{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0d9c8880",
   "metadata": {},
   "source": [
    "# Cement Retail Product Analytics: SKU Performance, Sales Trends & Portfolio Optimization\n",
    "\n",
    "**Synthetic Data — Portfolio Demonstration**  \n",
    "Jawa Timur, 1 Januari–31 Maret 2026. Semua entitas dan pola bisnis merupakan simulasi. Seluruh hasil dihitung dari CSV yang disertakan."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20cdd2bf",
   "metadata": {},
   "source": [
    "## 1. Project Overview\n",
    "Portofolio ini memperlihatkan alur end-to-end: generasi data, quality assurance, EDA, feature engineering, product analytics, uji statistik, dan rekomendasi. **Business question:** SKU, toko, dan wilayah mana yang perlu diprioritaskan?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78d6172b",
   "metadata": {},
   "source": [
    "## 2. Business Understanding\n",
    "Unit analisis adalah line item. Nomor transaksi dapat berulang pada beberapa SKU dalam satu order. Revenue = harga per zak × qty zak; tonase = qty zak × berat kemasan / 1.000. Analisis ini tidak mengukur laba atau causal lift."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c7fe3f3",
   "metadata": {},
   "source": [
    "## 3. Dataset Description\n",
    "Dataset mencakup transaksi Q1 2026, 18 SKU sintetis, 400 toko sintetis, dan 10 distributor sintetis. Hierarki provinsi–kabupaten/kota–area–regional–distributor dan personel SSM–ASM–TSO berasal dari master."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2294401",
   "metadata": {},
   "source": [
    "## 4. Data Dictionary\n",
    "| Field | Definisi |\n",
    "|---|---|\n",
    "| Tanggal | Tanggal transaksi |\n",
    "| No Transaksi | ID order; dapat memiliki beberapa SKU |\n",
    "| Kode Produk | Kunci master SKU |\n",
    "| Harga | Harga jual per zak (IDR) |\n",
    "| Qty zak | Jumlah zak pada line item |\n",
    "| Packaging KG | Berat zak dari master SKU |\n",
    "| Qty ton | Qty zak × Packaging KG / 1.000 |\n",
    "| Revenue | Harga × Qty zak |\n",
    "| ID Toko | Kunci master toko |\n",
    "| Kode Distributor | Kunci master distributor |\n",
    "| Promosi Simulasi | Penanda periode promosi buatan |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03683f30",
   "metadata": {},
   "source": [
    "## 5. Library Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c778d50a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T02:25:07.431366Z",
     "iopub.status.busy": "2026-10-13T02:25:07.431077Z",
     "iopub.status.idle": "2026-10-13T02:25:08.537829Z",
     "shell.execute_reply": "2026-10-13T02:25:08.537456Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Project root: /Users/firmansyahromadhoni/Documents/Codex/2026-10-13/files-pasted-by-the-user-role/outputs/cement-product-analytics\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import sys, json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from IPython.display import display, Image, Markdown\n",
    "ROOT = next(p for p in [Path.cwd(), Path.cwd().parent] if (p / \"src/generate_data.py\").exists())\n",
    "sys.path.insert(0, str(ROOT / \"src\"))\n",
    "from generate_data import generate\n",
    "from data_validation import validate\n",
    "from feature_engineering import load, tables\n",
    "from product_analytics import analyze\n",
    "print(\"Project root:\", ROOT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e1441c2",
   "metadata": {},
   "source": [
    "## 6. Data Loading\n",
    "Generator dijalankan ulang dengan seed tetap sehingga semua hasil dapat direproduksi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "443cc3a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T02:25:08.539617Z",
     "iopub.status.busy": "2026-10-13T02:25:08.539419Z",
     "iopub.status.idle": "2026-10-13T02:25:17.498186Z",
     "shell.execute_reply": "2026-10-13T02:25:17.497845Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 99,244 lines, 68,000 transactions, 400 stores, 18 SKUs\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tanggal</th>\n",
       "      <th>ID Toko</th>\n",
       "      <th>Nama Toko</th>\n",
       "      <th>Alamat Toko</th>\n",
       "      <th>Kabupaten Toko</th>\n",
       "      <th>Provinsi Toko</th>\n",
       "      <th>Regional Toko</th>\n",
       "      <th>Area Toko</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Kode Produk</th>\n",
       "      <th>...</th>\n",
       "      <th>ASM</th>\n",
       "      <th>TSO</th>\n",
       "      <th>DISTRIK</th>\n",
       "      <th>AREA</th>\n",
       "      <th>DAY</th>\n",
       "      <th>Promosi Simulasi</th>\n",
       "      <th>Sales Territory</th>\n",
       "      <th>Kategori Produk</th>\n",
       "      <th>Packaging KG</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2026-01-01</td>\n",
       "      <td>T-SYN-0093</td>\n",
       "      <td>Toko Bangunan Simulasi 0093</td>\n",
       "      <td>Jl. Simulasi No. 13, Kabupaten Gresik</td>\n",
       "      <td>Kabupaten Gresik</td>\n",
       "      <td>Jawa Timur</td>\n",
       "      <td>Jatim Utara</td>\n",
       "      <td>Pantura</td>\n",
       "      <td>NusaKuat</td>\n",
       "      <td>SYN-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>ASM Pantura</td>\n",
       "      <td>TSO GSK-2</td>\n",
       "      <td>Kabupaten Gresik</td>\n",
       "      <td>Pantura</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>False</td>\n",
       "      <td>TER-GSK-2</td>\n",
       "      <td>Portland Composite</td>\n",
       "      <td>40</td>\n",
       "      <td>1380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2026-01-01</td>\n",
       "      <td>T-SYN-0093</td>\n",
       "      <td>Toko Bangunan Simulasi 0093</td>\n",
       "      <td>Jl. Simulasi No. 13, Kabupaten Gresik</td>\n",
       "      <td>Kabupaten Gresik</td>\n",
       "      <td>Jawa Timur</td>\n",
       "      <td>Jatim Utara</td>\n",
       "      <td>Pantura</td>\n",
       "      <td>ArgaBina</td>\n",
       "      <td>SYN-05-02</td>\n",
       "      <td>...</td>\n",
       "      <td>ASM Pantura</td>\n",
       "      <td>TSO GSK-2</td>\n",
       "      <td>Kabupaten Gresik</td>\n",
       "      <td>Pantura</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>False</td>\n",
       "      <td>TER-GSK-2</td>\n",
       "      <td>Portland Composite</td>\n",
       "      <td>50</td>\n",
       "      <td>5864000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2026-01-01</td>\n",
       "      <td>T-SYN-0004</td>\n",
       "      <td>Toko Bangunan Simulasi 0004</td>\n",
       "      <td>Jl. Simulasi No. 4, Kota Surabaya</td>\n",
       "      <td>Kota Surabaya</td>\n",
       "      <td>Jawa Timur</td>\n",
       "      <td>Jatim Utara</td>\n",
       "      <td>Surabaya Raya</td>\n",
       "      <td>NusaKuat</td>\n",
       "      <td>SYN-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>ASM Surabaya Raya</td>\n",
       "      <td>TSO SUR-1</td>\n",
       "      <td>Kota Surabaya</td>\n",
       "      <td>Surabaya Raya</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>False</td>\n",
       "      <td>TER-SUR-1</td>\n",
       "      <td>Portland Composite</td>\n",
       "      <td>40</td>\n",
       "      <td>1682000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Tanggal     ID Toko                    Nama Toko  \\\n",
       "0 2026-01-01  T-SYN-0093  Toko Bangunan Simulasi 0093   \n",
       "1 2026-01-01  T-SYN-0093  Toko Bangunan Simulasi 0093   \n",
       "2 2026-01-01  T-SYN-0004  Toko Bangunan Simulasi 0004   \n",
