Enhancing Logistical Performance in a Colombian Citrus Supply Chain Through Joint Decision Making: A Simulation Study

1. Summary

Reviewer

: A simulation study in 3 Colombia. Single, joint, and joint consolidation systems were examined using Flexim software. Simulation results were analyzed and discussed. 

Response

We would like to thank the Editor and Reviewers for their careful assessment of our manuscript, which we believe has significantly improved as a result of their additional comments. We have addressed the remaining issues outlined by the reviewers and we hope that it is now considered acceptable for publication. Reviewers’ comments are pasted verbatim below in bold, along with our point-by-point response. Where required, new or changed sections of text are copied in red text.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Must be improved

Can be improved

Is the research design appropriate?

Must be improved

Can be improved

Are the methods adequately described?

Must be improved

Can be improved

Are the results clearly presented?

Must be improved

Can be improved

Are the conclusions supported by the results?

Must be improved

Can be improved

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: Appropriate reference citations in the text.

Response 1: Thank you for your suggestion. We agree with this comment and have reviewed the references, removing those that were not relevant to our study. Additionally, we have added a new literature review section, incorporating the most relevant studies related to our research.

Please see the Introduction and Literature Review sections.

Comments 2:   The research gap and problem definition are not well stated. There is no literature review section.

Response 2

Thank you for your suggestion. We have added a literature review section, helping us to provide context, identify study gaps and unexplored areas, and inform methodologies about joint-decision-making coordination mechanisms. We have compared these studies and categorize them in terms of Structure of supply chain, Coordination Problem, Methodology, and   Performance Measures, see Tables 1 and 2.  Based on this new section, we have redefined our study problem and hypothesis, improving the introduction section.

See new section of Literature review in the document

In the introduction:

Heretofore, background studies have only highlighted the social-economics needs and strategies to be implemented to overcome these longstanding challenges and strengthen regional food supply chains of the department. Therefore, the core questions in this study were: Does the implementation of joint decision-making mechanisms improve the logistical performance of the citrus supply chain in Chimichagua, Colombia? How does joint-decision making affect the integration of smallholder farmers into the agricultural value chains? Does joint-decision-making improve smallholder farmers access to better opportunities and minimize reliance on middlemen? What impact does joint decision-making have on the economic viability and sustainability of the citrus supply chain in Colombia?

See new section Literature review in the document

Comments 3: “The discussion section is general and should be concise”

Response 3

We appreciate this feedback and have revised the discussion section to provide a more concise synthesis of our findings, emphasizing the study's contributions and practical implications.

See discussion section in the document

Comments 4: “The effects of uncertain model parameters are not considered”

Response 4

Thank you for your suggestion. We addressed this by including a sensitivity analysis, which examines the impact of variations in some model parameters such as the number of distribution vehicles and fuel prices, on the simulation results.

Please see the sensitivity analysis subsections in the Methods and Results for further details.

Simulation

In the methods section:

A sensitivity analysis was carried out to evaluate the parametric uncertainty in some parameters on the simulation:

·        Number of three-axle trucks (m): variations of ±2 vehicles were analyzed, corresponding to a change of ±33.34%.

·        Fuel price per gallon (F): This parameter was evaluated considering a variation of its value of ±20%.

We used the KPI values obtained with the nominal model parameter values as the reference for each scenario. The scenarios were then re-simulated using adjusted parameter values as part of the sensitivity analysis. The resulting changes in KPI values were compared, and percentage variations were calculated to quantify the sensitivity of each parameter.

Results section.

4.4. Sensitivity analysis

Table 7 presents the sensitivity analysis of the number of three-axle trucks (m). The findings revealed that reducing the number of distribution trucks by 33% led to an average increase of 31% in Staytime, 12% in the loading time percentage, and 7,5% in the total load capacity across all scenarios. Additionally, the idle time percentage decreased by 44,8%. These changes can be explained by the reduced number of three-axle trucks, which required that middlemen or ASOCITRICOS to perform more trips to distribute the entire harvest. This also resulted in an average reduction of 12,6% in the total distribution distance due to the smaller fleet size. Regarding the financial indicators, the reduction in trucks led to a 6,32% increase in profits and a 22% decrease in total costs for ASOCITRICOS in the joint-consolidation-distribution scenario. Similarly, having fewer trucks resulted in lower distribution and maintenance costs. Financial KPIs remained unchanged in the other scenarios.

