The Role of Digital Transformation in Manufacturing: Discrete Event Simulation to Reshape Industrial Landscapes

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The article per se was quite interesting and pertinent; however, to make it publishable, there are some issues that need to be resolved. Please see the attachment.

 

The role of digital transformation in manufacturing: Discrete 2

event simulation to reshape industrial landscapes

 

Abstract:

1. You mention human complexity, but it is kind of slightly vague. Try to re-phrases.
2. There was no mention of study findings. Add a sentence explaining the simulations revealed, and the measurable improvements.

Introduction:

3. There ate some repetition in the section: It is a technology that not only digitizes business processes but also drives digital transformation through its ability to model, analyze and optimize operations in a virtual environment; it
   appear twice (lines 82–83 and again 84–85).
4. The section is quite long for an introduction — about 1,350 words. Try to trim redundant phrases, merge similar points, and consider moving some content to other sections like: literature review or methodology.
5. There are too Many Citations Without discussing them, like: [3] to [6], [8–9]). Try to refer them to support your claims, not just cite them.
6. Try to highlight the research questions in this form so it would be smoothie for reader to follow:

Q1: What is the evolutionary scenario and current status of DES becoming a Key Enabling Technologies (KET) for Industry 4.0.?

Q2: What are the distinguishing features of DES that offer significant benefits to manufacturing companies in terms of operational efficiency, cost reduction, and product quality improvement?

Q3: How DES can effectively drive digital transformation in processes, facilitating the adoption of emerging technologies and optimizing the entire production chain?

2. Background and research gaps
3. There are some redundancy and repetition: the discussion of SMEs and the lack of guidance is echoed multiple times across similar phrasing. Sentences like "navigating operational and strategic uncertainties" are repeated multiple times.
4. You had abruptly finished with a study summary, but then here's no concluding synthesis or bridge into your own research objectives. Try to add a short concluding paragraph summarizing what the literature reveals, what is still missing, and how your study positions itself to contribute.
5. You have used the same numbering for the sections:
6. Background and research gaps
7. Materials and Methods
8. Repetitive phrase with little added value. For instance, the purpose of validation is reiterated in both Step 2 and Step 3.
9. Reference scenario
10. Some paragraphs are very long and cover multiple topics like these lines 370–407), making it hard for the reader to follow or identify the main message.
11. These terms are not consistence expressed: One Day," "One week," "One month," "Six months?
12. Future developments
13. technologies have been discussed extensively, but the role of the workforce and decision-makers is only briefly mentioned in the context of collaboration. Try to increasing importance of human involvement in smart manufacturing in terms of reskilling, decision-making, and managing AI systems.
14. Conclusions
15. There’s some repetition in the section, particularly with the idea of digital transformation being complex and challenging. For instance, you mention "difficult challenges" and "such a radical change presents manufacturers with difficult challenges" closely together. Try to streamlined to avoid redundancy.
16. There was no mentioning of theoretical contribution, business contribution, study limitations, and future avenues. You can a paragraph in the conclusion section for each issue.
17. There was also no mentioning of Alternative Software, like Flexslim, AnyLogic, and Simul8?

 

Comments for author File: Comments.pdf

Comments on the Quality of English Language

There were several issues with the quality of the English language that needed to be extrapolated to make it readable for the reader. A native English speaker would be recommended.

Author Response

Detailed Response to Reviewers#1

ID: applsci-3630795

Title: The role of digital transformation in manufacturing: Discrete event simulation to reshape industrial landscapes

 

 

Dear Reviewers:

Enclosed please find the revised version of the manuscript "The role of digital transformation in manufacturing: Discrete event simulation to reshape industrial landscapes” by Fabio De Felice, Cristina De Luca, Antonella Petrillo *, Antonio Forcina, Miguel Angel Ortiz Barrios, Ilaria Baffo.

We would like to thank you for your comments and recommendations.

 

We have responded to all your recommendations point by point.

In this report, our responses are marked in blue, and the change made in the text is indicated in red

In the revised manuscript, the main changes requested are highlighted in red.

 

Sincerely,

The authors

 

== Answers to reviewer==================================================

 

Abstract

1. You mention human complexity, but it is kind of slightly vague. Try to re-phrases.

Authors: Thank you for the suggestion. We have clarified the concept and rephrased:

An operational framework is proposed to drive digital transformation in manufacturing by balancing automated systems efficiency with the complexity of human activities, which include decision-making flexibility, adaptability, tacit knowledge and collaborative interaction.

 

1. There was no mention of study findings. Add a sentence explaining the simulations revealed, and the measurable improvements.

Authors: Thank you for the suggestion. We mentioned the results of the study and added information explaining the revealed simulations and measurable improvements:

The implemented solutions led to a significant annual increase in productivity, the result of an overall improvement in process efficiency, which was also achieved through the identification and resolution of key process bottlenecks, confirming the method's effectiveness

 

Introduction

 

1. There are some repetition in the section: It is a technology that not only digitizes business processes but also drives digital transformation through its ability to model, analyze and optimize operations in a virtual environment; it
   appear twice (lines 82–83 and again 84–85).

Authors: Thank you for the suggestion. The repeated sentence has been deleted.

 

1. The section is quite long for an introduction — about 1,350 words. Try to trim redundant phrases, merge similar points, and consider moving some content to other sections like: literature review or methodology.

Authors: Thank you for the suggestion. The introduction has been reworked into a condensed form, reduced to about 800 words, removing redundant and overlapping concepts to improve coherence and clarity.

 

1. There are too Many Citations Without discussing them, like: [3] to [6], [8–9]). Try to refer them to support your claims, not just cite them.

Authors: Thank you for the suggestion. The overall rephrasing and reorganization of the introduction, as indicated above, made the statements more explicit and avoided the use of insufficiently contextualized quotations.

 

1. Try to highlight the research questions in this form so it would be smoothie for reader to follow:

Q1: What is the evolutionary scenario and current status of DES becoming a Key Enabling Technologies (KET) for Industry 4.0.?

Q2: What are the distinguishing features of DES that offer significant benefits to manufacturing companies in terms of operational efficiency, cost reduction, and product quality improvement?

Q3: How DES can effectively drive digital transformation in processes, facilitating the adoption of emerging technologies and optimizing the entire production chain?

Authors: As suggested, the research questions were made explicit and identified progressively with the abbreviation Q, followed by an increasing number.

 

Background and research gaps

 

1. There are some redundancy and repetition: the discussion of SMEs and the lack of guidance is echoed multiple times across similar phrasing. Sentences like "navigating operational and strategic uncertainties" are repeated multiple times.

Authors: We took his suggestion and rewarded the section on SMEs and lack of guidance, removing similar and redundant concepts.

 

1. You had abruptly finished with a study summary, but then here's no concluding synthesis or bridge into your own research objectives. Try to add a short concluding paragraph summarizing what the literature reveals, what is still missing, and how your study positions itself to contribute.

Authors: As suggested, we have introduced a short concluding paragraph summarizing what the literature reveals, what is still missing, and how your study is positioned to contribute to research:

In summary, the literature highlights the value of Discrete Event Simulation (DES) as a key enabling technology for digital transformation in manufacturing. However, significant gaps remain, particularly in terms of integrated methodologies, practical guidelines, and applications tailored to SMEs. These limitations underline the need for more concrete and replicable approaches. This study addresses these gaps by proposing an operational DES-based model, validated through a real case study, to support both strategic and operational decision-making in the digitalization process.

9. You have used the same numbering for the sections:

Background and research gaps

Materials and Methods

Authors: Thank you for the suggestion. We have appropriately changed the numbering of the sections:

2. Background and research gaps
3. Materials and Methods

 

1. Repetitive phrase with little added value. For instance, the purpose of validation is reiterated in both Step 2 and Step 3.

