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Volume 12
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10.3390/pr12061070
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Open AccessArticle
by Georgina Elizabeth Riosvelasco-Monroy SciProfilesScilitPreprints.orgGoogle Scholar Iván Juan Carlos Pérez-Olguín SciProfilesScilitPreprints.orgGoogle Scholar Salvador Noriega-Morales SciProfilesScilitPreprints.orgGoogle Scholar Luis Asunción Pérez-Domínguez SciProfilesScilitPreprints.orgGoogle Scholar Luis Carlos Méndez-González SciProfilesScilitPreprints.orgGoogle Scholar Luis Alberto Rodríguez-Picón SciProfilesScilitPreprints.orgGoogle Scholar
Georgina Elizabeth Riosvelasco-Monroy
, Iván Juan Carlos Pérez-Olguín, Salvador Noriega-MoralesLuis Asunción Pérez-Domínguez, Luis Carlos Méndez-González and Luis Alberto Rodríguez-Picón
Institute of Engineering and Technology, Department of Industrial and Manufacturing Engineering, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
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Processes 2024, 12(6), 1070; https://doi.org/10.3390/pr12061070 (registeringDOI)
Submission received: 1 May 2024/Revised: 17 May 2024/Accepted: 17 May 2024/Published: 23 May 2024
(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)
Abstract
As enterprises look forward to new market share and supply chain opportunities, innovative strategies and sustainable manufacturing play important roles for micro-, small, and mid-sized enterprises worldwide. Sustainable manufacturing is one of the practices aimed towards deploying green energy initiatives to ease climate change, presenting three main pillars—economic, social, and environmental. The issue of how to reach sustainability goals within the sustainable manufacturing of pillars is a less-researched area. This paper’s main purpose and novelty is two-fold. First, it aims to provide a hierarchy of the green energy indicators and their measurements through a multi-criteria decision-making point of view to implement them as an alliance strategy towards sustainable manufacturing. Moreover, we aim to provide researchers and practitioners with a forecasting method to re-prioritize green energy indicators through a linearity factor model. The CODAS–Hamming–Mahalanobis method is used to obtain preference scores and rankings from a 50-item list. The resulting top 10 list shows that enterprises defined nine items within the economic pillar as more important and one item on the environmental pillar; items from the social pillar were less important. The implication for MSMEs within the manufacturing sector represents an opportunity to work with decision makers to deploy specific initiatives towards sustainable manufacturing, focused on profit and welfare while taking care of natural resources. In addition, we propose a continuous predictive analysis method, the linearity factor model, as a tool for new enterprises to seek a green energy hierarchy according to their individual needs. The resulting hierarchy using the predictive analysis model presented changes in the items’ order, but it remained within the same two sustainable manufacturing pillars: economic and environmental.
Keywords: Mahalanobis distance; green energy supply chain; MCDM; sustainable manufacturing; predictive analysis model; CODAS; Hamming distance
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MDPI and ACS Style
Riosvelasco-Monroy, G.E.; Pérez-Olguín, I.J.C.; Noriega-Morales, S.; Pérez-Domínguez, L.A.; Méndez-González, L.C.; Rodríguez-Picón, L.A.CODAS–Hamming–Mahalanobis Method for Hierarchizing Green Energy Indicators and a Linearity Factor for Relevant Factors’ Prediction through Enterprises’ Opinions. Processes 2024, 12, 1070.https://doi.org/10.3390/pr12061070
AMA Style
Riosvelasco-Monroy GE, Pérez-Olguín IJC, Noriega-Morales S, Pérez-Domínguez LA, Méndez-González LC, Rodríguez-Picón LA.CODAS–Hamming–Mahalanobis Method for Hierarchizing Green Energy Indicators and a Linearity Factor for Relevant Factors’ Prediction through Enterprises’ Opinions. Processes. 2024; 12(6):1070.https://doi.org/10.3390/pr12061070
Chicago/Turabian Style
Riosvelasco-Monroy, Georgina Elizabeth, Iván Juan Carlos Pérez-Olguín, Salvador Noriega-Morales, Luis Asunción Pérez-Domínguez, Luis Carlos Méndez-González, and Luis Alberto Rodríguez-Picón.2024. "CODAS–Hamming–Mahalanobis Method for Hierarchizing Green Energy Indicators and a Linearity Factor for Relevant Factors’ Prediction through Enterprises’ Opinions" Processes 12, no. 6: 1070.https://doi.org/10.3390/pr12061070
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
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MDPI and ACS Style
Riosvelasco-Monroy, G.E.; Pérez-Olguín, I.J.C.; Noriega-Morales, S.; Pérez-Domínguez, L.A.; Méndez-González, L.C.; Rodríguez-Picón, L.A.CODAS–Hamming–Mahalanobis Method for Hierarchizing Green Energy Indicators and a Linearity Factor for Relevant Factors’ Prediction through Enterprises’ Opinions. Processes 2024, 12, 1070.https://doi.org/10.3390/pr12061070
AMA Style
Riosvelasco-Monroy GE, Pérez-Olguín IJC, Noriega-Morales S, Pérez-Domínguez LA, Méndez-González LC, Rodríguez-Picón LA.CODAS–Hamming–Mahalanobis Method for Hierarchizing Green Energy Indicators and a Linearity Factor for Relevant Factors’ Prediction through Enterprises’ Opinions. Processes. 2024; 12(6):1070.https://doi.org/10.3390/pr12061070
Chicago/Turabian Style
Riosvelasco-Monroy, Georgina Elizabeth, Iván Juan Carlos Pérez-Olguín, Salvador Noriega-Morales, Luis Asunción Pérez-Domínguez, Luis Carlos Méndez-González, and Luis Alberto Rodríguez-Picón.2024. "CODAS–Hamming–Mahalanobis Method for Hierarchizing Green Energy Indicators and a Linearity Factor for Relevant Factors’ Prediction through Enterprises’ Opinions" Processes 12, no. 6: 1070.https://doi.org/10.3390/pr12061070
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
Processes,EISSN 2227-9717,Published by MDPI
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