01 · Mechanics and boundaries
Mechanics and boundaries
Capacity analysis estimates how much conforming work a resource or connected process can complete in a stated period. Theoretical capacity assumes uninterrupted operation at a specified work time. Effective capacity reflects feasible availability, expected losses, order mix, changeovers, and actual constraints. Observed output is what was completed; it may lie below capacity because demand, material, or downstream space was insufficient.
Always state resource arrangement. Three independent pickers working different orders provide three parallel servers. Three people jointly picking one order provide one team whose timing must be measured as a team. Shared staff cannot be counted as fully available at every stage. The system's sustainable output is constrained by its limiting resource and operating pattern.
02 · Construct the analysis
Construct the analysis
Define demand in comparable work units and determine net scheduled time after planned exclusions. Measure repeatable work content by family. Calculate theoretical stage capacity as dedicated resources × available minutes ÷ minutes per order. Then account for relevant losses without double counting time already removed. Separate first-pass output from additional rework workload.
Compare capacity to demand by period as well as by day. Identify shared equipment and resources, replenishment, and batch restrictions. Build sensitivity cases for plausible work times and availability. Verify whether downstream stages can accept the proposed output. An analytical limit is not a promise; validate effective capacity under ordinary operating conditions.
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.
At 420 net minutes, three pickers and eight minutes per order provide 157.5 theoretical orders; three packers at five minutes provide 252; two checkers at two minutes provide 420; two loaders at three minutes provide 280. Demand is 240. Picking needs 1,920 labor minutes against 1,260 available, a theoretical deficit of 660. Packing's nominal margin is only 12 orders, before losses.
Suppose a planning case assumes 90% productive availability after the already excluded breaks. Picking then has 1,134 usable minutes, or 141.75 orders at eight minutes each. If 5% of original orders require exactly one full repeat and every repeat succeeds, the workload is 1.05 attempts per good order; dividing 141.75 by 1.05 gives 135 good orders. Those assumptions require evidence; the example does not claim an observed effective capacity.
| Stage | Resources | Time/order | Theoretical/day |
|---|---|---|---|
| Picking | 3 | 8 min | 157.5 |
| Packing | 3 | 5 min | 252 |
| Checking | 2 | 2 min | 420 |
| Loading | 2 | 3 min | 280 |
04 · Theoretical capacity comparison
Theoretical capacity comparison
05 · Interpretation and decisions
Interpretation and decisions
Capacity below demand explains persistent accumulation, but it does not explain every late order. Replenishment holds and uneven release can worsen performance independently. A proposed six-minute pick method with four dedicated pickers gives 280 theoretical orders. At 90% availability, that becomes 252 before rework: enough nominally, but with a small margin and uncertainty.
A weak analysis takes the fastest observed order, multiplies by a full shift, and promises output. A sound analysis uses a representative family, verifies dedicated time, includes loss mechanisms, and tests variation. Do not assume all resources should run at 100% utilization; practical slack can protect service when arrivals and task times vary.
06 · Sound application and adaptations
Sound application and adaptations
For mixed work, use labor-minute demand by family: volume multiplied by representative work content, then summed. For shared resources, include every competing flow. For a cutoff, calculate cumulative capacity before collection rather than hiding the deadline inside a daily average. Consider staggered staffing and replenishment windows as well as headcount.
Queueing or simulation can estimate service sensitivity near a capacity limit, but requires reliable arrival and service distributions. Distinguish storage capacity from processing capacity, and team capacity from machine speed. Recalculate after a verified redesign and check whether the constraint has moved. The purpose is to choose a feasible operating system, not to create a reassuring utilization spreadsheet.
07 · 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.
08 · 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.