       "\n",
       "                             Alamat Toko    Kabupaten Toko Provinsi Toko  \\\n",
       "0  Jl. Simulasi No. 13, Kabupaten Gresik  Kabupaten Gresik    Jawa Timur   \n",
       "1  Jl. Simulasi No. 13, Kabupaten Gresik  Kabupaten Gresik    Jawa Timur   \n",
       "2      Jl. Simulasi No. 4, Kota Surabaya     Kota Surabaya    Jawa Timur   \n",
       "\n",
       "  Regional Toko      Area Toko     Brand Kode Produk  ...                ASM  \\\n",
       "0   Jatim Utara        Pantura  NusaKuat   SYN-01-01  ...        ASM Pantura   \n",
       "1   Jatim Utara        Pantura  ArgaBina   SYN-05-02  ...        ASM Pantura   \n",
       "2   Jatim Utara  Surabaya Raya  NusaKuat   SYN-01-01  ...  ASM Surabaya Raya   \n",
       "\n",
       "         TSO           DISTRIK           AREA       DAY Promosi Simulasi  \\\n",
       "0  TSO GSK-2  Kabupaten Gresik        Pantura  Thursday            False   \n",
       "1  TSO GSK-2  Kabupaten Gresik        Pantura  Thursday            False   \n",
       "2  TSO SUR-1     Kota Surabaya  Surabaya Raya  Thursday            False   \n",
       "\n",
       "  Sales Territory     Kategori Produk Packaging KG  Revenue  \n",
       "0       TER-GSK-2  Portland Composite           40  1380000  \n",
       "1       TER-GSK-2  Portland Composite           50  5864000  \n",
       "2       TER-SUR-1  Portland Composite           40  1682000  \n",
       "\n",
       "[3 rows x 35 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(99244, 35) 2026-01-01 00:00:00 2026-03-31 00:00:00\n"
     ]
    }
   ],
   "source": [
    "generate(ROOT)\n",
    "raw = pd.read_csv(ROOT / \"data/raw/cement_transactions_q1_2026.csv\", parse_dates=[\"Tanggal\"])\n",
    "product_master = pd.read_csv(ROOT / \"data/master/product_master.csv\")\n",
    "store_master = pd.read_csv(ROOT / \"data/master/store_master.csv\")\n",
    "distributor_master = pd.read_csv(ROOT / \"data/master/distributor_master.csv\")\n",
    "display(raw.head(3))\n",
    "print(raw.shape, raw.Tanggal.min(), raw.Tanggal.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6a5ede8",
   "metadata": {},
   "source": [
    "## 7. Data Quality Assessment\n",
    "Validasi mencakup dimensi, tipe data, missing values, duplikasi tepat, pasangan transaksi–SKU, formula, tanggal, harga, relasi master, wilayah, dan log-IQR quantity flag. Multi-item order bukan duplikasi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "482b200e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T02:25:17.499894Z",
     "iopub.status.busy": "2026-10-13T02:25:17.499734Z",
     "iopub.status.idle": "2026-10-13T02:25:20.272378Z",
     "shell.execute_reply": "2026-10-13T02:25:20.272023Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>rows</th>\n",
       "      <th>columns</th>\n",
       "      <th>transactions</th>\n",
       "      <th>stores</th>\n",
       "      <th>skus</th>\n",
       "      <th>distributors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>99244</td>\n",
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       "      <td>10</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "    rows  columns  transactions  stores  skus  distributors\n",
       "0  99244       35         68000     400    18            10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>missing_required_columns</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>missing_cells</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>exact_duplicate_rows</th>\n",
       "      <td>0</td>\n",
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       "      <th>duplicate_transaction_sku</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transaction_multiple_stores</th>\n",
       "      <td>0</td>\n",
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       "      <th>transaction_multiple_dates</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invalid_dates</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wrong_day</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invalid_price</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invalid_quantity</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invalid_tonnage</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invalid_revenue</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown_product</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown_store</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown_distributor</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_master_mismatch</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>store_master_mismatch</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distributor_master_mismatch</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>warehouse_regency_mismatch</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>warehouse_regional_mismatch</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>warehouse_area_mismatch</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>province_mismatch</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_outside_15pct_master</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>potential_quantity_outliers_review_only</th>\n",
       "      <td>531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         count\n",
       "missing_required_columns                     0\n",
       "missing_cells                                0\n",
       "exact_duplicate_rows                         0\n",
       "duplicate_transaction_sku                    0\n",
       "transaction_multiple_stores                  0\n",
       "transaction_multiple_dates                   0\n",
       "invalid_dates                                0\n",
       "wrong_day                                    0\n",
       "invalid_price                                0\n",
       "invalid_quantity                             0\n",
       "invalid_tonnage                              0\n",
       "invalid_revenue                              0\n",
       "unknown_product                              0\n",
       "unknown_store                                0\n",
       "unknown_distributor                          0\n",
       "product_master_mismatch                      0\n",
       "store_master_mismatch                        0\n",
       "distributor_master_mismatch                  0\n",
       "warehouse_regency_mismatch                   0\n",
       "warehouse_regional_mismatch                  0\n",
       "warehouse_area_mismatch                      0\n",
       "province_mismatch                            0\n",
       "price_outside_15pct_master                   0\n",
       "potential_quantity_outliers_review_only    531"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quality = validate(ROOT)\n",
    "display(pd.DataFrame([quality[\"dimensions\"]]))\n",
    "display(pd.Series(quality[\"checks\"], name=\"count\").to_frame())\n",
    "assert quality[\"status\"] == \"PASS\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ade0b4b",
   "metadata": {},
   "source": [
    "## 8. Data Cleaning\n",
    "Tidak ada baris yang dihapus karena seluruh hard checks lulus. Flag log-IQR disimpan untuk review, tanpa membuang pesanan sah yang besar. Data bersih adalah salinan terurut dari data tervalidasi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f4a05c19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T02:25:20.273908Z",
     "iopub.status.busy": "2026-10-13T02:25:20.273778Z",
     "iopub.status.idle": "2026-10-13T02:25:20.830255Z",
     "shell.execute_reply": "2026-10-13T02:25:20.829894Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baris bersih: 99244 | Transaksi unik: 68000\n"
