Conceptual Framework for Managing Uncertainty in a Collaborative Agri-Food Supply Chain Context

[EN] Agri-food supply chains are subjected to many sources of uncertainty. If these uncertainties are not managed properly, they can have a negative impact on the agri-food supply chain (AFSC) performance, its customers, and the environment. In this sense, collaboration is proposed as a possible solution to reduce it. For that, a conceptual framework (CF) for managing uncertainty in a collaborative context is proposed. In this context, this paper seeks to answer the following research questions: What are the existing uncertainty sources in the AFSCs? Can collaboration be used to reduce the uncertainty of AFSCs? Which elements can integrate a CF for managing uncertainty in a collaborative AFSC? The CF proposal is applied to the weather source of uncertainty in order to show its applicability.The first author acknowledges the partial support of the Program of Formation of University Professors of the Spanish Ministry of Education, Culture, and Sport (FPU15/03595). The other authors acknowledge the partial support of the Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems, funded by the EU under its funding scheme H2020-MSCA-RISE-2015.Esteso-Álvarez, A.; Alemany Díaz, MDM.; Ortiz Bas, Á. (2017). Conceptual Framework for Managing Uncertainty in a Collaborative Agri-Food Supply Chain Context. IFIP Advances in Information and Communication Technology. 506:715-724. https://doi.org/10.1007/978-3-319-65151-4_64S715724506Taylor, D.H., Fearne, A.: Towards a framework for improvement in the management of demand in agri-food supply chains. Supply Chain Manag. Int. J. 11, 379–384 (2006)Matopoulos, A., Vlachopoulou, M., Manthou, V., Manos, B.: A conceptual framework for supply chain collaboration: empirical evidence from the agri-food industry. Supply Chain Manag. Int. J. 12, 177–186 (2007)Ahumada, O., Villalobos, J.R.: Application of planning models in the agri-food supply chain: a review. Eur. J. Oper. Res. 196, 1–20 (2009)Tsolakis, N.K., Keramydas, C.A., Toka, A.K., Aidonis, D.A., Iakovou, E.T.: Agrifood supply chain management: a comprehensive hierarchical decision-making framework and a critical taxonomy. Biosyst. Eng. 120, 47–64 (2014)van der Vorst, J.G., Da Silva, C.A., Trienekens, J.H.: Agro-industrial supply chain management: Concepts and applications. FAO (2007)Borodin, V., Bourtembourg, J., Hnaien, F., Kabadie, N.: Handling uncertainty in agricultural supply chain management: a state of the art. Eur. J. Oper. Res. 254, 348–359 (2016)van der Vorst, J.G.A.J., Beulens, A.J.M.: Identifying sources of uncertainty to generate supply chain redesign strategies. Int. J. Phys. Distrib. Logist. Manag. 32, 409–430 (2000)Klosa, E.: A concept of models for supply chain speculative risk analysis and management. J. Econ. Manag. 12, 45–59 (2013)Samson, S., Reneke, J.A., Wiecek, M.M.: A review of different perspectices on uncertainty and risk and an alternative modeling paradigm. Reliab. Eng. Syst. Saf. 94, 558–567 (2009)Backus, G.B.C., Eidman, V.R., Dijkhuizen, A.A.: Farm decision making under risk and uncertainty. Neth. J. Agric. Sci. 45, 307–328 (1997)van der Vorst, J.G.: Effective food supply chains; Generating, modelling and evaluating supply chain scenarios. (2000)Amorim, P., Günther, H.O., Almada-Lobo, B.: Multi-objective integrated production and distribution planning of perishable products. Int. J. Prod. Econ. 138, 89–101 (2012)Amorim, P., Meyr, H., Almeder, C., Almada-Lobo, B.: Managing perishability in production-distribution planning: a discussion and review. Flex. Serv. Manuf. 