Conversely, increasing the number of distribution trucks by 33% resulted in an average increment of 27,72% in idle time percentage and 13,8% in the total distribution distance (). Additionally, the increment in trucks reduced the Staytime by 12,5% and total load capacity by 13,1% across all scenarios. Changes in time percentage (TP) indicators can be explained by the higher number of three-axle trucks, which required that middlemen or ASOCITRICOS to perform less trips to distribute the entire harvest. Regarding the financial indicators, this increase in trucks led to a 6.32% reduction in profits while simultaneously increasing total costs by 22% for ASOCITRICOS in the joint-consolidation-distribution scenario. Similarly, having more trucks resulted in higher distribution and maintenance costs. Financial KPIs remained unchanged in the other scenarios.

Table S1 presents the sensitivity analysis of the fuel price (). The findings revealed that reducing (or increasing) the fuel price by 20% led to an average increase (or reduction) of 6,2% in profits while reducing (increasing) total costs 22,4% in the joint-consolidation-distribution scenario. The other scenarios were not affected by changes in the fuel prices as the middlemen assumed total liability of the distribution of the harvest while the transportation costs of the harvest to the food hub was incorporated in the selling price.  The efficiency indicators remained unchanged in all scenarios

Comments 5:” Model development section should be provided, and equations should be defined in the text” 

Response 5

Thank you for this suggestion. We have improved our Methods section by adding details on model development, including equations, parameters, variables, and agent attributes.

See 3.4. Modeling subsection in Methods in the document

Comments 6:” Model assumptions and parameters should be included in the text” 

Response 6

Thank you for this suggestion. We have improved our Methods section, describing the model assumptions, parameters (Tables 3) and variables (Tables 4).

See 3.4. Modeling subsection in Methods in the document.

The models were made based on the following assumptions:

•          The demand was known and equal to the supply, i.e., we assumed that the total farmers' harvest is sold to consumers.

•          Any vehicle had the following states: Idle/Available (0), which described the state when a vehicle was idle and available to travel; empty-travel (1), which represented the state when a vehicle was travelling empty; loading (2), which described the state when a vehicle was loading a harvest; load-travel (3), which represented the state when a vehicle was travelling with a load (during this state, the trucks may operate either at full or partial capacity); unloading (4), which described the state when a vehicle was unloading a harvest.

•          Collection and distribution were treated as independent systems to compare coordination scenarios clearly; two-axle trucks are used for collection, while three-axle trucks manage distribution.

•          Maintenance of trucks was performed every 20000km or every year [39]

•          Trucks were dedicated to delivering goods exclusively within their assigned customer.

•          An optimal route exists for each scenario, considering the distances between farms, customers, and the food hub. Farmers and customers were georeferenced to identify the optimal path.

•          Fixed costs related to storage and revenues from sales remain constant throughout the study scenarios

•          Simulation time starts when the product is harvested, and it ends when it is delivered to the customers.

•          Our study examined a citrus supply chain with seasonal production, where both demand and supply are predictable.

•          The number for collection vehicles was two, since it was closer to reality according to primary information and was more than enough to collect the current production managed by the association ; otherwise, with a larger number of trucks the capacity utilization of these would be very low in the system.

•          Single distribution scenario had no 2-axle vehicles since all sales were made independently and distributed in 3-axle vehicles, it means, there is no collection activity in this scenario.

Parameters:

Table 3 Study parameters and values

Comments 7: “Comparison of the results with those obtained in previous studies” 

Response 7

Thank you for this suggestion. In the literature review section, we conducted a selective and relevant background review to position our study, highlight similarities with previous studies, identify gaps in the field, and underscore our study's contributions. Similarly, we discussed our findings in relation to previous studies, highlighting similarities and differences to contextualize the contributions of our work.

See sections of Literature Review and Discussion in the document

Comments 8: “The results section should be enhanced and discussed effectively” 

Response 8

Thank you for this suggestion. We have revised the Results and Discussion sections by incorporating additional analyses, visualizations, and a more detailed discussion of the study's findings and contributions.

See section 4. Results and section 5. Discussion in the document

Comments 9: “Concise conclusions” 

Response 9

Thank you for this suggestion. We have revised the conclusion section to ensure it is concise, summarizing the main contributions, limitations, and avenues for future research in a clear and focused manner.

