Manufacturing Pokayoke — Working Paper · Version 1.0

    Making the Wrong Action Impossible: Software-Enforced Poka-Yoke in Automotive Assembly

    Architecture, Computational Methods, and Field Evidence from the MileSoft Pokayoke Suite

    MileSoft Engineering Research Group · August 2026 · 5 min read

    0Wrong-part picks since rollout
    92%Drop in OEM containment incidents
    5 daysOperator ramp-up, from 4-6 weeks
    +18%Throughput per shift, flagship line

    Abstract

    Overview

    Shigeo Shingo drew a distinction that most quality programmes still blur: between a device that warns an operator of a deviation and a device that makes the deviating action impossible. Only the second is poka-yoke in his sense. The first is instrumentation, and its effectiveness is bounded by operator attention — which is exactly the variable that fails under time pressure, on night shifts, and on the sixth close-cousin variant of a part.

    This paper presents the architecture, computational methods, and field evidence of the MileSoft manufacturing pokayoke suite: three station types (Pick-to-Light kitting, ADAS radar alignment, and end-of-line AC performance test) built on a shared station runtime in which the build recipe is governed master data, the interlock is the default control action, and every station outcome is bound to a unit identity at the moment it is produced.

    We describe the control-versus-warning decision and why it is an architectural property rather than a configuration choice; the recipe model that lets six visually similar variants be distinguished without relying on operator discrimination; the measurement chain at a validation station and the guard-banding calculation that converts measurement uncertainty into acceptance limits; and the escape and false-reject accounting that makes a station's real performance visible.

    A production deployment at a Tier-1 automotive supplier across plants at Pune and Nasik reports zero wrong-part picks since rollout against a pre-deployment operator error rate of approximately 1.4%, a 92% reduction in original-equipment-manufacturer containment incidents, operator ramp-up falling from four to six weeks to five days, and an 18% throughput gain on the flagship line. We close by proposing a capability reference framework for evaluating error-proofing stations.

    Keywords

    Poka-yokeError proofingSource inspectionPick-to-lightADAS radar alignmentEnd-of-line testZero defect manufacturingMeasurement systems analysisGuard bandingIATF 16949ISO 26262ISO 13849-1First-pass yieldAutomotive assembly

    Contributions

    What this paper covers

    Each contribution is designed to be independently useful to engineers, procurement teams, and platform evaluators.

    01

    Control as the default, warning as the exception

    The station runtime treats the interlock — bin lock, fixture hold, tool inhibit — as the default response to a deviation, with a warning-only mode available but requiring explicit, recorded authorisation per joint or per pick. This inverts the usual default and makes every warning-only station a deliberate, auditable decision rather than an unexamined one.

    02

    Recipe-governed variant discrimination

    The build recipe is versioned master data resolved from the unit identity, not a printed sheet or an operator's memory. Six visually similar variants are distinguished by the system before the operator reaches for a bin, which removes discrimination from the human task rather than training the human to do it better.

    03

    Measurement chain and guard banding

    For validation stations, the paper states the measurement chain explicitly — reference, fixture, sensor, method — and gives the guard-banding calculation that tightens acceptance limits inward from specification limits by a multiple of the combined measurement uncertainty, making the trade between false rejects and escapes explicit and configured rather than implicit.

    04

    Field evidence and a capability framework

    A two-plant production deployment on radar-module assembly, with all reported metrics traced to their source, plus an eight-dimension vendor-independent benchmark for evaluating whether a station is genuinely error-proofed or merely instrumented.

    Standards

    Governing standards addressed

    Computational methods and workflows in this paper are grounded in and validated against these published standards.

    IATF 16949:2016

    Quality management system requirements for automotive production and relevant service parts organizations

    Error-proofing verification, reaction plans, and evidence-of-control obligations the station records satisfy.

    ISO 26262:2018 (all parts)

    Road vehicles — Functional safety

    Safety lifecycle for the electronic systems being assembled, including production and operation requirements in Part 7.

    ISO 13849-1:2015

    Safety of machinery — Safety-related parts of control systems — Part 1: General principles for design

    Performance-level requirements for the interlocks that implement the control function.

