An Overview of Hesitant Fuzzy Linguistic Term Set Applications in Supply Chain Management: The State of the Art and Future Directions

Comment: Reviewer#1 General Comments: The manuscript systematically reviews the applications of Hesitant Fuzzy Linguistic Term Sets (HFLTSs) and their extensions in the context of Supply Chain Management (SCM) decision-making problems. The paper highlights the relevance of this subject and addresses the lack of literature review studies in this area. Overall, the manuscript is well written and presents valuable insights into the state of the art, research gaps, and avenues for future studies in this field. However, a few points could be addressed to improve the manuscript further. Specific Comments: #1. Corresponding author's Institute email is encouraged. Answer: Dear Reviewer, thank you for your valuable comments. The author's Institute email has been added to the manuscript. #2. The author's list should be separated with commas. Answer: This has been fixed. #3. The following sentence is too long "In terms of the structure of the rest of this article, Section 2 will present a brief review of HFLTSs and HFLTS extensions; Section 3 describes the methodology for this systematic review of the literature; Section 4 will discuss the results; Section 5 presents recommendations for future studies; and Section 6 will consist of our conclusions and the limitations of this study." Please separate it into several sentences. Answer: This has been corrected. #4. There are 2 Table 1s, but no Table 2. Please revise. Answer: This has been fixed. #5. There are 2 Table 4s. Please revise. Answer: This has been fixed. #6. Are you sure this explanation is for Table 1? Answer: The sentence has been modified as follows: “Among the 13 HFLTS extensions discussed in Section 2, we found applications...”) #7. Keywords: Please provide between 5 and 10 keywords that need not be repeated in the title's words. Answer: The following keywords were added: “Multicriteria Decision Making, Systematic Literature Review, Operations Management, Fuzzy Logic, Group Decision Making.” #8. Figure 1: The figure title should be below the figure. #9. Figure 2: The figure title should be below the figure. Answer: All of the figure titles have been repositioned. #10. Figure 4: Please present charts or line graphs. I am sure that they will be more effective and understandable than pie charts. Answer: Thank you for this great suggestion. Figure 4 has been modified. #11. References are not formatted as per the guidelines. Please align left and right. Answer: This has been fixed. #12. Author contributions: Please remove the quotes. Answer: This has been fixed. #13. Please add a flowchart for the decision-making process. Answer: This has been added in Figure 1. #14. All equations should be marked with a number and aligned right. They should be mentioned or explained in the text. Answer: This has been fixed. #15. Citations in the text and references should follow the journal template. The reviewer suggests that the authors add more scientific articles to enrich the literature review of MCDM. It would help if you searched Mathematics or other Journals for more references that could be used to improve your literature review. References should be numbered in order of appearance and indicated by a numeral or numerals in square brackets—e.g., [1] or [2,3], or [4–6]. In the text, reference numbers should be in square brackets [ ] and placed before the punctuation; for example, [1], [1–3], or [1,3]. For embedded citations in the text with pagination, use both parentheses and brackets to indicate the reference number and page numbers; for example, [5] (p. 10) or [6] (pp. 101–105). The following suggestions could help: + A Two-Stage Multi-Criteria Supplier Selection Model for Sustainable Automotive Supply Chain under Uncertainty + Wireless Communications for Data Security: Efficiency Assessment of Cybersecurity Industry—A Promising Application for UAVs Answer: Dear reviewer, thank you for this great suggestion. In order to improve the quality of the theoretical framework, we have added five new references. We analyzed the two studies suggested above and chose to include only the first one, as it is more closely related to the subject of this systematic review. #16. Table 5: the table and its title should be on the same page. Answer: This has been fixed. #17. Subsection 4.4 should be on the same page as its first paragraph. Answer: This has been fixed. #18. Figure 8: Please add titles for both axes. Answer: Axis titles have been added to all of the charts. #19. Please format the manuscript following the guidelines of the journal template. Answer: In the reviewed version in the manuscript, we have adopted the Journal Mathematics template. We have fixed all formatting errors. Constructive Questions: #20. What are the most common decision-making problems in supply chain management that have been addressed using techniques based on Hesitant Fuzzy Linguistic Term Sets (HFLTSs) and their extensions? Answer: The decision-making problems in supply chain management that have been addressed using techniques based on HFLTSs are presented in Table 7. As inserted in the first paragraph of Section 4.3, “Studies dealing with supplier selection have been the most frequent, totaling 23.5% of the sample. They have been followed by failure evaluations (11.8%) and performance evaluations (8.8%).” #21. Which industries or sectors have predominantly been the focus of studies applying HFLTSs and their extensions to supply chain management decision making? Answer: In the fourth