Training course
Overview
Industrial Engineering for
Professionals is a comprehensive professional training course designed
to equip engineers, technical specialists, operations professionals, and other
workplace practitioners with the practical knowledge and analytical skills
required to improve productivity, efficiency, quality, cost performance, and
resource utilization. The course provides a structured understanding of
industrial engineering principles and their application to manufacturing,
production, logistics, service delivery, maintenance, supply chain, and
business operations. Participants will learn how to evaluate processes
systematically, identify performance gaps, eliminate operational waste, and
develop practical engineering improvements.
This professional industrial
engineering training introduces widely used methodologies and frameworks
including Lean, Six Sigma, Kaizen, PDCA, DMAIC, Theory of Constraints, 5S,
Value Stream Mapping, FMEA, Statistical Process Control, and Total Productive
Maintenance. Participants will develop practical skills in process mapping,
work measurement, time study, productivity analysis, capacity planning, line
balancing, facility layout, material flow, inventory management, quality
improvement, and operational performance measurement. Emphasis is placed on
selecting appropriate tools for different operational problems and translating
analytical findings into realistic workplace improvements.
The course uses hands-on exercises,
case studies, process analysis activities, engineering calculations,
simulations, and real-world operational scenarios to develop practical
capability. Participants will learn how to measure work and process
performance, identify bottlenecks and constraints, analyze process variation,
improve workflows, optimize resources, reduce defects and waste, and strengthen
operational controls. Practical tools such as SIPOC diagrams, flowcharts, value
stream maps, Pareto charts, Fishbone diagrams, Five Whys, control charts, FMEA
matrices, capacity calculations, standard work sheets, and productivity
dashboards are incorporated throughout the program.
By the end of this five-day
industrial engineering course, participants will be able to apply industrial
engineering techniques confidently to practical workplace challenges and
contribute to measurable improvements in operational performance. The program
progresses from fundamental concepts and process analysis to work measurement,
capacity and quality improvement, optimization, reliability, digital
technologies, and continuous improvement. A final applied case study and
improvement exercise enables participants to integrate the tools learned
throughout the training into a practical industrial engineering improvement
plan that can be adapted to their professional environment.
Course
Duration
5 Days (40 Hours)
Target
Participants
·
Industrial Engineering Professionals
·
Engineers and Technical Specialists
·
Production and Manufacturing Professionals
·
Operations and Process Improvement Professionals
·
Supply Chain and Logistics Professionals
·
Quality and Continuous Improvement Professionals
·
Maintenance and Reliability Professionals
·
Production Planners and Scheduling Professionals
·
Technical Supervisors and Team Leaders
·
Professionals responsible for productivity,
efficiency, cost reduction, and process improvement
Course
Objectives
By the end of the training,
participants will be able to:
·
Explain the principles, scope, and professional
applications of industrial engineering
·
Analyze operational systems involving people,
processes, equipment, materials, and information
·
Map and evaluate processes using practical
process analysis and value stream tools
·
Conduct work studies, method studies, and time
studies to establish effective work standards
·
Measure productivity, utilization, efficiency,
capacity, cycle time, and process performance
·
Identify waste, bottlenecks, constraints,
delays, and non-value-adding activities
·
Apply Lean, Kaizen, Six Sigma, PDCA, DMAIC, and
Theory of Constraints principles
·
Improve workflows, workstations, production
lines, and material movement
·
Apply quality engineering tools to reduce
defects, variation, and process failures
·
Develop practical capacity, inventory,
scheduling, and resource allocation solutions
·
Apply FMEA, SPC, root cause analysis, and other
structured problem-solving methods
·
Evaluate equipment performance, maintenance
effectiveness, and operational reliability
·
Apply ergonomics and human factors principles to
workplace and process design
·
Use industrial data, dashboards, analytics, and
digital technologies to support improvement
·
Develop practical industrial engineering
