1198 lines
85 KiB
Plaintext
1198 lines
85 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "1a76b7ed",
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"metadata": {},
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"source": [
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"# 02 — Costs Analysis\n",
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"\n",
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"**Study:** Forrester TEI™ Of Amazon Connect (Feb 2026)\n",
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"\n",
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"Three cost categories, three-year horizon, 10% discount rate.\n",
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"Target risk-adjusted PV = **$22,983,076**."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "46446223",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"from pathlib import Path\n",
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"\n",
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"ROOT = Path.cwd().resolve()\n",
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"while ROOT != ROOT.parent and not (ROOT / 'core').is_dir():\n",
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" ROOT = ROOT.parent\n",
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"if str(ROOT) not in sys.path:\n",
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" sys.path.insert(0, str(ROOT))\n",
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"STUDY = ROOT / 'studies' / '202602_AmazonConnect'\n",
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"if str(STUDY) not in sys.path:\n",
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" sys.path.insert(0, str(STUDY))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "4ec64198",
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"metadata": {},
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"outputs": [],
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"source": [
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"import config\n",
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"import seed_data\n",
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"from core.calculations import npv, risk_adjust_cost\n",
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"from core.notebook_helpers import charts, display, tables"
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]
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},
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{
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"cell_type": "markdown",
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"id": "26f1d385",
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"metadata": {},
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"source": [
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"## Costs — nominal & risk-adjusted\n",
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"\n",
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"| Ref | Cost | Initial | Y1 | Y2 | Y3 | Risk Adj |\n",
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"|---|---|---|---|---|---|---|\n",
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"| Ft | Amazon Connect usage | — | $6.5M | $8.0M | $9.8M | ↑5% |\n",
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"| Gt | Implementation & migration | $1.09M | $188K | $188K | — | ↑10% |\n",
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"| Ht | Ongoing management | — | $256K | $187K | $187K | ↑15% |\n",
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"\n",
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"Note **costs are risk-adjusted *upward*** (higher risk → higher modelled cost)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "9635f334",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<style type=\"text/css\">\n",
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"</style>\n",
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"<table id=\"T_032a1\">\n",
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" <thead>\n",
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" <tr>\n",
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" <th class=\"blank level0\" > </th>\n",
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" <th id=\"T_032a1_level0_col0\" class=\"col_heading level0 col0\" >field_key</th>\n",
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" <th id=\"T_032a1_level0_col1\" class=\"col_heading level0 col1\" >label</th>\n",
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" <th id=\"T_032a1_level0_col2\" class=\"col_heading level0 col2\" >category</th>\n",
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" <th id=\"T_032a1_level0_col3\" class=\"col_heading level0 col3\" >risk_adjustment</th>\n",
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" <th id=\"T_032a1_level0_col4\" class=\"col_heading level0 col4\" >Initial</th>\n",
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" <th id=\"T_032a1_level0_col5\" class=\"col_heading level0 col5\" >Initial (RA)</th>\n",
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" <th id=\"T_032a1_level0_col6\" class=\"col_heading level0 col6\" >Year 1</th>\n",
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" <th id=\"T_032a1_level0_col7\" class=\"col_heading level0 col7\" >Year 1 (RA)</th>\n",
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" <th id=\"T_032a1_level0_col8\" class=\"col_heading level0 col8\" >Year 2</th>\n",
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" <th id=\"T_032a1_level0_col9\" class=\"col_heading level0 col9\" >Year 2 (RA)</th>\n",
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" <th id=\"T_032a1_level0_col10\" class=\"col_heading level0 col10\" >Year 3</th>\n",
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" <th id=\"T_032a1_level0_col11\" class=\"col_heading level0 col11\" >Year 3 (RA)</th>\n",
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" <th id=\"T_032a1_level0_col12\" class=\"col_heading level0 col12\" >Total</th>\n",
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" <th id=\"T_032a1_level0_col13\" class=\"col_heading level0 col13\" >Total (RA)</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th id=\"T_032a1_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
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" <td id=\"T_032a1_row0_col0\" class=\"data row0 col0\" >amazon_connect_usage</td>\n",
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" <td id=\"T_032a1_row0_col1\" class=\"data row0 col1\" >Amazon Connect usage cost</td>\n",
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" <td id=\"T_032a1_row0_col2\" class=\"data row0 col2\" >Subscription</td>\n",
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" <td id=\"T_032a1_row0_col3\" class=\"data row0 col3\" >0.050000</td>\n",
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" <td id=\"T_032a1_row0_col4\" class=\"data row0 col4\" >$0</td>\n",
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" <td id=\"T_032a1_row0_col5\" class=\"data row0 col5\" >$0</td>\n",
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" <td id=\"T_032a1_row0_col6\" class=\"data row0 col6\" >$6,456,448</td>\n",
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" <td id=\"T_032a1_row0_col7\" class=\"data row0 col7\" >$6,779,270</td>\n",
