Review Reports - Probabilistic Analysis of Meat Distribution Logistics: Application of Monte Carlo Simulation

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Although I read this paper with interest, I find the following issues affect the quality of the paper:

Whilst the topic relates to a logistics facility, this is really a business case, considering the demand and supply of product, rather than the logistics thereof. This may be better positioned in a business management journal
Some of the assumptions are confusing and require further clarification. An example would be meat consumption – the authors have based the assumptions on the pork consumption, as initially proposed by the Brazilian Swine Breeders. Typically, per capita pork consumption figures would already account for non-meat consumers. The authors have then further reduced the figures by adjusting for non-meat consumers – this may skew the results. Furthermore, based on these figures, the authors determine that the population growth rate is stronger than the meat consuming population growth rate and, therefore, that meat consumption per capita is likely to rise enormously. The growth rate in meat consumption, as per Table 1, seems unfeasibly high. It is little wonder then that this is likely to be such a significant influence on the profitability of the facility, however one feels that the authors may be overestimating the growth rate of meat consumption per capita
Several other assumptions should also be questioned – for example, the cost of the facility and equipment is unclear – the authors state that these are low, because of using an existing facility and existing equipment, but it is not clear what the costs are. The base costs are regarded as the tax costs, but calculation of these require clarification. Labour costs also appear very low for a facility that should handle these kinds of volumesvolumes
Assumptions such as market share are unclear – the authors speak of 5% fulfilment of total demand, which appears to be based on a discussion with the investor. Many similar assumptions are based on discussions with the investor – it would be good to see a robust foundation for more assumptions
In essence - the paper would benefit from fully justified assumptions, with assumptions from brainstorming supported by the literature
It would also be good to see a sound discussion on the selection of the components for the model
Technical editing is required – Table 1 for example shows that the meat consuming population is measured in kgs
L44 – the study tends to contributes OR the study contributes
L57 – not clear where it is stated that one of the study objectives was to hold a discussion on the results. Language editing is strongly recommended
L91 – meaning unclear
The brainstorming sessions seem fundamental to model building, and therefore the paper requires an appropriate description of the process and participants
Selic rate requires description – this appears to be Brazilian and therefore needs description for a wider audience
L115-120 – need description in the literature and motivation for selection here
Consistency required in use of separators in numbers – for example pork consumption is reported as 19,500 kg per capita, whereas beef is reported as 30.609 kg per capita. These need standardisation as they are easily misread
All figures show kg per CAPTA
Table 3 – figures need alignment
An explanation is required of how business risk and opportunity cost percentages were determined
There are several empty brackets, e.g. L252, L253, etc.

Whilst there is a lot of merit in the methodology, the paper requires solid justification of assumptions and selection of components. Technical and language editing are also strong recommendations.

Comments on the Quality of English Language

See comments above

Author Response

Although I read this paper with interest, I find the following issues affect the quality of the paper:

Whilst the topic relates to a logistics facility, this is really a business case, considering the demand and supply of product, rather than the logistics thereof. This may be better positioned in a business management journal.

Response: Thank you for your comment. While it is true that the study evaluates the economic feasibility of a distribution facility, we respectfully argue that Logistics is the most suitable journal for disseminating this research for several reasons.

First, the core of this study is not solely a business case, but rather a comprehensive analysis of distribution logistics in the meat supply chain, with a strong emphasis on logistics costs, cross-docking operations, and the impact of logistical variables on investment profitability. The project relies heavily on logistics-specific methodologies - such as cost estimation based on transport regulations (e.g., ANTT freight tariffs), distribution route planning, and storage optimization strategies - alongside probabilistic modeling through Monte Carlo simulation to assess logistical uncertainties and risks.

Furthermore, the discussion section of the manuscript addresses logistics-related challenges such as route optimization, cold chain requirements, vehicle choice, and traceability, which are all key topics within the scope of Logistics. The study contributes directly to logistics decision-making by identifying how logistical variables - rather than purely financial ones - are central to the viability and performance of the proposed distribution center.

