Ilton Curty Leal Junior
Fluminense Federal University - UFF, Niterói, RJ, Brazil.
Vitória Palmeira Maia
Fluminense Federal University - UFF, Niterói, RJ, Brazil.
Ricardo Cesar da Silva Guabiroba
Fluminense Federal University - UFF, Niterói, RJ, Brazil.
Ualison Rébula de Oliveira
Fluminense Federal University - UFF, Niterói, RJ, Brazil.
Highlights: (1) In Brazil, the addition of pure biodiesel (B100) to diesel oil is mandatory. (2) Waste cooking oil is one of the possible feedstocks for B100. (3) Uncontrolled costs and risks of collection activities make waste cooking oil less competitive. (4) Actions can be implemented to improve the competitiveness of waste cooking oil. Aim: Identify and compare the costs and risks of two modalities of the waste cooking oil reverse chain, known as door-to-door collection, and collection point utilization. Methodology: Data concerning the year 2020 of two waste cooking oil collecting organizations in the municipality of Volta Redonda-RJ were collected by means of interviews and documental research. After the data survey, it was possible to map the stages of the reverse chain of the two companies and compare the results obtained regarding costs and risks in both modalities. The Supply Chain Risk Management - SCRM methodology was used to support data analysis. Finally, actions were proposed to improve results based on a bibliographical survey. Results: Results show that the most risky and costly stage of the reverse chain is that of transportation and collection for both modalities, if compared to the other stages analyzed, identifying a greater need for the implementation of improvement actions. It was also possible to verify that, for some stages, simple control actions can impact the risks and costs identified. Research limitations: The analysis considered only a part of the waste cooking oil reverse chain from a given geographic region and considered only two companies. The other limitation was data collection restricted to the year 2020 and interviews conducted with a non-probabilistic sample. Thus, the results obtained in this study cannot be generalized. Practical Implications: Considering the study performed, it is possible to set up a workflow of the researched organizations so as to assist their managers in the analysis and measurement of their risks and costs. The results can also help waste cooking oil collecting companies in implementing actions that can potentially contribute to improving the performance of their operations. Originality/value: This research contributes to the area of operations, as it raises the main risks and cost ranges per stage of the reverse chains studied, based on the reality of small and medium-sized companies in a region, considering the limitations of financial and human capital resources of organizations operating in the segment. Additionally, the research points out ways for these organizations that have relevant performance from the economic, environmental, and social point of view.
Keywords: Reverse chain; Waste cooking oil; Risk analysis; SCRM.
According to the National Agency of Petroleum, Natural Gas, and Biofuels, ANP, (2019), the equivalent of 161,774,593 m³ of oil and 5,901,104 m³ of biofuel were produced in Brazil. In 2008, the mixture of pure biodiesel (B100) to diesel oil became mandatory, and as of September 2019, this mixture became 11% by volume, as stipulated by Law 13,263/2016, changing its consumption, which grew 25.7% (BEN, 2019).
In the case of biofuels, the supply chain plays a significant role by connecting different parts of the process, establishing a flow between geographically dispersed regions, and by generally having a significant cost (Santos and Ribeiro, 2013).
For Trkman and McCormack (2009), a supply chain that is resilient and potentially adaptable to changes arising from external means, such as changes in the dynamics between businesses and environmental issues, can give competitive advantage to the organization that owns it. A supply chain with such characteristics is essential to ensure the flow of its operations and, consequently, a good performance.
A poorly managed supply chain, in terms of risks, can lead to recurrent or isolated losses and costly processes, due to risks that have a considerable degree of predictability, but are not controlled (Hunt et al., 2010).
In this context, the analysis of the stages of preparation, transportation, collection, and storage of waste cooking oil, along with the survey of risks, costs, and suggestions for actions to control them, can be economically useful for organizations that are present in the area and justify the scientific exploration of the theme. In view of the above, the present study is based on the following questions to represent the research problem: (1) Given the considerable growth of biodiesel consumption, how can we contribute to improve the reverse logistics chain of waste cooking oil as a raw material, considering the associated risks and costs? (2) Which stages represent the greatest risk and cost in the process of waste cooking oil reverse logistics?
