https://orcid.org/0000-0001-5539-1924
ABSTRACT
Fluidity models in the supply chain privilege the sustainable integration of capabilities and collaboration among its members in order to guarantee an efficient and safe flow of resources throughout all its processes. This research proposes a fluidity model for the agroindustry supply chain as a solution with regard to the sector’s needs of supply chain processes, and opportunities to collaborate within the field of innovation and sustainability through of traceability and proactive risk management as a tool for creating resilient systems. The model is based on a holistic vision that will allow it to adapt to an ever more complex and continuously transformed global environment that demands solutions to assess the global impact of local decision-making in the supply chain over a period of time, considering its implications and contributions to the agroindustry and agro-logistics sector. Finally, pertinent research areas are identified in the integration of agroindustry supply chain echelons.
1. Introduction
Over the past years, agroindustry supply chain research has captured the attention of producers, governments, academics, and society due to its critical relevance to guarantee the availability of quality food that is safe for human consumption (Flynn et al., Citation2019; Handayati et al., Citation2015; Melkonyan et al., Citation2018).
To this effect, it is possible to identify several efforts and investments in the sector to achieve significant improvements in agricultural productivity (for example, in the use of electromechanical systems, the implementation of irrigation techniques, the development of agrochemicals and raw materials with new characteristics derived from genetically modified plants – to mention a few); nevertheless, losses and waste in those systems are still evident (Liddiard et al., Citation2017; Opara, Citation2017).
According to documented evidence, losses or waste in the agroindustry supply chains mainly take place within the early stages in the chain, considering that the problem resides in the poor and deficient handling of the product; likewise, another factor that has been identified is the lack of timely transport capacity to move production. Other approaches have addressed the problem from the need to improve nutritional value, gain client’s acceptance in new consumption points and minimize the cost of products by improving productive processes (Liddiard et al., Citation2017).
Recent research acknowledges that agro-logistics problems are not exclusive of a certain stage in the supply chain, revealing the need to develop strategies that allow the problem to be addressed from a holistic perspective (Narsimhalu et al., Citation2015). Likewise, the need to study and assess the concept of value in supply chains has been highlighted (Eggert et al., Citation2019).
From this perspective, this work proposes to address the study of agri-food supply chains as complex networks that add value and involve primary production activities (farms), processing plants, distribution centers, retailer chains, and even proper handling by the consumer (Bosona & Gebresenbet, Citation2013; Lazarides, Citation2011). Therefore, there is a need to understand and define processes that are present in food supply chains to identify productive methodologies and techniques, which result in an increase in their competitive advantage in a sustainable environment.
The first objective outlined in the sustainability of agro-logistic systems is the development of efficient production processes, which minimize waste in production, transformation and distribution, by attending to the assurance of food quality and safety as well as promoting fair and transparent distribution practices that guarantee access to food that is healthy and affordable. The second objective is related to providing the necessary support to promote the development of rural communities by encouraging the efficient use of regional resources. The third objective addresses the need to create an environment that fosters solutions to help balance quality, safety, and resource management problems by allowing the integration of global agroindustry supply chain systems that offer benefits derived from their involvement in the flow of international business, and that offer access to better practices, which translate into better yields in the productive systems within transparent environments that guarantee production traceability within the entire economic system (Fredriksson & Liljestrand, Citation2015; Zondag et al., Citation2017).
Within this context, the present document proposes the study of agroindustry supply chains from a holistic perspective which allows the integration of different echelons that conform the chain, considering traceability, risk management, quality and sustainability aspects. It is worth noting that fluidity in the agroindustry supply chain is defined as the integration of capabilities in order to guarantee safe and efficient agroindustry production flow throughout all its processes (Bueno-Solano et al., Citation2021). In the next section, there is a formal description of fluidity, as well as the tendencies and possible areas of research to be developed in the field, contributing, firstly, to the productivity and competitiveness in echelons, and secondly, to diminishing unexpected interruptions in the flow of agroindustry products, which result in food loss or waste in the chain.