     ]
    }
   ],
   "source": [
    "df = load(ROOT)\n",
    "print(\"Baris bersih:\", len(df), \"| Transaksi unik:\", df[\"No Transaksi\"].nunique())\n",
    "assert len(df) == len(raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fcd00c7",
   "metadata": {},
   "source": [
    "## 9. Exploratory Data Analysis\n",
    "**Business question:** bagaimana revenue, volume, transaksi, kemasan, brand, wilayah, toko, distributor, dan harga berbeda? Interpretasi berikut selalu berasal dari hasil eksekusi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a5326e1d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T02:25:20.831851Z",
     "iopub.status.busy": "2026-10-13T02:25:20.831693Z",
     "iopub.status.idle": "2026-10-13T02:25:25.533887Z",
     "shell.execute_reply": "2026-10-13T02:25:25.533481Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1 2026</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>revenue</th>\n",
       "      <td>2.249440e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tons</th>\n",
       "      <td>1.550056e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bags</th>\n",
       "      <td>3.437532e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transactions</th>\n",
       "      <td>6.800000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>active_stores</th>\n",
       "      <td>4.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skus</th>\n",
       "      <td>1.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aov</th>\n",
       "      <td>3.308000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue_per_store</th>\n",
       "      <td>5.623600e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue_per_ton</th>\n",
       "      <td>1.451199e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daily_cv</th>\n",
       "      <td>2.099434e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Q1 2026\n",
       "revenue            2.249440e+11\n",
       "tons               1.550056e+05\n",
       "bags               3.437532e+06\n",
       "transactions       6.800000e+04\n",
       "active_stores      4.000000e+02\n",
       "skus               1.800000e+01\n",
       "aov                3.308000e+06\n",
       "revenue_per_store  5.623600e+08\n",
       "revenue_per_ton    1.451199e+06\n",
       "daily_cv           2.099434e-01"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>revenue</th>\n",
       "      <th>tons</th>\n",
       "      <th>bags</th>\n",
       "      <th>transactions</th>\n",
       "      <th>mom_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>69790934000</td>\n",
       "      <td>47953.25</td>\n",
       "      <td>1062553</td>\n",
       "      <td>20947</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>68973066300</td>\n",
       "      <td>47587.56</td>\n",
       "      <td>1055615</td>\n",
       "      <td>21048</td>\n",
       "      <td>-0.011719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>86180014900</td>\n",
       "      <td>59464.80</td>\n",
       "      <td>1319364</td>\n",
       "      <td>26005</td>\n",
       "      <td>0.249473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month      revenue      tons     bags  transactions  mom_revenue\n",
       "0      1  69790934000  47953.25  1062553         20947          NaN\n",
       "1      2  68973066300  47587.56  1055615         21048    -0.011719\n",
       "2      3  86180014900  59464.80  1319364         26005     0.249473"
      ]
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     "metadata": {},
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    },
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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yRS+99JIuX76cq/2lGw+EfPbZZzn+/hQZGang4OBc1+kIjnpvbqfExER169ZNS5YsydXvA2fPnlXv3r21cePGXJ/j3LlzGjBgQL6SJeHh4Ro6dKhdsuRmp0+fVo8ePRQfH2+43ZH35K5duzR27FiH/S6WF7/++qv69u2b4+8uWeX2cyU7ycnJ+v777+3KczMKBwD+jkiYAADgQEbTH8XFxWnIkCFq3ry5evToofHjx2vVqlV2w+nz49///rddWbly5bRgwQIFBgYWuP7iILMTsHz58rr33nsNn07+/vvvrdMIODk5qUaNGoZPm1osFn355Ze3PKeLi4vuvvtu1a5dW0FBQbr33nvl4mI/MDcpKUkTJ07M07VkdvzcddddqlatWrb7bd68+Zb1eXl5qWrVqgoKClLt2rWzfVJ+9+7dWr9+fa7jzKp79+52Uy2tXbtWZrPZbt/169fb/cHepk2bbKdJkm4kVO655x7r6125cmXDqZ0uXLiQ7ZQwRqKjo62dvffee68CAgIM99uzZ4/hlHg7duzIdsFTX19f1a5d25okDQ8Pz3VcBZGYmGjtLLr77ruzHRkzY8YMa+yurq6qWbOmYUL3+vXrCgkJsStPS0vTwIEDdfbsWbttXl5eqlmzpipUqGC3LSkpScOGDct1p2hB2kSbNm3s3tOTJ08adrAfPnxYx44dsynz9/dX69atcxWno92u9zE7mZ3l7u7uql27drbTAIaGht4yYSjdGNFYvXp13XfffapZs6a8vb0N9/viiy907ty5XMeZOQqtVKlSNu0tq6+++sqw/L333tNff/1lV+7k5GRd7Fi6cd/+8ccfuY6rICIjI61fZ36OZH39r1y5on/84x+GncelS5dWrVq1VK5cObttly5d0uDBg+0SDEaJwdWrVxvGt2bNGruy5557zuZn4OnTp/X666/r+vXrdvuWLVtWtWrVMnxq/+TJk3rnnXcMz2sk8/3PfL+ye+J+5cqVeUqmF4Sj35vcKFu2rOrWrau6desatoHy5ctbt2f+yzrS7+233za8xz08PFStWjXdfffddj9309LSNHLkyFwnY2NiYqyvi6urq2rUqKF77703VyOtd+3apZSUFJlMJlWrVk1VqlQx3C8uLs5umlDJ8fdkdknqChUqKDAwUNWqVcvX6AsPDw9VqVJFderUUWBgYLb39NGjR/O04HpuPleyk5KSotdff12nTp2yKXdzc1O3bt1yHQMA/J0wJRcAAA700ksvGT6hJdlOtZLp3nvv1TPPPKPu3bs7ZFoYf39/LViwQDVq1ChwXcWFk5OTxo4dq86dO8tkMunnn39W//79bfaxWCxKT09X1apVNWfOHFWpUkVpaWkaPny4tmzZYrPvrl27lJqaavcHfJ06dfTggw/qgQceUM2aNe0SJMnJyfr222/12Wef2TxxHRoaqri4OMPOYyOlS5fWlClT1LJlS0k31iUYPHiwkpOTbfb75ZdfDNdbaN++vdq1a6cmTZoY/qEdExOjf/7zn9q1a5dN+Q8//KBnnnkmVzFmFRAQoDZt2ti8lklJSVq5cqVeeeUVm31z+xT//fffr+7du6t58+aqWrWqXUfNX3/9pZkzZ2revHk25atWrdKHH35omMAycvfdd2v69OmqV6+eJGnFihV6//337Z6w/fnnn+2mGJs9e7ZdfSaTSe+99566d+8uZ2dnXb9+XZ999pkWLlyYq3gcwcPDQ9OmTVOrVq0kSd99950++ugjm33S0tIkSY0aNdKMGTNUvnx5Xb16Va+99prdWku//PKL3TmWLVtm95Suh4eHxowZo2effdb6+kdGRurNN9+0eXL6woUL+vrrr/X222/n6nry2yacnJzUvXt3u/WblixZYreuhdEUQ1k7gW+32/E+5uTZZ5/VmDFjrMmNjRs36p133rHrcPzyyy/18ssv27TRMmXKqEePHmrdurUaNWpklyCxWCwKDw/Xu+++a/NwQGpqqlauXJmnRYT79OmjN998U25ubkpISNCAAQPsrn337t12n+u//vqrfvvtN7v6goKCNH36dGuybf/+/RoyZIjdYseFqUyZMvr000/Vtm1b6+t67Ngx6/oMc+fOtUsAlC1bVuPGjVPr1q2tDw7s3LlTb775ps10oEePHtWyZcvUtWtXa1nHjh01ZcoUm8+9n376SVevXlWpUqWsZSkpKYYjCrJ+hk+dOtXu6fd7771X48aNU9OmTSXduAfWrVunUaNG2dxToaGh+uWXX/TII4/c+oXSjZ/N06ZNs3agf/HFF3ZTqKWnp2vXrl16/vnnc1VnQTj6vcmNNm3aqE2bNpKM1+t6+eWXc5wWMjQ01G5EkZOTk959911169bNet+dOHFCI0aM0IEDB6z7JSUladq0aXmatq5Hjx56/fXXrQkFs9mc4xScmbK+10eOHNHgwYMVExNjs9+6dev05ptv6t5777WWOfqezJpgL1WqlObPn2/3e0JsbKzCw8MVGhqq33//3fC6WrdurVatWun+++/XPffcY/fgz8WLFzVu3Di7JOayZcuynUbWyK0+V4yYzWYNGjTIcLTUu++++7f6ewIA8oIRJgAAOFDDhg01ZMiQXO9/+vRpzZo1S48//ni2Uxzlxeuvv/63++Omc+fOevHFF61/YD766KOqVauW4b7BwcHWP7RdXFw0cOBAu30y113I6s0331Tv3r1Vp04dw05UT09P9e3b13B9hrxMQfTuu+9aO4Yl6aGHHjKcrirrH+vSjY7lyZMn6+mnn872qcSAgAC9++67BYrRiNH0R//3f/9n8/25c+fsOgwqVKhg2DE2ZswYde3aVdWrVzccTVK6dGm9++67dk+EJiUl5WnqnODgYGuyRJKef/55w0Wis77esbGxhh0InTt3Vq9evawL0Lu7u2v06NFq0qRJrmMqqIEDB1o72aUbidqbOz0zubi46D//+Y/1KdNSpUrptddes9svJiZG165dsynL+t5KN+7d559/3qZ91KtXT2PGjLHbd9myZbmeyqkgbeLFF1+0u/bNmzfbTdlilMjLaT2g2+F2vI/ZqVq1qsaPH2+T6GjXrp2GDh1qt++ZM2fs2vWDDz6o0aNH6+GHHzYcTWIymdS0aVPDz428fBY1bdpUI0eOtK614evra7j4tdHnutFICVdXV5tkiXRj/bCCLOyeH1OnTtUTTzxh89lXo0YNVa5cWRaLxfDBi/Hjx6tNmzY2na0PPfSQ4dPxWY+vWLGiTRuTpGvXrtmN2goNDbUbOdGoUSOb3ysSExMNRyzOnDnT2jEt3bgH2rdvb3ivZvdgSVZOTk6aOnWqzWiDf/zjH4ZrWOQ0daejFMZ7czsYfZ737t1br732mk1nerVq1ewW/ZZujBw1GlFq5Pnnn9fo0aNtRl94e3urcePGOR7n4uKi6dOn27zXtWvXNozHYrHYfKbfjnuyYsWKql+/vmF5+/btFRwcrFWrVhkeO3nyZD3//PMKCAgwHCXt7++vDz/80K785MmTeZrCLqfPFSNms1mvvfaa4e86w4YNU48ePXJ9bgD4u2GECQAADjZ8+HDde++9+ve//53reZyTk5P14YcfytPTs0BrPQQHBysgIMCwQ/hOZfR6BAQE2D2tWLlyZbsF2LNbGNRoihZJunr1qjZs2KDQ0FBFR0crNjZWSUlJt5zDOrdPJvv6+qpjx4525dWrV7cry2ke72PHjmn9+vX67bffdPLkSSUkJCg5OTnHecmvXr0qs9mc7VQ5t/LAAw+odu3aOnLkiLXsjz/+sFn8fd26dXad5M8//7w1uZBVfHy81q1bp927d+