{
  "assumption_density": 0.2222222222222222,
  "assumptions": [
    "Defects are visually detectable — all 12 defect types have surface-visible signatures amenable to camera-based inspection rather than requiring X-ray, ultrasonic, or functional testing",
    "Production line has physical space for an inline machine vision station with structured lighting dome before existing manual inspection stations",
    "The 0.2% defect rate is stable enough that 6 months of parallel data is representative of normal production variation including seasonal material batch changes",
    "Cognex ViDi deep learning module can be trained to adequate performance on 12 defect types within the $30K training budget allocation",
    "Current 9-inspector manual inspection is the primary cost center justifying automation — if inspectors perform other functions beyond visual inspection, the ROI case weakens"
  ],
  "confidence": 0.8,
  "id": "b5398079-03c6-4e21-b6df-bfa48bf25207",
  "next_action": "Write an RFQ (Request for Quote) to Cognex and Keyence specifying: 200K units/month throughput, 0.2% defect rate across 12 defect types (enumerate them), inline pre-inspection station placement, GigE camera with structured lighting dome for specular/metallic surfaces, ViDi deep learning module, and conveyor interface requirements — with delivery within 6 weeks.",
  "question": "Should we replace our manual QA inspection line with machine vision? 200K units/month, 3 shifts, 0.2% defect rate, $400K budget for automation.",
  "question_fit_score": 0,
  "rejected_alternatives": [
    {
      "path": "Evaluate off-the-shelf machine vision solutions generically",
      "rationale": "Branch b001 lacked specificity — no named hardware, no deployment architecture, no success thresholds, no failure modes. It restated the question as a recommendation without answering HOW to proceed."
    },
    {
      "path": "Hybrid system with machine vision primary + 2 human inspectors per shift for flagged units",
      "rationale": "Branch b002 (killed round 2) proposed immediate operational dependency on machine vision without validation data. Its 3-tier confidence classification is architecturally sound but premature — you cannot set confidence thresholds without the 6-month parallel run data that b003 generates first. Also risked throughput bottleneck if false positive rate exceeds 5%."
    },
    {
      "path": "Invest $400K in upstream SPC and defect prevention instead of inspection automation",
      "rationale": "Branch b004 (killed round 3) targeted a 0.2% defect rate already at ~4.4 sigma. SPC cannot eliminate stochastic defects at this level within $400K. Critically, even if defects drop 50%, you still need all 9 inspectors — no path to labor cost reduction, which is the actual cost center ($450K-$600K/year fully loaded)."
    },
    {
      "path": "Reject machine vision due to false negative risk and unquantified AI liability",
      "rationale": "Branch b005 (killed round 2) was empirically backwards — NIST data shows human inspection reliability degrades to 80-85% detection after 30 minutes of repetitive tasks, while machine vision maintains consistent rates with full traceability logging. Pure fear-based reasoning with no specifics."
    },
    {
      "path": "Redefine quality paradigm toward defect elimination via DFM and root cause analysis",
      "rationale": "Branch b006 (killed round 2) was a philosophical reframe with no actionable specifics, no named technology, and no path to eliminating the inspection cost center."
