A_Primer_for_Fully-Loading_Second_Trailers.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Abstract/ Objective \n",
    "\n",
    "Maximizing the number of items on trailers represents a potential reduction in operating expenses for companies that ship goods.  It also represents a potential reduction in environmental impact as the amount of fuel per unit shipped may be reduced.  There are a number of factors that prevent companies from fully-loading trailers.  Among them are customer demand, weight of the items, labor costs, and labor shortages.  The following series of logical premises and equations are designed to help companies fully utilize trailers without sacrificing customer expectations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e21b79",
   "metadata": {},
   "source": [
    "# The Business Problem\n",
    "\n",
    "Imagine the following scenario:  There is a company that ships goods.  In order to fulfill customer expectations, some items shipped need to be sent out on the earliest available trailer (a.k.a. \"high-priority-items\").  Other items need to be shipped in two to three days in order to meet customer expectations (a.k.a. \"middle-priority-items\").  And some need to be shipped in four or more days in order to meet customer expectations (a.k.a. \"low-priority-items\").  If one sends only the high-priority-items, the trailer will not be fully loaded.  If instead one sends all of the items, one will need more than one trailer and the second trailer will not be fully-loaded.  Moreover, there is no easy way to determine what is or isn't a 'high-priority-item' once it has been sent to the trailer to be loaded.  Ideally, one wants to send out 10 to 11 fully-loaded trailers per week to this destination as opposed to 14 trailers per week, 7 of which are only partially-loaded. \n",
    "\n",
    "One option to solve this problem is to program a calculator-like object to solve it.  This can be done through the programming language Python.  The first task one needs to complete is to define \"What is a fully loaded trailer?\".  Depending on what one is shipping and the methodology used to ship those items, this number can vary.   If one is shipping lighter items and is double-stacking pallets in a standard 53' trailer, that number is 60 pallets.  If one is instead using a fork-lift to fully-load a 53' trailer with single-stacked pallets, that number is 30 pallets.  If one is instead using a pallet jack to \"straight-load\" a 53' trailer with single-stacked pallets, that number is 24.  There are also other factors that can come into play such as non-standard pallets and trailers shorter than 53 feet such as box trucks. However, if one has a standardized way of shipping items to a destination, one can use that standard to define \"fully-loaded-trailer\".  \n",
    "\n",
    "In addition to knowing how many pallets can fit, it is useful to know how many units typically fit into a trailer for a particular destination considering some destinations may typically receive larger units and other destinations may typically receive smaller units.  In the real world, such numbers can be obtained from a warehouse's past shipment history.  In this notebook, random number generators will be used as substitute."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af520bb6",
   "metadata": {},
   "source": [
    "## The Data\n",
    "\n",
    "This is designed to be a simulation using random number generators to demonstrate a logical and mathematical framework real companies can use to help improve the fuel-efficiency of their logistical operations.  All data is fake, although it is designed to closely represent real-world scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ecca058f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random number generators are being used to create values for three different destinations, namely \n",
    "# Washington, DC, Boston, MA, and Charleston, WV.  The originating warehouse is in Pittsburgh, PA.\n",
    "# The variables named \"100d-60p\" represent the past 100 days of fully-loaded trailers sent to \n",
    "# a destination. 60p stands for 60 pallets as these hypothetical destinations all use trailers that \n",
    "# can fit 60 pallets.  This variable will be used to estimate how many units will be expected to fit\n",
    "# in a trailer. The second variable, past_100d_units, represents the number of units ordered by \n",
    "# customers from a particular destination that need to be shipped from the Pittsburgh warehouse.  \n",
    "# One objective of this notebook will be to try and use priority shipping to make the two \n",
    "# numbers match.\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "import random\n",
    " \n",
    "random.seed(3)\n",
    "\n",
    "\n",
    "\"Loads departing from Pittsburgh, PA\"\n",
    "\n",
    "\"Washington, DC\" \n",
    "\n",
    "DC_units_in_loads_with_60p_for_past100day = np.random.normal(loc=12500, scale= 1500, size =(1,100)).round().tolist()\n",
    "\n",
    "dc_100d_60p = DC_units_in_loads_with_60p_for_past100day[0]\n",
    "\n",
    "dc_units_for_past_100_days = np.random.normal(loc=17000, scale= 3000, size =(1,100)).round().tolist()\n",
    "dc_past_100d_units = dc_units_for_past_100_days[0]\n",
    "\n",
    "\"Boston, Mass\"\n",
    "Boston_units_in_loads_with_60p_for_past100day = np.random.normal(loc=10250, scale=1000, size=(1, 100)).round().tolist()\n",
    "Boston_100d_60p= Boston_units_in_loads_with_60p_for_past100day[0]\n",
    "\n",
    "Boston_units_for_past_100_days = np.random.normal(loc=14250, scale= 2500, size=(1, 100)).round().tolist()\n",
    "Boston_past_100d_units =Boston_units_for_past_100_days[0]\n",
    "\n",
    "\"Charleston, WV:\" \n",
    "WV_units_in_loads_with_60p_for_past100d = \"None\"\n",
    "\n",
    "WV_units_for_past_100_days = np.random.normal(loc=5300, scale= 1000, size=(1, 100)).round().tolist()\n",
    "WV_past_100d_units = WV_units_for_past_100_days[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "71d87023",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is a graphical representation of the DC destination.  The green stars represent the number of\n",
