A Supply Chain Framework for Corn Products in Sumenep to Support Sustainable Ethanol Production

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Review of the manuscript

1. Summary of the paper

The paper presents a supply-chain framework for corn products in the Sumenep region of Indonesia, intended to support sustainable bioethanol production in the context of the planned E10 fuel policy. The authors argue that a properly designed supply chain integrating smallholder farmers may contribute both to emission reduction and to improving the economic situation of local producers.

The study uses primary and secondary data on the locations of farmer groups, seed suppliers, fertilizer distributors, and corn storage warehouses. The data were collected through interviews and from public institutions.

The methodological approach is based on the p-median location model, which is used to group farmer entities in order to reduce transportation distances. The analysis is based on a distance matrix between farmer groups and on a MATLAB implementation of the model.

The empirical analysis covers 385 farmer groups and other supply-chain actors. As a result, the authors identify 15 corn production clusters that may serve as potential aggregation nodes for bioethanol production. According to the paper, the proposed decentralized supply-chain structure may reduce logistics costs and increase farmers’ income, with a reported profit improvement of about 16% compared with the non-clustered case.

2. General evaluation

The paper addresses an important problem related to supply-chain design for bioethanol production from agricultural feedstock. This topic is relevant both from the perspective of energy transition and emission reduction, and from the perspective of rural development.

The study also has practical value because it uses real data on corn production and logistics infrastructure in the Sumenep region. The application of the p-median model may be useful for planning agricultural logistics systems.

At the same time, the scientific contribution of the paper is not fully clear in its current form. The methodological part requires clarification, and some parts of the paper are more descriptive than analytical. In addition, not all elements of the empirical section are directly linked to the optimization model.

3. Main comments

3.1. Scientific contribution

The authors describe the study as a “novel supply-chain framework”, but the main methodological element is the use of a classical p-median location model. This model is well known in the literature on facility location and logistics network design. Therefore, the novelty of the proposed approach is not fully clear.

The authors should explain more clearly whether the contribution lies in:

• applying an existing method to a new empirical setting,
• extending the optimization model,
• or integrating socio-economic aspects into supply-chain design.

3.2. Literature review

The literature review is rather descriptive and does not clearly identify the research gap. In particular, relatively little attention is given to optimization models used in biomass and biofuel supply-chain planning.

It would strengthen the paper to include more discussion of:

• location models in biomass supply chains,
• the use of p-median and other facility location models in agricultural systems,
• decentralized feedstock aggregation systems for bioenergy production

3.3. Methodology

The methodology section needs clarification in several important areas.

First, the paper uses the terms “clustering” and “p-median” interchangeably. This is methodologically misleading. The p-median model is a classical facility location problem, not a clustering method in the statistical sense.

Second, the presentation of the model is not sufficiently clear. Equations (1)-(6) correspond to a standard p-median formulation, but the formal notation is very general and the explanation of variables and parameters appears only later in the text.

A particularly important issue concerns Equation (1). It is presented as an objective function, while in fact it has the form of an assignment constraint. In the classical p-median model, the objective function should minimize the total distance between demand points and selected centers. In its current form, the model formulation is formally unclear and may confuse the reader.

The paper also does not clearly explain:

• what exactly is minimized,
• what the units of the objective function are,
• which logistics or infrastructure constraints are included in the model.

For clarity, the model should be presented in a more structured way, including: indices, parameters, decision variables, the objective function, and constraints.

3.4. Integration of the clustering model with the supply-chain model

In the paper, the p-median model is used to determine clusters of farmer groups, while later Equations (7)-(9) describe transportation costs between different parts of the system. However, the relationship between these two parts of the analysis is not clearly explained.

In particular, it is not clear:

• how the clustering result affects the logistics network structure,
• whether the cluster centers represent actual physical aggregation points,
• or whether clustering is only an auxiliary analytical step.

At present, these two parts look like separate elements rather than one coherent model.

3.5. Number of clusters

A key parameter in the p-median model is the number of centers, p. The paper adopts a solution with 15 clusters, but no justification is provided for this choice.

It is unclear whether this number follows from:

• infrastructure limitations,
• logistics assumptions,
• or an arbitrary decision.

A sensitivity analysis showing how the results change with different values of p would be useful.

3.6. Data and empirical analysis

The paper contains a large empirical section based on data on 385 farmer groups and other system elements. These data may provide valuable empirical input, but their presentation is at times too descriptive.

Many tables include detailed information that does not appear to be directly used in the optimization model. The paper would benefit from a clearer indication of which variables are actual model inputs.

3.7. Interpretation of results

The results lead to the identification of 15 corn production clusters in the Sumenep region. However, the interpretation remains rather general.

In particular, the paper lacks:

• a quantitative analysis of transport distance reduction,
• a comparison with alternative logistics configurations,
• and a more detailed justification of the reported 16% increase in profit.

4. Minor comments

Methodological terminology should be used more consistently, especially the distinction between clustering and facility location.

Some figures are mainly illustrative. Their analytical value could be improved by adding distance scales and clearer legends.

The manuscript contains a large number of tables; some of them could be moved to the supplementary material.

It would also be useful to check the consistency of the references and their direct relevance to the problem studied.

5. Evaluation of the manuscript

Is the content succinctly described and contextualized with respect to previous and present theoretical background and empirical research (if applicable) on the topic?

The topic is generally described and contextualized, but the literature review remains rather descriptive and does not clearly identify the research gap or sufficiently discuss optimization models used in bioenergy supply chains.

Are the research design, questions, hypotheses and methods clearly stated?

The modelling framework and methodological assumptions are not sufficiently clear, particularly the formulation of the p-median model and the role of several equations.

The research design and methodological framework require clarification. In particular, the formulation of the p-median model is not presented in a fully transparent way. Equations (1–6) correspond to a standard p-median formulation, but the objective function, variables and parameters are not clearly defined, and Equation (1) appears to represent an assignment constraint rather than an objective function. The authors should present the model more systematically by clearly defining indices, parameters, decision variables, the objective function and constraints, and by explaining how the model is implemented in MATLAB and how the parameter (number of clusters) is determined.

Are the arguments and discussion of findings coherent, balanced and compelling?

The discussion of results remains rather general and lacks a clearer quantitative interpretation and comparison with alternative configurations.

For empirical research, are the results clearly presented?

The empirical results are presented in detail, but the link between the data, the model inputs and the optimization results could be clearer.

Is the article adequately referenced?

The references are generally adequate, but the review could include more studies on optimization and facility location models in bioenergy supply chains.

Are the conclusions thoroughly supported by the results presented in the article or referenced in secondary literature?

The conclusions are generally consistent with the results, but some claims, such as the reported profit increase, are not sufficiently supported by detailed analysis.

Quality of English Language - The language is generally understandable, but several sentences could be improved for clarity and consistency.

Originality: The study applies a well-known p-median facility location model to an empirical agricultural supply-chain case.

Contribution to Scholarship: The paper provides an empirical application, but the theoretical or methodological contribution remains limited.

Quality of Structure and Clarity: The structure is generally clear, although some parts of the empirical section are overly descriptive.

Logical Coherence / Strength of Argument / Academic Soundness: The overall argument is understandable, but the methodological formulation and model presentation need clarification.

Engagement with sources as well as recent scholarship: Relevant literature is cited, but the review could include more studies on optimization models in bioenergy supply chains.

Overall Merit: Average - The paper addresses a relevant problem and uses valuable data, but methodological clarity and analytical depth should be improved.

Author Response

Coment 1:

Scientific contribution

The authors describe the study as a “novel supply-chain framework”, but the main methodological element is the use of a classical p-median location model. This model is well known in the literature on facility location and logistics network design. Therefore, the novelty of the proposed approach is not fully clear.

The authors should explain more clearly whether the contribution lies in:

• applying an existing method to a new empirical setting,
• extending the optimization model,
• or integrating socio-economic aspects into supply-chain design.

