Maturity model for the Structural Elements of Coordination Mechanisms on the collaborative planning process

[ENG] Collaborative Planning (CP) can be defined as a joint decision making process for aligning plans of individual Supply Chains (SC) members with the aim of achieving a certain degree of coordination (Stadler, 2009). Coordination means identification and classification of existing interdependencies (Li et al., 2002). Different coordination processes manage different types of interdependencies. Coordination should be considered different from integration in that where coordination takes the target for granted, integration often involves determining this target simultaneously with the aligning of allocation decisions (Oliva and Watson, 2010). Typical features of supply chain coordination processes include demand planning (DP), supply planning (SP), available-to-promise/ capacity-to-promise (ATP/CTP), manufacturing planning, distribution planning (DP), etc. Generally, the execution of process depends on proper information management. Coordination mechanisms in supply chain should be tools by which, every member of a supply chain can achieve more benefits. Thus, organizations need to develop strategically aligned capabilities not only within the company itself, but also among the organizations that are part of its value-adding networks. Additionally, processes are now viewed as assets requiring investment and development as they mature. Thus the concept of process maturity is becoming increasingly important as firms adopt a process view of the organization

Modelling Pricing Policy Based on Shelf-Life of Non Homogeneous Available-To-Promise in Fruit Supply Chains

[EN] Fruit Supply Chains (SCs) are influenced by uncontrollable natural factors causing heterogeneity in their products, as regards certain attributes that are relevant to customers and vary over time because of the shelf-life. As a consequence customers should be served not only with the required quantity and due date as usual, but also with the quality, freshness and homogeneity specified in their orders. The order promising process (OPP) is based on the uncommitted availability of homogeneous product quantities in planned lots (ATP) that are uncertain. Therefore, there is a risk of not being reliable in the commitments because of discrepancies between the real and planned homogeneous quantities. Furthermore, due to the shelf-life (SL), serving customers with the freshest product introduce the risk of increasing waste because of the aging process. To efficiently manage these risks, this work proposes a mathematical model for handling the heterogeneous ATP in fruit SCs and a pricing policy based on the product SL in the moment of delivery. In order to illustrate the application of the modelling approach, a short numerical example is introduced. The example evidences a conflictive situation when optimizing the assignation of homogeneous ATP between serving orders with fresh and more valuable product, what could lead to increase the risk of having waste because of expiration, and consequently, more costs and less profit.This research has been supported by the Ministry of Science, Technology and Telecommunications, government of Costa Rica (MICITT), through the program of innovation and human capital for competitiveness (PINN) (PED-019-2015-1).Grillo-Espinoza, H.; Alemany Díaz, MDM.; Ortiz Bas, Á. (2016). Modelling Pricing Policy Based on Shelf-Life of Non Homogeneous Available-To-Promise in Fruit Supply Chains. IFIP Advances in Information and Communication Technology. 480:608-617. https://doi.org/10.1007/978-3-319-45390-3_52S608617480Alarcon, F., Alemany, M.M.E., Lario, F.C., Oltra, R.F.: The lack of homogeneity in the product (LHP) in the ceramic tile industry and its impact on the reallocation of inventories. Boletin Soc. Espanola Ceram. Vidr. 50, 49–57 (2011). doi: 10.3989/cyv.072011Alemany, M.M.E., Grillo, H., Ortiz, A., Fuertes-Miquel, V.S.: A fuzzy model for shortage planning under uncertainty due to lack of homogeneity in planned production lots. Appl. Math. Model. (2015). doi: 10.1016/j.apm.2014.12.057Alemany, M.M.E., Lario, F.-C., Ortiz, A., Gomez, F.: Available-To-Promise modeling for multi-plant manufacturing characterized by lack of homogeneity in the product: an illustration of a ceramic case. Appl. Math. Model. 37, 3380–3398 (2013). doi: 10.1016/j.apm.2012.07.022Blanco, 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). doi: 10.1016/j.jfoodeng.2004.05.075Grillo, 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)Kilic, O.A., van Donk, D.P., Wijngaard, J., Tarim, S.A.: Order acceptance in food processing systems with random raw material requirements. Spectrum 32, 905–925 (2010). doi: 10.1007/s00291-010-0213-4Lin, J.T., Hong, I.H., Wu, C.H., Wang, K.S.: A model for batch available-to-promise in order fulfillment processes for TFT-LCD production chains. Comput. Ind. Eng. 59, 720–729 (2010). doi: 10.1016/j.cie.2010.07.026Maihami, R., Karimi, B.: Optimizing the pricing and replenishment policy for non-instantaneous deteriorating items with stochastic demand and promotional efforts. Comput. Oper. Res. 51, 302–312 (2014). doi: 10.1016/j.cor.2014.05.022Mundi, M.I., Alemany, M.M.E., Poler, R., Fuertes-Miquel, V.S.: Fuzzy sets to model master production effectively in Make to Stock companies with Lack of Homogeneity in the Product. Fuzzy Sets Syst. 293, 95–112 (2016). http://dx.doi.org/10.1016/j.fss.2015.06.009Tsao, Y.-C., Sheen, G.-J.: Dynamic pricing, promotion and replenishment policies for a deteriorating item under permissible delay in payments. Part Spec. Issue Top. Real-Time Supply Chain Manag. 35, 3562–3580 (2008). doi: 10.1016/j.cor.2007.01.02

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. 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Responsive Production in Manufacturing: A Modular Architecture