vYsWO6ePGikpKSbjllSW5f79q1axu2hWrVqik0NNSmLOs9kd2Tol26dDEsf/HFFw2fZi8MWduEk5OT7rnnHrv1dJo0aWKdcihTTm0is+MsMTFRhw4dsttn8eLFWrZsmV250ft16dIlRUdHZ5vczFTQNuHt7a1OnTrZjPBJTU3VsmXLrCPRIiMj7TrTs3YCF4XCfh9vdW6jaXI6d+5s2EEZERFhmBTcs2ePNm/erIMHD+r06dMym81KTk7OMVmWl5EcRgkXo3tDsm/DRomZFi1aGE7N17JlS1WuXNkhU2XeSpMmTXL8GX3kyBHDNX2mTp2qadOm2ZUbLSZ98OBBu8/7zp076+eff7bZb82aNTbtzyjJ1KlTJ5vv9+7dax35lMnFxSXbtaBuHmGRKbs1Z7Jq1aqV4b1evXp1u2misvu57kiF9d4UNqPXe8uWLbl+H9LT0xUWFparKQwHDx6c1/Ak3UiO3jxiJFPDhg1Vo0YNu4T5zT+jC+OerFOnjk0S7vjx4+revbtat26t6tWrq1q1anbTtWb3u45044GSdevWac+ePTpx4oQuXbqk5ORku7iziouLU9myZXPcR7r154qRkSNH2k1HajKZ9M9//lOvvvpqnuoCgL8bEiYAABSC559/Xu3bt9fmzZu1efNm7dmzJ1fJk88++0zPPPNMjn905SQ5OVkDBw7U7Nmz/zZJE6MRHUZzeBvtZ7SOiSTDBMimTZv04YcfZruYaE5y21FTs2ZN65PSNzO6HqNO6NTUVI0dO1ZLly7N18KeiYmJBeqk6dmzp0aPHm1TtmTJEmvCxKizLbtFtTOnNzPqULqV3L7eQUFBhuVGT/FnvSeyW4w7uzqzK3e0UqVKGT4tanQPGa1jlN2c6zdf/7lz5wzvr9xMqXKzs2fP3jJhUtA2Id2Y/mXRokU2nfRLly5Vv379ZDKZ8nRf3i63433MSXZrXJUrV07lypWz6xTO2h7i4uL05ptvKiwsLFfnu1leOraN1gXLbh2T3LThnNppYGDgbUmYtGjRIsftRusGSTJMYmYnIyND58+fV82aNa1ljz32mHx9fW06i3fu3KmEhAT5+voqOTlZW7dutanHw8PDbipHo/jS0tJ08ODBXMd3+fJlJScny9PTM8f9snu/jO6B27E4d2G9N4Xp6tWrhgmCrOtV3Epu2kalSpWyXXvkVnJad6927dqGo0AzFcY92aNHD61bt87mQZSwsDCbzzwXFxfVrl1bzZs313PPPae6deva1WuxWDR9+nTNmTMnX+vXJCYm5mq/W32uZHXgwAHDtfLef/99kiUAIKbkAgCg0Li5ual9+/aaNGmSQkNDtWXLFk2ePFkvv/yy4YKg0o1OqLz8gWc0xcG1a9c0cOBAuzUssmP0NHJOIxUkGT4Rd6uOj/wySnoYPRmdXXIkN/bs2aPhw4fnK1kiKdfJi+ym0cptguxf//qXlixZkq9kiXTr9/VWnnvuObsnHTMXf4+JibFZdFS6sU6J0dPBP/74o8aMGZOvZIl0e17vrHOhSzc6R9zd3Q33z64T19Gyu8+N2kR+FqSVct9Bcyu5mUqkoG1CkqpUqWL35POpU6e0e/duWSwWu6laPD097dYHyo/8dH5luh3vY06MkoaZjJKqN7eHlJQUvfbaa/lKlki5b7+S8f2R23VnjNpwTtd9u9pwpUqVctzuqPaXtZPczc1NHTp0sClLTU3Vhg0bJEnbtm2z+0xu166d3f1QWPEZye7zoTDWHspNe76d1+4ouZ1K61ZyE/Ot7u2c5NT+bvWZVBjvS6NGjTR58uQcR3ekpaXp0KFDmj9/vjp16qT33nvP7vesWbNm6fPPP8/3z4vc/t6W19feaERs5cqVDUf1AUBJRMIEAIDb5J577lH79u318ccfa9u2bXr66acN9/vzzz9zXedXX32VY9Jk586dt6zDqOPuVn98Gj0hXJCERU6M1rcoyH5Gpk2bZteJd9ddd2ny5MnasWOH/vjjD0VFRSkqKspmPuy8MnqSXpLhnNZZxcTEGM6x/fjjj2vp0qUKCwuzxmj01KAjuLu76+WXX7Ypy1z83WiNiKxTuWSaOnWqXVnt2rX1xRdfaNeuXTp8+LD1Wu666658x1uQ19uoYzUtLS3bp5gvXryYt+DyKS/3eX7bhKM66HPTMV6Q9+hmRp08S5Ys0e+//2739LFRJ3BOspsy5dy5c3mK8Wa3433MiVEyIadtN7eHVatW2a0XYTKZ9I9//EPr169XRESEtf1++umnBYrT6P7I7b1h1IZzStLerjac3T2fyVHtz6ij1egzOXOh6awLTme3f2HGl5WjPh9ull3HdW7a8+28dkdx1NRfBfk8z42c2uatPpMK63158skn9dNPP+k///mPOnXqpKCgoBwfDvrhhx80f/586/dJSUn64osv7PZr2rSpFixYoF9//dX6WfnHH38UKPa8vvYuLi6qVq2azb+C/H4LAH83TMkFAEARcHd315AhQ7Ru3Tq7bXnpCPD29tZXX32lfv362c3Xfu3aNQ0aNEgzZ860W+z1ZlWqVLE79tSpU0pJScn2DzCjxVXzOw1DUUtJSVF4eLhd+ccff6xHH33UrrwgnaQFsXv3bruyChUqaNq0aXZP42c3bYgjvPLKK5o7d65Np9OSJUvs9vPy8jJMCp46dcpwao8pU6bYrSmRkpJiOF/87XD33Xcblh89etRw2o3jx48Xdki3TaVKleTk5GTXQbZ48eLburh9Xjz00EOqVauWzbRhmzdvNhypktN0XEYjPIw661JTU+/o9zwqKkqPP/64XfmlS5cMEwc3Jy6NRi8+/fTTeuutt+zKi+rzUroRc9bkfnbTylksFrs1MYpKdknin3/+OdsRF7l13333qU6dOjp8+LC1LCwsTEePHrVb36Ry5cp64IEHchWfl5eXfv311wJ1mBeG3LZnSTbrc2WnMN+bwlKqVCmVKVPGbg2of//733YjjopS1rWbbmb03tw8oqIw70kPDw89++yzevbZZ61l8fHxOnr0qL799lu7EYwrVqxQ3759Jd1YZ+XatWs2211dXfXFF1/YJbIK8/c2I6+++ipTbwFADhhhAgCAA40ZM0Zff/11jk/vZsquI7h8+fJ5Omdm0sSoIzMzaZJ1geubZV0oXZKuX7+ujRs3Gu7/22+/GXZ4F9eO1Fu5fPmy4ZOTfn5+dmXbt2+/LXPcGzG6X3x9fQ07hL/99ttCi6NixYp68sknbcoOHz5s0wEnSU899ZThE97Z3fdGr/fSpUtvy7z0Rho1amRY/uOPPxqWL168uBCjub1Kly5tuHZEbq8xPj4+39M1FUSPHj1svk9NTbV7av7ee+9V8+bNs63D6Ello0721atX52rKseJq5cqVhiNnli9fbrh/w4YNrV8bTV1o1H6TkpKyre92MBp9uWvXLsP1xLZs2ZLtukW3W2BgoOHrmdv2d+7cOR04cCDb7VlHjWRkZOjdd9+1G3nRqVMnwwc4mjdvbjclVlJSUrafjVkdO3bM8KGLwmDUno3OHRMTo59++umW9RX2e5MbRkmg5OTkHI8xSnz93//9X65Guly9elU7duzIfYD5FBYWptOnT9uV79+/3/A9u/lndGHckzmNePHz81OLFi00duxYu203X4PR7ztubm6GvxsV5u9tAIC8I2ECAIADxcbGasKECXr44Yf1zjvvaNOmTYadM+Hh4frwww/tyt3d3VWvXr08nzczaZJd8mPw4MHZJk2eeOIJwykGxo4da3fM/v37NWLECLt9TSaTnnvuuTzHXRxkN5XDzJkzbeb+3rZtm+G13y7ZdeTe3CGZlJSk4OBguyceHS03c1xn9xR/dq/3lClTrMmRjIwMLVu2TBMmTMh/kAVUvnx5w471b775RkuXLrUuMH79+nUFBwfnadHfO8GLL75oV7Zy5UqNHj1aMTExdtvOnj2rH3/8UcOHD1erVq0Mp48rbM8995x8fX1z3OeFF17IcRSf0ULMa9assfks/PXXXzV+/Ph8x1kcnDx5UiNHjrT5jNu8ebOmT59ut+8999xjkzAxmuJn9erVNutvnTt3ToMGDSqyBLMku8XKpRsPEQwfPtwmOXLgwAHDjs+iYjKZDD8/Z8+erUmTJtmNALJYLDp16pSWLFmi/v37q23btjl2/j/33HN2ne5ZP79MJpNeeOEFw+N9fHzUrl07u/LMB0ayTumZnp6uo0ePav78+erevbvat2+v/fv3ZxufIxm155iYGH3++ec237/++uu5Sh4U9nuTG0bTn+7evTvHpInR53nm2m1GIzsuXLigDRs2aOTIkXrkkUc0e/bsAsWcG2lpaRo+fLhNwuHo0aN655137PZ1cnKyGcFaGPfk2LFj1b17d4WEhOjo0aN2CeaUlBQtWrTI7pw3J26Mft+5evWqZs2aZb3fUlNTNWfOHH399dd2+xam6dOnKzAw0OZf1ocOAKAkY0ouAAAKQVJSklatWqVVq1ZJksqWLauyZcvKyclJFy5csJsaIdNzzz1nuAh7bpQqVUpffvml+vfvbzfFVGbS5PPPP9cjjzxis61s2bLq1auX3R/ECQkJ6tu3r8qVK6fy5cvr8uXLio2NNTz3s88+azed0p3Cy8tLdevWtensk248cfzwww/rnnvuUUJCgmHi63YymlvaYrFo1KhR+ve//y0/Pz+dPn3abvqHwtCwYUM1bNhQERERhturVq2a7VzYNWrUkJ+fn91T6t99951Wr16tu+++W3Fxcbd1UdzsDBo0SHv27LEpS0tL0+jRozVp0iSVL19eMTEx+V68vjh78cUX9e2339pNhbJ06VItXbpUZcuWVfny5a3Tpjlq0d2C8PT0VJcuXfTll18abndycsp2XZ1Mjz76qN2oiGvXrqlv376qVq2aUlJSijQJ4EirV6/Wpk2bVKVKFSUkJCguLs5wv/79+9uso9KsWTPrQuGZEhIS1LlzZ1WuXFlubm46efJknhZ3LwzNmzfX/fffb/fz8LffftNjjz2mqlWrKiUlxTABWNT69++vH3/80eY9sVgs+uKLL/Tll1+qXLly8vPzU3Jysi5evJinz6CyZcvqscces3sPb/bAAw+ocuXK2W5/44039NNPP9mcNzU1VRMmTNC///1v+fv7y9fXV1evXtWFCxd0/fr1XMfnSC1btpSzs7NdMmTatGlasmSJvL29deLEiTzdq4X53uRG7dq17aZzPXjwoFq2bKnKlStbk2H9+vVT+/btJd34XHvkkUf0yy+/2By3efNmbd68WWXKlFGFChWUlpamy5cvF9nP3z/++EPt2rVT1apVc5wm7+mnn9a9995rU+boe9JisWjv3r3au3evpBsjeypUqCBvb2+lpaXp3Llzhu/tzVN2NmrUSC4uLnbJlqlTpyokJEQVKlTQ2bNnbRLXAIDigREmAADcBpcvX9bx48cVHR2dbbIkICBAb775ZoHOk5k0MeqszkyaZJ2nXJKGDRumhx56yLDOS5cu6fDhw9kmSwIDAzVmzJgCxV3UBg4caFienJyso0ePWpMlDRo0sHnS+nYKDAxUmzZtDLddvHhRR44csSZLbsdTgjmNMslpjQgnJycNGDDAcJvZbNaRI0esnTVt27bNdi2R2+Ghhx7Kdo7v+Ph4RUVFWTtMsiYiMxVkceKilDnPenZz9l++fFlHjhzRyZMni0WyJNOrr75qNzVLpoceeshm3nsjjz/+uKpWrWq47cSJE9Zkiaurq12H3Z2kfv36km78XDhy5Ei2yZKHH35YL7/8sk3ZCy+8oAoVKtjta7FY9Oeff+r48ePKyMiQm5ub3bG327hx4wyfyE9PT9exY8esyZLSpUsbfrYXVfstU6aM5syZYxh7RkaGLly4oKioKJ0+fTpfHfK3ShzeanuVKlU0depUw/Uh0tPTFRsbq6ioKP35559FliyRbkwhmd06HbGxsTp27Jg1WZLbhz4K+725lXbt2hnel1evXtWRI0d08OBBHTx40O6hhMmTJ6tOnTqGdV65ckVHjx7ViRMniixZUrduXTk5OclisejEiRPZJksqVKig0aNH25UX9j2ZmpqqM2fOKCoqSseOHTN8b00mk83vN76+vurWrZthfQkJCTpy5Ig1WcLoDgAoXkiYAADgQM2aNcvzGiTSjU6phQsXqly5cgWOITNp0qxZM7ttKSkpGjJkiLZv325T7uL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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary = analyze(ROOT)\n",
    "k = summary[\"kpis\"]\n",
    "display(pd.Series(k, name=\"Q1 2026\").to_frame())\n",
    "monthly = pd.read_csv(ROOT / \"outputs/tables/monthly_kpis.csv\")\n",
    "display(monthly)\n",
    "display(Image(filename=str(ROOT / \"outputs/figures/02_daily.png\")))\n",
    "display(Image(filename=str(ROOT / \"outputs/figures/04_sku.png\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2cdc52f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T02:25:25.536856Z",
     "iopub.status.busy": "2026-10-13T02:25:25.536698Z",
     "iopub.status.idle": "2026-10-13T02:25:25.539937Z",
     "shell.execute_reply": "2026-10-13T02:25:25.539555Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Interpretasi:** Revenue kuartal mencapai Rp 224,944,015,200 dari 68,000 transaksi. Lima SKU teratas menyumbang 50.9% revenue. Pola ini hanya berlaku pada data simulasi."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(f\"**Interpretasi:** Revenue kuartal mencapai Rp {k['revenue']:,.0f} dari {k['transactions']:,} transaksi. Lima SKU teratas menyumbang {summary['top5_share']:.1%} revenue. Pola ini hanya berlaku pada data simulasi.\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f171d952",
   "metadata": {},
   "source": [
    "## 10. Feature Engineering\n",