    }
  ],
  "reversal_conditions": [
    {
      "condition": "If defects are primarily dimensional or internal (not surface-visible), requiring CMM, X-ray, or functional testing rather than camera-based inspection",
      "flips_to": "Invest in inline metrology (e.g., Zeiss AIMax or Keyence LJ-X8000 laser profiler) or X-ray inspection rather than optical machine vision"
    },
    {
      "condition": "If the 9 inspectors are near-minimum-wage or if union contract prevents any future headcount reduction regardless of validation results",
      "flips_to": "The ROI case collapses — retain manual inspection and spend $400K on upstream process improvements (SPC/predictive maintenance) to reduce defect rate and scrap costs instead"
    },
    {
      "condition": "If production volume increases to \u003e500K units/month within 12 months, making the 6-month validation timeline unacceptable due to immediate throughput constraints",
      "flips_to": "Accelerate to the hybrid model (branch b002's architecture) with machine vision primary + reduced human verification, accepting higher quality risk during ramp-up"
    }
  ],
  "unresolved_uncertainty": [
    "Whether $100K buffer is sufficient for full replacement deployment if validation succeeds — full replacement across 3 shifts likely requires additional capital beyond the original $400K budget",
    "The 12 defect types are referenced but not enumerated — some defect types (cosmetic vs. dimensional vs. functional) have fundamentally different machine vision detection profiles, and the approach may work for 10 of 12 but fail on 2",
    "Union contract implications of eventual full replacement are deferred, not resolved — the parallel run buys time but doesn't address the labor transition plan",
    "ROI timeline is unclear: if 9 inspectors cost $450K-$600K/year and validation takes 7 months, the payback period depends on a second capital expenditure for full replacement that isn't budgeted"
  ],
  "url": "https://vectorcourt.com/v/b5398079-03c6-4e21-b6df-bfa48bf25207",
  "verdict": "Do NOT do a full replacement. Deploy a Cognex In-Sight 9902 or Keyence CV-X480F machine vision system in PARALLEL with existing manual inspection for 6 months. Install one machine vision station inline BEFORE manual inspection stations. Run both systems on 100% of units, logging machine vision pass/fail alongside human decisions for every unit. After ~1.2M unit comparisons, make a data-driven go/no-go on full replacement.\n\nBudget: $180K hardware (Cognex In-Sight 9902 + GigE camera + structured lighting dome), $40K integration, $30K ViDi deep learning training on 12 defect types, $50K reserve for lighting/model iteration. Total $300K, $100K buffer.\n\nSuccess thresholds: ≥99.8% agreement with human inspectors AND ≥95% of human-caught defects detected, validated over ≥500K units.\n\nKey failure mode: Structured lighting fails on subtle waviness or discoloration due to alloy batch reflectance variation. Mitigated by $50K reserve and manual inspectors as backstop.\n\nKeep all 9 inspectors during validation. No union disruption. Timeline: 6 weeks procurement/install, months 2-4 parallel run + model training, months 5-6 statistical validation, month 7 go/no-go with real data.",
  "verdict_core": {
    "recommendation": "Deploy a Cognex In-Sight 9902 or Keyence CV-X480F machine vision system in PARALLEL with existing manual inspection for a 6-month validation period, at a budget of $250K-$300K for a single inspection station.",
    "mechanism": "Because running machine vision inline BEFORE manual inspection on 100% of units generates ~1.2M unit comparisons over 6 months, providing statistically rigorous data on whether machine vision matches the 0.2% defect catch rate across all defect types — while retaining all 9 inspectors as a backstop eliminates quality risk and avoids union contract disruption during validation.",
    "tradeoffs": [
      "No immediate labor cost savings — all 9 inspectors retained during 6-month validation, delaying ROI by at least 7 months",
      "Consumes $300K of $400K budget on validation alone, leaving only $100K buffer and no funds for full replacement if validation succeeds",
      "Single-station deployment may not capture all line-specific variation across 3 shifts"
    ],
    "failure_modes": [
      "Structured lighting fails on certain defect types (subtle waviness, discoloration on stamped parts) due to metal surface reflectance varying with alloy batch",
      "Deep learning model requires more than $30K allocated for ViDi training to cover all 12 defect types adequately",
      "6-month parallel run reveals machine vision catches different defects than humans (high agreement rate but different miss profiles), making go/no-go decision ambiguous"
    ],
    "thresholds": [
      "≥99.8% agreement with human inspectors on defect/no-defect classification",
      "≥95% of defects humans catch must be caught by machine vision",
      "Validated over ≥500K units",
      "$180K for Cognex In-Sight 9902 + GigE camera + structured lighting dome",
      "$40K integration/mounting/conveyor interface",
      "$30K Cognex ViDi deep learning module training",
      "$50K reserve for lighting and model tuning iteration",
      "Total: $300K, leaving $100K buffer"
    ]
  },
  "verdict_type": ""
}