Supply_Chain_Analysis_Case_Study.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Dsv6be8MrTB",
        "outputId": "70dc923e-455f-483b-a68c-eaa9e642846f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Product type   SKU      Price  Availability  Number of products sold  \\\n",
            "0     haircare  SKU0  69.808006            55                      802   \n",
            "1     skincare  SKU1  14.843523            95                      736   \n",
            "2     haircare  SKU2  11.319683            34                        8   \n",
            "3     skincare  SKU3  61.163343            68                       83   \n",
            "4     skincare  SKU4   4.805496            26                      871   \n",
            "\n",
            "   Revenue generated Customer demographics  Stock levels  Lead times  \\\n",
            "0        8661.996792            Non-binary            58           7   \n",
            "1        7460.900065                Female            53          30   \n",
            "2        9577.749626               Unknown             1          10   \n",
            "3        7766.836426            Non-binary            23          13   \n",
            "4        2686.505152            Non-binary             5           3   \n",
            "\n",
            "   Order quantities  ...  Location Lead time  Production volumes  \\\n",
            "0                96  ...    Mumbai        29                 215   \n",
            "1                37  ...    Mumbai        23                 517   \n",
            "2                88  ...    Mumbai        12                 971   \n",
            "3                59  ...   Kolkata        24                 937   \n",
            "4                56  ...     Delhi         5                 414   \n",
            "\n",
            "  Manufacturing lead time Manufacturing costs  Inspection results  \\\n",
            "0                      29           46.279879             Pending   \n",
            "1                      30           33.616769             Pending   \n",
            "2                      27           30.688019             Pending   \n",
            "3                      18           35.624741                Fail   \n",
            "4                       3           92.065161                Fail   \n",
            "\n",
            "   Defect rates  Transportation modes   Routes       Costs  \n",
            "0      0.226410                  Road  Route B  187.752075  \n",
            "1      4.854068                  Road  Route B  503.065579  \n",
            "2      4.580593                   Air  Route C  141.920282  \n",
            "3      4.746649                  Rail  Route A  254.776159  \n",
            "4      3.145580                   Air  Route A  923.440632  \n",
            "\n",
            "[5 rows x 24 columns]\n",
            "            Price  Availability  Number of products sold  Revenue generated  \\\n",
            "count  100.000000    100.000000               100.000000         100.000000   \n",
            "mean    49.462461     48.400000               460.990000        5776.048187   \n",
            "std     31.168193     30.743317               303.780074        2732.841744   \n",
            "min      1.699976      1.000000                 8.000000        1061.618523   \n",
            "25%     19.597823     22.750000               184.250000        2812.847151   \n",
            "50%     51.239831     43.500000               392.500000        6006.352023   \n",
            "75%     77.198228     75.000000               704.250000        8253.976921   \n",
            "max     99.171329    100.000000               996.000000        9866.465458   \n",
            "\n",
            "       Stock levels  Lead times  Order quantities  Shipping times  \\\n",
            "count    100.000000  100.000000        100.000000      100.000000   \n",
            "mean      47.770000   15.960000         49.220000        5.750000   \n",
            "std       31.369372    8.785801         26.784429        2.724283   \n",
            "min        0.000000    1.000000          1.000000        1.000000   \n",
            "25%       16.750000    8.000000         26.000000        3.750000   \n",
            "50%       47.500000   17.000000         52.000000        6.000000   \n",
            "75%       73.000000   24.000000         71.250000        8.000000   \n",
            "max      100.000000   30.000000         96.000000       10.000000   \n",
            "\n",
            "       Shipping costs   Lead time  Production volumes  \\\n",
            "count      100.000000  100.000000          100.000000   \n",
            "mean         5.548149   17.080000          567.840000   \n",
            "std          2.651376    8.846251          263.046861   \n",
            "min          1.013487    1.000000          104.000000   \n",
            "25%          3.540248   10.000000          352.000000   \n",
            "50%          5.320534   18.000000          568.500000   \n",
            "75%          7.601695   25.000000          797.000000   \n",
            "max          9.929816   30.000000          985.000000   \n",
            "\n",
            "       Manufacturing lead time  Manufacturing costs  Defect rates       Costs  \n",
            "count                100.00000           100.000000    100.000000  100.000000  \n",
            "mean                  14.77000            47.266693      2.277158  529.245782  \n",
            "std                    8.91243            28.982841      1.461366  258.301696  \n",
            "min                    1.00000             1.085069      0.018608  103.916248  \n",