25, 389–413 (2013)Costa, C., Antonucci, F., Pallottino, F., Aguzzi, J., Sarria, D., Menesatti, P.: A review on agri-food supply chain traceability by means of RFID technology. Food Bioprocess Technol. 6, 353–366 (2013)Pahl, J., Voss, S.: Integrating deterioration and lifetime constraints in production and supply chain planning: a survey. Eur. J. Oper. Res. 238, 654–674 (2014)Grillo, H., Alemany, M.M.E., Ortiz, A.: A review of Mathematical models for supporting the order promising process under Lack of Homogeneity in product and other sources of uncertainty. Comput. Ind. Eng. 91, 239–261 (2016)Zwietering, M.H., van’t Riet, K.: Modelling of the quality of food: optimization of a cooling chain. In: Management Studies and the Agri-business: Management of Agri-chains, Wageningen, The Netherlands, pp. 108–117 (1994)Akkerman, R., Farahani, P., Grunow, M.: Quality, safety and sustainability in food distribution: a review of quantitative operations management approaches and challenges. Spectrum 32, 863–904 (2010)Apaiah, R.K., Hendrix, E.M.T., Meerdink, G., Linnemann, A.R.: Qualitative methodology for efficient food chain design. Trends Food Sci. Technol. 16, 204–214 (2005)Lehmann, R.J., Reiche, R., Schiefer, G.: Future internet and the agri-food sector: State-of-the-art in literature and research. Comput. Electron. Agric. 89, 158–174 (2012)Kusumastuti, R.D., van Donk, D.P., Teunter, R.: Crop-related harvesting and processing planning: a review. Int. J. Prod. Econ. 174, 76–92 (2016)Dreyer, H.C., Strandhagen, J.O., Hvolby, H.H., Romsdal, A., Alfnes, E.: Supply chain strategies for speciality foods: a Norwegian case study. Prod. Plan. Control 27, 878–893 (2016)Baghalian, A., Rezapour, S., Farahani, R.Z.: Robust supply chain network design with service level against disruptions and demand uncertainties: a real-life case. Eur. J. Oper. Res. 227, 199–215 (2013)Aggarwal, S., Srivastava, M.K.: Towards a grounded view of collaboration in Indian agri-food supply chains: a qualitative investigation. Br. Food J. 115, 1085–1106 (2016)Teimoury, E., Nedaei, H., Ansari, S., Sabbaghi, M.: A multi-objective analysis for import quota policy making in a perishable fruit and vegetable supply chain: a system dynamics approach. Comput. Electron. Agric. 93, 37–45 (2013)Opara, L.U.: Traceability in agriculture and food supply chain: a review of basic concepts, technological implications, and future prospects. J. Food Agric. Environ. 1, 101–106 (2003)Kruize, J.W., Wolfert, S., Goense, D., Scholten, H., Beulens, A., Veenstra, T.: Integrating ICT applications for farm business collaboration processes using Fl Space. In: 2014 Annual SRII Global Conference, pp. 232–240. IEEE (2014)Oriade, C.A., Dillon, C.R.: Developments in biophysical and bioeconomic simulation of agricultural systems: a review. Agric. Econ. 17, 45–58 (1997)Camarinha-Matos, L.M., Afsarmanesh, H.: Collaborative networks: value creation in a knowledge society. In: Wang, Kesheng, Kovacs, G.L., Wozny, Michael, Fang, Minglun (eds.) PROLAMAT 2006. IIFIP, vol. 207, pp. 26–40. Springer, Boston, MA (2006). doi: 10.1007/0-387-34403-9_4Prima Dania, W.A., Xing, K., Amer, Y.: Collaboration and sustainable agri-food supply chain: a literature review. MATEC Web Conf. 58 (2016)Simatupang, T.M., Sridharan, R.: The collaborative index: a measure for supply chain collaboration. Int. J. Phys. Distrib. Logist. Manag. 35, 44–62 (2005)Fischer, C., Hartmann, M., Reynolds, N., Leat, P., Revoredo-Giha, C., Henchion, M., Albisu, L.M., Gracia, A.: Factors influencing contractual choice and sustainable relationships in European agri-food supply chains. Eur. Rev. Agric. Econ. 36, 541–569 (2009

