Process-Aware Procurement Lead Time Prediction for Shipyard Delay Mitigation

TL;DR

This paper introduces a process-aware deep learning framework that combines event logs and static attributes to improve procurement lead time prediction in shipbuilding, significantly reducing prediction errors.

Contribution

It presents a novel integration of dynamic event sequences with static data using deep neural networks for more accurate procurement lead time prediction.

Findings

Achieved up to 50.4% reduction in mean absolute error.

Validated on real-world shipyard procurement data.

Demonstrated improvements across production, post-processing, and procurement tasks.

Abstract

Accurately predicting procurement lead time (PLT) remains a challenge in engineered-to-order industries such as shipbuilding and plant construction, where delays in a single key component can disrupt project timelines. In shipyards, pipe spools are critical components; installed deep within hull blocks soon after steel erection, any delay in their procurement can halt all downstream tasks. Recognizing their importance, existing studies predict PLT using the static physical attributes of pipe spools. However, procurement is inherently a dynamic, multi-stakeholder business process involving a continuous sequence of internal and external events at the shipyard, factors often overlooked in traditional approaches. To address this issue, this paper proposes a novel framework that combines event logs, dataset records of the procurement events, with static attributes to predict PLT. The temporal attributes of each event are extracted to reflect the continuity and temporal context of the process. Subsequently, a deep sequential neural network combined with a multi-layered perceptron is employed to integrate these static and dynamic features, enabling the model to capture both structural and contextual information in procurement. Comparative experiments are conducted using real-world pipe spool procurement data from a globally renowned South Korean shipbuilding corporation. Three tasks are evaluated, which are production, post-processing, and procurement lead time prediction. The results show a 22.6% to 50.4% improvement in prediction performance in terms of mean absolute error over the best-performing existing approaches across the three tasks. These findings indicate the value of considering procurement process information for more accurate PLT prediction.