Thus, we believe that the article aligns well with the interdisciplinary and applied focus of Logistics, which welcomes studies that integrate logistics systems, supply chain challenges, and operations management in real-world contexts.

We kindly request the reviewer to consider this perspective regarding the journal's scope.

Some of the assumptions are confusing and require further clarification. An example would be meat consumption – the authors have based the assumptions on the pork consumption, as initially proposed by the Brazilian Swine Breeders. Typically, per capita pork consumption figures would already account for non-meat consumers. The authors have then further reduced the figures by adjusting for non-meat consumers – this may skew the results. Furthermore, based on these figures, the authors determine that the population growth rate is stronger than the meat consuming population growth rate and, therefore, that meat consumption per capita is likely to rise enormously. The growth rate in meat consumption, as per Table 1, seems unfeasibly high. It is little wonder then that this is likely to be such a significant influence on the profitability of the facility, however one feels that the authors may be overestimating the growth rate of meat consumption per capita.

Response: We sincerely thank the reviewer for the thoughtful and detailed feedback. We recognize the importance of clarity and accuracy in the formulation of assumptions, particularly regarding meat consumption estimates.

In response, we would like to clarify that our approach to estimating per capita meat consumption was intentionally conservative and structured in two stages: (i) we initially used official data on average per capita consumption of pork and beef as reported by national associations and statistical sources, and (ii) we adjusted these figures by applying the percentage of the population that actually consumes meat. This adjustment was made to avoid overestimating the effective demand, especially considering the growing segment of the population that abstains from meat consumption due to dietary, ethical, or cultural reasons.

We understand the reviewer’s concern regarding potential distortions introduced by this adjustment. However, our intent was to reflect the real volume of meat consumed in the market, rather than the average across the entire population, which could include non-consumers.

Regarding the growth rate of meat consumption, we would like to emphasize that it is directly linked to the projected population growth over the simulation period. We did not assume that the per capita consumption would grow significantly on its own; rather, total consumption was projected to increase as a function of demographic expansion. The increase in effective demand, therefore, stems primarily from the larger base of meat-consuming individuals, not from an assumption of a drastic change in dietary habits.

Nonetheless, we recognize the need to avoid any overestimation. In light of the reviewer’s observation, we have reviewed and refined the assumptions in Table 1, providing a clearer explanation of the relationship between population growth and consumption dynamics.

Several other assumptions should also be questioned – for example, the cost of the facility and equipment is unclear – the authors state that these are low, because of using an existing facility and existing equipment, but it is not clear what the costs are. The base costs are regarded as the tax costs, but calculation of these require clarification. Labour costs also appear very low for a facility that should handle these kinds of volumes

Response: Done. We thank the reviewer for these important observations. In response, we have revised the manuscript to explicitly present the base values of the initial investment, including legal expenses (such as business registration, operational licensing, and initial capital requirements). Regarding the facility and equipment, we clarified that the project is structured around a shared infrastructure model, leveraging the existing logistics yards, handling equipment, and cold storage units of a partner company. Therefore, no costs were allocated for the construction or acquisition of fixed facilities, and only operational costs related to the usage of such assets were considered.

Additionally, we have detailed the material handling equipment involved, such as forklifts, storage racks, reusable plastic boxes, weighing systems, cold chambers, labeling systems, and thermal sensors, most of which are provided through the partnership agreement. These points have been made explicit to improve transparency on capital allocation.

We also addressed the concern regarding labor costs by providing a detailed breakdown of the staff structure and functions, which includes one logistics analyst, three cleaning assistants, and two loading inspectors. The lean staffing approach is justified by the nature of the cross-docking operation, its short cargo dwell time, and the operational support absorbed by the partner facility.

Furthermore, we have expanded the explanation of tax cost calculations, specifying the assumptions adopted for IRPJ and CSLL, and how they are applied to gross profit and revenue. We believe these clarifications improve the consistency and transparency of the model.

