Update work/tbx/charter/meraki_info.md

work/tbx/charter/meraki_info.md

@@ -1,3 +1,102 @@
+To use Jupyter Notebook for displaying the calculations and visualizations, you can refactor the code into separate cells within a notebook. Here's how you can organize the code in a Jupyter Notebook:
+
+```python
+# Cell 1: Import necessary libraries
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Cell 2: Load the inventory data from a CSV file
+data = pd.read_csv("raw_data.csv")
+
+# Cell 3: Create a summary table for slow-moving inventory
+slow_moving = data[(data['Qty on hand'] > 0) & (data['12mo Iss Qty'] == 0)]
+slow_moving_table = slow_moving[['Material', 'Material description', 'Qty on hand', 'Value on hand']]
+print("Slow-Moving Inventory:")
+display(slow_moving_table)
+
+# Cell 4: Create a summary table for fast-moving inventory
+fast_moving = data[(data['Qty on hand'] > 0) & (data['12mo Iss Qty'] > 1000)]
+fast_moving_table = fast_moving[['Material', 'Material description', 'Qty on hand', '12mo Iss Qty', '12mo Iss Val']]
+print("Fast-Moving Inventory:")
+display(fast_moving_table)
+
+# Cell 5: Create a summary table for potential excess inventory
+excess_inventory = data[(data['Qty on hand'] > 500) & (data['YTD Issuance Qty'] < data['Qty on hand'] * 0.5)]
+excess_inventory_table = excess_inventory[['Material', 'Material description', 'Qty on hand', 'YTD Issuance Qty']]
+print("Potential Excess Inventory:")
+display(excess_inventory_table)
+
+# Cell 6: Perform ABC classification
+data['ABC_Class'] = pd.qcut(data['12mo Iss Val'], q=[0, 0.7, 0.9, 1.0], labels=['A', 'B', 'C'])
+abc_summary = data.groupby('ABC_Class').agg({'12mo Iss Val': 'sum', 'Material': 'count'}).reset_index()
+abc_summary.columns = ['ABC_Class', 'Issuance Value', 'Item Count']
+print("ABC Classification Summary:")
+display(abc_summary)
+
+# Cell 7: Create a pie chart for ABC classification
+plt.figure(figsize=(8, 6))
+plt.pie(abc_summary['Issuance Value'], labels=abc_summary['ABC_Class'], autopct='%1.1f%%')
+plt.title('ABC Classification by Issuance Value')
+plt.tight_layout()
+plt.show()
+
+# Cell 8: Create a bar chart for inventory turnover ratio
+data['Inventory Turnover Ratio'] = data['12mo Iss Val'] / data['Value on hand']
+inventory_turnover = data.groupby('Material')['Inventory Turnover Ratio'].mean()
+plt.figure(figsize=(12, 6))
+plt.bar(inventory_turnover.index, inventory_turnover.values)
+plt.xlabel('Material')
+plt.ylabel('Inventory Turnover Ratio')
+plt.title('Inventory Turnover Ratio by Material')
+plt.xticks(rotation=45)
+plt.tight_layout()
+plt.show()
+
+# Cell 9: Create a histogram for days of supply
+days_of_supply = data['Qty on hand'] / (data['12mo Iss Qty'] / 365)
+plt.figure(figsize=(8, 6))
+sns.histplot(days_of_supply, bins=20, kde=True)
+plt.xlabel('Days of Supply')
+plt.ylabel('Frequency')
+plt.title('Distribution of Days of Supply')
+plt.tight_layout()
+plt.show()
+
+# Cell 10: Calculate and print KPIs
+total_inventory_value = data['Value on hand'].sum()
+total_issuance_value = data['12mo Iss Val'].sum()
+
+inventory_turnover_ratio = total_issuance_value / total_inventory_value
+days_of_supply_avg = data['Qty on hand'].sum() / (data['12mo Iss Qty'].sum() / 365)
+stock_availability_percentage = (data[data['Qty on hand'] > 0].shape[0] / data.shape[0]) * 100
+
+print("Key Performance Indicators (KPIs):")
+print(f"Inventory Turnover Ratio: {inventory_turnover_ratio:.2f}")
+print(f"Days of Supply (Average): {days_of_supply_avg:.2f}")
+print(f"Stock Availability Percentage: {stock_availability_percentage:.2f}%")
+```
+
+In this refactored code:
+
+- The code is divided into separate cells for better organization and readability.
+- The `display()` function is used instead of `print()` to display the summary tables in a more visually appealing way within the notebook.
+- The `plt.show()` function is used to display the plots directly in the notebook.
+- The warnings related to `FigureCanvasAgg` being non-interactive should not appear in the notebook environment.
+
+To run this code in a Jupyter Notebook:
+
+1. Open a new notebook in Jupyter.
+2. Copy and paste each cell into separate cells in the notebook.
+3. Make sure the `raw_data.csv` file is in the same directory as the notebook or provide the correct file path.
+4. Run each cell in order by pressing Shift+Enter or using the "Run" button in the toolbar.
+
+The results, including the summary tables, visualizations, and KPIs, will be displayed inline in the notebook cells.
+
+Remember to adjust the file path for the `raw_data.csv` file if needed, and ensure that you have the necessary libraries (pandas, matplotlib, seaborn) installed in your Jupyter Notebook environment.
+
+---
+
 Here is the complete inventory analysis report for the Meraki MX and MS product lines, incorporating all the information provided:
 
 ```markdown