Predicting Delays In Delivery Process Using Machine Learning-Based Approach
thesisposted on 2020-12-16, 18:06 authored by Shehryar ShahidShehryar Shahid
There has been a great interest in applying Data Science, Machine Learning, and AI-related technologies in recent years. Industries are adopting these technologies very rapidly, which has enabled them to gather valuable data about their businesses. One such industry that can leverage this data to improve their business's output and quality is the logistics and transport industry. This phenomenon provides an excellent opportunity for companies who rely heavily on air transportation to leverage this data to gain valuable insights and improve their business operations. This thesis is aimed to leverage this data to develop techniques to model complex business processes and design a machine learning-based predictive analytical approach to predict process violations.
This thesis focused on solving delays in shipment delivery by modeling a prediction technique to predict these delays. The approach presented here was based on real airfreight shipping data, which follows the International Air and Transport Association industry standard for airfreight transportation, to identify shipments at risk of being delayed. By leveraging the shipment process structure, this research presented a new approach that solved the complex event-driven structure of airfreight data that made it difficult to model for predictive analytics.
By applying different data mining and machine learning techniques, prediction techniques were developed to predict delays in delivering airfreight shipments. The prediction techniques were based on random forest and gradient boosting algorithms. To compare and select the best model, the prediction results were interpreted in the form of six confusion matrix-based performance metrics. The results showed that all the predictors had a high specificity of over 90%, but the sensitivity was low, under 44%. Accuracy was observed to be over 75%, and a geometric mean was between 58% – 64%.
The performance metrics results provided evidence that our approach could be implemented to develop a prediction technique to model complex business processes. Additionally, an early prediction method was designed to test predictors' performance if complete process information was not available. This proposed method delivered compelling evidence suggesting that early prediction can be achieved without compromising the predictor’s performance.