{
"cells": [
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"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n",
"\n",
""
]
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Clarity with Data: Logistics Decision Making under Risk Part 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulating Disruption and Predicting Impact: Quantifying Supply Chain Risk Exposure"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In our previous post, we explored the importance of balancing cost and risk in supply chain optimization. We introduced a use case involving a Midwest valve manufacturer facing potential hurricane threats to its coastal suppliers. We walked through the initial steps of building a supply chain model, including defining the scope, bootstrapping the logistics network with OpenStreetMap data, and simulating a basic supplier network. In this post we will continue the example and show how simulation can be used to understand the operational and financial impacts of disruptions on the supply chain. As in previous posts, we will show our work with full implementations in code."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem Framing\n",
"**Problem**: Natural disasters like hurricanes can significantly disrupt supply chains, leading to delays, production downtimes, and financial losses.\n",
"\n",
"**Solution**: By simulating the impact of hurricanes on a supply chain network, we can estimate key performance indicators (KPIs) such as delivery delays, production downtimes, and financial losses. This simulation helps in understanding the potential risks and preparing mitigation strategies.\n",
"\n",
"**Benefit**: Organizations can use these insights to enhance their resilience against natural disasters, minimize disruptions, and reduce financial losses."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulation Code and Analysis\n",
"\n",
"The following sections contain the code and analysis for simulating the impact of hurricanes on a supply chain network.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Refactoring Previous Code for Simplicity and Clarity\n",
"\n",
"To enhance the clarity and maintainability of our code, we've refactored the functionalities from the previous post into reusable libraries. This modular approach not only simplifies the code but also promotes better organization and readability.\n",
"\n",
"Here's a glimpse of the refactored code:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"import json\n",
"import logging\n",
"import random\n",
"import math\n",
"import statistics\n",
"import networkx as nx\n",
"from joblib import Parallel, delayed\n",
"from scipy.spatial.distance import euclidean\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed\n",
"import copy\n",
"from matplotlib.ticker import FuncFormatter\n",
"\n",
"# Enable interactive plots\n",
"%matplotlib inline\n",
"\n",
"# Add the root directory to the Python path\n",
"script_dir = os.path.dirname(os.path.abspath(\"__file__\"))\n",
"project_root = os.path.abspath(os.path.join(script_dir, \"..\"))\n",
"sys.path.append(project_root)\n",
"\n",
"try:\n",
" from supplychain.simulation import build_supply_chain_from_config, simulate_supply_chain\n",
" from supplychain.visualization import visualize_supply_chain, assign_supplier_types\n",
"except ImportError as e:\n",
" logging.error(f\"Error importing modules: {e}\")\n",
" raise\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Configure Logging"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"tags": [
"hide-input"
]
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"outputs": [],
"source": [
"\n",
"# Remove all handlers associated with the root logger object\n",
"for handler in logging.root.handlers[:]:\n",
" logging.root.removeHandler(handler)\n",
"\n",
"# Configure logging\n",
"logging.basicConfig(\n",
" level=logging.ERROR, format=\"%(asctime)s - %(levelname)s - %(message)s\"\n",
")\n",
"\n",
"logging.info(\"Build supply chain from a configuration file\")\n",
"config_filepath = os.path.join(project_root, \"notebooks\", \"sim_config.json\")\n",
"combined_graph_filepath = os.path.join(\n",
" project_root, \"combined_graph.graphml\"\n",
") # Path to the GraphML file\n",
"visualization_filepath = os.path.join(project_root, \"supply_chain_graph.html\")\n",
"\n",
"# Check if config file exists\n",
"if not os.path.exists(config_filepath):\n",
" logging.error(f\"Configuration file not found: {config_filepath}\")\n",
" sys.exit(1)\n",
"\n",
"# Load configuration\n",
"with open(config_filepath, \"r\") as f:\n",
" config = json.load(f)\n",
"\n",
"G, road_networks = build_supply_chain_from_config(\n",
" config_filepath, combined_graph_filepath\n",
")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create the Supply Chain"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [
{
"data": {
"text/html": [
"
Make this Notebook Trusted to load map: File -> Trust Notebook
"
],
"text/plain": [
""
]
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"metadata": {},
"output_type": "display_data"
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"source": [
"logging.info(\"Simulate the supply chain\")\n",
"\n",
"# Assign supplier types to locations\n",
"locations_with_suppliers = assign_supplier_types(\n",
" config[\"locations\"], config[\"supplier_types\"]\n",
")\n",
"\n",
"# Visualize the graph\n",
"visualize_supply_chain(\n",
" G, locations_with_suppliers, config[\"color_map\"], visualization_filepath\n",
")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This refactoring allows us to focus on the core aspects of simulating disruptions and analyzing their impact, without getting bogged down in the complexities of building the initial network.\n",
"\n",
"Now, we'll delve deeper into simulating disruptions and predicting their impact on key performance indicators (KPIs). This will enable the valve manufacturer to proactively identify vulnerabilities and develop effective mitigation strategies."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 4: Simulate Disruption Events and Their Impact\n",
"\n",
"This step involves simulating various disruption scenarios and quantifying their potential impact on the supply chain. We will focus on hurricane events and their effects on transportation routes.\n",
