Enhancing Sustainable Global Supply Chain Performance: A Multi-Criteria Decision-Making-Based Approach to Industry 4.0 and AI Integration

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper is relevant, topical, and original. A robust methodology is employed, and experimental results are demonstrated. The body of knowledge on metrics concerning the impact of cutting-edge AI and Industry 4.0 on supply chain management and logistics is limited. This paper explores this emerging area, prioritizing sustainability-related indicators influenced by digital transformation. There is tremendous scope to expand this research by using other methods like AHP, Fuzzy AHP, ISM, etc. 

• What is the main question addressed by the research? A. The aim of the investigation is outlining and prioritizing of sustainability-related metrics pertaining to Industry 4.0 and Artificial Intelligence (AI) in the global supply chain network. 
• Do you consider the topic original or relevant to the field? Does it address a specific gap in the field? Please also explain why this is/ is not the case. A. Yes, there is a distinct gap in research concerning metrics pertaining to Industry 4.0 and Artificial Intelligence (AI) in the global supply chain network as a whole. A total of 79 references are cited with respect to sustainability-related metrics for said problem. As I am working in this area, I am aware of the gap in this field. The plagiarism report is attached. The similarity index in Turnitin is 14% after removing bibliography. 
• What does it add to the subject area compared with other published material? A. Metrics and rubrics in this cutting-edge area of AI and Industry 4.0 applications in SCM are limited. This investigation outlines 20 key sustainability indicators in four dimensions: environmental, operational, strategic, and social in the said area. This is a novel investigation.   
• What specific improvements should the authors consider regarding the methodology? A. A sectoral approach can be thought out as an extension of the research. It could be retail sector or airport or healthcare sector.      • Are the conclusions consistent with the evidence and arguments presented, and do they address the main question posed? Please also explain why this is/is not the case. Yes. The results are conclusive in the sense that they present a novel framework for identifying clear and concise sustainability metrics in the digital supply chain
• Are the references appropriate?   Yes. 79   • Any additional comments on the tables and figures. No.

Author Response

Dear Reviewer 

We would like to sincerely thank the reviewers for their valuable time, constructive comments, and insightful suggestions, which have greatly helped us improve the quality of our manuscript. We have carefully addressed all comments and revised the paper accordingly. Below, we provide a detailed explanation of the changes made in response to each comment.

• What is the main question addressed by the research? A. The aim of the investigation is outlining and prioritizing of sustainability-related metrics pertaining to Industry 4.0 and Artificial Intelligence (AI) in the global supply chain network. 

Response: Thank you for your positive assessment. The main research question—focused on identifying and prioritizing sustainability-related indicators influenced by Industry 4.0 and AI in global supply chains—is now explicitly stated at the end of the Introduction section to enhance clarity and focus.

• Do you consider the topic original or relevant to the field? Does it address a specific gap in the field? Please also explain why this is/ is not the case. A. Yes, there is a distinct gap in research concerning metrics pertaining to Industry 4.0 and Artificial Intelligence (AI) in the global supply chain network as a whole. A total of 79 references are cited with respect to sustainability-related metrics for said problem. As I am working in this area, I am aware of the gap in this field. The plagiarism report is attached. The similarity index in Turnitin is 14% after removing bibliography. 

Response: Thank you for recognizing the relevance and originality of the study. We have strengthened the literature review by clearly articulating the research gap concerning the integration of sustainability metrics with Industry 4.0 and AI in global supply chains. This gap is addressed through a novel BWM-based prioritization of 20 indicators across four sustainability dimensions. We have also ensured transparency and academic integrity, with a similarity index of 14% after excluding references, as verified by Turnitin.

• What does it add to the subject area compared with other published material? A. Metrics and rubrics in this cutting-edge area of AI and Industry 4.0 applications in SCM are limited. This investigation outlines 20 key sustainability indicators in four dimensions: environmental, operational, strategic, and social in the said area. This is a novel investigation. 

Response:Thank you for highlighting the novelty of our contribution. To further emphasize this point, we have clarified in the literature review how this study uniquely contributes to the field by identifying and prioritizing 20 sustainability indicators across four dimensions—environmental, operational, strategic, and social—in the context of AI and Industry 4.0 in global supply chains. This multidimensional and integrative approach has not been comprehensively addressed in existing literature, and we have made this distinction more explicit in Section 2.

