{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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>Order</th>\n",
       "      <th>Material Number</th>\n",
       "      <th>Material description</th>\n",
       "      <th>Order quantity (GMEIN)</th>\n",
       "      <th>Basic start date</th>\n",
       "      <th>Basic finish date</th>\n",
       "      <th>System Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100033364</td>\n",
       "      <td>S9992431</td>\n",
       "      <td>SUB 1/8 NBK, Clinic, Module 1, Medicines</td>\n",
       "      <td>14</td>\n",
       "      <td>2025-03-03</td>\n",
       "      <td>2025-03-07</td>\n",
       "      <td>REL  PRC  BCRQ MACM SETC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100034881</td>\n",
       "      <td>S9991123</td>\n",
       "      <td>SUB 3/5 f.S9901026 IEHK2017 part 1</td>\n",
       "      <td>58</td>\n",
       "      <td>2025-03-17</td>\n",
       "      <td>2025-03-21</td>\n",
       "      <td>REL  PRC  BCRQ MACM SETC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100035124</td>\n",
       "      <td>S9992442</td>\n",
       "      <td>SUB 2/3 NBK, Clinic,Module 2,Consumables</td>\n",
       "      <td>5</td>\n",
       "      <td>2025-03-14</td>\n",
       "      <td>2025-03-21</td>\n",
       "      <td>REL  PRC  BCRQ MACM SETC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100034003</td>\n",
       "      <td>S9901042</td>\n",
       "      <td>IEHK 2024,Basic Equipment UNIT</td>\n",
       "      <td>800</td>\n",
       "      <td>2025-03-24</td>\n",
       "      <td>2025-03-28</td>\n",
       "      <td>REL  PRC  BCRQ MANC SETC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100034017</td>\n",
       "      <td>S9901042</td>\n",
       "      <td>IEHK 2024,Basic Equipment UNIT</td>\n",
       "      <td>800</td>\n",
       "      <td>2025-03-24</td>\n",
       "      <td>2025-03-28</td>\n",
       "      <td>REL  PRC  BCRQ MANC SETC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order Material Number                      Material description  \\\n",
       "0  100033364        S9992431  SUB 1/8 NBK, Clinic, Module 1, Medicines   \n",
       "1  100034881        S9991123        SUB 3/5 f.S9901026 IEHK2017 part 1   \n",
       "2  100035124        S9992442  SUB 2/3 NBK, Clinic,Module 2,Consumables   \n",
       "3  100034003        S9901042            IEHK 2024,Basic Equipment UNIT   \n",
       "4  100034017        S9901042            IEHK 2024,Basic Equipment UNIT   \n",
       "\n",
       "   Order quantity (GMEIN) Basic start date Basic finish date  \\\n",
       "0                      14       2025-03-03        2025-03-07   \n",
       "1                      58       2025-03-17        2025-03-21   \n",
       "2                       5       2025-03-14        2025-03-21   \n",
       "3                     800       2025-03-24        2025-03-28   \n",
       "4                     800       2025-03-24        2025-03-28   \n",
       "\n",
       "              System Status  \n",
       "0  REL  PRC  BCRQ MACM SETC  \n",
       "1  REL  PRC  BCRQ MACM SETC  \n",
       "2  REL  PRC  BCRQ MACM SETC  \n",
       "3  REL  PRC  BCRQ MANC SETC  \n",
       "4  REL  PRC  BCRQ MANC SETC  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Use kernel 3.7.9 python\n",
    "#!pip install pandas\n",
    "import pandas as pd\n",
    "demand_path = \"data/real_data_excel/converted_csv/COOIS_Released_Prod_Orders.csv\"\n",
    "df = pd.read_csv(\"../\"+demand_path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order                      int64\n",
       "Material Number           object\n",
       "Material description      object\n",
       "Order quantity (GMEIN)     int64\n",
       "Basic start date          object\n",
       "Basic finish date         object\n",
       "System Status             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Demand data\n",
    "import matplotlib.pyplot as plt\n",
    "pure_quant = df.groupby('Basic finish date').sum()['Order quantity (GMEIN)']\n",
    "plt.figure()\n",
    "plt.title('Demand data')\n",
    "pure_quant.plot(kind='line', figsize=(10, 5))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- demand viz / date\n",
    "- resource viz\n",
    "- inventory capa viz\n",
    "- available hr viz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"../data/real_data_excel/converted_csv/\"\n",
    "human_resource_per_kit =  \"Kits__Calculation.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>Kit</th>\n",
       "      <th>Description</th>\n",