Assumptions such as market share are unclear – the authors speak of 5% fulfilment of total demand, which appears to be based on a discussion with the investor. Many similar assumptions are based on discussions with the investor – it would be good to see a robust foundation for more assumptions.

Response: Done.

In essence - the paper would benefit from fully justified assumptions, with assumptions from brainstorming supported by the literature.

Response: Completed. We appreciate the reviewer’s comment. In response, we have added more robust and detailed information regarding the underlying assumptions, including the definitions of costs, market share, interest rates, risk, opportunity cost, and the rationale for the selection of random variables.

It would also be good to see a sound discussion on the selection of the components for the model.

Response: Done.

Technical editing is required – Table 1 for example shows that the meat consuming population is measured in kgs

Response: Done. The periods and commas have been changed.

L44 – the study tends to contributes OR the study contributes

Response: Done. Excerpt taken from the text.

L57 – not clear where it is stated that one of the study objectives was to hold a discussion on the results. Language editing is strongly recommended

Response: Done (lines 56-57).

L91 – meaning unclear

Response: Thanks for your comment. The phrase was revised.

The brainstorming sessions seem fundamental to model building, and therefore the paper requires an appropriate description of the process and participants.

Response: Done.

Selic rate requires description – this appears to be Brazilian and therefore needs description for a wider audience

Response: Done. (lines 824-828).

L115-120 – need description in the literature and motivation for selection here

Response: Done. Excerpt taken from the text.

Consistency required in use of separators in numbers – for example pork consumption is reported as 19,500 kg per capita, whereas beef is reported as 30.609 kg per capita. These need standardisation as they are easily misread

Response: Done.

All figures show kg per CAPTA

Response: Yes, all figures related to meat consumption and production presented in the document are expressed in kilograms (kg) per inhabitant per year, i.e. per capita.

Table 3 – figures need alignment

Response: Done.

An explanation is required of how business risk and opportunity cost percentages were determined.

Response: Done.

There are several empty brackets, e.g. L252, L253, etc.

Response: Checked.

Whilst there is a lot of merit in the methodology, the paper requires solid justification of assumptions and selection of components. Technical and language editing are also strong recommendations.

Response: In response to the request for stronger justification of assumptions and component selection, we have thoroughly revised the Materials and Methods section to include detailed explanations for all key assumptions adopted throughout the study. This includes explicit rationale for:

the definition of market share and demand parameters,
the selection of financial and macroeconomic indicators,
and the choice of probability distributions used in the Monte Carlo Simulation (Table 6).

Furthermore, we have expanded the discussion on the participatory approach involving the investor and stakeholders, highlighting how this process helped ground the assumptions in realistic and consensus-based scenarios.

Regarding the technical and language editing, we have conducted a comprehensive revision of the manuscript to enhance clarity, coherence, and precision in both technical content and English language. This included grammar corrections, style adjustments, and terminology consistency throughout the text.

Although I read this paper with interest, I find the following issues affect the quality of the paper:

Whilst the topic relates to a logistics facility, this is really a business case, considering the demand and supply of product, rather than the logistics thereof. This may be better positioned in a business management journal.

We kindly request the reviewer to consider this perspective regarding the journal's scope.

Some of the assumptions are confusing and require further clarification. An example would be meat consumption – the authors have based the assumptions on the pork consumption, as initially proposed by the Brazilian Swine Breeders. Typically, per capita pork consumption figures would already account for non-meat consumers. The authors have then further reduced the figures by adjusting for non-meat consumers – this may skew the results. Furthermore, based on these figures, the authors determine that the population growth rate is stronger than the meat consuming population growth rate and, therefore, that meat consumption per capita is likely to rise enormously. The growth rate in meat consumption, as per Table 1, seems unfeasibly high. It is little wonder then that this is likely to be such a significant influence on the profitability of the facility, however one feels that the authors may be overestimating the growth rate of meat consumption per capita.

