DELIVERY TIME SLA PREDICTION AND PERFORMANCE

Abstract

The Delivery Time SLA Prediction and Performance system is a data-driven application designed to analyze delivery operations and predict whether an order is likely to meet its defined Service Level Agreement (SLA). Delivery performance is an important factor in logistics, e-commerce, food delivery, and courier services because delays can affect customer satisfaction and operational efficiency. The proposed system analyzes delivery-related information such as order time, dispatch time, pickup time, delivery time, distance, delivery location, vehicle type, traffic conditions, weather conditions, order priority, and delivery status. Data preprocessing techniques are applied to clean the collected dataset by handling missing values, duplicate records, inconsistent formats, and invalid entries. The system calculates important performance indicators such as average delivery time, on-time delivery percentage, delayed orders, SLA compliance rate, and average delay duration. The dashboard can provide delivery performance analysis based on regions, delivery partners, order categories, time periods, and other available attributes. Interactive charts, graphs, tables, and KPI cards make the results easier to understand. The prediction component uses historical delivery information to estimate the expected delivery time and determine the probability of an order meeting its SLA. Machine Learning models can identify relationships between delivery time and factors such as distance, traffic, weather, order volume, and time of day. The predicted results can help operations teams identify potentially delayed deliveries. Overall, the proposed system combines delivery performance analysis with predictive analytics to provide useful operational insights. It can help organizations monitor SLA performance, identify causes of delays, and improve delivery planning. Future enhancements can include real-time GPS tracking, live traffic integration, automated alerts, route optimization, dynamic ETA prediction, and advanced forecasting models.