       "      <th>Kit per day</th>\n",
       "      <th>Humanizer</th>\n",
       "      <th>UNICEF staff</th>\n",
       "      <th>Paid work hours per day</th>\n",
       "      <th>Humanizer hours</th>\n",
       "      <th>UNICEF hours</th>\n",
       "      <th>Total Hours</th>\n",
       "      <th>Hourly rate  humanizer staff in USD</th>\n",
       "      <th>Hourly rate UNICEF staff in USD</th>\n",
       "      <th>Cost for humanizer for 800 kits</th>\n",
       "      <th>Cost for UNICEF staff for 800 kits</th>\n",
       "      <th>Total cost</th>\n",
       "      <th>Hours per kit</th>\n",
       "      <th>Cost for subkit/prepack/Kits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S9935011</td>\n",
       "      <td>School-in-a-carton,40 students, 2016</td>\n",
       "      <td>800</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>82.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>97.5</td>\n",
       "      <td>25.87</td>\n",
       "      <td>30</td>\n",
       "      <td>2134.275</td>\n",
       "      <td>450</td>\n",
       "      <td>2584.275</td>\n",
       "      <td>0.121875</td>\n",
       "      <td>3.230344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S9999991</td>\n",
       "      <td>Education prepack 2 for new SIB  (I)</td>\n",
       "      <td>800</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>37.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>45.0</td>\n",
       "      <td>25.87</td>\n",
       "      <td>30</td>\n",
       "      <td>970.125</td>\n",
       "      <td>225</td>\n",
       "      <td>1195.125</td>\n",
       "      <td>0.056250</td>\n",
       "      <td>1.493906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S9999990</td>\n",
       "      <td>Education prepack 1 for new SIB  (I)</td>\n",
       "      <td>800</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>37.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>45.0</td>\n",
       "      <td>25.87</td>\n",
       "      <td>30</td>\n",
       "      <td>970.125</td>\n",
       "      <td>225</td>\n",
       "      <td>1195.125</td>\n",
       "      <td>0.056250</td>\n",
       "      <td>1.493906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S9935019</td>\n",
       "      <td>School-in-a-bag kit,40 student,1 teacher</td>\n",
       "      <td>800</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>67.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>25.87</td>\n",
       "      <td>30</td>\n",
       "      <td>1746.225</td>\n",
       "      <td>450</td>\n",
       "      <td>2196.225</td>\n",
       "      <td>0.103125</td>\n",
       "      <td>2.745281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S9999933</td>\n",
       "      <td>Prepack 1/1 School in a Bag S9935019</td>\n",
       "      <td>800</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>22.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.87</td>\n",
       "      <td>30</td>\n",
       "      <td>582.075</td>\n",
       "      <td>225</td>\n",
       "      <td>807.075</td>\n",
       "      <td>0.037500</td>\n",
       "      <td>1.008844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Kit                               Description  Kit per day  Humanizer  \\\n",
       "0  S9935011      School-in-a-carton,40 students, 2016          800       11.0   \n",
       "1  S9999991      Education prepack 2 for new SIB  (I)          800        5.0   \n",
       "2  S9999990      Education prepack 1 for new SIB  (I)          800        5.0   \n",
       "3  S9935019  School-in-a-bag kit,40 student,1 teacher          800        9.0   \n",
       "4  S9999933      Prepack 1/1 School in a Bag S9935019          800        3.0   \n",
       "\n",
       "   UNICEF staff  Paid work hours per day  Humanizer hours  UNICEF hours  \\\n",
       "0           2.0                      7.5             82.5          15.0   \n",
       "1           1.0                      7.5             37.5           7.5   \n",
       "2           1.0                      7.5             37.5           7.5   \n",
       "3           2.0                      7.5             67.5          15.0   \n",
       "4           1.0                      7.5             22.5           7.5   \n",
       "\n",
       "   Total Hours  Hourly rate  humanizer staff in USD  \\\n",
       "0         97.5                                25.87   \n",
       "1         45.0                                25.87   \n",
       "2         45.0                                25.87   \n",
       "3         82.5                                25.87   \n",