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" <td id=\"T_032a1_row0_col8\" class=\"data row0 col8\" >$7,951,164</td>\n",
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" <td id=\"T_032a1_row0_col9\" class=\"data row0 col9\" >$8,348,722</td>\n",
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" <td id=\"T_032a1_row0_col10\" class=\"data row0 col10\" >$9,832,961</td>\n",
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" <td id=\"T_032a1_row0_col11\" class=\"data row0 col11\" >$10,324,609</td>\n",
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" <td id=\"T_032a1_row0_col12\" class=\"data row0 col12\" >$24,240,573</td>\n",
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" <td id=\"T_032a1_row0_col13\" class=\"data row0 col13\" >$25,452,602</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th id=\"T_032a1_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
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" <td id=\"T_032a1_row1_col0\" class=\"data row1 col0\" >implementation_migration</td>\n",
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" <td id=\"T_032a1_row1_col1\" class=\"data row1 col1\" >Implementation and migration cost</td>\n",
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" <td id=\"T_032a1_row1_col2\" class=\"data row1 col2\" >Implementation</td>\n",
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" <td id=\"T_032a1_row1_col3\" class=\"data row1 col3\" >0.100000</td>\n",
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" <td id=\"T_032a1_row1_col4\" class=\"data row1 col4\" >$1,087,500</td>\n",
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" <td id=\"T_032a1_row1_col5\" class=\"data row1 col5\" >$1,196,250</td>\n",
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" <td id=\"T_032a1_row1_col6\" class=\"data row1 col6\" >$188,333</td>\n",
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" <td id=\"T_032a1_row1_col7\" class=\"data row1 col7\" >$207,166</td>\n",
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" <td id=\"T_032a1_row1_col8\" class=\"data row1 col8\" >$188,333</td>\n",
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" <td id=\"T_032a1_row1_col9\" class=\"data row1 col9\" >$207,166</td>\n",
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" <td id=\"T_032a1_row1_col10\" class=\"data row1 col10\" >$0</td>\n",
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" <td id=\"T_032a1_row1_col11\" class=\"data row1 col11\" >$0</td>\n",
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" <td id=\"T_032a1_row1_col12\" class=\"data row1 col12\" >$1,464,166</td>\n",
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" <td id=\"T_032a1_row1_col13\" class=\"data row1 col13\" >$1,610,583</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th id=\"T_032a1_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
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" <td id=\"T_032a1_row2_col0\" class=\"data row2 col0\" >ongoing_management</td>\n",
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" <td id=\"T_032a1_row2_col1\" class=\"data row2 col1\" >Ongoing management</td>\n",
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" <td id=\"T_032a1_row2_col2\" class=\"data row2 col2\" >Operations</td>\n",
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" <td id=\"T_032a1_row2_col3\" class=\"data row2 col3\" >0.150000</td>\n",
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" <td id=\"T_032a1_row2_col4\" class=\"data row2 col4\" >$0</td>\n",
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" <td id=\"T_032a1_row2_col5\" class=\"data row2 col5\" >$0</td>\n",
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" <td id=\"T_032a1_row2_col6\" class=\"data row2 col6\" >$256,200</td>\n",
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" <td id=\"T_032a1_row2_col7\" class=\"data row2 col7\" >$294,630</td>\n",
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" <td id=\"T_032a1_row2_col8\" class=\"data row2 col8\" >$187,200</td>\n",
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" <td id=\"T_032a1_row2_col9\" class=\"data row2 col9\" >$215,280</td>\n",
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" <td id=\"T_032a1_row2_col10\" class=\"data row2 col10\" >$187,200</td>\n",
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" <td id=\"T_032a1_row2_col11\" class=\"data row2 col11\" >$215,280</td>\n",
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" <td id=\"T_032a1_row2_col12\" class=\"data row2 col12\" >$630,600</td>\n",
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" <td id=\"T_032a1_row2_col13\" class=\"data row2 col13\" >$725,190</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n"
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],
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"text/plain": [
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"<pandas.io.formats.style.Styler at 0x10a22eab0>"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = tables.costs_table(seed_data.COSTS)\n",
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"df.style.format({c: '${:,.0f}' for c in df.columns if c not in ('field_key','label','category','risk_adjustment')})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0667d1da",
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"metadata": {},
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"source": [
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"## Local validation\n",
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"\n",
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"Reproduce the **$22,983,076** Costs PV from the PDF Cash Flow Analysis."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "3e35a794",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<style type=\"text/css\">\n",
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"</style>\n",
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"<table id=\"T_dc982\">\n",
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" <thead>\n",
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" <tr>\n",
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" <th class=\"blank level0\" > </th>\n",
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" <th id=\"T_dc982_level0_col0\" class=\"col_heading level0 col0\" >Cost</th>\n",
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" <th id=\"T_dc982_level0_col1\" class=\"col_heading level0 col1\" >Initial (RA)</th>\n",
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" <th id=\"T_dc982_level0_col2\" class=\"col_heading level0 col2\" >Y1 (RA)</th>\n",
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" <th id=\"T_dc982_level0_col3\" class=\"col_heading level0 col3\" >Y2 (RA)</th>\n",
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" <th id=\"T_dc982_level0_col4\" class=\"col_heading level0 col4\" >Y3 (RA)</th>\n",
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" <th id=\"T_dc982_level0_col5\" class=\"col_heading level0 col5\" >PV</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th id=\"T_dc982_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
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" <td id=\"T_dc982_row0_col0\" class=\"data row0 col0\" >Amazon Connect usage cost</td>\n",