2. Background
Nowadays, as a result of globalization, distances travelled by agroindustry products from the producer to the end consumer have increased. Given this scenario, being able to guarantee the constant flow of products in safe and quality environments has become a critical challenge from the standpoint of logistics and managing risk and variability (Aung & Chang, Citation2014; Parck et al., Citation2003).
To this respect, management of the supply chain suggests that the entire chain start with attending the client’s need and include all the processes directly or indirectly involved in the satisfaction of that need (Chopra & Meindl, Citation2008). However, this is not a simple task; in the last decade, supply chains have become more complex as a result of globalization, and they have increased their vulnerability to risk (Hans-Christian; Bueno-Solano et al., Citation2016; Pfohl et al., Citation2010).
Due to global competitiveness pressure and the need to have inter-organization collaboration, companies are forced to take their supply chains to the limit, demanding that they become more flexible and responsive. As a result, supply chains should be able to handle a great number of events, both expected and unexpected disturbances (disruptions). In a highly dynamic environment, in which client conditions and demands are ever-changing, deviations to the plan occur regularly and have costly consequences. Likewise, it has been proven that these unexpected events tend to have an effect throughout the supply chain partners, resulting in the well-known bullwhip effect and reverse bullwhip effect (Bueno & Cedillo, Citation2014; Rong et al., Citation2007).
In the face of this situation, supply chain stakeholders must build alternative options that not only enable them to improve performance in the productive process but also increase the level of reliability in the supply chain. In this sense, the capability of continuously reaching the supply chain objectives with reliability and safety, and simultaneously contributing to an efficient flow of finances, human talent, transportation, and information is known as the fluidity model in the supply chain. The term fluidity has been implemented in different sectors to offer effective support towards achieving the supply chain objectives, as it is able to balance the different interests involved in each echelon of the chain (I-95 Corridor Coalition, Citation2016).
In order to obtain an effective fluidity measurement, it is necessary to connect all information sources within the required areas in order to have more agile decision-making. Likewise, specific indicators must be identified to represent the behavior of each company and industrial sector (Cedillo-Campos & Cedillo-Campos, Citation2015). In this sense, from an analytical approximation with regard to the agroindustry, travel time, reliability in travel time, and the cost of the implemented measures to control deviations, are common variables in all the chains, and they can also be escalated and comparable in chains with different settings, and with operational, regional, national or global influence (I-95 Corridor Coalition, Citation2016).
Within this context, the fluidity measurement may be considered as a standard indicator that allows stakeholders to either remain in or access to new markets supported by the supply chain logistic reorganization, which in order to achieve their proper integration, they need to develop more robust systems, reduce delivery times, costs, as well as their vulnerability.
3. The fluidity model for agroindustry supply chain
Even though the fluidity model was developed to support transportation analysis in the main commercial corridors in Canada, the objective is to measure the degree of travel time reliability in order to identify the bottlenecks that may represent a disruptive risk for business. Nowadays, this model is experimentally used in other contexts, for example, in the manufacturing industry, where there is a need to improve the direct deliveries of assembly line raw materials (Eisele & Villa, Citation2015).
The model is also identified as a logistic challenge related to the need for developing strategies to implement the concept of fluidity in other supply chains. For instance, one of the sectors in which, due to its complexity and dynamics, the fluidity indicator may be implemented, is that of the agroindustry supply chain, as there is greater focus on customer service, due to the demand of faster response times for the delivery of fresh, safe food, that is also in appropriate consumption conditions with regard to health benefits, and therefore, the chain must prove its reliability and be able to timely attend to any deviation or interruption in the flow of goods (Aung & Chang, Citation2014).