See section 6. Conclusions in the document

Comments 10: “The organization of the paper sections should be enhanced; For example, section 2 literature review. Section 3 Model development. Section 4 model analysis and results discussion.” 

Response 10

Thank you for this suggestion. We have added a Literature Review section and restructured the paper as follows:

• Materials and Methods:
• 3.1. System Description and Base Scenario
• 3.2. Joint-Consolidation Scenario
• 3.3. Joint-Consolidation-Distribution Scenario
• 3.4. Modeling
• 3.5. Simulation
• 3.6. Sensitivity Analysis
• Results:
• 4.1. Modeling Parameters
• 4.2. Efficiency Key Performance Indicators
• 4.3. Financial Key Performance Indicators

This restructuring improves the clarity and logical flow of the paper.

Comments 11: “Please clearly state the research problems based on the research gap. The model development should include mathematical and technical details.” 

Response 11. Thank you for this suggestion. We have added a Literature Review section, where we conducted a selective and relevant background review to position our study, highlight similarities with previous studies, identify gaps in the field, and underscore our study's contributions. In addition, we have revised the introduction to clearly state the research problems derived from the identified gap. Regarding the model, section 3.4 Modeling includes detailed mathematical and technical descriptions of the model to provide a comprehensive understanding of the methodology.

See the Introduction Section and the 3.4. Modeling subsection in the document

In the Literature Review

2.3. Concluding remarks

Rather than introducing new methods, this study explored the underutilized potential of simulation approaches in enhancing supply chain coordination. We applied agent-based modeling to simulate and evaluate two joint coordination mechanisms aimed at improving logistical performance in a Colombian citrus supply chain. These mechanisms fostered collaboration among farmers to consolidate harvests and channel them through a food hub, optimizing logistics processes to streamline the flow of goods from production to final distribution. This work addressed the logistical challenges faced by a smallholder-dominated supply chain while supporting broader sustainability goals, including fostering collaboration among stakeholders, improving working conditions, and promoting inclusive growth.

In Introduction

However, both the department of Cesar and Chimichagua have faced historical challenges, including poor support from territorial entities, low social investment, and poor infrastructure, among others [1,4,5,7,11].These historical challenges have then led to low indices of competitiveness and productivity, with the Department ranking 20th out of 26 departments in Colombia. In addition, they have led to a lack of associativity and low returns to small farmers in Chimichagua [10]. To address these challenges, national and regional authorities must implement supportive public policies and foster initiatives that empower small farmers through strengthened associations. These policies should aim to integrate small farmers as key players in agricultural value chains by fostering collaboration, improving their access to resources and markets, and enhancing their capacity to adopt sustainable productive practices. Heretofore, background studies have focused on the social-economics needs and strategies required to overcome these longstanding challenges and strengthen the regional food supply chains [10]. Therefore, the core questions in this study were: Does the implementation of joint decision-making mechanisms improve the logistical performance of the citrus supply chain in Chimichagua, Colombia? How does joint-decision making affect the integration of smallholder farmers into the agricultural value chains? Does joint-decision-making improve smallholder farmers access to better opportunities and minimize reliance on middlemen? What impact does joint decision-making have on the economic viability and sustainability of the citrus supply chain in Colombia?

This study aimed to explore the value of coordinating collection and distribution to enhance logistical performance in a citrus FSC in Colombia. Our research evaluated whether joint decision-making mechanisms improve logistical performance. To achieve this, we simulated three scenarios: a first scenario, named Single distribution, which represented the existing food supply system, relying on middlemen and single distribution; a  second scenario, named Joint-consolidation, which simulated joint decision-making among farmers to consolidate harvests at ASOCITRICOS; and the third, named Joint-consolidation-distribution, which simulated joint decision-making among farmers and ASOCITRICOS to consolidate and distribute harvests without middlemen. ASOCITRICOS is a cooperative producer that operates a food hub. We used efficiency and financial key performance indicators (KPIs) to measure the impact of coordination on logistical performance. Our study examined a citrus supply chain with seasonal production, where both demand and supply are predictable. The study supply chain is multi-actor and multi-echelon constrained by capacity limitations. The findings of this study serve as a starting point to propose public policies and development plans aimed at strengthening local FSCs and producer cooperatives, enhancing the sustainability of these chains, and fostering the sustainable development of Chimichagua.

