Control Chart in Lean Six Sigma

Control Chart in Lean Six Sigma

Introduction

In Lean Six Sigma, a control chart is a fundamental statistical tool used to monitor process performance over time and ensure that improvements are sustained. It belongs primarily to the Control phase of DMAIC, where the focus is on maintaining process stability and preventing regression after improvements have been implemented.

A control chart helps distinguish between normal process variation and abnormal variation that requires investigation and corrective action.

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What Is a Control Chart?

A control chart is a time-ordered graphical display of process data with statistically determined limits. It answers a critical question:

Is the process stable and predictable, or is something unusual happening?

A typical control chart consists of:

• Center Line (CL) – the process average or mean

• Upper Control Limit (UCL) – upper boundary of expected variation

• Lower Control Limit (LCL) – lower boundary of expected variation

• Plotted data points over time

These limits are not specification limits; they are calculated from the process data itself.

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Purpose of Control Charts in Lean Six Sigma

Control charts support Lean Six Sigma objectives by:

• Detecting process instability

• Identifying special cause variation

• Preventing defects before they occur

• Sustaining gains achieved during improvement

• Enabling data-driven decision making

They shift organizations from reactive firefighting to proactive process control.

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Types of Variation

Understanding variation is central to control charts.

1. Common Cause Variation

• Inherent to the process

• Predictable and random

• Requires process redesign to improve

2. Special Cause Variation

• Due to unusual or assignable factors

• Unpredictable

• Requires immediate investigation and correction

Control charts help separate these two types clearly.

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Types of Control Charts

Control charts are chosen based on data type and sample size.

A. Variable Data Charts (Continuous data)

Used when measurements are numerical.

• X̄ – R Chart: Mean and range (small samples)

• X̄ – S Chart: Mean and standard deviation (large samples)

• I–MR Chart: Individual values and moving range

B. Attribute Data Charts (Discrete data)

Used when data are counts or proportions.

• P Chart: Proportion defective

• NP Chart: Number of defectives

• C Chart: Count of defects

• U Chart: Defects per unit

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Control Chart Rules (Signals of Trouble)

A process may be out of control if:

• A point falls outside UCL or LCL

• Seven or more points trend upward or downward

• Seven consecutive points lie on one side of the center line

• Cyclical or repeating patterns appear

These rules help identify hidden instability even when points are within limits.

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Role of Control Charts in DMAIC

DMAIC Phase	Role of Control Chart
Define	Understand customer requirements
Measure	Establish baseline performance
Analyze	Identify variation patterns
Improve	Validate improvement effectiveness
Control	Monitor and sustain gains

Control charts are most critical in the Control phase, where they act as an early warning system.

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Benefits of Using Control Charts

• Reduces defects and rework

• Improves process predictability

• Enhances customer satisfaction

• Supports standardization

• Builds a culture of continuous improvement

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Common Mistakes to Avoid

• Confusing control limits with specification limits

• Reacting to every data point (overcontrol)

• Using the wrong type of chart

• Ignoring patterns and trends

• Failing to update charts after process changes

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What happens when a point exceeds UCL or falls below LCL?

Let’s understand this with clear, practical examples 👇

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Example 1: Point Exceeds UCL (Upper Control Limit)

Scenario

You are monitoring call handling time in a customer support process using an I–MR control chart.

• Center Line (CL) = 5 minutes

• UCL = 8 minutes

• LCL = 2 minutes

What happened?

On Tuesday, the handling time recorded is 11 minutes, which is above the UCL.

Meaning

✔ This is special cause variation
✔ The process is out of statistical control
✔ Something unusual has occurred

Possible reasons

• New employee handling calls

• System outage or slow application

• Very complex customer issue

• No standard work followed

Action required

• Stop and investigate immediately

• Identify the root cause

• Fix the issue

• Prevent recurrence

🔴 Key point: A single point above UCL is a strong signal that the process behavior has changed.

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Example 2: Point Below LCL (Not Meeting LCL)

Scenario

You are monitoring fill volume in a bottling process.

• CL = 500 ml

• UCL = 520 ml

• LCL = 480 ml

What happened?

One bottle shows 460 ml, which is below the LCL.

Meaning

✔ This is also special cause variation
✔ The process is out of control
✔ Customer dissatisfaction or defect risk

Possible reasons

• Machine calibration issue

• Valve blockage

• Pressure fluctuation

• Operator error

Action required

• Quarantine affected products

• Check machine settings

• Recalibrate equipment

• Resume production only after stability

🔴 Key point: A point below LCL is just as serious as exceeding UCL.

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Important Clarification (Very Common Confusion)

Control Limits vs Specification Limits

Control Limits	Specification Limits
Based on process data	Based on customer requirement
Show process stability	Show acceptability
Used for monitoring	Used for inspection

👉 A point within spec but outside control limits still needs investigation.

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Simple Rule to Remember 🧠

• Above UCL → Process behaving unusually high

• Below LCL → Process behaving unusually low

• Either case → ❌ Process is out of control

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Real-Life Analogy 🚗

Imagine driving a car:

• Speed limit = Control limits

• Suddenly you hit 140 km/h → exceeds UCL

• Suddenly engine drops to 20 km/h → below LCL

Both are dangerous and need immediate action.

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Lean Six Sigma Mindset

Do not adjust the process for normal variation.
Always investigate special cause variation.

What are USL and LSL in Lean Six Sigma?

USL (Upper Specification Limit): The maximum acceptable value allowed by the customer or design

LSL (Lower Specification Limit): The minimum acceptable value allowed by the customer or design

Example 1: Manufacturing (Easy to visualize)

Scenario – Shaft Diameter

• Required diameter = 10 mm ± 0.5 mm

Term	Value
USL	10.5 mm
LSL	9.5 mm

• 10.3 mm → ✅ Acceptable (Average)

• 9.4 mm → ❌ Defect (below LSL)

• 10.6 mm → ❌ Defect (above USL)

Conclusion

Control charts are the heartbeat of Lean Six Sigma control. They transform raw data into meaningful insight, enabling organizations to maintain stable, efficient, and high-quality processes. Without control charts, improvements are temporary; with them, excellence becomes sustainable.

“You cannot improve what you do not measure, and you cannot sustain what you do not control.”