Optimizing Delivery Partner Allocation in Last-Mile Logistics

Problem Statement:

GoLogistics, a third-party logistics (3PL) company, faces inefficiencies in last-mile delivery operations due to suboptimal allocation of delivery partners. These inefficiencies lead to increased delivery times, higher operational costs, and reduced customer satisfaction. The company aims to optimize the allocation of delivery partners based on delivery volumes, regional demands, partner performance, and dynamic traffic conditions. The goal is to minimize delivery costs while ensuring on-time delivery.

Data Sources:

  1. Order Data:

    • order_id: Unique identifier for each order.

    • order_date: Date and time of the order.

    • customer_location: Latitude and longitude of the customer's location.

    • delivery_time_window: Time window for delivery.

    • package_weight: Weight of the package.

    • order_status: Status (Delivered, In-Transit, Canceled).

  2. Delivery Partner Data:

    • partner_id: Unique identifier for each delivery partner.

    • region: Region assigned to the partner.

    • average_speed: Partner's average speed (km/h).

    • on_time_delivery_rate: Percentage of on-time deliveries.

    • active_hours: Available hours per day.

  3. Traffic and Regional Data:

    • region_id: Unique identifier for each region.

    • traffic_density: Average traffic density per region.

    • delivery_volume: Daily average delivery volume.

  4. Cost Data:

    • delivery_cost_per_km: Cost per kilometer for deliveries.

    • penalty_cost_late_delivery: Cost for late deliveries.

    • partner_compensation: Fixed daily cost for each partner.

Project Objectives:

  1. Business Intelligence (BI):

    • Create interactive dashboards to analyze:

      • Delivery volume trends by region and partner.

      • Delivery performance metrics (e.g., on-time rates, average delivery time).

      • Cost analysis (total cost, cost per delivery).

      • Traffic density impact on delivery times.

  2. Machine Learning:

    • Build a model to predict delivery times based on:

      • Package weight.

      • Customer location.

      • Partner performance metrics.

      • Traffic conditions.

    • Develop an optimization algorithm to dynamically allocate delivery partners, considering:

      • Regional demand.

      • Partner performance.

      • Traffic density.

Proposed Solution Workflow:

  1. Data Preprocessing:

    • Clean and preprocess raw data (handle missing values, outliers, and inconsistencies).

    • Engineer features such as distance_to_customer, estimated_delivery_time, and cost_per_delivery.

  2. BI Dashboards:

    • Use Power BI or Tableau to create dashboards for real-time monitoring.

    • Visualize KPIs like average delivery cost, partner utilization rates, and delay trends.

  3. Machine Learning Models:

    • Prediction Model: Train a regression model (e.g., Gradient Boosting, Random Forest) to predict delivery times.

    • Optimization Algorithm: Use Reinforcement Learning or Linear Programming to allocate delivery partners efficiently.

  4. Evaluation Metrics:

    • BI Dashboards: Measure user engagement and insight clarity.

    • Machine Learning: Evaluate prediction accuracy (MAE, RMSE) and cost savings.

Deliverables:

  1. BI Dashboards showcasing:

    • Delivery performance.

    • Cost breakdowns.

    • Traffic density impact.

  2. Predictive model for delivery times.

  3. Optimization model for partner allocation.

  4. Documentation and presentation summarizing findings and recommendations.

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