       "4         30.0                                25.87   \n",
       "\n",
       "   Hourly rate UNICEF staff in USD  Cost for humanizer for 800 kits   \\\n",
       "0                               30                          2134.275   \n",
       "1                               30                           970.125   \n",
       "2                               30                           970.125   \n",
       "3                               30                          1746.225   \n",
       "4                               30                           582.075   \n",
       "\n",
       "   Cost for UNICEF staff for 800 kits  Total cost  Hours per kit  \\\n",
       "0                                 450    2584.275       0.121875   \n",
       "1                                 225    1195.125       0.056250   \n",
       "2                                 225    1195.125       0.056250   \n",
       "3                                 450    2196.225       0.103125   \n",
       "4                                 225     807.075       0.037500   \n",
       "\n",
       "   Cost for subkit/prepack/Kits  \n",
       "0                      3.230344  \n",
       "1                      1.493906  \n",
       "2                      1.493906  \n",
       "3                      2.745281  \n",
       "4                      1.008844  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "kit_resource_info = pd.read_csv(data_path + human_resource_per_kit, header=0, skiprows=[0])\n",
    "kit_resource_info.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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>Material Number</th>\n",
       "      <th>Order quantity (GMEIN)</th>\n",
       "      <th>Basic finish date</th>\n",
       "      <th>Kit</th>\n",
       "      <th>Description</th>\n",
       "      <th>Kit per day</th>\n",
       "      <th>Humanizer</th>\n",
       "      <th>UNICEF staff</th>\n",
       "      <th>Paid work hours per day</th>\n",
       "      <th>Humanizer hours</th>\n",
       "      <th>UNICEF hours</th>\n",
       "      <th>Total Hours</th>\n",
       "      <th>Hourly rate  humanizer staff in USD</th>\n",
       "      <th>Hourly rate UNICEF staff in USD</th>\n",
       "      <th>Cost for humanizer for 800 kits</th>\n",
       "      <th>Cost for UNICEF staff for 800 kits</th>\n",
       "      <th>Total cost</th>\n",
       "      <th>Hours per kit</th>\n",
       "      <th>Cost for subkit/prepack/Kits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S9992431</td>\n",
       "      <td>14</td>\n",
       "      <td>2025-03-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S9991123</td>\n",
       "      <td>58</td>\n",
       "      <td>2025-03-21</td>\n",
       "      <td>S9991123</td>\n",
       "      <td>SUB 3/5 f.S9901026 IEHK2017 part 1</td>\n",
       "      <td>800.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>67.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>25.87</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1746.225</td>\n",
       "      <td>450.0</td>\n",
       "      <td>2196.225</td>\n",
       "      <td>0.103125</td>\n",
       "      <td>2.745281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S9992442</td>\n",
       "      <td>5</td>\n",
       "      <td>2025-03-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S9901042</td>\n",
       "      <td>800</td>\n",
       "      <td>2025-03-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S9901042</td>\n",
       "      <td>800</td>\n",
       "      <td>2025-03-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Material Number  Order quantity (GMEIN) Basic finish date       Kit  \\\n",
       "0        S9992431                      14        2025-03-07       NaN   \n",
       "1        S9991123                      58        2025-03-21  S9991123   \n",
       "2        S9992442                       5        2025-03-21       NaN   \n",
       "3        S9901042                     800        2025-03-28       NaN   \n",
       "4        S9901042                     800        2025-03-28       NaN   \n",
       "\n",
       "                          Description  Kit per day  Humanizer  UNICEF staff  \\\n",
       "0                                 NaN          NaN        NaN           NaN   \n",
       "1  SUB 3/5 f.S9901026 IEHK2017 part 1        800.0        9.0           2.0   \n",
       "2                                 NaN          NaN        NaN           NaN   \n",
       "3                                 NaN          NaN        NaN           NaN   \n",