4. Response to Comments on the Quality of English Language

Point 1: “The authors should enhance the Quality of English. The authors used "We" many times”

Response 1. Thank you for your feedback regarding the language and style. We have carefully revised the manuscript to improve the overall quality of English. The repeated use of "We" has been minimized by restructuring sentences into a more formal, passive voice or using alternative phrasing where appropriate.

1. Summary

Reviewer: The manuscript is interesting, and all the sections were written by the authors. The references cited in this manuscript are appropriate and relevant to this research.

Response

We would like to thank the Editor and Reviewers for their careful assessment of our manuscript, which we believe has significantly improved as a result of their additional comments. We have addressed the remaining issues outlined by the reviewers and we hope that it is now considered acceptable for publication. Reviewers’ comments are pasted verbatim below in bold, along with our point-by-point response. Where required, new or changed sections of text are copied in red text.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Can be improved

Can be improved

Is the research design appropriate?

Yes

Can be improved

Are the methods adequately described?

Yes

Can be improved

Are the results clearly presented?

Must be improved

Can be improved

Are the conclusions supported by the results?

Must be improved

Can be improved

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: The abstract should be rewritten according to the rules.

Response 1

Thank you for this suggestion. We have revised the abstract to ensure it complies with the journal’s guidelines and reflects the adjustments made to the manuscript.

Abstract: Agriculture plays a key role in Colombia's economy. However, the sector faces persistent logistics, infrastructure, and supply chain integration challenges that hinder its development. Background studies have primarily addressed socio-economic needs and strategies to overcome these long-standing challenges. Supply chain coordination has become essential for synchronizing stakeholder activities to improve efficiency and sustainability. This study examined the impact of joint decision-making mechanisms on the logistical performance of a citrus food supply chain (FSC) in Colombia. We employed agent-based modeling and simulation (ABMS) to evaluate three scenarios: single distribution (the current system), joint consolidation, and joint consolidation distribution. Key performance indicators (KPIs), including Total Logistics Costs, Staytime, and Load Capacity Utilization, were used to evaluate the scenarios. The joint consolidation-distribution model emerged as the most effective, reducing logistical costs, improving load utilization, and increasing farmers' revenues by 100% through direct customer sales. The findings also underscored that harvest consolidation and distribution via a Food Hub significantly enhanced logistical performance. Our results provide actionable insights, demonstrating that joint decision-making can improve smallholder farmers' economic outcomes, support supply chain sustainability, and foster community development.

Comments 2:   The introduction section should be shortened. The aim and hypothesis of the manuscript should be given clearly.

Response 2

Thank you for this suggestion. We have shortened the introduction by moving background studies on supply chain coordination and joint decision-making mechanisms to a new section in the Literature Review. Additionally, the aim and hypothesis of the manuscript are now explicitly stated in the introduction.

In the introduction:

However, both the department of Cesar and Chimichagua have faced historical challenges, including poor support from territorial entities, low social investment, and poor infrastructure, among others [1,4,5,7,11]. These historical challenges have then led to low indices of competitiveness and productivity, with the Department ranking 20th out of 26 departments in Colombia. In addition, they have led to a lack of associativity and low returns to small farmers in Chimichagua [10]. To address these challenges, national and regional authorities must implement supportive public policies and foster initiatives that empower small farmers through strengthened associations. These policies should aim to integrate small farmers as key players in agricultural value chains by fostering collaboration, improving their access to resources and markets, and enhancing their capacity to adopt sustainable productive practices. Heretofore, background studies have focused on the social-economics needs and strategies required to overcome these longstanding challenges and strengthen the regional food supply chains [10]. Therefore, the core questions in this study were: Does the implementation of joint decision-making mechanisms improve the logistical performance of the citrus supply chain in Chimichagua, Colombia? How does joint-decision making affect the integration of smallholder farmers into the agricultural value chains? Does joint-decision-making improve smallholder farmers access to better opportunities and minimize reliance on middlemen? What impact does joint decision-making have on the economic viability and sustainability of the citrus supply chain in Colombia?

