optimize_manufacturing_level.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c9ed9f87",
   "metadata": {},
   "source": [
    "## The TwoPlus Earbuds Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b9e52b8",
   "metadata": {},
   "source": [
    "### Group 10: Dean Criscitiello, Daylyn Mosher, Tom Lyster, Amaarah Rahman, Franklin Tan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be3695e",
   "metadata": {},
   "source": [
    "#### Importing Numpy and Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f63e04e",
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c87ded",
   "metadata": {},
   "source": [
    "#### Function for random demand - Normal Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7ee43e50",
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def generate_random_demand_normal(mean, stddev, size):\n",
    "    \"\"\"\n",
    "    Generates a random sample of demand instances from a normal (Gaussian) distribution.\n",
    "    \"\"\"\n",
    "    demand = np.random.normal(mean,stddev,size)\n",
    "    for i in range(size):\n",
    "        demand[i] = round(demand[i],4)\n",
    "    return demand\n",
    "\n",
    "# print(generate_random_demand_normal(150,20,1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "424e850e",
   "metadata": {},
   "source": [
    "#### Function for random demand - Uniform Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "570399fa",
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def generate_random_demand_uniform(low, high, size):\n",
    "    \"\"\"\n",
    "    Generates a random sample of demand instances from a uniform distribution.\n",
    "    \"\"\"\n",
    "    demand = np.random.uniform(low,high,size)\n",
    "    for i in range(size):\n",
    "        demand[i] = round(demand[i],4)\n",
    "    return demand\n",
    "\n",
    "# print(generate_random_demand_uniform(100,200,1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb96166",
   "metadata": {},
   "source": [
    "#### Iterates through the demand list and calculates the average at a specific manufacture level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "44309406",
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def calc_optimal_production(mean, stddev, size, retail_price, production_cost, disposal_cost, units_manufactured, dist, low, high):\n",
    "    \"\"\"\n",
    "    Uses inputs to calculate the optimal production level given randomly generated demand\n",
    "    \"\"\"\n",
    "    \n",
    "    # User has to specify if they want a normal or uniform demand distribution\n",
    "    if dist == \"n\":\n",
    "        demand = generate_random_demand_normal(mean, stddev, size)\n",
    "    elif dist == \"u\":\n",
    "        demand = generate_random_demand_uniform(low, high, size)\n",
    "        \n",
    "    profit = retail_price - production_cost\n",
    "    profit_dict = {}\n",
    "\n",
    "    for x in range(size):\n",
    "        # Scenario where too many units are manufactured/higher than demand and results in loss from unsold units + disposal cost\n",
    "        if demand[x] < units_manufactured:\n",
    "            loss_units = units_manufactured - demand[x]\n",
    "            profit_dict[demand[x]] = (demand[x] * profit) - (loss_units * (production_cost + disposal_cost))\n",
    "\n",
    "        # Scenario where too little units are manufactured/lower than demand and results in loss profits from customers that want to buy\n",
    "        elif demand[x] > units_manufactured:\n",
    "            loss_customers = demand[x] - units_manufactured\n",
    "            profit_dict[demand[x]] = (units_manufactured * profit) - (loss_customers * (retail_price - production_cost))\n",
    "\n",
    "        else:\n",
    "        # Scenario where demand and units manufactured are equal\n",
    "            profit_dict[demand[x]] = (units_manufactured * profit)\n",
    "    # print(profit_dict) # Checking what the profit dictionary looks like\n",
    "    print(str(units_manufactured) + \" units manufactured\")\n",
    "\n",
    "    # Calculate mean profit of the production level\n",
    "    sum = 0\n",
    "    for val in profit_dict.values():\n",
    "        sum += val\n",
    "    result = round(sum / len(profit_dict),2)\n",
    "    print(\"AVG: \" + str(result))\n",
    "\n",
    "    # Calculate standard deviation of the profit\n",
