Operations and Supply Chain ManagementQuality Control

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1. Introduction to Variation:

2. Variation is a natural part of any process. Understanding the sources and magnitude of variation is key to process control.

3. Types of Variation:

4. Common Cause Variation: Inherent to the process, stable and predictable over time.

5. Special Cause Variation: Resulting from specific, identifiable factors, requires investigation and correction.

6. Role of Control Charts:

7. Control charts are tools used to monitor process behavior and distinguish between common and special causes of variation.

• Identify Sources of Variation: List and categorize sources into common and special causes.
• Use Control Charts: Implement control charts to monitor ongoing processes.

• Bottling Plant: A bottling plant may have variations in the fill level due to factors like bottle size (common cause) or a malfunctioning valve (special cause).
• Control Chart Setup: Using a control chart, a company could graph daily production levels to detect any points that signal a need for intervention.

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1. Setting Up Control Charts:
2. Key components: central line, upper control limit (UCL), lower control limit (LCL).

3. Charts for variables data often include X̄ (mean) and R (range) charts.

4. Interpreting Control Charts:

5. Look for points outside control limits, runs, and patterns that signal special causes.

6. Corrective Actions:

7. If a special cause is identified, investigate and eliminate the cause. Maintain control over the process through continual monitoring.

• Create X̄ and R Charts: Establish control limits based on historical data.
• Regular Monitoring: Evaluate the control charts frequently for signs of special causes.

• Manufacturing Line: A factory producing bolts may use X̄ and R charts to record daily measurements of bolt diameters, ensuring the consistency of output.
• Special Cause Detection: When a series of points fall outside the UCL, it could indicate a broken machine part that needs immediate attention.

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1. Different Types of Attribute Charts:

2. p-charts for proportion defectives, np-charts for number of defectives, c-charts for count of defects, and u-charts for defects per unit.

3. Attributes vs. Variables:

4. Attributes data is categorical and involves counts (e.g., defectives), whereas variables data is continuous (e.g., measurements).

5. Application of Attribute Charts:

6. Use attribute charts when the data consists of defect counts or defect rates.

• Select Appropriate Chart: Choose the correct attribute chart based on the type of data.
• Consistent Data Collection: Ensure accurate and consistent data recording of defects.

• Call Center: A call center may use a c-chart to count the number of customer complaints per day, helping identify days with unusually high complaint rates.
• Quality Inspection: An electronics manufacturer might use a p-chart to track the proportion of defective components in each batch.

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1. Process Capability Indices:

2. Cp and Cpk are measures that compare the width of the process distribution to the width of the specification limits.

3. Assessing Capability:

4. A capable process has Cp and Cpk values greater than 1, indicating that the process variation is less than the specification range.

5. Improving Capability:

6. Identifying and reducing sources of variation can improve process capability.

• Calculate Cp and Cpk: Use collected data to compute capability indices.
• Initiate Improvement Projects: Target processes with low capability for quality improvement initiatives.

• Automotive Industry: An automotive parts supplier calculates Cpk for the diameter of a piston to ensure it meets the stringent specifications required for engine assembly.
• Capability Improvement: If the Cpk is less than desired, the company might introduce tighter control over temperature settings during manufacturing.

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1. Real-World Implementation:

2. Understanding how various industries implement SPC can provide useful insights and guidelines.

3. Continuous Improvement Cycle:

4. Incorporates Plan-Do-Check-Act (PDCA) cycle for systematic improvement.

5. Management Involvement:

6. Active involvement of management in the SPC efforts is crucial for success.

• Study Case Studies: Draw lessons from other industries’ experiences with SPC.
• Engage in PDCA Cycle: Implement the cycle for continuous assessment and improvement of processes.

• Food Processing Industry: A bakery uses SPC charts to monitor the weight of dough pieces, learning from other bakeries’ practices to improve consistency.
• Management Retreats: Companies may organize retreats where management and workforce collaboratively review SPC data and set targets.

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Operations and Supply Chain ManagementQuality Control