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" <td id=\"T_dc982_row0_col1\" class=\"data row0 col1\" >$0</td>\n",
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" <td id=\"T_dc982_row0_col2\" class=\"data row0 col2\" >$6,779,270</td>\n",
|
|
" <td id=\"T_dc982_row0_col3\" class=\"data row0 col3\" >$8,348,722</td>\n",
|
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" <td id=\"T_dc982_row0_col4\" class=\"data row0 col4\" >$10,324,609</td>\n",
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" <td id=\"T_dc982_row0_col5\" class=\"data row0 col5\" >$20,819,775</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th id=\"T_dc982_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
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" <td id=\"T_dc982_row1_col0\" class=\"data row1 col0\" >Implementation and migration cost</td>\n",
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" <td id=\"T_dc982_row1_col1\" class=\"data row1 col1\" >$1,196,250</td>\n",
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" <td id=\"T_dc982_row1_col2\" class=\"data row1 col2\" >$207,166</td>\n",
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" <td id=\"T_dc982_row1_col3\" class=\"data row1 col3\" >$207,166</td>\n",
|
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" <td id=\"T_dc982_row1_col4\" class=\"data row1 col4\" >$0</td>\n",
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" <td id=\"T_dc982_row1_col5\" class=\"data row1 col5\" >$1,555,795</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th id=\"T_dc982_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
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" <td id=\"T_dc982_row2_col0\" class=\"data row2 col0\" >Ongoing management</td>\n",
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" <td id=\"T_dc982_row2_col1\" class=\"data row2 col1\" >$0</td>\n",
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" <td id=\"T_dc982_row2_col2\" class=\"data row2 col2\" >$294,630</td>\n",
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" <td id=\"T_dc982_row2_col3\" class=\"data row2 col3\" >$215,280</td>\n",
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" <td id=\"T_dc982_row2_col4\" class=\"data row2 col4\" >$215,280</td>\n",
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" <td id=\"T_dc982_row2_col5\" class=\"data row2 col5\" >$607,506</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th id=\"T_dc982_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
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" <td id=\"T_dc982_row3_col0\" class=\"data row3 col0\" >TOTAL</td>\n",
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" <td id=\"T_dc982_row3_col1\" class=\"data row3 col1\" >$1,196,250</td>\n",
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" <td id=\"T_dc982_row3_col2\" class=\"data row3 col2\" >$7,281,067</td>\n",
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" <td id=\"T_dc982_row3_col3\" class=\"data row3 col3\" >$8,771,168</td>\n",
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" <td id=\"T_dc982_row3_col4\" class=\"data row3 col4\" >$10,539,889</td>\n",
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" <td id=\"T_dc982_row3_col5\" class=\"data row3 col5\" >$22,983,076</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n"
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],
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"text/plain": [
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"<pandas.io.formats.style.Styler at 0x10ac1fbf0>"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"rows = []\n",
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"for c in seed_data.COSTS:\n",
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" rf = c['risk_adjustment']\n",
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" init_ra = risk_adjust_cost(c.get('initial') or 0, rf)\n",
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" yr = [c['year_values'][str(y)] for y in (1, 2, 3)]\n",
|
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" yr_ra = [risk_adjust_cost(v, rf) for v in yr]\n",
|
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" pv = npv(yr_ra, config.DISCOUNT_RATE, initial=init_ra)\n",
|
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" rows.append({\n",
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" 'Cost': c['label'],\n",
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" 'Initial (RA)': init_ra,\n",
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" 'Y1 (RA)': yr_ra[0],\n",
|
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" 'Y2 (RA)': yr_ra[1],\n",
|
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" 'Y3 (RA)': yr_ra[2],\n",
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" 'PV': pv,\n",
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" })\n",
|
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"df_check = pd.DataFrame(rows)\n",
|
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"totals = df_check.drop(columns='Cost').sum()\n",
|
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"df_check.loc[len(df_check)] = ['TOTAL'] + totals.tolist()\n",
|
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"df_check.style.format({c: '${:,.0f}' for c in df_check.columns if c != 'Cost'})"
|
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]
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},
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{
|
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"cell_type": "code",
|
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"execution_count": 5,
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"id": "4109784e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div style=\"padding:10px 14px;border-left:4px solid #2e7d32;background:#e8f5e9;color:#1a1a1a;border-radius:4px;margin:6px 0;\">Computed Costs PV: <b>$22,983,076</b><br>Forrester target: <b>$22,983,076</b><br>Δ = $-0</div>"
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],
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"text/plain": [
|
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"<IPython.core.display.HTML object>"
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
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"source": [
|
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"expected_pv = 22_983_076\n",
|
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"computed_pv = df_check.iloc[-1]['PV']\n",
|
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"delta = computed_pv - expected_pv\n",
|
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"kind = 'success' if abs(delta) < 1_000 else 'warning'\n",
|
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"display.alert(\n",
|
|
" f'Computed Costs PV: <b>${computed_pv:,.0f}</b><br>'\n",
|
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" f'Forrester target: <b>${expected_pv:,.0f}</b><br>'\n",
|
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" f'Δ = ${delta:,.0f}',\n",
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" kind,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dd1b3c04",
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"metadata": {},
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"source": [
|
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"## Cost mix\n",