    "    profit_list = []\n",
    "    for val in profit_dict.values():\n",
    "        profit_list.append(val)\n",
    "    std = round(np.std(profit_list),2)\n",
    "    print(\"STD: \" + str(std))\n",
    "    print(\"===========================\")\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "038ac173",
   "metadata": {},
   "source": [
    "#### Outputs a recommendation to the user as well as a scatterplot after running a specifed range of simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d8345162",
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def run_simulation(min_lvl, max_lvl, mean, stddev, size, retail_price, production_cost, disposal_cost, dist, low, high):\n",
    "    \"\"\"\n",
    "    Simulation run - finds optimal production level by calculating average profit given a demand at various manufacturing levels\n",
    "    Plots results on a graph and prints the optimal number of units to manufacture.\n",
    "    \"\"\"\n",
    "    x_manufacture_level = []\n",
    "    y_profit = []\n",
    "    for i in range(min_lvl, max_lvl):\n",
    "        x_manufacture_level.append(i)\n",
    "        y_profit.append(calc_optimal_production(mean, stddev, size, retail_price, production_cost, disposal_cost, i, dist, low, high))\n",
    "\n",
    "    # Print out the recommendation to the user\n",
    "    optimal_value_profit = max(y_profit)\n",
    "    optimal_number = x_manufacture_level[y_profit.index(optimal_value_profit)]\n",
    "    print(\"Recommendation: \" + str(optimal_number) + \" units yields an average profit of \" + \"$\" + str(optimal_value_profit))\n",
    "\n",
    "    # Plotting using Matplotlib\n",
    "    plt.title(\"Manufacturing Level Simulation\", loc = 'left', fontsize = 20)\n",
    "    plt.xlabel('Manufacture Level')\n",
    "    plt.ylabel('Profit')\n",
    "    plt.xticks(np.arange(min(x_manufacture_level), max(x_manufacture_level)+1, 10.0))\n",
    "    sctr = plt.scatter(x_manufacture_level, y_profit, c=y_profit, cmap='RdYlBu')\n",
    "    plt.colorbar(sctr, format='$%d')\n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d06ad292",
   "metadata": {},
   "source": [
    "#### Allows user input to be taken to run the functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b7daf2d5",
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def run_program():\n",
    "    min_lvl = int(input(\"Enter the lowest manufacturing level you want to simulate: \"))\n",
    "    max_lvl = int(input(\"Enter the highest manufacturing level you want to simulate: \"))\n",
    "    mean = float(input(\"Enter the mean: \"))\n",
    "    stddev = float(input(\"Enter the standard deviation: \"))\n",
    "    size = int(input(\"Enter the number of demand simulations you want to run: \"))\n",
    "    retail_price = float(input(\"Enter the retail price: \"))\n",
    "    production_cost = float(input(\"Enter the production cost: \"))\n",
    "    disposal_cost = float(input(\"Enter the disposal cost: \"))\n",
    "    dist = input(\"Enter the 'n' for normal distribution or 'u' for uniform distribution: \")\n",
    "    low = int(input(\"Enter the low only for uniform distribution. Enter 0 for normal distribution:  \"))\n",
    "    high = int(input(\"Enter the high only for uniform distribution. Enter 0 for normal distribution: \"))\n",
    "\n",
    "    run_simulation(min_lvl, max_lvl, mean, stddev, size, retail_price, production_cost, disposal_cost, dist, low, high)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4070fbfa",
   "metadata": {},
   "source": [
    "##### Running simulation with normal distribution - How many earbuds will you manufacture and why? Please restrict your answer to at most 50 words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "480c8c1e",
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the lowest manufacturing level you want to simulate: 140\n",
      "Enter the highest manufacturing level you want to simulate: 200\n",
      "Enter the mean: 150\n",
      "Enter the standard deviation: 20\n",
      "Enter the number of demand simulations you want to run: 1000\n",
      "Enter the retail price: 150\n",
      "Enter the production cost: 28.5\n",
      "Enter the disposal cost: 8.5\n",
      "Enter the 'n' for normal distribution or 'u' for uniform distribution: n\n",