    "# units that were ordered on a particular day and the red line represents the number of units \n",
    "# that were able to fit on the trailer for that particular day.  As can be seen by the number of \n",
    "# green dots above the red line, on most days more than one trailer needs to be used to send \n",
    "# out everything that is ordered on a particular day, but on a few days only one trailer is needed.\n",
    "# Moreover, the second trailer is unlikely to be fully-loaded when it is used. \n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14,8), dpi=80)\n",
    "X = np.arange(-100, 0)\n",
    "\n",
    "ax.plot(X, dc_100d_60p, c='#ef4209', alpha=0.8, label=\"Number of units in a fully-loaded trailer\")\n",
    "ax.scatter(X, dc_past_100d_units, c='darkgreen', marker='*', s=50, label=\"Customer orders to DC per day\");\n",
    "ax.set_ylim(0, 24000);\n",
    "ax.grid(True)\n",
    "\n",
    "plt.title(\"Units in Fully-Loaded Trucks and Customer Orders\\n Departing from Pittsburgh, PA to Washington, DC\", fontsize=20)\n",
    "plt.xlabel('Past 100 Days', fontsize=18)\n",
    "plt.ylabel('Number of Units per Day', fontsize=18);\n",
    "plt.legend(fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "17bcaa7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A similar situation occurs with the Boston destination. \n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14,8), dpi=80)\n",
    "\n",
    "ax.plot(X, Boston_100d_60p, c='#ef4209', alpha=0.8, label=\"Number of units in a fully-loaded trailer\")\n",
    "ax.scatter(X, Boston_past_100d_units, c='darkgreen', marker='*', s=50, label=\"Customer orders to Boston per day\");\n",
    "ax.set_ylim(0, 24000);\n",
    "ax.grid(True)\n",
    "\n",
    "plt.title(\"Units in Fully-Loaded Trucks and Customer Orders\\n Departing from Pittsburgh, PA to Boston, Mass.\", fontsize=20)\n",
    "plt.xlabel('Past 100 Days', fontsize=18)\n",
    "plt.ylabel('Number of Units per Day', fontsize=18);\n",
    "plt.legend(fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f863946e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In the case of Charleston, WV, the number of customer orders never exceeds the number of units a trailer\n",
    "# can fit so one trailer is always sufficient.  \n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14,8), dpi=80)\n",
    "\n",
    "ax.scatter(X, WV_units_for_past_100_days, c='darkgreen', marker='*', s=50, label=\"Customer orders to Charleston, WV per day\");\n",
    "ax.set_ylim(0, 24000);\n",
    "ax.grid(True)\n",
    "\n",
    "plt.title(\"Units in Fully-Loaded Trucks and Customer Orders\\n Departing from Pittsburgh, PA to Charleston, WV\", fontsize=20)\n",
    "plt.xlabel('Past 100 Days', fontsize=18)\n",
    "plt.ylabel('Number of Units per Day', fontsize=18);\n",
    "plt.legend(fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0ce37044",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\eincr\\AppData\\Local\\Temp/ipykernel_56512/843489692.py:23: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(ax.get_yticks(), fontsize=14)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  This is a box-and-whisker plot that represents the same thing in a slightly different way.\n",
    "#  For the Boston and DC destinations, the average number of customer orders per day \n",
    "#  usually exceeds the number of units a trailer can fit, but not always as there is significant\n",
    "#  variation in both the number of units that are ordered and the number of units that can fit\n",
    "#  into a trailer.  If one doesn't have a logical framework to deal with the aforementioned \n",
    "#  variations, it will result in logistical issues such as as underutilized trailers or customers\n",
    "#  not getting their products on time.  \n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14,8), dpi=80)\n",
    "data = (dc_100d_60p, dc_past_100d_units, Boston_100d_60p, Boston_units_for_past_100_days, WV_units_for_past_100_days)\n",
    "boxplot = sns.boxplot(data = data)\n",
    "plt.xlabel(\"Units Headed to a Destination\", size=20)\n",
    "plt.ylabel('Number of Units per Day', size=20)\n",
    "plt.title(\"Distribution of Units in Fully-Loaded Trucks\\n and Customer Orders Departing from Pittsburgh, PA\", size=20)\n",
    "ax.set_ylim(0, 25000)\n",
    "ax.set_xticklabels(['DC Fully-\\n Loaded Truck', 'DC Customer-\\n Orders', 'Boston Fully-\\n Loaded Truck',\n",
    "                    'Boston Customer-\\n Orders', 'WV Customer-\\n Orders'], fontsize=17, color='r')\n",
    "ax.set_yticklabels(ax.get_yticks(), fontsize=14)\n",
    "sns.set_style(\"whitegrid\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a338fb7",
   "metadata": {},
   "source": [
    "## Fully-Utilize Trailers Using Statistical Updating and Priority Shipping\n",
    "\n",
    "In order to fully-utilize outbound trailers, there are two primary problems one must solve.  The first is determining how many units can fit into a trailer and not sending more units than the trailer can fit.  This is more difficult than it sounds.  If one uses the average number of units a trailer can fit, then roughly half the time one will exceed trailer capacity as it is the \"average\".  Exceeding trailer capacity could mean customers not getting their packages on time which, in turn, may necessitate the need for an additional trailer.  If instead one goes well below the average to play it safe, then one won't be filling trailers to the fullest extent possible.  \n",
    "\n",
    "The second problem one must solve is determining how many trailers are needed to meet customer demand. This is also a difficult problem to solve as some of the customer demand is not known until the day it is supposed to be shipped, hence it is challenging to know how many trucks to schedule.  Poor planning may result in under-utilized trailers as it takes time to fully load an additional trailer and a warehouse may be forced to send a half-empty trailer due to not having enough labor to fully load it.\n",
