{
  "schema": "vedokrok.public-item.v1",
  "release_id": "MHC-RPUB-20260920-75ad787a",
  "url": "/knowledge/maintain-a-local-forecast-error-baseline",
  "id": "MHC-D-RESEARCH-0636",
  "version": "0.1.0",
  "title": "Maintain a local forecast-error baseline",
  "summary": "Your own misses are a dataset if you keep them.",
  "kind": "protocol",
  "body": "For recurring work, store original forecast, scope snapshot, actual outcome and major error components. Periodically summarize bias and spread for comparable work. Use this local history as one input to future forecasts and to check whether estimation changes are actually improving calibration.",
  "limits": [
    "Small or changing samples can mislead; audit comparability before treating history as a stable base rate."
  ],
  "topics": [
    "union-project-estimation-and-delivery-realism"
  ],
  "intents": [],
  "source_ids": [
    "RS-F874002E4A58CC3B",
    "RS-3AABED087865223D"
  ],
  "evidence": [
    {
      "claim": "The 2026 Green Book recommends explicitly accounting for optimism bias in cost, benefit and duration estimates and using historical forecast errors from similar proposals where available.",
      "source_id": "RS-F874002E4A58CC3B",
      "role": "supports",
      "note": "The guidance is for UK public appraisal; the transferable principle is empirical correction, not a universal percentage uplift.",
      "locator": "Optimism bias"
    },
    {
      "claim": "Supplementary Green Book optimism-bias guidance recommends basing adjustments on data from past or similar projects and collecting local data to improve future estimates.",
      "source_id": "RS-3AABED087865223D",
      "role": "supports",
      "note": "Reference classes must be genuinely comparable; irrelevant historical projects can make an estimate worse.",
      "locator": "Introduction and making adjustments"
    }
  ],
  "use_when": [
    "Teams repeatedly estimate similar work but every new estimate starts from intuition."
  ],
  "avoid_when": [
    "Small or changing samples can mislead; audit comparability before treating history as a stable base rate."
  ],
  "example": "A migration team learns that approval wait is consistently underforecast even when build effort is accurate, so future elapsed forecasts reflect that history.",
  "check": "A new estimate can cite comparable local forecast errors instead of relying only on memory.",
  "steps": [
    "Original forecasts preserved.",
    "Actuals recorded.",
    "Comparable work grouped.",
    "Systematic over/underforecast checked.",
    "Spread, not only average error, inspected.",
    "Method changes evaluated over time."
  ],
  "sources": [
    {
      "id": "RS-F874002E4A58CC3B",
      "title": "The Green Book (2026)",
      "url": "https://www.gov.uk/government/publications/the-green-book-appraisal-and-evaluation-in-central-government/the-green-book-2026"
    },
    {
      "id": "RS-3AABED087865223D",
      "title": "Supplementary Green Book Guidance: Optimism Bias",
      "url": "https://assets.publishing.service.gov.uk/media/5a74dae740f0b65f61322c72/Optimism_bias.pdf"
    }
  ],
  "relations": [
    {
      "from": "MHC-D-RESEARCH-0636",
      "to": "MHC-D-RESEARCH-0402",
      "type": "useful_with",
      "url": "/knowledge/build-an-outside-view-reference-class"
    }
  ],
  "collections": [
    {
      "id": "RC-3C67E080415E2677",
      "title": "Estimate the delivery system, not only the hands-on work",
      "url": "/collections/estimate-the-delivery-system-not-only-the-hands-on-work"
    }
  ]
}