Manufacturing Technology Curriculum

Course Overview

8 Modules

This course introduces learners to the foundational concepts, practical applications, and strategic value of data analytics, automation, and artificial intelligence in modern manufacturing environments. Designed as a 16-hour training experience, the course helps participants understand how manufacturing has evolved into the Industry 4.0 era and how connected technologies are reshaping decision-making across the value chain. Learners explore how manufacturing data is captured, managed, analyzed, and translated into action through dashboards, predictive models, automation tools, robotics, and emerging AI capabilities. Through discussions, case studies, and hands-on exercises, participants gain a practical understanding of how these technologies can improve quality, efficiency, visibility, and competitiveness in manufacturing settings.

Course Objectives:

Define core concepts related to data analytics, artificial intelligence, machine learning, automation, robotics, and the Industrial Internet of Things (IIoT) in a manufacturing context.
Explain the evolution of manufacturing from Industry 1.0 through Industry 4.0 and describe the strategic drivers behind digital transformation.
Identify major sources of manufacturing data and understand how data is captured, stored, governed, and protected.
Apply foundational data analysis and visualization concepts to turn operational data into meaningful insights.
Recognize how AI and machine learning can support predictive maintenance, defect detection, forecasting, and process optimization.
Understand the role of automation and robotics across both shop-floor and back-office manufacturing processes.
Evaluate real-world manufacturing use cases and identify opportunities, risks, and implementation considerations.
Build awareness of future trends in smart manufacturing and how organizations can prepare their teams and processes for continued digital change.

Module Breakdown:

Module 1 - Introduction to Data Analytics, Automation, and AI in Manufacturing

Introduces the course foundation by defining key terminology, tracing the evolution of manufacturing from Industry 1.0 through Industry 4.0, and highlighting the strategic importance of analytics, AI, and automation across the manufacturing value chain.

Module 2 - Data Collection and Management in Manufacturing

Explores how manufacturing data is generated, captured, stored, and governed. Topics include data sources across office operations, shop floor systems, logistics, and product design; collection technologies such as sensors, PLCs, SCADA, MES, and APIs; and key considerations for data quality, governance, security, and architecture.

Module 3 - Data Analysis and Visualization Tools in Manufacturing

Focuses on how raw manufacturing data is transformed into actionable insights. Learners examine data preparation, cleaning, descriptive through prescriptive analytics, foundational statistical concepts, and visualization best practices using tools such as Excel, Power BI, Tableau, and Python-based workflows.

Module 4 - AI and Machine Learning in Manufacturing

Builds on the analytics foundation by introducing practical AI and machine learning concepts for manufacturing. Topics include predictive maintenance, anomaly detection, computer vision for quality inspection, demand forecasting, and the data and governance requirements that support successful model deployment.

Module 5 - Automation, Robotics, and Intelligent Operations

Examines the role of automation and robotics in both plant-floor and business-process environments. Learners review fixed, programmable, and flexible automation as well as use cases such as welding, assembly, packaging, material handling, and software-based automation for repetitive digital tasks.

Module 6 - Real-World Manufacturing Use Cases and Business Value

Uses case examples to show how analytics, AI, and automation create measurable operational impact. Learners review scenarios tied to quality control, maintenance, throughput improvement, labor productivity, supply chain visibility, and cost reduction while discussing common implementation barriers.

Module 7 - Strategy, Readiness, and Responsible Adoption

Helps participants connect technology capabilities to business decision-making. Topics include prioritizing use cases, assessing return on investment, building stakeholder buy-in, addressing skills gaps, and adopting AI and automation responsibly within operational and regulatory constraints.

Module 8 - Future Trends and Applied Manufacturing Transformation

Concludes the course by exploring the future of smart manufacturing, including connected factories, digital twins, advanced robotics, edge analytics, and next-generation AI applications. Learners reflect on practical next steps for applying course concepts within their own organizations.

Program Cost

The Introduction to Data Analytics, Automation, and AI in Manufacturing course is offered at a fee of $3,000 per student. This all-inclusive cost covers the following:

Tuition and Instructional Support: Instructor-led training, facilitated discussions, and guided exercises.
Curriculum and Tooling Access: Course materials and any required software or sample datasets used during instruction.
Experiential Learning Activities: Case-based learning, applied activities, and coordination for optional guest speaker participation are covered under the per participant fee.

The per-student fee ensures a comprehensive educational experience that blends manufacturing context, data and AI fundamentals, operational use cases, and practical exposure to the technologies shaping Industry 4.0.