A conceptual framework for crop-based agri-food supply chain characterization under uncertainty

[EN] Crop-based Agri-food Supply Chains (AFSCs) are complex systems that face multiple sources of uncertainty that can cause a significant imbalance between supply and demand in terms of product varieties, quantities, qualities, customer requirements, times and prices, all of which greatly complicate their management. Poor management of these sources of uncertainty in these AFSCs can have negative impact on quality, safety, and sustainability by reducing the logistic efficiency and increasing the waste. Therefore, it becomes crucial to develop models in order to deal with the key sources of uncertainty. For this purpose, it is necessary to precisely understand and define the problem under study. Even, the characterisation process of this domains is also a difficult and time-consuming task, especially when the right directions and standards are not in place. In this chapter, a Conceptual Framework is proposed that systematically collects those aspects that are relevant for an adequate crop-based AFSC management under uncertainty.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015Alemany Díaz, MDM.; Esteso, A.; Ortiz Bas, Á.; Hernández Hormazabal, JE.; Fernández, A.; Garrido, A.; Martin, J.... (2021). A conceptual framework for crop-based agri-food supply chain characterization under uncertainty. Studies in Systems, Decision and Control. 280:19-33. https://doi.org/10.1007/978-3-030-51047-3_2S1933280Taylor, D.H., Fearne, A.: Towards a framework for improvement in the management of demand in agri-food supply chains. Supply Chain Manage. 11, 379–384 (2006)Matopoulos, A., Vlachopoulou, M., Manthou, V., Manos, B.: A conceptual framework for supply chain collaboration: empirical evidence from the agri-food industry. Supply Chain Manage. 12, 177–186 (2007)Ahumada, O., Villalobos, J.R.: Application of planning models in the agri-food supply chain: a review. Eur. J. Oper. Res. 196, 1–20 (2009)Iakovou, E., Vlachos, D., Achillas, C., Anastasiadis, F.: A methodological framework for the design of green supply chains for the agrifood sector. Paper presented at the 2nd international conference on supply chains, Katerini, 5–7 Oct 2012Manzini, R., Accorsi, R.: The new conceptual framework for food supply chain assessment. J. Food Eng. 115, 251–263 (2013)Shukla, M., Jharkharia, S.: Agri-fresh produce supply chain management: a state-of-the-art literature review. Int. J. Oper. Prod. Manage. 33, 114–158 (2013)Lemma, Y., Kitaw, D., Gatew, G.: Loss in perishable food supply chain: an optimization approach literature review. Int. J. Sci. Eng. Res. 5, 302–311 (2014)Tsolakis, N.K., Keramydas, C.A., Toka, A.K., Aidonis, D.A., Iakovou, E.T.: Agrifood supply chain management: a comprehensive hierarchical decision-making framework and a critical taxonomy. Biosyst. Eng. 120, 47–64 (2014)Van der Vorst, J.G., Da Silva, C.A., Trienekens, J.H.: Agro-industrial Supply Chain Management: Concepts and Applications. FAO (2007)Hernandez, J., Mortimer, M., Patelli, E., Liu, S., Drummond, C., Kehr, E., Calabrese, N., Iannacone, R., Kacprzyk, J., Alemany, M.M.E., Gardner, D.: RUC-APS: enhancing and implementing knowledge based ICT solutions within high risk and uncertain conditions for agriculture production systems. In: 11th International Conference on Industrial Engineering and Industrial Management, Valencia, Spain (2017)Miles, M.B., Huberman, A.M.: Qualitative Data Analysis: An Expanded Sourcebook. Sage Publications, Thousand Oaks (1994)Alemany, M.M.E., Alarcón, F., Lario, F.C., Boj, J.J.: An application to support the temporal and spatial distributed decision-making process in supply chain collaborative planning. Comput. Ind. 62, 519–540 (2011)Teimoury, E., Nedaei, H., Ansari, S., Sabbaghi, M.: A multi-objective analysis for import quota policy making in a perishable fruit and vegetable supply chain: a system dynamics approach. Comput. Electron. Agric. 