      "Enter the low only for uniform distribution. Enter 0 for normal distribution:  0\n",
      "Enter the high only for uniform distribution. Enter 0 for normal distribution: 0\n",
      "140 units manufactured\n",
      "AVG: 14630.2\n",
      "STD: 1738.81\n",
      "===========================\n",
      "141 units manufactured\n",
      "AVG: 14772.46\n",
      "STD: 1709.24\n",
      "===========================\n",
      "142 units manufactured\n",
      "AVG: 14967.46\n",
      "STD: 1689.16\n",
      "===========================\n",
      "143 units manufactured\n",
      "AVG: 15153.4\n",
      "STD: 1657.44\n",
      "===========================\n",
      "144 units manufactured\n",
      "AVG: 15233.43\n",
      "STD: 1692.54\n",
      "===========================\n",
      "145 units manufactured\n",
      "AVG: 15356.08\n",
      "STD: 1736.01\n",
      "===========================\n",
      "146 units manufactured\n",
      "AVG: 15440.11\n",
      "STD: 1704.41\n",
      "===========================\n",
      "147 units manufactured\n",
      "AVG: 15619.96\n",
      "STD: 1664.65\n",
      "===========================\n",
      "148 units manufactured\n",
      "AVG: 15781.5\n",
      "STD: 1691.93\n",
      "===========================\n",
      "149 units manufactured\n",
      "AVG: 15890.46\n",
      "STD: 1714.13\n",
      "===========================\n",
      "150 units manufactured\n",
      "AVG: 16182.39\n",
      "STD: 1681.14\n",
      "===========================\n",
      "151 units manufactured\n",
      "AVG: 16030.54\n",
      "STD: 1817.93\n",
      "===========================\n",
      "152 units manufactured\n",
      "AVG: 16145.99\n",
      "STD: 1801.67\n",
      "===========================\n",
      "153 units manufactured\n",
      "AVG: 16272.12\n",
      "STD: 1764.96\n",
      "===========================\n",
      "154 units manufactured\n",
      "AVG: 16340.57\n",
      "STD: 1837.73\n",
      "===========================\n",
      "155 units manufactured\n",
      "AVG: 16524.92\n",
      "STD: 1813.25\n",
      "===========================\n",
      "156 units manufactured\n",
      "AVG: 16570.74\n",
      "STD: 1897.87\n",
      "===========================\n",
      "157 units manufactured\n",
      "AVG: 16498.32\n",
      "STD: 1977.16\n",
      "===========================\n",
      "158 units manufactured\n",
      "AVG: 16670.56\n",
      "STD: 1935.61\n",
      "===========================\n",
      "159 units manufactured\n",
      "AVG: 16700.31\n",
      "STD: 2041.93\n",
      "===========================\n",
      "160 units manufactured\n",
      "AVG: 16702.52\n",
      "STD: 2164.88\n",
      "===========================\n",
      "161 units manufactured\n",
      "AVG: 16749.89\n",
      "STD: 2240.68\n",
      "===========================\n",
      "162 units manufactured\n",
      "AVG: 16831.75\n",
      "STD: 2124.59\n",
      "===========================\n",
      "163 units manufactured\n",
      "AVG: 16825.94\n",
      "STD: 2263.12\n",
      "===========================\n",
      "164 units manufactured\n",
      "AVG: 16826.29\n",
      "STD: 2308.74\n",
      "===========================\n",
      "165 units manufactured\n",
      "AVG: 16892.86\n",
      "STD: 2424.89\n",
      "===========================\n",
      "166 units manufactured\n",
      "AVG: 17012.81\n",
      "STD: 2301.56\n",
      "===========================\n",
      "167 units manufactured\n",
      "AVG: 16892.97\n",
      "STD: 2495.29\n",
      "===========================\n",
      "168 units manufactured\n",
      "AVG: 16915.74\n",
      "STD: 2529.35\n",
      "===========================\n",
      "169 units manufactured\n",
      "AVG: 16948.1\n",
      "STD: 2490.12\n",
      "===========================\n",
      "170 units manufactured\n",
      "AVG: 17051.43\n",
      "STD: 2647.7\n",
      "===========================\n",
      "171 units manufactured\n",
      "AVG: 17049.62\n",
      "STD: 2578.45\n",
      "===========================\n",
      "172 units manufactured\n",
      "AVG: 17059.24\n",
      "STD: 2668.68\n",
      "===========================\n",
      "173 units manufactured\n",
      "AVG: 17038.56\n",
      "STD: 2695.98\n",
      "===========================\n",
      "174 units manufactured\n",
      "AVG: 16935.28\n",
      "STD: 2615.24\n",