            "25%                    7.00000            22.983299      1.009650  318.778455  \n",
            "50%                   14.00000            45.905622      2.141863  520.430444  \n",
            "75%                   23.00000            68.621026      3.563995  763.078231  \n",
            "max                   30.00000            99.466109      4.939255  997.413450  \n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "import plotly.io as pio\n",
        "import plotly.graph_objects as go\n",
        "pio.templates.default = \"plotly_white\"\n",
        "\n",
        "data = pd.read_csv(\"supply_chain_data.csv\")\n",
        "print(data.head())\n",
        "print(data.describe())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.scatter(data, x='Price',\n",
        "                 y='Revenue generated',\n",
        "                 color='Product type',\n",
        "                 hover_data=['Number of products sold'],\n",
        "                 trendline=\"ols\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "rkCGOlxsNUHf",
        "outputId": "91a29947-0c3c-47e6-9e74-456c6c0e3cea"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "sales_data = data.groupby('Product type')['Number of products sold'].sum().reset_index()\n",
        "\n",
        "pie_chart = px.pie(sales_data, values='Number of products sold', names='Product type',\n",
        "                   title='Sales by Product Type',\n",
        "                   hover_data=['Number of products sold'],\n",
        "                   hole=0.5,\n",
        "                   color_discrete_sequence=px.colors.qualitative.Pastel)\n",
        "\n",
        "pie_chart.update_traces(textposition='inside', textinfo='percent+label')\n",
        "pie_chart.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "BcyDddOMOCOF",
        "outputId": "b3944039-b03f-406d-e16b-df1c35ccdad4"
      },
      "execution_count": 5,
      "outputs": [
        {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Skincare goods account for 45% of the business, followed by haircare (29.5%) and cosmetics (25.5%). Now, let's look at the entire income made by shipping carriers."
      ],
      "metadata": {
        "id": "kHjxifCBSGtO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "total_revenue = data.groupby('Shipping carriers')['Revenue generated'].sum().reset_index()\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Bar(x=total_revenue['Shipping carriers'],\n",
        "                     y=total_revenue['Revenue generated']))\n",
        "fig.update_layout(title='Total Revenue by Shipping Carrier',\n",
        "                  xaxis_title='Shipping Carrier',\n",
        "                  yaxis_title='Revenue Generated')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "yv-KG7rWOHM0",
        "outputId": "d3cf4f36-1c95-4a9a-b9b9-1274535bd45b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The corporation uses three carriers for transportation, with Carrier B helping it generate more income. Let us now look at the average lead time and manufacturing costs for all of the company's goods."
      ],
      "metadata": {
        "id": "mcvd97xCOexd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "avg_lead_time = data.groupby('Product type')['Lead time'].mean().reset_index()\n",
        "avg_manufacturing_costs = data.groupby('Product type')['Manufacturing costs'].mean().reset_index()\n",
        "result = pd.merge(avg_lead_time, avg_manufacturing_costs, on='Product type')\n",
        "result.rename(columns={'Lead time': 'Average Lead Time', 'Manufacturing costs': 'Average Manufacturing Costs'}, inplace=True)\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mT-_ZaEfOZMf",
        "outputId": "e96d0515-2b91-42a5-9e77-a6602bf04a4a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Product type  Average Lead Time  Average Manufacturing Costs\n",
            "0    cosmetics          13.538462                    43.052740\n",
            "1     haircare          18.705882                    48.457993\n",
            "2     skincare          18.000000                    48.993157\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analyze SKUs\n",
        "There is a column in the dataset called SKUs. You must've heard it for the first time. So, SKU stands for Stock Keeping Unit. They're like unique codes that allow businesses to keep track of all the different items they have for sale. Assume you own a huge toy store with a lot of toys. Each toy is unique, with its own name and price, but if you want to know how many you have left, you must first identify them. So you assign each toy a unique code, similar to a secret number that only the retailer knows. This secret number is known as SKU.\n",
        "\n",
        "Now let's look at the income generated by each SKU:"
      ],
      "metadata": {
        "id": "ZLRdfU_ROoDE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "revenue_chart = px.line(data, x='SKU',\n",
        "                        y='Revenue generated',\n",
        "                        title='Revenue Generated by SKU')\n",
        "revenue_chart.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "9I_0yjqROkCI",
        "outputId": "055ec602-28ea-4e95-fc9b-e0f8c2faf226"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is another column in the dataset called Stock Levels. Stock levels are the quantity of items in a store or business's inventory. Let's have a look at the stock levels of each SKU:"
      ],
      "metadata": {
        "id": "fO-5V0g8Ov7P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock_chart = px.line(data, x='SKU',\n",