       "4                                 NaN          NaN        NaN           NaN   \n",
       "\n",
       "   Paid work hours per day  Humanizer hours  UNICEF hours  Total Hours  \\\n",
       "0                      NaN              NaN           NaN          NaN   \n",
       "1                      7.5             67.5          15.0         82.5   \n",
       "2                      NaN              NaN           NaN          NaN   \n",
       "3                      NaN              NaN           NaN          NaN   \n",
       "4                      NaN              NaN           NaN          NaN   \n",
       "\n",
       "   Hourly rate  humanizer staff in USD  Hourly rate UNICEF staff in USD  \\\n",
       "0                                  NaN                              NaN   \n",
       "1                                25.87                             30.0   \n",
       "2                                  NaN                              NaN   \n",
       "3                                  NaN                              NaN   \n",
       "4                                  NaN                              NaN   \n",
       "\n",
       "   Cost for humanizer for 800 kits   Cost for UNICEF staff for 800 kits  \\\n",
       "0                               NaN                                 NaN   \n",
       "1                          1746.225                               450.0   \n",
       "2                               NaN                                 NaN   \n",
       "3                               NaN                                 NaN   \n",
       "4                               NaN                                 NaN   \n",
       "\n",
       "   Total cost  Hours per kit  Cost for subkit/prepack/Kits  \n",
       "0         NaN            NaN                           NaN  \n",
       "1    2196.225       0.103125                      2.745281  \n",
       "2         NaN            NaN                           NaN  \n",
       "3         NaN            NaN                           NaN  \n",
       "4         NaN            NaN                           NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demand = pd.read_csv(data_path + \"COOIS_Released_Prod_Orders.csv\")\n",
    "demand.head()\n",
    "demand['Material Number'] =demand['Material Number'].str.strip()\n",
    "kit_resource_info['Kit'] = kit_resource_info['Kit'].str.strip()\n",
    "df_merged = pd.merge(demand[['Material Number', 'Order quantity (GMEIN)','Basic finish date']], kit_resource_info, left_on = 'Material Number', right_on = 'Kit', how = 'left')\n",
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "kit_resource_info.columns\n",
    "column_name_cleaning_kit_resource_info = {\n",
    "    'Humanizer': 'Number of humanizer to finish 800 in 7.5 hours',\n",
    "    'UNICEF staff' : 'Number of UNICEF staff to finish 800 in 7.5 hours'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Is the information in the kit_resource_info correct? How is it calculated? Can we discuss how it is calculated?\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ratio = kit_resource_info['UNICEF staff'] / kit_resource_info['Humanizer']\n",
    "\n",
    "plt.hist(ratio, bins=30, edgecolor='black')\n",
    "plt.title(\"Distribution of efficiency ratio of UNICEF staff / Humanizer Ratio\")\n",
    "plt.xlabel(\"Ratio\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()\n",
    "#This data ->  (Lack of confidence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Common values: {'S9999133', 'S9991125', 'S9999143', 'S9991225', 'S9991123', 'S9999137', 'S9999945', 'S9999125', 'S9999124', 'S9999933', 'S9935084', 'S9999979', 'S9935090', 'S9999987', 'S9999986', 'S9991229', 'S9999994', 'S9910004', 'S9991222', 'S9991228', 'S9999974'}\n",
      "Only in kit: {'S9991122', 'S9999923', 'S9993739', 'S9990034', 'S9902221', 'S9999882', 'S9998281', 'S9997733', 'S9997735', 'S9986710', 'S9935100', 'S9999592', 'S9935011', 'S9993730', 'S9901030', 'S9999128', 'S9906801', 'S9996716', 'S9999884', 'S9993843', 'S9906712', 'S9901024', 'S9935019', 'S9986723', 'S9994733', 'S9999995', 'S9935095', 'S9999862', 'S9999898', 'S9997737', 'S9994738', 'S9990041', 'S9991124', 'S9999842', 'S9993734', 'S9908402', 'S9901022', 'S9999502', 'S9999494', 'S9986722', 'S9910002', 'S9910006', 'S9906734', 'S9999132', 'S9993736', 'S9996714', 'S9903005', 'S9998837', 'S9999591', 'S9994737', 'S9999924', 'S9906710', 'S9901023', 'S9996710', 'S9986711', 'S9999136', 'S9910005', 'S9991321', 'S9997734', 'S9999841', 'S9908302', 'S9999126', 'S9993841', 'S9999491', 'S9999493', 'S9935030', 