To answer the questions of the research problem, the main objective was to identify and compare the costs and risks of two modalities of the waste cooking oil reverse chain: door to door (where the collector goes to the generator) and the use of a collection site (where the generator goes to a predetermined collection point). To this end, the stages of the waste cooking oil reverse chain performed by two small and medium-sized collection companies, operating in the city of Volta Redonda-RJ, were analyzed. The specific objectives of this study are (1) to raise studies on supply chains from the point of view of risks and costs; and (2) to suggest actions that can contribute to the improvement of the performance of the reverse chain of waste cooking oil.
To improve the form of risk management in the supply chain in the case of residual oil, the research is based on Supply Chain Risk Management (SCRM) in four stages to identify, assess, mitigate, and control the possible risks present in the chain (Rangel et al., 2014). This research is justified by the importance of the topic for organizations and society, and because it aims to present a contribution to the reverse chain of waste, including the proposal of actions aimed at reducing and/or mitigating the risks present and, consequently, their costs. This study contributes to increasing the competitive potential of waste cooking oil as a raw material for biofuel production.
As a research delimitation, only the preparation, transportation, collection, and storage operations within the entire biodiesel production process were considered. Furthermore, the research was limited to the average costs and risks raised specifically in the southern region of the state of Rio de Janeiro.
Based on this introduction, this study is organized as follows: Section 2 presents the concepts and studies that were the basis for this research; Section 3 presents the procedures for conducting the research, followed by Section 4, the application, analysis, and discussion of results; in Section 5, the final considerations are made and, finally, the list of references used in the work is presented.
In this section, a literature survey was conducted on waste cooking oil for biodiesel production, reverse supply chain assessment, and Supply Chain Risk Management.
Waste cooking oil, once used, becomes an unwanted residue and its recycling as an alternative biofuel would not only remove a pollutant from the environment, but would also allow the generation of an alternative energy source. Thus, two basic needs would be met at once (Pasqualetto and Barbosa, 2008).
As disclosed by the National Agency of Petroleum, Natural Gas, and Biofuels (ANP) in 2019, the liter of diesel oil came out of the refinery for R$1.98, on average, and B100 of the producer came out for R$3.80, i.e., almost double. This significant difference directly impacts the competitiveness of the products in the market.
Biodiesel is a generic denomination for fuels derived from renewable sources, such as vegetable oils (soybean, corn, dendê palm, castor, palm, etc.) and animal fat (beef tallow, and chicken fat) (Holland, 2004). According to ANP (2019), soybean oil represents the main raw material for biodiesel production in Brazil (68.33% of the total). In second place are others, such as palm oil, peanut oil, turnip rape oil, sunflower oil, castor oil, sesame oil, used cooking oil and other fatty materials (16.48%); and in third place, there is beef fat (14.06%).
Biodiesel replaces, totally or partially, diesel from fossil origin in trucks, tractors, vans, and automobile engines, and can be used pure or added to diesel oil in several proportions (Brandão et al., 2009). According to Costa Neto et al. (2000), in the economic aspect, biodiesel becomes competitive as it complements all the new diesel technologies with similar performance and without the need to install an infrastructure or training policy.
In relation to biodiesel production, it is also important to consider some issues, such as the need for some technological adaptations and measures to reduce production costs. According to specialists, to reduce these costs, the use of residual cooking oil has been pointed out as one of the alternatives, since this residue is commonly discarded. However, there are costs to collect and reduce the acidity of this oil so that it can be transformed into biodiesel (Costa Neto et al., 2000).
Residual cooking oil comes from the consumption of refined virgin edible vegetable oils. After this consumption, there are basically two possible destinations for this material: (1) sanitary sewage, causing water pollution and costing its treatment, and (2) processors that transform the residue into a new product. In this context, it would be desirable to forward the residue to processors, such as biodiesel plants. For this, the residual cooking oil needs to be collected (Guabiroba and D'Agosto, 2011).
After use, the material is collected and filtered to remove possible solid residues and then stored in containers that prevent spillage. Afterwards, it is directed to ecopoints, recycling cooperatives, or specialized companies, which receive the product and process it, separating the vegetable oil from the glycerin. In this context, the oil is commonly sold for biodiesel production, and the glycerin (byproduct of the biodiesel production process) is used to make soap or meal for animal feed (ECOLEO, 2016).
However, the residual cooking oil is commonly found dispersed in urban areas, which can make the collection cost so high as to compromise biodiesel production, especially when this operation is done in a disorganized and unplanned manner. For this, it is necessary to verify whether the cost of this collection in urban areas makes it more expensive than virgin vegetable oil for biodiesel production (Guabiroba and D'Agosto, 2011).
