Quality at its best
This course provides a comprehensive introduction to Statistical Quality Control (SQC), equipping students with the analytical tools necessary to monitor and improve organizational processes . Students will begin by learning the formal definition of statistical quality control and its critical role in modern manufacturing and service industries ,here various control charts will be used to detect if our product is within the control limits or not. Peradventure if the lots is not within the control limit some critical investigations will be conducted.
What you’ll learn
- Definition of Quality Control.
- Importance of Quality Control in the industries.
- They will learn causes of variation.
- They will learn the types of control charts.
- Concept of Acceptance plans will be studied.
- Types of Sampling inspection will be learn.
- Producer Risk and Consumer Risk will be explained.
Course Content
- Introduction –> 1 lecture • 2min.
- Introduction to Statistical Quality Control –> 1 lecture • 5min.
- Causes of Variation –> 1 lecture • 7min.
- Uses of Statistical Quality Control –> 1 lecture • 7min.
- Product and Process control in the statistical quality control –> 1 lecture • 3min.
- The Control Charts –> 1 lecture • 9min.
- The 3-Sigma Control limits will be explained –> 1 lecture • 9min.
- Tools used for Statistical Quality Control –> 1 lecture • 1min.
- Control Charts for Variables –> 1 lecture • 4min.
- Control Charts for Mean –> 1 lecture • 5min.
- Calculation of Mean and Range Charts –> 1 lecture • 3min.
- Computation of Control limits –> 1 lecture • 6min.
- Control Chart for the Range chart (R-chart) –> 1 lecture • 2min.
- Interpretation of Mean and Range Charts –> 1 lecture • 9min.
- EXAMPLE ONE –> 1 lecture • 7min.
- EXAMPLE TWO –> 1 lecture • 5min.
- Example Three –> 1 lecture • 7min.
- Example Four –> 1 lecture • 3min.
- Controls Charts for Standard Deviation –> 1 lecture • 9min.
- Example 5: Control Chart –> 1 lecture • 4min.
- Control Chart for Attributes –> 1 lecture • 7min.
- Control Chart for fraction defective or P-chart –> 1 lecture • 6min.
- Fraction defective chart example –> 1 lecture • 4min.
- Example seven on the P-chart –> 1 lecture • 9min.
- Control Chart for Number of Defective or np chart –> 1 lecture • 2min.
- Np Chart versus P-chart –> 1 lecture • 2min.
- Example Eight –> 1 lecture • 5min.
- Control chart for number of defective per unit or C- chart –> 1 lecture • 3min.
- Example of C-chart –> 1 lecture • 5min.
- Acceptance sampling plans –> 1 lecture • 3min.
- Concept of inspection plans –> 1 lecture • 4min.
- Dodge and Romig’s Sampling plans –> 1 lecture • 2min.
- Procedure for single sampling plans –> 1 lecture • 2min.
- Merit and Demerit of single sampling plans –> 1 lecture • 1min.
- Double sampling plans –> 1 lecture • 6min.
- Producer Risk and Consumer Risk –> 1 lecture • 2min.
- Curves for sampling plans –> 1 lecture • 5min.
Requirements
This course provides a comprehensive introduction to Statistical Quality Control (SQC), equipping students with the analytical tools necessary to monitor and improve organizational processes . Students will begin by learning the formal definition of statistical quality control and its critical role in modern manufacturing and service industries ,here various control charts will be used to detect if our product is within the control limits or not. Peradventure if the lots is not within the control limit some critical investigations will be conducted.
A core focus of the curriculum is understanding the types of variation inherent in any process, specifically distinguishing between common (chance) causes and assignable (special) causes . By identifying these variations, students will learn the strategic uses of SQC to maintain stability, reduce defects, and enhance overall productivity .
The course also delves into the economics of inspection through acceptance sampling. Participants will explore the critical concepts of producer’s risk (the probability of rejecting a good batch) and consumer’s risk (the probability of accepting a defective batch), learning how to balance these risks to protect both the manufacturer and the end-user .
By the end of the term, students will be proficient in utilizing control charts and sampling plans to drive data-driven decision-making.