      "===========================\n",
      "175 units manufactured\n",
      "AVG: 17002.71\n",
      "STD: 2765.53\n",
      "===========================\n",
      "176 units manufactured\n",
      "AVG: 16849.48\n",
      "STD: 2798.25\n",
      "===========================\n",
      "177 units manufactured\n",
      "AVG: 16906.02\n",
      "STD: 2877.14\n",
      "===========================\n",
      "178 units manufactured\n",
      "AVG: 17003.54\n",
      "STD: 2921.48\n",
      "===========================\n",
      "179 units manufactured\n",
      "AVG: 16888.07\n",
      "STD: 2886.28\n",
      "===========================\n",
      "180 units manufactured\n",
      "AVG: 17033.32\n",
      "STD: 2857.85\n",
      "===========================\n",
      "181 units manufactured\n",
      "AVG: 17009.24\n",
      "STD: 2966.68\n",
      "===========================\n",
      "182 units manufactured\n",
      "AVG: 16986.23\n",
      "STD: 2918.95\n",
      "===========================\n",
      "183 units manufactured\n",
      "AVG: 16997.28\n",
      "STD: 2946.15\n",
      "===========================\n",
      "184 units manufactured\n",
      "AVG: 16766.36\n",
      "STD: 2958.54\n",
      "===========================\n",
      "185 units manufactured\n",
      "AVG: 16994.85\n",
      "STD: 2961.13\n",
      "===========================\n",
      "186 units manufactured\n",
      "AVG: 16863.69\n",
      "STD: 2774.05\n",
      "===========================\n",
      "187 units manufactured\n",
      "AVG: 16664.36\n",
      "STD: 3065.02\n",
      "===========================\n",
      "188 units manufactured\n",
      "AVG: 16702.7\n",
      "STD: 3035.38\n",
      "===========================\n",
      "189 units manufactured\n",
      "AVG: 16713.4\n",
      "STD: 3134.31\n",
      "===========================\n",
      "190 units manufactured\n",
      "AVG: 16753.4\n",
      "STD: 3051.54\n",
      "===========================\n",
      "191 units manufactured\n",
      "AVG: 16587.51\n",
      "STD: 3089.66\n",
      "===========================\n",
      "192 units manufactured\n",
      "AVG: 16590.46\n",
      "STD: 3093.61\n",
      "===========================\n",
      "193 units manufactured\n",
      "AVG: 16382.85\n",
      "STD: 3185.59\n",
      "===========================\n",
      "194 units manufactured\n",
      "AVG: 16540.34\n",
      "STD: 3106.48\n",
      "===========================\n",
      "195 units manufactured\n",
      "AVG: 16571.02\n",
      "STD: 3178.97\n",
      "===========================\n",
      "196 units manufactured\n",
      "AVG: 16329.37\n",
      "STD: 3159.74\n",
      "===========================\n",
      "197 units manufactured\n",
      "AVG: 16512.69\n",
      "STD: 3151.25\n",
      "===========================\n",
      "198 units manufactured\n",
      "AVG: 16356.84\n",
      "STD: 2992.12\n",
      "===========================\n",
      "199 units manufactured\n",
      "AVG: 16349.23\n",
      "STD: 3180.11\n",
      "===========================\n",
      "Recommendation: 172 units yields an average profit of $17059.24\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_program()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e84d6d9",
   "metadata": {},
   "source": [
    "##### Running simulation with normal distribution - Your policy analysts report that there is a 50% chance of the government imposing additional taxes on the disposal of electronic waste, which will increase the cost of disposing of unsold earbuds to $17."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f0196da4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the lowest manufacturing level you want to simulate: 130\n",
      "Enter the highest manufacturing level you want to simulate: 200\n",
      "Enter the mean: 150\n",
      "Enter the standard deviation: 20\n",
      "Enter the number of demand simulations you want to run: 1000\n",
      "Enter the retail price: 150\n",
      "Enter the production cost: 28.5\n",
      "Enter the disposal cost: 17\n",
      "Enter the 'n' for normal distribution or 'u' for uniform distribution: n\n",
      "Enter the low only for uniform distribution. Enter 0 for normal distribution:  0\n",
      "Enter the high only for uniform distribution. Enter 0 for normal distribution: 0\n",
