• Simulating warehouse and material handling operations

• Evaluating logistics performance and inventory strategies

• Improving supply chain responsiveness through simulation

Module 10: Digital Twins and Virtual Manufacturing

• Fundamentals of digital twin technology in manufacturing systems

• Integrating simulation models with real-time operational data

• Virtual manufacturing environments for process optimization

• Future applications of digital twin-based simulation systems

Module 11: Artificial Intelligence in Simulation Modelling

• Artificial intelligence applications in manufacturing simulation

• Machine learning techniques supporting predictive modelling

• AI-based optimization of simulation experiments

• Intelligent decision-support systems for manufacturing operations

Module 12: Industry 4.0 and Smart Manufacturing Simulation

• Smart factory concepts and simulation-based optimization

• Industrial IoT integration with manufacturing models

• Connected manufacturing systems and real-time analytics

• Digital transformation strategies using simulation technologies

Module 13: Advanced Simulation Experimentation

• Designing simulation experiments for manufacturing improvement

• Scenario analysis and alternative system evaluation methods

• Optimization techniques for simulation-based decision-making

• Interpreting simulation outputs for engineering applications

Module 14: Simulation Applications in Manufacturing Industries

• Simulation applications in automotive production systems

• Modelling pharmaceutical and process manufacturing operations

• Industrial applications in electronics and high-volume production

• Case studies of successful simulation implementation projects

Module 15: Emerging Issues and Future Trends

• Autonomous manufacturing systems and intelligent simulation models

• Cloud-based simulation platforms and collaborative modelling

• Sustainable manufacturing simulation and resource optimization

• Future developments in digital manufacturing technologies

Module 16: Integrated Manufacturing Simulation Project

• Developing a complete manufacturing simulation model project

• Applying simulation methods to practical industrial challenges

• Evaluating improvement opportunities using simulation results

• Creating implementation plans for optimized manufacturing systems

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.