93, 37–45 (2013)Kusumastuti, R.D., van Donk, D.P., Teunter, R.: Crop-related harvesting and processing planning: a review. Int. J. Prod. Econ. 174, 76–92 (2016)Zhang, W., Wilhelm, W.E.: OR/MS decision support models for the specialty crops industry: a literature review. Ann. Oper. Res. 190, 131–148 (2011)Grillo, H., Alemany, M.M.E., Ortiz, A.: A review of mathematical models for supporting the order promising process under lack of homogeneity in product and other sources of uncertainty. Comput. Ind. Eng. 91, 239–261 (2016)Blanco, A.M., Masini, G., Petracci, N., Bandoni, J.A.: Operations management of a packaging plant in the fruit industry. J. Food Eng. 70, 299–307 (2005)Grillo, H., Alemany, M.M.E., Ortiz, A., Fuertes-Miquel, V.S.: Mathematical modelling of the order-promising process for fruit supply chains considering the perishability and subtypes of products. Appl. Math. Model. 49, 255–278 (2017)Verdouw, C.N., Beulens, A.J.M., Trienekens, J.H., Wolferta, J.: Process modelling in demand-driven supply chains: a reference model for the fruit industry. Comput. Electron. Agric. 73, 174–187 (2010)Amorim, P., Günther, H., Almada-Lobo, B.: Multi-objective integrated production and distribution planning of perishable products. Int. J. Prod. Econ. 138, 89–101 (2012)Nahmias, S.: Perishable inventory theory: a review. Oper. Res. 30, 680–708 (1982)Mowat, A., Collins, R.: Consumer behavior and fruit quality: supply chain management in an emerging industry. Supply Chain Manage. 5, 45–54 (2000)Kazaz, B., Webster, S.: The impact of yield-dependent trading costs on pricing and production planning under supply uncertainty. M&SOM Manuf. Serv. Oper. Manage. 13, 404–417 (2011)Van der Vorst, J.G.: Effective food supply chains: generating, modelling and evaluating supply chain scenarios (2000)Fuertes-Miquel, V.S., Cuenca, L., Boza, A., Guyon, C., Alemany, M.M.E.: Conceptual framework for the characterization of vegetable breton supply chain sustainability in an uncertain context. In: 12th International Conference on Industrial Engineering and Industrial Management, XXII Congreso de Ingeniería de Organización, Girona, Spain, 12–13 July 2018Kummu, M., de Moel, H., Porkka, M., Siebert, S., Varis, O., Ward, P.J.: Lost food, wasted resources: global food supply chain losses and their impacts on freshwater, cropland, and fertiliser use. Sci. Total Environ. 438, 477–489 (2012)Hoekstra, S., Romme, J.: Integral Logistic Structures: Developing Customer-Oriented Goods Flow. Industrial Press Inc., New York (1992)Borodin, V., Bourtembourg, J., Hnaien, F., Labadie, N.: Handling uncertainty in agricultural supply chain management: a state of the art. Eur. J. Oper. Res. 254, 348–359 (2016)Handayati, Y., Simatupang, T.M., Perdana, T.: Agri-food supply chain coordination: the state-of-the-art and recent developments. Logist. Res. 8, 1–15 (2015)Mintzberg, H.: The Structuring of Organisations. Prentice-Hall, Upper Saddle River (1979)Keuning, D.: Grondslagen Van Het Management. Stenfert Kroese, Houten (1995) (in Dutch)Esteso, A., Alemany, M.M.E., Ortiz, A.: Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. Int. J. Prod. Res. (2018)Backus, G.B.C., Eidman, V.R., Dijkhuizen, A.A.: Farm decision making under risk and uncertainty. Neth. J. Agr. Sci. 45, 307–328 (1997)Esteso, A., Alemany, M.M.E., Ortiz, A.: Conceptual framework for managing uncertainty in a collaborative agri-food supply chain context. In: IFIP Advances in Information and Communication Technology, vol. 506, pp. 715–724 (2017)Mundi, I., Alemany, M.M.E., Poler, R., Fuertes-Miquel, V.S.: Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model. Int. J. Prod. Res. (2019)Grillo, H., Alemany, M.M.E., Ortiz, A., De Baets, B.: Possibilistic compositions and state functions: application to the order promising process for perishables. Int. J. Prod. Res. (2019)Soto-Silva, W.E., Nadal-Roig, E., González-Araya, M.C., Pla-Aragones, L.M.: Operational research models applied to the fresh fruit supply chain. Eur. J. Oper. Res. 251, 345–355 (2016)Farahani, R.Z., Rezapour, S., Drezner, T., Fallah, S.: Competitive supply chain network design: an overview of classifications, models, solution techniques and applications. Omega 45, 92–118 (2014)Banasik, A., Bloemhof-Ruwaard, J.M., Kanellopoulos, A., Claassen, G.D.H., van der Vorst, J.G.: Multi-criteria decision making approaches for green supply chains: a review. Flex. Serv. Manuf. J. 1–31 (2016)Paam, P., Berretta, R., Heydar, M., Middleton, R.H., García-Flores, R., Juliano, P.: Planning models to optimize the agri-fresh food supply chain for loss minimization: a review. In: Reference Module in Food Science (2016)Soysal, M., Bloemhof-Ruwaard, J.M., Meuwissen, M.P., van der Vorst, J.G.: A review on quantitative models for sustainable food logistics management. Int. J. Food Syst. Dyn. 3, 136–155 (2012