To better understand the theme, Chart 1 was created, based on bibliographic research that shows a compilation of studies on Supply Chain and/or Supply Chain Risk Management (SCRM). They were classified and analyzed by the cases that presented practical application, by material transported, methods used for analysis, their classification as to the type of logistics used, whether direct or reverse, and, if there were any, the identification of improvement actions or enhancements in the chain studied.
Chart 1. Improvement actions and methods
Source: The authors themselves
On the methodological issues for the analysis of the chains addressed, it is observed that the studies use literature review. In some cases, the studies use techniques specifically related to the supply chain such as SCM in Seuring and Gold (2012) and Dias et al. (2020), and SCRM in Dias et al. (2019). Pouriani et al. (2019) propose a solid waste management network from the public initiative as a solution for solid waste disposal, while the other works focus on improvement aspects of existing networks and/or literature survey.
It is also noted that Dias et al. (2020) focus on identifying risks and response actions commonly used in a supply chain of a Brazilian automotive industry, having the use of a chain management tool (SCRM) as the centerpiece of the study while authors such as Carvalho et al. (2013), despite also conducting the study of a supply chain, make use of a case study analysis without a specific tool.
It is also noticeable that, among the articles analyzed and selected during the literature survey, semi-structured interviews are widely used in the construction of the studies, as in Soto-Silva et al. (2016). Their main role is to bring the research as close as possible to the practical reality experienced by professionals who perform activities pertinent to the theme. The method used by Dias et al. (2020) in their study on risk analysis in supply chains presents the development of a risk matrix through the classification of risk points raised by the application of questionnaires and interviews.
For the improvement actions identified in the studies broken down in Table 1, actions aimed at strategically establishing routes as a way to reduce costs stand out and are observed in Pouriani et al. (2019) and Guabiroba and D'Agosto (2011). In Carvalho et al. (2013), investment in agent training and technological tools are cited as a way to promote improvement in the chain's operation and management activities.
The identified action alternatives are geared towards creating communication links between stakeholders, as noted in Dias et al. (2019) and Almeida et al. (2017). As the only work analyzed that proposes an improvement action starting from a public initiative, Zhang et al. (2014) cite the creation of tax incentives for biofuel companies that join partnerships with recycling agents or specialized organizations as a way to increase the dynamism in the waste cooking oil market aimed at biodiesel production.
Aspects such as standardization of analysis and monitoring are present in the improvement actions identified in Almeida et al. (2017) and Luna et al. (2014), respectively.
To carry out this research, the stages and sub-stages in Chart 2 were established.
Chart 2. Stages and sub-stages of the methodological procedures used in the research
Source: The authors themselves
It is important to emphasize that, for this study, two waste cooking oil collection modalities were considered in order to provide a comparative analysis and, consequently, their risk points and costs. This choice was based on observations of collection models analyzed in the works of Guabiroba and D'Agosto (2011) and Pouriani et al. (2019).
The models analyzed were the door-to-door model, in which the collection agent goes to the generator agent, and the collection point model, where the generator agent has pre-defined points for the disposal of the waste to be collected by the collection agent.
For the initial part of this paper, literature and documentary research was conducted. A bibliographic search of relevant articles was conducted using the Web of Science™ database, filtered by subject, considering the title, abstract, and keywords. This literature search (Stage 1) contributed to broadening knowledge and definition of concepts about: (1) supply chain; (2) risk management method, using the SCRM tool; and (3) proposal of actions related to the reduction or extinction of costs and/or risks in the supply chain.
To build Chart 1 of the theoretical framework (Sub-stages 1.1 and 1.2), research papers were gathered that addressed the supply chain theme and used risk or cost analysis tools and/or provided proposals for improvement actions with potential replicability for the residual cooking oil reverse chain.
Moving on to Stage 2, the measurement and classification forms for the risks were developed, based on the analysis method adopted by Dias et al. (2020), in which scores are related from 1 to 5 for two variables, and their respective interactions generate a final score, which is related to a classification.
For the analysis, two variables were defined to be measured: (1) the first is the probability of occurrence of each risk by assigning a score from 1 to 5, where 5 = daily; 4 = very frequent; 3 = frequent; 2 = infrequent; and 1 = never happened; (2) the second variable determined was the impact generated for the chain in the event that that risk occurs, by assigning a score, where 5 = critical; 4 = high; 3 = moderate; 2 = low; and 1 = negligible.