    "\n",
    "This notebook provides solutions to both problems.  The first solution involves using the number of loaded pallets to provide a real-time update on how many units can fit into a trailer.  The second solution uses priority shipping to adjust how many trailers to schedule.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7271909b",
   "metadata": {},
   "source": [
    "## Statistical Updating \n",
    "\n",
    "As mentioned earlier, trying to predetermine how many units can fit into a fully-loaded trailer is imprecise as there is a lot of space to fill which can lead to a wide range of possible outcomes.  It is far more accurate to make ones estimates off of a half-filled or a three-quarter filled trailer as there is less space to fill and consequently a smaller range of possible outcomes.  In the case of standardized trailers being completely filled with pallets of a standardized size (which is commonplace in the industry), one can use the known number of pallets that can fit into a trailer to update one’s estimates.  The proposed methodology is as follows:\n",
    "1.  Use a warehouse's past shipment history to provide an estimate for the minimum number of units expected to fit into a fully-loaded trailer. For example, one can look for all trailers that have 60 pallets in them and find the number of units that is -3 standard deviations away from the mean of said trailers.  This is intentionally a low-ball estimate and will be used as the initial planned volume.   \n",
    "2.  Divide the minimum number of units expected to fit into a fully-loaded trailer by the number of pallets that fit in said trailers (60 in the above example).  This number enables one to determine how may units are expected to fit onto a pallet.\n",
    "3.  Subtract the total number of pallets a trailer can fit by the number of loaded (or created) pallets to provide an updated number of remaining pallets.   \n",
    "4.  Multiply the number of remaining pallets by the minimum number of units expected to fit on a pallet to provide an updated estimate of what can fit.  \n",
    "\n",
    "All of this may seem quite overwhelming, but it is actually quite simple if one works in a warehouse that electronically keeps track of all this information.  All one needs to do is plug in a couple of numbers and one can get an accurate updated estimate of how many units are expected to fit.   As one is using a low-ball estimate, the risk of going over capacity is minimal.  And as one is updating ones estimates as one loads pallets, the risk of going to low is also minimal as the forecasts will become more accurate the closer one gets to having a fully-loaded trailer.\n",
    "\n",
    "I have created two calculator-like objects to demonstrate the aforementioned approach.  They are fully operational.  If one wishes to use this approach, just substitute actual warehouse data with the fake data I am using.  The first set of functions updates one’s estimate based off of loaded pallets.   The second set of functions adds created pallets and sent units into the equation.  \n",
    "\n",
    "A note on the statistics:  \n",
    "I calculated level of risk based off of standard deviations and assumed that the number of units that can fit into a fully-loaded trailer is normally distributed.  In a normal distribution, 68.2% of values fall within 1 standard deviation of the mean, 95.4% of values fall within 2 standard deviations, and 99.7% fall within 3 standard deviations. Thus, if one wants to have less than .3% level of risk of going beyond trailer capacity, one can set the level of risk to -3 standard deviations and plan on sending the number of units that corresponds to that level of risk.   Alternatively, one could incorporate other statistical approaches such as z-scores and percentiles.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "19b4a8af",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please select operation 1. DC  2. Boston \n",
      "Select operations from 1, 2: 1\n",
      "Enter number of pallets in a fully loaded trailer: 60\n",
      "Enter level of risk measured in standard deviations: 3\n",
      "Enter number of loaded pallets: 30\n",
      "Capacity estimate for the remaining units to DC is 3670.2912000160914\n",
      "Continue? y/n:n\n"
     ]
    }
   ],
   "source": [
    "# These functions enable one to use past warehouse data, the known number of \n",
    "# pallets that can fit into a trailer, and the number of pallets that have been \n",
    "# loaded to provide an updated capacity estimate.  Try it out (if you are using\n",
    "# this in a Jupyter notebook).  This is simple code and could be put on a website\n",
    "# anyone can use or incorporated into a company's employee website.\n",
    "\n",
    "\n",
    "max_number_of_pallets = []  \n",
    "level_of_risk = []  \n",
    "loaded_pallets = [] \n",
    "cont = 'y'\n",
    "\n",
    "def min_expected_items(destination):\n",
    "    return (np.mean(destination) - (level_of_risk)*(np.std(destination)))\n",
    "\n",
    "def min_expected_items_per_pallet(destination):\n",
    "    return min_expected_items(destination)/(max_number_of_pallets)\n",
    "\n",
    "def number_of_remaining_pallets(loaded_pallets):\n",
    "    return (max_number_of_pallets - loaded_pallets)\n",
    "    \n",
    "def estimate_for_remaining_units(destination): \n",
    "    return (min_expected_items_per_pallet(destination))*(number_of_remaining_pallets(loaded_pallets))    \n",
    "\n",
    "print('Please select operation' \\\n",
    "        ' 1. DC ' \\\n",
    "        ' 2. Boston ')\n",
    "\n",
    "while cont == 'y':      \n",
    "    \n",
    "    select = input('Select operations from 1, 2: ')\n",
    "    max_number_of_pallets = int(input('Enter number of pallets in a fully loaded trailer: '))\n",
    "    level_of_risk = int(input('Enter level of risk measured in standard deviations: '))\n",
    "    loaded_pallets = int(input('Enter number of loaded pallets: '))\n",
    "\n",
    "    if select == '1':\n",