        "                      y='Stock levels',\n",
        "                      title='Stock Levels by SKU')\n",
        "stock_chart.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "lmOr4B6OOqrU",
        "outputId": "19ff8f42-d7ed-44e7-9fda-e2329bf25c00"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "markdown",
      "source": [
        "The order quantity of each SKU:"
      ],
      "metadata": {
        "id": "ByxCBZEJPRim"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "order_quantity_chart = px.bar(data, x='SKU',\n",
        "                              y='Order quantities',\n",
        "                              title='Order Quantity by SKU')\n",
        "order_quantity_chart.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "neWCuUyfOyyP",
        "outputId": "68aa467e-e9a8-4fd2-c08a-13db67ba7e59"
      },
      "execution_count": 10,
      "outputs": [
        {
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    {
      "cell_type": "markdown",
      "source": [
        "Cost Analysis\n",
        "analyze the shipping cost of Carriers:"
      ],
      "metadata": {
        "id": "oH3yrkQCPX6J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "shipping_cost_chart = px.bar(data, x='Shipping carriers',\n",
        "                             y='Shipping costs',\n",
        "                             title='Shipping Costs by Carrier')\n",
        "shipping_cost_chart.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Ncl8WHwIPURN",
        "outputId": "2bfac6a7-3362-474b-f67a-36f6355f725a"
      },
      "execution_count": 11,
      "outputs": [
        {
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    {
      "cell_type": "markdown",
      "source": [
        "In one of the aforementioned visualizations, I noticed that Carrier B increases the company's income. It is also the most expensive Carrier of the three. Let's have a look at the cost distribution by transit method:"
      ],
      "metadata": {
        "id": "bB354VcBPkn_"
      }
    },
    {
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      "source": [
        "transportation_chart = px.pie(data,\n",
        "                              values='Costs',\n",
        "                              names='Transportation modes',\n",
        "                              title='Cost Distribution by Transportation Mode',\n",
        "                              hole=0.5,\n",
        "                              color_discrete_sequence=px.colors.qualitative.Pastel)\n",
        "transportation_chart.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "mTVQxfx8PeRX",
        "outputId": "b131ff3c-8dfb-4868-ae8f-4b0d6845b2f1"
      },
      "execution_count": 12,
      "outputs": [
        {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "The corporation invests more in road and rail forms of transportation for goods."
      ],
      "metadata": {
        "id": "hx85eF_GPrjv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analyzing the Defect Rate\n",
        "The supply chain defect rate is the percentage of items that are discovered to be defective or damaged after shipment. Let us look at the average failure rate of all product kinds:"
      ],
      "metadata": {
        "id": "5TsIkLZrPwm6"
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    {
      "cell_type": "code",
      "source": [
        "defect_rates_by_product = data.groupby('Product type')['Defect rates'].mean().reset_index()\n",
        "\n",
        "fig = px.bar(defect_rates_by_product, x='Product type', y='Defect rates',\n",
        "             title='Average Defect Rates by Product Type')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Z7rkURxePpFZ",
        "outputId": "28f25feb-ada0-4adf-9468-de9949568817"
      },
      "execution_count": 13,
      "outputs": [
        {
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    },
    {
      "cell_type": "markdown",
      "source": [
        " the defect rate of haircare products is higher. Now let’s have a look at the defect rates by mode of transportation:"
      ],
      "metadata": {
        "id": "lVIrh-gPP46e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pivot_table = pd.pivot_table(data, values='Defect rates',\n",
        "                             index=['Transportation modes'],\n",
        "                             aggfunc='mean')\n",
        "\n",
        "transportation_chart = px.pie(values=pivot_table[\"Defect rates\"],\n",
        "                              names=pivot_table.index,\n",
        "                              title='Defect Rates by Transportation Mode',\n",
        "                              hole=0.5,\n",
        "                              color_discrete_sequence=px.colors.qualitative.Pastel)\n",
        "transportation_chart.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "CbwkGwsOP0y7",
        "outputId": "5caa0243-75a0-47d8-931e-aa0d88ff0d95"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
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    {
      "cell_type": "markdown",
      "source": [
        "Road transport has a greater failure rate, but air transportation has the lowest defect rate.\n",
        "\n",
        "So this is one method for analyzing a company's supply chain using the Python programming language.\n",
        "\n",
        "Summary\n",
        "Supply Chain Analysis is the process of assessing various components of a supply chain to determine ways to enhance its performance and produce more value for consumers. I hope you like this post about Supply Chain Analysis with Python."
      ],
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}