In order to properly implement the fluidity model, managers and researchers need to accept the fact that we are currently living in a new market-led reality. In this scenario, a feasible alternative for a redesign consists on implementing collaboration techniques that permit to create and share value to the final client (Eggert et al., Citation2019). Simultaneously, fluidity model must help create a corporate social responsibility system must be created to attend to the international demands of such sustainable systems in every organization (Beulens et al., Citation2005; Trienekens et al., Citation2017).
Nevertheless, the literature study reveals that there is no consensus with regard to which collaborative models are more effective or ideal to replace traditional schemes in agroindustry supply chains. The lack of consensus may be explained, first of all, with respect to the current problems in the retail channel, where the following can be identified: different operation formats, differential power among the different stakeholders of such channel, adverse government regulations, and unfair competition. A second factor is that which the sector faces due to the products’ own conditions, such as their expiration date, seasonality, regional influences and regulations (both government and sanitary safety) which are necessary to access international markets. The third factor, shown in literature, is related to the need to increase the number of multidisciplinary researches in the agri-food industry aimed towards the development of more productive supply chains, which focus on network analysis that encourages collaboration relationships (Fredriksson & Liljestrand, Citation2015; Narsimhalu et al., Citation2015; Zondag et al., Citation2017).
To contribute to these three factors, this document takes as a reference the results of various projects aimed at introducing the fluidity model in the Roma tomato supply chain in northwestern Mexico. Navarro et al., Citation2017 build a graphical interface to evaluate the information of scenarios associated with performance indicators as a strategy to provide certainty in the configuration of the vegetable supply chain in Sonora, Mexico. Lagarda et al. Citation2018 propose a model for the distribution of Roma tomato from Sonora in Mexico to Arizona in the U.S. Morales-Gáytan, Citation2019 proposes a reengineering of processes to design a traceability model for direct and reverse flows of the tomato chain. Finally, Bueno-Solano et al., Citation2021 propose a quantitative model to measure risk resilience in the tomato distribution system.
In this sense, the present document suggests that it is possible to achieve success in the agroindustry supply chain by administrating and coordinating processes which commence at producing farms, and conclude until the satisfaction of the end client’s demand. Moreover, as presented by Kelepouris et al., Citation2007; Opara, Citation2017, the fact that agroindustry chain clients require more information for decision-making on products available in the market must be taken into consideration, acknowledging that this need to be informed is beyond knowing production and expiration dates; nowadays, clients wish to know the region where products are produced, the techniques used, the transportation systems involved in the distribution, as well as the markets which were visited by the products before reaching the purchase or consumption point, and even how problems and risks were addressed if these occurred.
To attend to clients’ requirements, data management must involve all the echelons in the chain, favoring a culture in which it is common knowledge that success is shared among all stakeholders, and therefore, failure in handling products affects the reputation of the entire chain. This demonstrates, on the one hand, the interdependence of all stakeholders, and on the other, it suggests that, if success is to be shared, the consequences of deviations must be shared as well; therefore, strategies that enable the flow of information must be developed, which undertake the network coordination as a critical stage in agroindustry chains that not only struggle to access the best distribution routes, facing factors such as food seasonality, expiration and safety, but also are capable of developing traceability strategies that add value and build end consumers’ trust (Handayati et al., Citation2015).
3.1. Traceability
The term traceability has been used in several industries with different connotations. Particularly in the agroindustry, traceability refers to information collection, documentation, maintenance and application related to all the processes involved in the supply chain, in such a way that it may provide and guarantee the consumer and the investor data related to the origin, current location, and life history of the product; it also should be able to assist in handling crisis management as a result of safety events or losses in quality (Opara, Citation2017, Aung et al., Citation2014). To be more specific, within the field of food production, traceability refers to the ability to identify the farm where the food was grown, the source of the materials that were utilized, the storage warehouses or distributors, the retailer, as well as to make available all communication history, both reverse and forward, in order to determine the exact location and condition of the agroindustry product in the chain. See .
Figure 1. Traceability and visibility in the supply chain.