'S9998834', 'S9999134', 'S9986718', 'S9999939', 'S9997738', 'S9902220', 'S9935097', 'S9999147', 'S9993737', 'S9991223', 'S9935034', 'S9935064', 'S9999496', 'S9999858', 'S9986715', 'S9993733', 'S9999144', 'S9999992', 'S9935065', 'S9997736', 'S9999975', 'S9906733', 'S9994734', 'S9998833', 'S9996711', 'S9999129', 'S9906802', 'S9991227', 'S9901027', 'S9999492', 'S9999883', 'S9903001', 'S9998831', 'S9901154', 'S9910001', 'S9991226', 'S9990031', 'S9993842', 'S9901153', 'S9986712', 'S9999938', 'S9998282', 'S9999990', 'S9999501', 'S9990038', 'S9999944', 'S9999859', 'S9999495', 'S9908300', 'S9999993', 'S9994732', 'S9998832', 'S9903000', 'S9994831', 'S9903003', 'S9997739', 'S9998838', 'S9901152', 'S9990032', 'S9999593', 'S9999199', 'S9993732', 'S9990036', 'S9906737', 'S9997730', 'S9999131', 'S9901026', 'S9994735', 'S9999941', 'S9991224', 'S9990037', 'S9986716', 'S9986714', 'S9996713', 'S9993738', 'S9901029', 'S9999138', 'S9999881', 'S9999886', 'S9996712', 'S9999146', 'S9996715', 'S9975020', 'S9991323', 'S9906751', 'S9997731', 'S9903002', 'S9994739', 'S9910003', 'S9986719', 'S9999957', 'S9994736', 'S9993735', 'S9999940', 'S9991221', 'S9991121', 'S9997732', 'S9994730', 'S9935082', 'S9935024', 'S9986713', 'S9998835', 'S9991322', 'S9993731', 'S9935081', 'S9990035', 'S9999857', 'S9906714', 'S9999885', 'S9999899', 'S9998836', 'S9903004', 'S9908305', 'S9901025', 'S9999843', 'S9999500', 'S9999991', 'S9986717', 'S9999145', 'S9999973', 'S9986720', 'S9993840', 'S9999861', 'S9902217', 'S9994731', 'S9999135', 'S9986721', 'S9990042', 'S9908303', 'S9935080', 'S9999942', 'S9901028', 'S9990033', 'S9910000', 'S9999844', 'S9994832'}\n",
      "Only in demand: {'S9992431', 'S9992441', 'S9901044', 'S9901046', 'S9901047', 'S9992458', 'S9902600', 'S9902700', 'S9991045', 'S9992432', 'S9902460', 'S9991042', 'S9901048', 'S9901042', 'S9992413', 'S9999976', 'S9992442', 'S9999989', 'S9992467', 'S9991041', 'S9992457', 'S9992450', 'S9991043', 'S9992583'}\n"
     ]
    }
   ],
   "source": [
    "common_values = set(demand['Material Number']).intersection(set(kit_resource_info['Kit']))\n",
    "only_in_kit = set(kit_resource_info['Kit']) - set(demand['Material Number'])\n",
    "only_in_demand = set(demand['Material Number']) - set(kit_resource_info['Kit'])\n",
    "print(\n",
    "    f\"Common values: {common_values}\\n\"\n",
    "    f\"Only in kit: {only_in_kit}\\n\"\n",
    "    f\"Only in demand: {only_in_demand}\"\n",
    ")\n",
    "\n",
    "#problem1  - some of id only in demand\n",
    "#We can set up database RDB shape - Let's see where the data is from\n",
    "#Inconsistent data naming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged['Basic finish date'] = pd.to_datetime(df_merged['Basic finish date'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "kit_resource_info['humanizer_per_hour_production'] = 800 / (kit_resource_info['Humanizer'] * kit_resource_info['Paid work hours per day'])\n",
    "kit_resource_info['unicef_per_hour_production'] = 800 / (kit_resource_info['UNICEF staff'] * kit_resource_info['Paid work hours per day'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'humanizer_per_hour_production'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'humanizer_per_hour_production'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/t7/ldqv1xt97rs4jyjvhxf4shgw0000gn/T/ipykernel_21503/3288443996.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_merged\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'required_humanizer'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_merged\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order quantity (GMEIN)'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mdf_merged\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'humanizer_per_hour_production'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf_merged\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'required_unicef'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_merged\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order quantity (GMEIN)'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mdf_merged\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'unicef_per_hour_production'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf_merged\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3453\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3454\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3455\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'humanizer_per_hour_production'"
     ]
    }
   ],
   "source": [
    "df_merged['required_humanizer'] = df_merged['Order quantity (GMEIN)'] / df_merged['humanizer_per_hour_production']\n",
