Positional Variation is a critical concept in the Analyze Phase of Lean Six Sigma that refers to differences in process output based on the specific location or position where work is performed. This type of variation occurs when the physical placement of equipment, workstations, or operators influ…Positional Variation is a critical concept in the Analyze Phase of Lean Six Sigma that refers to differences in process output based on the specific location or position where work is performed. This type of variation occurs when the physical placement of equipment, workstations, or operators influences the quality or consistency of results.
In manufacturing environments, positional variation commonly manifests when multiple machines, tools, or fixtures produce different outcomes despite being designed for identical operations. For example, if a production line has five identical drilling machines, positional variation exists when Machine 1 consistently produces parts with slightly different specifications compared to Machine 4, even though both follow the same procedures.
During the Analyze Phase, Green Belts investigate positional variation using several statistical tools. Multi-vari charts are particularly effective for visualizing how output changes across different positions. Analysis of Variance (ANOVA) helps determine whether observed differences between positions are statistically significant or merely due to random chance.
Common sources of positional variation include environmental factors such as temperature gradients, lighting differences, or humidity levels at various locations. Equipment wear patterns, calibration discrepancies, and maintenance histories can also contribute to position-based inconsistencies. Additionally, ergonomic factors affecting operator performance at different workstations may introduce variation.
Identifying positional variation is essential because it helps teams pinpoint root causes of defects and process instability. Once detected, corrective actions might include equipment recalibration, environmental controls, standardized maintenance schedules, or workstation redesign.
Green Belts should collect data stratified by position to reveal hidden patterns. This stratification allows for meaningful comparisons and helps distinguish positional effects from other variation sources like temporal or cyclical patterns. Understanding and controlling positional variation leads to more predictable processes, reduced defects, and improved overall quality performance in any operational setting.
Positional Variation in Six Sigma: A Complete Guide
What is Positional Variation?
Positional variation refers to differences in process output that occur based on the location or position within a process, machine, or system. This type of variation is associated with where something is physically located during production or processing. Common examples include variations between different cavities in a mold, different spindles on a machine, different lanes on a filling line, or different positions on a conveyor belt.
Why is Positional Variation Important?
Understanding positional variation is crucial for Six Sigma practitioners because:
• It helps identify root causes of defects that may be hidden when data is aggregated • It enables targeted improvements rather than broad, expensive changes • It reveals whether a process issue is systemic or location-specific • It supports data stratification, a key analytical technique in the Analyze phase • It can significantly impact process capability calculations when not properly accounted for
How Positional Variation Works
Positional variation manifests when certain positions consistently produce different results than others. For example:
• A 4-cavity injection mold where cavity 3 produces parts with larger dimensions • A multi-head filling machine where head 2 consistently underfills • A heat treatment furnace where parts near the door have different properties
To detect positional variation, practitioners use stratification by collecting data tagged with position information, then analyzing each position separately using tools like:
• Box plots comparing positions • Multi-vari charts • ANOVA (Analysis of Variance) • Control charts by position
How to Answer Exam Questions on Positional Variation
When facing exam questions about positional variation, follow these strategies:
1. Recognize the indicators: Look for keywords like position, location, cavity, spindle, lane, head, station, or zone in the question stem.
2. Connect to stratification: Positional variation questions often test your understanding of data stratification as an analytical technique.
3. Think about multi-vari analysis: This tool is specifically designed to separate positional variation from cyclical and temporal variation.
4. Consider the appropriate statistical test: ANOVA is commonly used to determine if positional differences are statistically significant.
Exam Tips: Answering Questions on Positional Variation
• Tip 1: When a question describes output differences based on physical location, positional variation is likely the correct answer choice.
• Tip 2: Remember that positional variation is one of the three families of variation in multi-vari studies (positional, cyclical, and temporal).
• Tip 3: If asked how to investigate suspected positional variation, stratifying data by position and creating comparative visualizations is the recommended approach.
• Tip 4: Positional variation is often a special cause that can be addressed through targeted corrective actions at specific locations.
• Tip 5: Questions may present scenarios where combining data from all positions masks a problem - recognize this as a case where positional stratification is needed.
• Tip 6: Be prepared to identify which graphical tool best displays positional variation - box plots and multi-vari charts are primary choices.