Knowledge centre · Analyze
Analyze tool guides
30 guides for the Analyze phase. Learn the method, its reasoning and assumptions, the developed example, and the decisions the result supports.
Analyze · 01
Cause-Effect Diagram
Organize plausible explanations for a precisely defined effect, then test the mechanisms with evidence.
Analyze · 02
FMEA
Anticipate process failure modes, prioritize preventive action, and connect risk controls to evidence.
Analyze · 03
Input–process–output measurement matrix
Connect outcomes, process behavior, and candidate inputs with operational measurement definitions.
Analyze · 04
Process Analysis
Explain how work, resources, decisions, and interruptions combine to produce process performance.
Analyze · 05
Spaghetti Diagram
Trace actual movement on a scaled layout to identify unnecessary travel, crossings, and congestion.
Analyze · 06
Process Flow Chart
Show the actual sequence, decisions, and rework routes needed to complete one unit of work.
Analyze · 07
Cross-functional diagram (swimlane map)
Expose responsibility, handovers, and decision rights across teams participating in one process.
Analyze · 08
Value Stream Map
Connect material and information flow with demand, inventory, time, and a proposed future operating pattern.
Analyze · 09
Value Analysis
Classify work by customer value and current necessity, then redesign waste without discarding required controls.
Analyze · 10
Time Analysis
Separate elapsed time, processing, labor, and waiting to explain delay and support credible redesign.
Analyze · 11
Process Efficiency
Use explicitly defined ratios to distinguish customer flow efficiency, labor productivity, and quality performance.
Analyze · 12
Capacity Analysis
Compare demand with resource capability while distinguishing theoretical limits from sustainable effective capacity.
Analyze · 13
Process Flow Analysis
Analyze arrivals, departures, work in process, batching, and queues as a connected flow system.
Analyze · 14
Analyze Data
Use graphical and quantitative evidence to distinguish patterns, uncertainty, and plausible explanations.
Analyze · 15
Data Stratification
Separate meaningful groups to expose hidden patterns and avoid misleading aggregate comparisons.
Analyze · 16
Confidence Interval
Estimate an operational quantity with an interval that makes sampling uncertainty visible, then compare its precision with the decision you need to make.
Analyze · 17
Hypothesis Tests
Use a prespecified statistical comparison to judge evidence against a process claim without confusing significance, practical value, or causality.
Analyze · 18
ANOVA/One Way ANOVA
Compare several group means by separating between group differences from within group variation, then investigate the specific differences that matter.
Analyze · 19
ANOVA/Two Way ANOVA
Separate two categorical factors and their interaction to evaluate an improvement under the conditions where it operates.
Analyze · 20
Correlation Coefficient
Quantify linear association in paired measurements while checking shape, groups, outliers, and uncertainty before developing a causal hypothesis.
Analyze · 21
Simple Linear Regression
Fit a line to one predictor and a continuous response, interpret its slope in process units, and judge predictions using residuals and intervals.
Analyze · 22
Multiple Linear Regression
Estimate conditional relationships among several inputs and a continuous outcome, distinguishing useful adjustment from confounding, instability, and overfitting.
Analyze · 23
Logistic Regression
Model a binary warehouse outcome as a probability while interpreting coefficients as log odds and evaluating calibration, dependence, and decision consequences.
Analyze · 24
Design Of Experiments (DOE)
Plan deliberate changes, randomization, blocking, and independent replication so the resulting comparison can test a mechanism and support an improvement decision.
Analyze · 25
Full Factorial Design
Cross every selected factor level combination to estimate main effects and interactions with transparent replication and a model suited to the experiment.
Analyze · 26
Fractional Factorial Designs
Screen factors with fewer treatment combinations while explicitly accounting for defining relations, alias chains, and the follow-up needed to separate plausible effects.
Analyze · 27
Variation Reduction
Identify and reduce the sources of process spread, separating stable within condition variation from shifts, measurement noise, and changes in workload composition.
Analyze · 28
Response Surface Methods
Model local curvature in continuous settings, distinguish stationary points from feasible optima, and confirm an operating region rather than trusting a single fitted best point.
Analyze · 29
Further Experimental Designs
Choose blocking, split plot, mixture, and custom designs when operational constraints change the experimental unit or prevent a conventional factorial study.
Analyze · 30
Derive Main Causes
Turn candidate explanations into verified, actionable causes by combining mechanism evidence, conditional associations, controlled interventions, and independent confirmation.