    "df_merged['required_unicef'] = df_merged['Order quantity (GMEIN)'] / df_merged['unicef_per_hour_production']\n",
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "grouped = df_merged.groupby('Basic finish date').sum()\n",
    "\n",
    "plt.plot(grouped.index, grouped['required_humanizer'], label='Humanizer')\n",
    "plt.plot(grouped.index, grouped['required_unicef'], label='UNICEF')\n",
    "\n",
    "plt.xlabel('Basic finish date')\n",
    "plt.ylabel('Required labor')\n",
    "plt.title('Labor Requirement Over Time')\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)  # 날짜가 겹치지 않게 회전\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Available UNICEF Staff (Fixed resource)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "available_unicef_staff = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'grouped' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/t7/ldqv1xt97rs4jyjvhxf4shgw0000gn/T/ipykernel_21503/475179701.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mshortfall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mgrouped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'required_unicef'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mavailable_unicef_staff\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Shortfall of UNICEF staff'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mshortfall\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'line'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Basic finish date'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mylabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Shortfall'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'grouped' is not defined"
     ]
    }
   ],
   "source": [
    "shortfall = (grouped['required_unicef'] - available_unicef_staff).clip(lower=0)\n",
    "plt.figure()\n",
    "plt.title('Shortfall of UNICEF staff')\n",
    "shortfall.plot(kind='line', xlabel='Basic finish date', ylabel='Shortfall')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bottleneck due to Inventory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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>Work Area</th>\n",
       "      <th>Per hour capacity</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Receiving</td>\n",
       "      <td>73.0</td>\n",
       "      <td>pallets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shipping</td>\n",
       "      <td>94.0</td>\n",
       "      <td>pallets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Depalletizing station</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pre-pack lines</td>\n",
       "      <td>300.0</td>\n",
       "      <td>cartons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pre-pick station</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LONG_LINE</td>\n",
       "      <td>300.0</td>\n",
       "      <td>cartons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SHORT_LINE</td>\n",
       "      <td>54.0</td>\n",
       "      <td>pallets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>To not be considered currently</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Work Area  Per hour capacity     Unit\n",
       "0                       Receiving               73.0  pallets\n",
       "1                        Shipping               94.0  pallets\n",
       "2           Depalletizing station                NaN      NaN\n",
       "3                  Pre-pack lines              300.0  cartons\n",
       "4                Pre-pick station                NaN      NaN\n",
       "5                       LONG_LINE              300.0  cartons\n",
       "6                      SHORT_LINE               54.0  pallets\n",
       "7                             NaN                NaN      NaN\n",
       "8  To not be considered currently                NaN      NaN"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workcenter_capa = pd.read_csv(data_path+\"Work_Centre_Capacity.csv\")\n",
    "workcenter_capa\n",
    "\n",
    "capacity_info = workcenter_capa['Capacity Campus 2'].str.extract(r'(?P<capacity>\\d+)\\s*(?P<unit>\\w+)')\n",
    "capacity_info['capacity'] = capacity_info['capacity'].astype(float)\n",
    "workcenter_capa['Per hour capacity'] = capacity_info['capacity']\n",
    "workcenter_capa['Unit'] = capacity_info['unit']\n",
    "workcenter_capa[['Work Area', 'Per hour capacity', 'Unit']]\n",
    "\n",
    "#Need to clarify on the data (work area naming)\n",
    "column_pair = {'Short / flex lines':'SHORT_LINE', 'Long lines':'LONG_LINE'}\n",