    "        print('Capacity estimate for the remaining units to DC is', estimate_for_remaining_units(dc_100d_60p))\n",
    "    elif select == '2':\n",
    "        print('Capacity estimate for the remaining units to Boston is', estimate_for_remaining_units(Boston_100d_60p))\n",
    "    else:\n",
    "        print('Invalid input')\n",
    "    cont = input('Continue? y/n:') \n",
    "    if cont == 'n':\n",
    "         break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "25cffe37",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please select operation 1. DC  2. Boston \n",
      "Select operations from 1, 2: 1\n",
      "Enter number of pallets in a fully loaded trailer: 60\n",
      "Enter level of risk measured in standard deviations: 3\n",
      "Enter number of loaded pallets: 35\n",
      "Enter number of created pallets: 2\n",
      "Enter number of sent units: 500\n",
      "Capacity estimate for the remaining units to DC is 2331.2077611379996\n",
      "Continue? y/n:n\n"
     ]
    }
   ],
   "source": [
    "# These set of functions are similar to the ones above, only this time we \n",
    "# are subtracting the number of created pallets and the number of sent units\n",
    "# from the total capacity as created pallets and sent units will have to go\n",
    "# onto a trailer.  As the sent units get added to the already created \n",
    "# pallets and loaded, the forecast will become more accurate by removing\n",
    "# sent units and created pallets from the calculation.  \n",
    "\n",
    "max_number_of_pallets = []  \n",
    "level_of_risk = [] \n",
    "loaded_pallets = []\n",
    "created_pallets = []\n",
    "sent_units = []\n",
    "cont = 'y'\n",
    "\n",
    "def remaining_pallets_minus_created(created_pallets):\n",
    "    return (number_of_remaining_pallets(loaded_pallets) - created_pallets)\n",
    "def remaining_units_minus_sent(destination):\n",
    "    return (((min_expected_items_per_pallet(destination))*(remaining_pallets_minus_created(created_pallets))- sent_units))\n",
    " \n",
    "            \n",
    "print('Please select operation' \\\n",
    "        ' 1. DC ' \\\n",
    "        ' 2. Boston ')  \n",
    "\n",
    "while cont == 'y':      \n",
    "    \n",
    "    select = input('Select operations from 1, 2: ')\n",
    "    max_number_of_pallets = int(input('Enter number of pallets in a fully loaded trailer: '))\n",
    "    level_of_risk = int(input('Enter level of risk measured in standard deviations: '))\n",
    "    loaded_pallets = int(input('Enter number of loaded pallets: '))\n",
    "    created_pallets = int(input('Enter number of created pallets: '))\n",
    "    sent_units = int(input('Enter number of sent units: '))\n",
    "\n",
    "    if select == '1':\n",
    "        print('Capacity estimate for the remaining units to DC is', remaining_units_minus_sent(dc_100d_60p_round))\n",
    "    elif select == '2':\n",
    "        print('Capacity estimate for the remaining units to Boston is', remaining_units_minus_sent(Boston_100d_60p_round))\n",
    "    else:\n",
    "        print('Invalid input')\n",
    "    cont = input('Continue? y/n:') \n",
    "    if cont == 'n':\n",
    "         break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49e31c66",
   "metadata": {},
   "source": [
    "## Priority Shipping\n",
    "\n",
    "If everything that was ordered had to be shipped the same day it was ordered, there would be no way to maximize the utilization of trailers that ship customer orders.  But, as anyone who has had something upgraded to UPS 2-day or who has an Amazon Prime membership knows, there are different tiers of priority shipping.  The following code takes advantage of those tiers to calculate how many trailers are needed.  The same random number generators used to create the past 100-day warehouse histories are used to create the next 14 days of customer orders and fully-loaded trailers.  Customer orders are divided by one-third to create the priority shipments.  One-third of orders are high-priority, one-third is middle-priority, and one-third is low-priority.  High-priority items are always sent, and if a unit of lower priority isn't sent, then it gets upgraded to the next tier.  \n",
    "\n",
    "While the present formulation differs significantly from a real-world scenario, this demonstration should provide some insight into the potential for fuel and cost savings through the use of priority shipping.  Some variation of this is likely already in place in warehouses that ship customer orders, but as such code is proprietary (and based on proprietary data), there cannot be an open-source academic discussion about how to best achieve fully-loaded outbound trailers, hence the need for a proxy. \n",
    "\n",
    "This demonstration is set up in two parts.  The first section determines how many trailers have to be sent if everything that was ordered was sent the same day it was ordered.  The second section predicts how many trailers how many trailers need to be sent based off of past historical trends and orders the warehouse already knows need to be sent. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "60f89d53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This section creates the next 14 days of customer orders and the number of units\n",
    "# that can fit into a trailer using the same random number generators as before.  \n",
    "\n",
    "random.seed(3)\n",
    "\n",
    "\"Washington, DC\" \n",
    "dc_cust_for_next_14_days = np.random.normal(loc=17000, scale= 3000, size =(1,14)).round().tolist()\n",
    "dc_cust_next14 = dc_cust_for_next_14_days[0]\n",
    "\n",
    "dc_60p_for_next_14_days = np.random.normal(loc=12500, scale= 1500, size =(1,28)).round().tolist()\n",
    "dc_60p_next14 = dc_60p_for_next_14_days[0]\n",
    "\n",
    "\"Boston, Mass\"\n",
    "Boston_cust_for_next_14_days = np.random.normal(loc=14250, scale= 2500, size=(1, 14)).round().tolist()\n",
    "Boston_cust_next14 = Boston_cust_for_next_14_days[0]\n",
    "\n",
    "Boston_60p_for_next_14_days = np.random.normal(loc=10250, scale=1000, size=(1, 28)).round().tolist()\n",