"\n",
"Now, we'll delve deeper into simulating disruptions and predicting their impact on key performance indicators (KPIs). This will enable the valve manufacturer to proactively identify vulnerabilities and develop effective mitigation strategies."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Objectives:**\n",
"\n",
"* **Simulate Hurricane Events:** Model hurricane events with varying intensities and paths, affecting different parts of the coastal supplier regions.\n",
"* **Disrupt Transportation Routes:** Simulate the disruption of transportation routes based on hurricane intensity and proximity to the affected areas.\n",
"* **Calculate Disruption Impact:** Quantify the impact of disruptions on KPIs such as delivery delays, production downtime, and potential financial losses.\n",
"\n",
"**Steps:**\n",
"\n",
"1. **Define Hurricane Parameters:** Define parameters like hurricane intensity (e.g., categories 1-5), size, and path.\n",
"2. **Simulate Hurricane Impact:** Develop a function to simulate the impact of a hurricane on the transportation network. This function should consider the hurricane parameters and the proximity of transportation routes to the hurricane's path. The function could, for example, assign a probability of disruption to each route based on the hurricane's intensity and distance.\n",
"3. **Calculate KPIs:** Develop functions to calculate the impact of disruptions on KPIs. For instance:\n",
" * **Delivery Delays:** Calculate the increase in transportation time due to disrupted routes.\n",
" * **Production Downtime:** Estimate the potential downtime at the Wausau plant due to delayed deliveries of critical components.\n",
" * **Financial Losses:** Estimate potential financial losses due to production downtime, expedited shipping costs, or lost sales.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Define the Huricane Simulation Parameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"hide-input"
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"source": [
"def define_hurricane_parameters():\n",
" hurricane_intensity = random.randint(1, 5)\n",
" hurricane_size = random.randint(50, 200) # km\n",
" \n",
" # Generate a random hurricane path\n",
" start_lat = 44.0 + random.uniform(-1, 1)\n",
" start_lon = -88.0 + random.uniform(-1, 1)\n",
" path_length = random.randint(3, 5)\n",
" hurricane_path = [(start_lat + i * random.uniform(-0.5, 0.5), start_lon + i * random.uniform(-0.5, 0.5)) for i in range(path_length)]\n",
" \n",
" return hurricane_intensity, hurricane_size, hurricane_path\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Calculate the Impact of the Hurrican on the Supply Chain"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"def simulate_hurricane_impact(graph, hurricane_intensity, hurricane_size, hurricane_path):\n",
" disrupted_edges = []\n",
" for u, v, key, data in graph.edges(data=True, keys=True):\n",
" # Calculate the distance of the edge from the hurricane path\n",
" edge_coords = [(graph.nodes[u]['y'], graph.nodes[u]['x']), (graph.nodes[v]['y'], graph.nodes[v]['x'])]\n",
" min_distance = min([euclidean(edge_coords[0], point) for point in hurricane_path] +\n",
" [euclidean(edge_coords[1], point) for point in hurricane_path])\n",
" \n",
" # Assign a probability of disruption based on hurricane intensity and distance\n",
" # Adjusted disruption probability with a scaling factor\n",
" scaling_factor = 0.1 # Adjust this factor to control the probability\n",
" disruption_probability = min(1.0, scaling_factor * hurricane_intensity / 5 * (hurricane_size / (min_distance + 1)))\n",
" if random.random() < disruption_probability:\n",
" disrupted_edges.append((u, v, key))\n",
" \n",
" return disrupted_edges\n",
"\n",
"def calculate_delivery_delay(graph, disrupted_edges):\n",
" total_delay = 0\n",
" for u, v, key in disrupted_edges:\n",
" # Estimate delay based on edge length and a fixed delay factor\n",
" edge_length = graph.edges[u, v, key]['length'] \n",
" delay = edge_length / 1000 / 100 # Convert meters to hours assuming average speed\n",
" total_delay += delay\n",
" return total_delay\n",
"\n",
"def calculate_kpis(graph, disrupted_edges):\n",
" delivery_delay = calculate_delivery_delay(graph, disrupted_edges)\n",
" production_downtime = delivery_delay * 0.5 # Example factor\n",
" financial_losses = production_downtime * 10000 # Example cost per hour of downtime\n",
" return delivery_delay, production_downtime, financial_losses\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Run the Disruption Impact Simulation"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"\n",
"def run_single_simulation(graph):\n",
" graph_copy = copy.deepcopy(graph) # Create an independent copy of the graph\n",
" hurricane_intensity, hurricane_size, hurricane_path = define_hurricane_parameters()\n",
" disrupted_edges = simulate_hurricane_impact(graph_copy, hurricane_intensity, hurricane_size, hurricane_path)\n",
" delivery_delay, production_downtime, financial_loss = calculate_kpis(graph_copy, disrupted_edges)\n",
" return hurricane_intensity, hurricane_size, delivery_delay, production_downtime, financial_loss\n",
"\n",
"def run_simulation(graph, num_simulations=1000):\n",
" results = Parallel(n_jobs=-1)(delayed(run_single_simulation)(graph) for _ in range(num_simulations))\n",
" \n",
" intensities, sizes, delivery_delays, production_downtimes, financial_losses = zip(*results)\n",
" \n",
" return intensities, sizes, delivery_delays, production_downtimes, financial_losses\n"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"num_simulations = 100\n",
"intensities, sizes, delivery_delays, production_downtimes, financial_losses = run_simulation(G, num_simulations)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 5: Visualize and Analyze the Results\n",
"Visualizing the simulation results is crucial for understanding the impact of disruptions and communicating findings effectively.\n",
"\n",
"#### Objectives:\n",
"\n",
"- **Visualize KPI Impact**: Create visualizations to show the impact of disruptions on KPIs like delivery delays, production downtime, and ultimately financial loss.\n",
"- **Analyze Scenarios**: Compare the impact of different hurricane scenarios (e.g., varying intensities and paths) on the supply chain."