As developed by Dias et al. (2020), the interaction between the scores of two analyzed variables results in a matrix, generating a final score for a given risk and its consequent classification. The combination of the probability and impact scores gave rise to the matrix of risk points to be prioritized, which is an easy way to visualize and guide managers on which points should be prioritized or postponed in case of limitations of financial resources and/or available time. The result of this interaction in this study is presented in matrix form in Table 1.
Table 1. Matrix of priority on attackable risk points
Source: Elaborated from Dias et al. (2020)
From the construction of the matrix, it is possible to classify each risk found according to the score obtained, as shown in Table 2.
Table 2. Risk classification.
Source: Elaborated from Dias et al. (2020)
Two managers responsible for the operational area of two waste cooking oil collecting companies, operating in the city of Volta Redonda-RJ, were interviewed. During the interviews, the managers identified the stages executed by their organizations within the waste cooking oil supply chain, with each stage designed according to the peculiarities of the process observed in each model. Subsequently, respondents defined cost ranges in percentages for each defined stage. The three proposed stages were detailed based on the reality of the organizations studied and on influential factors in the structuring of their reverse channels (Sub-stage 3.1).
Subsequently, according to the definition of Rangel et al. (2014), the application of the 4-phase SCRM was initiated, as presented in Figure 1.
Figure 1. Stages of the study application
Source: The authors themselves
Besides the individual risk classification, in order to aggregate for the discussion established in the comparative analysis section, general classifications were established for each of the stages, resulting from the interaction between the scores obtained by the risks, producing a numerical result, in which the higher the score obtained, the riskier the stage is considered, thus building a risk hierarchy among the stages.
For this classification, the individual scores of each risk in their respective stages were added up, thus generating an overall score, as shown in Equation 1.
In which:
Cr: risk classification;
X: risk probability, determined by its score set according to the criteria in the risk score priority matrix (Table 1);
Y: impact, also established according to the score given in Table 1.
For data organization (Stage 4), the information and data gathered through the bibliographical survey, interviews, and questionnaires are schematized as presented in Tables 3 and 4.
Through the suggestion of mitigation and control actions (Stage 5), in which alternatives are proposed to reduce or extinguish the identified risks, the last two SCRM stages were applied according to Rangel et al. (2014). As pointed out in Hallikas (2004), these stages depend directly on the previous stages of the SCRM.
For the proposals of improvement actions, the issues and/or suggestions for improvement cited as potential in previous works were used as a basis and were raised in the theoretical framework (Chart 1) through bibliographic research, according to Sub-stage 1.1.
Finally, Stage 6 is dedicated to the comparative analysis between the collection modalities analyzed in this study, considering the information gathered and the research limitations.
This section presents the information gathered during the research and, subsequently, its analysis in a comparative manner in order to understand which modality presents the greatest risk, weight relative to total cost, and which stages are decisive for this finding, considering the percentage of cost per stage and their respective classifications as to the degree of risk presented.
After understanding the concepts, a semi-structured questionnaire was set up that sought to understand the steps, peculiarities, and processes contemplated in the activities performed within the reverse chain of the material by the organizations, besides the potential risks observed and/or experienced by the managers and those responsible. The questionnaire was answered by an operations manager and a general manager from different organizations that work directly in the waste cooking oil reverse chain for the generation of new products such as biodiesel. Their experience in the function varies from 4 to 9 years.
The results found by the research are organized in two tables, the first referring to the door-to-door collection model (Table 3) and the second, to the collection point (Table 4).
Table 3 presents the structure and description of the stages of the waste cooking oil reverse logistics, adopting the door-to-door collection mode used by organization A, in which the collection unit travels according to the demand of the generating unit. In this modality, collection focuses on domestic waste generated by family homes.
Source: The authors themselves
As raised in the interviews and described in Table 3, the route definition process in the preparation stage uses the volumetric history and calls as determining criteria. Although relevant, when considered in isolation, the criteria used lose efficiency precisely because they do not consider other variables that can be crucial for improving performance and reducing potential costs, such as the distance traveled and the intersection between routes.
It is observed that the stage of transport and collection corresponds to the most significant percentage in relation to the total cost attributed to this modality, highlighting the relevance of this stage in the chain and, consequently, the need for mitigation and control actions to reduce costs in the logistics process in general. This observation was also verified by Demirbas (2009) in his studies.
