palladium/studies/202602_AmazonConnect/notebooks/02_costs.ipynb

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    "# 02 — Costs Analysis\n",
    "\n",
    "**Study:** Forrester TEI™ Of Amazon Connect (Feb 2026)\n",
    "\n",
    "Three cost categories, three-year horizon, 10% discount rate.\n",
    "Target risk-adjusted PV = **$22,983,076**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46446223",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from pathlib import Path\n",
    "\n",
    "ROOT = Path.cwd().resolve()\n",
    "while ROOT != ROOT.parent and not (ROOT / 'core').is_dir():\n",
    "    ROOT = ROOT.parent\n",
    "if str(ROOT) not in sys.path:\n",
    "    sys.path.insert(0, str(ROOT))\n",
    "STUDY = ROOT / 'studies' / '202602_AmazonConnect'\n",
    "if str(STUDY) not in sys.path:\n",
    "    sys.path.insert(0, str(STUDY))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ec64198",
   "metadata": {},
   "outputs": [],
   "source": [
    "import config\n",
    "import seed_data\n",
    "from core.calculations import npv, risk_adjust_cost\n",
    "from core.notebook_helpers import charts, display, tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26f1d385",
   "metadata": {},
   "source": [
    "## Costs — nominal & risk-adjusted\n",
    "\n",
    "| Ref | Cost | Initial | Y1 | Y2 | Y3 | Risk Adj |\n",
    "|---|---|---|---|---|---|---|\n",
    "| Ft | Amazon Connect usage | — | $6.5M | $8.0M | $9.8M | ↑5% |\n",
    "| Gt | Implementation & migration | $1.09M | $188K | $188K | — | ↑10% |\n",
    "| Ht | Ongoing management | — | $256K | $187K | $187K | ↑15% |\n",
    "\n",
    "Note **costs are risk-adjusted *upward*** (higher risk → higher modelled cost)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9635f334",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = tables.costs_table(seed_data.COSTS)\n",
    "df.style.format({c: '${:,.0f}' for c in df.columns if c not in ('field_key','label','category','risk_adjustment')})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0667d1da",
   "metadata": {},
   "source": [
    "## Local validation\n",
    "\n",
    "Reproduce the **$22,983,076** Costs PV from the PDF Cash Flow Analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e35a794",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "rows = []\n",
    "for c in seed_data.COSTS:\n",
    "    rf = c['risk_adjustment']\n",
    "    init_ra = risk_adjust_cost(c.get('initial') or 0, rf)\n",
    "    yr = [c['year_values'][str(y)] for y in (1, 2, 3)]\n",
    "    yr_ra = [risk_adjust_cost(v, rf) for v in yr]\n",
    "    pv = npv(yr_ra, config.DISCOUNT_RATE, initial=init_ra)\n",
    "    rows.append({\n",
    "        'Cost': c['label'],\n",
    "        'Initial (RA)': init_ra,\n",
    "        'Y1 (RA)': yr_ra[0],\n",
    "        'Y2 (RA)': yr_ra[1],\n",
    "        'Y3 (RA)': yr_ra[2],\n",
    "        'PV': pv,\n",
    "    })\n",
    "df_check = pd.DataFrame(rows)\n",
    "totals = df_check.drop(columns='Cost').sum()\n",
    "df_check.loc[len(df_check)] = ['TOTAL'] + totals.tolist()\n",
    "df_check.style.format({c: '${:,.0f}' for c in df_check.columns if c != 'Cost'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4109784e",
   "metadata": {},
   "outputs": [],
   "source": [
    "expected_pv = 22_983_076\n",
    "computed_pv = df_check.iloc[-1]['PV']\n",
    "delta = computed_pv - expected_pv\n",
    "kind = 'success' if abs(delta) < 1_000 else 'warning'\n",
    "display.alert(\n",
    "    f'Computed Costs PV: <b>${computed_pv:,.0f}</b><br>'\n",
    "    f'Forrester target:  <b>${expected_pv:,.0f}</b><br>'\n",
    "    f'Δ = ${delta:,.0f}',\n",
    "    kind,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd1b3c04",
   "metadata": {},
   "source": [
    "## Cost mix\n",
    "\n",
    "Most of the three-year cost (~90%) is Amazon Connect *usage* (Ft) —\n",
    "consistent with the PDF's framing that consumption-based pricing dominates,\n",
    "with implementation a one-time investment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90e9b5e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "charts.cost_breakdown_pie(seed_data.COSTS).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d15ae10",
   "metadata": {},
   "source": [
    "## Push to Athena"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03547040",
   "metadata": {},
   "outputs": [],
   "source": [
    "if config.TOOL_PUBLIC_ID:\n",
    "    from core.tei_client import TEIClient\n",
    "\n",
    "    client = TEIClient()\n",
    "    client.update_values(config.TOOL_PUBLIC_ID, seed_data.COSTS)\n",
    "    display.alert(f'Pushed {len(seed_data.COSTS)} cost rows to '\n",
    "                  f'tool <code>{config.TOOL_PUBLIC_ID}</code>.', 'success')\n",
    "else:\n",
    "    display.alert('No TOOL_PUBLIC_ID set — skipped Athena push.', 'info')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5befbb",
   "metadata": {},
   "source": [
    "Continue with [`03_business_case.ipynb`](03_business_case.ipynb) →"
   ]
  }
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