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hurricane Intensity: Mean: 2.92, Std Dev: 1.33, 5th: 1.00, Median: 3.00, Mode: 2, 95th: 5.00\n",
"Hurricane Size: Mean: 123.54, Std Dev: 43.22, 5th: 61.00, Median: 122.50, 95th: 191.10\n",
"Delivery Delay: Mean: 923.32 hours, Std Dev: 307.03 hours, 5th: 422.99 hours, Median: 946.37 hours, 95th: 1,386.39 hours\n",
"Production Downtime: Mean: 461.66 hours, Std Dev: 153.52 hours, 5th: 211.50 hours, Median: 473.18 hours, 95th: 693.20 hours\n",
"Financial Loss: Mean: $4,616,578.47, Std Dev: $1,535,171.53, 5th: $2,114,962.19, Median: $4,731,848.19, 95th: $6,931,954.41\n"
]
},
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xprXJt9+aNicnT0pz5pjh6Sm1b29C765dpdKlra4cwC3O8qAbAAAAyDGJiY5A2yUyUsWOHpVbTIy5PCve3plXaZcoIXl45GrZAADkCUWKSL17m5GcLP32mwm9v/lG2r/ftDtZssTMbdjQhN533CHVqsWbwQBynSVB95QpU65rvs1m09ChQ3OoGgAAAOR7drt09qwUFWXG8ePm65kzjimu/xuSzMkhLz4pZHqoXbgwL8wBAMiKu7vUpo0Z77wjRUSY0Pvbb6V160yf77/+kkaOlMqXl+68U7rrLnNiS/p6A8gFlgXdtmt8AWG32wm6AQAAkCEx0by43rxZ2rLFfJT68GHpwoWs5/v6SoGBSi1RQme9vFQ4JETuQUG86AYA4EbZbFKNGma88IJ5czn9ZJbLl0sHD0rvvWdG8eJmpfddd5lWJ15eVlcPoICyJOhu2LDhZbclJCToxIkTOnHihGw2m3r27KnixYvnYnUAAADIM06cMGF2+ti8Wdqxw5w48lLpq7SDgjJWaAcFST4+kqS05GQlnDypwiVKEHIDAJCdgoKkhx4yIz7ehN1ffWVOannqlPThh2YULix16mRC786dTWsUAMgmlgTd8+bNu+qcf/75R08//bTWr1+vL7/8Mltu9/HHH9eyZcs0btw43X333dmyTwAAAGSD1FRp1y7nQHvLFikyMuv5AQFS7dpSnTpSdLRZHVa8uOTGKWgAALCUj4/UvbsZKSnSr7+a0Pvrr6UjR6RFi8xwd5fatTOhd/fu5g1qALgJefaVQK1atfTOO++oV69emjZtmp599tmb2t+iRYu0bNmybKoOAAAAN+zsWemff5wD7X//lRISMs+12aTQUBNo166dEW6XKZPRS3vGjMsH4gAAwDpublLbtmZMmiRt2GBC76++Mp/Q+vFHMwYPlpo1M6H3XXdJlSpZXTmAfCjPBt2SFB4ervLly2v58uU3FXTv379fr7/+ejZWBgAAgKuy203v7E2bMgLtLVukffuynl+okFSrlnOgXbOm+ZgzAADI32w2qWFDM15/3QTd6aH3X39Jv/9uxjPPmOcD6aF3rVqcKBrANcnTQbckeXl5af/+/Td8/aSkJD399NNycXFR9erVFRERkY3VAQAAQJJpPbJzpwm0N23KGNHRWc8vWzbzKu2QENNnGwAAFHxVq5oTWb7wgmlp8vXXJvRevdp88uuff6TRo83q7h49pJ49TUhO6A3gMvJ00L1v3z7t2bNHAQEBN7yPd999V9u2bdObb76pZcuWEXQDAADcrAsXpK1bnQPtf/7JuvWIm5tUvboJstNHrVpSsWK5XDQAAMizypaVhg0z4/Rp6fvvTei9bJn5JNhbb5lRrlxG6N20KW+QA3BiSdC9du3aK25PSkrSvn37NGfOHKWlpalp06Y3dDt//PGH5syZoy5duqh79+706AYAALheMTGZV2lv325WcF/Kx8eszq5bN2PUrCl5euZ21QAAIL8qVkwaMMCM8+dND+8vvjDh9+HD0sSJZpQqZULvHj2kFi0kV1erKwdgMUuC7gcffFC2a/ioid1uV6FChfTYY49d921ER0frueeeU1BQkF555ZUbqPLKdcXHx2frPgErJPxv5V1CVivwctG1/D7IL+x2u9Ul4CJ59WcrKSlJ3t7eSkpKumqNNptNXl5eSk5JkZKTc6nCHJCSIneZ3zf5/TjJqz9X18tmszn/X9jtUmSkXLZscQzbli1yOXAgy+vbixVTWu3aSqtdW/b0r6GhmV9kpqVlvdI7m+7D9Rwfyf+bk5wXj6UCcozYbDZ5e3sXmN9ZBeV+WPlzlR3PN/m5ymMKyv2QCs59yeJYzyuv9a5XpudZLi5S585mXLggl59/luvXX8v1++9li4yUpkyRpkyRvWRJpd5xh1LvvFNpLVtK7u7W3IGL5Oe/5+kKwvPe3Hw9laPPNfPA33Sr2O32a/5ZtKx1yZX+U1xdXVW0aFHVq1dPQ4YMUcWKFa97/y+++KJOnz6tuXPnys/P72ZKzSQ5OVnbt2/P1n0CVjpwmRAjN7i7u6tm9epyccvTnZSuSVpKiv6NiMibAcotKC//bHl7e8vf3/+6rnM2NlaJJ0/mTEG5wN3bWyVkThCd315wXSwv/1xdl7Q02Xfvlm3LFueV2idOZD2/fHnnVdp168pWpoxcbTblhbVT13t8xMTE5FwxN6igHCPe3t6qXr26Ys6cUXI+/p3lfe6cikqKO3dOCfn4fuSln6ubeb7Jz1XeUlDuh1Rw7suVjnUrX+tdr6s+z/L2lnr1