This study aimed to explore the value of coordinating collection and distribution to enhance logistical performance in a citrus FSC in Colombia. Our research evaluated whether joint decision-making mechanisms improve logistical performance. To achieve this, we simulated three scenarios: a first scenario, named Single distribution, which represented the existing food supply system, relying on middlemen and single distribution; a  second scenario, named Joint-consolidation, which simulated joint decision-making among farmers to consolidate harvests at ASOCITRICOS; and the third, named Joint-consolidation-distribution, which simulated joint decision-making among farmers and ASOCITRICOS to consolidate and distribute harvests without middlemen. ASOCITRICOS is a cooperative producer that operates a food hub. We used efficiency and financial key performance indicators (KPIs) to measure the impact of coordination on logistical performance. Our study examined a citrus supply chain with seasonal production, where both demand and supply are predictable. The study supply chain is multi-actor and multi-echelon constrained by capacity limitations. The findings of this study serve as a starting point to propose public policies and development plans aimed at strengthening local FSCs and producer cooperatives, enhancing the sustainability of these chains, and fostering the sustainable development of Chimichagua.

Comments 3: “What is the name of Figure 1”

Response 3

Thank you for identifying this oversight. We have revised Figure 1 by splitting it into three separate figures, each with its own explanation to improve clarity and readability. These are now presented as Figure 1, Figure 2, and Figure 3 in the document.

Figure 1. Overview of the study’s supply chain. The current logistics distribution relies on single-distribution through middlemen, who purchase the harvest in bulk from farmers and resell it to retailers or food service providers.

Figure 2. Overview of the joint-consolidation scenario. Farmers jointly decided to consolidate the harvests at ASOCITRICOS’s food hub, incorporating transportation costs in the selling price. ASOCITRICOS sets the price and sells to middlemen, who distribute to customers, assuming full liability for their loads.

Figure 3. Overview of the direct distribution scenario. Farmers and ASOCITRICOS jointly consolidate harvests at the food hub for direct distribution to customers, eliminating middlemen. ASOCITRICOS sets prices, markets the harvest, and manages centralized logistics, assuming delivery liability.

Comments 4: “The discussion section should be supported by more references”

Response 4

We appreciate this suggestion. We have revised the Discussion section and incorporated additional references to better support the discussion of the study’s findings and contributions.

See 5. Discussion section in the document

1. Summary

Reviewer:

The article handles an interesting problem regarding the logistical and financial performance of a citrus supply chain in LA. Although interesting and with a high-profile focus on practical implications, the study has some issues that require corrective actions.

Response

We would like to thank the Editor and Reviewers for their careful assessment of our manuscript, which we believe has significantly improved as a result of their additional comments. We have addressed the remaining issues outlined by the reviewers and we hope that it is now considered acceptable for publication. Reviewers’ comments are pasted verbatim below in bold, along with our point-by-point response. Where required, new or changed sections of text are copied in red text.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Can be improved

Can be improved

Is the research design appropriate?

Must be improved

Can be improved

Are the methods adequately described?

Must be improved

Can be improved

Are the results clearly presented?

Must be improved

Can be improved

Are the conclusions supported by the results?

Yes

Can be improved

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: “Major issue:

I could not see the necessity of using simulation in a well-known problem, the non-linear optimization min z = Ax+ B/x. Perhaps the most noted problem of this type is the EOQ calculation. Furthermore, the results shown in Figure 2 ensure that this is a non-linear, non- random problem. The authors state that feature in lines 253-257. Therefore, I suggest rewriting the study to present not a simulation but a non-linear optimization procedure. You can easily derive deterministic equations to the two outcomes (staytime and TLC) as a function of n, the number of trucks, sum them, take the first derivative, and then equal to zero. The optimal n should emerge at the end of the procedure. (Please observe a double usage for the symbol n, number of trucks, and number of states please amend)

Thank you for this suggestion. We have revised the Methods section to improve the description of model development, including assumptions, parameters (Table 3), variables (Table 4), and equations. Additionally, we have ensured consistency in notation, resolving the double usage of the symbol n to prevent ambiguity.