 

• What specific improvements should the authors consider regarding the methodology? A. A sectoral approach can be thought out as an extension of the research. It could be retail sector or airport or healthcare sector.     

Response: We appreciate the reviewer’s thoughtful suggestion. While this study intentionally adopted a generalizable, cross-sectoral framework to establish a broad foundation for indicator prioritization, we agree that sector-specific applications would enrich contextual understanding. As such, we have included a discussion in the conclusion recommending future research to apply this framework within specific sectors—such as healthcare, retail, or logistics—to validate its relevance and adaptability.

• Are the conclusions consistent with the evidence and arguments presented, and do they address the main question posed? Please also explain why this is/is not the case. Yes. The results are conclusive in the sense that they present a novel framework for identifying clear and concise sustainability metrics in the digital supply chain

Response: Thank you for affirming the consistency between our conclusions and the presented evidence. We have further reinforced this alignment by explicitly linking the main research question with the findings and implications in the conclusion section. This ensures a coherent narrative from problem formulation through methodology to results and their interpretation.

 

• Are the references appropriate?   Yes. 79   • Any additional comments on the tables and figures. No.

Response: Thank you for your positive feedback. We are pleased to hear that the references were found appropriate and that the tables and figures were satisfactory.

 

 

Reviewer 2 Report

Comments and Suggestions for Authors

The paper investigates how the integration of Industry 4.0 and AI technologies can enhance sustainability performance in global supply chains. In particular, it explores the question as to which sustainability indicators are most critical in the digital transformation of global supply chains, and how can these be systematically prioritized? The study applies the Best-Worst Method (BWM), a Multi-Criteria Decision-Making (MCDM) tool, to rank 20 sustainability indicators categorized under environmental, operational, strategic, and social dimensions. Data was gathered from 37 experts from academia and industry through structured questionnaires and validated through sensitivity analysis. In my view, the manuscript could merit favorable consideration following changes based on the below comments.

1/ The integration of sustainability, AI, and Industry 4.0 has been explored previously in literature. The authors are suggested to further highlight the distinct conceptual contribution. In other words, the claims of filling a gap could be further substantiated with a clear contrast against existing integrated MCDM studies.

2/ The authors are recommended to clarify that the sample size of thirty-seven experts, while relatively small, is not uncommon in the literature on expert surveys, by considering recent literature including but not limited to:

https://doi.org/10.1016/j.tre.2024.103753
https://doi.org/10.1016/j.omega.2016.02.003

3/ The authors are recommended to further enhance the sensitivity analysis part by considering – (i) equal weighting, (ii) experience-based weighting, and (iii) familiarity-based weighting – for the various respondents. If it may not be possible to do as at this stage, the authors are recommended to add a discussion as to how such a sensitivity analysis could be performed by considering the relevant literature.

4/ Along the lines of the previous comment, the authors are encouraged to enrich the methodological design by combining BWM with qualitative validation (e.g., expert interviews or case vignettes) to deepen interpretation and relevance. If it may not be possible to do so at this stage, then the authors are encouraged to highlight it as part of future research work.

5/ The paper treats indicators as independent, but in practice, many are interlinked (e.g., emissions reduction and energy efficiency). And in the real-world decision-making, there can be trade-offs as well. The authors are encouraged to acknowledge this limitation more explicitly and propose frameworks that could capture these inter-relations for future work.

6/ As part of the future work discussion, the authors are suggested to discuss highlight possible expansion of the framework with longitudinal perspective. That is, comment on how priorities might shift over time due to evolving technology or policy landscapes (e.g., EU AI regulations) and propose future updates through longitudinal expert tracking or dynamic BWM models.

7/ The authors are suggested to clarify and streamline the introduction and conclusion for better flow.

Author Response

We would like to sincerely thank the reviewers for their valuable time, constructive comments, and insightful suggestions, which have greatly helped us improve the quality of our manuscript. We have carefully addressed all comments and revised the paper accordingly. Below, we provide a detailed explanation of the changes made in response to each comment.

The paper investigates how the integration of Industry 4.0 and AI technologies can enhance sustainability performance in global supply chains. In particular, it explores the question as to which sustainability indicators are most critical in the digital transformation of global supply chains, and how can these be systematically prioritized? The study applies the Best-Worst Method (BWM), a Multi-Criteria Decision-Making (MCDM) tool, to rank 20 sustainability indicators categorized under environmental, operational, strategic, and social dimensions. Data was gathered from 37 experts from academia and industry through structured questionnaires and validated through sensitivity analysis. In my view, the manuscript could merit favorable consideration following changes based on the below comments.