improvement projects with measurable performance outcomes
Course
Content
Day
1: Industrial Engineering Foundations, Systems Thinking, and Process Analysis
Module 1: Industrial Engineering
Foundations, Systems Thinking, and Process Analysis
1. Introduction
to Industrial Engineering and Its Professional Applications
2. Industrial
Engineering Systems: People, Processes, Technology, Materials, and Information
3. Systems
Thinking and Integrated Analysis of Operational Performance
4. Productivity,
Efficiency, Effectiveness, Utilization, and Key Performance Measures
5. Process
Mapping, Flowcharts, SIPOC, and Basic Process Analysis
6. Value
Stream Mapping and Identification of Value-Adding Activities
7. Lean
Principles, Waste Identification, 5S, Kaizen, and PDCA
8. Identifying
Bottlenecks, Constraints, Delays, and Process Inefficiencies
9. Case
Study: Diagnosing Productivity and Workflow Problems in an Operational Process
10. Practical
Exercise: Developing a Process Map and Identifying Industrial Engineering
Improvement Opportunities
Day
2: Work Measurement, Capacity Planning, Facility Layout, and Human Factors
Module 2: Work Measurement, Capacity
Planning, Facility Layout, and Human Factors
1. Work
Study Principles, Method Study, and Work Improvement Techniques
2. Time
Study, Work Sampling, Performance Rating, and Allowances
3. Standard
Time Development and Standard Work Documentation
4. Productivity
Measurement, Labor Utilization, and Performance Analysis
5. Capacity
Planning, Capacity Utilization, and Resource Requirements
6. Takt
Time, Cycle Time, Lead Time, and Production Flow
7. Line
Balancing, Workstation Design, and Production Flow Improvement
8. Facility
Layout, Material Handling, Workplace Organization, and Flow
9. Case
Study: Improving Production Capacity Through Work Measurement and Layout
Redesign
10. Practical
Exercise: Conducting a Work Measurement Study and Developing an Improved
Process Design
Day
3: Quality Engineering, Inventory, Scheduling, and Problem Solving
Module 3: Quality Engineering, Inventory,
Scheduling, and Problem Solving
1. Quality
Engineering Principles and the Relationship Between Quality and Productivity
2. Statistical
Process Control, Control Charts, and Process Variation
3. Process
Capability, Defect Prevention, and Six Sigma Fundamentals
4. FMEA
for Process Risk Identification and Failure Prevention
5. Root
Cause Analysis Using Five Whys, Fishbone, and Pareto Analysis
6. Inventory
Management, Economic Order Quantity, Safety Stock, and Reorder Points
7. Production
Planning, Scheduling, Sequencing, and Resource Coordination
8. Bottleneck
Management and Theory of Constraints
9. Case
Study: Resolving Quality, Inventory, and Production Scheduling Problems
10. Practical
Simulation: Balancing Quality, Inventory, Capacity, and Production Requirements
Day
4: Reliability, Optimization, Cost Management, and Operational Improvement
Module 4: Reliability, Optimization, Cost
Management, and Operational Improvement
1. Reliability
Engineering Fundamentals and Equipment Performance Analysis
2. Total
Productive Maintenance and Overall Equipment Effectiveness
3. Preventive,
Predictive, and Condition-Based Maintenance Concepts
4. Operations
Research and Quantitative Methods for Industrial Engineering
5. Resource
Allocation, Optimization, and Engineering Decision-Making
6. Cost
Analysis, Cost Drivers, Waste Costs, and Cost of Poor Performance
7. Process
Improvement Economics and Evaluating Improvement Alternatives
8. Lean
Six Sigma DMAIC and Structured Improvement Projects
9. Case
Study: Reducing Equipment Losses, Operating Costs, and Process Inefficiencies
10. Practical
Exercise: Developing a Reliability, Cost Reduction, and Process Improvement
Proposal
Day
5: Digital Industrial Engineering, Sustainability, and Continuous Improvement
Module 5: Digital Industrial Engineering,
Sustainability, and Continuous Improvement
1. Digital
Industrial Engineering and Data-Driven Operational Improvement
2. Industrial
Data Collection, Quality Metrics, Dashboards, and Performance Reporting
3. Simulation
Concepts and Scenario Analysis for Operational Decision-Making
4. Automation,
Robotics, IoT, and Industry 4.0 Applications
5. Artificial
Intelligence, Predictive Analytics, and Intelligent Process Improvement
6. Sustainable
Industrial Engineering, Energy Efficiency, and Resource Optimization
7. Operational
Resilience, Risk Management, and Continuous Improvement
8. Benchmarking,
Best Practices, and Industrial Engineering Performance Maturity
9. Case
Study: Designing a Lean, Digital, Sustainable, and High-Performance Operation
10. Capstone
Exercise: Developing a Practical Industrial Engineering Improvement Project and
Implementation Roadmap