Dashboard-III

Detailed Description for Dashboard 3: Delivery Dataset

Objective:

Analyze courier performance, identify delivery inefficiencies, and enhance customer satisfaction by pinpointing key improvement areas.

KPIs with Calculations:

  1. Delivery Count by Hour:

    • Formula: Count of deliveries grouped by delivery_time hour.

    • Purpose: Identify peak hours to optimize resource allocation.

  2. Average Delivery Time by Region:

    • Formula: Avg Delivery Time=∑(delivery_time−accept_time)Total Deliveries\text{Avg Delivery Time} = \frac{\sum (\text{delivery\_time} - \text{accept\_time})}{\text{Total Deliveries}}.

    • Purpose: Highlight regions with delays for targeted actions.

  3. Top 5 Couriers by Deliveries:

    • Formula: Sort couriers by delivery count and display top 5.

    • Purpose: Recognize high-performing couriers.

  4. Customer Complaints per Region:

    • Data Dependency: Requires a complaints column. If unavailable, skip.

    • Purpose: Assess satisfaction trends across regions.

  5. Failed Deliveries Reasons:

    • Data Dependency: Categorize reasons from a failure_reason column if present.

    • Purpose: Address systemic issues causing failures.

Filters and Slicers:

  • City: Filter data for one or multiple cities.

  • Region: Drill down to specific regions.

  • Courier ID: Analyze individual courier performance.

  • Delivery Status: View data for "On-Time," "Delayed," or "Failed" deliveries.

Visuals:

  1. Heatmap: Delivery density by hour and region to visualize peak times.

  2. Bar Chart: Display top failure reasons grouped by category.

  3. Donut Chart: Visualize SLA compliance split into "On-Time" and "Delayed."

  4. Map Visualization: Plot delivery success rates across regions.

  5. Table: Courier-wise metrics with delivery count and SLA performance.

Detailed Description for Dashboard 3: Pickup Dataset

Objective:

Focus on monitoring pickup location trends, adherence to time windows, and overall courier reliability to enhance pickup operations.

KPIs with Calculations:

  1. Total Pickups Completed by Courier:

    • Formula: Count pickups grouped by courier_id.

    • Purpose: Measure courier contributions to operations.

  2. Average Pickup Time:

    • Formula: Avg Pickup Time=∑(pickup_time−accept_time)Total Pickups\text{Avg Pickup Time} = \frac{\sum (\text{pickup\_time} - \text{accept\_time})}{\text{Total Pickups}}.

    • Purpose: Identify inefficiencies in pickup duration.

  3. Failed Pickup Rate:

    • Formula: Failed Rate=Failed PickupsTotal Pickups×100\text{Failed Rate} = \frac{\text{Failed Pickups}}{\text{Total Pickups}} \times 100.

    • Purpose: Track operational issues affecting pickups.

  4. Pickup Time Window Adherence:

    • Formula: Count of pickups within [time_window_start,time_window_end][time\_window\_start, time\_window\_end].

    • Purpose: Ensure compliance with SLA for pickups.

  5. Top 5 AOI Types for Pickups:

    • Formula: Group pickups by aoi_type and display top 5.

    • Purpose: Pinpoint areas of high pickup demand.

Filters and Slicers:

  • City: Filter for one or more cities.

  • Region: Focus on specific regions.

  • Time Window Status: "Within Window" or "Outside Window."

  • Pickup Status: Filter for "Successful" or "Failed."

Visuals:

  1. Bar Chart: Pickup count grouped by aoi_type.

  2. Line Chart: Average pickup time trends over a date range.

  3. Map Visualization: Geospatial view of pickup locations and compliance rates.

  4. Pie Chart: Distribution of pickups within and outside time windows.

  5. Table: Courier-wise performance metrics.

General Notes for Implementation:

  • Ensure consistent date formats across datasets before loading them into BI tools.

  • Perform joins for city-specific data as needed for combined views.

  • Use filters dynamically to toggle between specific regions and overall trends.

Deadline:

30th December for full implementation and presentation.

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