INTEGRATING INVENTORY AND TRANSPORT CAPACITY PLANNING IN A FOOD SUPPLY CHAIN

[EN] The general objective of this paper is to simulate a supply chain to assess the effects that different inventory management policies and transport capacity systems have on costs (transport) and service levels (stockouts). This paper specifically aimed to facilitate the decision-making process about planning distribution capacities, particularly when contracting a transport fleet in a supply chain under uncertainty with a 1-year time horizon by evaluating different types of scenarios, which vary depending on availability of vehicles and obtaining vehicles. The system dynamics simulation model was applied to a real-world food supply chain and can be adopted by chains related to diversified cropping systems. The results provide the best decision alternative in terms of costs and inventory levels by considering the transport capacity life cycle, the time to acquire additional transport capacity, the reorder point in days of stock and the target inventory.This work was supported by the European Commission Horizon 2020 project entitled 'Crop diversification and low-input farming cross Europe: from practitioners' engagement and ecosystems services to increased revenues and value chain organisation' (Diverfarming), grant agreement 728003.Freile, A.; Mula, J.; Campuzano Bolarin, F. (2020). INTEGRATING INVENTORY AND TRANSPORT CAPACITY PLANNING IN A FOOD SUPPLY CHAIN. International Journal of Simulation Modelling. 19(3):434-445. https://doi.org/10.2507/IJSIMM19-3-52343444519

Propuesta de programa docente de simulación de la cadena de suministro

[ES] En este artículo se aborda el perfil docente de Simulación de la Cadena de Suministro. Para ello, se analiza, de forma global, la titulación de Máster Universitario en Ingeniería Avanzada de Producción, Logística y Cadena de Suministro (MUIAPLCS): los objetivos y el perfil de competencias del egresado, el acceso y perfil del alumnado, la estructura del plan de estudios y cómo se satisface la demanda de dicha titulación en el territorio nacional. Seguidamente, se desarrolla la propuesta de programa docente para la asignatura de Simulación de la Cadena de Suministro de la titulación de MUIAPLCS. Así, se contextualiza la asignatura en el plan de estudios en vigor, atendiendo a las competencias, la coordinación y organización de la asignatura y las metodologías de enseñanza-aprendizaje. Posteriormente, se propone el temario, la bibliografía recomendada y el sistema de evaluación, entre otros.[EN] In this paper, we address the teaching profile of Supply Chain Simulation. For this, we analyse, in a general way, the Master¿s degree in Advanced Engineering of Production, Logistics and Supply Chain (MUIAPLCS): the aims and the profile of competences of the graduate, the access and profile of the students, the structure of the syllabus and how satisfies the demand of this Master¿s degree in the national territory. Next, it develops the proposal of the syllabus for the subject of Supply Chain Simulation of the MUIAPLCS degree. Thus, it contextualises the subject in the current syllabus, attending to the competences, the coordination and organisation of the subject and the teaching-learning methodologies. Then, it proposes the temary, the recommended bibliography and the system of evaluation, among othersMula, J.; Campuzano Bolarin, F. (2019). Propuesta de programa docente de simulación de la cadena de suministro. Universitat Politècnica de València. 407-414. http://hdl.handle.net/10251/179358S40741

Modelling performance management measures through statistics and system dynamics-based simulation

[EN] The objective of this paper is to establish a methodology that combines performance measurement, a statistical record of measures to identify any relations among them, and system dynamics-based simulation modeling with the aim of supporting operations decision systems. This methodology intends to provide the comprehensive analysis of performance in such a way that it also analyzes the sensitivity and optimization of certain metrics according to requirements in each case. In the literature, this appears as a poorly developed research area. Some relevant studies have been identified which have attempted this combination, but have not completely established it.Grillo-Espinoza, H.; Campuzano Bolarin, F.; Mula, J. (2018). Modelling performance management measures through statistics and system dynamics-based simulation. Direccion y Organizacion. 65:20-35. http://hdl.handle.net/10251/120641S20356

A rolling horizon simulation approach for managing demand with lead time variability