We employed agent-based modeling (ABMS) to capture local knowledge and stakeholder interactions through autonomous agents, each with distinct objectives, attributes, and decision-making rules (see Figures 4, 5, and supplemental material). The objective of this study was to simulate and evaluate the impact of joint decision-making coordination on the logistical performance of a citrus supply chain rather than solving a well-defined optimization problem with clear constraints, such as determining the optimal batch size for coordinating joint costs between production and inventory or the optimal number of distribution trucks to coordinate production and distribution. We simulated the existing single-distribution scenario (baseline) and two joint coordination scenarios to test our hypothesis that such a joint decision-making mechanism could enhance supply chain performance and sustainability. Moreover, the simulation framework allows us to incorporate real-world logistical complexities that would be challenging to model analytically. The software used in our study (FlexSim v24.2.2) integrates a GIS and routing module, dynamically adjusting for real-time route conditions when determining optimal paths. This level of detail enhances the practical relevance of our findings, particularly in evaluating coordination mechanisms within the citrus supply chain.

We agree with the reviewer that the number of trucks could be determined using a non-linear optimization procedure, where the equations for Staytime and Total Logistics Costs (TLC) could be formulated and optimized as functions of the number of trucks. However, in this study, model parameters were estimated using raw data collected during fieldwork in September 2023. The number of three-axle trucks was determined using a heuristic method that balanced efficiency and financial performance. We simulated each scenario multiple times using different fleet sizes, ranging from 1 to 20 distribution trucks. The mean values of Staytime (Equation 5) and TLC (Equation 12) were calculated across all scenarios and normalized to a mean of 0 and a standard deviation of 1. These normalized values were then plotted as functions of the number of trucks, and the fleet size was selected at the intersection point of the two curves.

Rather than employing a non-linear optimization procedure to determine the optimal number of trucks, as suggested by the reviewer, we opted for a heuristic brute-force approach. Since the number of trucks is a discrete parameter with predefined values (i.e., a limited search space), this method was computationally feasible within the ABMS framework. Additionally, normalizing the data and visualizing both KPIs as functions of the number of trucks provided a simplified yet effective decision-making approach. We selected the intersection point between both KPIs as the best balance between efficiency and cost performance. While our approach may not guarantee a global optimum, we believe it offers a reasonable and practical estimate based on trade-off analysis within a feasible time frame, aligning with the study’s objectives.

We have added this methodology and these comments in the Methods and Discussions

Methods:

Tables 3 y 4 provide the parameters and variables used in this study. The parameter values were estimated using raw data collected in fieldwork in September 2023. Researchers employed a questionnaire-based instrument to guide data collection from farmers and ASOCITRICOS members, requesting information such as annual sales history, customer details, and farm location. This data, gathered from the stakeholders' experiences and records, was essential for accurately predicting each farmer's supply and customer demand. The number of trucks was determined by simulating each scenario multiple times using different fleet sizes, ranging from 1 to 20 distribution trucks. We calculated the mean values of Staytime (Equation 5) and Total Logistics Costs (Equation 12) across all scenarios and normalized them to a mean of 0 and a standard deviation of 1. These normalized values were then plotted as functions of the number of trucks. We selected the number of trucks at the intersection point of the two curves, balancing between efficiency and financial performance.

Discussion section:

Another contribution of this study was the heuristic method used to determine the optimal number of trucks for collection and distribution. In logistics network design, previous studies have defined model parameters using a combination of optimization and heuristic-based methods, such as local search, genetic algorithms, and integer linear programming [64,65]. Instead of employing a mathematical optimization method, our approach used a grid search simulation over a range of truck numbers to balance Staytime and Total Logistics Costs. The intersection of these KPIs served as a rule of thumb for determining the optimal number of trucks, achieving the best balance between efficiency and cost. We chose this brute-force approach because the truck number was a discrete parameter with predefined values (i.e., a smaller search space), making it computationally feasible to implement with ABMS. Additionally, after normalizing the data, the visual representation (plot) of both KPIs as function of truck number simplified the decision-making process, focusing on the intersection point to balance between efficiency and cost. Therefore, although our approach may not guarantee a global optimum, we believe it provided a reasonable and practical estimate based on the trade-off analysis within a feasible time frame.

Comments 2: “You should also perform a sensitivity analysis by taking increments and decrements of 5% to main parameters and observing when the outcome changes more. The larger the change, the more influential the parameter. I believe that the scenario analysis that follows (it is a good analysis) can also be made from non-linear optimization, not only simulation.”

Response 2. Thank you for this suggestion. We have included a sensitivity analysis, where we explored the impact of variations in some parameters such as the number of distribution vehicles and its capacity of load, on the simulation results.

