Enhanced efficiency assessment in manufacturing: Leveraging machine learning for improved performance analysis

被引:0
作者
Guillen, Maria D. [1 ]
Charles, Vincent [2 ]
Aparicio, Juan [1 ,3 ]
机构
[1] Miguel Hernandez Univ UMH, Ctr Operat Res CIO, Elche 03202, Spain
[2] Queens Univ Belfast, Queens Business Sch, Belfast BT9 5EE, North Ireland
[3] ValgrAI Valencian Grad Sch & Res Network Artificia, Valencia, Spain
来源
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE | 2025年 / 134卷
关键词
PCB; Undesirable outputs; Data Envelopment Analysis; Machine learning; Gradient boosting; DATA ENVELOPMENT ANALYSIS; UNDESIRABLE OUTPUTS; TRANSLATION-INVARIANCE; DESIGN SCIENCE; DEA; POWER; METHODOLOGY; MODELS;
D O I
10.1016/j.omega.2025.103300
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
This paper introduces EATBoosting, a novel application of gradient tree boosting within the Data Envelopment Analysis (DEA) framework, designed to address undesirable outputs in printed circuit board (PCB) manufacturing. Recognizing the challenge of balancing desirable and undesirable outputs inefficiency assessments, our approach leverages machine learning to enhance the discriminatory power of traditional DEA models, facilitating more precise efficiency estimations. By integrating gradient tree boosting, EATBoosting optimizes the handling of complex data patterns and maximizes accuracy in predicting production functions, thus improving upon the deterministic nature of conventional DEA and Free Disposal Hull methods. The practicality of our approach is demonstrated through its application to a PCB assembly process, highlighting its capacity to discern subtle inefficiencies that traditional methods might overlook. This methodology not only enriches the analytical toolkit available for operational efficiency analysis but also sets a precedent for incorporating advanced machine learning techniques in performance evaluation across various industries. Looking forward, the continued integration of such innovative methods promises to revolutionize efficiency analysis, making it more adaptive to complex industrial challenges and more reflective of real-world production dynamics. This work not only broadens the scope of DEA applications but also invites further research into the integration of machine learning to refine performance measurement and management.
引用
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页数:13
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