    "Boston_60p_next14 = Boston_60p_for_next_14_days[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f216756e",
   "metadata": {},
   "source": [
    "### Number of trailers needed if priority shipping isn't used to fully-utilize trailers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cbca8dfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28 trucks are needed if DC loads are not merged.\n",
      "26 trucks are needed if Boston loads are not merged.\n"
     ]
    }
   ],
   "source": [
    "# This code adds one trailer for each day the number of units ordered by customers\n",
    "# exceeds the number of units that can fit into the trailer for that day.  It is assumed that \n",
    "# at no point three trailers will be needed.\n",
    "\n",
    "compare_list = [sum(x > y for x,y in zip(dc_cust_next14, dc_60p_next14))] + [len(dc_cust_next14)]\n",
    "\n",
    "number_trucks = sum(compare_list) \n",
    "print (f\"{number_trucks} trucks are needed if DC loads are not merged.\")\n",
    "\n",
    "compare_list2 =  [sum(x > y for x,y in zip(Boston_cust_next14, Boston_60p_next14))] + [len(Boston_cust_next14)]\n",
    "number_trucks2 = sum(compare_list2)\n",
    "print (f\"{number_trucks2} trucks are needed if Boston loads are not merged.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2411361",
   "metadata": {},
   "source": [
    "### Predicting the number of trailers needed based off of past warehouse history and priority shipping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0947b494",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The function \"new truck\" uses the new number of high priority items, the number\n",
    "# of middle priority items that didn't get shipped the prior day, and the minimum \n",
    "# amount of units expected to fit into a trailer to calculate how many trucks \n",
    "# need to be scheduled.  In this demonstration, high priority is defined as 1/3 of \n",
    "# all daily orders.  Hence, the number of units that must be shipped can be calculated by \n",
    "# dividing the number of new units by one-third and adding the amount of middle priority \n",
    "# units that didn't get shipped the prior day.  If this sum exceeds the minimum number \n",
    "# of items expected to fit into one trailer, but is less than the amount two trailers\n",
    "# can fit, it returns two trucks are sufficient.  If it exceeds the amount two trailers \n",
    "# can fit, it returns three trucks are needed. This demonstration assumes four trailers \n",
    "# will never be needed which may not hold true in all real-world cases.\n",
    "\n",
    "\n",
    "def min_expected_items2(destination):\n",
    "    return (np.mean(destination) - (3)*(np.std(destination)))\n",
    "\n",
    "def high_new_units(new_units):\n",
    "     return (new_units)/3\n",
    "\n",
    "def double_min_expected_items(destination):\n",
    "    return 2*(min_expected_items2(destination))\n",
    "    \n",
    "def new_truck(new_units, middle_upgraded, destination):\n",
    "    if high_new_units(new_units) + middle_upgraded < min_expected_items2(destination):\n",
    "        return  \"One truck is sufficient.\"\n",
    "    if double_min_expected_items(destination) > daily_new_units(new_units) + middle_upgraded > min_expected_items2(destination):\n",
    "        return \"Two trucks are sufficient.\"\n",
    "    if daily_new_units(new_units) + middle_upgraded > double_min_expected_items(destination):\n",
    "        return \"Three trucks are needed.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0f2e9d26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example 1: One truck is sufficient.\n",
      "Example 2: Two trucks are sufficient.\n",
      "Example 3: Three trucks are needed.\n"
     ]
    }
   ],
   "source": [
    "# This code demonstrates that the functions work.  The first number in the parenthesis \n",
    "# is the number of units being sent out at a day, the second number is the number of \n",
    "# middle-priority units that weren't shipped the prior day, and the third variable is \n",
    "# the number of units that had fit in the past 100 days of fully-loaded trailers.\n",
    "# The function new truck uses the warehouse history to calculate the minimum amount of units \n",
    "# expected to fit.  This has been hard-coded as -3 standard deviations from the mean,\n",
    "# but other levels of risk and statistical methods could also be used.\n",
    "\n",
    "print(f\"Example 1: {new_truck(15000, 0, dc_100d_60p)}\")\n",
    "\n",
    "print(f\"Example 2: {new_truck(10000, 10000, dc_100d_60p)}\")\n",
    "\n",
    "print(f\"Example 3: {new_truck(30000, 20000, dc_100d_60p)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acfba6cb",
   "metadata": {},
   "source": [
    "### Calculating the number of units remaining after a trailer has been sent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "970e905a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# These functions determine what remains after a truck has been fully loaded.\n",
    "# If only one truck is need, only the first function is used.  If more than one truck \n",
    "# is used, the second function is employed after the first function is executed.\n",
    "# It is assumed that higher-priority units will always be sent when one has to choose \n",
    "# between sending a higher-priority unit and a lower-priority unit. \n",
    "\n",
    "def loaded_truck(new_units, middle_upgraded, low_upgraded, unitsin60p):\n",
    "    if daily_new_units(new_units) + middle_upgraded > unitsin60p:\n",
    "        print(\"High-priority items remain\")\n",
    "        return daily_new_units(new_units) + middle_upgraded - unitsin60p\n",
    "    elif 2 *daily_new_units(new_units) + middle_upgraded + low_upgraded > unitsin60p:\n",
    "        print(\"Middle-priority items remain\")\n",
    "        return 2*daily_new_units(new_units) + middle_upgraded + low_upgraded - unitsin60p\n",
    "    else:\n",
    "        print('Low-priority items remain')\n",
    "        return 3*daily_new_units(new_units) + middle_upgraded + low_upgraded - unitsin60p\n",
