01 · Mechanics and boundaries
Mechanics and boundaries
A cause-effect diagram is a structured hypothesis map. Put a single undesirable effect at the head and arrange candidate mechanisms on branches. A branch is neither a proven cause nor a vote for an improvement. The diagram is valuable because it exposes missing explanations, ambiguous statements, and the evidence needed to distinguish rival mechanisms.
Define the effect with a unit, boundary, and threshold: parcel orders exceeding 120 minutes from acceptance to handover. “Poor warehouse performance” is too broad; damage, delay, and cost can involve different mechanisms. Use categories such as methods, equipment, materials, people, measurement, and environment only when they help the team think. The categories organize ideas; they do not establish six independent causal systems.
02 · Construct the analysis
Construct the analysis
First agree the effect and the population affected. Second observe complete late and on-time routes. Third ask workers from release, picking, packing, and loading to identify physical mechanisms rather than personalities. Fourth group ideas without erasing meaningful differences. Fifth add subbranches by asking how a proposed factor could produce the effect. Sixth label each leaf with its evidence status and an investigation.
Write “pick list arrives after replenishment slot closes” rather than “bad communication.” The first statement predicts an observable sequence. Check that a proposed cause precedes the delay; a supervisor entering an escalation after an order is already late cannot explain its onset. Add connections outside the fishbone when two factors interact, because a simple tree can conceal combinations such as large orders during replenishment.
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.
For the 100-order sample, 20 orders are late. Candidate leaves include late release waves, empty forward-pick locations, printer retries, and orders held for address clarification. Suppose investigators find replenishment holds in 12 orders, of which 8 are late; among the other 88, 12 are late. The corresponding late proportions are 66.7% and 13.6%. This is a useful association, not proof that replenishment alone causes lateness.
A more precise hypothesis says that an empty forward location forces an eight-minute reserve search and pushes already marginal orders beyond the cutoff. Trace the sequence for those eight late orders and compare similar line counts and release times. Printer retries remain on the diagram even if none occurred in this sample: absence in a small sample can weaken a frequent-delay explanation without proving the failure is impossible.
04 · Interpretation and decisions
Interpretation and decisions
Prioritize hypotheses by consequence, plausibility, frequency, and cost of verification. Investigate replenishment timing before adding a printer when the observed mechanism points to reserve searches. The capacity arithmetic independently shows insufficient theoretical picking capacity, so a replenishment intervention alone should not be promised to deliver 240 orders per day.
A weak diagram ends with “training” on a people branch and an action to retrain everyone. A sound diagram separates unclear location coding, missing stock, competing priorities, and knowledge gaps. Each has a different test and a different countermeasure. Ask what observation would make the preferred explanation less credible; otherwise the workshop risks becoming a justification exercise.
05 · Sound application and adaptations
Sound application and adaptations
Use evidence labels such as observed, associated, experimentally supported, and unresolved. Keep alternative mechanisms visible after a promising finding. For a recurrent delay, combine the diagram with stratification by route and time of day. For an interaction, draw a small causal model and specify which variables precede which others.
The diagram cannot estimate effect size, forecast throughput, or replace controlled investigation. It also cannot make an unobserved judgment about worker motivation reliable. Revisit the structure after new evidence; remove disproved claims and preserve the reasoning that led to their rejection. A living hypothesis map is more useful than a polished poster whose branches never change.
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.