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Foundations of Industrial Engineering

Role and Scope of Industrial Engineering

Work Systems and Human Factors

Modeling, Analysis, and Optimization

Operations Research Fundamentals

Stochastic Systems and Simulation

Production and Supply Chain Systems

Manufacturing Systems and Quality Engineering

Supply Chain Design and Logistics

Data, Digitalization, and Systems Engineering

Analytics and Decision Support for Industrial Engineers

Systems Engineering, Project Management, and Economics

Capstone Integration and Applied Practice

Integrated System Design Capstone

Knowledge Checks

Quiz 1: Foundations and Human Factors

Quiz 2: Operations Research Essentials

Quiz 3: Stochastic Models and Simulation

Quiz 4: Manufacturing, Quality, and Lean

Quiz 5: Supply Chains, Analytics, and Economics

Feedback and Continuous Improvement

End-of-Course Survey

Badge Card

Designing and Optimizing Socio-Technical Systems in Industrial Engineering is a comprehensive, practice-oriented course that equips you to design high-performing operations where people, processes, data, and technology work together. You’ll build a robust foundation in industrial engineering—covering human factors, operations research, stochastic modeling, quality, and supply chains—then apply these tools to real manufacturing and service contexts. The course blends analytical rigor with human-centered design so you can optimize flow, cost, quality, safety, sustainability, and stakeholder value. Through hands-on modeling, simulation, and decision-support techniques, you’ll learn to diagnose system performance, evaluate trade-offs, and implement improvements that are technically sound and organizationally viable. You’ll also develop fluency in lean, Six Sigma, reliability, analytics, project economics, and systems engineering practices. The capstone challenges you to integrate these methods into an end-to-end solution—scoping a real problem, modeling alternatives, planning a pilot, and creating a control plan for sustained benefits. Whether you’re modernizing a factory, streamlining a hospital unit, or designing resilient supply chains, this course will help you translate data and models into better decisions and measurable outcomes.

Designing and Optimizing Socio-Technical Systems in Industrial Engineering

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            <p>Designing and Optimizing Socio-Technical Systems in Industrial Engineering equips learners to analyze, design, and improve complex operations where people, processes, data, and technology intersect. The course starts with the foundations of the discipline—its history and impact across manufacturing and services—then foregrounds a socio-technical perspective that balances stakeholder requirements with performance, cost, quality, and sustainability. Learners will examine ethics, standards, and professional practice, and build fluency in work systems and human factors, including time study, ergonomics, job design, and safety with applications in domains such as healthcare, logistics, and contact centers.</p>

<p>The analytical core focuses on modeling, analysis, and optimization. Participants will construct mathematical models of decision problems and apply linear, integer, and nonlinear programming, complemented by network models, sensitivity analysis, and multi-objective trade-off evaluation. For systems with uncertainty, the course develops probability and queueing models, and advances discrete-event simulation techniques—covering model building, verification, validation, output analysis, and robustness to risk and variability. This toolkit enables evidence-based decisions that integrate human-centered design with quantitative rigor.</p>

<p>From strategy to execution, the course deepens practice in production and supply chain systems. Learners will engage with manufacturing system design, lean principles, value stream mapping, Just-in-Time, and quality engineering through SPC, capability analysis, design of experiments, Six Sigma, and maintenance and reliability engineering. The supply chain sequence addresses demand forecasting, inventory control policies, network design, transportation, warehousing, last-mile logistics, S&OP, capacity and aggregate planning, and sustainability themes such as circularity, emissions, and resilience.</p>

<p>Data and digitalization are woven throughout. Participants will learn data acquisition and cleaning, descriptive–predictive–prescriptive analytics, and operations-focused regression, classification, and time series. They will integrate optimization with analytics through digital twins and what-if analysis, design dashboards and KPIs, and assess decision quality while mitigating bias. Complementary systems-engineering content builds capabilities in lifecycle thinking, requirements engineering, architecture, and interfaces, along with engineering economics (cost estimation, NPV/IRR, sensitivity analysis), project planning and scheduling (CPM/PERT), risk and portfolio management, and change management for stakeholder-centered implementation.</p>

<p>Learning culminates in an integrated capstone where each participant scopes a real problem in manufacturing or services, conducts stakeholder analysis, benchmarks flow, cost, quality, and sustainability metrics, and generates concepts that blend operations research, simulation, and ergonomics. The capstone delivers an implementation roadmap with pilot design, control plans, and benefits realization. Knowledge checks throughout—spanning foundations, human factors, optimization, stochastic systems, manufacturing, lean, supply chains, analytics, and economics—reinforce mastery, while an end-of-course survey supports continuous improvement. By the end, learners will be prepared to design and optimize socio-technical systems that are efficient, resilient, and humane.</p>
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