As it can be observed in the figure, traceability provides the communication linkage that enables variability sources to be identified, verified and isolated, and allows processes to take place in an environment of conformity to the established norms and client expectations. Traceability systems are utilized as tools that assist both quality assurance systems and activities that are responsible for safety as they simultaneously acquire the client’s reliance by minimizing conditions that are unsafe in production and distribution stages (Aung & Chang, Citation2014; Galimberti et al., Citation2013). Conversely, although the client may not need to know all the information that is generated, the supply chain may use the information derived from its traceability strategy to offer visibility that the client may require with regard to the origin of the product and the possible risks encountered during transportation to the destination market.
3.2. Risk administration
As a result of the increase of the journey of agroindustry products within the global supply chains, maintaining the level of traceability and visibility demanded by both the producers and the client has become a significant logistic challenge. In the past decades, agroindustry chains have faced several crises as a result of poor safety levels, which is a fact that has led to the increased attention of research and investments in the chain (Accorsi et al., Citation2017; Kelepouris et al., Citation2007).
In this context, the Codex Alimentarius Commission CAC (Citation2003) defines safety as all the activities that guarantee flow in the supply chain, making sure that agriculture products arrive to the last link knowing that there will be no damages to clients’ health. Moreover, it sets forth that safety is not a negotiable topic, and that not attending to problems at early stages can affect millions of people in the event of contamination or shortage of products. In view of this situation, agroindustry safety is not a topic that should be studied from a static point of view, and therefore, the efforts to guarantee the same must be continuous and dynamic (Flynn et al., Citation2019).
The Asian Producers Organization published in 2009 that both developed and developing countries share their concern for safety in agroindustry chains, in an environment in which international trade and border crossing are constantly rising. Thus, besides traceability, proactive risk measures are identified as a critical challenge for agri-food chains, in which deviations to the plan may occur at any point, and consequently, the risk and responsibility must be shared among producers, processors, distributors, retailers and clients (Sgarbossa & Russo, Citation2017).
Shamah-Leyva et al. stated in 2017 that during the last 20 years in emerging markets, a sufficient amount of agri-food products have been produced to satisfy the population’s consumption needs. However, he explains that due to different historical factors, the elaboration structure of agroindustry products remains virtually unchanged for the systems that took place 25 years ago; the lack of investment to enable updating processes contributes to the fact that 13.6% of the population does not have enough food as a result of losses and waste in the sector. Alternatively, he agrees with what was presented by Pérez-Escamilla et al., in 2017, and highlights the need to design governance strategies that place risk management as a critical element in the development of the sector. He also suggests that such governance system must capture the complexity within the agroindustry supply chain, and assess risks as part of the whole. Finally, he exhibits the need to guarantee food availability, access, and proper utilization, without these elements being interrupted during their trajectory throughout the supply chain.
3.3. Collaboration strategy
Within the international context, and regarding some of the methodologies assessed to address these issues in agroindustry supply chains, it has been detected that for several years one of the first attempts to attend to this challenge consists of the application of HACCP systems (Aung & Chang, Citation2014). Likewise, other methodologies assessed by Handayati and associates may be identified in the state-of-the-art revision published in 2015. See .
Table 1. Methodologies to address agroindustry issues (Handayati et al., Citation2015)
All research analyzed in the table assesses safety from a traceability perspective. However, as it has been discussed, agroindustry supply chain management and coordination is more complex, and thus, all efforts should not be reduced to a single element. To this regard, in 2015 Handayati and associates suggested that safety analysis in food chains be addressed from four perspectives: availability, access, utilization, and stability.
Another approach that has been recently adopted in the study of supply chains is that of collective intelligence, a term used to describe the process of drawing upon the knowledge that each actor possesses, and that it be shared with all the other members in the supply chain (Cedillo-Campos, Citation2017). This knowledge conjunction must give place to the generation of higher knowledge which contributes to the creation of a more resilient supply chain as sources of variation and uncertainty are identified between each node (Christopher & Peck, Citation2004).
