- Project Overview
- Business Problem
- Project Tools
- Repository Structure
- Dataset Overview & Data Model
- Dashboard Overview
- Dashboard Screenshots
- Key Insights
- Recommendations
- Assumptions & Limitations
- Future Enhancements
- Author
This project delivers an end-to-end Business Intelligence solution designed to analyze and optimize last-mile delivery operations. By transforming raw logistics data into actionable insights, the solution enables stakeholders to monitor operational performance, identify inefficiencies, and improve delivery reliability.
The dashboard provides a unified analytical view across orders, hubs, drivers, and fleet operations, supporting both strategic planning and day-to-day operational decision-making. Through interactive reporting and performance analysis, the project helps uncover delivery bottlenecks, evaluate workforce efficiency, and track key logistics KPIs across the delivery network.
Last-mile delivery operations are highly sensitive to operational inefficiencies that can negatively impact service quality, delivery reliability, and overall business performance. Challenges such as delivery delays, uneven workload distribution across hubs, inconsistent driver performance, and increasing vehicle breakdowns often make it difficult for logistics teams to maintain efficient operations and meet customer expectations.
Without a centralized analytics solution, these issues remain fragmented across multiple operational areas, limiting visibility into performance trends and making root-cause analysis difficult. This project addresses these challenges by developing an interactive Business Intelligence dashboard that enables performance monitoring, operational analysis, and data-driven decision-making across the logistics network.
- Microsoft Power BI – Used for data modeling, interactive dashboard development, and data visualization.
- Power Query – Used for data loading and validation during the data preparation stage.
- DAX (Data Analysis Expressions) – Used to create calculated measures and KPI metrics such as On-Time Delivery Rate, Average Delivery Time, Customer Satisfaction Score, and Hub Performance indicators.
- Microsoft Excel / CSV Files – Used as the source format for the logistics operations dataset.
- GitHub — Project documentation and version control.
The dataset was assumed to be clean and analysis-ready; therefore, extensive data cleaning and transformation processes were not required for this project.
FlashPoint-Logistics-Dashboard/
│
├── dashboard/
│ └── FlashPoint Dashboard.pbix
│
├── data/
│ └── raw/
│ ├── drivers.csv
│ ├── hubs.csv
│ ├── orders.csv
│ └── vehicles.csv
│
├── docs/
│ ├── business-requirements.md
│ ├── dax-calculations.md
│ ├── domain-knowledge.md
│ └── insights-report.md
│
├── images/
│ ├── model-view.jpg
│ ├── flashpoint-summary-dashboard.jpg
│ ├── drivers-overview.jpg
│ ├── hubs-overview.jpg
│ └── vehicles-overview.jpg
│
├── LICENSE
└── README.md
The analysis integrates four relational datasets:
- Orders (Fact Table) → Delivery transactions and performance metrics
- Hubs (Dimension) → Capacity and operational structure
- Drivers (Dimension) → Workforce attributes and performance
- Vehicles (Dimension) → Fleet characteristics and maintenance
- Orders → Drivers via Driver ID
- Orders → Hubs via Hub ID
- Orders → Vehicles via Vehicle Code
This structure enables cross-functional analysis across logistics operations.
- FlashPoint Summary Dashboard Tracks core KPIs:
- Total Orders
- On-Time Delivery Rate
- Customer Satisfaction (CSAT)
- Average Delivery Time
- Month-over-Month trends
Purpose: Provide a high-level operational overview of delivery performance, customer satisfaction, and service efficiency trends.
- Hubs Overview Dashboard Focus areas:
- Orders vs Capacity
- Hub Performance Ranking
- Processing Time Analysis
Purpose: Identify overloaded hubs and optimize workload distribution.
- Drivers Overview Dashboard Focus areas:
- Experience vs Performance
- Delay contribution by driver
- Individual performance profiles
Purpose: Improve workforce efficiency and target performance gaps.
- Vehicles Overview Dashboard Focus areas:
- Fleet availability (Active vs Inactive)
- Vehicle utilization
- Breakdown trends and risk analysis
Purpose: Enhance fleet reliability and support maintenance planning.
- Delivery operations maintained a relatively stable customer satisfaction score despite extended average delivery times, suggesting service quality remains consistent under operational pressure.
- Hub and fleet performance indicators reveal that operational efficiency varies significantly across locations and assets, highlighting opportunities for targeted optimization.
- Hub performance rankings show that higher capacity utilization does not always translate to stronger delivery efficiency, indicating operational management quality plays a major role.
- Some lower-volume hubs achieved performance levels close to larger hubs, suggesting opportunities to replicate efficient operational practices across the network.
- Driver performance distribution indicates that operational consistency differs noticeably across the workforce, with a small group of drivers contributing disproportionately to delayed deliveries.
- The relationship between driver experience and ratings suggests that workforce maturity positively impacts delivery quality and customer experience.
