Introduction

Advanced Automation Systems Design in Manufacturing Training Course is designed to equip learners with cutting-edge competencies in smart manufacturing, Industrial Automation, Industry 4.0 technologies, and intelligent production systems. As global manufacturing rapidly evolves toward fully digitized and connected ecosystems, organizations are increasingly adopting IIoT (Industrial Internet of Things), AI-driven automation, robotics integration, PLC/SCADA systems, and digital twin technologies to optimize productivity, reduce downtime, and enhance operational efficiency. This course provides a structured pathway to mastering these advanced systems, enabling professionals to design, implement, and manage next-generation automated manufacturing environments.

With a strong emphasis on cyber-physical systems, predictive maintenance, edge computing, machine learning in manufacturing, and smart factory architecture, this training bridges the gap between traditional manufacturing engineering and modern intelligent automation frameworks. Participants will gain hands-on knowledge of system design, process optimization, real-time data analytics, and integrated control systems. The course is ideal for engineers, technicians, and industry professionals aiming to advance their expertise in automated production lines, robotics process automation (RPA), and AI-powered manufacturing systems, ensuring alignment with global digital transformation trends in industrial operations.

Programme Curriculum

Advanced Automation Systems Design in Manufacturing Training Course

Introduction

Course Duration

10 days

Course Objectives

Understand core principles of Industry 4.0 smart manufacturing ecosystems
Design and integrate PLC-based automation control systems
Implement SCADA systems for real-time industrial monitoring
Develop skills in Industrial Internet of Things (IIoT) architecture
Apply AI and machine learning for predictive maintenance
Configure and program industrial robotics and robotic arms
Analyze and optimize manufacturing process automation workflows
Design digital twin models for production systems
Implement edge computing solutions in manufacturing environments
Enhance system efficiency using data-driven manufacturing analytics
Develop secure cyber-physical manufacturing systems
Optimize production using lean automation and smart factory design
Integrate MES (Manufacturing Execution Systems) with ERP platforms

Target Audience

Manufacturing Engineers
Automation and Control Engineers
Industrial Maintenance Technicians
Electrical and Electronics Engineers
Robotics Engineers and Technicians
Production Managers and Supervisors
Industry 4.0 Consultants
Technical Students in Mechanical/Electrical Engineering

Course Modules

Module 1: Fundamentals of Advanced Manufacturing Systems

Evolution of manufacturing technologies
Introduction to smart factories
Role of automation in modern industries
Cyber-physical systems overview
Case Study: Transition from traditional to smart factory in automotive industry

Module 2: Industry 4.0 Framework

Core principles of Industry 4.0
Smart manufacturing ecosystems
Connected devices and systems
Digital transformation strategies
Case Study: Siemens digital factory implementation

Module 3: PLC Systems Design

PLC architecture and components
Ladder logic programming basics
Industrial control applications
Troubleshooting PLC systems
Case Study: Automated packaging line control system

Module 4: SCADA Systems Integration

SCADA architecture and functions
Real-time monitoring systems
Human Machine Interface (HMI)
Data acquisition systems
Case Study: Power plant SCADA control system

Module 5: Industrial Robotics

Types of industrial robots
Robotic arm programming
Motion control systems
Safety standards in robotics
Case Study: Robotic welding in automotive manufacturing

Module 6: Industrial IoT (IIoT)

IoT sensors in manufacturing
Connectivity protocols (MQTT, OPC-UA)
Data collection and transmission
Smart device integration
Case Study: Smart factory IoT deployment in electronics industry

Module 7: Artificial Intelligence in Manufacturing

AI fundamentals in industrial systems
Machine learning applications
Predictive maintenance models
Quality control automation
Case Study: AI-based defect detection in semiconductor production

Module 8: Digital Twin Technology

Concept of digital twin systems
Simulation of manufacturing processes
Real-time system synchronization
Performance optimization techniques
Case Study: Aerospace production line digital twin implementation

Module 9: Edge Computing Systems

Edge vs cloud computing
Real-time processing at edge
Industrial edge devices
Latency reduction techniques
Case Study: Smart factory edge analytics deployment

Module 10: Manufacturing Execution Systems (MES)

MES architecture and functions
Production tracking systems
Workflow automation
Integration with ERP systems
Case Study: MES implementation in pharmaceutical manufacturing

Module 11: Predictive Maintenance Systems

Condition monitoring techniques
Sensor-based diagnostics
AI predictive algorithms
Failure prevention strategies
Case Study: Wind turbine predictive maintenance system

Module 12: Smart Factory Design

Smart factory architecture
Automation integration strategies
Data-driven production systems
Energy-efficient manufacturing
Case Study: Fully automated smart warehouse system

Module 13: Cybersecurity in Industrial Automation

Industrial network security
Cyber-physical threats
Secure communication protocols
Risk mitigation strategies
Case Study: Cyberattack prevention in manufacturing plant

Module 14: Robotics Process Automation (RPA)

RPA fundamentals in industry
Software-based automation tools
Workflow optimization
Human-robot collaboration
Case Study: RPA in supply chain automation

Module 15: Advanced System Integration & Optimization

System interoperability techniques
Multi-platform integration
Performance optimization tools
Future trends in automation
Case Study: End-to-end smart factory integration project

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Control system design Industrial automation System integration Process optimization

Programme Fee

Duration: 10 Days

Method: Online or Physical

Online Price: $2,000

Physical Price: $3,000

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 30 Oct 2026

💻 Online / Virtual session

Online $2,000