MiMxUVqxwqz0/uYb2U6ckNvs2XKbPVsKCJDuvNOs9G7fXvLwyNX7IRWM14YF5nnv/+Tm66mceK6Zl/6mW8HjGo9jS35ad+zYkaP7X7BggVauXKlHHnlEjRo1yvb9u7u7KzQ0NNv3C+S2hIQEHThwQBUqVJC3t7clNdhsNrm4uSll4ULZ8/GTS1uJEnK75x5Vrlz5lnt3Na/Kyz9bKSkpijt3ToV9feV2lSeOtsqV5XbbbfIrUkQqUSKXKswBRYtKkipWrJivj5G8/HN1WampUmSkbIcPm3HkiGyRkbJl8QTZbrNJgYGylysne9myjqFChTImHTpkRh5wvcdHcnKyYmJi5O/vL/c8sNLLSQE6RiTJv2jRHFvJnyt8fSVJhX19VZjfvTclO55v8nOVxxSU+yEVnPuSxbGeF17rXa8bep5Vv75Up45su3bJtnmz+URadLT04YfShx/K7u0te3i40urUkb1atVxZ6V1QXhvmy+e9WcjN11M5+lwzD/xNt8qePXuueW7BeFvmIrt379Ybb7yhGjVq6IknnsiR27DZbPLx8cmRfQNW8Pb2tvxn2u3MGSkf//HU/8LK/PIk8laSJ3+2kpOVfPKk3EqUuPoToKAgSZK7m1ue+AjmDStgx0ie/LmSTKh94oR07JgUGWnG8eNZ99P28pICA00fzFKlpKAg2QIDJXd3OX0wMD7ejLzoBo8Pd3f3vBd0F7BjpKD8zioo9yMv/Fxlx/PNgvL/wf3IQwrKfbnCsZ4XXutdrxt6nlW8uFm93bateUM+IkLavl22uDjZ1q+Xy/r1ZmV3lSpSjRpSaKjjccv+O5B3fvdmhzz7vPdaWfB6Kkeeaxawn6vrcT0tdApc0P32228rMTFRXl5eeuGFF5y2bdu2TZK0cOFC/fHHH2rYsKF69+5tRZkAAAA3JyXFhNjpgXZ6qJ2Wlnmup6d5kv+/QFstW0rDh0uzZ5vrAQAAFAQuLlKFCmZ06mROXvm/0Ftnz0r//muGp6cUFmZC75AQTmQJFBCWBN3PPffcTe/DZrPpjTfeyHR5+kkiN27cqI0bN2Z53U2bNmnTpk1yc3Mj6AYAAHlfcnJGqH3smBQVZVZuZxVqe3mZQDt9lC5tPup48UqI4OCcW8UEAACQF9hs5jlPcLDUsaN09Ki0bZsZ585J//xjhpeXVLWqCb0rViT0BvIxS17hfPvtt5Kcl55f2l/mcttsNpvjbJtZBd3z5s277O0OGTJEK1as0Lhx43T33XffcP0AAAA5JjnZBNnpgfaxY+bjoln14vP2dg60S5WS/P2dQ20AAIBbnc0mlS1rxm23mZXe27aZ1d5xcdLmzWZ4e0vVqkk1a5qTcbu4WF05gOtgSdD96quvaseOHVqwYIHc3NzUokUL1a5dW/7+/rpw4YJ27dql5cuX69y5c6pcubJq1qxpRZkAAAA5KynJufVIZKR06lTWobaPT0aYnT6KFCHUBgAAuB6XrvQ+dCgj9I6Pl/7+24xChTJC7+BgnnMB+YAlQXfDhg01fvx4lS9fXlOnTlVISEimOc8884yGDh2qiIgIvfnmm6pWrZoFlQIAAGSTxMSsQ+2sFC6cuf2Iry8vsAAAALLTpT29DxwwPbx37JDOn5c2bDDD11eqXt20NylbludkQB5lSdA9ceJEJSQkaPLkyVmG3JIUEBCgiRMnql27dpo0aZKmTZuWy1UCAADcoAsXnPtpHzsmRUdnPdfX1wTZQUEZK7Z9fXO3XgAAgFudi4tUqZIZXbpI+/ebld7bt5ue3uvWmeHnZwLvmjXN8zZCbyDPsCToXrdunUJDQ1WlSpUrzgsMDFSVKlW0YcOGbLndqVOnZst+AAAAHNJXah87ljHOnMl6bpEiziu1S5Uyq7cBAACQd7i6SqGhZnTpIu3da0LvnTuls2eltWvNKFbMBN7h4eZ7AJayJOhOTExUWlraNc2Ni4u75rkAAAA5Kj5e2rJF+uUX85HWY8cu337E3z9zqF2oUK6WCwAAgJvk5iaFhZmRnCzt2ZMRep8+La1ebUbp0ibwbtvW6oqBW5YlQXf58uW1Y8cObdu2TTVq1LjsvLVr1+rQoUNq0KBBLlYHAAAgc6LIrVulv/4yvRn/+su8qElNzTzXz08qUyajn3bp0pK3d+7XDAAAgJzj7m5OUFmtmvlU344dpqf33r0Zn+z76Sfp55+l++6T7rrLfKIPQK6wJOju3r27tm/friFDhuiNN95QkyZNMs355Zdf9OKLL8pms+m+++6zoEoAAHDLSEkx/RfTQ+0NG8zK7aSkzHMDA6WSJaWAgIxwm/YjAAAAtxZPT6l2bTPi4qSICLNI4sgRaflyMwYPlu64w4TenTub6wDIMZYE3ffdd59+/PFHbd68WQ8++KBKly6tKlWqyMfHR+fPn9eOHTt0/Phx2e123XHHHercubMVZQIAgIIoLU3avdt5pfamTVJCQua5AQFSgwZmNGxovpYpI82cafpyAwAAAIULS40ameH2v6htwQKz4vuLL8woUkTq2VPq21dq2dL0AQeQrSwJuj08PDRr1iy9/vrr+vrrr3X06FEdPXrUaY6Xl5cefPBBDR8+3IoSAQBAQWC3SwcOOIfaGzdK585lnuvrK9Wv7xxqV6wo2Wy5XjYAAADyqRIlpEcflV56Sdq8WfrkE+nTT6WjR6XZs80oU0bq08es9K5bl+ebQDaxJOiWJF9fX40bN05PPvmkVq5cqUOHDikuLk6FCxdWlSpV1KpVKxUtWtSq8gAAQH5jt5sXEOmtR9LD7ejozHO9vc2LivRAu0EDqUoVycUl9+sGAABAwWOzmeebdetK48dLa9aYVd5ffGGes06YYEbVqlK/fmaUL2911UC+ZlnQnS4wMFB9+vSxugwAAJDfnDiROdSOiso8z93d9E68ONSuXj3jY6UAAABATnJ1lVq3NmPKFOmHH8xK7+++M+1NRowwo3VracAAqUcP82