    "    \n",
    "    \n",
    "def loaded_truck_2(highest_priority_items, next_highest_priority_items, lowest_priority_items, unitsin60p_truck2):\n",
    "    if highest_priority_items > unitsin60p_truck2:\n",
    "        print(\"Highest-priority items remain\")\n",
    "        return highest_priority_items - unitsin60p_truck2\n",
    "    elif (highest_priority_items + next_highest_priority_items) > unitsin60p_truck2:\n",
    "        print (\"Next-highest priority items remain\")\n",
    "        return (highest_priority_items + next_highest_priority_items) -  unitsin60p_truck2\n",
    "    elif (highest_priority_items + next_highest_priority_items + lowest_priority_items) > unitsin60p_truck2:\n",
    "        print (\"Lowest-priority items remain\")\n",
    "        return (highest_priority_items + next_highest_priority_items + lowest_priority_items) -  unitsin60p_truck2\n",
    "    else: \n",
    "        print (\"No remaining units\")\n",
    "        return"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac2cb3e3",
   "metadata": {},
   "source": [
    "## Demonstration - Pittsburgh to Washington, DC\n",
    "\n",
    "The preliminary analysis said that 28 trucks were needed if priority shipping wasn't used to help maximize the loads.  The following demonstration will determine how many trailers are needed if priority shipping is accounted for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "fdc7292b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13382.0, 16654.0, 19466.0, 17450.0, 15308.0, 15373.0, 20468.0, 15326.0, 15523.0, 16163.0, 18213.0, 17087.0, 17077.0, 23220.0]\n"
     ]
    }
   ],
   "source": [
    "# These are the customer orders for the next 14 days that need to be moved\n",
    "# from Pittsburgh to Washington, DC\n",
    "print(dc_cust_next14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "59b72f5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11028.0, 13233.0, 11143.0, 13608.0, 11754.0, 14384.0, 14250.0, 11769.0, 10950.0, 15280.0, 10253.0, 11341.0, 14234.0, 11899.0, 14771.0, 10917.0, 14154.0, 11359.0, 12791.0, 11618.0, 12231.0, 11893.0, 15155.0, 11913.0, 16312.0, 11882.0, 12588.0, 10936.0]\n"
     ]
    }
   ],
   "source": [
    "# These are the number of units that can fit into fully loaded trailers for the next \n",
    "# 14 days from Pittsburgh to Washington, DC.\n",
    "print(dc_60p_next14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d93a2748",
   "metadata": {},
   "source": [
    "### Day 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "eb7161d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One truck is sufficient.\n",
      "Low-priority items remain\n",
      "2354.0\n"
     ]
    }
   ],
   "source": [
    "# The functions created earlier will now be employed for the next 14 days \n",
    "# of Pittsburgh, PA to Washington, DC customer shipments.  There are no \n",
    "# middle upgraded units for the first day as we are starting fresh.  The \n",
    "# number of new units for the day is 13382.  This number was obtained from\n",
    "# the dc_cust_next14 list. Only one truck was needed. 2354 low priority \n",
    "# units remained after everything was shipped.  The first truck in the \n",
    "# dc_60p_next14 list (11028) was used to help calculate this number.\n",
    "\n",
    "print (new_truck(13382, 0, dc_100d_60p))\n",
    "\n",
    "print (loaded_truck(13382, 0, 0, 11028))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae51b1f",
   "metadata": {},
   "source": [
    "### Day 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7c84365d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One truck is sufficient.\n",
      "Middle-priority items remain\n",
      "223.66666666666606\n"
     ]
    }
   ],
   "source": [
    "# The lower priority units were only upgraded to middle-priority and thus\n",
    "# do not factor into the calculation of a new truck for the second day.\n",
    "# These 2354 units are accounted for, however, in the loaded_truck function.  \n",
    "# This time, middle priority units remain as well as low priority units\n",
    "\n",
    "print (new_truck(16654, 0, dc_100d_60p))\n",
    "\n",
    "print (loaded_truck(16654, 0, 2354, 13233))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c69f2430",
   "metadata": {},
   "source": [
    "### Day 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b8c02371",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One truck is sufficient.\n",
      "Middle-priority items remain\n",
      "7609.333333333336\n"
     ]
    }
   ],
   "source": [
    "# While there were middle-priority units that became high-priority, the 224 \n",
    "# units weren't enough to necessitate the need for a second truck.  There \n",
    "# were 5551 units of low priority units from the prior day (i.e. 1/3 of \n",
    "# 16654) so the backlog is beginning to accumulate which may soon necessitate\n",
    "# the need for a second trailer.\n",
    "\n",
    "print (new_truck(19466, 224, dc_100d_60p))\n",
    "\n",
    "print (loaded_truck(19466, 224, 5551, 11143))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93d67998",
   "metadata": {},
   "source": [
    "### Day 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "b9a82ae7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two trucks are sufficient.\n",
      "Middle-priority items remain\n",
      "12123.333333333336 from truck 1 (and sent in truck 2)\n",
      "Next-highest priority items remain\n",
      "369 from truck 2\n"
     ]
    }
   ],
   "source": [
    "# The accumulation of backlog units did necessitate the need for a second trailer\n",
    "# on day 4.  While there was enough space to fill all of the highest priority units \n",
    "# in the first trailer, there was risk of it going over and the \n",
    "# additional trailer didn't add to the overall cost as it was fully utilized by \n",
    "# lower priority units. As a second trailer was needed, the second function, loaded_truck_2, \n",
    "# had to be employed.  The first number, 0, is the highest-priority units that couldn't\n",