paragraph of Section 4.3, we have “Figure 5 displays the classification of these studies according to the sector of the participating company (RQ2.4). The automotive sector stands out with 10 applications (29.4%). It was followed by the health and electro-electronic sectors with 5 and 4 applications respectively. The food, glass, high-technology, infrastructure, manufacturing, retail, and transportation sectors were represented by just one application apiece.” #22. What are the identified research gaps in the literature regarding applying HFLTS and its extensions to supply chain management, and what specific avenues for future research are suggested to address these gaps? Answer: The research gaps and avenues for future research are discussed in Sections 5.1 to 5.5. #23. The manuscript provides a valuable systematic review of the applications of HFLTS and its extensions in SCM decision making. Addressing the points mentioned above will further enhance the manuscript's clarity, rigor, and overall quality. Thank you for reading. Answer: We have modified the text to address all of your suggested recommendations, and this has improved the overall quality of the article. Reviewer#2 #24. The abstract mentions that the study presents a systematic review of Hesitant Fuzzy Linguistic Term Sets (HFLTS) and their extensions in the context of Supply Chain Management (SCM) decision-making problems. Could you elaborate on the significance and benefits of using HFLTS and its extensions in SCM? How do these techniques address the uncertainty and hesitation faced by decision-makers in SCM? Answer: Dear Reviewer, thank you for your valuable suggestions. The significance and benefits of using HFLTS and HFLTS extensions in SCM is included in the third paragraph of Section 1 and in Section 2 (beginning with the second paragraph). #25. The abstract mentions the classification of selected papers based on factors such as the origins of the studies, SCM, HFLTS, and decision making. Could you provide more details about the classification criteria used in the study? How did you ensure the relevance and consistency of the selected papers for the systematic review? Answer: Dear reviewer, based on your recommendation, we have included a description of the factors considered in the classification of selected papers in Table 4. Concerning the second question, the consistency and relevance of the study have been guaranteed through the adoption of the PRISMA method for the elaboration of this study’s research protocol. We observed all of the steps of PRISMA and complied with all the checklist items provided by this method. We considered the indexing databases to be of great relevance. The selection of the articles in the systematic review was made by two of the researchers involved. Its quality assessment was realized by the two authors through a complete reading of the articles to confirm whether they involved real applications, were written in English, and were peer-reviewed. In addition, we surveyed the number of citations of these articles to help understand the relevance of each article. #26. The results of the study indicate that the source and enable processes have been studied with greater frequency, and common problems relate to supplier selection, failure evaluation, and performance evaluation. Could you explain why these processes and problems have received more attention in the literature? Are there any specific challenges or complexities associated with these areas in SCM decision-making? Answer: The following sentence has been added to the second paragraph of Section 4.3: “The high frequency of applications related to the source process seems to be related to the fact that this process requires decision-making processes that affect several areas of the business. The main one is supplier selection, which determines part of the supply chain structure and also influences production costs, product quality, and customer satisfaction. Similarly, the enable process encompasses other decision-making processes that are essential for supply chain structuring and operation, such as personnel selection, location selection, performance evaluation, and risk assessment [48].” #27. The abstract mentions the predominance of the automotive sector and sustainable SCM and green SCM strategies in the analyzed studies. Could you elaborate on the reasons behind this focus? Are there any unique characteristics or considerations within the automotive sector that make it particularly relevant for HFLTS and its extensions in SCM? Answer: The following excerpt has been added to the fifth paragraph of Section 4.3: “The predominance of applications in sustainable and green SCM is due to the importance that environmental management and social responsibility have achieved in recent decades. Companies began adapting to deal with stricter regulations and customers who were more concerned about the socio-environmental impacts of business activities [8]. As the use of environmental and social performance indicators is relatively recent in most companies, and such indicators usually involve qualitative aspects or variables for which there is no performance history, the use of HFLTS techniques and HFLTS extensions is quite appropriate [19].” Additionally, the following sentence has been added to the sixth paragraph of Section 4.3: “The large number of company applications seems to be related to the fact that these chains are of great economic importance and have a complex structure. Assembly companies purchase many components externally, for which there are often more than one