A sensitivity analysis was carried out to evaluate the parametric uncertainty in some parameters on the simulation:

·        Number of three-axle trucks (m): variations of ±2 vehicles were analyzed, corresponding to a change of ±33.34%.

·        Maximum load capacity of three-axle trucks (): Changes of ±2 tons were evaluated, equivalent to ±28.57%.

·        Fuel price per gallon (F): This parameter was evaluated considering a variation of its value of ±20%.

·        Food Hub maximum storage capacity (): Modifications in storage capacity were explored over a ±50% range.

We used the KPI values obtained with the nominal model parameter values as the reference for each scenario. The scenarios were then re-simulated using adjusted parameter values as part of the sensitivity analysis. The resulting changes in KPI values were compared, and percentage variations were calculated to quantify the sensitivity of each parameter.

See section 4.4. Sensitivity analysis and Supplementary Materials in the document for more details of results.

Comments 3: “You have a huge number of lumped references, which do not make the life of the audience if they wish to triangulate your findings. Please avoid the most you can.”

Response 3. Thank you for this suggestion. We agree with this comment. We have reviewed the references and eliminated those references that are not relevant for our study. We have also added a new section of literature review, including those most important studies related to our research, to clarify our findings and contributions.

See the 1. Introduction section and the new section 2. Literature review in the document

Comments 4: “The captions are too large. For the sake of clarity, they should entail at most eight words.”

Response 4. Thank you for your feedback. We have revised the captions of all figures and tables to be more concise while maintaining clarity. We believe that captions should contain enough information to be self-explanatory without requiring the reader to refer to the main text. Therefore, while we have shortened them where possible, we have ensured they still provide sufficient context for interpretation.

Comments 5: “Figure 1 and related explanations are wordy and fussy. I believe you should split the figure into 3, individuating comments and explanations.”

Response 5. Thank you for this suggestion. We have split Figure 1 into three separate figures, each with a concise caption and reduced explanation to improve clarity and readability. The revised figures can be seen in the document as follows:

Figure 1. Overview of the study’s supply chain. The current logistics distribution relies on single-distribution through middlemen, who purchase the harvest in bulk from farmers and resell it to retailers or food service providers.

Figure 2. Overview of the joint-consolidation scenario. Farmers jointly consolidate the harvests at ASOCITRICOS’s food hub, incorporating transportation costs into the selling price. ASOCITRICOS sets the price and sells to middlemen, who then distribute to customers, assuming full liability for their loads.

Figure 3. Overview of the joint-consolidation-distribution scenario. Farmers and ASOCITRICOS jointly consolidate harvests at the food hub for direct distribution to customers, eliminating middlemen. ASOCITRICOS sets prices, markets the harvest, and manages centralized logistics, assuming delivery liability.

Comments 6: “Also, review references to figures and tables in the text as they seem crossed.”

Response 6. Thank you for your observation. We have carefully reviewed and corrected all references to figures and tables in the text to ensure accuracy and consistency.

Comments 7: “Finally, please observe that a table in the second chapter positioning your study in front of others in the literature should help the audience to underscore your contribution within the state of the art of the issue in the literature”

Response 7. Thank you for this suggestion. We have added two tables (Table 1 and Table 2) in the Literature Review to highlight similarities with previous studies, identify gaps in the field, and underscore our study's contributions. This addition provides a clearer comparison and underscores the novelty of our work. As a concluding remarks of this section, we noted:

“…Rather than introducing new methods, this study explored the underutilized potential of simulation approaches in enhancing supply chain coordination. We applied agent-based modeling to simulate and evaluate two joint coordination mechanisms aimed at improving logistical performance in a Colombian citrus supply chain. These mechanisms fostered collaboration among farmers to consolidate harvests and channel them through a food hub, optimizing logistics processes to streamline the flow of goods from production to final distribution. This work addressed the logistical challenges faced by a smallholder-dominated supply chain while supporting broader sustainability goals, including fostering collaboration among stakeholders, improving working conditions, and promoting inclusive growth.”

4. Response to Comments on the Quality of English Language

Point 1: “Explanative pasts should be allocated in the text.”

Response 1: Thank you for your suggestion. We have revised the quality of English past in the whole document to improve readability and coherence.