Industrial Engineer Course

Lesson 1 • IE Roles in Organisations

Maps IE functions across manufacturing, logistics, healthcare, and services. Students recognise where IE creates strategic and operational value.

Lesson 2 • Core IE Principles and Concepts

Defines efficiency, productivity, waste, and value as foundational IE constructs. Links these concepts to measurable workplace outcomes.

Lesson 3 • History and Evolution of IE

Traces IE from scientific management through modern systems engineering. Provides context for why current tools and methods exist.

Lesson 1 • Probability and Distributions

Covers discrete and continuous probability distributions used in IE modelling. Connects statistical theory to process variability and reliability analysis.

Lesson 2 • Statistical Inference and Hypothesis Testing

Teaches estimation, confidence intervals, and hypothesis tests for process data. Enables evidence-based decisions about process performance.

Lesson 3 • Optimisation Mathematics

Introduces linear programming, calculus-based optimisation, and integer programming. Provides the mathematical foundation for resource allocation and scheduling.

Lesson 4 • Data Collection and Sampling Methods

Covers sampling strategies, measurement systems, and data integrity for IE studies. Ensures valid data underpins all subsequent analysis.

Lesson 1 • Motion Study and Principles of Motion Economy

Analyses body movements using therbligs and motion economy rules to reduce fatigue and waste. Directly improves operator efficiency and ergonomic safety.

Lesson 2 • Work Sampling and Standard Data

Uses statistical sampling to estimate activity proportions and builds standard data libraries. Reduces time study effort for repetitive IE projects.

Lesson 3 • Predetermined Motion Time Systems

Introduces MTM and MOST as analytical alternatives to stopwatch study. Enables standard time development before physical production begins.

Lesson 4 • Method Study and Process Analysis

Applies systematic observation and charting to document and critique current work methods. Establishes the baseline for improvement in any IE project.

Lesson 1 • Systematic Layout Planning

Applies Muther's SLP methodology to develop and rank layout alternatives systematically. Connects relationship analysis to practical floor plan development.

Lesson 2 • Facility Layout Fundamentals

Covers layout types, design objectives, and space requirements for industrial facilities. Establishes criteria for evaluating and selecting layout alternatives.

Lesson 3 • Material Flow Analysis

Quantifies material movement using from-to charts and flow diagrams to minimise travel distance. Feeds directly into layout and handling equipment decisions.

Lesson 4 • Warehouse and Storage System Design

Designs storage systems, slotting strategies, and order-picking layouts for warehouses. Integrates with material flow and inventory management decisions.

Lesson 1 • Material Requirements Planning

Uses MRP logic to explode bills of materials and generate time-phased order schedules. Connects product structure to procurement and production timing.

Lesson 2 • Production Scheduling Techniques

Applies priority rules, Gantt charts, and sequencing algorithms to shop floor scheduling. Minimises makespan, tardiness, and work in progress inventory.

Lesson 3 • Demand Forecasting Methods

Covers qualitative and quantitative forecasting techniques including moving averages and exponential smoothing. Accurate forecasts drive all downstream planning decisions.

Lesson 4 • Inventory Management and Control

Applies EOQ, reorder point, and safety stock models to minimise total inventory cost. Balances service levels against holding and ordering costs.

Lesson 1 • Acceptance Sampling Plans

Designs single, double, and sequential sampling plans using OC curves. Balances producer and consumer risk in incoming and outgoing inspection.

Lesson 2 • Quality Management Principles

Covers total quality management philosophy, quality costs, and process capability concepts. Frames quality as a strategic and measurable engineering objective.

Lesson 3 • Statistical Process Control

Develops and interprets control charts for variables and attributes data. Distinguishes common cause from special cause variation to guide corrective action.

Lesson 4 • Design of Experiments for Quality

Introduces factorial and Taguchi experimental designs for optimising process parameters. Connects experimental results to robust product and process design.

Lesson 1 • 5S and Visual Management

Implements 5S workplace organisation and visual controls to sustain lean improvements. Creates self-explaining workplaces that expose abnormalities instantly.

Lesson 2 • Flow, Pull, and Takt Time

Applies takt time, one-piece flow, and pull systems to synchronise production with customer demand. Reduces lead time and work in progress inventory.

Lesson 3 • Value Stream Mapping

Teaches current-state and future-state VSM to visualise and redesign end-to-end process flow. Identifies improvement priorities and quantifies waste reduction potential.

Lesson 4 • Kaizen and Continuous Improvement

Structures kaizen events and PDCA cycles to deliver rapid, team-based process improvements. Builds organisational capability for sustained continuous improvement.

Lesson 1 • Simulation-Based Optimisation

Combines simulation with optimisation algorithms to find near-optimal system configurations. Addresses problems too complex for analytical optimisation alone.

Lesson 2 • Building and Validating Simulation Models

Covers model construction, verification, and validation techniques for manufacturing and service systems. Ensures simulation outputs accurately represent real system behaviour.

Lesson 3 • Fundamentals of Discrete-Event Simulation

Introduces simulation concepts, event scheduling, and random variate generation for IE systems. Provides the conceptual foundation for building and interpreting simulation models.

Lesson 4 • Simulation Output Analysis

Applies statistical methods to analyse simulation outputs and compare system alternatives. Translates simulation data into actionable design recommendations.