    ISO 5725-1 / ISO 5725-2

    Accuracy (trueness and precision) of measurement methods and results

    Trueness and precision decomposition underlying the measurement-chain and guard-band analysis.

    AIAG MSA (4th edition)

    Measurement Systems Analysis reference manual

    Gauge repeatability and reproducibility study design for validation stations.

    ISO 22514-1 / ISO 22514-2

    Statistical methods in process management — Capability and performance

    Capability and performance indices for station outputs, and the control-state distinction between them.

    VDI/VDE 2862 Blatt 1

    Minimum restrictions for application of fastening systems and tools — Applications in the automotive industry

    Risk classification determining which joints require detection of incorrect tightening.

    IEC 62264-1 / ANSI-ISA-95.00.01

    Enterprise-Control System Integration — Part 1: Models and Terminology

    Placement of the station runtime at operations level, with defined interfaces to MES and enterprise systems.

    Full paper

    Read the complete text

    The entire paper is published here in full — sections, equations, figures, tables, and appendices.

    1Introduction

    Shingo's argument in Zero Quality Control is that inspection which detects defects after they occur cannot produce zero defects, however thorough it is, because it acts on the output rather than the cause. What produces zero defects is source inspection: detecting the condition that would cause a defect, at the moment it arises, and preventing the action that would realise it.

    The distinction that follows is the one this paper is built on. A poka-yoke device has a setting function — how it detects a deviation — and a control function — what it does about one. Shingo separates control-type devices, which stop the process or make the wrong action impossible, from warning-type devices, which signal and leave the decision to the operator. Both are useful. Only the first is independent of attention.

    Most industrial error-proofing deployments drift toward the warning type, for a reason that is organisational rather than technical: a warning never stops the line, and a control device sometimes does. The drift is rarely a decision anyone made; it accumulates through defaults. The architecture described here inverts the default so that the drift must be deliberate and recorded.

    Figure 1. Schematic. Where a defect is detected determines what it costs. The bars are illustrative orders of magnitude rather than measured values; the claim is the shape, not the scale. Each step to the right adds the value that was built onto the defect before it was found, and the rightmost step adds containment and warranty exposure that is not proportional to the part's cost at all.

    1.1The variant discrimination problem

    The deployment that motivated this work assembles radar modules in six variants that differ by small visual features and by substantially different downstream calibration. An operator distinguishing them by sight is performing a discrimination task under time pressure, and the observed error rate before deployment was approximately 1.4% — roughly one wrong pick in seventy.

    That rate is not a training failure. It is close to what the human-factors literature would predict for a rapid discrimination task between similar items under production tempo. Training moves it somewhat; it does not move it to zero, and it does not hold under staff turnover. The only durable remedy is to remove the discrimination from the human task.

    If a process depends on an operator reliably telling two similar things apart at speed, the defect rate is a property of the process design, not of the operator.

    1.2Contributions

    1. A station architecture in which the interlock is the default control action and warning-only operation requires explicit, recorded authorisation per joint or pick.
    2. A recipe model that resolves the build specification from unit identity, removing variant discrimination from the operator's task.
    3. An explicit measurement chain and guard-banding method for validation stations, converting measurement uncertainty into acceptance limits with the false-reject and escape trade stated.
    4. Field evidence from a two-plant automotive deployment, and an eight-dimension capability reference framework distinguishing error-proofed stations from instrumented ones.

    2Background and Related Work

    Three bodies of work bear on the design: the source-inspection tradition, the automotive quality and functional-safety regimes, and measurement systems analysis.

    2.1Source inspection and the poka-yoke taxonomy

    Shingo classifies detection methods into contact, fixed-value, and motion-step types, and control responses into control and warning types. The classification is orthogonal: a contact sensor can drive either an interlock or a lamp. Which it drives is an engineering decision that the taxonomy makes visible and that most implementations leave implicit.

    The related idea of successive and self-check inspection — that the next operation, or the operator themself, checks immediately rather than waiting for a downstream gate — is the organisational form of the same principle: shorten the interval between error and detection until it approaches zero, at which point detection becomes prevention.