Response 1: Nevertheless, such approaches may fail to consider critical socio-economic factors, including income distribution, market accessibility, and the inclusion of marginalized rural producers. Within the context of Sumenep Regency, where agricultural production is characterized by fragmented smallholder systems and limited access to formal markets, supply chain design plays a dual role; as a logistical mechanism, but also as a tool for rural economic empowerment. The proposed clustering-based supply chain model contributes to this integration by improving the spatial organization of farmer groups, thereby reducing transportation barriers and enhancing their access to centralized aggregation points. The establishment of connections between smallholders and the participation of these actors in structured value chains has the potential to enhance their bargaining power and income stability. Moreover, by reducing logistical inefficiencies, the model helps ensure that a greater share of value creation is retained at the farm level, addressing common issues of value leakage in traditional agricultural supply chains. These outcomes are consistent with broader findings in sustainable supply chain management, which emphasize that efficiency improvements should be aligned with equitable value distribution and inclusive development goals. Nevertheless, it is important to acknowledge that the current model integrates socio-economic aspects implicitly through economic performance indicators, such as increased farmer profit and improved market access, rather than explicitly incorporating social variables into the optimization function

Comment 2 Literature review

The literature review is rather descriptive and does not clearly identify the research gap. In particular, relatively little attention is given to optimization models used in biomass and biofuel supply-chain planning.

It would strengthen the paper to include more discussion of:

• location models in biomass supply chains,
• the use of p-median and other facility location models in agricultural systems,
• decentralized feedstock aggregation systems for bioenergy production

Response 2:

The conceptual framework of this study is based on the interaction between agricultural production systems, supply-chain logistics, and renewable energy development. Corn production from farmer groups represents the primary feedstock source for bioethanol production. However, fragmented production and inefficient logistics often lead to high transportation costs and supply instability. To address this issue, the study proposes a cluster-based supply-chain structure, where farmer groups are aggregated into clusters and connected to optimal collection points using the p-median optimization model. This approach minimizes transportation distance while improving coordination between supply-chain actors. The optimized network then supports ethanol production facilities, which convert corn feedstock into bioethanol for blended fuel applications. Through this framework, the research evaluates how optimized logistics networks can improve supply-chain efficiency, increase farmer profitability, and support renewable energy targets simultaneously.

Nevertheless, such approaches may fail to consider critical socio-economic factors, including income distribution, market accessibility, and the inclusion of marginalized rural producers. Within the context of Sumenep Regency, where agricultural production is characterized by fragmented smallholder systems and limited access to formal markets, supply chain design plays a dual role; as a logistical mechanism, but also as a tool for rural economic empowerment. The proposed clustering-based supply chain model contributes to this integration by improving the spatial organization of farmer groups, thereby reducing transportation barriers and enhancing their access to centralized aggregation points. The establishment of connections between smallholders and the participation of these actors in structured value chains has the potential to enhance their bargaining power and income stability. Moreover, by reducing logistical inefficiencies, the model helps ensure that a greater share of value creation is retained at the farm level, addressing common issues of value leakage in traditional agricultural supply chains. These outcomes are consistent with broader findings in sustainable supply chain management, which emphasize that efficiency improvements should be aligned with equitable value distribution and inclusive development goals. Nevertheless, it is important to acknowledge that the current model integrates socio-economic aspects implicitly through economic performance indicators, such as increased farmer profit and improved market access, rather than explicitly incorporating social variables into the optimization function.

 

Comment 3: Methodology

The methodology section needs clarification in several important areas.

First, the paper uses the terms “clustering” and “p-median” interchangeably. This is methodologically misleading. The p-median model is a classical facility location problem, not a clustering method in the statistical sense.

Second, the presentation of the model is not sufficiently clear. Equations (1)-(6) correspond to a standard p-median formulation, but the formal notation is very general and the explanation of variables and parameters appears only later in the text.

A particularly important issue concerns Equation (1). It is presented as an objective function, while in fact it has the form of an assignment constraint. In the classical p-median model, the objective function should minimize the total distance between demand points and selected centers. In its current form, the model formulation is formally unclear and may confuse the reader.

The paper also does not clearly explain:

• what exactly is minimized,
• what the units of the objective function are,
• which logistics or infrastructure constraints are included in the model.

For clarity, the model should be presented in a more structured way, including: indices, parameters, decision variables, the objective function, and constraints.

Response 3:

This study employs a p-median model, a classical optimization approach in facility location problems, to determine optimal aggregation points in the corn supply chain. It is important to clarify that, although the term “clustering” is used in a practical sense to describe the grouping of farmers, the underlying method is not a statistical clustering technique but a location-allocation optimization model. The p-median model minimizes the total distance between supply points from farmers and selected facility locations, thereby improving logistical efficiency in feedstock collection

2.1 Model Formulation

To enhance clarity, the model is presented using a structured formulation consisting of indices, parameters, decision variables, an objective function, and constraints.

Indices

?∈?: Set of supply points (corn farmers)

?∈?: Set of candidate facility locations (collection centers)

Parameters

dij : Distance between supply point i and facility location ? (km)

p: Number of facilities to be selected

Decision Variables

???=1 if supply point ? is assigned to facility ?; 0 otherwise

??=1 if facility ? is selected; 0 otherwise

 

Objective Function

∑_(i=1)^n▒〖x_ij=1      ∀i〗         (1)

 

Assignment Constrain Function

x_ij≤y_j        ∀i,j

(2)

Facility Selection Constraint

∑_(i=1)^n▒〖y_j=p〗      (3)

Binary Constrain

x_ij={0,1}     ∀i,j

(4)

y_j={0,1}      ∀j

(5)

The equation uses Index with notation i as the farmer group and j as the center point of the group. Once the points of each cluster and cluster members are known, the next step is to determine the objective function and constraint function. This equation then used as the basis for creating a Matlab program to solve the farmer group clustering, similar to this research [16,17]. The next step is to verify the model that has been created by declaring each unit in the model and checking the final output results as desired. The following is the mathematical model

Objective function of breeders and farmer groups

∑_(∀i)▒∑_(∀k)▒(((TDS_ik.bp_ik.d_ik)/(Kap_i ))+btp_ik )  (7)

Objective Function Fertilizer shops and farmer groups

∑_(∀i)▒∑_(∀k)▒(((TDS_jk.bp_jk.d_ik)/(Kap_i ))+btp_ik ) +∑_(∀i)▒∑_(∀i)▒〖TDS_ik.bp_ik.d_ik 〗      (8)

Objective Functions of Farmer Groups and Agricultural Product Collectors

∑_(∀i)▒〖∑_(∀k)▒(((TDS_kc.bp_kc.d_kc)/(Kap_c ))+btp_kc ) +∑_(∀i)▒∑_(∀i)▒〖TDS_ik.bp_ik.d_ik 〗〗 (9)

Comments 4 : Integration of the clustering model with the supply-chain model

In the paper, the p-median model is used to determine clusters of farmer groups, while later Equations (7)-(9) describe transportation costs between different parts of the system. However, the relationship between these two parts of the analysis is not clearly explained.

In particular, it is not clear:

• how the clustering result affects the logistics network structure,
• whether the cluster centers represent actual physical aggregation points,
• or whether clustering is only an auxiliary analytical step.

At present, these two parts look like separate elements rather than one coherent model.

Response 4:

The model minimizes total logistics effort by assigning each farmer to the nearest selected aggregation centre, weighted by production volume. This approach reflects real-world supply chain behaviour, where transportation cost is influenced by both distance and quantity transported. By optimizing facility locations, the model improves feedstock consolidation efficiency, reduces transportation burden, and enhances accessibility for smallholder farmers. Although the model focuses on logistical optimization, its outcomes also contribute indirectly to socio-economic improvements, such as reduced transportation costs for farmers and improved market access. However, infrastructure limitations (e.g., road conditions, storage capacity) and operational constraints are not explicitly modeled and are discussed as limitations of this study.

Table 1. Equation symbol and description on this study.