As far as the storage stage is concerned, the treatment process is incorporated into the stage as a kind of guarantor of the integrity of the collected material. In some cases, it is possible to recover the waste contaminated by other materials, such as water and debris.
As for the risks identified in this collection modality, three items were raised by the interviewees for each stage. One can see that the preparation stage has the lowest representativeness of the chain and presents varied risk classifications, highlighting the risk of an outdated route, which was assessed as an extreme risk due to its high probability of occurrence and high impact on the chain. The importance of efficient routing for a sustainable final cost to the activity was evidenced in Guabiroba and D'Agosto (2011).
To structure the chain, following the collection point mode, Table 4 was constructed.
Table 4. Waste cooking oil supply chain in the collection point model
Source: The authors
The preparation stage adopted in this modality contemplates, in addition to registration, the collection and issuance of documentation, along with some bureaucratic processes due to the partnership with firms that demand the delivery of disposal certifications. This peculiarity impacts on the demand for more qualified personnel and, consequently, on the stage costs.
As for the risks raised in the collection and transportation stage, the risk of cargo and/or container theft was identified. This risk is mentioned only in the collection point modality, offering a risk classified as high, while the accident along the way is present in both modalities.
Departing for a comparative analysis of the results found in the two modalities, one can notice points of similarity and significant divergences due to the particularities observed.
In terms of processes, it is possible to observe greater similarity in the storage stage, considering that both modalities follow similar protocols, except for a slight variation in the representativeness of the cost percentages, which can be attributed to the outsourcing of the laboratory analysis of the waste collected in the door-to-door collection modality. Due to process similarity, the risks and classifications are the same. The preparation stage is distinguished in relation to the adoption of distinct processes, such as routing according to a collection schedule and pre-registration of partners in the preparation stage. Thus, it is possible to draw a parallel between the adoption of this process and the increase in representativity, in terms of cost of this stage compared to the same stage in the door-to-door modality, in which this process is not incorporated.
Considering the percentage of representativity in terms of costs, it is possible to identify, both in the door-to-door and at the collection point modality, the collection and transportation stage as the most representative among the three analyzed, even though they adopt similar processes in relation to the previous stage and the collection modality used. As noted in a prior study by Santos and Ribeiro (2013), transportation is the primary stage within the logistics process since, besides having higher costs, it is responsible for interconnecting different geographical spaces and meeting inter-regional demands.
As to the risk points identified by the interviewees, it is intuitive to associate them with the processes defined as pertinent to each stage analyzed, with risks directly linked to the technical aspects of each modality. As an example of the previous statement, container theft is a risk identified in the collection point chain, since this model uses its own container for transportation, while in the door-to-door chain, the container used for transportation comes from third parties.
A higher concentration of risks classified as extreme and/or high can be seen in the collection and transportation stage. This occurs both in the door-to-door modality and at the collection point, with scores of 46 and 42, respectively. This observation is potentially linked to the fact that this stage also presents the highest percentages of costs.
In the overall risk score obtained at each stage, both modalities present the same sequence of positions, with the collection and transportation stage being the riskiest, achieving the highest score, followed by the storage stage and the preparation stage, considered the least risky, scoring 18 and 30 for door-to-door and collection point modality, respectively.
For the proposed improvement actions, suggestions and issues addressed in previous works were adapted, as seen in Chart 1. They include programmed circulation and routing for the door-to-door collection model, as cited in Guabiroba and D'Agosto (2011), in addition to the investment in training and qualification addressed in Carvalho et al. (2013).
Through the classification of the risks raised during the research, it is possible to identify those that present the greatest need for mitigation and control. By establishing priorities for the development of possible improvement projects, it is possible to guide the managers in the decision-making process, as they were oriented towards actions that present feasibility, practical benefits, cost reduction, and, hence, are able to contribute, in part, to the competitiveness of waste cooking oil as raw material for biodiesel production.
Throughout the study, with the application of the SCRM tool and the analysis method adopted, the general goal of the study to identify and compare the costs and risks of two modes of the waste cooking oil reverse chain was achieved. It was also possible to identify the points that represent the greatest risks and costs for each stage in the chain. From the understanding of the concepts and the survey of literature on the subject, it was possible to achieve specific objectives 1 and 2, the latter being related to the establishment of proposed improvement actions aimed at risk mitigation and control.