Currently, government efforts together with the private sector are centered in mitigating the risk of criminal organizations that exploit supply systems for the contamination of loads and the displacement of massive destruction weapons to specific points, or the direct destruction of those systems (Bueno & Cedillo, Citation2014; Hintsa, Citation2010), therefore understanding that safety and security risks as such have direct and indirect effects in the nature and operation development of the business and the supply chain. According to the ORM (Operational Risk Management), risk prevention has been defined as the process of decision-making, which can minimize the effects of losses that are generated by the above mentioned. Thus, the importance of generating collaboration strategies that allow proper risk handling, which result in less severe, less frequent, and more predictable losses.
For practical purposes, the occurrence of disturbances in any stage of the chain may lead to considerable financial consequences, as a result of waste, delays in delivery, production stoppages, fines derived from breach of contracts, and unsatisfied clients (Kommerskollegium, Citation2008). Thus, a disturbance in any point of the chain may cause failure in the entire supply system. For example, in the manufacturing industry, it has been estimated that the impact of a substantial interruption in supply may have a cost of 50 to 100 million dollars a day within the entire chain (Wu et al., Citation2007).
To this effect, derived from the need to develop research in terms of risk management of agroindustry chains, and acknowledging the fact that we are now facing a practically untapped field, the present document suggests addressing the creation of a fluidity model from three perspectives that commence with focusing on attention to disruptive risks on the basis of defining the cause or failure mode present in each echelon, that is, the event that may potentially interrupt the flow in the supply chain. Secondly, a holistic vision must be adopted to recognize that, regardless of the cause of security and safety events, the effect will have global consequences throughout the entire chain. Finally, derived from this statement, methodological developments must be aligned, and collaboration strategies, as well as the role of each actor in the agroindustry supply chains, must be defined.
This context allows us to go back to what is stated in in terms of traceability and visibility, and create a proposal of analysis that circumscribes the vision of guaranteeing an efficient and safe flow of products in the agroindustry supply chain. See .
Figure 2. A model to study risk in agroindustry supply chains.
In the proposed model, there are four categories of disruptive risks that may generate one or more of the six failure modes in supply chains suggested by Piket 2003, which account for i) disruptions in supply (D.S), which refer to the delay or lack of capability to supply raw materials, including scarcity of the same. This event may lead to a stoppage in the supply chain; ii) disruption in infrastructure (D.I), which indicates the lack of availability in resources to continue with the operation, which hinders activities in one of the links of the chain; iii) disruption in transportation (D.T), which accounts for lack of means to displace agroindustry products among the different stages in the supply chains; iv) infringements in transportation (I.T) as a result of altering merchandise, violating the integrity of goods, for lucrative purposes, piracy, and smuggling, among others; v) disruption in communication (D.C), which refers to internal and external problems that impede coordinating and executing transactions among the different stages in the chain; vi) disruption in demand (D.D), which refers to the actions that lead to the partial or total loss of the demand, with potentially devastating consequences for all the processes involved in the agroindustry chain. In addition to the failure modes, it is important to point out the existence of international borders that represent a partial, temporary or total interruption in the supply chain significantly affecting flow throughout the same (Bueno & Cedillo, Citation2014).
The proposal to integrate elements such as traceability, inverse logistics, and the study of risk in agroindustry chain productive processes will allow assessment of the problem from a holistic approach; it will also specifically contribute to the strategy of creating an indicator that enables standardized measurements of flow levels in agroindustry supply chains. Such indicator, from a logistics perspective, is created by grouping different dimensions: processes, collaboration strategies, traceability and safety risk management. presents a conceptual map that shows the sub-processes that integrate each of these dimensions. See .
Figure 3. Proposed framework for the integration of the fluidity index in agroindustry supply chains.
The model herein presented suggests a base structure with four generalizable subsystems (Supply chain processes, Collaboration, Traceability, Risk Management) that can be adaptable according to the supply chain context in which it is to be applied. It also allows the inclusion of future new systems and subsystems according to evidence generated in future research.