      "130 units manufactured\n",
      "AVG: 12857.41\n",
      "STD: 1968.59\n",
      "===========================\n",
      "131 units manufactured\n",
      "AVG: 13083.59\n",
      "STD: 1844.08\n",
      "===========================\n",
      "132 units manufactured\n",
      "AVG: 13241.48\n",
      "STD: 1895.48\n",
      "===========================\n",
      "133 units manufactured\n",
      "AVG: 13526.37\n",
      "STD: 1825.22\n",
      "===========================\n",
      "134 units manufactured\n",
      "AVG: 13669.43\n",
      "STD: 1798.52\n",
      "===========================\n",
      "135 units manufactured\n",
      "AVG: 13812.7\n",
      "STD: 1792.97\n",
      "===========================\n",
      "136 units manufactured\n",
      "AVG: 13990.69\n",
      "STD: 1801.11\n",
      "===========================\n",
      "137 units manufactured\n",
      "AVG: 14212.0\n",
      "STD: 1746.69\n",
      "===========================\n",
      "138 units manufactured\n",
      "AVG: 14438.76\n",
      "STD: 1740.07\n",
      "===========================\n",
      "139 units manufactured\n",
      "AVG: 14514.19\n",
      "STD: 1672.48\n",
      "===========================\n",
      "140 units manufactured\n",
      "AVG: 14608.56\n",
      "STD: 1752.16\n",
      "===========================\n",
      "141 units manufactured\n",
      "AVG: 14788.56\n",
      "STD: 1646.01\n",
      "===========================\n",
      "142 units manufactured\n",
      "AVG: 14876.12\n",
      "STD: 1735.63\n",
      "===========================\n",
      "143 units manufactured\n",
      "AVG: 15080.6\n",
      "STD: 1670.39\n",
      "===========================\n",
      "144 units manufactured\n",
      "AVG: 15282.46\n",
      "STD: 1657.05\n",
      "===========================\n",
      "145 units manufactured\n",
      "AVG: 15329.81\n",
      "STD: 1740.16\n",
      "===========================\n",
      "146 units manufactured\n",
      "AVG: 15457.64\n",
      "STD: 1696.9\n",
      "===========================\n",
      "147 units manufactured\n",
      "AVG: 15621.57\n",
      "STD: 1786.64\n",
      "===========================\n",
      "148 units manufactured\n",
      "AVG: 15718.41\n",
      "STD: 1764.37\n",
      "===========================\n",
      "149 units manufactured\n",
      "AVG: 15928.11\n",
      "STD: 1679.46\n",
      "===========================\n",
      "150 units manufactured\n",
      "AVG: 15922.27\n",
      "STD: 1837.56\n",
      "===========================\n",
      "151 units manufactured\n",
      "AVG: 16108.84\n",
      "STD: 1655.45\n",
      "===========================\n",
      "152 units manufactured\n",
      "AVG: 16045.0\n",
      "STD: 1848.34\n",
      "===========================\n",
      "153 units manufactured\n",
      "AVG: 16280.41\n",
      "STD: 1784.21\n",
      "===========================\n",
      "154 units manufactured\n",
      "AVG: 16290.46\n",
      "STD: 1889.66\n",
      "===========================\n",
      "155 units manufactured\n",
      "AVG: 16279.32\n",
      "STD: 1995.39\n",
      "===========================\n",
      "156 units manufactured\n",
      "AVG: 16368.14\n",
      "STD: 2030.03\n",
      "===========================\n",
      "157 units manufactured\n",
      "AVG: 16455.69\n",
      "STD: 2059.14\n",
      "===========================\n",
      "158 units manufactured\n",
      "AVG: 16572.1\n",
      "STD: 2124.29\n",
      "===========================\n",
      "159 units manufactured\n",
      "AVG: 16646.94\n",
      "STD: 2133.94\n",
      "===========================\n",
      "160 units manufactured\n",
      "AVG: 16614.89\n",
      "STD: 2192.77\n",
      "===========================\n",
      "161 units manufactured\n",
      "AVG: 16653.37\n",
      "STD: 2298.24\n",
      "===========================\n",
      "162 units manufactured\n",
      "AVG: 16725.87\n",
      "STD: 2280.0\n",
      "===========================\n",
      "163 units manufactured\n",
      "AVG: 16768.16\n",
      "STD: 2362.7\n",
      "===========================\n",
      "164 units manufactured\n",
      "AVG: 16719.05\n",
      "STD: 2432.97\n",
      "===========================\n",
      "165 units manufactured\n",
      "AVG: 16943.64\n",
      "STD: 2447.21\n",
      "===========================\n",