Symbol	Description	Symbol	Description
i	index for identifying seed breeders	TDSik	Number of seeds shipped from seed breeder i to farmer group k
j	index for identifying fertilizer shop	bpik	Cost of shipping corn seed raw materials from seed producer i to farmer group k
k	Index for identifying farmer group	dik	Demand for corn seeds in farmer group k
c	Index for identifying agricultural warehouse	Kapi	Maximum vehicle capacity for transporting seeds
n	Total of farmer group points	btpik	Fixed shipping cost from breeder i to farmer group k
xij	1 if point i is a member of cluster point j; 0 for others	TDSik	Number of seeds shipped from seed producer i to farmer group k
∀i	Constrain for every farmer group	TDSik	Number of seeds shipped from seed producer i to farmer group k
∀j	Constrain for every candidate collection center	bpjk	Shipping cost of fertilizer raw materials from fertilizer store j to farmer group k
dij	distance between points i and j	btpkc	Fixed shipping cost from farmer group k to c agricultural warehouse
	The constraint must hold for every farmer group ? and every potential fertilizer shop ?	bpik	Shipping cost of corn seed raw materials from seed producer i to farmer group k
p	demand of clusters	∑∀i ∑​∀k ​	The summation over all constrains values of ? will set to all constrain of ?
yj	1 if the cluster point is located at point j; 0 for others

 

Comments 5: Number of clusters

A key parameter in the p-median model is the number of centers, p. The paper adopts a solution with 15 clusters, but no justification is provided for this choice.

It is unclear whether this number follows from:

• infrastructure limitations,
• logistics assumptions,
• or an arbitrary decision.

A sensitivity analysis showing how the results change with different values of p would be useful.

Number of Facilities (p-Median Parameter)

Response 5 : A key parameter in the p-median model is the number of facilities (p), which de-termines how many aggregation centers are established within the supply chain net-work. In this study, a configuration of ?=15 facilities is adopted. This value is not arbi-trary but reflects a balance between logistical efficiency, spatial coverage, and practi-cal considerations related to regional conditions. From a logistical perspective, in-creasing the number of facilities reduces the average transportation distance between supply points and assigned centers, thereby lowering transportation costs. However, a larger number of facilities may lead to higher infrastructure and operational costs, as each facility requires investment in land, storage, and management. Conversely, a smaller number of facilities may reduce infrastructure costs but increase transporta-tion burden, particularly for geographically dispersed smallholder farmers. Therefore, the selection of ?=15 represents a compromise between these competing factors, en-suring reasonable accessibility for farmers while maintaining a manageable number of aggregation points.

In addition, the choice of p is informed by the spatial distribution of corn produc-tion in Sumenep Regency, where agricultural activities are dispersed across multiple sub-districts with varying production capacities. The selected number of facilities al-lows for effective clustering of production areas without excessively concentrating supply flows into a limited number of centers, which could create bottlenecks in logis-tics operations. To further evaluate the robustness of this assumption, a sensitivity analysis is conducted by varying the number of facilities across several scenarios such as p=10,12,15,18,20. The results indicate that while increasing ? consistently reduces total weighted distance, the marginal improvement diminishes beyond a certain threshold. In particular, the reduction in total transportation distance becomes less significant when p>15, suggesting diminishing returns in logistical efficiency. This finding supports the selection of ?=15 as a near-optimal solution that balances effi-ciency and practicality. It is important to note that the model does not explicitly in-corporate infrastructure capacity constraints or investment cost functions. Therefore, the selection of ? should be interpreted as a planning-level decision rather than a de-finitive operational recommendation. Future research could extend this analysis by in-tegrating facility cost structures, capacity limitations, and multi-objective optimization to determine the optimal number of facilities under both economic and socio-economic criteria.

Comment 6: Data and empirical analysis

The paper contains a large empirical section based on data on 385 farmer groups and other system elements. These data may provide valuable empirical input, but their presentation is at times too descriptive.

Many tables include detailed information that does not appear to be directly used in the optimization model. The paper would benefit from a clearer indication of which variables are actual model inputs.

Response 6: The implementation of the p-median–based clustering approach significantly improved the efficiency of the corn supply chain. Prior to optimization, the total delivery distance for farmer groups reached 346.29 km, reflecting dispersed and uncoordinated transportation patterns. After applying the clustering model, the total delivery distance was reduced to 74.26 km, resulting in a substantial decrease of 272.03 km. This reduction demonstrates the effectiveness of spatial optimization in minimizing logistical inefficiencies and improving accessibility to aggregation centers. From an operational perspective, this improvement also translates into tangible economic benefits. Assuming that pickup trucks commonly used by farmers have an average fuel consumption of 16 km per liter, the reduced travel distance leads to a significant decrease in fuel usage and associated transportation costs. Consequently, the optimized supply chain not only enhances logistical performance but also contributes to cost savings and improved economic outcomes for smallholder farmers.

Comment 7: Interpretation of results

The results lead to the identification of 15 corn production clusters in the Sumenep region. However, the interpretation remains rather general.

In particular, the paper lacks:

• a quantitative analysis of transport distance reduction,
• a comparison with alternative logistics configurations,
• and a more detailed justification of the reported 16% increase in profit.

Response 7:

This study developed an optimized supply-chain framework for corn products in Sumenep Regency, Indonesia, with the objective of supporting sustainable ethanol production and improving the economic participation of smallholder farmers. By integrating field data, farmer clustering techniques, and mathematical modeling using the p-median method, the research demonstrates that a decentralized and simplified supply-chain architecture can significantly improve the efficiency of agricultural logistics and the economic outcomes of farmer groups. The results show that Sumenep’s corn production potential can generate approximately 381,416 liters of bioethanol, which could contribute to the production of E10 blended fuel while strengthening local agricultural value chains. By applying clustering supply chain model, the farmers' group profit is Rp 205,693,725,826, while Rp 177,394,823,353 profit for non-clustering model. It The majority of this revenue increase came from savings in delivery distance: before applying the clustering model, the distance was 346.29 km, but after applying the clustering model, it was reduced to 74.26 km, resulting in a substantial reduction of 272.03 km in delivery distance.

The findings highlight that clustering farmer groups and optimizing supply routes can increase farmer group profit by approximately 16%, demonstrating that supply chain design plays a crucial role in ensuring that value creation from bioethanol feedstock reaches smallholder farmers. This outcome supports previous studies emphasizing that efficient biofuel supply chains can simultaneously enhance economic viability and environmental sustainability by reducing logistical costs, improving feedstock aggregation efficiency, and lowering carbon emissions associated with transportation activities

Reviewer 2 Report

Comments and Suggestions for Authors

In this study, the authors developed a framework to study the corn products in Sumenep to support sustainable ethanol production. Although the study could contribute to the literature, the significant revision must be made in the current version:

1. In the current version, there lacks literature review section. The authors should provide significant literature analysis related to the study theme and identify accurate research gaps to justify the research values and novelty.

2. In section 2, the readability of the model development should be significantly improved. The notation definition is missing. I would recommend the authors to provide a table in the beginning of Section 2 and list all notations with their definitions.

3. Figure 1 should be reproduced. The figure in the text should be self-explanatory. However, the current version has little information and can confuse readers. This comment also applies to all other figures in this study.

4. The figure 2 is missing, as there seems only figure 1 and figure 3 in the manuscript.

5. The result presentation should be largely improved. In Table 7, the authors listed different colours for different clusters. However, such relationship should be provided in the figures, instead of in the table.

6. The conclusion should be significantly improved by citing relevant supporting literature.

7. Research limitations and future research directions should be added.

4. 

 

Author Response

[Comments 1] : In the current version, there lacks literature review section. The authors should provide significant literature analysis related to the study theme and identify accurate research gaps to justify the research values and novelty.

Although there the proliferation of research endeavors concerning bioethanol production and renewable fuel policies, a plethora of significant research gaps persist. Firstly, the majority of studies on bioethanol supply chains have focused on large-scale industrial systems or national policy perspectives. However, there has been limited attention paid to feedstock supply-chain structures at the smallholder level, particularly in developing countries. Secondly, although the Republic of Indonesia has explored the implementation of ethanol blending policies as part of its renewable energy transition, empirical studies examining regional feedstock logistics and farmer participation in ethanol supply chains are still scarce. Thirdly, previous research rarely integrates facility location optimization with farmer clustering approaches to design efficient biomass aggregation systems that can simultaneously improve logistical efficiency and farmer income. Agricultural production in Sumenep Regency is dominated by smallholder farmers with fragmented supply structures, which poses a significant challenge to the efficient collection of feedstock for bioethanol development. In the absence of a meticulously designed supply chain, the capacity of corn-based ethanol to facilitate both the attainment of renewable energy objectives and the advancement of rural economic development may not be fully actualized. The objective of this study is to develop a model for optimizing the supply chain for corn. This will be achieved by employing a p-median clustering approach, with the aim of improving the efficiency of aggregation of feedstock and supporting sustainable ethanol production. The integration of supply-chain optimisation with rural agricultural systems is a novel contribution to the existing literature on sustainable bioenergy systems and inclusive rural development.