4. Conclusions
Creating a fluidity index is a process that requires a willingness from all the actors and stakeholders in the supply chain. To this effect, the present research highlighted, first of all, the importance of having sustainable and efficient channels to share information given the interdependence among the different links in the supply chain. Likewise, the need to develop strategies that enable improvement in production processes in order to increase levels of traceability, resilience and sustainability was also underlined. Finally, it was possible to identify that risk management in agroindustry supply chains is a critical area, and that it is practically untapped.
Therefore, as a result of wide-ranging considerations, contributions made by the present document in the creation of a model that leads to future research towards an indicator that allows standardized measurement of fluidity in agroindustry supply chains have been structured in two main aspects: i) Methodology and ii) Logistics.
As far as Methodology, the objective is to propose a relevant model to integrate a traceability collaboration strategy among participants in the echelons and the risk analysis in agroindustry supply chains with regional and global operations. Alternatively, due to the fact that there was no consensus identified related to the procedure in which a study on interruptions in the flow may be created, and an initial approach consists of defining possible failure modes that enable quantification of the impact of disruptions from a global supply perspective. The document suggests developing an assessment system that enables systematic and simultaneous behavior analysis of the actors involved in the supply chain processes.
Finally, from a logistic point of view, analyzing the behavior of the different echelons in the export supply chain security initiatives, is a topic of great interest. These analyses will allow the development of strategies that result in more robust and resilient chains to face unexpected and sudden events, such as natural disasters and criminal acts.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
References
- Accorsi, R., Bartolini, M., Baruffaldi, G., Polati, F., & Ferrari, E. (2017). Internet-of-Things paradigm in food supply chains control and management. Procedia Manufacturing, 11, 889–895. ISBN:2351-9789. https://doi.org/10.1016/j.promfg.2017.07.192
- Aung, M. M., & Chang, Y. S. (2014). Traceability in a food supply chain: Safety and quality perspectives. Food Control, 39, 172–184. ISBN:. https://doi.org/10.1016/j.foodcont.2013.11.007
- Beulens, A. J. M., Broens, D., Folstar, P., & Hofstede, G. J. (2005). Food safety and transparency in food chains and networks relationships and challenges. Food Control, 16(6), 481–486. https://doi.org/10.1016/j.foodcont.2003.10.010
- Bosona, T., & Gebresenbet, G. (2013). Food traceability as an integral part of logistics management in food and agricultural supply chain. Food Control, 33(1), 32–48. https://doi.org/10.1016/j.foodcont.2013.02.004
- Bueno, A., & Cedillo, M. G. (2014). Dynamic impact on global supply chains performance of disruptions propagation produced by terrorist acts. Transportation Research Part E, 61, 1–12. ISBN:. https://doi.org/10.1016/j.tre.2013.09.005
- Bueno-Solano, A., Cedillo-Campos, M. C., & Velarde-Cantú, J. M. (2016). Reliability of the supply chain: Method of self-assessment as a first step to building resilient systems. International Journal of Combinatorial Optimization Problems and Informatics, 7(1), 3–9. ISBN: . https://www.ijcopi.org/ojs/article/view/41.
- Bueno-Solano, A., Vega-Telles, E. A., & Mireya, R.-M. (2021). Modelo dinámico para el control y manejo de factores de riesgo en la cadena de suministro del tomate. Logística 2020: Nuevos paradigmas y aplicaciones. Editorial ITSON.