Thus, in general, the aims of the study were achieved, and it was possible to analyze the stages of the waste cooking oil reverse chain contemplated by the collecting organizations, identify risk points, and propose control and mitigation actions through the application of SCRM, which is a risk management technique in chains and can be applied both in direct and reverse chains.
The research results indicated the stages included in the waste cooking oil reverse chain, their respective cost and risk percentage ranges, and the classification of each risk according to the analysis of its likelihood of occurrence and impact generated, concluding that the riskiest stage, among those analyzed, is collection and transportation, as pointed out by Guabiroba and D'Agosto (2011) and Santos and Ribeiro (2013). It should be noted that this conclusion is for the case under study. In the case of transportation cost, this can represent a smaller percentage in relation to the other preparation and storage steps. This cost can be lower if non-motorized transport mode is used and if the waste cooking oil processing point is located close to the generating sites.
As a theoretical contribution to the area, works related to the theme were surveyed, pointing out the main risks and costs per stage and the possible analysis forms. As a practical contribution, the study suggests improvement actions addressed in previous studies, considering the limitations of financial and human capital resources of organizations operating in the segment.
In terms of study limitations, the analysis was based on the interview with only two respondents, whose perspectives are based on individual experiences and, therefore, subject to their opinion as to the risk criteria presented. There is also a geographical limitation, focusing on southern Rio de Janeiro state, a limitation regarding the costs analyzed, and the focus is only on the stages of the waste cooking oil reverse logistics chain executed by the collecting organizations and not on the chain as a whole, thus providing proposals for improvement actions and risk assessment limited to this universe.
As opportunities for future research, since the waste cooking oil is one among other possibilities of raw material for biodiesel production, it is proposed to analyze the other chains of competing materials in order to obtain results for comparative purposes. We also propose a survey of replicable actions of more competitive chains in the residual cooking oil chain. Another possibility is to examine the chain after the implementation of the proposed improvement actions through simulation and/or practical application, thus establishing a parallel between efficient actions in practice and in theory.
Almeida, A. V. C.; Melo, I. M.; Pinheiro, I. S.; Freitas, J. F.; Melo, A. C. S. (2017), ‘“Appreciation of acai core of a pulp producer from Ananindeua/PA: proposal of reverse channel structure oriented by NPSW and reverse logistics’”, Revista Gestão da Produção, Operações e Sistemas, [s.l.], vol. 12, No. 4, pp. 59-83, disponível em: http://dx.doi.org/10.15675/gepros.v12i3.1668> (acesso em 05 dez. 2019).
ANP – Agência Nacional do Petróleo, Gás Natural e Biocombustíveis (2019), Dados estatísticos 2019, disponível em: http://www.anp.gov.br/dados-estatisticos (acesso em 05 dez. 2019).
BEN - Balanço Energético Nacional (2019), Relatório Executivo, disponível em: http://epe.gov.br/sites-pt/publicacoes-dados-abertos/publicacoes/PublicacoesArquivos/publicacao-377/topico-470/Relat%C3%B3rio%20S%C3%ADntese%20BEN%202019%20Ano%20Base%202018.pdf (acesso em 10 fev. 2021)
Brandão, A. K.; Zordan, P. F. P.; Vendrame, F. C.; Sarraceni. J. M.; Vendrame, M. C. R. (2009), “Biodiesel como fonte de energia renovável”, artigo apresentado no II Encontro Científico Unisalesiano, Lins, SP, 28-31 de jun. 2009.
Brandenburg, M.; Govindan, K.; Sarkis, J.; Seuring, S. (2019), “Quantitative models for sustainable supply chain management: Developments and directions”, European Journal of Operational Research, vol. 233, Issue 2, pp. 299-312, disponível em: http://www.sciencedirect.com/science/article/pii/S037722171300787X (acesso em 05 dez. 2019).
Carvalho, K. L.; Costa, R. P.; Souza, R. C. (2013), “Gestão estratégica dos relacionamentos na cadeia de suprimentos da alface”, Production, [s.l.], vol. 24, No. 2, pp. 271-282, disponível em: http://dx.doi.org/10.1590/s0103-65132013005000031 (acesso em 15 jan. 2020).
Castellanelli, C. A. (2008), Estudo da viabilidade de produção do biodiesel, obtido através do óleo de cozinha usado, na cidade de Santa Maria – RS, Dissertação de Mestrado em Engenharia de Produção, Universidade Federal de Santa Maria, Santa Maria, RS.