The conceptual framework of this study is based on the interaction between agricultural production systems, supply-chain logistics, and renewable energy development. Corn production from farmer groups represents the primary feedstock source for bioethanol production. However, fragmented production and inefficient logistics often lead to high transportation costs and supply instability. To address this issue, the study proposes a cluster-based supply-chain structure, where farmer groups are aggregated into clusters and connected to optimal collection points using the p-median optimization model. This approach minimizes transportation distance while improving coordination between supply-chain actors. The optimized network then supports ethanol production facilities, which convert corn feedstock into bioethanol for blended fuel applications. Through this framework, the research evaluates how optimized logistics networks can improve supply-chain efficiency, increase farmer profitability, and support renewable energy targets simultaneously

[Comment 2] :  In section 2, the readability of the model development should be significantly improved. The notation definition is missing. I would recommend the authors to provide a table in the beginning of Section 2 and list all notations with their definitions.

[Comment 2] we already add in table 1.

[Comment 3] : Figure 1 should be reproduced. The figure in the text should be self-explanatory. However, the current version has little information and can confuse readers. This comment also applies to all other figures in this study.

[Answer 3] : We have considered the readability of Figure 1, assuming that the names of each farmers' group are included in the figure. Additionally, our focus in Figure 1 is to illustrate the distribution of farmers' groups in Sumenep Regency.

[Comment 4] : The figure 2 is missing, as there seems only figure 1 and figure 3 in the manuscript.

[Answer 4] : We already revised it and so on.

[Comments 5] : The result presentation should be largely improved. In Table 7, the authors listed different colours for different clusters. However, such relationship should be provided in the figures, instead of in the table.

[Response 5] : We concerns have been raised regarding the readability of the content in Table 7 when transferred to Figure 5. The text will be rendered too small and the spacing between the points will be too narrow, causing the text to overlap.

[Comments 6] : The conclusion should be significantly improved by citing relevant supporting literature.

[Response 6] : This study developed an optimized supply-chain framework for corn products in Sumenep Regency, Indonesia, with the objective of supporting sustainable ethanol production and improving the economic participation of smallholder farmers. By integrating field data, farmer clustering techniques, and mathematical modeling using the p-median method, the research demonstrates that a decentralized and simplified supply-chain architecture can significantly improve the efficiency of agricultural logistics and the economic outcomes of farmer groups. The results show that Sumenep’s corn production potential can generate approximately 381,416 liters of bioethanol, which could contribute to the production of E10 blended fuel while strengthening local agricultural value chains.

The findings highlight that clustering farmer groups and optimizing supply routes can increase farmer group profit by approximately 16%, demonstrating that supply chain design plays a crucial role in ensuring that value creation from bioethanol feedstock reaches smallholder farmers. This outcome supports previous studies emphasizing that efficient biofuel supply chains can simultaneously enhance economic viability and environmental sustainability by reducing logistical costs, improving feedstock aggregation efficiency, and lowering carbon emissions associated with transportation activities [6] [7]. From a broader sustainability perspective, the integration of corn-based bioethanol production into regional agricultural systems offers multiple benefits. Bioethanol derived from biomass has been widely recognized as a renewable alternative to fossil fuels that can reduce greenhouse gas emissions and support national decarbonization strategies [49]. Furthermore, ethanol–gasoline blends have been shown to improve engine performance and reduce harmful emissions such as carbon monoxide and hydrocarbons, making bioethanol an important component of cleaner transportation system [50]. In the Indonesian context, the proposed supply-chain framework contributes to ongoing efforts to implement bioethanol blending policies, which aim to enhance national energy security while reducing dependence on imported fossil fuels. Studies assessing Indonesia’s readiness for ethanol-blended fuels indicate that logistical infrastructure and feedstock supply chains are critical determinants of successful implementation [8]. Therefore, the decentralized clustering approach proposed in this study provides a practical mechanism for connecting smallholder farmers to emerging bioenergy markets while supporting inclusive rural development.

[Comments 7] : Research limitations and future research directions should be added

[Response 7] : Despite these contributions, several limitations should be acknowledged. First, the study relies on cross-sectional data obtained from interviews and secondary institutional sources, which may not fully capture dynamic fluctuations in corn production, transportation costs, or market prices over time. Second, the analysis focuses primarily on logistical optimization and economic outcomes, without conducting a comprehensive life-cycle environmental assessment of ethanol production and distribution. Third, the proposed model assumes relatively stable demand for ethanol feedstock and does not explicitly account for potential policy changes, market volatility, or climate-related agricultural risks that could influence feedstock availability. Future studies should extend this work by incorporating dynamic cost models and real-time logistics data to improve supply-chain responsiveness under uncertain market conditions. Additionally, integrating life-cycle assessment (LCA) and environmental impact analysis would provide a more comprehensive evaluation of the sustainability performance of corn-based ethanol supply chains. Further research could also examine multi-feedstock systems, including cassava, sugarcane, or agricultural residues, to reduce the risk of food–fuel competition and diversify bioethanol production sources.

Reviewer 3 Report

Comments and Suggestions for Authors

During reviewing of this article, I found that the authors did not sufficiently discussed the research gap and why they are proceeding this research work. Without answering to the "What is the research gap? why this research need to be conducted? and how this research will enrich the field knowledge and what are the novelty and originality of the research? It is not conclusive research article. Therefore, I suggest authors to first answers these key answers in the begining of the study to avoid any confusion.

 

Author Response

Comments 1: What is the research gap? why this research need to be conducted? and how this research will enrich the field knowledge and what are the novelty and originality of the research
Response 1 : Although there the proliferation of research endeavors concerning bioethanol production and renewable fuel policies, a plethora of significant research opportunity to observe. Firstly, the majority of studies on bioethanol supply chains have focused on large-scale industrial systems or national policy perspectives. However, there has been limited attention paid to feedstock supply-chain structures at the smallholder level, particularly in developing countries. Secondly, although the Republic of Indonesia has explored the implementation of ethanol blending policies as part of its renewable energy transition, empirical studies examining regional feedstock logistics and farmer participation in ethanol supply chains are still scarce. Thirdly, previous research rarely integrates facility location optimization with farmer clustering approaches to design efficient biomass aggregation systems that can simultaneously improve logistical efficiency and farmer income. Agricultural production in Sumenep Regency is dominated by smallholder farmers with fragmented supply structures, which poses a significant challenge to the efficient collection of feedstock for bioethanol development. In the absence of a meticulously designed supply chain, the capacity of corn-based ethanol to facilitate both the attainment of renewable energy objectives and the advancement of rural economic development may not be fully actualized. The objective of this study is to develop a model for optimizing the supply chain for corn. This will be achieved by employing a p-median clustering approach, with the aim of improving the efficiency of aggregation of feedstock and supporting sustainable ethanol production. The integration of supply-chain optimisation with rural agricultural systems is a novel contribution to the existing literature on sustainable bioenergy systems and inclusive rural development.

The conceptual framework of this study is based on the interaction between agricultural production systems, supply-chain logistics, and renewable energy development. Corn production from farmer groups represents the primary feedstock source for bioethanol production. However, fragmented production and inefficient logistics often lead to high transportation costs and supply instability. To address this issue, the study proposes a cluster-based supply-chain structure, where farmer groups are aggregated into clusters and connected to optimal collection points using the p-median optimization model. This approach minimizes transportation distance while improving coordination between supply-chain actors. The optimized network then supports ethanol production facilities, which convert corn feedstock into bioethanol for blended fuel applications. Through this framework, the research evaluates how optimized logistics networks can improve supply-chain efficiency, increase farmer profitability, and support renewable energy targets simultaneously.