    2.2Automotive quality and functional-safety regimes

    IATF 16949 requires error-proofing devices to be verified, and requires a documented reaction plan for the case where a device fails. Both requirements have architectural consequences: verification must be scheduled and recorded per device, and the reaction plan must be enforceable rather than advisory, which in practice means the station must know what to do when its own sensor is suspect.

    For radar modules specifically, the assembled product falls under ISO 26262, whose Part 7 addresses production, operation, service and decommissioning. The relevant consequence is that the alignment record produced at assembly is part of the safety argument for the finished vehicle, not merely a quality record, and its integrity requirements follow accordingly.

    The interlocks themselves are safety-related parts of control systems in the sense of ISO 13849-1, which sets performance-level requirements for their design. A bin lock that fails open on power loss and a bin lock that fails closed are different designs with different performance levels, and the choice must be made against the hazard rather than convenience.

    2.3Measurement systems analysis

    A validation station produces a verdict, and a verdict is only as trustworthy as the measurement chain behind it. ISO 5725 decomposes accuracy into trueness — closeness of the mean of many results to the reference value — and precision, with Part 2 giving the method for determining repeatability and reproducibility. The AIAG Measurement Systems Analysis manual provides the study designs that automotive practice uses for the same purpose.

    The consequence too often skipped is that a station whose gauge variation is a significant fraction of the specification width will produce both false rejects and escapes at rates that no amount of tightening the limits can eliminate, because the limits are being applied to a noisy measurement. Section 4.3 states the guard-banding calculation that makes this explicit.

    2.4Incumbent practice and its failure modes

    • Warning drift. Devices are commissioned as controls and quietly reconfigured to warnings after the first line stop, with no record that the change was made or why.
    • Recipe on paper. The build specification lives on a printed sheet at the station; a revision reaches some stations and not others, and no record shows which revision built which unit.
    • Unbound outcomes. A station records a pass or fail against a timestamp rather than a unit identity, so the record cannot answer a question about a specific vehicle later.
    • Unexamined measurement. Acceptance limits are set equal to specification limits, with no allowance for gauge variation, so the escape rate is whatever the measurement noise makes it.

    The first failure mode is the most damaging and the hardest to see, because a warning-configured station looks identical to a control-configured one on a plant walk.

    3System Overview and Architecture

    Figure 2. Schematic. The poka-yoke control loop at one station. The Gate step implements Shingo's control function. Its default outcome is the upper branch, in which the next step is physically impossible; the lower warning branch is available but must be authorised per joint or pick and is recorded as a deviation from the default. Recorded outcomes feed back into sensor thresholds and recipe limits, closing the loop. A station wired only to the lower branch is instrumented, not error-proofed — and on a plant walk the two look identical.

    3.1Design goals

    • G1 — Control by default. An interlock is the default response to a deviation. Warning-only operation is configurable but requires explicit authorisation, recorded with an author and a reason.
    • G2 — Recipe from identity. The build specification is resolved from the unit identity by the system, never selected by the operator.
    • G3 — Bound outcomes. Every station outcome is written against a unit identity at the moment it is produced, not reconciled to one later.
    • G4 — Versioned recipes. Recipes are versioned; every outcome records the version in force, so the specification that built a given unit is recoverable.
    • G5 — Declared measurement. Every validation station declares its measurement chain and its combined uncertainty, and derives acceptance limits from them.
    • G6 — Verified devices. Each error-proofing device carries a verification schedule; a device overdue for verification degrades the station to a defined reaction state rather than continuing silently.

    3.2The shared station runtime

    All three station types run the same runtime, differing in their sensing hardware and their recipe schema. The runtime owns five things: identity resolution from a scan or fixture read; recipe resolution and version pinning; the gate decision and its interlock outputs; the outcome record; and the device verification state.

    What the runtime deliberately does not own is the process logic of the operation itself. A radar alignment routine and an air-conditioning performance test have nothing in common computationally. Keeping the shared layer to identity, recipe, gate, record, and verification is what lets a third station type be added without touching the first two.

