The MECE Principle in Methods Engineering
Define mutually exclusive, collectively exhaustive categories and check their consistent use by observers.
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MECE means mutually exclusive and collectively exhaustive. Each observation of the target resource receives one primary state, and the state dictionary covers situations within the study. This is a classification rule, not proof that a sample represents the population.
Build the state dictionary around the decision
Define the resource and denominator first: a person during included working time, or a machine during planned production time. A person may prepare material while a machine runs. Those are two resources with compatible states; do not force them into one shared category.
A person-state dictionary might distinguish direct execution, preparation, travel, waiting, breaks and undetermined state. Use subordinate cause tags where appropriate. Missing material is a waiting cause. Maintenance may be planned work or a machine breakdown depending on the observed resource. Agree boundaries before collecting data.
| Primary state | Illustrative observable rule | Boundary to resolve |
|---|---|---|
| Direct execution | Performs the defined operation | Does in-process inspection belong here? |
| Preparation | Prepares tools or settings | When does first-piece production start? |
| Travel | Moves between work locations | Does this include moving the product? |
| Waiting | Cannot proceed; the state is known | Do not guess the cause |
| Break | A pause within included study time | Distinguish time excluded by design |
| Undetermined | Resource could not be classified | Do not relabel it productive |
These are examples, not mandatory standard codes. Necessary support work is not automatically removable waste.
Test the rules
Have two observers independently classify selected situations and examine disagreement. A 50–100-reading pilot can be an operational exercise if chosen in the protocol, but does not automatically validate the dictionary or detect rare states.
A zero count may reflect rarity, a study period without that task, or a poor definition. Do not delete a category only because it did not occur. Percentages summing to 100% demonstrate an accounting identity when every record has a code; they do not demonstrate coverage of every shift or error-free classification. Retain incidents and dictionary versions.
Absolute, relative and simultaneous precision
For an approximately binomial proportion with independent sampling, plan n = ceil(z²p₀(1−p₀)/e²). With z=1.96, p₀=0.10 and e=0.03, ceil(384.16)=385 readings. This is approximate planning for an absolute margin of three percentage points, not a guarantee that the realized interval will be 7–13%.
For a fixed absolute margin, p(1−p) is largest at 0.5, not in the rarest category. For a fixed relative margin, a small proportion may need more readings: with e=r×p, n≈z²(1−p)/(r²p). Decide which precision matters.
Separate 95% intervals do not automatically give 95% simultaneous coverage for all categories. States within one record are related. Use a design-appropriate analysis when simultaneous coverage is required or observations are clustered in rounds.
Observation and comparison
Random schedules reduce selection of convenient moments. They do not remove reactivity or guarantee independence among readings in one round. Familiarization may help, but there is no universal number of days that restores “normal” behavior. Explain the purpose, minimize personal data and document operational changes.
Consider a simulated reclassification: 65 of 100 readings were called operation. Dictionary review finds that 12 were necessary cleaning and 53 direct execution. The new figure is 53%, but the process has not improved or deteriorated merely through relabeling. The 12 percentage points are not proven recoverable capacity.
To compare lines, align definitions and also examine product mix, load, shifts, maintenance and uncertainty. A percentage difference does not establish its own cause.
What the dictionary cannot calculate alone
Wrench time needs an explicit direct-work definition and denominator; it is not universally identical to value-added time. OEE requires availability, performance and quality:
- A = run time / planned production time.
- P = ideal cycle time × total count / run time.
- Q = good count / total count.
- OEE = A×P×Q.
A machine waiting during planned production time is not running. Seeing a conforming item at an instant does not replace representative unit counts: longer-processing items may be overrepresented. Sampling supplies context and state estimates; combine it with consistent time, production and quality data.
WorkSamp supports study preparation and sampling visits. Cronometras complements duration analysis, while Induly provides working-time records rather than instantaneous task states. The ASETEMYT directory lists methods and time-study resources.