      "166 units manufactured\n",
      "AVG: 16870.03\n",
      "STD: 2466.56\n",
      "===========================\n",
      "167 units manufactured\n",
      "AVG: 16639.5\n",
      "STD: 2610.84\n",
      "===========================\n",
      "168 units manufactured\n",
      "AVG: 16814.01\n",
      "STD: 2527.47\n",
      "===========================\n",
      "169 units manufactured\n",
      "AVG: 16886.88\n",
      "STD: 2665.33\n",
      "===========================\n",
      "170 units manufactured\n",
      "AVG: 16900.52\n",
      "STD: 2538.17\n",
      "===========================\n",
      "171 units manufactured\n",
      "AVG: 16920.16\n",
      "STD: 2773.39\n",
      "===========================\n",
      "172 units manufactured\n",
      "AVG: 16788.53\n",
      "STD: 2848.88\n",
      "===========================\n",
      "173 units manufactured\n",
      "AVG: 16906.16\n",
      "STD: 2742.68\n",
      "===========================\n",
      "174 units manufactured\n",
      "AVG: 16870.9\n",
      "STD: 2817.67\n",
      "===========================\n",
      "175 units manufactured\n",
      "AVG: 16749.73\n",
      "STD: 2911.36\n",
      "===========================\n",
      "176 units manufactured\n",
      "AVG: 16765.59\n",
      "STD: 2948.36\n",
      "===========================\n",
      "177 units manufactured\n",
      "AVG: 16827.32\n",
      "STD: 2988.7\n",
      "===========================\n",
      "178 units manufactured\n",
      "AVG: 16639.51\n",
      "STD: 3071.29\n",
      "===========================\n",
      "179 units manufactured\n",
      "AVG: 16711.96\n",
      "STD: 3015.54\n",
      "===========================\n",
      "180 units manufactured\n",
      "AVG: 16770.35\n",
      "STD: 3028.41\n",
      "===========================\n",
      "181 units manufactured\n",
      "AVG: 16678.19\n",
      "STD: 3176.58\n",
      "===========================\n",
      "182 units manufactured\n",
      "AVG: 16534.75\n",
      "STD: 3107.54\n",
      "===========================\n",
      "183 units manufactured\n",
      "AVG: 16509.02\n",
      "STD: 3080.45\n",
      "===========================\n",
      "184 units manufactured\n",
      "AVG: 16645.09\n",
      "STD: 3141.09\n",
      "===========================\n",
      "185 units manufactured\n",
      "AVG: 16541.36\n",
      "STD: 3046.16\n",
      "===========================\n",
      "186 units manufactured\n",
      "AVG: 16649.34\n",
      "STD: 3082.59\n",
      "===========================\n",
      "187 units manufactured\n",
      "AVG: 16310.69\n",
      "STD: 3246.4\n",
      "===========================\n",
      "188 units manufactured\n",
      "AVG: 16405.97\n",
      "STD: 3075.79\n",
      "===========================\n",
      "189 units manufactured\n",
      "AVG: 16272.43\n",
      "STD: 3322.14\n",
      "===========================\n",
      "190 units manufactured\n",
      "AVG: 16306.18\n",
      "STD: 3158.91\n",
      "===========================\n",
      "191 units manufactured\n",
      "AVG: 16168.06\n",
      "STD: 3247.78\n",
      "===========================\n",
      "192 units manufactured\n",
      "AVG: 16085.69\n",
      "STD: 3209.5\n",
      "===========================\n",
      "193 units manufactured\n",
      "AVG: 16222.14\n",
      "STD: 3230.59\n",
      "===========================\n",
      "194 units manufactured\n",
      "AVG: 16175.12\n",
      "STD: 3369.52\n",
      "===========================\n",
      "195 units manufactured\n",
      "AVG: 16214.39\n",
      "STD: 3256.69\n",
      "===========================\n",
      "196 units manufactured\n",
      "AVG: 16127.65\n",
      "STD: 3373.36\n",
      "===========================\n",
      "197 units manufactured\n",
      "AVG: 15875.44\n",
      "STD: 3189.33\n",
      "===========================\n",
      "198 units manufactured\n",
      "AVG: 15981.0\n",
      "STD: 3332.64\n",
      "===========================\n",
      "199 units manufactured\n",
      "AVG: 15818.13\n",
      "STD: 3342.93\n",
      "===========================\n",
      "Recommendation: 165 units yields an average profit of $16943.64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_program()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a27db62",
   "metadata": {},