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"\n",
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"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",
|
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"with implementation a one-time investment."
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]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
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|
"id": "90e9b5e2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
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"data": {
|
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"application/vnd.plotly.v1+json": {
|
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"config": {
|
|
"plotlyServerURL": "https://plot.ly"
|
|
},
|
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"data": [
|
|
{
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|
"hole": 0.35,
|
|
"labels": [
|
|
"Amazon Connect usage cost",
|
|
"Implementation and migration cost",
|
|
"Ongoing management"
|
|
],
|
|
"type": "pie",
|
|
"values": [
|
|
25452601.650000002,
|
|
1610582.6,
|
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725190
|
|
]
|
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}
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],
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|
"layout": {
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"margin": {
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"b": 40,
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"l": 40,
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"style": "light"
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},
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"paper_bgcolor": "white",
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"plot_bgcolor": "#E5ECF6",
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"polar": {
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"angularaxis": {
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": ""
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},
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"bgcolor": "#E5ECF6",
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"radialaxis": {
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": ""
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}
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},
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"scene": {
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"xaxis": {
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"backgroundcolor": "#E5ECF6",
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"gridcolor": "white",
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"gridwidth": 2,
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"linecolor": "white",
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"showbackground": true,
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"ticks": "",
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"zerolinecolor": "white"
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},
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"yaxis": {
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"backgroundcolor": "#E5ECF6",
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"gridcolor": "white",
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"gridwidth": 2,
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"linecolor": "white",
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"showbackground": true,
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"ticks": "",
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"zerolinecolor": "white"
|
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},
|
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"zaxis": {
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"backgroundcolor": "#E5ECF6",
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"gridcolor": "white",
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"gridwidth": 2,
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"linecolor": "white",
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"showbackground": true,
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"ticks": "",
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"zerolinecolor": "white"
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}
|
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},
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"shapedefaults": {
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"line": {
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"color": "#2a3f5f"
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}
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},
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"ternary": {
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"aaxis": {
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": ""
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},
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"baxis": {
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": ""
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},
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"bgcolor": "#E5ECF6",
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"caxis": {
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": ""
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}
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},
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"title": {
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"x": 0.05
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},
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"xaxis": {
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"automargin": true,
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|
"gridcolor": "white",
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"linecolor": "white",
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"ticks": "",
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"title": {
|
|
"standoff": 15
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},
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"zerolinecolor": "white",
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"zerolinewidth": 2
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},
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"yaxis": {
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"automargin": true,
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": "",
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"title": {
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"standoff": 15
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},
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"zerolinecolor": "white",
|
|
"zerolinewidth": 2
|
|
}
|
|
}
|
|
},
|
|
"title": {
|
|
"text": "Cost Breakdown (Three-Year, Risk-Adjusted)"
|
|
}
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}
|
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},
|
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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"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) →"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|