    "| Fitur | Rumus | Interpretasi / tujuan |\n",
    "|---|---|---|\n",
    "| Revenue contribution | Revenue SKU / total revenue | Konsentrasi portofolio |\n",
    "| Volume contribution | Ton SKU / total ton | Peran volume |\n",
    "| SKU growth | Revenue Mar / revenue Jan − 1 | Perubahan periode |\n",
    "| Daily CV | SD zak harian / mean zak harian | Variasi permintaan |\n",
    "| Penetration | Toko aktif pembeli SKU / semua toko aktif | Jangkauan produk |\n",
    "| Recency | 31 Mar − tanggal pembelian terakhir | Aktivitas toko |\n",
    "| Purchase frequency | Transaksi toko / 90 hari | Intensitas beli |\n",
    "| AOV | Revenue toko / transaksi toko | Besar order rata-rata |\n",
    "| Revenue per store | Revenue regional/distributor / toko aktif | Produktivitas cakupan |\n",
    "| Growth regional/distributor | Revenue Mar / revenue Jan − 1 | Dinamika periode |\n",
    "\n",
    "Year, month, ISO week, day of week, weekend, dan quarter diturunkan dari tanggal. Fitur kuartal ini **deskriptif**; pemodelan prediktif harus menghitungnya hanya dari jendela historis sebelum tanggal prediksi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2053b5c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T02:25:25.541640Z",
     "iopub.status.busy": "2026-10-13T02:25:25.541507Z",
     "iopub.status.idle": "2026-10-13T02:25:25.760071Z",
     "shell.execute_reply": "2026-10-13T02:25:25.759726Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sku (18, 24)\n",
      "store (400, 19)\n",
      "region (4, 8)\n",
      "distributor (10, 9)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Kode Produk</th>\n",
       "      <th>Nama Produk</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Kategori Produk</th>\n",
       "      <th>Packaging KG</th>\n",
       "      <th>revenue</th>\n",
       "      <th>tons</th>\n",
       "      <th>bags</th>\n",
       "      <th>transactions</th>\n",
       "      <th>active_stores</th>\n",
       "      <th>...</th>\n",
       "      <th>growth_jan_mar</th>\n",
       "      <th>daily_mean_bags</th>\n",
       "      <th>daily_std_bags</th>\n",
       "      <th>demand_cv</th>\n",
       "      <th>sku_rank</th>\n",
       "      <th>cumulative_share</th>\n",
       "      <th>abc</th>\n",
       "      <th>growth_matrix</th>\n",
       "      <th>stability</th>\n",
       "      <th>opportunity_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SYN-01-01</td>\n",
       "      <td>NusaKuat Portland Composite 40 KG</td>\n",
       "      <td>NusaKuat</td>\n",
       "      <td>Portland Composite</td>\n",
       "      <td>40</td>\n",
       "      <td>29580381800</td>\n",
       "      <td>20862.44</td>\n",
       "      <td>521561</td>\n",
       "      <td>15025</td>\n",
       "      <td>400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.301702</td>\n",
       "      <td>5795.122222</td>\n",
       "      <td>1340.586463</td>\n",
       "      <td>0.231330</td>\n",
       "      <td>1</td>\n",
       "      <td>0.131501</td>\n",
       "      <td>A</td>\n",
       "      <td>High Contribution / High Growth</td>\n",
       "      <td>Stable</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SYN-01-02</td>\n",
       "      <td>NusaKuat Portland Composite 50 KG</td>\n",
       "      <td>NusaKuat</td>\n",
       "      <td>Portland Composite</td>\n",
       "      <td>50</td>\n",
       "      <td>24628500000</td>\n",
       "      <td>17828.55</td>\n",
       "      <td>356571</td>\n",
       "      <td>10354</td>\n",
       "      <td>400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.271972</td>\n",
       "      <td>3961.900000</td>\n",
       "      <td>946.273520</td>\n",
       "      <td>0.238843</td>\n",
       "      <td>2</td>\n",
       "      <td>0.240988</td>\n",
       "      <td>A</td>\n",
       "      <td>High Contribution / High Growth</td>\n",
       "      <td>Stable</td>\n",
       "      <td>67.916899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SYN-02-01</td>\n",
       "      <td>BatuPrima Portland Composite 40 KG</td>\n",
       "      <td>BatuPrima</td>\n",
       "      <td>Portland Composite</td>\n",
       "      <td>40</td>\n",
       "      <td>23477881600</td>\n",
       "      <td>16280.76</td>\n",
       "      <td>407019</td>\n",
       "      <td>11636</td>\n",
       "      <td>400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214224</td>\n",
       "      <td>4522.433333</td>\n",
       "      <td>1073.694515</td>\n",
       "      <td>0.237415</td>\n",
       "      <td>3</td>\n",
       "      <td>0.345360</td>\n",
       "      <td>A</td>\n",
       "      <td>High Contribution / Low Growth</td>\n",
       "      <td>Stable</td>\n",
       "      <td>50.465963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SYN-02-02</td>\n",
       "      <td>BatuPrima Portland Composite 50 KG</td>\n",
       "      <td>BatuPrima</td>\n",
       "      <td>Portland Composite</td>\n",
       "      <td>50</td>\n",
       "      <td>18853185900</td>\n",
       "      <td>13458.35</td>\n",
       "      <td>269167</td>\n",
       "      <td>7768</td>\n",
       "      <td>400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251511</td>\n",
       "      <td>2990.744444</td>\n",
       "      <td>748.873920</td>\n",
       "      <td>0.250397</td>\n",
       "      <td>4</td>\n",
       "      <td>0.429173</td>\n",
       "      <td>A</td>\n",
       "      <td>High Contribution / High Growth</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>54.937075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SYN-03-01</td>\n",
       "      <td>CiptaSemen Portland Composite 40 KG</td>\n",
       "      <td>CiptaSemen</td>\n",
       "      <td>Portland Composite</td>\n",
       "      <td>40</td>\n",
       "      <td>17991680000</td>\n",
       "      <td>12266.80</td>\n",
       "      <td>306670</td>\n",
       "      <td>8838</td>\n",
       "      <td>400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.244622</td>\n",
       "      <td>3407.444444</td>\n",
       "      <td>806.826884</td>\n",
       "      <td>0.236784</td>\n",
       "      <td>5</td>\n",
       "      <td>0.509156</td>\n",
       "      <td>A</td>\n",
       "      <td>High Contribution / High Growth</td>\n",
       "      <td>Stable</td>\n",
       "      <td>54.310406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Kode Produk                          Nama Produk       Brand  \\\n",
       "0   SYN-01-01    NusaKuat Portland Composite 40 KG    NusaKuat   \n",
       "1   SYN-01-02    NusaKuat Portland Composite 50 KG    NusaKuat   \n",
       "2   SYN-02-01   BatuPrima Portland Composite 40 KG   BatuPrima   \n",
       "3   SYN-02-02   BatuPrima Portland Composite 50 KG   BatuPrima   \n",
       "4   SYN-03-01  CiptaSemen Portland Composite 40 KG  CiptaSemen   \n",
       "\n",
       "      Kategori Produk  Packaging KG      revenue      tons    bags  \\\n",
       "0  Portland Composite            40  29580381800  20862.44  521561   \n",
       "1  Portland Composite            50  24628500000  17828.55  356571   \n",
       "2  Portland Composite            40  23477881600  16280.76  407019   \n",
       "3  Portland Composite            50  18853185900  13458.35  269167   \n",
       "4  Portland Composite            40  17991680000  12266.80  306670   \n",
       "\n",
       "   transactions  active_stores  ...  growth_jan_mar  daily_mean_bags  \\\n",
       "0         15025            400  ...        0.301702      5795.122222   \n",
       "1         10354            400  ...        0.271972      3961.900000   \n",