[EN] This paper proposes a rolling horizon (RH) approach to deal with management problems under dynamic demand in planning horizons with variable lead times using system dynamics (SD) simulation. Thus, the nature of dynamic RH solutions entails no inconveniences to contemplate planning horizons with unpredictable demands. This is mainly because information is periodically updated and replanning is done in time. Therefore, inventory and logistic costs may be lower. For the first time, an RH is applied for demand management with variable lead times along with SD simulation models, which allowed the use of lot-sizing techniques to be evaluated (Wagner-Whitin and Silver-Meal). The basic scenario is based on a real-world example from an automotive single-level SC composed of a first-tier supplier and a car assembler that contemplates uncertain demands while planning the RH and 216 subscenarios by modifying constant and variable lead times, holding costs and order costs, combined with lot-sizing techniques. Twenty-eight more replications comprising 504 new subscenarios with variable lead times are generated to represent a relative variation coefficient of the initial demand. We conclude that our RH simulation approach, along with lot-sizing techniques, can generate more sustainable planning results in total costs, fill rates and bullwhip effect terms.This work was supported by the European Commission Horizon 2020 project Diverfarming [grant number 728003].Campuzano Bolarin, F.; Mula, J.; Díaz-Madroñero Boluda, FM.; Legaz-Aparicio, Á. (2020). A rolling horizon simulation approach for managing demand with lead time variability. International Journal of Production Research. 58(12):3800-3820. https://doi.org/10.1080/00207543.2019.1634849S380038205812Agaran, B., W. W. Buchanan, and M. K. Yurtseven. 2007. “Regulating Bullwhip Effect in Supply Chains through Modern Control Theory.” in PICMET ‘07 – 2007 Portland International Conference on Management of Engineering & Technology, 2391–2398. IEEE. http://doi.org/10.1109/PICMET.2007.4349573.Baker, K. R. (1977). AN EXPERIMENTAL STUDY OF THE EFFECTIVENESS OF ROLLING SCHEDULES IN PRODUCTION PLANNING. Decision Sciences, 8(1), 19-27. doi:10.1111/j.1540-5915.1977.tb01065.xBhattacharya, R., & Bandyopadhyay, S. (2010). A review of the causes of bullwhip effect in a supply chain. The International Journal of Advanced Manufacturing Technology, 54(9-12), 1245-1261. doi:10.1007/s00170-010-2987-6Boulaksil, Y., Fransoo, J. C., & van Halm, E. N. G. (2007). Setting safety stocks in multi-stage inventory systems under rolling horizon mathematical programming models. OR Spectrum, 31(1). doi:10.1007/s00291-007-0086-3Brown, M. E., & Kshirsagar, V. (2015). Weather and international price shocks on food prices in the developing world. Global Environmental Change, 35, 31-40. doi:10.1016/j.gloenvcha.2015.08.003Campuzano, F., Mula, J., & Peidro, D. (2010). Fuzzy estimations and system dynamics for improving supply chains. Fuzzy Sets and Systems, 161(11), 1530-1542. doi:10.1016/j.fss.2009.12.002Campuzano-Bolarín, F., Mula, J., & Peidro, D. (2013). An extension to fuzzy estimations and system dynamics for improving supply chains. International Journal of Production Research, 51(10), 3156-3166. doi:10.1080/00207543.2012.760854De Sampaio, R. J. B., Wollmann, R. R. G., & Vieira, P. F. G. (2017). A flexible production planning for rolling-horizons. International Journal of Production Economics, 190, 31-36. doi:10.1016/j.ijpe.2017.01.003Díaz-Madroñero, M., Mula, J., & Jiménez, M. (2014). Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions. International Journal of Production Research, 52(23), 6971-6988. doi:10.1080/00207543.2014.920115Díaz-Madroñero, M., Mula, J., & Peidro, D. (2017). A mathematical programming model for integrating production and procurement transport decisions. Applied Mathematical Modelling, 52, 527-543. doi:10.1016/j.apm.2017.08.009Disney, S. M., Naim, M. M., & Potter, A. (2004). Assessing the impact of e-business on supply chain dynamics. International Journal of Production Economics, 89(2), 109-118. doi:10.1016/s0925-5273(02)00464-4Dominguez, R., Cannella, S., & Framinan, J. M. (2015). The impact of the supply chain structure on bullwhip effect. Applied Mathematical Modelling, 39(23-24), 7309-7325. doi:10.1016/j.apm.2015.03.012Fransoo, J. C., & Wouters, M. J. F. (2000). Measuring the bullwhip effect in the supply chain. Supply Chain Management: An International Journal, 5(2), 78-89. doi:10.1108/13598540010319993Geary, S., Disney, S. M., & Towill, D. R. (2006). On bullwhip in supply chains—historical review, present practice and expected future impact. International Journal of Production Economics, 101(1), 2-18. doi:10.1016/j.ijpe.2005.05.009Giard, V., & Sali, M. (2013). The bullwhip effect in supply chains: a study of contingent and incomplete literature. International Journal of Production Research, 51(13), 3880-3893. doi:10.1080/00207543.2012.754552Hosoda, T., & Disney, S. M. (2018). A unified theory of the dynamics of closed-loop supply chains. European Journal of Operational Research, 269(1), 313-326. doi:10.1016/j.ejor.2017.07.020Hussain, M., & Drake, P. R. (2011). Analysis of the bullwhip effect with order batching in multi‐echelon supply chains. International Journal of Physical Distribution & Logistics Management, 41(10), 972-990. doi:10.1108/09600031111185248Jakšič, M., & Rusjan, B. (2008). The effect of replenishment policies on the bullwhip effect: A transfer function approach. European Journal of Operational Research, 184(3), 946-961. doi:10.1016/j.ejor.2006.12.018Karimi, B., Fatemi Ghomi, S. M. T., & Wilson, J. M. (2003). The capacitated lot sizing problem: a review of models and algorithms. Omega, 31(5), 365-378. doi:10.1016/s0305-0483(03)00059-8Li, J., Ghadge, A., & Tiwari, M. K. (2016). Impact of replenishment strategies on supply chain performance under e-shopping scenario. Computers & Industrial Engineering, 102, 78-87. doi:10.1016/j.cie.2016.10.005Lian, Z., Liu, L., & Zhu, S. X. (2010). Rolling-horizon replenishment: Policies and performance analysis. Naval Research Logistics (NRL), 57(6), 489-502. doi:10.1002/nav.20416D. Mendoza, J., Mula, J., & Campuzano-Bolarin, F. (2014). Using systems dynamics to evaluate the tradeoff among supply chain aggregate production planning policies. International Journal of Operations & Production Management, 34(8), 1055-1079. doi:10.1108/ijopm-06-2012-0238Moreno, J. R., Mula, J., & Campuzano-Bolarin, F. (2015). Increasing the Equity of a Flower Supply Chain by Improving Order Management and Supplier Selection. International Journal of Simulation Modelling, 14(2), 201-214. doi:10.2507/ijsimm14(2)2.284Mula, J., Peidro, D., & Poler, R. (2010). The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand. International Journal of Production Economics, 128(1), 136-143. doi:10.1016/j.ijpe.2010.06.007Mula, J., Poler, R., & Garcia, J. P. (2006). MRP with flexible constraints: A fuzzy mathematical programming approach. Fuzzy Sets and Systems, 157(1), 74-97. doi:10.1016/j.fss.2005.05.045Mula, J., Poler, R., & Garcia-Sabater, J. P. (2007). Material Requirement Planning with fuzzy constraints and fuzzy coefficients. Fuzzy Sets and Systems, 158(7), 783-793. doi:10.1016/j.fss.2006.11.003Mula, J., Poler, R., & Garcia-Sabater, J. P. (2008). Capacity and material requirement planning modelling by comparing deterministic and fuzzy models. International Journal of Production Research, 46(20), 5589-5606. doi:10.1080/00207540701413912Ostberg, S., Schewe, J., Childers, K., & Frieler, K. (2018). Changes in crop yields and their variability at different levels of global warming. Earth System Dynamics, 9(2), 479-496. doi:10.5194/esd-9-479-2018Pacheco, E. de O., Cannella, S., Lüders, R., & Barbosa-Povoa, A. P. (2017). Order-up-to-level policy update procedure for a supply chain subject to market demand uncertainty. Computers & Industrial Engineering, 113, 347-355. doi:10.1016/j.cie.2017.09.015Nyoman Pujawan, I. (2004). The effect of lot sizing rules on order variability. European Journal of Operational Research, 159(3), 