Authors: Thank you for the suggestion. In step 3, the purpose and value of validation was removed because, as you pointed out to us, it is repetitive and without added value

 

Reference scenario

1. Some paragraphs are very long and cover multiple topics like these lines 370–407), making it hard for the reader to follow or identify the main message.

Authors: Thank you for the suggestion. To facilitate reading and make the identification of the main message more immediate, the structure of the section has been reorganized. In particular, sub-sections have been added:

4.1 Company Background and Industrial Challenges

4.2 Strategic Use of DES in Production Systems

4.4 Data Analysis for Process Modeling Accuracy

 

1. These terms are not consistence expressed: One Day," "One week," "One month," "Six months?

Authors: Thank you for the suggestion. Expressions have been modified to make them more consistent:

One Working Day

One Working week

One Working month

Six Working months

 

Future developments

1. technologies have been discussed extensively, but the role of the workforce and decision-makers is only briefly mentioned in the context of collaboration. Try to increasing importance of human involvement in smart manufacturing in terms of reskilling, decision-making, and managing AI systems.

Authors: As suggested, the growing importance of human involvement in intelligent production in terms of retraining, decision making and management of artificial intelligence systems has been clarified:

However, in an increasingly automated and data-driven manufacturing ecosystem, the human role assumes central importance. The evolution toward intelligent production models requires a rethinking of skills: operators can no longer be limited to executive tasks, but must be able to interact with digital systems, interpret data and contribute consciously to decision-making. In this context, reskilling and upskilling initiatives are key to transforming traditional skills into digital skills that can effectively dialogue with automation and simulation. Decision making at the managerial level is also evolving: decision makers need to be able to interpret simulation results, assess the operational and environmental implications of hypothesized strategies, and take timely action. Artificial intelligence must be integrated as decision support, without replacing human judgment. In fact, the management of intelligent systems requires continuous supervision that can recognize when it is necessary to adapt to complex or unexpected contexts. In such situations, human intuition, experience and critical capacity remain indispensable elements.

 

Conclusions

1. There’s some repetition in the section, particularly with the idea of digital transformation being complex and challenging. For instance, you mention "difficult challenges" and "such a radical change presents manufacturers with difficult challenges" closely together. Try to streamlined to avoid redundancy.
2. There was no mentioning of theoretical contribution, business contribution, study limitations, and future avenues. You can a paragraph in the conclusion section for each issue.
3. There was also no mentioning of Alternative Software, like Flexslim, AnyLogic, and Simul8?

Authors: Thank you for the suggestion. The conclusions have been rewritten and reorganized so as to eliminate some repetition and redundancy in the section.

The theoretical contribution, business contribution, study limitations and future avenues have been clarified in appropriate sections.

In section Limitations of the study have been mention of alternative software, such as Flexslim, AnyLogic, and Simul8:

Digital transformation is a systemic business strategy, applicable to any industry to address structural inefficiencies and generate new opportunities through digital technologies. In manufacturing, it involves the adoption of enabling tools to increase revenues, reduce costs, improve quality and enhance operational flexibility while pursuing industrial sustainability. The extreme variety of available solutions is a competitive advantage, but it also introduces significant management complexity, especially for small and medium-sized enterprises. In this context, this research proposes a methodological framework supported by discrete event simulation (DES) to rigorously guide the digital transition in real manufacturing environments.

 Theoretical contribution

From a theoretical perspective, the study contributes to the advancement of the literature on Industry 4.0 and Operational Research by introducing an integrated approach between digital simulation, statistical analysis of process data, and empirical validation. The proposed model is not limited to the optimization of physical flows, but includes decision-making elements and human dynamics, highlighting the relevance of human-in-the-loop design even in highly automated contexts. The contribution is in the vein of hybrid methodologies that combine simulation, decision science and digital transformation.

Enterprise contribution

At the application level, the research provides a concrete case of using simulation to support the digital transformation journey in a manufacturing SME. The modeling enabled the identification of bottlenecks, critical operational issues and optimization margins, returning simulated alternative scenarios to support strategic decisions with low operational impact. The proposed approach is transferable and scalable to similar manufacturing contexts.