lDANfF8ld4aWlp2rZtm/bt26dz586pX79+Sk5OVlRUlMqVK2d1eQAAIC84c8a0HLk41D50KPM8V1epRo2MULthQ6lmTU78AwAAgLzB01O6804zzp6VvvxS+vhjaeVKadUqM4YMke6+W+rfX2rXjn7ewDWyNOhevHixJk+erOPHjzsu69evn44dO6bOnTurU6dOGjt2rDx5cQoAwK0jIcGcHHL9ehNqr18v7dmTeZ7NJoWFOYfatWtLPj65XzMAAABwvfz8pAceMOPgQdPaZO5cadcu8/2CBVLp0qatSf/+ZkEHgMuyLOh+5513NGvWLNntdrm4uMjFxUWpqamSpKioKKWmpmrJkiU6fvy45syZIzc+XgwAQMGTmipFRJgwOz3Y/ucfc/mlQkIyWo80bGj6Hfr55X7NAAAAQHYrX1568UXphRfM8+KPPzYnsTx2THrzTTPq1TOB9733SiVLWl0xkOdYkh7/+eefmjlzpry9vfXf//5XXbt21aBBg7Rp0yZJUuPGjfXmm2/qlVde0YYNG/T555+rb9++VpQKAACyi91uVqqkh9rr10t//y2dP595bmCg1Lix1KhRxortgIDcrxkAAADITTabeR7cuLH0zjvS0qVmlfeSJea5899/S888I3XpIg0cKHXqZM5JA8CaoHvevHmy2Wx6/fXX1alTpyzndOvWTT4+Pho2bJi+++47gm4AAPKb+HhzRvmjR6XTp80Jdk6dyjzP19cE2Y0aZQTbZcuaJ/kAAADArcrTU7rrLjNOnZI++8yE3hs2SN98Y0ZgoFnl/eCDUrVqVlcMWMqSoHvz5s0qXrz4ZUPudO3bt1fJkiW1J6u+nAAAIO9ISpKiojKC7aNHpZiYzPPc3U0f7YtD7bAwTrADAAAAXEnx4tKwYWZs2ybNmWPamxw/Lr31lhlNmphV3r170+IPtyRLgu7Y2FhVrVr1muYGBgZq+/btOVwRAAC4Zmlp0okTGYH2sWPm33Z75rnFikllypjVJf/5j1SrluTllfs1AwAAAAVFjRrS229L48aZliZz5pivf/5pxhNPSL16mVXeLVtKLi5WVwzkCkuCbn9/fx0+fPiq8+x2u44cOaKiRYvmQlUAACATu106cyYj0D56VIqMlFJSMs/19TWhdunSGV/TQ+1SpcwKbgAAAADZw91duvNOM6KipHnzpA8/lHbsMKu9P/5YqlTJBN4DBkjlylldMZCjLAm669Wrp+XLl2vJkiXq0qXLZed99dVXOnPmjG677bZcrA4AgFtYXFxGoJ3+NSEh8zxPz4xAOz3U5uORAAAAgDWCgqRnnzUnqly3zgTen30m7dsnjRwpjRplTlw5aJDUubPkZkkkCOQoS36q77//fv30008aM2aMvLy81K5dO6ftaWlpWrx4scaOHSubzaY+ffpYUSYAAAVbUlLmUDs2NvM8V1fzxPniYLtYMU4WCQAAAOQ1Npvp1d2kifTuu9KXX0qzZ0urV5v2JkuWmOf1Dz1kRvnyVlcMZBtLgu6GDRvq4Ycf1gcffKBhw4apUKFCSk5OliT17NlTBw4c0Pnz52W323XPPfeoWbNmVpQJAEDBkZqaua/2yZNZ99UuUcI51A4M5GSRAAAAQH5TqJB0//1m7NolffCB6ed97Jj06qvSa69Jt99uVnl36WJaoQD5mGWfU3jmmWdUtmxZTZ48WadPn3Zc/u+//0qSfH19NWjQID3yyCNWlQgAQP5kt0vR0c6hdmSkCbsv5eeXua+2p2fu1wwAAAAg51SpIr35pgm4v/lGmjlTWrFC+uEHM0qVkgYONKu8K1a0ulrghljakKdPnz7q0aOHNm3apN27d+vcuXPy9vZWxYoV1bBhQ3l7e1tZHgAA+cO5cxmtR9KD7QsXMs/z8socavv65n69AAAAAKzh6Sndc48Ze/ZkrPKOjJTGjpVef13q0MGs8u7WzepqgetiSdCdvpr7kUceUaFChdSoUSM1atTIilIAAMhfEhMz99U+ezbzPFdXsyrj4mA7IIC+2gAAAACM0FBp/HhpzBjp22/NKu/ly6WffjKjVCmpXj2zGpwTzyMfsCToXrNmjVxcXDRs2DArbh4AgPwhJcWsspg6VZo3z3x/6lTWc0uWdO6rXbIkfbUBAAAAXJ2Hh9Szpxn79plV3h9+aFZ5L1kiLV0qVasmNWggVajA4hnkWZYE3RcuXFBISIjc3CztnAIAQN5ht0unTzu3H4mKyrqvdpEiGYF26dJmpQV9tQEAAADcrEqVTPuSV16RvvpKGjHCLLiJiDCjeHGpYUOpdm1egyDPsSRpbty4sdauXat9+/apUqVKVpQAAIClXM6fly062oTZx46ZkZiYeWLhwlLz5mZ1dvqJIwsVyv2CAQAAANw6PDyk3r2lmBhp82bpr7+kf/4xnzD94Qfp55+lWrVM6B0YaHW1gCSLgu7XXntNDz30kPr27au+ffuqXr16KlGihLy8vC57nXLlyuVihQAAZKOL+2ofPSq3I0cUFBeXeZ6bW0Zf7fTRooX5COGMGeajgwAAAACQmwIDpa5dzUkqt2wxofepU9LGjWYEB5vAu1o12ifCUpYE3XfffbeSk5MVGxur999//6rzbTabIiIicqEyAABuUmqqdOJERguSo0elkyedptgk2W02qUQJ2S4OtUuWlFxcnPdH/zsAAAAAeYGnp9SokQm1DxyQNmyQtm+XDh0yo1Ah08e7QQPzyVQgl1kSdJ+66ERadrv9qvOvZQ4AALnObpdiY6UjRzJC7chIcxLJS13UVzslMFAn3dxUvHRpubu7537dAAAAAHCjbDapYkUzzp6V/v7brOyOi5NWr5Z++02qWVNq3Nh8YhXIJZYE3StWrLDiZgEAuDkJCaYFyZEjGV/j4zPP8/R0bj9SpozTigZ7crLsl6zyBgAAAIB8x89Pat3atFzcvl3680+zAGjLFjOCg03gXbVq5k+vAtnMkqC7TJkyVtwsAADXLiVFOn7cuQXJ6dOZ57m4SEFBzqF2sWK0HAEAAABw63B1Nau4a9Y0C4LWrZMiIjLamhQpYtqe1K0reXtbXS0KqBwPuuPi4uTq6ipvfogBAHmV3S5FRzuH2lFRpt/2pQICnEPtoCBzEkkAAAAAgFS2rBkdOpgTV27caFo+Ll8urVol1a5tVnkXL251pShgcvyVeYMGDdSgQQPNnz8/p28KAIBrc/68c6h99Kh04ULmed7e5gla6dIZX318cr9eAAAAAMhv/Pykdu2kli2lrVvNKu8TJ8xJLDdskEJCpKZNpUqV+EQsskWuLEG70skk+/fvr7CwML300ku5UQoA4FaTlGQ+KndxqB0Tk3meq6s5UUr6Su2yZSV/f55wAQAAAMDNcHeX6tUzbUsOHDCB986d0t69ZgQGmsC7Zk3zugy4QZZ/1nr9+vVKzeqj4QAAXK+0NGnHDvPEaf1683XLFnP5pYoXd25BEhjIkyoAAAAAyCk2m1SxohnR0eb12qZN5txIX38trVhhWprUry95eVldLfIhy4NuAABuWGSkeXKUHmz/9Zd07lzmeYULO4fapUvzxAkAAAAArBIQIHXqJLVubdqYrF9vXsv9/LP0668m7G7c2JzEErhGBN0AgPzh/HnzBOji1dpHjmSe5+MjNWhgnhQ1aiTt3m36b9OCBAAAAADyFm9vqUUL07pk61bpjz+kU6ektWvNa76aNc22oCCrK0U+QNANAMh70tJMQP3nnxnjn38ytyBxcTFPfBo1ygi2q1eX3C768zZjhln5DQAAAADIm9zcTA/vOnXMa8E//pAOHjSvA//5x5ywslkzTlyJKyLoBgBY78wZ8279n39mtCI5cybzvLJlpSZNMoLtevVMWxIAAAAAQP5ns0lVqphx9KhZ2R0RIe3bZ0ZgoPR//yfVqGEWPgEXIegGAOSulBTp33+dV2vv3Jl5nre3aUHSpIkZjRub/toAAAAAgIKvTBmpZ0+zCOrPPzNOXPnll9LKlSbwrl3b+RO9uKXxkwDcwmw2m7y9vWXjYz/ISVFRzqH2X39J8fGZ51WunBFqN2kihYdL7u65Xy8AAMg2PN8EANy0okUzTlyZfr6mM2ek77+XVq82Pbzr15c8PKyuFBbLlaD79OnT+vrrr294uyTdeeed2VoTblBaWsH4aAj3Q5Lk7e2t6tWrZ2NBt7DChQvOz5V04/clMdG8y35xsH3wYOZ5fn5mhXZ6qN2okVS8+M3XDQAAMuSB5yc83wQAZBtvb6lVKxNsb9xo2pqcOyf99JO0Zk3GeZu8va2uFBbJlaD74MGDeuGFF7LcZrPZrrg9fQ5Bdx7h4iItXmzOgJtfFS8u9ehhdRXZ4yb/P5JTUhRz5oz8ixaVu1Uf9QkNldq1s+a2s5OXV8E4PqRrP0bsdhNiXxxqb9okJSU5z7PZzAkjL16tXbVqwXlTAACAvCoPPD/JluebBeX5IgAge3h4mLC7YUNzosrffjMrvFetMiexrF/fbPf1tbpS5LJcSbbsdrul10c2O3VKioy0ugqku5n/j+RkJZ88KSUkWNcioqCt4i3Ix0d8vGk7snZtRrB9/HjmeSVKmCcV6Su2GzbkCQYAAFay8vlJdjzfLGjPFwEA2cPNTapXT6pTx5yw8rffzGvUtWtNi5M6daSSJa2uErkox4PuHTt25PRNAABywpEj5t3w3383XzdvNieSvJi7u1S3rvNq7QoVzCpuAAAAAABymouL+RRxjRrS7t0m8D582LQ32bTJBN+hoVZ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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def calculate_statistics(data):\n",
" mean = np.mean(data)\n",
" std_dev = np.std(data)\n",
" quantiles = np.percentile(data, [5, 50, 95])\n",
" median = np.median(data)\n",
" try:\n",
" mode = statistics.mode(data)\n",
" except statistics.StatisticsError:\n",
" mode = None # In case there's no unique mode\n",
" return mean, std_dev, quantiles, median, mode\n",
"\n",
"# Calculate statistics\n",
"intensity_stats = calculate_statistics(intensities)\n",
"size_stats = calculate_statistics(sizes)\n",
"delivery_delay_stats = calculate_statistics(delivery_delays)\n",
"production_downtime_stats = calculate_statistics(production_downtimes)\n",
"financial_loss_stats = calculate_statistics(financial_losses)\n",
"\n",
"# Print the statistics\n",
"print(f\"Hurricane Intensity: Mean: {intensity_stats[0]:,.2f}, Std Dev: {intensity_stats[1]:,.2f}, 5th: {intensity_stats[2][0]:,.2f}, Median: {intensity_stats[3]:,.2f}, Mode: {intensity_stats[4]}, 95th: {intensity_stats[2][2]:,.2f}\")\n",
"print(f\"Hurricane Size: Mean: {size_stats[0]:,.2f}, Std Dev: {size_stats[1]:,.2f}, 5th: {size_stats[2][0]:,.2f}, Median: {size_stats[3]:,.2f}, 95th: {size_stats[2][2]:,.2f}\")\n",