    Station outcomes are streamed to a shared dashboard and to the plant's manufacturing execution system over OPC UA, positioning the runtime at operations level in the IEC 62264 sense with defined interfaces upward.

    3.3Degraded modes and the reaction plan

    IATF 16949 requires a reaction plan for error-proofing device failure. The runtime implements this as an explicit station state rather than a document: a device that fails its verification check, reports an implausible reading, or exceeds its verification interval moves the station into a declared degraded state.

    The degraded state is configured per station and is one of three: halt, in which the station refuses work; fallback, in which a secondary sensing path takes over at a stated lower confidence; or supervised, in which work continues under a named authoriser with every unit flagged for downstream re-check. What is not available is silent continuation, which is what an undeclared reaction plan amounts to in practice.

    A station that cannot state which of these three it is currently in does not have a reaction plan; it has a document describing one.

    4Computational Methods by Station Type

    Notation is collected in Appendix A; worked numerical examples in Appendix B.

    4.1Pick-to-Light kitting

    The station resolves the unit identity from a scan, resolves the pinned recipe version for that identity, and illuminates exactly the bin the recipe names. Every other bin is held locked. Confirmation is a sensor read at the bin, not a button press, so the record states that the correct bin was opened rather than that the operator asserted it was.

    The effect on error rate is structural rather than statistical. Under a warning-only regime the residual wrong-pick probability is the product of the operator's discrimination error rate and the probability that the warning is not acted on. Under an interlock regime a wrong pick requires a device failure, and device failure is covered by the verification schedule and the reaction plan of Section 3.3.

    p(wrong | warning) = p(discrimination error) x (1 - p(warning acted on)) versus p(wrong | control) = p(device failure)(pickerr)
    where p(d) is the operator discrimination error rate, p(a) the probability the warning is noticed and acted on, and p(f) the interlock failure probability. The two regimes differ in kind: the first scales with human factors, the second with device reliability and verification interval.

    Ramp-up time follows from the same shift. When variant discrimination is the system's job rather than the operator's, a new operator must learn the physical motion and the station's rhythm, not the differences between six similar parts. Section 5 reports the observed effect.

    4.2ADAS radar alignment

    The alignment station seats the module in a fixture, reads a fiducial by vision to establish the datum, drives a sealed alignment routine against a calibrated reference target, and emits a signed alignment report bound to the module identity. The routine is sealed in the sense that its parameters come from the pinned recipe version and cannot be adjusted at the station.

    The verdict rests on the measurement chain, and the chain must be declared (G5). Its links are the calibrated reference artefact, the fixture's datum repeatability, the sensor's bias and resolution, and the method's sampling and filtering. Each contributes a variance component, and independent contributions combine in the usual way.

    Figure 3. Schematic. The measurement chain at a validation station, and the guard band that follows from it. Acceptance limits are tightened inward from specification limits by a multiple of the combined uncertainty, so that a unit the station passes is conforming even in the worst case the chain admits.
    u(combined) = sqrt( u(reference)^2 + u(fixture)^2 + u(sensor)^2 + u(method)^2 )(uc)
    the combined standard uncertainty of the alignment measurement, assuming the four contributions are independent. Where a fixture and a sensor share a thermal drift they are not independent, and a correlation term must be added rather than assumed away.

    Because the assembled module falls under ISO 26262, the alignment record forms part of the finished vehicle's safety argument. The record therefore carries the recipe version, the reference artefact's calibration state, and the combined uncertainty in force at the time, not merely the measured angle and a verdict.

    4.3Guard banding and the accept/reject trade

    Setting acceptance limits equal to specification limits guarantees escapes at a rate determined by measurement noise, because a unit measured just inside the limit may be truly outside it. Guard banding tightens the acceptance limits inward.

    AL(upper) = USL - k x u(combined), AL(lower) = LSL + k x u(combined)(guard)
    where k is the guard-band multiplier, chosen from the tolerable escape probability. k = 2 corresponds to roughly 95% confidence for a normally distributed measurement error; safety-relevant characteristics are typically banded harder.