1/ The integration of sustainability, AI, and Industry 4.0 has been explored previously in literature. The authors are suggested to further highlight the distinct conceptual contribution. In other words, the claims of filling a gap could be further substantiated with a clear contrast against existing integrated MCDM studies.

Response: Thank you for this insightful observation. In response, we have revised the literature review section to more clearly differentiate our work from existing MCDM studies that integrate sustainability, AI, and Industry 4.0. Specifically, we emphasize that while prior research has applied MCDM techniques—often focusing on narrow industry scopes or single sustainability dimensions—our study offers a novel integration of 20 indicators across four dimensions using the BWM technique. This distinction is now explicitly articulated in Section 2, where we highlight the multidimensional and cross-sectoral nature of our framework, which has not been comprehensively addressed in the current literature.

Response: 2/ The authors are recommended to clarify that the sample size of thirty-seven experts, while relatively small, is not uncommon in the literature on expert surveys, by considering recent literature including but not limited to:

https://doi.org/10.1016/j.tre.2024.103753
https://doi.org/10.1016/j.omega.2016.02.003

Response: Thank you for the helpful observation regarding expert panel size. We have now added a clarification in the methodology section to justify our sample of 37 experts by referencing recent literature that supports the validity of similar sample sizes in BWM and other MCDM applications (see Govindan et al. (2024); Reefke and Sundaram (2017)). These references confirm that our sample size aligns with accepted practices in the field.

3/ The authors are recommended to further enhance the sensitivity analysis part by considering – (i) equal weighting, (ii) experience-based weighting, and (iii) familiarity-based weighting – for the various respondents. If it may not be possible to do as at this stage, the authors are recommended to add a discussion as to how such a sensitivity analysis could be performed by considering the relevant literature.

Response: Thank you for this insightful recommendation. While our current sensitivity analysis focused on input variation for specific criteria, we acknowledge the importance of exploring respondent-level weighting schemes such as experience-based or familiarity-based aggregation. We have added a paragraph in Section sentesivy analysis a how these alternative approaches could be implemented in future research and cited relevant literature (Rezaei, 2016; Haji Abadi & Darestani, 2023). We appreciate this valuable direction for methodological enhancement.

 

4/ Along the lines of the previous comment, the authors are encouraged to enrich the methodological design by combining BWM with qualitative validation (e.g., expert interviews or case vignettes) to deepen interpretation and relevance. If it may not be possible to do so at this stage, then the authors are encouraged to highlight it as part of future research work.

Response:  Thank you for this excellent suggestion. While our study applied a structured BWM-based quantitative method, we agree that the integration of qualitative methods such as expert interviews or case vignettes would significantly enhance interpretive depth. We have now added a paragraph in the discussion section highlighting this as a key avenue for future research. This approach can help contextualize and validate the rankings across industry-specific scenarios.

 

5/ The paper treats indicators as independent, but in practice, many are interlinked (e.g., emissions reduction and energy efficiency). And in the real-world decision-making, there can be trade-offs as well. The authors are encouraged to acknowledge this limitation more explicitly and propose frameworks that could capture these inter-relations for future work.

Response: Thank you for highlighting this important point. We agree that in practice, many sustainability indicators are interlinked and may involve trade-offs. We have now explicitly acknowledged this limitation in the discussion section and suggested the application of hybrid MCDM frameworks such as BWM-DEMATEL or BWM-ANP for future studies to address these interdependencies more rigorously.

 

6/ As part of the future work discussion, the authors are suggested to discuss highlight possible expansion of the framework with longitudinal perspective. That is, comment on how priorities might shift over time due to evolving technology or policy landscapes (e.g., EU AI regulations) and propose future updates through longitudinal expert tracking or dynamic BWM models.

Response:  We thank the reviewer for this valuable suggestion. We agree that sustainability priorities are not static and can evolve due to changes in technology and policy. We have added a paragraph in the conclusion highlighting the potential for longitudinal expert tracking and dynamic BWM models as future research directions. These would enhance the adaptability and real-world relevance of the framework.

 

7/ The authors are suggested to clarify and streamline the introduction and conclusion for better flow.