supplier. Moreover, many automotive companies have traditionally been among the early adopters of management tools and practices [83,84].” #28. The abstract mentions the utilization of various HFLTS extensions, with Double Hierarchy Hesitant Fuzzy Linguistic Term Sets and Probabilistic Linguistic Term Sets being the most utilized. Could you explain the advantages or specific applications of these extensions in SCM decision making? How do they enhance the modeling and analysis of uncertain and hesitant information in SCM? Answer: The following excerpt has been added to the third paragraph of Section 4.4: “the use of DHHFLTSs may be associated with the possibility of using complex linguistic expressions, which combine two scales of linguistic terms in each judgement. In addition to increasing the flexibility of DM preferences, the use of DHHFLTSs increases the possibilities of values that can be attributed to the evaluated object [40]. Furthermore, the interest of using PLTS lies in the fact that this approach takes into account a possibility degree for each linguistic term selected by a DM. For each judgment provided by a DM, the sum of the possibility degrees must be equal to 1. For example, if a DM selects only the term “s3: Medium”, without hesitation, his judgment will be quantified as (s3, 1.0). When selecting “between s4: good and s6: extremely good”, the DM judgment will be quantified as (s4, 0.33; s5, 0.33; s6, 0.33). Since the values of the degrees of possibility act as weighting factors for the linguistic terms, the terms chosen in situations of greater hesitation will have less influence on the final result [37].” #29. The abstract suggests potential avenues for future studies, such as innovative applications, the integration of techniques, comparisons of techniques, group decision making, and validation procedures for new models. Could you provide more insights into these potential areas of research? Which specific aspects or gaps in the literature do you believe require further exploration or development? Answer: The identified research gaps and potential avenues for future studies have been included in Sections 5.1 to 5.5. Several research gaps and suggestions concerning each of the five topics mentioned above have been discussed. #30. In terms of the state of the art in using HFLTSs and their extensions in SCM decision making, are there any notable limitations or challenges that have been identified? How can these limitations be addressed or overcome in future research? Are there any specific advancements or advancements in methodology or techniques that could contribute to the improvement of HFLTS-based SCM decision making? Answer: Yes, there are several study limitations and challenges. Some limitations are related to the lack of methods for weighting decision-makers, the scarcity of methods for obtaining consensus and the absence of comparative studies among HFLTS techniques. Sections 5.3 to 5.5 discuss issues related to these topics and make some recommendations for future research. #31. How does the use of HFLTSs and their extensions in SCM decision making compare to other traditional or conventional decision-making approaches? Are there any specific advantages or limitations of using HFLTSs in SCM that set them apart from other methods? Have there been any comparative studies conducted to evaluate the performance or effectiveness of HFLTS-based approaches in SCM? Answer: The reasons for adopting HFLTS approaches over traditional methods are to be found in the third paragraph of Section 1 and the second paragraph of Section 2. The benefits of using HFLTS extensions are discussed in Section 2 and the third paragraph of Section 4.4. The features of HFLTSs and HFLTS extensions are discussed in Section 2. Moreover, several suggestions for conducting comparative studies are presented in Section 5.3. #32. In terms of practical applications, how readily adoptable are HFLTSs and their extensions in real world SCM settings? Are there any implementation challenges or considerations that organizations should be aware of when applying these techniques to SCM decision making? Have there been any success stories or case studies that showcase the benefits of using HFLTSs in SCM? Answer: As noted in Table 3 of Section 3 ("Inclusion criteria"), all studies included in our systematic review study have successful real world applications. The results suggest that companies from any economic sector can adopt the techniques discussed in this study in their decision-making processes, whether they are strategic, tactical or operational. This demonstrates the high applicability of HFLTSs and their extensions in business practices. A challenge for real applications is how to validate the results. Section 5.5 has been added to discuss this topic. In addition, another challenge is computationally implementing HFLTS techniques and their extensions, since decision-making software products based on such techniques are still rare. Although most of them can be implemented using spreadsheet software, such as MS Excel, the development of new forms of software with graphical interfaces would simplify their use and contribute to the greater adoption of these techniques by users who are not specialists in HFLTSs. Two excerpts have been added to Section 6 to explain this topic. #33. The author can read the following papers to improve the quality of research: 10.1016/j.stae.2022.100032, 10.3390/SU12176969 Answer: Dear reviewer, thank you for this great suggestion. In order to improve the quality of the theoretical framework, we have added five new