Limitations of the study

The main limitation of the study lies in its application to a single industrial case and the adoption of a single simulation environment. Although the software used has high discrete modeling capabilities, alternative tools such as FlexSim, Simul8, or AnyLogic, which offer specific capabilities such as multi-method simulation (discrete-event, agent-based, system dynamics), advanced support for cyber-physical systems, or integration with real-time data, were not considered. Comparative evaluation could offer greater generalizability and methodological robustness.

Future Perspectives

Development perspectives include the integration of simulation with emerging technologies such as Digital Twin, artificial intelligence and IoT in order to build adaptive models based on real data. Expansion of the framework to multi-paradigm systems and integration with environmental and social metrics (e.g., Life Cycle Assessment) represent further research directions, with the goal of strengthening the sustainability and resilience of digitized production systems

 

Comments on the Quality of English Language

There were several issues with the quality of the English language that needed to be extrapolated to make it readable for the reader. A native English speaker would be recommended.

 

 

Authors: We thank the reviewers for valuable feedback on the linguistic quality and clarity of the manuscript. In response, the text was fully revised by a professional, with the aim of improving its grammatical correctness, terminological consistency, and expository fluency. Excessively long or complex sentences were simplified to make them more readable, linguistic inaccuracies were corrected to ensure clarity and academic rigor, technical terminology was standardized to strengthen internal consistency, and transitions between sections were improved to ensure smoother logical development. In addition, some paragraphs have been restructured to eliminate redundancies and enhance essential information. These changes aim to make the manuscript more effective in communicating complex content to an international scientific audience, while adhering to high standards of clarity and accuracy.

 

We trust that the revised version adequately responds to the comments received and meets expectations in terms of linguistic and structural quality.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors,

Thank you for this interesting contribution.

These documents present an exploration of DES alongside digital technology in Industry 4.0 applications for manufacturing process optimization within an Italian aluminum cabinet producer for electric vehicle charging stations. These documents present an approach for developing simulation models which are validated and used for bottleneck detection and workflow optimization and scenario-based decision support. 

Some suggestions are:

• The introduction requires updated citations involving latest and diverse content to better present both Industry 4.0 and discrete event simulation (DES) research context as well as show current knowledge gaps.  
• The paper needs to provide a detailed explanation about the statistical validation methods along with test descriptions and their relevance to enhance the credibility of the simulation model.  
• The manuscript should provide a more detailed explanation about how human factors are incorporated into the simulation framework since this constitutes a fundamental part of Industry 4.0 digital transformation.  
• The manuscript quality must improve because an English language update will provide sentences with improved structures and terminology to deliver clear and precise information across all text sections.  
• The manuscript would benefit from including visual aids through diagrams and flowcharts that show the simulation model structure and process pathways to improve understanding.  
• The manuscript should discuss the study limitations and obstacles that might arise when trying to apply the simulation methodology beyond this research environment and small and medium enterprises.
• The paper should include additional quantitative research and practical suggestions regarding implementation results from what-if analyses.  
• The proposed framework needs additional explanation for its uniqueness because it sets itself apart from previously documented DES applications for manufacturing and Industrial 4.0 research.  
• It should focus on deriving both sustainable long-term advantages and environmental benefits from DES and digital twin adoption in addition to short-term operational enhancements.

I hope that these comments and suggestions may help increase the quality of the paper.

Kindest regards

Comments on the Quality of English Language

• Clear descriptions of research purposes and study methods run through most parts of the manuscript. Some lengthy and complex sentences appear in the text making it challenging to read smoothly.
• Overall the manuscript contains grammatical mistakes along with expressions that need refinement to create a more precise and easy-to-understand text.  
• Technical jargon appears properly understood in most instances yet the author should enhance definition clarity as well as term consistency across the document.  
• Better transitional elements between different sections should be incorporated to improve both coherence and logical development.  
• Certain sections need restructuring because it would decrease repetition and keep essential information at the forefront of each paragraph.  
• Proofreading and language editing at a professional level will create major improvements in both clarity and formulation quality of the manuscript.  
• The manuscript will communicate complex ideas more effectively to various readers by working on sentence structure and condensing textual sections.