Costa Neto, P. R.; Rossi, L. F. S.; Zagonel, G. F.; Ramos, L. Z. P. (2000), “Produção de Biocombustível alternativo ao óleo diesel através da transesterificação de óleo de soja usado em cozinhas”. Quím., vol. 23, No. 4, pp. 531-537. ISSN 0100-4042.
Demirbas, A. (2009), “Biodiesel from waste cooking oil via base-catalytic and supercritical methanol transesterification”, Energy Conversion and Management, vol. 50, pp. 923–927.
Dias, G. C.; Cesar, A. S.; Oliveira, U. R. (2017), “Supply Chain Risk Management (SCRM): uma revisão sistemática de literatura”, Revista Brasileira de Administração Científica, vol.8, No.2, pp. 190-207.
Dias, G. C.; Hernandez, C. T.; Oliveira, U. R. (2020), “Supply chain risk management and risk ranking in the automotive industry”, Gestão & Produção, 27(1), e3800, disponível em: https://doi.org/10.1590/0104-530X3800-20 (acesso em 3 abr. 2021).
Dias, G. C.; Leal Junior, I. C.; Oliveira, U. R. (2019), “Supply chain risk management at seaport container terminals”, Gestão e Produção, São Carlos, vol. 26, No. 3, e 4900, Disponível em http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2019000300218&lng=en&nrm=iso (acesso em 3 abr. 2021).
Dubey R.; Gunasekaran A.; Shibin, K. T. (2017), “Explaining sustainable supply chain performance using a total interpretive structural modeling approach” Sustainable Production and Consumption, vol. 12, pp. 104-118, Disponível em: https://www.sciencedirect.com/science/article/pii/S2352550917300210?casa_token=Bne65ttNAvEAAAAA:DDpWmiRv0Gpc1gpD8k_HBRqo4My6kXvy8SGQcYTZ-Ddt1HfrXF3xZcIHRRUieoPP8kcL4Z0JKQ (acesso em 20 jul. 2019).
ECOLEO – Associação Brasileira para Sensibilização, Coleta e Reciclagem de Resíduos de Óleo Comestível (2016), Cartilha da Associação Brasileira para Sensibilização, Coleta e Reciclagem de Óleo Comestível (ECOLEO). Disponível em: http://ecoleo.org.br/ (acesso em 14 nov. 2016).
Guabiroba, R. C. S.; D’Agosto, M. A. (2011), “O impacto do custo de coleta do óleo residual de cozinha disperso em áreas urbanas no custo total de produção de biodiesel – Estudo De Caso”, Transportes, [S. l.], vol. 19, No. 1, pp. 59–67, Disponível em: https://revistatransportes.org.br/anpet/article/view/372 (acesso em 20 jul. 2019).
Guabiroba, R. C. S.; D'Agosto M. A.; Leal Junior, I. C.; Silva, M. A. V. (2014), “Eco-efficiency as an auxiliary measure for the definition of interregional public consortia responsible for the collection of recyclable domestic waste”, Journal of Cleaner Production, Vol. 68, pp. 36-45
HalliIkas, J. (2004), “Risk management processes in supplier networks”. International Journal of Production Economics, vol.90, pp.47-58.
Holanda, A. (2004), “Biodiesel e inclusão social”, Série cadernos de altos estudos Câmara dos Deputados. Coordenação de Publicações, Brasília, DF, No. 1, 200 p., Disponível em https://www2.camara.leg.br/a-camara/estruturaadm/altosestudos/pdf/biodiesel-e-inclusao-social/biodiesel-e-inclusao-social (acesso em acesso em 20 jul. 2019)
Hunt, G. T. M.; Craighead, C. W.; & Ketchen, D. J. (2010), “Risk uncertainty and supply chain decisions: a real options perspective”, Decision Sciences, Vol. 41, No. 3, pp. 435-458, Disponível em: http://dx.doi.org/10.1111/j.1540-5915.2010.00276.x (acesso em 05 dez. 2019).
Jin, H.; Liu, S.; Liu, C.; Udawatta, N. (2019), "Optimizing the concession period of PPP projects for fair allocation of financial risk", Engineering, Construction and Architectural Management, Vol. 26 No. 10, pp. 2347-2363, Disponível em https://doi.org/10.1108/ECAM-05-2018-0201 (acesso em 15 jan. 2021).