[EN] This paper proposes an architecture aiming at promoting the convergence of the physical and digital worlds, through CPS and IoT technologies, to accommodate more customized and higher quality products following Industry 4.0 concepts. The architecture combines concepts such as cyber-physical systems, decentralization, modularity and scalability aiming at responsive production. Combining these aspects with virtualization, contextualization, modeling and simulation capabilities it will enable self-adaptation, situational awareness and decentralized decision-making to answer dynamic market demands and support the design and reconfiguration of the manufacturing enterprise.The research leading to these results has received funding from the European Union H2020 project C2 NET (FoF-01-2014) nr 636909.Marques, M.; Agostinho, C.; Zacharewicz, G.; Poler, R.; Jardim-Goncalves, R. (2018). Responsive Production in Manufacturing: A Modular Architecture. 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Optimization model to support sustainable crop planning for reducing unfairness among farmers

File replaced (incorrect version) on 8/8/2022 by KT (LDS).Agri-food production must increase while food waste needs to be reduced for improving the position of farmers. To do so it is necessary to sustainably manage agri-food supply chains beginning with the crop planning decisions. Although the centralized approach has usually been adopted for this purpose, it can lead to unfair solutions due to inequitable distribution of profits among farmers causing their unwillingness to collaborate in the implementation of decisions made. To solve this, in this paper a novel centralized multi-objective mathematical programming model is proposed to support the sustainable crop planning definition for a region that jointly optimize three objectives aligned to the sustainability aspects: supply chain profits maximization (economic objective), waste minimization (environmental objective) and unfairness among farmers minimization (social objective), being the last two objectives novel in the crop planning literature. It has also shown the conflicting nature of the three objectives finding trade-offs among them. Other novelties of this proposal are: (1) anticipation of operative decisions (such as harvest, transport, sale, clearance sale, waste and unmet demand) when defining the crop planning, (2) possibility of clearing the oversupply of crops as a means of increasing the farmers’ profits and reducing waste, and (3) the modelling of a agri-food supply chain characterized by the lack of intermediaries between farmers and retailers, fostering the freshest product delivery and farmers’ power position. The model is solved by applying the weighted sum method concluding that the crop waste generated along the chain and the unfairness among farmers can be considerably reduced by little decreasing the optimal SC profits

Structural elements of coordination mechanisms in collaborative planning processes and their assessment through maturity models : application to a ceramic tile company

Maturity is defined as a measure to evaluate the capabilities of an organization in regards to a certain discipline. The Collaborative Planning Process is a very complex process and Coordination mechanisms are especially relevant in this field to align the plans of the supply chain members. The objective of this paper is to develop a maturity model and a methodology to perform assessment for the Structural Elements of Coordination Mechanisms in the Collaborative Planning Process. Structural elements are specified in order to characterize coordination mechanisms in a collaborative planning context and they have been defined as key areas to be assessed by the maturity model. The identified structural elements are: number of decision-makers, collaboration level, interdependence relationships nature, interdependence relationships type, number of coordination mechanisms, information exchanged, information processing, decision sequence characteristics and stopping criteria. Structural elements are assessed using the scheme of five levels: Initial, Repeatable, Defined, Managed and Optimized. This proposal has been applied to a ceramic tile company and the results are also reported

Group Decision Support for agriculture planning by a combination of Mathematical Model and Collaborative Tool

International audienceDecision making in the Agriculture domain can be a complex task. The land area allocated to each crop should be fixed every season according to several parameters: prices, demand, harvesting periods, seeds, ground, season etc… The decision to make becomes more difficult when a group of farmers must fix the price and all parameters all together. Generally, optimization models are useful for farmers to find no dominated solutions, but it remains difficult if the farmers have to agree on one solution. We combine two approaches in order to support a group of farmers engaged in this kind of decision making process. We firstly generate a set of no dominated solutions thanks to a centralized optimization model. Based on this set of solution we then used a Group Decision Support System called GRUS for choosing the best solution for the group of farmers. The combined approach allows us to determine the best solution for the group in a consensual way. This combination of approaches is very innovative for the Agriculture. This approach has been tested in laboratory in a previous work. In the current work the same experiment has been conducted with real business (farmers) in order to benefit from their expertise. The two experiments are compared

Literature Can: A Sociological Reading of Ngozi Chuma-Udeh's The Presidential Handshake

[EN] Decision making for farms is a complex task. Farmers have to fix the price of their production but several parameters have to be taken into account: harvesting, seeds, ground, season etc. This task is even more difficult when a group of farmers must make the decision. Generally, optimization models support the farmers to find no dominated solutions, but the problem remains difficult if they have to agree on one solution. In order to support the farmers for this complex decision we combine two approaches. We firstly generate a set of no dominated solutions thanks to a centralized optimization model. Based on this set of solution we then used a Group Decision Support System called GRUS for choosing the best solution for the group of farmers. The combined approach allows us to determine the best solution for the group in a consensual way. This combination of approaches is very innovative for the Agriculture domain.The authors acknowledge 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. One of the authors acknowledges the partial support of the Programme of Formation of University Professors of the Spanish Ministry of Education, Culture, and Sport (FPU15/03595).Zaraté, P.; Alemany Díaz, MDM.; Del Pino, M.; Esteso, A.; Camilleri, G. (2019). How to Support Group Decision Making in Horticulture: An Approach Based on the Combination of a Centralized Mathematical Model and a Group Decision Support System. Lecture Notes in Business Information Processing. 348:83-94. https://doi.org/10.1007/978-3-030-18819-1_7S839434