       "2         11636            400  ...        0.214224      4522.433333   \n",
       "3          7768            400  ...        0.251511      2990.744444   \n",
       "4          8838            400  ...        0.244622      3407.444444   \n",
       "\n",
       "   daily_std_bags  demand_cv  sku_rank  cumulative_share  abc  \\\n",
       "0     1340.586463   0.231330         1          0.131501    A   \n",
       "1      946.273520   0.238843         2          0.240988    A   \n",
       "2     1073.694515   0.237415         3          0.345360    A   \n",
       "3      748.873920   0.250397         4          0.429173    A   \n",
       "4      806.826884   0.236784         5          0.509156    A   \n",
       "\n",
       "                     growth_matrix  stability  opportunity_score  \n",
       "0  High Contribution / High Growth     Stable          75.000000  \n",
       "1  High Contribution / High Growth     Stable          67.916899  \n",
       "2   High Contribution / Low Growth     Stable          50.465963  \n",
       "3  High Contribution / High Growth   Moderate          54.937075  \n",
       "4  High Contribution / High Growth     Stable          54.310406  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_tables = tables(df)\n",
    "for name, tab in feature_tables.items():\n",
    "    print(name, tab.shape)\n",
    "display(feature_tables[\"sku\"].head(5))\n",
    "assert np.allclose(feature_tables[\"sku\"].revenue_share.sum(), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96476ac",
   "metadata": {},
   "source": [
    "## 11. Advanced Product Analytics\n",
    "ABC memasukkan SKU yang melintasi batas 80% ke A dan yang melintasi 95% ke B. Growth matrix menggunakan median contribution dan median Jan–Mar growth. Stabilitas: CV ≤0,25 stable; ≤0,50 moderate; >0,50 variable. Opportunity score = 30% growth + 25% contribution + 25% penetration gap + 20% stability. Semua komponen min-max; growth di-winsorize p5/p95. Skor merupakan heuristik, belum dioptimasi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c4359b4c",
   "metadata": {
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     "shell.execute_reply": "2026-10-13T02:25:25.771577Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Kode Produk</th>\n",
       "      <th>revenue_share</th>\n",
       "      <th>growth_jan_mar</th>\n",
       "      <th>demand_cv</th>\n",
       "      <th>penetration</th>\n",
       "      <th>abc</th>\n",
       "      <th>growth_matrix</th>\n",
       "      <th>opportunity_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SYN-01-01</td>\n",
       "      <td>0.131501</td>\n",
       "      <td>0.301702</td>\n",
       "      <td>0.231330</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>A</td>\n",
       "      <td>High Contribution / High Growth</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SYN-01-02</td>\n",
       "      <td>0.109487</td>\n",
       "      <td>0.271972</td>\n",
       "      <td>0.238843</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>A</td>\n",
       "      <td>High Contribution / High Growth</td>\n",
       "      <td>67.916899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SYN-02-01</td>\n",
       "      <td>0.104372</td>\n",
       "      <td>0.214224</td>\n",
       "      <td>0.237415</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>A</td>\n",
       "      <td>High Contribution / Low Growth</td>\n",
       "      <td>50.465963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SYN-02-02</td>\n",
       "      <td>0.083813</td>\n",
       "      <td>0.251511</td>\n",
       "      <td>0.250397</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>A</td>\n",
       "      <td>High Contribution / High Growth</td>\n",
       "      <td>54.937075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SYN-03-01</td>\n",
       "      <td>0.079983</td>\n",
       "      <td>0.244622</td>\n",
       "      <td>0.236784</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>A</td>\n",
       "      <td>High Contribution / High Growth</td>\n",
       "      <td>54.310406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SYN-03-02</td>\n",
       "      <td>0.065437</td>\n",
       "      <td>0.207397</td>\n",
       "      <td>0.264170</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>A</td>\n",
       "      <td>High Contribution / Low Growth</td>\n",
       "      <td>36.504943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SYN-01-03</td>\n",
       "      <td>0.062271</td>\n",
       "      <td>0.217071</td>\n",
       "      <td>0.274094</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>A</td>\n",
       "      <td>High Contribution / High Growth</td>\n",
       "      <td>37.360097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SYN-04-01</td>\n",
       "      <td>0.058706</td>\n",
       "      <td>0.250094</td>\n",
       "      <td>0.252681</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>A</td>\n",
       "      <td>High Contribution / High Growth</td>\n",
       "      <td>49.027540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SYN-02-03</td>\n",
       "      <td>0.048796</td>\n",
       "      <td>0.236502</td>\n",
       "      <td>0.281327</td>\n",
       "      <td>0.9975</td>\n",
       "      <td>A</td>\n",
       "      <td>High Contribution / High Growth</td>\n",
       "      <td>39.732628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SYN-04-02</td>\n",
       "      <td>0.048221</td>\n",
       "      <td>0.175989</td>\n",
       "      <td>0.255530</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>A</td>\n",
       "      <td>Low Contribution / Low Growth</td>\n",
       "      <td>25.112841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SYN-05-01</td>\n",
       "      <td>0.039409</td>\n",
       "      <td>0.200788</td>\n",
       "      <td>0.293254</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>A</td>\n",
       "      <td>Low Contribution / Low Growth</td>\n",
       "      <td>25.739916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SYN-03-03</td>\n",
       "      <td>0.036813</td>\n",
       "      <td>0.188690</td>\n",
       "      <td>0.314208</td>\n",
       "      <td>0.9875</td>\n",
       "      <td>B</td>\n",
       "      <td>Low Contribution / Low Growth</td>\n",
       "      <td>21.715192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SYN-05-02</td>\n",
       "      <td>0.032626</td>\n",
       "      <td>0.219581</td>\n",
       "      <td>0.308702</td>\n",
       "      <td>0.9875</td>\n",
       "      <td>B</td>\n",
       "      <td>Low Contribution / High Growth</td>\n",
       "      <td>30.282472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SYN-04-03</td>\n",
       "      <td>0.027154</td>\n",
       "      <td>0.229438</td>\n",
       "      <td>0.293710</td>\n",
       "      <td>0.9850</td>\n",
       "      <td>B</td>\n",
       "      <td>Low Contribution / High Growth</td>\n",
       "      <td>33.964837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SYN-06-01</td>\n",
       "      <td>0.023594</td>\n",
       "      <td>0.151279</td>\n",
       "      <td>0.313567</td>\n",
       "      <td>0.9975</td>\n",
       "      <td>B</td>\n",
       "      <td>Low Contribution / Low Growth</td>\n",
       "      <td>12.635790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SYN-06-02</td>\n",
       "      <td>0.019338</td>\n",
       "      <td>0.182235</td>\n",
       "      <td>0.333526</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>C</td>\n",
