In the realm of quality management and process improvement, identifying patterns that signal a process has gone out of control is crucial for maintaining operational excellence. Out of control patterns indicate that special causes of variation are affecting your process, requiring immediate investigation and corrective action. This comprehensive guide will walk you through the methodology of recognizing these patterns, understanding their implications, and implementing effective solutions.
Understanding Process Control and Variation
Before diving into out of control patterns, it is essential to understand the fundamental concept of process variation. Every process experiences two types of variation: common cause variation, which is inherent to the process and occurs naturally, and special cause variation, which results from external factors that are not part of the normal process operation. You might also enjoy reading about What is Process Improvement?.
Control charts serve as the primary tool for monitoring process stability and detecting out of control patterns. These statistical tools help distinguish between common and special cause variation, enabling you to make informed decisions about when to intervene and when to leave the process alone. You might also enjoy reading about How to Calculate and Improve First Pass Yield: A Complete Guide for Quality Excellence.
The Eight Primary Out of Control Patterns
Statistical process control recognizes eight distinct patterns that indicate a process has deviated from its expected behavior. Each pattern tells a unique story about what might be affecting your process.
1. Single Point Beyond Control Limits
This pattern occurs when one or more data points fall outside the upper or lower control limits. Consider a manufacturing facility producing precision components with a target diameter of 50mm. The control limits are set at 49.5mm (LCL) and 50.5mm (UCL). If a measurement registers at 50.7mm, this single point beyond the control limit signals immediate attention is required.
Sample Data Example:
- Measurement 1: 50.1mm
- Measurement 2: 49.8mm
- Measurement 3: 50.2mm
- Measurement 4: 50.7mm (Out of Control)
- Measurement 5: 50.0mm
This pattern typically indicates equipment malfunction, operator error, or defective raw materials requiring immediate investigation.
2. Nine Points in a Row on Same Side of Center Line
When nine consecutive data points fall on the same side of the process average, this indicates a shift in the process mean. For instance, in a call center measuring average handling time with a center line at 8 minutes, observing nine consecutive measurements all above this line suggests a systematic change has occurred.
Sample Data Set:
- Call 1: 8.3 minutes
- Call 2: 8.5 minutes
- Call 3: 8.2 minutes
- Call 4: 8.6 minutes
- Call 5: 8.4 minutes
- Call 6: 8.7 minutes
- Call 7: 8.3 minutes
- Call 8: 8.5 minutes
- Call 9: 8.4 minutes
This pattern might indicate new employee training needs, system upgrades affecting performance, or changes in customer inquiry complexity.
3. Six Points in a Row Steadily Increasing or Decreasing
A trend pattern shows six or more consecutive points that continuously increase or decrease. In a chemical processing plant monitoring pH levels, six consecutive readings showing steady decline would trigger this rule.
Example Trend Pattern:
- Reading 1: 7.2 pH
- Reading 2: 7.0 pH
- Reading 3: 6.8 pH
- Reading 4: 6.6 pH
- Reading 5: 6.4 pH
- Reading 6: 6.2 pH
Common causes include tool wear, equipment degradation, operator fatigue, or gradual depletion of process materials.
4. Fourteen Points Alternating Up and Down
This sawtooth pattern indicates systematic variation, often caused by alternating factors such as multiple operators, different machines, or fluctuating environmental conditions. In a bottling facility measuring fill volume, consistent alternation between high and low values suggests two different filling heads with different calibrations.
5. Two Out of Three Consecutive Points Beyond Two Sigma
When two out of three consecutive points fall beyond two standard deviations from the mean (on the same side), this signals a potential process shift. For a delivery service tracking daily package volume with an average of 1000 packages and a two sigma limit of 1150 packages, recording 1160 and 1170 packages within three consecutive days indicates instability.
6. Four Out of Five Points Beyond One Sigma
This pattern is more subtle but equally important. In a customer satisfaction survey with an average score of 8.0 and one sigma at 8.5, having four out of five consecutive scores above 8.5 suggests a positive shift that warrants investigation to understand and sustain the improvement.
7. Fifteen Points in a Row Within One Sigma
Ironically, too little variation can also indicate problems. This pattern suggests stratification, where data from different sources are being mixed, or measurement system issues are masking true variation. This often occurs when combining data from multiple machines or shifts without proper subgrouping.
8. Eight Points in a Row Beyond One Sigma
When eight consecutive points fall beyond one standard deviation from the mean on either side, this indicates mixture or bimodal distribution. This commonly occurs in processes with multiple operating conditions or when combining data from fundamentally different sources.