A mathematical formulation of time-cost and reliability optimization for supply chain management in research-development projects

One of the most important strategic decisions in Research-Development projects is network design. It needs to be optimized for the long-term efficient operation. This paper aims at designing the network of Supply Chain for R&D projects. Accordingly, it proposes a Goal programming model for solving a Project-oriented Supply Chain Management problem. The proposed model is developed to determine the optimal combination of the main contractors, executers, and various alternatives for project implementation. The model optimizes time, cost and reliability in the whole lifecycle for the R&D projects. A case study is presented to validate and illustrate the proposed model. The main reason for the high cost and time in the case study was due to the incorrect choice of the network of suppliers and consultants. The model has been tested by the numerical data, revealing that the model could have a significant contribution to the productivity of project-oriented organization. This model could serve as a guideline for managers and decision makers in R&D projects, enabling them to identify the best networks of the SC in their organizations to resolve and improve problems. It also acts as a useful basis for researchers to continue research concerning SCM in R&D projects

Fuzzy Queuing Approach for Designing Multi Supplier Systems (Case: SAPCO Company)

The importance of reliable supply is increasing with supply chain network extension and just-in-time (JIT) production. Just in time implications motivate manufacturers towards single sourcing, which often involves problems with unreliable suppliers. If a single and reliable vendor is not available, manufacturer can split the order among the vendors in order to simultaneously decrease the supply chain uncertainty and increase supply reliability. In this paper we discuss with the aim of minimizing the shortage cost how we can split orders among suppliers with different lead times. The (s,S) policy is the basis of our inventory control system and for analyzing the system performance we use the fuzzy queuing methodology. After applying the model for the case study (SAPCO), the result of the developed model will be compared in the single and multiple cases and finally we will find that order splitting in optimized condition will conclude in the least supply risk and minimized shortage cost in comparison to other cases

An Integrated Queuing Model for Site Selection and Inventory Storage Planning of a Distribution Center with Customer Loss Consideration

Discrete facility location,   Distribution center,   Logistics,   Inventory policy,   Queueing theory,   Markov processes, The distribution center location problem is a crucial question for logistics decision makers. The optimization of these decisions needs careful attention to the fixed facility costs, inventory costs, transportation costs and customer responsiveness. In this paper we study the location selection of a distribution center which satisfies demands with a M/M/1 finite queueing system plus balking and reneging. The distribution center uses one for one inventory policy, where each arrival demand orders a unit of product to the distribution center and the distribution center refers this demand to its supplier. The matrix geometric method is applied to model the queueing system in order to obtain the steady-state probabilities and evaluate some performance measures. A cost model is developed to determine the best location for the distribution center and its optimal storage capacity and a numerical example is presented to determine the computability of the results derived in this study

Wireless mesh networks channel reservation: modelling and delay analysis

In order to overcome the negotiation procedure bottleneck of the standard DCF in wireless mesh networks, the authors propose a new channel reservation function (CRF) that reduces the negotiation overhead of the DCF, which as a result reduces the overall transmission delay effectively without of any extra bandwidth consumption. Furthermore, the authors provide an analytical model for the proposed scheme for which the simulation results measure the amount that the new method can reduce the average total delay for both regular and fragmented mesh topologies demonstrating superiority of the new method over the classic 802.11 solution. Additionally, the authors extend the scheme to multichannel CRF upon which the proposed method can be used for multichannel applications