Response: Thank you for the suggestion. We have revised both the introduction and conclusion to improve their logical flow and clarity. In the introduction, we clearly distinguish the background, research gap, and objectives. In the conclusion, we summarize the aim, main findings, and implications in a more structured and reader-friendly format.

Reviewer 3 Report

Comments and Suggestions for Authors

The paper studies the gap in the literature regarding the structured prioritization of sustainability-related indicators influenced by digital transformation (integration of Industry 4.0 and Artificial Intelligence (AI) technologies). The paper is well-structured and well-written, but before publication, the following should be addressed.

• Add a methodology paragraph at the end of the introduction (what each section consists of)
• Add more information regarding the experts (years of experience, position, etc.)
• Add a figure describing the methodology of the paper
• Considering that there were multiple experts, how did you determine a single value when applying the BWM method? Was the average value of the experts' evaluations used, or the most frequently occurring value? This should be explained in the paper
• On what basis did you determine that the sample of experts is representative?
• Enhance the theoretical implications of the paper
• Enhance the managerial implications of the paper
• Enhance future research directions

Author Response

We would like to sincerely thank the reviewers for their valuable time, constructive comments, and insightful suggestions, which have greatly helped us improve the quality of our manuscript. We have carefully addressed all comments and revised the paper accordingly. Below, we provide a detailed explanation of the changes made in response to each comment.

The paper studies the gap in the literature regarding the structured prioritization of sustainability-related indicators influenced by digital transformation (integration of Industry 4.0 and Artificial Intelligence (AI) technologies). The paper is well-structured and well-written, but before publication, the following should be addressed.

• Add a methodology paragraph at the end of the introduction (what each section consists of)

Response: Thank you for the helpful suggestion. We have added a paragraph at the end of the Introduction that outlines the structure of the paper. This roadmap paragraph guides the reader through the main sections of the study, including the literature review, methodology, results, sensitivity analysis, discussion, and conclusion.

 

• Add more information regarding the experts (years of experience, position, etc.)

Response: Thank you for the valuable suggestion. We have expanded the Methodology section to include additional information about the expert panel, such as their average years of experience, professional positions, and sectoral backgrounds. This enhancement provides a clearer picture of the diversity and relevance of the expert judgments used in the BWM analysis.

• Add a figure describing the methodology of the paper

Response: Thank you for this helpful suggestion. We have added a new figure in the methodology section that visually summarizes the research design and implementation process, including indicator identification, expert consultation, application of the Best-Worst Method, and sensitivity analysis.

 

• Considering that there were multiple experts, how did you determine a single value when applying the BWM method? Was the average value of the experts' evaluations used, or the most frequently occurring value? This should be explained in the paper

Response: Thank you for this important observation. We have now clarified in the methodology section that individual BWM weights obtained from 37 experts were aggregated using the arithmetic mean to produce global weights. This method ensures equal representation of all expert views and aligns with accepted practices in MCDM literature.

 

• On what basis did you determine that the sample of experts is representative?

Response: Thank you for your thoughtful comment. We have added a paragraph to the methodology section clarifying that expert representativeness was determined based on subject-matter expertise, sectoral diversity, and relevance to supply chain sustainability and digital transformation. This aligns with accepted practices in MCDM literature.

 

• Enhance the theoretical implications of the paper

Response: Thank you for the valuable suggestion. We have now enhanced the theoretical implications in the discussion section by explicitly stating how the study extends the literature on sustainable supply chains, digital transformation, and MCDM theory. The paragraph highlights our contribution to indicator prioritization, methodological expansion of BWM, and integration of digital enablers with sustainability frameworks.

 

• Enhance the managerial implications of the paper

Response: Thank you for this helpful suggestion. We have added a paragraph in the discussion section explicitly outlining the managerial implications of our findings. This addition highlights how supply chain leaders and policy makers can use the prioritized indicators and decision framework to guide investment, assess trade-offs, and align digital transformation with sustainability goals.

 

• Enhance future research directions

Response: Thank you for your constructive feedback. We have now expanded the future research section to offer a more comprehensive outlook, covering longitudinal studies, hybrid MCDM methods, sectoral extensions, and cross-regional analyses. These enhancements provide a clearer path for advancing theory and practice in sustainability and digital supply chain management.

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

All of my previous round comments have been adequately addressed. The manuscript merits acceptance in my view, pending revision based on one minor comment below.

1/ The references section needs to be formatted as per the journal guidelines.