Author Response

Detailed Response to Reviewers #2

ID: applsci-3630795

Title: The role of digital transformation in manufacturing: Discrete event simulation to reshape industrial landscapes

 

 

Dear Reviewers:

Enclosed please find the revised version of the manuscript "The role of digital transformation in manufacturing: Discrete event simulation to reshape industrial landscapes” by Fabio De Felice, Cristina De Luca, Antonella Petrillo *, Antonio Forcina, Miguel Angel Ortiz Barrios, Ilaria Baffo.

We would like to thank you for your comments and recommendations.

 

We have responded to all your recommendations point by point.

In this report, our responses are marked in blue, and the change made in the text is indicated in red

In the revised manuscript, the main changes requested are highlighted in red.

 

Sincerely,

The authors

 

 

== Answers to reviewer==================================================

 

• The introduction requires updated citations involving latest and diverse content to better present both Industry 4.0 and discrete event simulation (DES) research context as well as show current knowledge gaps.  

Authors: Thank you for the suggestion. The introduction has been modified to include more up-to-date and diverse content to more comprehensively and articulately present the research context related to Industry 4.0 and discrete event simulation (DES), while highlighting key gaps in the literature. Specifically:

Update and relevance of content:

Updated market data were integrated, including the global value of digital transformation in 2022 ($731.13 billion) and the annual growth forecast to 2030. The impact of the COVID-19 pandemic was also highlighted as a determining factor in the acceleration of digitization across sectors. These elements were included to strengthen the relevance and topicality of the framework.

Reframing the theoretical framework of Industry 4.0:

The framing of digital transformation has been expanded, placing it explicitly in the Industry 4.0 paradigm and presenting discrete event simulation (DES) as an essential enabling technology (KET). Its strategic role in the analysis, optimization and testing of manufacturing solutions in the virtual environment was also clarified.

Explication of knowledge gaps:

In direct response to the comments, explicit remarks were made about the current difficulties of enterprises in adopting structured approaches to digitization. In particular, the lack of effective methodological tools and the insufficiency of decision-making supports capable of handling complex changes and benchmarking were emphasized.

Definition of research questions:

Finally, the introduction was restructured to include three research questions (Q1-Q3) designed to address the main theoretical and application gaps noted in the literature:

1. the evolution and current status of DES as an enabling technology for Industry 4.0;
2. the operational and strategic benefits offered to manufacturing companies;
3. the potential of DES in facilitating the adoption of new technologies and optimizing the entire production chain.

• The paper needs to provide a detailed explanation about the statistical validation methods along with test descriptions and their relevance to enhance the credibility of the simulation model.  

Authors: Thank you for the suggestion. The methodology section reports step 2 in which detailed explanation of statistical validation methods are clarified :

