Process behaviour charts, also known as control charts, represent one of the most powerful yet underutilized tools for understanding how processes perform over time. Whether you manage a manufacturing operation, oversee healthcare delivery, or coordinate service processes, learning to create and interpret these charts will transform how you make decisions based on data. This comprehensive guide will walk you through everything you need to know about process behaviour charts, from basic concepts to practical application.
Understanding Process Behaviour Charts
A process behaviour chart is a graphical tool that displays data points in chronological order, along with statistically calculated limits that help distinguish between normal process variation and signals that indicate something has genuinely changed. Unlike simple trend graphs, these charts incorporate the voice of the process itself, allowing you to separate meaningful signals from background noise. You might also enjoy reading about How to Use Variables Control Charts to Monitor and Improve Process Quality.
The fundamental principle behind process behaviour charts is that all processes exhibit variation. Some variation is natural and expected, while other variation indicates that special circumstances have influenced the process. Making this distinction is critical because responding to natural variation as if it were special often makes matters worse, while ignoring genuine signals allows problems to persist or grow. You might also enjoy reading about How to Apply Nelson Rules for Statistical Process Control: A Complete Guide.
The Two Types of Variation
Before creating your first chart, you must understand the two categories of variation that exist in every process.
Common Cause Variation
Common cause variation represents the natural, inherent fluctuation that exists within a stable system. This variation is predictable within calculable limits and results from the routine factors that are always present in the process. For example, if you measure your commute time to work each day, you will notice slight differences even when taking the same route at the same time. Traffic light timing, other vehicles, and your own driving speed create natural variation.
Special Cause Variation
Special cause variation occurs when something exceptional influences the process. These are signals that indicate a change has occurred. Using the commute time example, a special cause might be an accident blocking traffic, road construction, or an unusually severe weather event. These factors are not part of the normal system and create results that fall outside the predictable pattern.
Components of a Process Behaviour Chart
Every process behaviour chart contains several key elements that work together to provide insight into process performance.
The Central Line
The central line typically represents the average or mean of your data. This line shows the central tendency of your process and serves as the baseline for comparison. All other calculations derive from this fundamental measure.
The Upper and Lower Natural Process Limits
These limits, often called control limits, are calculated using the data itself. They are not specifications, targets, or goals. Instead, they represent the range within which you can expect future data points to fall if the process remains stable. Approximately 99 to 100 percent of points from a stable process will fall within these limits.
The Data Points
Each individual measurement is plotted chronologically on the chart. The time sequence is crucial because it allows you to detect patterns and shifts that would be invisible in other forms of data analysis.
Creating Your First Process Behaviour Chart: A Step by Step Example
Let us walk through creating a process behaviour chart using a practical example. Imagine you manage a customer service center and want to understand the variation in call handling times.
Step 1: Collect Your Data
You have gathered the average call handling time in minutes for 20 consecutive days:
Day 1: 8.2, Day 2: 7.9, Day 3: 8.5, Day 4: 8.1, Day 5: 7.8, Day 6: 8.3, Day 7: 8.0, Day 8: 7.7, Day 9: 8.4, Day 10: 8.2, Day 11: 8.1, Day 12: 7.9, Day 13: 8.3, Day 14: 8.0, Day 15: 7.8, Day 16: 8.5, Day 17: 8.2, Day 18: 7.9, Day 19: 8.1, Day 20: 8.3
Step 2: Calculate the Central Line
Add all data points and divide by the number of observations. In this example, the sum is 162.2 divided by 20, giving us an average of 8.11 minutes. This becomes your central line.
Step 3: Calculate the Moving Range
The moving range is the absolute difference between consecutive data points. For our example:
Between Day 1 and 2: |8.2 minus 7.9| = 0.3, Between Day 2 and 3: |7.9 minus 8.5| = 0.6, Between Day 3 and 4: |8.5 minus 8.1| = 0.4, and so forth for all consecutive pairs.
Calculate the average of these moving ranges. In our dataset, the average moving range is approximately 0.31.
