Robert Helewka
e88449d15a
studies/202512_GenesysCX -> studies/202512_TEI_Genesys_CX_Cloud, rebuilt as pattern Variant 4 (TEI composite reproduction): - teicalc/ self-contained engine: Forrester's tables as the never-edited verbatim anchor (incl. the p.14 typo note and the $0 AI-token line), generic model/scenarios/staging carried over from the Amazon Connect study, ClientDrivers overlay (agents / weekly interactions / revenue, flat composite so no growth re-base) with ai_tokens_annual as a direct input for the token line the published study models at $0 - one deliverable notebook (business_case.ipynb): widget-pair sidebar drivers incl. the AI-token price, published-vs-overlay KPI columns, cash-flow/waterfall/scenario charts, verification gate, backstage JSON data appendix - gate + tests reproduce the published totals within $2: NPV $10.8M / ROI 266% (engine $10,783,466 / 265.79%; payback 3.3 months, not headlined in the PDF); 29 study tests, headless nbconvert green, stage simulation leak-free, exports carry the appendix - old Athena workflow (00_provision..04_export, config.py, seed_data.py, PALLADIUM_GENESYSCX_* keys, ATHENA_EXPECTED reconciliation) deleted; git history preserves it With the last legacy study migrated, the retirement lands too: - app/ (Streamlit UI) and core/notebook_helpers deleted; nothing else imported them - streamlit stripped from pyproject extras, requirements.txt, Makefile; .env.example reduced to the Athena keys; 00_setup.ipynb and core/bootstrap.py repointed at the pattern studies - root README reworked: self-contained studies + slim core/ Athena toolkit (tei_client, calculations, export, cli) All suites green: Genesys 29, Amazon Connect 27, CTM 55, template 7, root 58. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
"""
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Scenario stress — transplanted from the retired shared ``core/calculations/scenarios.py``
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with identical semantics.
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Forrester TEI risk-adjusts benefits *down* and costs *up*; scenarios stress
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both levers:
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* ``adoption`` scales nominal values (``year_values`` and ``initial``).
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* ``risk_delta`` is *added* to a benefit's risk factor and *subtracted*
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from a cost's (conservative = more uncertainty on benefits, less padding
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on costs), then clamped to [0, 1].
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``"moderate"`` is the identity — the headless default reproduces the
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published study. Note the counterintuitive corollary: the conservative
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scenario *lowers* costs PV, because 80% adoption shrinks consumption-priced
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usage and the clamp caps cost padding.
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"""
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from __future__ import annotations
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from copy import deepcopy
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SCENARIOS: dict[str, dict[str, float]] = {
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"conservative": {"adoption": 0.80, "risk_delta": 0.10},
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"moderate": {"adoption": 1.00, "risk_delta": 0.00},
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"aggressive": {"adoption": 1.15, "risk_delta": -0.05},
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}
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def apply_scenario(
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items: list[dict],
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scenario: str = "moderate",
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*,
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multipliers: dict[str, dict[str, float]] | None = None,
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table: str | None = None,
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) -> list[dict]:
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"""
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Deep-copied value rows with the scenario applied; inputs are not mutated.
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Each row needs ``year_values`` (year-string → float), optionally
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``initial`` and ``risk_adjustment``, and a ``table`` of ``"benefits"``
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or ``"costs"`` (or pass ``table=`` to force one) — the table decides the
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sign of ``risk_delta``.
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"""
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cfg = (multipliers or SCENARIOS).get(scenario)
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if cfg is None:
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raise KeyError(f"Unknown scenario: {scenario!r}")
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adoption = float(cfg.get("adoption", 1.0))
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risk_delta = float(cfg.get("risk_delta", 0.0))
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out: list[dict] = []
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for raw in items:
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item = deepcopy(raw)
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item_table = item.get("table") or table or "benefits"
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item["table"] = item_table
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item["year_values"] = {
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k: float(v) * adoption for k, v in item["year_values"].items()
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}
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if item.get("initial") is not None:
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item["initial"] = float(item["initial"]) * adoption
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ra = float(item.get("risk_adjustment") or 0.0)
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new_ra = ra + risk_delta if item_table == "benefits" else ra - risk_delta
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item["risk_adjustment"] = max(0.0, min(1.0, new_ra))
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out.append(item)
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return out
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