• Randomness: Randomness in the input data is essential to ensure that simula-tions are not affected by unanticipated patterns or correlations. In a discrete-event simulation, events should occur independently, reflecting the stochastic nature of the real system. To analyze randomness, statistical tests such as Pearson's test for independence are used, which tests whether two variables are independent of each other [91]. If the variables show strong dependence, it could indicate the presence of a pattern that must be eliminated to obtain a correct simulation. Another meth-od is the runs test, which examines the sequence of the data to detect the presence of patterns. Finally, the autocorrelation test helps identify any temporal dependen-cies between successive data, ensuring that the inputs are not influenced by previ-ous events [92].
• Homogeneity: Homogeneity of input data is crucial to ensure that all data come from the same population or that their variances are equivalent. This is especially important when modeling complex systems with multiple components or opera-tional steps. To check for homogeneity, statistical tests such as Bartlett's test, which assesses whether multiple samples come from populations with the same variance, are used. However, because this test can be sensitive to deviations from normality, Levene's test offers a more robust alternative [93]. Homogeneity is further analyzed using nonparametric tests such as the Kruskal-Wallis test, which compares the av-erages of multiple groups without assuming a normal distribution. In addition, visual tools such as box plots and Q-Q plots make it possible to observe the distri-butions of the data and visually verify the equality of variances between groups [94].
• Goodness of fit: Goodness of fit of the input data refers to how well the observed data follow an expected theoretical distribution. This is critical to ensure that the models used in the simulation accurately represent the behavior of the real system. The chi-square test is commonly used to compare the observed distribution with an expected distribution, detecting significant discrepancies. The Kolmogorov-Smirnov test, on the other hand, compares a sample distribution with a reference distribu-tion, checking their congruence over all points in the distribution [95]. A more sen-sitive test, the Anderson-Darling, places more emphasis on the tails of the distribu-tion, where discrepancies can have a significant impact on simulation results. Us-ing these goodness-of-fit tests helps ensure that the input data are well represented by the theoretical models used in the simulation [96].

While the test descriptions are given in section 4.4 Data Analysis for Process Modeling Accuracy

 

• The manuscript should provide a more detailed explanation about how human factors are incorporated into the simulation framework since this constitutes a fundamental part of Industry 4.0 digital transformation.  

Authors: Thank you for the suggestion . In the 5. future developments section, a section was added on how human factors are incorporated into the simulation framework and how this constitutes a key part of the digital transformation of Industry 4.0:

However, in an increasingly automated and data-driven manufacturing ecosystem, the human role assumes central importance. The evolution toward intelligent production models requires a rethinking of skills: operators can no longer be limited to executive tasks, but must be able to interact with digital systems, interpret data and contribute consciously to decision-making. In this context, reskilling and upskilling initiatives are key to transforming traditional skills into digital skills that can effectively dialogue with automation and simulation.

Decision making at the managerial level is also evolving: decision makers need to be able to interpret simulation results, assess the operational and environmental implications of hypothesized strategies, and take timely action. Artificial intelligence must be integrated as decision support, without replacing human judgment. In fact, the management of intelligent systems requires continuous supervision that can recognize when it is necessary to adapt to complex or unexpected contexts. In such situations, human intuition, experience and critical capacity remain indispensable elements.

 

• The manuscript quality must improve because an English language update will provide sentences with improved structures and terminology to deliver clear and precise information across all text sections.  

Authors: Thank you for the suggestion . The entire manuscript was carefully revised in English in order to improve its linguistic quality, expository clarity, terminological accuracy and stylistic consistency in all sections. The revision involved both the syntactic structure of sentences and the adaptation of technical-specialist vocabulary, ensuring more effective scholarly communication aligned with international standards required for academic publications. This intervention helped to make the content clearer, more coherent, and more easily interpreted by an international scientific audience.

 

• The manuscript would benefit from including visual aids through diagrams and flowcharts that show the simulation model structure and process pathways to improve understanding.  

Authors: Thank you for the suggestion. Diagrams and flowcharts showing the simulation model structure and process paths have been included in the manuscript to enhance understanding. In particular:

 

Figure 5 - Production flow of the company

This figure graphically represents the flowchart of the real production process of the studied company. As described in the text, this diagram divides the production flow into three macro-sections: (1) processing of the structure, (2) production of doors and covers, and (3) final assembly of the cabinet. The figure highlights the presence of parallel flows, which converge at the assembly stage, and clearly shows the points of interconnection between the production areas. The manuscript reads:

“Figure 5 shows the complete production flow, highlighting the main activities of the three processing paths: structure, doors/cover, and assembly. The diagram also highlights the interdependencies between the flows, which is a critical aspect in time management and synchronization.”

