Integrating lean management and emerging technologies for enhanced sustainable performance: empirical evidence based on double machine learning approach

The rapid increase in sustainability has pushed manufacturing firms to rethink their production systems to make them more efficient, produce less waste, and improve their working conditions. In this landscape, many manufacturing firms that rely on traditional quality improvement methods such as lean management seek ways to improve their performance, align with market needs, and achieve zero-defect production. In this sense, digital technologies seem to have promising features that can enhance manufacturing firms' sustainable positions. However, recent studies on zero-defect manufacturing have often overlooked the possible integration of existing quality improvement practices and technologies, with a major focus on the latter. Employing a survey analyzed through a double machine-learning algorithm, we provide guidelines to manufacturing firms that extensively use lean management practices, showing the best technologies to significantly impact sustainable metrics. The results of this study provide guidelines for firms by demonstrating the best technologies that can be integrated with different lean management practices to advance toward zero-defect production and achieve superior sustainable performance.

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Publication Details

Journal
Data Science and Management
Published
2025-09-01
DOI
https://doi.org/10.1016/j.dsm.2025.08.004
Citations
2
Primary Topic
Quality and Supply Management
Type
article
Field-Weighted Citation Impact
4.57
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article

Integrating lean management and emerging technologies for enhanced sustainable performance: empirical evidence based on double machine learning approach

Alessandra Cantini, Stefano Frecassetti, Alberto Portioli Staudacher, Matteo Ferrazzi et al.
2 citations
Data Science and Management
Quality and Supply Management
4.57
article

Integrating lean management and emerging technologies for enhanced sustainable performance: empirical evidence based on double machine learning approach

Alessandra Cantini, Stefano Frecassetti, Alberto Portioli Staudacher, Matteo Ferrazzi, Anna Presciuttini
article en
2 citations

Abstract

The rapid increase in sustainability has pushed manufacturing firms to rethink their production systems to make them more efficient, produce less waste, and improve their working conditions. In this landscape, many manufacturing firms that rely on traditional quality improvement methods such as lean management seek ways to improve their performance, align with market needs, and achieve zero-defect production. In this sense, digital technologies seem to have promising features that can enhance manufacturing firms' sustainable positions. However, recent studies on zero-defect manufacturing have often overlooked the possible integration of existing quality improvement practices and technologies, with a major focus on the latter. Employing a survey analyzed through a double machine-learning algorithm, we provide guidelines to manufacturing firms that extensively use lean management practices, showing the best technologies to significantly impact sustainable metrics. The results of this study provide guidelines for firms by demonstrating the best technologies that can be integrated with different lean management practices to advance toward zero-defect production and achieve superior sustainable performance.

Data Science and Management
Politecnico di Milano (IT)
Openalex Percentile: Top 5%
Quality and Supply Management
4.57
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