Nevertheless, such approaches may fail to consider critical socio-economic factors, including income distribution, market accessibility, and the inclusion of marginalized rural producers. Within the context of Sumenep Regency, where agricultural production is characterized by fragmented smallholder systems and limited access to formal markets, supply chain design plays a dual role; as a logistical mechanism, but also as a tool for rural economic empowerment. The proposed clustering-based supply chain model contributes to this integration by improving the spatial organization of farmer groups, thereby reducing transportation barriers and enhancing their access to centralized aggregation points. The establishment of connections between smallholders and the participation of these actors in structured value chains has the potential to enhance their bargaining power and income stability. Moreover, by reducing logistical inefficiencies, the model helps ensure that a greater share of value creation is retained at the farm level, addressing common issues of value leakage in traditional agricultural supply chains. These outcomes are consistent with broader findings in sustainable supply chain management, which emphasize that efficiency improvements should be aligned with equitable value distribution and inclusive development goals. Nevertheless, it is important to acknowledge that the current model integrates socio-economic aspects implicitly through economic performance indicators, such as increased farmer profit and improved market access, rather than explicitly incorporating social variables into the optimization function.

Reviewer 4 Report

Comments and Suggestions for Authors

The topic of the article aligns with the realistic context of the national energy transition, but there are still the following shortcomings:

1. Lines 440-444 read more like a description of the research background.

2. The title 3.1 is on line 270, so what is the secondary heading for lines 154-269? The second and third parts both lack clear secondary headings.

3. There is a lack of discussion and policy recommendations.

4. The use of the word 'gap' in line 80 is too absolute. Also, it is recommended to add the research framework of the article after line 94.

5. Most importantly, the research method lacks innovation; the clustering method is a classic approach for site selection problems. However, in real-world activities, risk factors such as agricultural product prices and extreme weather can affect the stability of the supply chain. It is recommended to supplement with scenario forecasting analysis to enhance the guidance of the method for practical work.

Author Response

[Comment 1] : Lines 440-444 read more like a description of the research background.

[Answer 1] : Thank you for revision, we revised and move to Introduction section.

[Comments 2] : The title 3.1 is on line 270, so what is the secondary heading for lines 154-269? The second and third parts both lack clear secondary headings.

[Answer 2] : Thank you for revision, we already adding heading 3.1 to 3.5 for this section.

[Comment 3] : There is a lack of discussion and policy recommendations.

[Answer 3] : The clustering-based model demonstrates that more efficient feedstock aggregation and transportation can increase farmer profits and strengthen the reliability of biomass supply for ethanol production. These results suggest that supply chain optimization is an important enabling factor for sustainable bioenergy development, particularly in regions dominated by smallholder farmers. Efficient agricultural logistics systems have been widely recognized as a key component in improving the viability of biofuel supply chains and reducing operational costs [52]. From a policy perspective, the development of decentralized biomass aggregation centers should be prioritized to reduce transportation distances and improve feedstock consolidation. Strategic placement of such facilities can enhance supply reliability for ethanol processing industries and support the implementation of ethanol blending policies. Strengthening farmer cooperatives and producer networks is also essential to improve coordination within the supply chain and increase farmers’ bargaining power in bioenergy markets [53]. Collective institutions have been shown to play a crucial role in improving market access and income stability among smallholder farmers involved in bioenergy feedstock production [54]. Integrating agricultural development policies with national renewable energy strategies will therefore be critical for Indonesia’s ethanol blending program. Such integrated approaches can simultaneously enhance rural livelihoods, improve energy security, and support greenhouse gas mitigation target [55].

[Comments 4} : The use of the word 'gap' in line 80 is too absolute. Also, it is recommended to add the research framework of the article after line 94.

[Answer 4] : Although there the proliferation of research endeavors concerning bioethanol production and renewable fuel policies, a plethora of significant research opportunity to observe. Firstly, the majority of studies on bioethanol supply chains have focused on large-scale industrial systems or national policy perspectives. However, there has been limited attention paid to feedstock supply-chain structures at the smallholder level, particularly in developing countries. Secondly, although the Republic of Indonesia has explored the implementation of ethanol blending policies as part of its renewable energy transition, empirical studies examining regional feedstock logistics and farmer participation in ethanol supply chains are still scarce. Thirdly, previous research rarely integrates facility location optimization with farmer clustering approaches to design efficient biomass aggregation systems that can simultaneously improve logistical efficiency and farmer income. Agricultural production in Sumenep Regency is dominated by smallholder farmers with fragmented supply structures, which poses a significant challenge to the efficient collection of feedstock for bioethanol development. In the absence of a meticulously designed supply chain, the capacity of corn-based ethanol to facilitate both the attainment of renewable energy objectives and the advancement of rural economic development may not be fully actualized. The objective of this study is to develop a model for optimizing the supply chain for corn. This will be achieved by employing a p-median clustering approach, with the aim of improving the efficiency of aggregation of feedstock and supporting sustainable ethanol production. The integration of supply-chain optimisation with rural agricultural systems is a novel contribution to the existing literature on sustainable bioenergy systems and inclusive rural development.

The conceptual framework of this study is based on the interaction between agricultural production systems, supply-chain logistics, and renewable energy development. Corn production from farmer groups represents the primary feedstock source for bioethanol production. However, fragmented production and inefficient logistics often lead to high transportation costs and supply instability. To address this issue, the study proposes a cluster-based supply-chain structure, where farmer groups are aggregated into clusters and connected to optimal collection points using the p-median optimization model. This approach minimizes transportation distance while improving coordination between supply-chain actors. The optimized network then supports ethanol production facilities, which convert corn feedstock into bioethanol for blended fuel applications. Through this framework, the research evaluates how optimized logistics networks can improve supply-chain efficiency, increase farmer profitability, and support renewable energy targets simultaneously.

 

[Comment 5] :  Most importantly, the research method lacks innovation; the clustering method is a classic approach for site selection problems. However, in real-world activities, risk factors such as agricultural product prices and extreme weather can affect the stability of the supply chain. It is recommended to supplement with scenario forecasting analysis to enhance the guidance of the method for practical work.

[Answer 5} : Although the clustering-based approach used in this study provides a practical framework for optimizing the location of corn aggregation centers, it should be acknowledged that clustering techniques represent a classical method commonly applied in facility location and supply chain design problems. While the approach is effective in minimizing transportation distances and improving feedstock aggregation efficiency, the model assumes relatively stable supply and demand conditions. In real-world agricultural systems, however, supply chain stability can be affected by several external risk factors, including fluctuations in agricultural commodity prices, variations in crop yields, and disruptions caused by extreme weather events. These uncertainties may significantly influence feedstock availability, logistics costs, and the overall reliability of the bioethanol supply chain.

The people of Sumenep engage in the cultivation of corn during the dry season. The initiation of the planting season is invariably contingent on the involvement of community leaders, such as kyai (religious leaders) or klebun (village chief). The transition to a clustering model will present its own challenges, as members have historically based their planting decisions on the counsel of these leaders. In order to proceed, coordination with the local BABINSA (Village Supervisory Non-Commissioned Officer) is required. The implementation of this process necessitates the coordination of various elements within the community.

Reviewer 5 Report

Comments and Suggestions for Authors

In the Introduction, lines 31–49 discuss corn bioethanol, emissions, and circularity, but they do so in a broad and weakly connected way. The section does not clearly establish the link between ethanol, supply chain design, smallholder integration, and the specific case of Sumenep. This section should be reorganized to show, in a logical sequence, why the supply chain is the central issue, why including smallholders is critical in this context, and why Sumenep represents a relevant case.

In addition, although the gap and the proposal appear in lines 80–93, both remain too generic. The manuscript should state the central objective directly, in a single clear sentence, specifying what will be modeled, in which context, and with what expected outcome.

The manuscript moves directly from the Introduction to Materials and Methods without a short section that explains, in simple terms, the minimum concepts needed to understand the study, such as sustainable supply chains, the p-median model, clustering, and smallholder integration in agroindustrial chains. This omission weakens the article’s clarity, especially for readers who are not familiar with the topic. A concise theoretical background should be added, focusing only on the basic concepts required to understand the problem, the model, and the analytical logic of the study, while avoiding an excessively theoretical review.

In the methodology, lines 96–123 report interviews, secondary data, Google Maps, and Matlab modeling, but they do not specify how many interviews were conducted, who the respondents were, how the data were validated, the exact data collection period, how the variables were operationalized, why the number of clusters was defined as it was, or which specific parameters fed the model. As a result, the study is still not replicable. The authors should rewrite the methodology in clear stages, reporting the source of each dataset, the selection criteria, data treatment procedures, model parameters, software used, validation logic, and the rationale behind the analytical decisions.