references. Several other changes have also been made to improve the overall quality of the manuscript. Unfortunately, we cannot include the studies suggested above in the systematic review, as they do not apply HFLTSs or HFLTS extensions. However, we have decided to cite one of them in the text of the manuscript (reference [76]). #34. How does the systematic review and overview presented in this paper contribute to the field of SCM and decision making? What are the main takeaways or implications for researchers and practitioners in terms of using HFLTSs and their extensions in SCM? How can this research assist in the development of new studies and the advancement of decision-making methodologies in SCM? Answer: The main takeaways have been inserted in Section 6. In the fifth paragraph of Section 1 and the third paragraph of Section 6, we have explained how this research assists in the development of new studies and the advancement of decision-making methods in SCM. Reviewer#3 This paper identifies gaps in the literature for supply chain problems and fuzzy theory, and future studies focusing on innovative applications, the integration of techniques, the comparison of techniques, group decision making, and validation methods for new models are presented. This article helps researchers and professionals develop new studies by examining the characteristics of various studies. This study is very useful for the development of mathematical models and decision making. However, there are some things that should be considered to improve the quality of the article. #35. The research method is presented in a form after the introduction. This research methodology should include a flowchart to help the reader understand the topic easily. Answer: Figure 1 has been modified in order to better explain the research method. #36. In the last part of the conducted studies, the differences between various fuzzy methods in supply chain management should be expressed in the form of a separate table. Answer: Table 9 has been added to the manuscript in order to describe the features of the methods found in the systematic review. #37. It seems that these articles were not properly reviewed as of March 2023. for example * Saha, A., Pamucar, D., Gorcun, O. F., & Mishra, A. R. (2023). Warehouse site selection for the automotive industry using a fermatean fuzzy-based decision-making approach. Expert Systems with Applications, 211, 118497. * Sathyan, R., Parthiban, P., Dhanalakshmi, R., & Sachin, M. S. (2023). An integrated Fuzzy MCDM approach for modelling and prioritising the enablers of responsiveness in automotive supply chain using Fuzzy DEMATEL, Fuzzy AHP and Fuzzy TOPSIS. Soft Computing, 27(1), 257-277. * Kumar Singh, R., & Modgil, S. (2023). Assessment of lean supply chain practices in Indian automotive industry. Global Business Review, 24(1), 68-105. Answer: Dear reviewer, thank you for the suggestion. However, since the scope of our study is limited to applications of HFLTS methods and HFLTS extensions, studies based on traditional fuzzy logic are outside the scope of our study. Therefore, unfortunately we cannot include the studies suggested above in our systematic review, as they do not apply HFLTS nor HFLTS extensions. Moreover, in the literature there are already systematic review studies focused on the application of fuzzy logic in supply chain management. Two review studies on this topic have been cited in the new version of the manuscript (references [20,21]). Although we could not include the aforementioned studies in the systematic review, we decided to cite one of them in the text of the article (reference [83]). #38. In Table 7, the studies conducted with the robust fuzzy method should also be mentioned. Answer: Dear reviewer, as explained in our response to comment #37, our study is focused on HFLTS and HFLTS extensions, since this approach is more recent and there are no systematic review studies regarding this subject. Applications of traditional fuzzy logic in supply chain management have already been reviewed by previous studies, such as those presented in references [20, 21]. #39. Figure 4(b) is not of sufficient quality. to be drawn again. Answer: It has been fixed. #40. The article should be set in journal format. Answer: In the reviewed version in the manuscript, we have adopted the Journal Mathematics template. We have fixed all of the formatting errors. Reviewer#4 #41. The study presents a systematic review of Hesitant Fuzzy Linguistic Term Sets and their extension applications to Supply Chain Management decision-making problems. The abstract of the paper is well structured. In the introduction, a good transposition is made concerning the research subject and the argument as to why this study has been carried out. A systematic review of the literature on the treated subject is presented. The methodological procedures, the classification criteria of the explored studies and the obtained results are described. The results are discussed in relation to other similar studies in the literature. Future research directions are represented. The bibliography is comprehensive with 79 references and well written. There are some small editing errors that need to be corrected. I congratulate the authors for this interesting review. Answer: Dear Reviewer#4, thank you for your valuable comments. In the new version of the manuscript, we have performed an extensive review of the text in order to correct editing errors. We have also made other improvements to the manuscript based on suggestions from other reviewers. Best regards, The authors.