       "      <td>Low Contribution / Low Growth</td>\n",
       "      <td>18.776406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SYN-05-03</td>\n",
       "      <td>0.018520</td>\n",
       "      <td>0.205247</td>\n",
       "      <td>0.397910</td>\n",
       "      <td>0.9350</td>\n",
       "      <td>C</td>\n",
       "      <td>Low Contribution / Low Growth</td>\n",
       "      <td>25.365163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SYN-06-03</td>\n",
       "      <td>0.009959</td>\n",
       "      <td>0.199364</td>\n",
       "      <td>0.458252</td>\n",
       "      <td>0.8525</td>\n",
       "      <td>C</td>\n",
       "      <td>Low Contribution / Low Growth</td>\n",
       "      <td>32.800831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Kode Produk  revenue_share  growth_jan_mar  demand_cv  penetration abc  \\\n",
       "0    SYN-01-01       0.131501        0.301702   0.231330       1.0000   A   \n",
       "1    SYN-01-02       0.109487        0.271972   0.238843       1.0000   A   \n",
       "2    SYN-02-01       0.104372        0.214224   0.237415       1.0000   A   \n",
       "3    SYN-02-02       0.083813        0.251511   0.250397       1.0000   A   \n",
       "4    SYN-03-01       0.079983        0.244622   0.236784       1.0000   A   \n",
       "5    SYN-03-02       0.065437        0.207397   0.264170       1.0000   A   \n",
       "6    SYN-01-03       0.062271        0.217071   0.274094       1.0000   A   \n",
       "7    SYN-04-01       0.058706        0.250094   0.252681       1.0000   A   \n",
       "8    SYN-02-03       0.048796        0.236502   0.281327       0.9975   A   \n",
       "9    SYN-04-02       0.048221        0.175989   0.255530       1.0000   A   \n",
       "10   SYN-05-01       0.039409        0.200788   0.293254       1.0000   A   \n",
       "11   SYN-03-03       0.036813        0.188690   0.314208       0.9875   B   \n",
       "12   SYN-05-02       0.032626        0.219581   0.308702       0.9875   B   \n",
       "13   SYN-04-03       0.027154        0.229438   0.293710       0.9850   B   \n",
       "14   SYN-06-01       0.023594        0.151279   0.313567       0.9975   B   \n",
       "15   SYN-06-02       0.019338        0.182235   0.333526       0.9625   C   \n",
       "16   SYN-05-03       0.018520        0.205247   0.397910       0.9350   C   \n",
       "17   SYN-06-03       0.009959        0.199364   0.458252       0.8525   C   \n",
       "\n",
       "                      growth_matrix  opportunity_score  \n",
       "0   High Contribution / High Growth          75.000000  \n",
       "1   High Contribution / High Growth          67.916899  \n",
       "2    High Contribution / Low Growth          50.465963  \n",
       "3   High Contribution / High Growth          54.937075  \n",
       "4   High Contribution / High Growth          54.310406  \n",
       "5    High Contribution / Low Growth          36.504943  \n",
       "6   High Contribution / High Growth          37.360097  \n",
       "7   High Contribution / High Growth          49.027540  \n",
       "8   High Contribution / High Growth          39.732628  \n",
       "9     Low Contribution / Low Growth          25.112841  \n",
       "10    Low Contribution / Low Growth          25.739916  \n",
       "11    Low Contribution / Low Growth          21.715192  \n",
       "12   Low Contribution / High Growth          30.282472  \n",
       "13   Low Contribution / High Growth          33.964837  \n",
       "14    Low Contribution / Low Growth          12.635790  \n",
       "15    Low Contribution / Low Growth          18.776406  \n",
       "16    Low Contribution / Low Growth          25.365163  \n",
       "17    Low Contribution / Low Growth          32.800831  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sku = feature_tables[\"sku\"]\n",
    "display(sku[[\"Kode Produk\",\"revenue_share\",\"growth_jan_mar\",\"demand_cv\",\"penetration\",\"abc\",\"growth_matrix\",\"opportunity_score\"]])\n",
    "display(Image(filename=str(ROOT / \"outputs/figures/09_abc.png\")))\n",
    "display(Image(filename=str(ROOT / \"outputs/figures/15_opportunity.png\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b48366ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T02:25:25.773305Z",
     "iopub.status.busy": "2026-10-13T02:25:25.773188Z",
     "iopub.status.idle": "2026-10-13T02:25:25.775905Z",
     "shell.execute_reply": "2026-10-13T02:25:25.775601Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Interpretasi:** SKU A berjumlah 11 dari 18 SKU. Peringkat opportunity teratas adalah SYN-01-01; prioritas ini perlu diuji dengan margin dan stok."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(f\"**Interpretasi:** SKU A berjumlah {summary['abc_counts'].get('A',0)} dari {len(sku)} SKU. Peringkat opportunity teratas adalah {summary['opportunity_top'][0]['Kode Produk']}; prioritas ini perlu diuji dengan margin dan stok.\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e3d7375",
   "metadata": {},
   "source": [
    "## 12. Statistical Analysis\n",
    "**Hipotesis:** distribusi revenue harian akhir pekan dan hari kerja berbeda. Mann–Whitney U dua sisi dipilih untuk membandingkan hari sebagai unit; Cliff’s delta sebagai effect size. P-value pada data sintetis tidak menggeneralisasi ke pasar. Serial correlation antarhari dapat melanggar asumsi independensi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1516117a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T02:25:25.777258Z",
     "iopub.status.busy": "2026-10-13T02:25:25.777146Z",
     "iopub.status.idle": "2026-10-13T02:25:25.782769Z",
     "shell.execute_reply": "2026-10-13T02:25:25.782504Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hasil</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hypothesis</th>\n",
       "      <td>Distribusi revenue harian akhir pekan berbeda ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <td>Mann-Whitney U, dua sisi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assumptions</th>\n",
       "      <td>Hari sebagai unit observasi; independensi hari...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_value</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cliffs_delta</th>\n",
       "      <td>-0.942308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekend_mean</th>\n",
       "      <td>1875404338.461539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday_mean</th>\n",
       "      <td>2752867225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_weekend</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_weekday</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          hasil\n",
       "hypothesis    Distribusi revenue harian akhir pekan berbeda ...\n",
       "method                                 Mann-Whitney U, dua sisi\n",
       "assumptions   Hari sebagai unit observasi; independensi hari...\n",
       "p_value                                                     0.0\n",
       "cliffs_delta                                          -0.942308\n",
       "weekend_mean                                  1875404338.461539\n",
       "weekday_mean                                       2752867225.0\n",
       "n_weekend                                                    26\n",
       "n_weekday                                                    64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Interpretasi:** p=3.063e-12, Cliff’s delta=-0.942; perbedaan berasal dari proses simulasi dan tidak boleh ditafsirkan sebagai causal effect."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "st = summary[\"stats\"]\n",