Step 4: Calculate the Natural Process Limits
For individual measurements, use these formulas:
Upper Natural Process Limit = Average + (2.66 times Average Moving Range)
Lower Natural Process Limit = Average minus (2.66 times Average Moving Range)
For our example:
Upper Limit = 8.11 + (2.66 times 0.31) = 8.11 + 0.82 = 8.93 minutes
Lower Limit = 8.11 minus (2.66 times 0.31) = 8.11 minus 0.82 = 7.29 minutes
Step 5: Plot Your Chart
Create a graph with time on the horizontal axis and your measurement on the vertical axis. Plot each data point in sequence, draw the central line at 8.11, and add the upper limit at 8.93 and lower limit at 7.29. Connect the data points with lines to make patterns easier to see.
Interpreting Your Process Behaviour Chart
Once you have created your chart, the real work begins. You must learn to read what the process is telling you.
Signals of Special Cause Variation
Several patterns indicate that special causes have affected your process:
- Any single point outside the natural process limits: This is the most obvious signal that something exceptional has occurred.
- Eight or more consecutive points on one side of the average: This pattern suggests the process has shifted.
- Three out of four consecutive points in the outer third of the region: This indicates the process is becoming less stable.
- A long run upward or downward: Six or more consecutive points steadily increasing or decreasing suggests a trend.
Stable Process Indication
If your data points fall randomly within the limits with roughly equal numbers above and below the central line, and no patterns appear, your process is likely stable and predictable. This does not necessarily mean the process is good or meeting your needs, only that it is consistent.
Common Mistakes to Avoid
Many people new to process behaviour charts make predictable errors that undermine their effectiveness.
Confusing Limits with Specifications
The natural process limits tell you what the process is actually doing, not what you want it to do. Specifications are separate considerations. A process can be stable but still fail to meet specifications, or it can meet specifications while being unstable.
Reacting to Every Fluctuation
When all points fall within the limits and show no patterns, the variation is natural to the process. Reacting to individual data points within these limits typically increases variation rather than reducing it. This phenomenon, called tampering, represents one of the most common mistakes in management.
Using Insufficient Data
You need at least 20 to 25 data points to calculate reliable limits. Using fewer points may give you misleading limits that do not accurately represent the process behaviour.
Practical Applications Across Industries
Process behaviour charts prove valuable in virtually any field where data is collected over time. Healthcare facilities use them to monitor infection rates, patient wait times, and medication errors. Manufacturing operations track defect rates, cycle times, and equipment performance. Service industries monitor customer satisfaction scores, delivery times, and error rates. Even individuals use these charts to understand personal metrics like weight, exercise performance, or household expenses.
Moving Forward with Process Behaviour Charts
Mastering process behaviour charts requires practice and dedication, but the investment pays substantial dividends. These tools enable you to make better decisions by helping you understand when to act and when to leave a process alone. They facilitate more productive conversations about performance because they are based on the actual behaviour of the process rather than arbitrary targets or gut feelings.
As you gain experience, you will develop intuition about process behaviour and become more skilled at identifying improvement opportunities. You will learn to ask better questions about why special causes occur and how to either eliminate problematic special causes or incorporate beneficial ones into the standard process.
Take Your Skills to the Next Level
Process behaviour charts represent just one tool within the comprehensive toolkit of process improvement methodologies. To truly transform how your organization operates and to develop deep expertise in statistical process control, quality management, and continuous improvement, formal training makes all the difference.
Lean Six Sigma training provides structured education in process behaviour charts along with dozens of other powerful tools and methodologies. You will learn from experienced practitioners, work through real world case studies, and earn credentials recognized across industries worldwide. Whether you are seeking to advance your career, improve your organization’s performance, or simply become more effective in your current role, Lean Six Sigma certification offers proven value.
Enrol in Lean Six Sigma Training Today and join thousands of professionals who have transformed their approach to quality, efficiency, and continuous improvement. Take the first step toward becoming a certified problem solver who can drive measurable results in any organization.