    "workcenter_capa['Work Area'] = workcenter_capa['Work Area'].replace(column_pair)\n",
    "workcenter_capa[['Work Area', 'Per hour capacity', 'Unit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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>Work Area</th>\n",
       "      <th>Capacity Campus 2</th>\n",
       "      <th>Pallets per hour</th>\n",
       "      <th>Cartons per hour</th>\n",
       "      <th>Per hour capacity</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Total_line_count_fake</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Receiving</td>\n",
       "      <td>73 pallets per hour</td>\n",
       "      <td>73.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.0</td>\n",
       "      <td>pallets</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shipping</td>\n",
       "      <td>94 pallets per hour</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "      <td>pallets</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Depalletizing station</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pre-pack lines</td>\n",
       "      <td>300 cartons per hour</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>cartons</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pre-pick station</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LONG_LINE</td>\n",
       "      <td>300 cartons per hour</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>cartons</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SHORT_LINE</td>\n",
       "      <td>54 pallets per hour</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>pallets</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>To not be considered currently</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Work Area     Capacity Campus 2  Pallets per hour  \\\n",
       "0                       Receiving   73 pallets per hour              73.0   \n",
       "1                        Shipping   94 pallets per hour              94.0   \n",
       "2           Depalletizing station                   NaN               NaN   \n",
       "3                  Pre-pack lines  300 cartons per hour               NaN   \n",
       "4                Pre-pick station                   NaN               NaN   \n",
       "5                       LONG_LINE  300 cartons per hour               NaN   \n",
       "6                      SHORT_LINE   54 pallets per hour              54.0   \n",
       "7                             NaN                   NaN               NaN   \n",
       "8  To not be considered currently                   NaN               NaN   \n",
       "\n",
       "   Cartons per hour  Per hour capacity     Unit Total_line_count_fake  \n",
       "0               NaN               73.0  pallets                     1  \n",
       "1               NaN               94.0  pallets                     2  \n",
       "2               NaN                NaN      NaN                     3  \n",
       "3             300.0              300.0  cartons                     4  \n",
       "4               NaN                NaN      NaN                     5  \n",
       "5             300.0              300.0  cartons                     6  \n",
       "6               NaN               54.0  pallets                     7  \n",
       "7               NaN                NaN      NaN                   Nan  \n",
       "8               NaN                NaN      NaN                   Nan  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Need info on how many work area we have\n",
    "\n",
    "supply_unit_info=pd.DataFrame([\n",
    "    {\"Unit\": \"pallets\", \"Per_hour_capacity_fake\": 100},\n",
    "    {\"Unit\": \"cartons\", \"Per_hour_capacity_fake\": 1000}])\n",
    "\n",
    "workcenter_capa['Total_line_count_fake'] = [1, 2, 3, 4, 5, 6, 7, \"Nan\", \"Nan\"]\n",
    "workcenter_capa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SHORT_LINE', 'LONG_LINE', 'MINILOAD', 'VIRTUAL_PACKING',\n",
       "       'BAGGING_LINE'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "Material_master = pd.read_csv(data_path + \"Material_Master_WMS.csv\")\n",
    "Material_master.head()\n",
    "\n",
    "Material_master.columns\n",
    "\n",
    "Material_master['Material Number'] = Material_master['Material Number'].str.strip()\n",
    "Material_master['KIT Routing'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Max product per day due to work center capacity => Number of days required minimum => labor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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