"print(f\"Delivery Delay: Mean: {delivery_delay_stats[0]:,.2f} hours, Std Dev: {delivery_delay_stats[1]:,.2f} hours, 5th: {delivery_delay_stats[2][0]:,.2f} hours, Median: {delivery_delay_stats[3]:,.2f} hours, 95th: {delivery_delay_stats[2][2]:,.2f} hours\")\n",
"print(f\"Production Downtime: Mean: {production_downtime_stats[0]:,.2f} hours, Std Dev: {production_downtime_stats[1]:,.2f} hours, 5th: {production_downtime_stats[2][0]:,.2f} hours, Median: {production_downtime_stats[3]:,.2f} hours, 95th: {production_downtime_stats[2][2]:,.2f} hours\")\n",
"print(f\"Financial Loss: Mean: ${financial_loss_stats[0]:,.2f}, Std Dev: ${financial_loss_stats[1]:,.2f}, 5th: ${financial_loss_stats[2][0]:,.2f}, Median: ${financial_loss_stats[3]:,.2f}, 95th: ${financial_loss_stats[2][2]:,.2f}\")\n",
"# Function to format the financial loss axis\n",
"def millions(x, pos):\n",
" 'The two args are the value and tick position'\n",
" return f'${x*1e-6:.1f}M'\n",
"\n",
"# Increase font size\n",
"sns.set(style=\"whitegrid\", font_scale=1.5)\n",
"\n",
"\n",
"# Display results in seaborn plots\n",
"fig, axes = plt.subplots(5, 1, figsize=(15, 30)) # 5 rows, 1 column\n",
"\n",
"# Plot Hurricane Intensity\n",
"sns.histplot(intensities, bins=25, kde=True, color='blue', ax=axes[0])\n",
"axes[0].set_title('Hurricane Intensity')\n",
"axes[0].set_xlabel('Intensity')\n",
"axes[0].set_ylabel('Frequency')\n",
"axes[0].annotate(f'Mean: {intensity_stats[0]:.2f}\\nStd Dev: {intensity_stats[1]:.2f}\\n5th: {intensity_stats[2][0]:.2f}\\nMedian: {intensity_stats[3]:.2f}\\nMode: {intensity_stats[4]}\\n95th: {intensity_stats[2][2]:.2f}',\n",
" xy=(0.95, 0.95), xycoords='axes fraction', fontsize=12, ha='right', va='top', bbox=dict(boxstyle='round,pad=0.3', edgecolor='black', facecolor='white'))\n",
"\n",
"# Plot Hurricane Size\n",
"sns.histplot(sizes, bins=25, kde=True, color='green', ax=axes[1])\n",
"axes[1].set_title('Hurricane Size')\n",
"axes[1].set_xlabel('Size (km)')\n",
"axes[1].set_ylabel('Frequency')\n",
"axes[1].annotate(f'Mean: {size_stats[0]:.2f}\\nStd Dev: {size_stats[1]:.2f}\\n5th: {size_stats[2][0]:.2f}\\nMedian: {size_stats[3]:.2f}\\n95th: {size_stats[2][2]:.2f}',\n",
" xy=(0.95, 0.95), xycoords='axes fraction', fontsize=12, ha='right', va='top', bbox=dict(boxstyle='round,pad=0.3', edgecolor='black', facecolor='white'))\n",
"\n",
"# Plot Delivery Delay\n",
"sns.histplot(delivery_delays, bins=25, kde=True, color='red', ax=axes[2])\n",
"axes[2].set_title('Delivery Delay')\n",
"axes[2].set_xlabel('Delay (hours)')\n",
"axes[2].set_ylabel('Frequency')\n",
"axes[2].annotate(f'Mean: {delivery_delay_stats[0]:.2f}\\nStd Dev: {delivery_delay_stats[1]:.2f}\\n5th: {delivery_delay_stats[2][0]:.2f}\\nMedian: {delivery_delay_stats[3]:.2f}\\n95th: {delivery_delay_stats[2][2]:.2f}',\n",
" xy=(0.95, 0.95), xycoords='axes fraction', fontsize=12, ha='right', va='top', bbox=dict(boxstyle='round,pad=0.3', edgecolor='black', facecolor='white'))\n",
