Introduction to Statistical Quality Control - MONTGOMERY

This course offers an in-depth exploration of statistical methods for quality control and process improvement, grounded in the widely respected textbook Introduction to Statistical Quality Control by Douglas C. Montgomery. It is designed to prepare students, engineers, analysts, and professionals to apply statistical techniques to real-world problems in manufacturing, service, and industrial settings, where quality and efficiency are critical to success.

The course begins by establishing a strong conceptual understanding of what quality means in today’s competitive, customer-focused world. Students will explore the historical evolution of quality thinking, from inspection to statistical control and modern continuous improvement methodologies such as Six Sigma and Lean. The foundational role of variation in quality management is emphasized throughout the course.

Core topics include Statistical Process Control (SPC), the design and interpretation of control charts, and the use of descriptive and inferential statistics to understand and manage process variation. Students will gain hands-on experience with control charts for variables (such as Xˉ\bar{X} and R charts) and attributes (such as p and c charts), enabling them to monitor process stability and performance effectively. Advanced SPC tools such as CUSUM and EWMA charts will also be introduced for detecting small shifts in processes.

Another major component of the course is Process Capability Analysis, where students learn to evaluate how well a process performs relative to specified limits using indices like CpC_p, CpkC_{pk}, and CpmC_{pm}. The course also delves into Measurement System Analysis (including Gage R&R studies) to assess the adequacy of measurement tools and systems.

Acceptance Sampling is covered to equip students with methods for decision-making when inspecting incoming or outgoing product lots, including the design of sampling plans and interpretation of Operating Characteristic (OC) curves.

The course also introduces the principles of Design of Experiments (DOE)—a powerful statistical approach to process optimization. Students will learn how to structure experiments, interpret interaction effects, and use data to identify key process variables.

Throughout the course, practical applications, case studies, and the use of statistical software are emphasized to help students bridge theory and practice. By the end of the course, participants will be able to collect and analyze quality data, design effective control systems, and contribute meaningfully to quality improvement initiatives in a variety of industries.

This course is ideal for upper-level undergraduate and graduate students in engineering, applied statistics, or business analytics, as well as industry professionals involved in quality assurance, process improvement, or operations management.