In the world of process improvement, understanding how inputs affect outputs is fundamental to achieving operational excellence. The Analyse phase of the DMAIC (Define, Measure, Analyse, Improve, Control) methodology represents a critical juncture where data transforms into actionable insights. This phase focuses on examining the relationships between process inputs and outputs to identify the root causes of performance issues and opportunities for improvement.
What Is the Analyse Phase?
The Analyse phase is the third step in the Lean Six Sigma DMAIC framework. After defining the problem and measuring current performance, teams must now dig deeper to understand why processes behave the way they do. This phase involves examining process input and output relationships to determine which factors have the most significant impact on performance outcomes. You might also enjoy reading about 5 Whys Technique: How to Dig Deep and Discover Root Causes in Problem-Solving.
During this phase, practitioners use statistical tools and analytical techniques to separate the vital few factors from the trivial many. The goal is to establish cause and effect relationships between process inputs (independent variables) and process outputs (dependent variables), enabling teams to focus their improvement efforts where they will have the greatest impact. You might also enjoy reading about Analyse Phase: Identifying Value Added vs Non Value Added Activities in Lean Six Sigma.
Understanding Process Inputs and Outputs
Before diving into analysis techniques, it is essential to understand what constitutes process inputs and outputs. Process outputs are the results or outcomes of a process, often referred to as Y variables. These might include metrics such as customer satisfaction scores, defect rates, delivery times, or production yields.
Process inputs, conversely, are the factors that influence these outputs. These X variables can be categorized into several types, including materials, methods, machines, measurements, people, and environment. The fundamental equation in Six Sigma expresses this relationship simply: Y = f(X), meaning that the output is a function of the inputs.
Types of Process Inputs
- Controllable Inputs: Variables that can be adjusted or controlled by the process team, such as machine settings, temperature, or training programs.
- Noise Inputs: Variables that affect the process but are difficult or impossible to control, such as weather conditions or supplier variability.
- Critical Inputs: Variables that have been identified as having the most significant impact on process outputs.
Key Analytical Tools for Examining Input Output Relationships
Several powerful analytical tools help practitioners understand the relationships between process inputs and outputs during the Analyse phase.
Correlation Analysis
Correlation analysis measures the strength and direction of relationships between variables. A correlation coefficient ranges from negative 1 to positive 1, where values closer to 1 or negative 1 indicate stronger relationships, while values near zero suggest weak or no linear relationship.
Consider a manufacturing example where a team investigates the relationship between oven temperature and product hardness. After collecting data over 30 production runs, they calculate a correlation coefficient of 0.87, indicating a strong positive relationship. This suggests that as oven temperature increases, product hardness tends to increase as well.
Regression Analysis
Regression analysis takes correlation a step further by creating a mathematical equation that describes the relationship between inputs and outputs. This tool not only identifies relationships but also quantifies them, allowing teams to predict outcomes based on input values.
Using the previous example, the team might develop a regression equation: Product Hardness = 12.5 + (0.45 x Oven Temperature). This equation indicates that for every one degree increase in oven temperature, product hardness increases by 0.45 units, with a baseline hardness of 12.5 units.
Hypothesis Testing
Hypothesis testing allows teams to determine whether observed differences or relationships are statistically significant or simply due to random variation. Common tests include t-tests, ANOVA (Analysis of Variance), and chi-square tests.
Imagine a call center analyzing whether training method affects customer satisfaction scores. They compare two training approaches using 50 representatives in each group. Group A receives traditional classroom training while Group B receives interactive simulation training. After three months, Group A averages 7.8 out of 10 in customer satisfaction, while Group B averages 8.4. Using a t-test, the team determines that this difference is statistically significant with 95 percent confidence, suggesting the training method genuinely impacts performance.
Practical Example with Sample Data
Let us examine a detailed example from a bakery experiencing inconsistent bread quality. The output variable (Y) is bread volume measured in cubic centimeters, and the team has identified several potential input variables (X) including yeast amount, proofing time, oven temperature, and mixing duration.
Over 25 baking cycles, the team collected the following sample data:
Sample Data Summary
The team recorded average values as follows: bread volume ranged from 820 to 1150 cubic centimeters with a mean of 985 cubic centimeters. Yeast amounts varied from 8 to 14 grams, proofing times ranged from 45 to 75 minutes, oven temperatures were set between 180 and 210 degrees Celsius, and mixing duration varied from 8 to 15 minutes.