   "source": [
    "##### Running simulation with uniform distribution - Your business analyst says the forecasted demand for the earbuds will be normally distributed with mean 150 and standard deviation 20 with probability 50% and uniformly distributed between 100 to 200 with probability 50%. How many earbuds would you manufacture now?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "27d15271",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the lowest manufacturing level you want to simulate: 140\n",
      "Enter the highest manufacturing level you want to simulate: 200\n",
      "Enter the mean: 150\n",
      "Enter the standard deviation: 20\n",
      "Enter the number of demand simulations you want to run: 1000\n",
      "Enter the retail price: 150\n",
      "Enter the production cost: 28.5\n",
      "Enter the disposal cost: 8.5\n",
      "Enter the 'n' for normal distribution or 'u' for uniform distribution: u\n",
      "Enter the low only for uniform distribution. Enter 0 for normal distribution:  100\n",
      "Enter the high only for uniform distribution. Enter 0 for normal distribution: 200\n",
      "140 units manufactured\n",
      "AVG: 13520.84\n",
      "STD: 1946.26\n",
      "===========================\n",
      "141 units manufactured\n",
      "AVG: 13783.0\n",
      "STD: 2000.21\n",
      "===========================\n",
      "142 units manufactured\n",
      "AVG: 13773.39\n",
      "STD: 2028.42\n",
      "===========================\n",
      "143 units manufactured\n",
      "AVG: 13920.22\n",
      "STD: 1998.55\n",
      "===========================\n",
      "144 units manufactured\n",
      "AVG: 13989.49\n",
      "STD: 2000.87\n",
      "===========================\n",
      "145 units manufactured\n",
      "AVG: 14351.26\n",
      "STD: 1997.17\n",
      "===========================\n",
      "146 units manufactured\n",
      "AVG: 14301.02\n",
      "STD: 1987.97\n",
      "===========================\n",
      "147 units manufactured\n",
      "AVG: 14410.46\n",
      "STD: 1999.82\n",
      "===========================\n",
      "148 units manufactured\n",
      "AVG: 14574.73\n",
      "STD: 2074.98\n",
      "===========================\n",
      "149 units manufactured\n",
      "AVG: 14576.04\n",
      "STD: 2092.01\n",
      "===========================\n",
      "150 units manufactured\n",
      "AVG: 14602.41\n",
      "STD: 2162.51\n",
      "===========================\n",
      "151 units manufactured\n",
      "AVG: 14809.58\n",
      "STD: 2133.89\n",
      "===========================\n",
      "152 units manufactured\n",
      "AVG: 14882.17\n",
      "STD: 2201.15\n",
      "===========================\n",
      "153 units manufactured\n",
      "AVG: 15021.12\n",
      "STD: 2195.46\n",
      "===========================\n",
      "154 units manufactured\n",
      "AVG: 15138.29\n",
      "STD: 2226.86\n",
      "===========================\n",
      "155 units manufactured\n",
      "AVG: 15307.58\n",
      "STD: 2318.62\n",
      "===========================\n",
      "156 units manufactured\n",
      "AVG: 15158.11\n",
      "STD: 2409.33\n",
      "===========================\n",
      "157 units manufactured\n",
      "AVG: 15288.93\n",
      "STD: 2427.16\n",
      "===========================\n",
      "158 units manufactured\n",
      "AVG: 15499.66\n",
      "STD: 2445.76\n",
      "===========================\n",
      "159 units manufactured\n",
      "AVG: 15643.88\n",
      "STD: 2490.55\n",
      "===========================\n",
      "160 units manufactured\n",
      "AVG: 15637.46\n",
      "STD: 2542.06\n",
      "===========================\n",
      "161 units manufactured\n",
      "AVG: 15721.11\n",
      "STD: 2584.53\n",
      "===========================\n",
      "162 units manufactured\n",
      "AVG: 15577.95\n",
      "STD: 2690.18\n",
      "===========================\n",
      "163 units manufactured\n",
      "AVG: 15792.16\n",
      "STD: 2737.13\n",
      "===========================\n",
      "164 units manufactured\n",
      "AVG: 15979.57\n",
      "STD: 2754.23\n",
      "===========================\n",
      "165 units manufactured\n",
      "AVG: 15915.77\n",
      "STD: 2828.87\n",
      "===========================\n",
      "166 units manufactured\n",
      "AVG: 15923.64\n",
      "STD: 2971.24\n",