    The trade is explicit. Increasing k reduces escapes and increases false rejects, which are good units scrapped or reworked. Both costs are real, and the correct k depends on their ratio, which differs between a safety-relevant alignment and a cosmetic characteristic. The architecture requires k to be configured per characteristic rather than defaulted globally, precisely so the trade is made rather than inherited.

    The measurement system's own adequacy is assessed by gauge repeatability and reproducibility against the tolerance width, following the AIAG study designs.

    %GRR = 100 x sigma(GRR) / (USL - LSL)(grr)
    expressed against tolerance rather than against total variation, because the question at a validation station is whether the gauge can resolve conformance, not whether it can resolve part-to-part variation. Automotive practice treats below 10% as acceptable and above 30% as unacceptable.

    4.4End-of-line AC performance test

    The air-conditioning performance station is a functional test rather than a dimensional one: the unit is run against a defined duty and its measured performance compared against the recipe's envelope. The same runtime applies — identity, pinned recipe, gate, bound record, device verification — with the sensing being a set of temperature, pressure, and current measurements rather than a vision read.

    Two properties distinguish functional testing from dimensional testing, and both affect the gate. Measurements are time-dependent, so the recipe specifies a settling period before the acceptance window is evaluated; and the test consumes cycle time, so the station is frequently the line's constraint. The runtime records settling time separately from measurement time, which makes it possible to distinguish a slow test from a slow product.

    4.5Yield and escape accounting

    Station performance is reported as first-pass yield, and line performance as the rolled product of station yields — the measure that reveals how a line of individually respectable stations produces a poor finished-unit yield.

    RTY = FPY(1) x FPY(2) x ... x FPY(n)(rty)
    where FPY(i) is the first-pass yield of station i. Ten stations at 99% each give RTY = 0.904, so roughly one unit in ten requires intervention somewhere — a result no single station's report would suggest.

    Escapes are counted separately from failures, because they are found downstream and are the number that predicts containment exposure. A station's escape count is knowable only from downstream detection, so it is attributed retrospectively to the station whose recipe should have caught the condition, which is possible only because outcomes are bound to unit identities (G3).

    5Field Evidence: A Production Deployment

    The deployment described here is at a Tier-1 automotive supplier operating plants at Pune and Nasik, India, on a radar-module assembly line. All figures are operator-reported from the production system and are itemised with their provenance below.

    5.1Context and prior workflow

    Radar modules ship in six close-cousin variants with small visual differences and substantially different downstream calibration. The operator pick-error rate hovered around 1.4%. Off-specification radar alignment passed quality control and surfaced at the original equipment manufacturer, generating containment incidents and damaging the customer relationship.

    Training a new operator to hold cycle time on more than two variants took four to six weeks. Audit preparation for customer line walks consumed a senior process engineer's full week each quarter.

    5.2What was deployed

    Pick-to-Light was installed on every kitting bay with bins physically locked until the correct pick is confirmed. The ADAS radar alignment station was integrated with the manufacturing execution system so each module boots through a sealed alignment routine and ships a signed alignment report. An AC performance test station was added downstream as a cross-check on a sister product family running on the same line. All three stream their events to one dashboard for shift leads and audit playback.

    5.3Reported outcomes

    Table 1. Operator-reported outcomes following deployment, measured against the pre-deployment baseline on the same line.
    MeasureBeforeAfter
    Operator wrong-part picksapprox. 1.4% error rate0 since rollout
    OEM containment incidentsBaseline92% reduction
    Operator ramp-up to full cycle time4-6 weeks5 days
    Throughput per shift, flagship lineBaseline+18%
    Variants handled without operator discrimination0 of 66 of 6

    The zero result should be read precisely. It is zero wrong-part picks recorded since rollout, under a regime in which a wrong pick requires an interlock failure rather than an operator error. It is not a claim that the failure probability is zero; it is a claim that the failure mode changed from one governed by human factors to one governed by device reliability, and that no device failure has produced a wrong pick within the observation window.

    The throughput gain is the least intuitive result and the most instructive. Interlocks were expected to cost cycle time, because a locked bin is a pause. In practice they removed more time than they added: operators stopped double-checking, and the rework loop for wrong picks — which had consumed both the station's time and the downstream station's — largely disappeared.