Lopes, J. M.; Leal Junior, I. C.; Guimaraes, V. A. (2016), “Impact of modal choice in energy consumption and carbon dioxide emissions: analysis of brazilian bioethanol supply chain”, Brazilian Journal of Operations and Production Management, vol. 13, pp. 138-148.
Luna, R. A.; Sousa Filho, J. M. Viana, F. L. E. (2014), “Análise da gestão de logística reversa de vasilhames de vidro em uma empresa de bebidas alcoólicas”, GEPROS - Gestão da Produção, Operações e Sistemas, Vol. 9, No 4, pp. 77-89. 2014.
Miller, P.; De Barros, A. G.; Kattan, L.; Wirasinghe, S. C. (2016), “Analyzing the sustainability performance of public transit”, Transportation Research Part D: Transport and Environment, Vol. 44, pp. 177-198.
Pasqualetto, A.; Barbosa, G. N. (2008), “Aproveitamento do óleo residual de cozinha na produção de biodiesel”, XXXI Congresso Interamericano AIDIS, Santiago, Chile, 12-15 de outubro 2008.
Pereira, H.N.; Luna, M. M. M.; Nunes, I. M. L.; Fook, A. V. L. (2017), “As atividades da logística reversa e a cadeia de suprimentos do papel para embalagem”, XVI ENGEMA - Encontro internacional sobre gestão empresarial e meio ambiente, São Paulo, SP, 1-3 de dezembro 2014. Disponível em: https://www.researchgate.net/publication/317545539_Encontro_Internacional_sobre_Gestao_Empresarial_e_Meio_Ambiente_2014.
Pires, S. R. I.; Sacomano Neto, M. (2010), “Características estruturais, relacionais e gerenciais na cadeia de suprimentos de um condomínio industrial na indústria automobilística”, Production, Vol. 20, No. 2, pp.172-185, Disponível em: http://dx.doi.org/10.1590/s0103-65132010005000032.
Pouriani, S.; Asadi-Gangraj, E.; Paydar, MM. (2019), “A robust bi-level optimization modelling approach for municipal solid waste management; a real case study of Iran”, Journal of Cleaner Production, Vol. 240, pp. 118-125, Disponível em https://doi.org/10.1016/j.jclepro.2019.118125 (acesso em 02 jan. 2021).
Predojevic, Z. J. (2008), “The production of biodiesel from waste frying oils: A comparison of different purification steps”, Fuel, vol. 87, pp. 3522–3528.
Rangel, D. A.; Oliveira, T. K.; Leite, M. S. A. (2014) “Supply chain risk classification: discussion and proposal”, International Journal of Production Research, pp.1-20.
Santos, A. S.; Ribeiro, S. K. (2013), “The use of sustainability indicators in urban passenger transport during the decision-making process: the case of Rio de Janeiro, Brazil”, Current Opinion in Environmental Sustainability, Vol.5, pp.251-260.
Seuring S.; Gold S. (2012), “Conducting content-analysis based literature reviews in supply chain management”, Supply Chain Management: An International Journal vol. 17, No. 5, pp. 544–555.
Soto-Silva, W. E.; Nadal-Roig, E.; González-Araya, M. C.; Pla-Aragones, L. M. (2016), “Operational research models applied to the fresh fruit supply chain”, European Journal of Operational Research, Vol. 251, No. 2, pp. 345-355.
Trkman, P., & McCormack, K. (2019), “Supply chain risk in turbulent environments: A conceptual model for managing supply chain network risk”, International Journal of Production Economics, Vol. 119, No. 2, pp. 247-258, Disponível em: http://dx.doi.org/10.1016/j.ijpe.2009.03.002, (acesso em 02 jan. 2021).
Zhang, H.; Ozturk, U. A.; Zhou, D.; Qiu, Y.; Wu, Q. (2014) “How to increase the recovery rate for waste cooking oil-to-biofuel conversion: A comparison of recycling modes in China and Japan”, Ecological Indicators, Vol. 51, pp. 146-150.
Received: June 17, 2021
Approved: July 5, 2021
DOI: 10.20985/1980-5160.2021.v16n2.1732
How to cite: Leal Junior, I.C., Maia, V.P., Guabiroba, R.C.S., Oliveira, U.R. (2021). The reverse chain of waste cooking oil for biodiesel production from the perspective of collectors. Revista S&G 16, 2. https://revistasg.emnuvens.com.br/sg/article/view/1732