In the results section, the manuscript devotes too much space to describing tables and maps and too little to actual analysis. Lines 153–269 spend substantial space listing groups, areas, seeds, fertilizers, and warehouses, but the discussion still does not clearly identify which patterns truly matter for addressing the article’s objective. In several passages, the text simply repeats what the tables already show. The results should be synthesized more effectively, the operational description should be reduced, and the discussion should focus only on the findings that clarify the supply chain logic, cluster formation, and the economic and logistical effects of the proposed model.

The manuscript also contains internal inconsistencies that undermine its credibility. In lines 161–162, the text refers to Table 1, but the discussion is actually based on Table 2. In lines 154–155, the data are presented as from March 2025, whereas lines 161–162 mention planting corn in 2024. In lines 214–235, the text states that Figure 3 shows the distribution of fertilizer stores, but the previous figure is associated with seed breeders, and the warehouse locations are later linked confusingly to Figure 4 and Figure 5. The authors should carefully review the numbering of tables and figures, the reference year of the data, and all cross-references, because the manuscript currently gives the impression of an incomplete assembly.

The clustering section also requires stronger analytical rigor. In lines 271–294, the study obtains distances from interviews and checks them on Google Maps, but it does not clarify whether the criterion was actual distance, travel time, shortest route, or usual route. Then, lines 295–345 present the 15 clusters, but the text does not justify why 15 clusters were selected or provide any sensitivity test or comparison with alternative solutions. The authors should technically justify the number of clusters, explain the spatial criterion adopted, and show why the selected solution performs better than other possible configurations.

In the Conclusion, the text reinforces the novelty and benefits of the model, but it still does not clearly separate the theoretical contribution, the practical contribution, and the study’s limitations. Lines 463–490 present broad implications, but the theoretical contribution remains weakly defined, and the limitations appear only marginally. The conclusion should close the article with three clear moves: state exactly what the study adds to the literature on agroindustrial supply chains and bioethanol, explain concretely how managers and policymakers can use the results, and clearly acknowledge the limitations related to the local scope, the database, and the model assumptions.

Author Response

Comments :

In the Introduction, lines 31–49 discuss corn bioethanol, emissions, and circularity, but they do so in a broad and weakly connected way. The section does not clearly establish the link between ethanol, supply chain design, smallholder integration, and the specific case of Sumenep. This section should be reorganized to show, in a logical sequence, why the supply chain is the central issue, why including smallholders is critical in this context, and why Sumenep represents a relevant case.

Response :

The transition toward low-carbon energy systems has intensified global interest in bioethanol as a renewable alternative to fossil fuels, particularly in the transportation sector [1]. Corn-based ethanol, supported by established agricultural systems and con-version technologies, offers significant potential to reduce greenhouse gas emissions while contributing to circular economy practices through the utilization of agricultural products [2]. Despite its environmental and technological feasibility, the sustainability of bioethanol production is not determined solely by feedstock availability or conversion efficiency, but critically depends on the design and performance of the supply chain [3]. Inefficiencies in feedstock collection, transportation, storage, and distribution can lead to increased costs, higher emissions, and reduced economic viability. As highlighted in prior studies, optimizing supply chain networks is essential to minimize logistical costs and carbon footprints while ensuring stable biomass supply for biofuel production [4]. Therefore, supply chain design emerges as the central issue in realizing the full sustainability potential of corn-based ethanol systems. Within this context, the role of smallholder farmers becomes particularly important, especially in developing countries such as Indonesia, where agricultural production is highly fragmented. Smallholders are often characterized by limited access to markets, infrastructure, and financial resources, which constrains their participation in emerging bioenergy value chains. Without deliberate integration into supply chain systems, these farmers risk being excluded from the economic benefits associated with bioethanol development [5]. Moreover, fragmented production structures increase transaction costs and com-plicate feedstock aggregation, further undermining supply chain efficiency. Previous research has shown that inclusive supply chain configurations such as farmer clustering, cooperative systems, and decentralized aggregation centers can improve coordination, reduce logistical inefficiencies, and enhance value capture at the farm level [6]. Consequently, integrating smallholders into supply chain design is not only a social imperative but also a technical requirement for ensuring reliable and cost-effective biomass supply. These challenges are particularly evident in Indonesia’s current energy transition strategy, which includes plans to expand ethanol blending in gasoline (E10) as part of its decarbonization and energy security agenda. While the country possesses significant potential feedstock resources, including corn, cassava, and sug-arcane, the existing supply chain infrastructure remains underdeveloped and region-ally fragmented [7,8]. This problem between resource potential and logistical capability highlights the need for localized, context-specific supply chain solutions. Sumenep Regency, located in Madura, represents a highly relevant case study in this regard. The region is characterized by a substantial corn production base alongside widespread rural poverty and limited market integration among smallholder farmers [9]. Many households face economic vulnerability due to low product prices, weak supply chain linkages, and restricted access to inputs and finance [10]. At the same time, the availability of agricultural feedstock presents an opportunity to connect local production systems with the emerging bioethanol market. Developing a simplified and decentral-ized supply chain model tailored to Sumenep is essential to facilitate between small-holder production and industrial bioethanol demand [11].

 

Comment:

In addition, although the gap and the proposal appear in lines 80–93, both remain too generic. The manuscript should state the central objective directly, in a single clear sentence, specifying what will be modeled, in which context, and with what expected outcome.

Response :

This study proposes a p-median–based optimization framework to redesign the corn supply chain in Sumenep, Indonesia, aiming to minimize logistical distances, enhance cost efficiency, and enable inclusive integration of smallholder farmers into sustainable bioethanol production systems.

Comments :

The manuscript moves directly from the Introduction to Materials and Methods without a short section that explains, in simple terms, the minimum concepts needed to understand the study, such as sustainable supply chains, the p-median model, clustering, and smallholder integration in agroindustrial chains. This omission weakens the article’s clarity, especially for readers who are not familiar with the topic. A concise theoretical background should be added, focusing only on the basic concepts required to understand the problem, the model, and the analytical logic of the study, while avoiding an excessively theoretical review.

Response :

Sustainable supply chain in this research refers to efficient coordination of bio-mass flows from production points to processing or aggregation centers while mini-mizing economic and environmental costs. In Sumenep, the dispersed nature of farmer groups and reliance on small-scale transportation create logistical inefficiencies, re-flected in long travel distances and high fuel consumption. Therefore, reducing trans-portation distance becomes a critical proxy for improving both cost efficiency and en-vironmental performance. The dataset used in this study includes inter-location dis-tances between farmer groups and potential aggregation points, which form the basis for evaluating supply chain performance before and after optimization. Term cluster-ing in this study is therefore used in an operational sense, referring to the grouping of farmer groups based on optimized allocation results rather than similarity measures. Each cluster represents a service area of an aggregation center, where farmer groups deliver their corn harvest. This distinction is important to clarify that the clusters are derived from an optimization framework rather than conventional data clustering al-gorithms such as k-means. In addition, smallholder integration is a key consideration in the design of the supply chain. The 385 farmer groups in Sumenep represent small-scale producers who typically face constraints related to transportation access, market connectivity, and cost efficiency. Without an optimized aggregation system, these constraints can lead to higher logistics costs and reduced participation in emerging bioethanol value chains. By minimizing travel distance and improving ac-cessibility to aggregation centers, the model directly supports the inclusion of small-holders in a more efficient and economically viable supply chain

Comments :

In the methodology, lines 96–123 report interviews, secondary data, Google Maps, and Matlab modeling, but they do not specify how many interviews were conducted, who the respondents were, how the data were validated, the exact data collection period, how the variables were operationalized, why the number of clusters was defined as it was, or which specific parameters fed the model. As a result, the study is still not replicable. The authors should rewrite the methodology in clear stages, reporting the source of each dataset, the selection criteria, data treatment procedures, model parameters, software used, validation logic, and the rationale behind the analytical decisions.

Response :
Model Formulation

To enhance clarity, the model is presented using a structured formulation consisting of indices, parameters, decision variables, an objective function, and constraints.

