01 · Mechanics and boundaries
Mechanics and boundaries
Data analysis converts observations into evidence relevant to a decision. Begin with a question, unit, population, and operational definition. Describe the data before choosing a statistical test: timing, distribution, missingness, order mix, and collection process all influence interpretation. A method that assumes independent observations is questionable when the same order contributes repeated records or consecutive orders share an interruption.
Distinguish description, association, prediction, and causation. A late-order rate describes performance. A relationship between stock holds and lateness is an association. A forecast model predicts outcomes for stated conditions. A causal conclusion requires a defensible mechanism and evidence addressing alternative explanations, often through a planned intervention or experiment.
02 · Construct the analysis
Construct the analysis
Check completeness and reconcile counts with the process boundary. Plot outcomes in time order before pooling them. Inspect distributions of elapsed time, line count, and waits; use categorical comparisons for route and hold status. State denominators beside percentages. Retain outliers until the record or mechanism is understood rather than deleting uncomfortable observations.
Select analysis based on the question and assumptions. Compare late proportions for groups, compare like-route times, or model several inputs when sufficient data and justified assumptions exist. Report uncertainty and practical significance alongside numerical differences. Avoid searching dozens of subgroup splits until one looks interesting and then reporting it as a prespecified finding.
03 · Worked warehouse-dispatch example
Worked warehouse-dispatch example
The fictional North Quay warehouse dispatches a single parcel-order family. Its boundary starts when an order is accepted for release and ends at confirmed carrier handover. Demand is 240 orders in a 420-minute net working day. Each order contains three lines on average, but the example keeps the parcel order as the unit of flow. No number below describes a real organization or a demonstrated improvement. The purpose is to make the reasoning traceable rather than to present a success story.
A representative order spends 12 minutes awaiting release, 2 minutes being released, 18 minutes awaiting picking, 8 minutes being picked, 24 minutes awaiting packing, 5 minutes being packed, 10 minutes awaiting checking, 2 minutes being checked, 15 minutes awaiting loading, and 3 minutes being loaded. These mutually exclusive intervals total 99 minutes: 20 minutes of processing and 79 minutes of waiting. They describe one complete normal-route trace, not a statistically estimated mean for every order. A separate 100-order diagnostic sample contains 20 late orders, defined as handover more than 120 minutes after acceptance. The normal trace and late-order sample therefore answer different questions.
Three pickers, three packers, two checkers, and two loaders work in parallel within their respective stages. At the illustrated processing times, theoretical stage capacities are 157.5, 252, 420, and 280 orders per day. Picking is below the 240-order requirement even before interruptions or rework. Release has two staff and a theoretical capacity of 420. These capacities assume dedicated resources, identical order mix, and no overlapping assignments. They are not observed outputs and should never be reported as effective capacity.
The 100-order sample contains 20 late orders. Twelve orders have replenishment holds; eight are late. The other 88 include 12 late orders. Late proportions are 66.7% and 13.6%, a difference of 53.0 percentage points. The observed risk ratio is about 4.89. The hold group is small, so its estimated rate is uncertain; the contrast is nevertheless a strong reason to investigate the route.
Do not conclude that eliminating holds will prevent eight late orders. Held orders may have larger line counts or arrive later in the shift. Record those variables and inspect comparable groups. Trace whether the hold occurred before the delay threshold and whether the order would otherwise have remained on time. The capacity deficit is another plausible contributor that the binary comparison does not isolate.
| Hold status | Orders | Late | Late proportion |
|---|---|---|---|
| Replenishment hold | 12 | 8 | 66.7% |
| No replenishment hold | 88 | 12 | 13.6% |
| Total | 100 | 20 | 20.0% |
04 · Interpretation and decisions
Interpretation and decisions
Use plots and process knowledge to make a targeted next decision. The evidence supports examining replenishment timing and measuring pick queues. It does not support a guaranteed savings claim or ranking workers by an unadjusted late percentage. For the 99-minute trace, arithmetic decomposes time but cannot estimate the sample's mean lead time.