Systematic Approach to Investigating Out of Control Patterns
When you identify an out of control pattern, follow this structured methodology to address the issue effectively.
Step 1: Document the Pattern
Record the specific pattern observed, the time period, and any relevant contextual information. Include the actual data points and any concurrent events or changes in the process environment.
Step 2: Form an Investigation Team
Assemble a cross-functional team including operators, supervisors, quality personnel, and maintenance staff who possess intimate knowledge of the process.
Step 3: Brainstorm Potential Causes
Utilize tools such as fishbone diagrams and the five whys technique to identify possible special causes. Consider the six Ms: Methods, Materials, Machines, Measurements, Mother Nature (environment), and Manpower.
Step 4: Collect Additional Data
Gather supplementary information to test hypotheses about the root cause. This may include equipment logs, maintenance records, environmental data, or material certifications.
Step 5: Implement Corrective Actions
Once the root cause is identified, implement appropriate corrective actions and verify their effectiveness through continued monitoring.
Step 6: Update Control Systems
Modify standard operating procedures, training materials, and control plans to prevent recurrence of the special cause.
Real World Application: Manufacturing Case Study
A precision engineering company producing automotive components noticed a trend pattern in their dimensional measurements over twelve consecutive hours. The nominal dimension was 25.0mm with control limits at 24.7mm and 25.3mm.
Observed Measurements:
- Hour 1: 25.0mm
- Hour 2: 25.1mm
- Hour 3: 25.1mm
- Hour 4: 25.2mm
- Hour 5: 25.2mm
- Hour 6: 25.3mm
- Hour 7: 25.3mm
- Hour 8: 25.4mm (Beyond UCL)
The investigation team discovered that the cutting tool was wearing progressively, causing dimensional drift. They implemented a preventive tool change schedule, which eliminated the trend pattern and improved overall process capability.
Benefits of Mastering Out of Control Pattern Recognition
Developing expertise in identifying and resolving out of control patterns delivers substantial benefits to organizations. These include reduced defect rates, improved product consistency, decreased waste, enhanced customer satisfaction, and lower overall operating costs. Companies that systematically address special cause variation typically experience quality improvements of 30 to 50 percent within the first year of implementation.
Furthermore, the proactive identification of these patterns prevents small issues from escalating into major quality incidents or customer complaints. This preventive approach aligns perfectly with modern quality management philosophies emphasizing defect prevention over detection.
Tools and Software for Pattern Detection
Modern quality management relies on sophisticated software tools that automatically detect out of control patterns. These systems provide real-time alerts, enabling immediate response to process deviations. However, understanding the underlying principles remains essential for effective problem-solving and continuous improvement.
Statistical software packages offer automated pattern recognition algorithms, but human judgment and process knowledge remain irreplaceable in determining appropriate corrective actions and preventing false alarms.
Building Organizational Capability
Mastering out of control patterns requires comprehensive training and organizational commitment. Successful companies integrate this knowledge throughout their operations, from shop floor operators to senior management. They create a culture where everyone understands the importance of process stability and possesses the tools to maintain it.
Organizations that excel in statistical process control typically invest heavily in developing their workforce capabilities through structured training programs. These programs provide both theoretical knowledge and practical application opportunities, ensuring team members can confidently identify and address out of control conditions.
Taking the Next Step in Your Quality Journey
Understanding out of control patterns represents a fundamental skill in the quality professional’s toolkit. However, this knowledge becomes truly powerful when integrated with broader process improvement methodologies and statistical techniques. The comprehensive framework provided by Lean Six Sigma combines these concepts with structured problem-solving approaches, delivering transformative results.
Whether you are beginning your quality journey or seeking to deepen your existing expertise, formal training provides the structure, practice, and certification that distinguishes true professionals. Lean Six Sigma training equips you with a complete arsenal of tools and methodologies for driving operational excellence, including advanced techniques for process control, variation reduction, and systematic problem-solving.
Enrol in Lean Six Sigma Training Today and transform your ability to identify, analyze, and resolve out of control patterns. Gain internationally recognized certification while developing practical skills that deliver immediate value to your organization. Join thousands of quality professionals who have accelerated their careers and driven measurable improvements through comprehensive Lean Six Sigma training. Visit our website to explore training options tailored to your experience level and career goals, from Yellow Belt fundamentals through Black Belt mastery.