Several other assumptions should also be questioned – for example, the cost of the facility and equipment is unclear – the authors state that these are low, because of using an existing facility and existing equipment, but it is not clear what the costs are. The base costs are regarded as the tax costs, but calculation of these require clarification. Labour costs also appear very low for a facility that should handle these kinds of volumes

Assumptions such as market share are unclear – the authors speak of 5% fulfilment of total demand, which appears to be based on a discussion with the investor. Many similar assumptions are based on discussions with the investor – it would be good to see a robust foundation for more assumptions.

Response: Done.

In essence - the paper would benefit from fully justified assumptions, with assumptions from brainstorming supported by the literature.

It would also be good to see a sound discussion on the selection of the components for the model.

Response: Done.

Technical editing is required – Table 1 for example shows that the meat consuming population is measured in kgs

Response: Done. The periods and commas have been changed.

L44 – the study tends to contributes OR the study contributes

Response: Done. Excerpt taken from the text.

L57 – not clear where it is stated that one of the study objectives was to hold a discussion on the results. Language editing is strongly recommended

Response: Done (lines 56-57).

L91 – meaning unclear

Response: Thanks for your comment. The phrase was revised.

The brainstorming sessions seem fundamental to model building, and therefore the paper requires an appropriate description of the process and participants.

Response: Done.

Selic rate requires description – this appears to be Brazilian and therefore needs description for a wider audience

Response: Done. (lines 824-828).

L115-120 – need description in the literature and motivation for selection here

Response: Done. Excerpt taken from the text.

Consistency required in use of separators in numbers – for example pork consumption is reported as 19,500 kg per capita, whereas beef is reported as 30.609 kg per capita. These need standardisation as they are easily misread

Response: Done.

All figures show kg per CAPTA

Response: Yes, all figures related to meat consumption and production presented in the document are expressed in kilograms (kg) per inhabitant per year, i.e. per capita.

Table 3 – figures need alignment

Response: Done.

An explanation is required of how business risk and opportunity cost percentages were determined.

Response: Done.

There are several empty brackets, e.g. L252, L253, etc.

Response: Checked.

Whilst there is a lot of merit in the methodology, the paper requires solid justification of assumptions and selection of components. Technical and language editing are also strong recommendations.

the definition of market share and demand parameters,
the selection of financial and macroeconomic indicators,
and the choice of probability distributions used in the Monte Carlo Simulation (Table 6).

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for the interesting study on meat distribution logistics in Brazil.
Major improvements are required to increase the quality of the paper. Main concerns are:

Please improve the scientific language.
Please explain why this study is of relevance for research on logistics. In the current form, the study provides an analysis of an investment and the relevance for logistic aspects is missing.
Please include literature sources for the assumptions of the Monte Carlo simulation: Distribution
Please validate and compare the results with studies introduced in Section 4.
Further remarks are provided in the attached document.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

The language quality is quite informal and should be more scientific.

Author Response

Thank you for the interesting study on meat distribution logistics in Brazil.
Major improvements are required to increase the quality of the paper. Main concerns are:

Please improve the scientific language.

Response: Response: We appreciate the reviewer’s feedback. Accordingly, we have revised the manuscript to enhance the clarity, precision, and scientific rigor of the language throughout the text. All sections were carefully reviewed to ensure consistency with academic standards.

Please explain why this study is of relevance for research on logistics. In the current form, the study provides an analysis of an investment and the relevance for logistic aspects is missing.

Response: Thanks for your comments. To address this point, we have revised both the Introduction and Discussion sections to explicitly highlight how the study contributes to the field of logistics. More specifically, the study focuses on the economic and operational feasibility of a cross-docking distribution center, which is a logistics-intensive model aimed at improving efficiency in meat distribution networks.

The research investigates key logistics variables—such as transportation costs, route coverage, cold chain integrity, and demand fulfillment across 139 municipalities—and demonstrates how these factors directly influence investment viability. By integrating Monte Carlo Simulation with deterministic financial modeling, the study provides a novel framework for quantifying the impact of logistics uncertainties on project profitability, a gap often overlooked in traditional feasibility studies.