- Vehicle utilization is concentrated among a few core fleet models, increasing operational dependency on specific vehicle types.
- Breakdown trends show a clear increase in maintenance issues as vehicle age rises, emphasizing the importance of fleet lifecycle management and preventive maintenance planning.
- FlashPoint Logistics processed over 14,000 orders, demonstrating strong operational scale across hubs, drivers, and vehicle fleets.
- The overall On-Time Delivery Rate of 79.3% indicates delivery consistency challenges, with approximately 1 in 5 orders arriving late.
- Despite operational inefficiencies, Customer Satisfaction (84.1%) remains relatively strong, suggesting customers still perceive service quality positively.
- Houston Hub demonstrated the strongest combination of operational performance and delivery volume across the logistics network.
- Austin Hub recorded the lowest performance, indicating possible dispatch, staffing, or route optimization inefficiencies.
- Fleet availability is a major operational concern, with 26.7% of vehicles under maintenance, reducing delivery capacity and increasing pressure on active vehicles.
- The Ford Transit emerged as the most utilized vehicle model, serving as the operational backbone of the logistics network.
- Breakdown frequency appeared to increase with vehicle age, suggesting aging fleet assets may contribute to higher maintenance risk.
- Freightliner M2 vehicles recorded high delivery utilization alongside elevated breakdown frequency, suggesting possible operational strain and maintenance pressure.
- Driver analysis revealed that more experienced drivers generally maintained stronger performance ratings and lower delay rates.
- Certain drivers consistently recorded higher delayed delivery percentages, highlighting opportunities for targeted coaching and route optimization.
- Implement preventive maintenance scheduling to reduce vehicle downtime and improve fleet availability.
- Prioritize replacement or refurbishment of aging high-breakdown vehicles to minimize operational disruptions.
- Optimize delivery route planning and dispatch coordination to improve on-time delivery performance.
- Rebalance workload distribution across hubs to reduce operational strain on high-volume locations.
- Introduce driver performance monitoring and targeted coaching programs for consistently delayed deliveries.
- Improve fleet allocation strategies by increasing utilization of underused vehicle models where operationally feasible.
- Expand operational capacity at high-performing hubs to support future order growth without sacrificing efficiency.
- Leverage predictive analytics to identify maintenance risks, delivery bottlenecks, and performance trends before they impact operations.
- Monitor hub capacity utilization regularly to ensure infrastructure and staffing levels align with delivery demand.
- Establish operational KPI tracking frameworks to continuously evaluate delivery speed, fleet efficiency, and customer satisfaction performance.
- The dataset represents completed and operationally valid delivery records within the selected reporting period.
- Delivery timestamps, hub assignments, driver records, and vehicle information are assumed to be accurate and consistently captured.
- Customer satisfaction scores are assumed to reflect post-delivery customer feedback without sampling bias.
- Vehicle maintenance status is assumed to be updated correctly at the time of analysis.
- Hub performance metrics are evaluated based on available operational KPIs and may not capture all real-world logistics constraints.
- Driver ratings are assumed to represent overall delivery performance and customer experience quality.
- All delivery orders are treated with equal operational importance regardless of package size or delivery complexity.
- The analysis does not include real-time GPS, traffic, or weather data that may significantly affect delivery performance.
- Fuel costs, operational expenses, and profitability metrics were not included in the dataset.
- The dashboard focuses primarily on operational performance and does not include financial or revenue analysis.
- Customer satisfaction analysis is limited to available CSAT scores and does not include qualitative customer feedback.
- Vehicle breakdown analysis does not account for maintenance history, repair costs, or part replacement details.
- Driver performance analysis may not fully capture external factors such as route difficulty or regional traffic conditions.
- Seasonal demand fluctuations and holiday delivery patterns were not incorporated into the analysis.
- The dashboard provides historical and descriptive insights but does not include predictive or machine learning forecasting models.
- Data quality issues such as missing values, inconsistent records, or reporting delays may impact analytical accuracy.
- Integrate real-time GPS tracking data to enable live delivery monitoring and route optimization.
- Implement predictive maintenance models to proactively identify vehicles at high risk of breakdown.
- Add fuel consumption and operational cost analysis to improve fleet efficiency monitoring.
- Incorporate weather and traffic data to evaluate external factors affecting delivery performance.
- Develop driver productivity scorecards with advanced KPI tracking and benchmarking.
- Introduce delivery forecasting models to predict demand spikes and optimize resource allocation.
- Expand the dashboard with customer segmentation analysis to better understand satisfaction trends across regions.
- Add automated alert systems for delayed deliveries, vehicle downtime, and hub capacity thresholds.
- Build mobile-responsive dashboard views for operational teams and field managers.
- Integrate warehouse and inventory datasets to enable end-to-end supply chain visibility.
- Implement AI-driven route optimization to reduce delivery times and operational costs.
Godwin Deborah
Data Analyst