A weak analysis reports a correlation coefficient or a p-value without a decision context. A sound analysis explains the magnitude, the denominator, the relevant mechanism, the uncertainty, and the limitations of the design. If a statistical result conflicts with the observed process, revisit definitions and assumptions before dismissing either source of evidence.
05 · Sound application and adaptations
Sound application and adaptations
For repeated or clustered data, account for shift, worker, wave, or day rather than treating every row as independent. For lead-time distributions, inspect the upper tail because service promises often depend on it. For predictions, hold out later periods or new groups to check performance beyond the fitting data.
Control charts address behavior over time under suitable subgroup and distribution choices; hypothesis tests address specified comparisons. Neither automatically establishes causality. After an intervention, retain comparable measurement rules and examine unintended effects on accuracy, WIP, and workload. The analysis is complete when the evidence supports a proportionate operational decision, with uncertainty visible, rather than when a software package produces output.
06 · Evidence and follow-through
Evidence and follow-through
Make the observation auditable before debating a countermeasure. Preserve an anonymous order identifier, acceptance and handover timestamps, route, line count, shift, exception flag, and the observer's definitions. A timestamp from a completed screen action may describe data entry rather than physical completion. Reconcile several records with direct observation and ask the person doing the work what each event actually means. Keep an uncertainty note when clocks differ or two actions overlap; rounding cannot repair an ambiguous boundary.
Observe ordinary work and exceptions over a period that includes opening, replenishment, breaks, and the carrier cutoff. Selecting only easily followed orders produces a clean-looking but biased picture. When an order is unfinished at observation end, keep it visible as unfinished rather than silently deleting it. For multi-line orders, establish whether timing ends at the first line or the complete order. Include all three lines in the latter case, because the customer receives the order only after its final requirement is satisfied.
Turn the analysis into a bounded decision. Describe the proposed change, the mechanism expected to alter performance, the measure that would detect the change, and the conditions under which it would be withdrawn. A small layout change might reduce walking yet increase congestion; an extra release wave might lower one queue yet overload picking. Monitor handover performance, first-pass accuracy, work in process, and staff workload together. Treat the proposed numbers as planning assumptions until a comparable observation period supplies evidence.
07 · Boundary and robustness review
Boundary and robustness review
Before implementing the selected countermeasure, check the boundary one more time. If the analysis starts after order release, an upstream acceptance hold is outside its measured lead time even though the customer experiences it. If the analysis ends at the loading bay, a parcel waiting for the carrier is still incomplete within the stated dispatch outcome. Keep those events inside the customer-facing boundary, or state exactly why a narrower operational measure is also being used.
Assess robustness across normal variation. A proposed operating rule should specify what happens when an urgent order arrives, stock is unavailable, a staff member is absent, or the carrier changes collection time. Those conditions do not invalidate the main analysis; they define where its assumptions need adaptation. Rehearse an exception through the proposed route with the people responsible for each decision. Record which assumptions remain unverified and what evidence would be sufficient to proceed.
Finish with an explicit learning statement: what the evidence currently supports, what it does not establish, and which observation is needed next. A quantified hypothesis can be useful without becoming a guaranteed result. Retain the current-state observations so a later comparison can use the same event definitions and population. When the observed outcome differs from the prediction, revisit the mechanism and resource assumptions rather than adjusting the target after the fact.
Connections
Related methods and phase use
Placed under Analyze because of the outcome it supports. Use the method in other phases when its purpose and assumptions fit the work; the phase list is a guide to selection, not a compulsory sequence.
Sources
Professional foundations and further reading
The explanation and fictional teaching examples are written for LeanTrainingLab. Linked primary sources provide professional foundations; example values and local conventions are stated in the guide. The accepted toolset draws on Six Sigma+Lean Toolset, second edition (Lunau, editor; Springer, 2013). No book text or examples are reproduced here.