Additionally, the probabilistic sensitivity analysis (Figures 7 and 8) reveals that logistics costs are the primary constraint on profitability, reinforcing the centrality of logistics decision-making to the project's success. These insights offer valuable contributions to logistics operations planning, especially for projects in perishable goods distribution, where timing, infrastructure, and route optimization are critical.

We have now made these points explicit in the revised manuscript to better align the study’s contributions with the logistics research community.

Please include literature sources for the assumptions of the Monte Carlo simulation: Distribution

Response: Thank you for the suggestion. We have updated the Materials and Methods section to include appropriate literature sources supporting the use of Pert and Triangular distributions in Monte Carlo simulations, particularly in logistics and feasibility studies. These sources justify the selection based on the availability of expert estimates and the need to model uncertainty in scenarios with limited historical data.

References [21], [22], [25], and [26] have been cited to support these methodological choices.

Please validate and compare the results with studies introduced in Section 4.

Response: Done. We thank the reviewer for the insightful comment. In response, we revised the manuscript to explicitly validate and compare the results of our study with the key works presented in Section 4. Throughout the revised discussion, we have integrated specific connections between our findings and those from the referenced literature, highlighting methodological and empirical convergences.

For instance, we reinforced the alignment between our route optimization results and the findings of [15], who emphasized cost and emission reductions through optimized delivery strategies. Similarly, our use of Monte Carlo simulation and stochastic modeling was validated against the approaches adopted by [25] and [26], who applied comparable probabilistic frameworks in the food and agricultural sectors. We also addressed storage, preservation, and traceability aspects by referencing studies [16–21], thereby contextualizing our assumptions on vehicle types, short-term cold storage, and operational safety.

This structured comparison demonstrates the coherence of our model with current research and strengthens the reliability and relevance of our conclusions. All these validations are now explicitly presented in the revised version of the manuscript, as requested.

Further remarks are provided in the attached document.

Response: We thank the reviewer for the valuable comments and suggestions. The authors have made the necessary adjustments to the manuscript as requested, aiming to improve the clarity and quality of the work.

Please explain why the type of distribution was selected. Please include literature evidence (Table 6).

Response: Done.

Usually, the number of least iterations for Monte Carlo simulation is 10.000 runs. Please reflect on this.

Response: We thank the reviewer for this valuable observation. In response, we revised our simulation procedures and conducted new analyses using 100,000 iterations. This adjustment enhances the robustness and stability of the probabilistic estimates, ensuring greater reliability of the results obtained from the Monte Carlo Simulation.

Such a diagramm is called "Butterfly" or "Tornado" diagram. It shows the most relevant impact parameters. Please consider to rename Fig. 7 accordingly.

Response: Done.

In general, please improve the language towards a more scientific style.

Response: Done.

“In this study, logistics costs were estimated for a set of municipalities located in Minas Gerais and São Paulo.” This is mentioned for the first time. Please check.

Response: Done.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

In general, the authors have done well to address the concerns from the previous submission.

It would be good to understand more about the brainstorming sessions – although the authors have provided more information, it would still be good to understand the profile of the participants, the questions and/or discussion topics, the number of sessions, etc.

Selic rate is explained, but only at 3^rd mention – it should be explained at first mention

The authors have explained their growth rate of meat consumption, however it seems unlikely that is a probable scenario. Whilst the beef scenario is appropriate, it is hard to envisage that the pork market will grow so fast. 12% over 7 years seems exceptionally high. Also, there is usually a ceiling to this type of growth, as seen in the beef market. This should be taken into account. Finally, the projected growth rate for pork rates is based on the preceding growth rates. The authors should try and explain why the previous rates were high, and whether this strong growth is likely to be sustained. It seems unlikely that this will be a usual growth trajectory.

WRT the percentage of meat-eating population, whilst the average of 86% is justified, the broad variation in the parameter range seems exceptionally high. The authors should consider reducing the range. The same goes for population growth.

Table 7 is difficult to read. Also, can the authors explain the target meat-consuming population of 105%?