After conducting correlation analysis, the team discovered that proofing time had the strongest correlation with bread volume at 0.82, followed by yeast amount at 0.71. Oven temperature showed a moderate correlation of 0.54, while mixing duration demonstrated a weak correlation of 0.23.
The team then performed multiple regression analysis, which revealed that proofing time and yeast amount together explained 76 percent of the variation in bread volume. The resulting equation was: Bread Volume = 245 + (8.7 x Proofing Time) + (32.4 x Yeast Amount).
This analysis clearly indicated that proofing time and yeast amount were critical inputs, while mixing duration had minimal impact and could be removed from intensive monitoring. Armed with these insights, the team could focus improvement efforts on standardizing proofing time and yeast measurement processes.
Common Pitfalls in Analysing Input Output Relationships
While the Analyse phase provides powerful insights, several common mistakes can undermine results. Confusing correlation with causation is perhaps the most frequent error. Just because two variables move together does not mean one causes the other. There may be a hidden third variable driving both, or the relationship may be purely coincidental.
Another pitfall involves analyzing data without verifying its quality. The principle of “garbage in, garbage out” applies strongly here. If measurement systems are flawed or data collection is inconsistent, the resulting analysis will be unreliable regardless of how sophisticated the statistical techniques employed.
Teams also sometimes fall into the trap of analysis paralysis, conducting endless statistical tests without moving toward actionable conclusions. The Analyse phase should balance thoroughness with efficiency, focusing on identifying the vital few inputs that drive performance.
Moving from Analysis to Action
The ultimate purpose of analyzing process input output relationships is to enable effective improvement. Once critical inputs have been identified and their relationships with outputs understood, teams can develop targeted interventions that address root causes rather than symptoms.
In the bakery example, understanding that proofing time and yeast amount were critical inputs enabled the team to implement precise controls on these variables. They established standard operating procedures for yeast measurement, calibrated scales more frequently, and installed timers to ensure consistent proofing duration. These focused improvements, based on solid analysis, resulted in a 34 percent reduction in bread volume variation within two months.
The Strategic Value of Input Output Analysis
Mastering process input output relationships delivers strategic advantages beyond immediate problem solving. Organizations that excel at this capability develop deeper process knowledge, enabling faster troubleshooting, more effective process design, and better decision making under uncertainty.
This analytical competency also builds a culture of evidence based improvement. Rather than relying on opinions or assumptions, teams make decisions grounded in data and statistical validation. This approach reduces conflict, increases buy in for changes, and improves the sustainability of improvements over time.
Developing Your Analytical Capabilities
Understanding process input output relationships requires both theoretical knowledge and practical application. While this article provides an introduction to key concepts, developing true proficiency requires structured learning and hands on practice with real world problems.
Lean Six Sigma training programs provide comprehensive instruction in analytical tools and methodologies, along with opportunities to apply these techniques to actual improvement projects. Whether you are just beginning your process improvement journey or looking to advance your existing skills, professional training accelerates your capability development and enhances your career prospects.
The Analyse phase represents a turning point in process improvement projects, where understanding deepens and the path to improvement becomes clear. By mastering the techniques for examining input output relationships, you gain the ability to uncover root causes, focus improvement efforts effectively, and deliver sustainable results that impact organizational performance.
Take the Next Step in Your Process Improvement Journey
The ability to analyze process input output relationships is a valuable skill in today’s data driven business environment. Organizations across all industries need professionals who can transform data into actionable insights and drive measurable improvements. Whether you work in manufacturing, healthcare, finance, or service industries, Lean Six Sigma capabilities enhance your effectiveness and career potential.
Do not let this opportunity pass you by. Enrol in Lean Six Sigma Training Today and gain the knowledge, tools, and credentials that will distinguish you as a process improvement professional. Our comprehensive programs provide expert instruction, practical exercises, and real world case studies that prepare you to deliver results from day one. Join thousands of professionals who have transformed their careers and their organizations through Lean Six Sigma expertise. Take action now and invest in skills that will serve you throughout your career. Enrol in Lean Six Sigma Training Today and start your journey toward process improvement mastery.