    "display(pd.Series(st).to_frame(\"hasil\"))\n",
    "display(Image(filename=str(ROOT / \"outputs/figures/17_weekend.png\")))\n",
    "display(Markdown(f\"**Interpretasi:** p={st['p_value']:.4g}, Cliff’s delta={st['cliffs_delta']:.3f}; perbedaan berasal dari proses simulasi dan tidak boleh ditafsirkan sebagai causal effect.\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ec32d26",
   "metadata": {},
   "source": [
    "## 13. Key Findings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6e933e1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-13T02:25:25.784191Z",
     "iopub.status.busy": "2026-10-13T02:25:25.784041Z",
     "iopub.status.idle": "2026-10-13T02:25:25.793142Z",
     "shell.execute_reply": "2026-10-13T02:25:25.792811Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "1. Lima SKU teratas berkontribusi 50.9% revenue; perhatikan ketersediaan produk inti."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "2. Brand NusaKuat berkontribusi 30.3% revenue."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "3. 10% toko teratas berkontribusi 20.1% revenue."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "4. 40 KG berkontribusi 43.8% revenue; evaluasi tonase juga."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "5. Regional Jatim Utara berkontribusi 41.8% revenue."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temuan</th>\n",
       "      <th>Bukti Kuantitatif</th>\n",
       "      <th>Interpretasi</th>\n",
       "      <th>Potensi Dampak</th>\n",
       "      <th>Rekomendasi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Konsentrasi SKU</td>\n",
       "      <td>Lima SKU teratas menyumbang 50.9% revenue.</td>\n",
       "      <td>Revenue terkonsentrasi pada beberapa SKU.</td>\n",
       "      <td>Gangguan pasokan SKU inti dapat memengaruhi re...</td>\n",
       "      <td>Pantau fill rate dan stockout SKU A setiap hari.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dominasi brand</td>\n",
       "      <td>NusaKuat menyumbang 30.3% revenue.</td>\n",
       "      <td>Brand teratas memegang porsi besar dalam simul...</td>\n",
       "      <td>Ketergantungan brand dapat meningkatkan risiko...</td>\n",
       "      <td>Uji substitusi dan cross-sell brand pada toko ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Konsentrasi toko</td>\n",
       "      <td>10% toko teratas menyumbang 20.1% revenue.</td>\n",
       "      <td>Sebagian kecil toko mendorong porsi besar penj...</td>\n",
       "      <td>Penurunan aktivitas toko utama dapat berdampak...</td>\n",
       "      <td>Terapkan layanan prioritas dan pantau recency ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Perubahan bulanan</td>\n",
       "      <td>Revenue Maret berubah 24.9% dari Februari.</td>\n",
       "      <td>Kapasitas penjualan bervariasi antarbulan dala...</td>\n",
       "      <td>Rencana stok statis dapat menghasilkan ketidak...</td>\n",
       "      <td>Perbarui proyeksi mingguan dan evaluasi kapasi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Variasi harian</td>\n",
       "      <td>CV revenue harian 0.21.</td>\n",
       "      <td>Permintaan harian tidak sepenuhnya rata.</td>\n",
       "      <td>Jadwal pengiriman tetap mungkin kurang cocok u...</td>\n",
       "      <td>Uji penyesuaian jadwal dan safety stock berdas...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Temuan                           Bukti Kuantitatif  \\\n",
       "0    Konsentrasi SKU  Lima SKU teratas menyumbang 50.9% revenue.   \n",
       "1     Dominasi brand          NusaKuat menyumbang 30.3% revenue.   \n",
       "2   Konsentrasi toko  10% toko teratas menyumbang 20.1% revenue.   \n",
       "3  Perubahan bulanan  Revenue Maret berubah 24.9% dari Februari.   \n",
       "4     Variasi harian                     CV revenue harian 0.21.   \n",
       "\n",
       "                                        Interpretasi  \\\n",
       "0          Revenue terkonsentrasi pada beberapa SKU.   \n",
       "1  Brand teratas memegang porsi besar dalam simul...   \n",
       "2  Sebagian kecil toko mendorong porsi besar penj...   \n",
       "3  Kapasitas penjualan bervariasi antarbulan dala...   \n",
       "4           Permintaan harian tidak sepenuhnya rata.   \n",
       "\n",
       "                                      Potensi Dampak  \\\n",
       "0  Gangguan pasokan SKU inti dapat memengaruhi re...   \n",
       "1  Ketergantungan brand dapat meningkatkan risiko...   \n",
       "2  Penurunan aktivitas toko utama dapat berdampak...   \n",
       "3  Rencana stok statis dapat menghasilkan ketidak...   \n",
       "4  Jadwal pengiriman tetap mungkin kurang cocok u...   \n",
       "\n",
       "                                         Rekomendasi  \n",
       "0   Pantau fill rate dan stockout SKU A setiap hari.  \n",
       "1  Uji substitusi dan cross-sell brand pada toko ...  \n",
       "2  Terapkan layanan prioritas dan pantau recency ...  \n",
       "3  Perbarui proyeksi mingguan dan evaluasi kapasi...  \n",
       "4  Uji penyesuaian jadwal dan safety stock berdas...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "findings = [\n",
    " f\"Lima SKU teratas berkontribusi {summary['top5_share']:.1%} revenue; perhatikan ketersediaan produk inti.\",\n",
    " f\"Brand {summary['top_brand']['name']} berkontribusi {summary['top_brand']['share']:.1%} revenue.\",\n",
    " f\"10% toko teratas berkontribusi {summary['store_top_decile_share']:.1%} revenue.\",\n",
    " f\"40 KG berkontribusi {summary['pack40_share']:.1%} revenue; evaluasi tonase juga.\",\n",
    " f\"Regional {summary['top_region']['name']} berkontribusi {summary['top_region']['share']:.1%} revenue.\"\n",
    "]\n",
    "for i, item in enumerate(findings,1): display(Markdown(f\"{i}. {item}\"))\n",
    "# Five-field, evidence-linked insight register\n",
    "display(pd.read_csv(ROOT / \"outputs/tables/business_insights.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa3b000c",
   "metadata": {},
   "source": [
    "## 14. Business Recommendations\n",
    "1. Pantau fill rate dan stockout SKU A.\n",
    "2. Uji perluasan SKU dengan opportunity score tinggi pada toko yang belum membeli.\n",
    "3. Prioritaskan layanan toko segmen RFM tertinggi.\n",
    "4. Sesuaikan alokasi kemasan dengan tonase, margin, dan profil toko.\n",
    "5. Coaching distributor menggunakan revenue per active store dan SKU coverage.\n",
    "\n",
    "**Peluang:** penetrasi SKU, cross-sell kemasan, produktivitas distributor. **Risiko:** konsentrasi SKU, volatilitas permintaan, validitas eksternal simulasi. Semua tindakan komersial memerlukan validasi profitabilitas dan pilot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27efd96c",
   "metadata": {},
   "source": [
    "## 15. Project Limitations\n",
    "Seluruh data dan entitas adalah sintetis. Preferensi brand, pola promosi, dan perbedaan regional disetel dalam generator. Harga–volume observasional bukan causal price elasticity. Skor peluang belum divalidasi terhadap margin, stok, dan eksperimen. Growth Jan–Mar dapat dipengaruhi base effect."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ddabad",
   "metadata": {},
   "source": [
    "## 16. Conclusion\n",
    "Proyek ini menyediakan alur reproduktif dari generator hingga report. Hasil berfungsi untuk menunjukkan kemampuan analitis dan cara merumuskan keputusan produk, bukan menggambarkan pasar semen aktual."
   ]
  }
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