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

Maria D. Guillen, Vincent Charles and Juan Aparicio

Omega, 2025, vol. 134, issue C

Abstract: 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 in efficiency 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.

Keywords: PCB; Undesirable outputs; Data Envelopment Analysis; Machine learning; Gradient boosting (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030504832500026X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jomega:v:134:y:2025:i:c:s030504832500026x

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.omega.2025.103300

Access Statistics for this article

Omega is currently edited by B. Lev

More articles in Omega from Elsevier
Bibliographic data for series maintained by Catherine Liu ().