    The ramp-up reduction follows directly from Section 4.1. Five days is the time to learn a motion and a rhythm; four to six weeks was the time to learn to tell six similar parts apart reliably at speed. Removing the second task from the operator removed it from the training curve.

    6Discussion

    6.1The default is the design

    Of the six design goals, G1 carries most of the result. The technical capability to interlock a bin is not novel and was available in the incumbent tooling. What was absent was a default that made interlocking the normal case and warning-only an authorised exception.

    This matters because the drift toward warnings is driven by a real and legitimate pressure. A control device stops the line, and stopping the line is visible, costly, and attributable, while an escape is invisible until it reaches the customer. Any architecture that leaves the choice to local discretion under that asymmetry will converge on warnings. Making the exception explicit and recorded does not remove the pressure; it makes yielding to it visible.

    6.2Guard banding as an honesty requirement

    A station whose acceptance limits equal its specification limits is passing units it cannot distinguish from failing ones. This is not a subtle statistical point; it is arithmetic that follows from any non-zero measurement uncertainty. Yet acceptance-equals-specification remains a common default, because guard banding visibly increases the reject rate and a higher reject rate looks like worse performance.

    The reject rate does rise, and this paper does not present that as costless. What changes is which error the station makes: it moves from silently passing marginal units to visibly rejecting some good ones. For a safety-relevant characteristic under ISO 26262 that is the correct direction; for a cosmetic one it may not be. Requiring k to be configured per characteristic is what forces the question to be asked.

    6.3A capability reference framework for error-proofing stations

    Table 2. Capability reference framework. Each dimension distinguishes a genuinely error-proofed station from an instrumented one, and each is answerable by demonstration at the station within minutes.
    DimensionQuestion the station must answer by demonstration
    D1 Control by defaultAttempt the wrong action. Is it prevented, or merely announced?
    D2 Authorised exceptionsCan the station list every characteristic in warning-only mode, with who authorised it and why?
    D3 Recipe from identityDoes the operator ever select the variant, or does the system resolve it from the unit?
    D4 Version recoveryFor a unit built six months ago, can the station state which recipe version built it?
    D5 Declared measurementCan the station state its measurement chain and its combined uncertainty?
    D6 Guard bandAre acceptance limits tighter than specification limits, and is k configured per characteristic?
    D7 Device verificationWhat happens when a device is overdue for verification? Demonstrate the degraded state.
    D8 Escape attributionCan a downstream escape be attributed back to the station whose recipe should have caught it?

    6.4Generalisability

    The evidence base is one operator, two plants, one product family. The variant-discrimination result should generalise wherever similar-looking parts are picked under tempo, which is common across automotive and appliance assembly. The alignment and guard-banding results are specific to stations producing a measured verdict, and their magnitude depends on the ratio of measurement uncertainty to tolerance width — a ratio that varies widely and must be established per station rather than assumed.

    7Threats to Validity and Limitations

    1. Single-operator evidence. All field figures come from one supplier, two plants, and one product family. There is no control line and no matched comparison against an alternative error-proofing approach.
    2. A zero is a censored observation. Zero wrong picks since rollout bounds the rate below the reciprocal of the units built, but it does not establish that the rate is zero. The observation window and unit count required to make that bound meaningful are not published here.
    3. Operator-reported metrics. Figures are reported from the production system by the operator rather than independently audited.
    4. Confounded deployment. Three station types were introduced alongside process and training change; no single station's contribution to the throughput or containment figures can be isolated.
    5. Throughput mechanism inferred. The explanation offered in Section 5.3 for the throughput gain — reduced double-checking and rework — is consistent with the operator's account but was not measured directly by time study.
    6. No cost data. Operational outcomes are reported; device, integration, and maintenance costs are not, so no return-on-investment claim is made or implied.

    The second limitation deserves emphasis because zero is the most rhetorically powerful and least statistically informative result a paper of this kind can report.