There are several technical issues that have not yet been addressed, for example, (1) Table 1 Projected total meat consumption (tkg) - different number onventions used in line 1 to line 2 , (2) Per capita vs capta, (3) duplication of conclusionconclusion, (4) formatting around lines 638-647, etc.

In general, the paper is much improved and the authors are to be commended on addressing most of the issues. The major concern however remains the assumptions around the meat-eating population figures and ranges. It would be essential for the authors to address these or provide very strong motivations for the broad parameters and assumptions.

Comments on the Quality of English Language

See comments above

Author Response

In general, the authors have done well to address the concerns from the previous submission.

Response: Thank you for your valuable comment. In response, we have expanded the description of the brainstorming sessions to include additional methodological details. Specifically, we now clarify the number of sessions conducted (three), the average duration (90 minutes), the participant profile (logistics managers, analysts, supply chain specialists, and academic researchers), as well as the key discussion topics (cost drivers, demand variability, infrastructure, and technology). These details have been incorporated into the revised manuscript to improve transparency and provide a clearer understanding of how the sessions contributed to the definition of variables used in the modeling phase.

Selic rate is explained, but only at 3^rd mention – it should be explained at first mention.

Response: Thank you for your valuable comment. Done.

Response: Thank you for your observation. The projected 12% growth in pork consumption over seven years is based on historical data (2015–2022) that show a consistent upward trend, driven by increased affordability, improvements in production and quality, and changing consumer habits. While we recognize that long-term growth may face saturation, especially as seen in the beef market, the pork sector in Brazil still presents expansion potential, particularly in regions with traditionally lower consumption. To address uncertainty, we modeled this variable probabilistically using Monte Carlo Simulation, with a defined range that captures both optimistic and conservative scenarios. For these reasons, we consider it appropriate to maintain the current growth range in the analysis.

Response: Thank you for your observation. We acknowledge the broad range considered for the percentage of the meat-eating population and population growth. However, we opted to maintain these ranges based on two main factors: (i) the diversity of consumption patterns across different regions of Brazil, particularly when considering urban versus rural populations and socioeconomic variability, and (ii) the use of Monte Carlo Simulation, which benefits from a wider parameter range to properly account for uncertainty and ensure robustness in sensitivity analysis. Narrowing the range could underestimate the impact of potential demographic and cultural shifts, which are critical when assessing long-term investment feasibility in food logistics infrastructure. Therefore, we consider the adopted variability both realistic and essential to support a more resilient and comprehensive risk analysis.

Table 7 is difficult to read. Also, can the authors explain the target meat-consuming population of 105%?

Response: Thank you for your comment. In response, we have clarified in the manuscript that the 105% value used for the meat-consuming population in Table 7 serves exclusively as an analytical upper bound within the Monte Carlo Simulation. This value was not intended as a literal estimate but as a way to capture potential variability due to factors such as informal market dynamics, internal migration, and data underreporting. The text was revised to explain that this probabilistic approach aims to test the project's resilience under optimistic but plausible demand scenarios, thereby reinforcing the robustness of the economic feasibility analysis.

Response: Table 1 was revised.

Response: Thank you for your comment and for acknowledging the improvements made. Regarding the meat-eating population figures and their parameter ranges, we maintained the broader variability based on two key justifications. First, Brazil presents substantial regional and socioeconomic diversity, which results in significant variation in meat consumption habits across urban and rural areas. Second, given the use of Monte Carlo Simulation, broader ranges are methodologically important to properly capture uncertainty and reflect potential demographic or cultural shifts over time. This approach ensures the robustness of the sensitivity analysis and supports a more realistic and resilient long-term investment evaluation. For these reasons, we believe it is appropriate to retain the original assumptions.

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for taking the reviewers comments into account. The manuscript has improved in quality and can be published without any amendsments.

Author Response

Thank you for taking the reviewers comments into account. The manuscript has improved in quality and can be published without any amendsments.

Response: Thank you for your positive evaluation. We appreciate your time and feedback throughout the review process and are pleased to know that the revised manuscript meets the expected standards.