      "===========================\n",
      "167 units manufactured\n",
      "AVG: 16324.3\n",
      "STD: 2908.17\n",
      "===========================\n",
      "168 units manufactured\n",
      "AVG: 16170.94\n",
      "STD: 3136.51\n",
      "===========================\n",
      "169 units manufactured\n",
      "AVG: 16191.57\n",
      "STD: 3159.67\n",
      "===========================\n",
      "170 units manufactured\n",
      "AVG: 16103.46\n",
      "STD: 3168.01\n",
      "===========================\n",
      "171 units manufactured\n",
      "AVG: 16371.25\n",
      "STD: 3260.12\n",
      "===========================\n",
      "172 units manufactured\n",
      "AVG: 16194.42\n",
      "STD: 3355.81\n",
      "===========================\n",
      "173 units manufactured\n",
      "AVG: 16205.96\n",
      "STD: 3463.56\n",
      "===========================\n",
      "174 units manufactured\n",
      "AVG: 16432.24\n",
      "STD: 3546.27\n",
      "===========================\n",
      "175 units manufactured\n",
      "AVG: 16516.38\n",
      "STD: 3578.95\n",
      "===========================\n",
      "176 units manufactured\n",
      "AVG: 16180.04\n",
      "STD: 3648.57\n",
      "===========================\n",
      "177 units manufactured\n",
      "AVG: 16349.44\n",
      "STD: 3748.62\n",
      "===========================\n",
      "178 units manufactured\n",
      "AVG: 16457.51\n",
      "STD: 3796.36\n",
      "===========================\n",
      "179 units manufactured\n",
      "AVG: 16514.97\n",
      "STD: 3892.22\n",
      "===========================\n",
      "180 units manufactured\n",
      "AVG: 16622.66\n",
      "STD: 3835.45\n",
      "===========================\n",
      "181 units manufactured\n",
      "AVG: 16478.46\n",
      "STD: 3952.68\n",
      "===========================\n",
      "182 units manufactured\n",
      "AVG: 16592.93\n",
      "STD: 3944.52\n",
      "===========================\n",
      "183 units manufactured\n",
      "AVG: 16610.2\n",
      "STD: 4029.78\n",
      "===========================\n",
      "184 units manufactured\n",
      "AVG: 16461.93\n",
      "STD: 4099.05\n",
      "===========================\n",
      "185 units manufactured\n",
      "AVG: 16766.49\n",
      "STD: 4147.92\n",
      "===========================\n",
      "186 units manufactured\n",
      "AVG: 16366.88\n",
      "STD: 4184.26\n",
      "===========================\n",
      "187 units manufactured\n",
      "AVG: 16871.38\n",
      "STD: 4184.86\n",
      "===========================\n",
      "188 units manufactured\n",
      "AVG: 16603.95\n",
      "STD: 4228.68\n",
      "===========================\n",
      "189 units manufactured\n",
      "AVG: 16717.43\n",
      "STD: 4224.02\n",
      "===========================\n",
      "190 units manufactured\n",
      "AVG: 16776.19\n",
      "STD: 4399.35\n",
      "===========================\n",
      "191 units manufactured\n",
      "AVG: 16580.09\n",
      "STD: 4369.97\n",
      "===========================\n",
      "192 units manufactured\n",
      "AVG: 16653.17\n",
      "STD: 4453.94\n",
      "===========================\n",
      "193 units manufactured\n",
      "AVG: 16501.65\n",
      "STD: 4409.53\n",
      "===========================\n",
      "194 units manufactured\n",
      "AVG: 16504.5\n",
      "STD: 4601.73\n",
      "===========================\n",
      "195 units manufactured\n",
      "AVG: 16488.02\n",
      "STD: 4508.36\n",
      "===========================\n",
      "196 units manufactured\n",
      "AVG: 16518.51\n",
      "STD: 4540.33\n",
      "===========================\n",
      "197 units manufactured\n",
      "AVG: 16494.33\n",
      "STD: 4681.35\n",
      "===========================\n",
      "198 units manufactured\n",
      "AVG: 16458.34\n",
      "STD: 4552.39\n",
      "===========================\n",
      "199 units manufactured\n",
      "AVG: 16211.93\n",
      "STD: 4554.54\n",
      "===========================\n",
      "Recommendation: 187 units yields an average profit of $16871.38\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_program()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df9850cf",
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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