RiskAnalysis_MBA416_IllinoisStateUniversity

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   "source": [
    "# Risk Analysis for a New Product Launch\n",
    "The management of Madeira Computing is considering the introduction of a wearable electronic device with the functionality of a phone. The fixed cost to launch this new product is 300,000.  The variable cost for the product is expected to be between 160 and 240, with a most likely value (mode) of 200 per unit. The product will sell for 300 per unit. Demand for the product is expected to range from 0 to approximately 20,000 units, with 4,000 units being the most likely (i.e. the mode)."
   ]
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Starting projected profit simulation:\n",
      "0.73  profit chance\n",
      "0.27  loss chance\n",
      "$ 320446.2\n",
      "Calculations Done.\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import gamma\n",
    "import statistics\n",
    "import random\n",
    "import numpy as np\n",
    "\n",
    "print(\"Starting projected profit simulation:\")\n",
    "#Variables:\n",
    "sell_price=300\n",
    "fixed_cost=300_000\n",
    "estimations=1000\n",
    "\n",
    "#Storing for calculations:\n",
    "profitList=[]\n",
    "loss_count=0\n",
    "profit_count=0\n",
    "\n",
    "variableList=list(range(160, 241))\n",
    "shape, scale = 2, 3  \n",
    "unitList = list(np.random.gamma(2, 3, 1000)*1000)\n",
    "\n",
    "for num in range(estimations+1):\n",
    "    rand_variable_cost=random.choice(variableList)\n",
    "    rand_units=random.choice(unitList)\n",
    "    margin_cost=sell_price-rand_variable_cost\n",
    "    margin_money=margin_cost*rand_units\n",
    "    profit=margin_money-fixed_cost\n",
    "    profitList.append(profit)\n",
    "    \n",
    "    if profit>0:\n",
    "        profit_count+=1\n",
    "    else:\n",
    "        loss_count+=1\n",
    "        \n",
    "print(round(profit_count/len(profitList),2), \" profit chance\")\n",
    "print(round(loss_count/len(profitList),2), \" loss chance\")\n",
    "print(\"$\", round(statistics.mean(profitList), 2))\n",
    "print(\"Calculations Done.\")"
   ]
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   "source": [
    "### Gamma Distribution Graph\n",
    "Demand Distribution: Assume that the product demand can be well-approximated as 1,000 times the value of a gamma random variable with an alpha parameter of 3 and a beta parameter of 2. This is unitList that is randomly generated and then graphed. "
   ]
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import scipy.special as sps  \n",
    "def plotGamma():\n",
    "    count, bins, ignored = plt.hist(unitList, 50, density=True)\n",
    "    y = bins**(shape-1)*(np.exp(-bins/scale) /  \n",
    "                         (sps.gamma(shape)*scale**shape))\n",
    "    plt.plot(bins, y, linewidth=2, color='r')\n",
    "    plt.show()\n",
    "plotGamma()"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [],
   "dockerImageVersionId": 30301,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 13.07334,
   "end_time": "2025-06-12T13:55:05.686238",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2025-06-12T13:54:52.612898",
   "version": "2.3.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}