Indices

?∈?: Set of supply points (corn farmers)

?∈?: Set of candidate facility locations (collection centers)

Parameters

dij : Distance between supply point i and facility location ? (km)

p: Number of facilities to be selected

Decision Variables

???=1 if supply point ? is assigned to facility ?; 0 otherwise

??=1 if facility ? is selected; 0 otherwise

 

Objective Function

(1)

 

Assignment Constrain Function

(2)

Facility Selection Constraint

(3)

Binary Constrain

	(4)
	(5)

The equation uses Index with notation i as the farmer group and j as the center point of the group. Once the points of each cluster and cluster members are known, the next step is to determine the objective function and constraint function. This equation then used as the basis for creating a Matlab program to solve the farmer group clustering, similar to this research [20,21]. The next step is to verify the model that has been created by declaring each unit in the model and checking the final output results as desired. The following is the mathematical model

Objective function of breeders and farmer groups

(7)

Objective Function Fertilizer shops and farmer groups

(8)

Objective Functions of Farmer Groups and Agricultural Product Collectors

(9)

2.2 Model Interpretation in Supply Chain Context

The model minimizes total logistics effort by assigning each farmer to the nearest selected aggregation centre, weighted by production volume. This approach reflects real-world supply chain behaviour, where transportation cost is influenced by both distance and quantity transported. By optimizing facility locations, the model improves feedstock consolidation efficiency, reduces transportation burden, and enhances accessibility for smallholder farmers. Although the model focuses on logistical optimization, its outcomes also contribute indirectly to socio-economic improvements, such as reduced transportation costs for farmers and improved market access. However, infrastructure limitations (e.g., road conditions, storage capacity) and operational constraints are not explicitly modeled and are discussed as limitations of this study.

Comments

In the results section, the manuscript devotes too much space to describing tables and maps and too little to actual analysis. Lines 153–269 spend substantial space listing groups, areas, seeds, fertilizers, and warehouses, but the discussion still does not clearly identify which patterns truly matter for addressing the article’s objective. In several passages, the text simply repeats what the tables already show. The results should be synthesized more effectively, the operational description should be reduced, and the discussion should focus only on the findings that clarify the supply chain logic, cluster formation, and the economic and logistical effects of the proposed model.

Response :

3.9 Number of Facilities (p-Median Parameter)

A key parameter in the p-median model is the number of facilities (p), which de-termines how many aggregation centers are established within the supply chain net-work. In this study, a configuration of ?=15 facilities is adopted. This value is not arbi-trary but reflects a balance between logistical efficiency, spatial coverage, and practi-cal considerations related to regional conditions. From a logistical perspective, in-creasing the number of facilities reduces the average transportation distance between supply points and assigned centers, thereby lowering transportation costs. However, a larger number of facilities may lead to higher infrastructure and operational costs, as each facility requires investment in land, storage, and management. Conversely, a smaller number of facilities may reduce infrastructure costs but increase transporta-tion burden, particularly for geographically dispersed smallholder farmers. Therefore, the selection of ?=15 represents a compromise between these competing factors, en-suring reasonable accessibility for farmers while maintaining a manageable number of aggregation points.

In addition, the choice of p is informed by the spatial distribution of corn produc-tion in Sumenep Regency, where agricultural activities are dispersed across multiple sub-districts with varying production capacities. The selected number of facilities al-lows for effective clustering of production areas without excessively concentrating supply flows into a limited number of centers, which could create bottlenecks in logis-tics operations. To further evaluate the robustness of this assumption, a sensitivity analysis is conducted by varying the number of facilities across several scenarios such as p=10,12,15,18,20. The results indicate that while increasing ? consistently reduces total weighted distance, the marginal improvement diminishes beyond a certain threshold. In particular, the reduction in total transportation distance becomes less significant when p>15, suggesting diminishing returns in logistical efficiency. This finding supports the selection of ?=15 as a near-optimal solution that balances effi-ciency and practicality. It is important to note that the model does not explicitly in-corporate infrastructure capacity constraints or investment cost functions. Therefore, the selection of ? should be interpreted as a planning-level decision rather than a de-finitive operational recommendation. Future research could extend this analysis by in-tegrating facility cost structures, capacity limitations, and multi-objective optimization to determine the optimal number of facilities under both economic and socio-economic criteria.

3.10 Research Limitations

Although the clustering-based approach used in this study provides a practical framework for optimizing the location of corn aggregation centers, it should be acknowledged that clustering techniques represent a classical method commonly ap-plied in facility location and supply chain design problems. While the approach is effective in minimizing transportation distances and improving feedstock aggregation efficiency, the model assumes relatively stable supply and demand conditions. In re-al-world agricultural systems, however, supply chain stability can be affected by sev-eral external risk factors, including fluctuations in agricultural commodity prices, variations in crop yields, and disruptions caused by extreme weather events. These uncertainties may significantly influence feedstock availability, logistics costs, and the overall reliability of the bioethanol supply chain.

The people of Sumenep engage in the cultivation of corn during the dry season. The initiation of the planting season is invariably contingent on the involvement of community leaders, such as kyai (religious leaders) or klebun (village chief). The tran-sition to a clustering model will present its own challenges, as members have histori-cally based their planting decisions on the counsel of these leaders. In order to proceed, coordination with the local BABINSA (Village Supervisory Non-Commissioned Of-ficer) is required. The implementation of this process necessitates the coordination of various elements within the community.

3.11   Policy Implications for Sustainable Bioethanol Development in Sumenep

The clustering-based model demonstrates that more efficient feedstock aggrega-tion and transportation can increase farmer profits and strengthen the reliability of biomass supply for ethanol production. These results suggest that supply chain opti-mization is an important enabling factor for sustainable bioenergy development, par-ticularly in regions dominated by smallholder farmers. Efficient agricultural logistics systems have been widely recognized as a key component in improving the viability of biofuel supply chains and reducing operational costs [57]. From a policy perspective, the development of decentralized biomass aggregation centers should be prioritized to reduce transportation distances and improve feedstock consolidation. Strategic placement of such facilities can enhance supply reliability for ethanol processing in-dustries and support the implementation of ethanol blending policies. Strengthening farmer cooperatives and producer networks is also essential to improve coordination within the supply chain and increase farmers’ bargaining power in bioenergy markets [58]. Collective institutions have been shown to play a crucial role in improving mar-ket access and income stability among smallholder farmers involved in bioenergy feedstock production [59]. Integrating agricultural development policies with national renewable energy strategies will therefore be critical for Indonesia’s ethanol blending program. Such integrated approaches can simultaneously enhance rural livelihoods, improve energy security, and support greenhouse gas mitigation target [60].

Comment :

The manuscript also contains internal inconsistencies that undermine its credibility. In lines 161–162, the text refers to Table 1, but the discussion is actually based on Table 2. In lines 154–155, the data are presented as from March 2025, whereas lines 161–162 mention planting corn in 2024. In lines 214–235, the text states that Figure 3 shows the distribution of fertilizer stores, but the previous figure is associated with seed breeders, and the warehouse locations are later linked confusingly to Figure 4 and Figure 5. The authors should carefully review the numbering of tables and figures, the reference year of the data, and all cross-references, because the manuscript currently gives the impression of an incomplete assembly.

Response :

This study was conducted in March 2025; the data used is from 2024 because the harvest season had not yet begun in March 2025 and there was insufficient data for annually.

Comments :

The clustering section also requires stronger analytical rigor. In lines 271–294, the study obtains distances from interviews and checks them on Google Maps, but it does not clarify whether the criterion was actual distance, travel time, shortest route, or usual route. Then, lines 295–345 present the 15 clusters, but the text does not justify why 15 clusters were selected or provide any sensitivity test or comparison with alternative solutions. The authors should technically justify the number of clusters, explain the spatial criterion adopted, and show why the selected solution performs better than other possible configurations.

Response :
In addition, the choice of p is informed by the spatial distribution of corn produc-tion in Sumenep Regency, where agricultural activities are dispersed across multiple sub-districts with varying production capacities. The selected number of facilities al-lows for effective clustering of production areas without excessively concentrating supply flows into a limited number of centers, which could create bottlenecks in logis-tics operations. To further evaluate the robustness of this assumption, a sensitivity analysis is conducted by varying the number of facilities across several scenarios such as p=10,12,15,18,20. The results indicate that while increasing ? consistently reduces total weighted distance, the marginal improvement diminishes beyond a certain threshold. In particular, the reduction in total transportation distance becomes less significant when p>15, suggesting diminishing returns in logistical efficiency. This finding supports the selection of ?=15 as a near-optimal solution that balances effi-ciency and practicality. It is important to note that the model does not explicitly in-corporate infrastructure capacity constraints or investment cost functions. Therefore, the selection of ? should be interpreted as a planning-level decision rather than a de-finitive operational recommendation. Future research could extend this analysis by in-tegrating facility cost structures, capacity limitations, and multi-objective optimization to determine the optimal number of facilities under both economic and socio-economic criteria.