    8Future Work

    • Bounding the zero. Publishing units built and the observation window alongside the zero count would convert a rhetorical result into a statistical bound on the interlock failure rate.
    • Direct measurement of the throughput mechanism. A time study separating double-check time, rework time, and interlock wait time would test the explanation offered in Section 5.3 rather than inferring it.
    • Guard-band optimisation from realised costs. Choosing k per characteristic currently rests on judgement; deriving it from observed false-reject and escape costs would make the trade quantitative.
    • Warning-mode telemetry. Recording how often a warning is issued and not acted upon would give an empirical value for the term p(a) in Equation (pickerr), which is currently an assumption.
    • Cross-plant device-reliability data. Aggregating interlock failure and verification-overdue events across sites would let p(f) be estimated rather than bounded by the absence of observed failures.

    9Conclusion

    Shingo's distinction between control and warning devices is sixty years old and still the most consequential decision in an error-proofing programme. The finding of this work is that the decision is rarely made explicitly: it is inherited from defaults, and the defaults drift toward warnings because stopping a line is visible while an escape is not.

    This paper has described a station architecture that inverts the default — interlock as the normal case, warning-only as an authorised and recorded exception — together with a recipe model that removes variant discrimination from the operator's task, and an explicit measurement chain with guard banding that converts measurement uncertainty into acceptance limits. A two-plant production deployment reports zero wrong-part picks since rollout against a 1.4% baseline error rate, a 92% reduction in customer containment incidents, operator ramp-up falling from four to six weeks to five days, and an 18% throughput gain.

    The capability reference framework of Section 6.3 is offered as the durable contribution, and its first question is the one that matters most: attempt the wrong action, and observe whether the station prevents it or merely announces it.


    Appendix ANomenclature

    Table 3. Symbols and abbreviations used in this paper.
    Symbol / termMeaning
    p(d)Operator discrimination error rate at a picking task
    p(a)Probability that an issued warning is noticed and acted upon
    p(f)Interlock device failure probability
    u(ref), u(fix), u(sen), u(met)Uncertainty contributions from reference, fixture, sensor, and method
    u(combined)Combined standard uncertainty of the measurement chain
    USL, LSLUpper and lower specification limits for a characteristic
    AL(upper), AL(lower)Guard-banded acceptance limits applied by the station
    kGuard-band multiplier, configured per characteristic
    sigma(GRR)Standard deviation attributable to gauge repeatability and reproducibility
    %GRRGauge repeatability and reproducibility as a percentage of tolerance width
    FPYFirst-pass yield of a single station
    RTYRolled throughput yield, the product of station first-pass yields
    EscapeA non-conforming unit passed by a station and detected downstream
    False rejectA conforming unit rejected by a station
    Control deviceA poka-yoke device that makes the wrong action impossible
    Warning deviceA poka-yoke device that signals a deviation and leaves the decision to the operator
    ADASAdvanced driver-assistance system
    MESManufacturing execution system

    Appendix BWorked Numerical Examples

    Appendix B.1Expected wrong picks under warning versus control

    A line builds 690 units per shift, each requiring four picks from close-cousin bins, so 2,760 picks per shift. The observed operator discrimination error rate before deployment was p(d) = 0.014.

    Under a warning-only regime, suppose a warning is noticed and acted upon 80% of the time, so p(a) = 0.8. Applying Equation (pickerr): p(wrong) = 0.014 x (1 — 0.8) = 0.0028, giving 2,760 x 0.0028 = 7.7 wrong picks per shift.

    Under an interlock regime the wrong-pick probability is the device failure probability. For an interlock verified daily with a demonstrated failure rate on the order of 1e-5 per actuation, the expected count is 2,760 x 1e-5 = 0.028 per shift, or roughly one per thirty-six shifts.

    The comparison is instructive precisely because p(a) is an assumption. Even at a generous p(a) = 0.95, the warning regime yields 1.9 wrong picks per shift — still two orders of magnitude worse than the interlock. The conclusion does not depend on the exact value, which is why removing the term matters more than estimating it well.

