MGMT 456 - Statistical Quality Control for Manufacturing

Credits: 5
Variable Credit Course: No

Lecture Hours: 55
Lab Hours: 0
Worksite/Clinical Hours: 0
Other Hours (LIA/Internships): 0

Course Description: Focuses on statistical methods for quality improvement in manufacturing. Use tools such as control charts, capability studies, and design of experiments (DOE) to identify and address process variability. Emphasizes approaches such as failure mode and effects analysis (FMEA), continuous improvement frameworks, and data-driven management strategies, reinforced through relevant software applications.

Prerequisite: Admission to BASAMD program and Dept. Chair permission.
Strongly Recommended:
Special Requirements:

Meets FQE Requirement: No
Integrative Experience Requirement: No

Student Learning Outcomes

Examine foundational Statistical Quality Control principles through industry reports to determine how quantitative methods enhance manufacturing processes.
Solve complex process variability issues by applying advanced SPC tools and capability analysis to reduce defects and improve production efficiency.
Create visual aids and analytical reports to effectively communicate quality metrics and facilitate collaborative decision-making.
Develop a software-based SQC model to automate data collection, monitor statistical trends, and support continuous improvement in production environments.

Course Contents

Software Tools for Statistical Process Control - Utilizes specialized programs (e.g., Minitab, JMP) to analyze production metrics and guide decision-making.
Statistical Process Control (SPC) Fundamentals - Teaches control chart construction, interpretation, and out-of-control action plans for stable processes.
Quality Control Charts & Sampling Methods - Focuses on methods like X-bar/R charts, p-charts, and appropriate sampling frequencies to maintain product standards.
Statistical Methods for Business Decisions - Includes hypothesis testing, confidence intervals, and regression analysis to support cost-benefit trade-offs.
Process Capability & Cp/Cpk Analysis - Evaluates the ability of a process to consistently produce items within specification limits.
Design of Experiments (DOE) for Optimization - Structures experiments to isolate key variables, driving robust improvements in product or process performance.
Failure Mode & Effects Analysis (FMEA) - Anticipates potential system failures, assigning severity and risk levels to prioritize corrective actions.
Continuous Improvement & QMS Implementation - Demonstrates how to integrate ISO-style or other industry-standard quality systems into production.
Communicating Process Findings & Team-Based Analysis - Emphasizes clarity in presenting statistical results, fostering collaborative problem-solving.
Data-Driven Management & Strategic Planning - Uses real-time production metrics and historical performance data to shape long-term operational strategies.

Instructional Units: 5