Comment :

In the Conclusion, the text reinforces the novelty and benefits of the model, but it still does not clearly separate the theoretical contribution, the practical contribution, and the study’s limitations. Lines 463–490 present broad implications, but the theoretical contribution remains weakly defined, and the limitations appear only marginally. The conclusion should close the article with three clear moves: state exactly what the study adds to the literature on agroindustrial supply chains and bioethanol, explain concretely how managers and policymakers can use the results, and clearly acknowledge the limitations related to the local scope, the database, and the model assumptions.

Response :

The findings highlight that clustering farmer groups and optimizing supply routes can increase farmer group profit by approximately 16%, demonstrating that supply chain design plays a crucial role in ensuring that value creation from bioetha-nol feedstock reaches smallholder farmers. This outcome supports previous studies emphasizing that efficient biofuel supply chains can simultaneously enhance economic viability and environmental sustainability by reducing logistical costs, improving feedstock aggregation efficiency, and lowering carbon emissions associated with transportation activities [61] [4]. From a broader sustainability perspective, the inte-gration of corn-based bioethanol production into regional agricultural systems offers multiple benefits. Bioethanol derived from biomass has been widely recognized as a renewable alternative to fossil fuels that can reduce greenhouse gas emissions and support national decarbonization strategies [54]. Furthermore, ethanol–gasoline blends have been shown to improve engine performance and reduce harmful emis-sions such as carbon monoxide and hydrocarbons, making bioethanol an important component of cleaner transportation system [55]. In the Indonesian context, the pro-posed supply-chain framework contributes to ongoing efforts to implement bioethanol blending policies, which aim to enhance national energy security while reducing de-pendence on imported fossil fuels. Studies assessing Indonesia’s readiness for etha-nol-blended fuels indicate that logistical infrastructure and feedstock supply chains are critical determinants of successful implementation [12]. Therefore, the decentral-ized clustering approach proposed in this study provides a practical mechanism for connecting smallholder farmers to emerging bioenergy markets while supporting in-clusive rural development.

 

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The revised manuscript has improved in several respects, particularly in terms of clarity of presentation and interpretation of results. However, I would like to draw the authors’ attention to a minor point concerning the formulation and labeling of the objective function.

In the current version, Equation (1), labeled as the objective function, appears to have the form of an assignment constraint (i.e., ensuring that each demand point is assigned to exactly one facility). As such, it may be clearer to distinguish explicitly between the objective function and the constraint set.

In optimization models, the objective function typically represents the criterion being minimized or maximized (e.g., total distance or transportation cost), while assignment conditions are formulated as constraints. To improve clarity and avoid potential confusion for readers, the authors may consider either (i) explicitly stating the objective function of the p-median model, or (ii) revising the labeling of Equation (1) so that it is not interpreted as the objective function.

This is a minor clarification, but addressing it would improve the methodological transparency of the paper.

Additionally, the attached cover letter (DOCX file) appears to contain manuscript content rather than a response to reviewers or a formal cover letter. This may be an unintended submission and could be verified.

Author Response

Please forgive us for the typographical error in "constrain function"; it should have been "Assignment Constraint Function." We would like to express our sincere apologies for this minor error.
With regard to the uploading of the paper in response to the reviewers, an error was also made on our part. This was due to the fact that this was the first time we had reached this stage.

Reviewer 2 Report

Comments and Suggestions for Authors

I would thank the authors' efforts to improve the manuscript. They can further consider the following points:

1. Figures 1 to 7 can still causing confusing for readers. One suggestion for enhance readability of the figures is to add remarks under the figure.

2. In Section 1, it would be better if the authors list their research questions in a separate paragraph. Based on that, in final section, the authors can answer the questions to enhance the readability.

3. There seems no Equation (6). Please check equation sequences.

 

Thanks!

Author Response

I would thank the authors' efforts to improve the manuscript. They can further consider the following points:

Comment 1:
Figures 1 to 7 can still causing confusing for readers. One suggestion for enhance readability of the figures is to add remarks under the figure.
Response 1: We already labeling each point 

2. In Section 1, it would be better if the authors list their research questions in a separate paragraph. Based on that, in final section, the authors can answer the questions to enhance the readability.

Although there the proliferation of research endeavors concerning bioethanol production and renewable fuel policies, a plethora of significant research opportunity to observe. Firstly, the majority of studies on bioethanol supply chains have focused on large-scale industrial systems or national policy perspectives. However, there has been limited attention paid to feedstock supply-chain structures at the smallholder level, particularly in developing countries. Republic of Indonesia has explored the implementation of ethanol blending policies as part of its renewable energy transition, empirical studies examining regional feedstock logistics and farmer participation in ethanol supply chains are still scarce. Previous research rarely integrates facility location optimization with farmer clustering approaches to design efficient biomass aggregation systems that can simultaneously improve logistical efficiency and farmer income. Agricultural production in Sumenep Regency is dominated by smallholder farmers with fragmented supply structures, which poses a significant challenge to the efficient collection of feedstock for bioethanol development. In the absence of a meticulously designed supply chain, the capacity of corn-based ethanol to facilitate both the attainment of renewable energy objectives and the advancement of rural economic development may not be fully actualized. The objective of this study is to develop a model for optimizing the supply chain for corn. This will be achieved by employing a p-median clustering approach, with the aim of improving the efficiency of aggregation of feedstock and supporting sustainable ethanol production. The integration of supply-chain optimisation with rural agricultural systems is a novel contribution to the existing literature on sustainable bioenergy systems and inclusive rural development. The conceptual framework of this study is based on the interaction between agricultural production systems, supply-chain logistics, and renewable energy development. Corn production from farmer groups represents the primary feedstock source for bioethanol production. However, fragmented production and inefficient logistics often lead to high transportation costs and supply instability. To address this issue, the study proposes a cluster-based supply-chain structure, where farmer groups are aggregated into clusters and connected to optimal collection points using the p-median optimization model. This approach minimizes transportation distance while improving coordination between supply-chain actors. The optimized network then supports ethanol production facilities, which convert corn feedstock into bioethanol for blended fuel applications. Through this framework, the research evaluates how optimized logistics networks can improve supply-chain efficiency, increase farmer profitability, and support renewable energy targets simultaneously.

Nevertheless, such approaches may fail to consider critical socio-economic factors, including income distribution, market accessibility, and the inclusion of marginalized rural producers. Within the context of Sumenep Regency, where agricultural production is characterized by fragmented smallholder systems and limited access to formal markets, supply chain design plays a dual role; as a logistical mechanism, but also as a tool for rural economic empowerment. The proposed clustering-based supply chain model contributes to this integration by improving the spatial organization of farmer groups, thereby reducing transportation barriers and enhancing their access to centralized aggregation points. The establishment of connections between smallholders and the participation of these actors in structured value chains has the potential to enhance their bargaining power and income stability. Moreover, by reducing logistical inefficiencies, the model helps ensure that a greater share of value creation is retained at the farm level, addressing common issues of value leakage in traditional agricultural supply chains. These outcomes are consistent with broader findings in sustainable supply chain management, which emphasize that efficiency improvements should be aligned with equitable value distribution and inclusive development goals. it is important to acknowledge that the current model integrates socio-economic aspects implicitly through economic performance indicators, such as increased farmer profit and improved market access, rather than explicitly incorporating social variables into the optimization function.

3. There seems no Equation (6). Please check equation sequences.
We already revise it

 

Thanks!

Reviewer 3 Report

Comments and Suggestions for Authors

Authors have incorporated all the raised concerns.

Author Response

Thank you for your feedback; it’s very helpful in improving the quality of our articles. We wish you all the best.

Reviewer 4 Report

Comments and Suggestions for Authors

accept

Author Response

Thank you for your feedback; it’s very helpful in improving the quality of our articles. We wish you all the best.

Reviewer 5 Report

Comments and Suggestions for Authors

Thank you forproviding the improved version.

Author Response

We are grateful for your feedback; it is instrumental in enhancing the quality of our articles. We extend our best wishes to you.