- CAC. (2003). Basic texts on food hygiene. 3rd ed. Codex Alimentarius Commission. (Accessed 02.November.2012) http://www.fao.org/docrep/006/y5307e/y5307e00.HTM
- Cedillo-Campos, M., & Cedillo-Campos, H. (2015). w@reRISK method: Security risk level classification of stock keeping units in a warehouse. Safety Science, 79, 358–368. https://doi.org/10.1016/j.ssci.2015.06.009
- Cedillo-Campos, M. C. (2017). Inteligencia Colectiva en Logística: Motor de la Innovación Empresarial.Julio: Forbes México. https://es.linkedin.com/pulse/inteligencia-colectiva-en-log%C3%ADstica-motor-de-la-gaston-cedillo
- Chopra, S., & Meindl, P. (2008). Administración de la Cadena de Suministro: Estrategia, Planeación y Operación (Tercera ed. ed.). Pearson Educación.
- Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15(2), 1–13. https://doi.org/10.1108/09574090410700275
- Eggert, A., Kleinaltenkamp, M., & Kashyap, V. (2019). Mapping value in business markets: An integrative framework. Industrial Marketing Management, 79(2019), 13–20. https://doi.org/10.1016/j.indmarman.2019.03.004
- Eisele, W., & Villa, J. (2015). Freight fluidity scale of analysis. Advancing freight fluidity performance measures workshop Turnbull, Katherine. Advancing Freight Fluidity Performance Measure: Summary of a workshop 1(1), (TRB Publications) 98 . AccessedDecember 9-10, 2015 2015. https://doi.org/10.17226/23623
- Flynn, K., Pérez-Villarreal, B., Barranco, A., Belc, N., Björnsdóttir, B., Fusco, V., Rainieri, S., Smaradóttir, S. E., Smeu, I., Teixeira, P., & Jörundsdóttir, H. O. (2019). An introduction to current food safety needs. Trends in Food Science & Technology, 84(2019), 1–3. https://doi.org/10.1016/j.tifs.2018.09.012
- Fredriksson, A., & Liljestrand, K. (2015). Capturing food logistics: A literature review and research agenda. International Journal of Logistics Research and Applications, 18(1), 16–34. https://doi.org/10.1080/13675567.2014.944887
- Galimberti, A., Mattia, F. D., Losa, A., Bruni, I., Federici, S., Casiraghi, M., Martellos, S., & Labra, M. (2013). DNA barcoding as a new tool for food traceability. Food Research International, 50(1), 55e63. https://doi.org/10.1016/j.foodres.2012.09.036
- Handayati, Y., Simatupang, T. M., & Perdana, T. (2015). Agri-food supply chain coordination: The state of the art and recent developments. Springerlink. Logistics Research, 8(5), 1–15 doi:https://doi.org/10.1007/s12159-015-0125-4.
- Hintsa, J. (2010). A Comprehensive Framework for analysis and design of supply chain security standards. Journal of Transportation Security, 3(2), 105–125. https://doi.org/10.1007/s12198-010-0042-3
- I-95 Corridor Coalition. (2016). Freight performance measurement: Measuring the performance of supply chains across multistate jurisdictions. White Paper, [ On Line] http://i95coalition.org/wp-content/uploads/2016/03/FR1_I95CC_Freight_Performance_Measurement_White_Paper_Final-20160314.pdf?dd650d (August 2016).
- Kelepouris, T., Pramatari, K., & Doukidis, G. (2007). RFID-enabled traceability in the food supply chain. Industrial Management & Data Systems, 107(2), 183–200. https://doi.org/10.1108/02635570710723804
- Kommerskollegium. (2008) . supply chain security initiatives: A trade facilitation perspective. The national board of trade.
