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Advancing sustainability through efficient production processes: integrating ISO standards, Lean Six Sigma and artificial intelligence

António Rocha et al.

International Journal of Productivity and Quality Management2026https://doi.org/10.1504/ijpqm.2026.152584article
AJG 1
Weight
0.50

What the paper says

In a circular economy, products must be designed and produced to a high standard of quality, durability, and reliability, to be easy to maintain and repair, and manufacturing processes must be efficient to minimise waste, information, and energy losses. Our research focuses on sustainable business success while addressing environmental concerns. Data from the European Environmental Agency's PRTR database has been analysed to identify industries contributing the most to air and water pollution and whether emissions have decreased over time. Findings highlight the need for stronger commitment and investment to achieve net-zero greenhouse gas emissions. As a contribution to this end, a baseline for an integrated management system for industrial sustainability (IMSIS) is proposed, combining ISO standards, Lean Six Sigma, and artificial intelligence (AI), making use of advanced technologies. This approach aims to address productivity and sustainability challenges, leading to reduced manufacturing waste and improved energy and information use.

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https://doi.org/https://doi.org/10.1504/ijpqm.2026.152584

Or copy a formatted citation

@article{antónio2026,
  title        = {{Advancing sustainability through efficient production processes: integrating ISO standards, Lean Six Sigma and artificial intelligence}},
  author       = {António Rocha et al.},
  journal      = {International Journal of Productivity and Quality Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijpqm.2026.152584},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.