Dashboard-I

Dashboard 1: Delivery Dataset Dashboard

Objective

This dashboard aims to provide insights into delivery performance, efficiency, and trends across regions and cities. It is designed to help operations teams identify bottlenecks, monitor courier performance, and enhance delivery SLA compliance.

Tools to Use:

• Power BI or Tableau for dashboard creation.

• Excel (optional) for intermediate data preparation or manual calculations.

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Step 1: Import Data

1. Open Power BI or Tableau.

2. Import the preprocessed Delivery dataset:

   • Connect to your CSV/Excel file.

   • Load the data into the tool.

Step 2: Data Modeling

• Ensure relationships between tables (if there are multiple tables).

• Create calculated columns:

  • Delivery Duration: DATEDIFF(delivery_time, accept_time, MINUTE) (for Power BI).

Step 3: Create KPIs

1. Average Delivery Time:

   • Measure: Average of Delivery Duration.

2. Total Deliveries Per Region:

   • Count of order_id, grouped by region_id.

3. Delivery Efficiency:

   • Ratio of on-time deliveries to total deliveries.

4. SLA Compliance:

   • Measure: IF(Delivery Duration <= SLA time, "On-Time", "Delayed").

5. Busiest Courier:

   • Count of order_id, grouped by courier_id.

Step 4: Design Visuals

• Bar Chart: Total Deliveries by Region.

• Line Chart: Delivery Efficiency over Time (grouped by ds).

• Pie Chart: SLA Compliance Distribution.

• Table: Busiest Couriers (with delivery counts).

• Heatmap: Delivery volumes across regions.

Step 5: Add Filters and Slicers

• Filters: Region, City, AOI Type, Courier ID, and Time Range.

• Slicers: SLA Compliance and Delivery Time Categories (e.g., "Fast," "Moderate," "Delayed").

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Dashboard 2: Pickup Dashboard Implementation

Objective

This dashboard focuses on analyzing pickup operations, providing a clear view of time adherence, courier performance, and trends in pickup requests across cities and AOIs.

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Step 1: Import Data

1. Load the preprocessed Pickup dataset into Power BI or Tableau.

Step 2: Data Modeling

• Create Calculated Columns:

  • Pickup Duration: DATEDIFF(pickup_time, accept_time, MINUTE).

  • Time Window Compliance:

    • IF(pickup_time BETWEEN time_window_start AND time_window_end, "Compliant", "Non-Compliant").

Step 3: Create KPIs

1. Average Pickup Time:

   • Measure: Average of Pickup Duration.

2. Total Pickups Per City:

   • Count of package_id, grouped by city.

3. Pickup Window Compliance:

   • Percentage of Compliant Pickups.

4. Average Pickup Distance:

   • Calculate distance between accept and pickup GPS coordinates using a Haversine formula.

5. Top AOI Type:

   • Count of aoi_type, ranked by pickup count.

Step 4: Design Visuals

• Bar Chart: Total Pickups by City.

• Scatter Plot: Pickup Distance vs. Duration.

• Line Chart: Time Window Compliance over Time.

• Map Visualization: Geospatial representation of pickup locations.

• Pie Chart: Distribution of AOI Types.

Step 5: Add Filters and Slicers

• Filters: Region, City, AOI Type, Courier ID, and Time Range.

• Slicers: Time Window Compliance and Pickup Task Status.

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Part 3: Finalizing Dashboards

Step 6: Interactivity

• Link slicers and filters to all visuals for dynamic analysis.

• Ensure that clicking on a visual (e.g., map points) highlights related data in other visuals.

Step 7: Formatting and Aesthetics

• Use consistent colors and themes.

• Add titles, labels, and tooltips for clarity.

• Optimize layout for readability and usability.

Step 8: Test the Dashboards

• Validate KPIs and calculations.

• Ensure visuals are updating correctly with filters and slicers.

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Optional Enhancements

1. Delivery and Pickup Comparison Tab:

   • Merge insights from both dashboards.

   • Use combined KPIs like total packages handled and efficiency.

2. Export Options: Enable exporting filtered views to Excel or PDF for reporting.

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