An analytical review of predictive methods for delivery delays in supply chains

Norman Müller, Peter Burggräf, Fabian Steinberg, Carl René Sauer, Maximilian Schütz
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Abstract

Predicting delivery delays is crucial for companies, especially in times of increasing global uncertainty and vulnerable supply chains. Machine learning (ML) offers significant potential to improve the forecast performance and quality of delivery delay prediction. Although various prediction approaches have been proposed in research, a structured and comprehensive overview is lacking. This paper addresses this gap by conducting a systematic literature review on the direct prediction of delivery delays. The objective is to identify applied prediction approaches and data sources, assess their readiness for real-world implementation, and derive a research agenda. The findings reveal that current research often focuses on marginal optimization of prediction performance while lacking practical applicability. Furthermore, most studies emphasize classifying deliveries as on time or delayed, rather than predicting the actual delay magnitude. Regarding the data used for prediction, combining enterprise resource planning (ERP) data with data from logistics improves prediction performance. However, environmental and location data, which could be easily integrated into ERP-based ML models, are rarely considered. This indicates a misalignment in current research, emphasizing the need for models combining practical applicability with predictive accuracy. Further research is required to address these identified deficits. Therefore, the present paper proposes a research agenda, to prioritize the most important deficits. These include, among others the industrial application, optimal prediction timing and ideal data combinations to achieve high prediction accuracy. It also highlights the need for integrated decision support systems that provide prediction-based recommendations, enhancing the practical value of predictive models in supply chain management.
供应链中交货延迟预测方法的分析回顾
预测交货延迟对企业来说至关重要,尤其是在全球不确定性增加、供应链脆弱的情况下。机器学习(ML)为提高交付延迟预测的预测性能和质量提供了巨大的潜力。虽然研究中提出了各种预测方法,但缺乏结构化和全面的概述。本文通过对直接预测交货延迟进行系统的文献回顾来解决这一差距。目标是确定应用的预测方法和数据源,评估其对现实世界实施的准备情况,并得出研究议程。研究结果表明,目前的研究往往侧重于预测性能的边际优化,缺乏实际适用性。此外,大多数研究强调将交付分类为准时或延迟,而不是预测实际的延迟程度。对于用于预测的数据,将企业资源规划(ERP)数据与物流数据相结合可以提高预测性能。然而,很少考虑环境和位置数据,这些数据可以很容易地集成到基于erp的ML模型中。这表明目前的研究存在偏差,强调需要将实际适用性与预测准确性结合起来的模型。需要进一步的研究来解决这些已确定的缺陷。因此,本文提出了一个研究议程,优先考虑最重要的赤字。其中包括工业应用,最佳预测时机和理想的数据组合,以实现高预测精度。它还强调需要集成的决策支持系统,提供基于预测的建议,提高预测模型在供应链管理中的实用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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