    "# fit into the first truck.  The second number, 12123, is the number of middle \n",
    "# priority units that remained left over from the first truck.  The third number (5817)\n",
    "# is the number of low priority units scheduled for the day and the fourth number (11754)\n",
    "# is the next number in the truck list.  The addition of the second trailer led to almost\n",
    "# all of the middle priority units being sent.  329 middle-priority and 5817 low-priority\n",
    "# units remain behind.\n",
    "\n",
    "print (new_truck(17450, 7609, dc_100d_60p))\n",
    "\n",
    "print (f\"{loaded_truck(17450, 7609, 6489, 13608)} from truck 1 (and sent in truck 2)\")\n",
    "\n",
    "print (f\"{loaded_truck_2(0, 12123, 5817, 11754)} from truck 2\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2808c17",
   "metadata": {},
   "source": [
    "### Day 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "270e8a5d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One truck is sufficient.\n",
      "Middle-priority items remain\n",
      "2007.3333333333358\n"
     ]
    }
   ],
   "source": [
    "# Days 1 through 4 demonstrated the utility of the functions so the rest of the \n",
    "# calculations for the days will be presented without commentary.\n",
    "\n",
    "print(new_truck(15308, 369, dc_100d_60p))\n",
    "\n",
    "print(loaded_truck(15308, 369, 5817, 14384))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9dfb1eb",
   "metadata": {},
   "source": [
    "### Day 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "2fa5ac0f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One truck is sufficient.\n",
      "Middle-priority items remain\n",
      "3108.6666666666642\n"
     ]
    }
   ],
   "source": [
    "print(new_truck(15373, 2007, dc_100d_60p))\n",
    "\n",
    "print(loaded_truck(15373, 2007, 5103, 14250))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c094858",
   "metadata": {},
   "source": [
    "### Day 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "145584ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two trucks are sufficient.\n",
      "Middle-priority items remain\n",
      "10109.333333333336 from truck 1 (and sent in truck 2)\n",
      "Lowest-priority items remain\n",
      "5982 from truck 2\n"
     ]
    }
   ],
   "source": [
    "print(new_truck(20468, 3069, dc_100d_60p))\n",
    "\n",
    "print(f\"{loaded_truck(20468, 3109, 5124, 11769)} from truck 1 (and sent in truck 2)\")\n",
    "\n",
    "print(f\"{loaded_truck_2(0, 10109, 6823, 10950)} from truck 2\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb82f370",
   "metadata": {},
   "source": [
    "### Day 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "d88963eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One truck is sufficient.\n",
      "Middle-priority items remain\n",
      "919.3333333333339\n"
     ]
    }
   ],
   "source": [
    "print(new_truck(15326, 0, dc_100d_60p))\n",
    "\n",
    "print(loaded_truck(15326, 0, 5982, 15280))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f040514",
   "metadata": {},
   "source": [
    "### Day 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "b03c70a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One truck is sufficient.\n",
      "Middle-priority items remain\n",
      "6122.666666666666\n"
     ]
    }
   ],
   "source": [
    "print(new_truck(15523, 919, dc_100d_60p))\n",
    "\n",
    "print(loaded_truck(15523, 919, 5108, 10253))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5a3914f",
   "metadata": {},
   "source": [
    "### Day 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "d819ea20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two trucks are sufficient.\n",
      "High-priority items remain\n",
      "169.66666666666788 from truck 1 (and sent in truck 2)\n",
      "Lowest-priority items remain\n",
      "1886 from truck 2\n"
     ]
    }
   ],
   "source": [
    "print(new_truck(16163, 6123, dc_100d_60p))\n",
    "\n",
    "print(f\"{loaded_truck(16163, 6123, 5174, 11341)} from truck 1 (and sent in truck 2)\")\n",
    "\n",
    "print(f\"{loaded_truck_2(170, 10562, 5388, 14234)} from truck 2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca4b6f01",
   "metadata": {},
   "source": [
    "### Day 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "d7d5317f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One truck is sufficient.\n",
      "Middle-priority items remain\n",
      "2129.0\n"
     ]
    }
   ],
   "source": [
    "print(new_truck(18213, 0, dc_100d_60p))\n",
    "\n",
    "print(loaded_truck(18213, 0, 1886, 11899))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8939d615",
   "metadata": {},
   "source": [
    "### Day 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "20437a31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two trucks are sufficient.\n",
      "Middle-priority items remain\n",
      "4820.333333333336 from truck 1 (and sent in truck 2)\n",
      "No remaining units\n",
      "None from truck 2\n",
      "The second truck had space for 401 additional units\n"
     ]
    }
   ],
   "source": [
    "print(new_truck(17087, 2129, dc_100d_60p))\n",
    "\n",
    "print(f\"{loaded_truck(17087, 2129, 6071, 14771)} from truck 1 (and sent in truck 2)\")\n",
    "\n",
    "print(f\"{loaded_truck_2(0, 4820, 5696, 10917)} from truck 2\")\n",
    "\n",
    "print(f\"The second truck had space for {(10917 - 4820 - 5696)} additional units\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4db3dff",
   "metadata": {},
   "source": [
    "### Day 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "736cb5da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One truck is sufficient.\n",
      "Low-priority items remain\n",
      "2923.0\n"
     ]
    }
   ],
   "source": [
    "print(new_truck(17077, 0, dc_100d_60p))\n",
    "\n",
    "print(loaded_truck(17077, 0, 0, 14154))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4153e99b",
   "metadata": {},
   "source": [
    "### Day 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "0e535207",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two trucks are sufficient.\n",