 

Figure 6 - Simulation model created in WITNESS Horizon

This figure provides a direct visualization of the digital model built in discrete event simulation (DES) software. It reproduces the workstations, resources (operators and machines), buffers, stores, and logical relationships between system entities. The model was developed to faithfully reflect the observed production reality. As explained in the text:

“Figure 6 shows the model developed in WITNESS Horizon. The layout reflects the physical structure of production and allows visualization of material flows, resource allocation, and operational logic. This model has been validated by comparing simulated results with actual production data.”

 

• The manuscript should discuss the study limitations and obstacles that might arise when trying to apply the simulation methodology beyond this research environment and small and medium enterprises.

Authors: Thank you for the suggestion. The conclusions of the manuscript were appropriately rewritten to include an articulate discussion of the limitations and obstacles that may arise in applying the simulation methodology outside the specific context analyzed. In particular, the following were highlighted Theoretical contribution, Enterprise contribution, Limitations of the study, Future Perspectives:

Digital transformation is a systemic business strategy, applicable to any industry to address structural inefficiencies and generate new opportunities through digital technologies. In manufacturing, it involves the adoption of enabling tools to increase revenues, reduce costs, improve quality and enhance operational flexibility while pursuing industrial sustainability. The extreme variety of available solutions is a competitive advantage, but it also introduces significant management complexity, especially for small and medium-sized enterprises. In this context, this research proposes a methodological framework supported by discrete event simulation (DES) to rigorously guide the digital transition in real manufacturing environments.

Theoretical contribution

From a theoretical perspective, the study contributes to the advancement of the literature on Industry 4.0 and Operational Research by introducing an integrated approach between digital simulation, statistical analysis of process data, and empirical validation. The proposed model is not limited to the optimization of physical flows, but includes decision-making elements and human dynamics, highlighting the relevance of human-in-the-loop design even in highly automated contexts. The contribution is in the vein of hybrid methodologies that combine simulation, decision science and digital transformation.

Enterprise contribution

At the application level, the research provides a concrete case of using simulation to support the digital transformation journey in a manufacturing SME. The modeling enabled the identification of bottlenecks, critical operational issues and optimization margins, returning simulated alternative scenarios to support strategic decisions with low operational impact. The proposed approach is transferable and scalable to similar manufacturing contexts.

Limitations of the study

The main limitation of the study lies in its application to a single industrial case and the adoption of a single simulation environment. Although the software used has high discrete modeling capabilities, alternative tools such as FlexSim, Simul8, or AnyLogic, which offer specific capabilities such as multi-method simulation (discrete-event, agent-based, system dynamics), advanced support for cyber-physical systems, or integration with real-time data, were not considered. Comparative evaluation could offer greater generalizability and methodological robustness.

Future Perspectives

Development perspectives include the integration of simulation with emerging technologies such as Digital Twin, artificial intelligence and IoT in order to build adaptive models based on real data. Expansion of the framework to multi-paradigm systems and integration with environmental and social metrics (e.g., Life Cycle Assessment) represent further research directions, with the goal of strengthening the sustainability and resilience of digitized production systems.

 

• The paper should include additional quantitative research and practical suggestions regarding implementation results from what-if analyses.  

Authors: Thank you for the suggestion. The quantitative results of the what-if analysis are presented in Table 10 of the manuscript, which provides a detailed comparison of three production process configurations: the current state (non-optimized model) and two simulated scenarios (Scenario 1 and Scenario 2). The table reports key indicators such as the number of units produced, machine blockage rate and resource idle time, allowing for an objective assessment of the expected benefits of the simulated changes. Specifically, the results show that Scenario 1 achieves a 50 percent increase in monthly production and a significant reduction in operational inefficiencies, with a 25 percent decrease in blocking time. Scenario 2 pushes the improvements even further, up to +70% in throughput and a 40% decrease in machine downtime, but with a larger implementation impact. Based on these results, the company chose to adopt the changes under Scenario 2, concretely introducing a second assembly station and reorganizing resources along the production line. The actual implementation of these changes is documented in Figure 10, which shows photographs of the updated real process, in which the practical application of the simulated solution can be seen. This shift from simulation to industrial practice confirms the application validity of the model and highlights its relevance as a decision-making tool in the context of digital transformation.