    Appendix B.2Guard banding an alignment characteristic

    A radar alignment characteristic is specified at 0.00 degrees with limits of plus or minus 0.30 degrees, so USL = 0.30 and LSL = -0.30. The declared measurement chain contributes u(reference) = 0.020, u(fixture) = 0.045, u(sensor) = 0.030, and u(method) = 0.015 degrees.

    Applying Equation (uc): u(combined) = sqrt(0.020^2 + 0.045^2 + 0.030^2 + 0.015^2) = sqrt(0.000400 + 0.002025 + 0.000900 + 0.000225) = sqrt(0.003550) = 0.0596 degrees.

    With k = 2, Equation (guard) gives AL(upper) = 0.30 — 2 x 0.0596 = 0.181 degrees and AL(lower) = -0.181 degrees. The station accepts a band 60% as wide as the specification. A unit measuring 0.25 degrees — comfortably inside specification — is rejected, because the chain cannot distinguish it from a unit truly at 0.37 degrees.

    The fixture dominates the uncertainty budget at 0.045 of 0.0596 combined. Halving fixture repeatability would give u(combined) = 0.0424 and widen the acceptance band to plus or minus 0.215 degrees. That is the correct engineering response: improve the largest contributor rather than reduce k and accept more escapes.

    Appendix B.3Is the gauge adequate?

    For the same characteristic, a gauge study returns sigma(GRR) = 0.052 degrees against a tolerance width of USL — LSL = 0.60 degrees.

    Applying Equation (grr): %GRR = 100 x 0.052 / 0.60 = 8.7%. This is below the 10% threshold automotive practice treats as acceptable, so the measurement system is adequate to judge conformance against this tolerance.

    Had the tolerance been plus or minus 0.10 degrees instead, the width would be 0.20 and %GRR would be 26% — in the marginal band, and adequate only with justification. The same gauge is adequate or inadequate depending entirely on the tolerance it is asked to judge, which is why %GRR is expressed against tolerance rather than against part variation at a validation station.

    Appendix B.4Why respectable stations make a poor line

    A line has twelve stations. Eleven run at 99.5% first-pass yield and one — the AC performance test — runs at 96.0%.

    Applying Equation (rty): RTY = 0.995^11 x 0.960 = 0.9464 x 0.960 = 0.9086. Roughly one finished unit in eleven required intervention somewhere on the line.

    Improving the weakest station from 96.0% to 99.0% raises RTY to 0.9464 x 0.990 = 0.9369. Improving all eleven others from 99.5% to 99.8% instead raises it to 0.9782 x 0.960 = 0.9391. The two interventions are comparable in effect, which is not what a station-by-station report suggests — and only the rolled measure reveals it.

    Provenance

    Where these numbers come from

    Every quantitative claim in this paper traces to a named deployment, a product specification, or an explicitly labelled model.

    MetricReported valueProvenance
    Wrong-part picks since rollout0Operator-reported — Tier-1 Manufacturers (Pune & Nasik plant, India)/case-studies/pokayoke

    Baseline operator pick-error rate was approximately 1.4%.

    Pre-deployment operator pick-error rateapprox. 1.4%Operator-reported — Tier-1 Manufacturers (Pune & Nasik plant, India)/case-studies/pokayoke

    Six close-cousin radar-module variants with small visual differences.

    OEM containment incidents92% reductionOperator-reported — Tier-1 Manufacturers (Pune & Nasik plant, India)/case-studies/pokayoke
    Operator ramp-up to full cycle-time competence5 days (from 4-6 weeks)Operator-reported — Tier-1 Manufacturers (Pune & Nasik plant, India)/case-studies/pokayoke
    Throughput per shift, flagship line+18%Operator-reported — Tier-1 Manufacturers (Pune & Nasik plant, India)/case-studies/pokayoke
    Wrong-part reduction attributed to Pick-to-Lightover 90%Product specification/products/pick-to-light

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    MileSoft Engineering Research Group (2026). Making the Wrong Action Impossible: Software-Enforced Poka-Yoke in Automotive Assembly: Architecture, Computational Methods, and Field Evidence from the MileSoft Pokayoke Suite. Working Paper Version 1.0. MileSoft Software Technologies. https://milesoft.net/research/pokayoke

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