      "Middle-priority items remain\n",
      "7044.0 from truck 1 (and sent in truck 2)\n",
      "Lowest-priority items remain\n",
      "1993 from truck 2\n"
     ]
    }
   ],
   "source": [
    "print(new_truck(23220, 0, dc_100d_60p))\n",
    "\n",
    "print(f\"{loaded_truck(23220, 0, 2923, 11359)} from truck 1 (and sent in truck 2)\")\n",
    "\n",
    "print(f\"{loaded_truck_2(0, 7044, 7740, 12791)} from truck 2\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4856cf0e",
   "metadata": {},
   "source": [
    "### Check of Work\n",
    "If everything was imported and calculated correctly, the total number of units sent in trailers minus unit space not used plus the number of units left over should match the total number of units customers ordered.  There was only one trailer that wasn't fully-utilized and it had space for an additional 401 additional units and there were an additional 1993 units customers ordered that weren't sent out after 14 days.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "51cfe451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "240710.0"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is the total number of units customers ordered that needed to be shipped from\n",
    "# Pittsburgh to Washington, DC during the 14-day demonstration.\n",
    "\n",
    "13382.0 + 16654.0 + 19466.0 + 17450 + 15308 + 15373 + 20468 + 15326 + 15523 \\\n",
    "+ 16163 + 18213 + 17087 + 17077 + 23220"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "86d647e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "240710.0"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The above number matches the total number of units sent in outbound trailers \n",
    "# plus the amount of customer orders not yet sent (1993) minus the space in \n",
    "# trailers that were not fully-utilized (401)\n",
    "\n",
    "11028 + 13233.0 + 11143.0 + 13608.0 + 11754.0 + 14384 + 14250.0 + 11769 + 10950 \\\n",
    "+ 15280 + 10253 + 11341 + 14234 + 11899 + 14771 + 10917 + 14154 + 11359 + 12791 + 1993 - 401"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7916fd2",
   "metadata": {},
   "source": [
    "### Number of trailers used \n",
    "\n",
    "In this demonstration, there were 5 days were two trailers needed to be sent from Pittsburgh, PA to Washington, DC.  In total, this meant that 19 trailers had to be used when priority shipping was taken into account.   This compares favorably to the 28 trailers that would have to have been sent had one always sent a trailer whenever customer orders exceed trailer capacity.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0e685ef",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "* The calculator functions I created in the first part of this notebook are fully-operational.  If one wanted to, one could download this Jupyter notebook and run the code as is or modify it to one’s particular specifications. All one needs to know is the number of units that fit into past fully-utilized trailers, the level of risk of going over one wishes to have, how many pallets fit into the trailer, how many pallets have been loaded and created, and how many units have been sent.  Not all warehouses have this ability, but the warehouse I work at tracks everything electronically and I am sure it is not the only one that does so.  Thus, doing these calculations can be as simple as entering a couple of numbers. This real-time statistical updating solves one of the problems warehouses have, namely knowing how many items to ship in order to fully-load a trailer.\n",
    "\n",
    "*  The function used to determine how many trailers to schedule (i.e. new truck) works well. In the demonstration, there was only one time that a trailer wasn't fully-utilized and it wasn't off by much.   Moreover, there wasn't any time when high-priority units weren't shipped.  This function can be modified to be used in a real-world scenario, but actual numbers will need to be used instead of just dividing a random number by 3.\n",
    "\n",
    "* The functions loaded_truck and loaded_truck_2 are rough as they require manual entry of numbers and there is no real-world uses for them without significant modifications. There entire purpose was to create a simple logistical simulation. There is no plan to develop them further as they differ too significantly from real-world scenarios.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c158ab7",
   "metadata": {},
   "source": [
    "## Next Steps\n",
    "\n",
    "* The plan is to use random number generators with the superstore dataframe in order to create my own fake warehouse that can be used to address logistical issues in a more realistic way (using SQL). This is in the works. \n",
    "* \"Hitch-hiking\" (for example, sending something from Pittsburgh to Boston so it can go to Foxborough) should be incorporated.\n",
    "* Weight should be incorporated as size is not always the limiting factor.  For certain heavy items, such as sending pallets of water and paper, trailers can become overloaded.\n",
    "* The amount of time it takes to load a trailer should also be taken into account as sometimes a lack of labor is what can lead to under-utilization.\n",
    "* A \"Snowstorm Andon\" should be created in order to calculate how best to modify what one sends due to a warehouse not being operational due to an event such as a large snowstorm.\n",
    "* Create code that calculates the amount of cost savings expected for reducing the number of trailers sent.\n",
    "* Create code that calculates the amount of fuel savings expected for reducing the number of trailers sent.\n",
    "* If there is a way a trailer can be more fully-loaded by employing more labor, create code that compares the labor cost of making the trailer \"super-loaded\" versus the cost-savings generated by adding x amount of units to the trailer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1efdd470",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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