"\n",
"# Plot Production Downtime\n",
"sns.histplot(production_downtimes, bins=25, kde=True, color='purple', ax=axes[3])\n",
"axes[3].set_title('Production Downtime')\n",
"axes[3].set_xlabel('Downtime (hours)')\n",
"axes[3].set_ylabel('Frequency')\n",
"axes[3].annotate(f'Mean: {production_downtime_stats[0]:.2f}\\nStd Dev: {production_downtime_stats[1]:.2f}\\n5th: {production_downtime_stats[2][0]:.2f}\\nMedian: {production_downtime_stats[3]:,.2f}\\n95th: {production_downtime_stats[2][2]:.2f}',\n",
" xy=(0.95, 0.95), xycoords='axes fraction', fontsize=12, ha='right', va='top', bbox=dict(boxstyle='round,pad=0.3', edgecolor='black', facecolor='white'))\n",
"\n",
"# Plot Financial Loss\n",
"sns.histplot(financial_losses, bins=25, kde=True, color='orange', ax=axes[4])\n",
"axes[4].set_title('Financial Loss from Disruptions')\n",
"axes[4].set_xlabel('Loss ($)')\n",
"axes[4].set_ylabel('Frequency')\n",
"axes[4].xaxis.set_major_formatter(FuncFormatter(millions))\n",
"axes[4].annotate(f'Mean: ${financial_loss_stats[0]:,.2f}\\nStd Dev: ${financial_loss_stats[1]:,.2f}\\n5th: ${financial_loss_stats[2][0]:,.2f}\\nMedian: ${financial_loss_stats[3]:,.2f}\\n95th: ${financial_loss_stats[2][2]:,.2f}',\n",
" xy=(0.95, 0.95), xycoords='axes fraction', fontsize=12, ha='right', va='top', bbox=dict(boxstyle='round,pad=0.3', edgecolor='black', facecolor='white'))\n",
"\n",
"# Adjust layout with increased spacing\n",
"plt.tight_layout()\n",
"plt.subplots_adjust(hspace=0.5) # Increase the height space between plots\n",
"plt.show()\n"
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"## What next?\n",
"\n",
"Based on the insights gained from the simulation, organizations may:\n",
"\n",
"1. **Develop Mitigation Strategies**: Create plans to mitigate the impact of hurricanes on supply chains, such as diversifying suppliers, increasing inventory levels, and improving logistics.\n",
"2. **Enhance Resilience**: Implement measures to enhance the resilience of supply chains, such as strengthening infrastructure and improving communication channels.\n",
"3. **Continuous Monitoring**: Continuously monitor the supply chain network and update the simulation model with new data to ensure preparedness for future natural disasters.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusion\n",
"By simulating disruptions and quantifying their impact, the valve manufacturer can gain valuable insights into its supply chain vulnerabilities. This information empowers the company to make data-driven decisions, develop effective mitigation strategies, and build a more resilient supply chain.\n",
"\n",
"In the next installment of this series, we will explore how to leverage these insights to optimize the supply chain for resilience, considering factors like alternative sourcing, inventory management, and flexible transportation options. Stay tuned!"
]
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