 

• The proposed framework needs additional explanation for its uniqueness because it sets itself apart from previously documented DES applications for manufacturing and Industrial 4.0 research.  

Authors: Thank you for the suggestion. To highlight the uniqueness of the manuscript, which differs from previously documented DES applications for industrial 4.0 manufacturing and research, the following was added:

Compared to the existing literature, this study is distinguished by its integrated methodological approach and its concrete application in a real manufacturing SME context. Unlike previously documented DES applications, which often focus on single operational improvements or simulated assessments in controlled environments, this work proposes a structured and scalable framework that combines digital modeling, what-if analysis, empirical validation, and decision support. By integrating simulation with process analysis and actual implementation of solutions, it bridges the gap between theory and practice, making an original contribution to industrial research in Industry 4.0. In addition, the proposed approach explicitly considers the role of the human factor and interaction with digital technologies, an aspect often overlooked in previous studies.

 

• It should focus on deriving both sustainable long-term advantages and environmental benefits from DES and digital twin adoption in addition to short-term operational enhancements.

Authors: Thank you for the suggestion. To clarify the long-term sustainable and environmental benefits of adopting discrete event simulation (DES) and digital twin, in addition to short-term operational improvements, the following passage has been integrated into the section on future developments:

In addition to short-term operational benefits, the integrated adoption of discrete-event simulation and digital twin enables the pursuit of long-term sustainable benefits. These tools support more efficient and rational use of resources, contributing to the systematic reduction of waste, the design of more resilient production systems, and large-scale energy optimization. In particular, the digital twin enables continuous life-cycle monitoring of production assets, fostering predictive maintenance practices, waste reduction, and improved operating conditions. Looking forward, these approaches enable industrial strategies geared toward environmental sustainability and compliance with increasingly stringent green regulations, integrating economic goals with ecological ones and enhancing business competitiveness in the long term.

 

Comments on the Quality of English Language

• Clear descriptions of research purposes and study methods run through most parts of the manuscript. Some lengthy and complex sentences appear in the text making it challenging to read smoothly.
• Overall the manuscript contains grammatical mistakes along with expressions that need refinement to create a more precise and easy-to-understand text.  
• Technical jargon appears properly understood in most instances yet the author should enhance definition clarity as well as term consistency across the document.  
• Better transitional elements between different sections should be incorporated to improve both coherence and logical development.  
• Certain sections need restructuring because it would decrease repetition and keep essential information at the forefront of each paragraph.  
• Proofreading and language editing at a professional level will create major improvements in both clarity and formulation quality of the manuscript.  
• The manuscript will communicate complex ideas more effectively to various readers by working on sentence structure and condensing textual sections.

Authors: Thank you for the suggestion. We thank the reviewers for their detailed and constructive feedback regarding the quality of the English language and the clarity of expression throughout the manuscript.

In response to these observations, the entire manuscript has been thoroughly revised and professionally proofread to enhance grammatical accuracy, improve sentence structure, and ensure consistency in terminology and technical language. Particular attention has been paid to:

• Simplifying overly long and complex sentences to improve readability and ensure a smoother flow for the reader;
• Correcting grammatical errors and refining expressions to ensure clarity, precision, and adherence to academic style;
• Standardizing technical terminology and definitions throughout the text to reinforce consistency and facilitate comprehension;
• Enhancing transitions between sections, thereby improving coherence and strengthening the logical development of the content;
• Restructuring certain paragraphs to eliminate redundancy, prioritize essential information, and improve the clarity of argumentation.

These revisions have been made to ensure that the manuscript communicates complex ideas more effectively to an international scientific audience, while maintaining high standards of clarity, technical accuracy, and academic rigor.

 

We trust that the revised version addresses the concerns raised and meets expectations for linguistic and structural quality.

 

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thanks the authors for resolving the previous issues; I think now it’s sufficient and could be ready for publishing.