- Lazarides, H. N. (2011). Food processing technology in a sustainable food supply chain. Procedia Food Science. 11th International Congress on Engineering and Food (ICEF11) Vol. 1,1918–1923
- Liddiard, R., Gowreesunker, B., & Spataru, G. (2017). The vulnerability of refrigerated food to unstable power supplies. Energy Procedia, 123, 196–203. ISBN:. https://doi.org/10.1016/j.egypro.2017.07.238
- Lagarda-Leyva, E. A., Manuel, A., Oshima, J. G., and Naranjo-Flores, A. A , & . (2018). Case study of the Roma tomato distribution chain: A dynamic interface for an agricultural enterprise in Mexico. International Journal of Economics and Management Engineering, 12(8), 2018. https://doi.org/10.5281/zenodo.1340404
- Melkonyan, A., Krumme, K., Gruchmann, T., & De la Torre, G. (2017). Sustainability assessment and climate change resilience in food production and supply. Energy Procedia, 123, 131–138. ISBN:. https://doi.org/10.1016/j.egypro.2017.07.236
- Morales-Gáytan, R. (2019). Reingeniería del proceso de empaque en la empresa unión de cooperativas agropecuarias hortiparque sc de RL de CV. [ Master Thesis, Instituto Tecnológico de Sonora]
- Narsimhalu, U., Potdar, V., & Kaur, A. (2015). A case study to explore influence of traceability factors on Australian food supply chain performance. Procedia - Social and Behavioral Sciences, 189, 17–32. https://doi.org/10.1016/j.sbspro.2015.03.188
- Navarro, J. C., Lagarda-Leyva, E. A., Vega, E. A., & Bueno-Solano, A. (2017). Interfaz dinámica para el empaque y almacenanaje del tomate Roma en una empresa agropecuaria del sur de sonora. In López-Acosta, Mauricio, Cedillo-Campos, Miguel Gastón, Velarde-Cantú, José Manuel, Bueno-Solano, Alfredo, Lagarda-Leyva, Ernesto Alonso. Pearson. Gestión en la cadena de suministro (pp. 272). ISBN:9786073243896).
- Opara, L. (2003). Traceability in agriculture and food supply chain: A review of basic concepts, technological implications, and future prospects. Food, Agriculture & Environment English, 1(1), 101–106. Accessed 26 05 2021. https://agris.fao.org/agris-search/search.do?recordID=FI2016100260 .
- Parck, M., Kim, M., & Ryu, S. (2019). The relationship between network governance and unilateral governance in dynamic consumer demand. Industrial Marketing Management R. Pérez-Escamillaa, M. B. Guberta, B. Rogersb, & A. Hromi-Fiedler (Eds.), Food security measurement and governance: Assessment of the usefulness of diverse food insecurity indicators for policy makers (Vol. 14, pp. 96–104). Global Food Security. 2017.
- Pfohl, H., Köhler, H., & Thomas, D. (2010). State of the art in supply chain risk management research: Empirical and conceptual findings and a roadmap for the implementation in practice. Logistics Research, 1(2), 33–44. https://doi.org/10.1007/s12159-010-0023-8
- Rong, L., Akhil, K., & Wil van der, A. (2007). A formal modeling approach for supply chain event management. Decision Support Systems, 43(3), 761–778. https://doi.org/10.1016/j.dss.2006.12.009
- Sgarbossa, F., & Russo, I. (2017). A proactive model in sustainable food supply chain: Insight from a case study. International Journal of Production Economics, 183, 596–606. https://doi.org/10.1016/j.ijpe.2016.07.022 .
- Trienekens, J. H., Wognum, P. M., Beulens, A. J. M., & Van der Vorst, J. G. A. J. (2012). Transparency in complex dynamic food supply chains. Advanced Engineering Informatics, 26(1), 55–65. https://doi.org/10.1016/j.aei.2011.07.007
- Wu, T., Blackhurst, J., & O’Grady, P. (2007). Methodology for supply chain disruption analysis. International Journal of Production Research, 45(7), 1665–1682. https://doi.org/10.1080/00207540500362138
- Zondag, M. M., Mueller, E. F., & Ferrin, B. G. (2017). The application of value nets in food supply chains: A multiple case study. Scandinavian Journal of Management, 33(4), 199–212. https://doi.org/10.1016/j.scaman.2017.10.002
![Supercharge Your Next Research Paper: Research and write your next paper with Jenni AI.]({"type":"image" , "src" : "/sda/112440/MPU-Jenni-AI-ECONOMICS.png"})