617-635. doi:10.1016/s0377-2217(03)00419-3Rafiei, R., Nourelfath, M., Gaudreault, J., Santa-Eulalia, L. A., & Bouchard, M. (2013). A periodic re-planning approach for demand-driven wood remanufacturing industry: a real-scale application. International Journal of Production Research, 52(14), 4198-4215. doi:10.1080/00207543.2013.869631Sahin, F., Narayanan, A., & Robinson, E. P. (2013). Rolling horizon planning in supply chains: review, implications and directions for future research. International Journal of Production Research, 51(18), 5413-5436. doi:10.1080/00207543.2013.775523Sahin, F., & Robinson, E. P. (2002). Flow Coordination and Information Sharing in Supply Chains: Review, Implications, and Directions for Future Research. Decision Sciences, 33(4), 505-536. doi:10.1111/j.1540-5915.2002.tb01654.xSahin, F., & Robinson, E. P. (2004). Information sharing and coordination in make-to-order supply chains. Journal of Operations Management, 23(6), 579-598. doi:10.1016/j.jom.2004.08.007Schmidt, M., Münzberg, B., & Nyhuis, P. (2015). Determining Lot Sizes in Production Areas – Exact Calculations versus Research Based Estimation. Procedia CIRP, 28, 143-148. doi:10.1016/j.procir.2015.04.024Simpson, N. . (1999). Multiple level production planning in rolling horizon assembly environments. European Journal of Operational Research, 114(1), 15-28. doi:10.1016/s0377-2217(98)00005-8Sridharan, S. V., Berry, W. L., & Udayabhanu, V. (1988). MEASURING MASTER PRODUCTION SCHEDULE STABILITY UNDER ROLLING PLANNING HORIZONS. Decision Sciences, 19(1), 147-166. doi:10.1111/j.1540-5915.1988.tb00259.xTaylor, D. H., & Fearne, A. (2006). Towards a framework for improvement in the management of demand in agri‐food supply chains. Supply Chain Management: An International Journal, 11(5), 379-384. doi:10.1108/13598540610682381Van den Heuvel, W., & Wagelmans, A. P. M. (2005). A comparison of methods for lot-sizing in a rolling horizon environment. Operations Research Letters, 33(5), 486-496. doi:10.1016/j.orl.2004.10.001Vargas, V., & Metters, R. (2011). A master production scheduling procedure for stochastic demand and rolling planning horizons. International Journal of Production Economics, 132(2), 296-302. doi:10.1016/j.ijpe.2011.04.025Wagner, H. M., & Whitin, T. M. (1958). Dynamic Version of the Economic Lot Size Model. Management Science, 5(1), 89-96. doi:10.1287/mnsc.5.1.89WEMMERLÖV, U., & WHYBARK, D. C. (1984). Lot-sizing under uncertainty in a rolling schedule environment. International Journal of Production Research, 22(3), 467-484. doi:10.1080/00207548408942467Zhang, C., & Qu, X. (2015). The effect of global oil price shocks on China’s agricultural commodities. Energy Economics, 51, 354-364. doi:10.1016/j.eneco.2015.07.01

The impact of fiscal policies and community services on housing market dynamics and urban land rent in crisis \u2013 The comparative analysis between Florida and Spain

Florida and Spanish coastal areas are two major destinations, where American and European citizens, mainly older persons from North of these two continents, are buying properties where they intend to move when they retire. Therefore until 2007, the flow of older inhabitants to the south was increasing rapidly, influencing the dynamics of housing construction. For example, in 2006 only the yearly growth of transactions in Spain was 30%, but after the crises, the inflows were fallen, and a yearly number of all housing transactions since 2007 till 2014 has fallen more than 30%. The flow of foreign buyers participates to the housing transactions more than 17%. When total number of all transactions in Spain was also decreasing in the time window 2012-2016 with minimum in 2014, so that the index 2014/2012 was 47 only, the value of these transactions with foreign buyers was growing again since 2012 from 6,4 19109 to 11,6 19109 in 2016, which means more than 1 % of Spanish GDP per year (Observatorio de Vivenda y Suelo no. 21, 2017). The tax policy and communal services in both continents influence this intensity of flows. In the paper, we shall present how some factors influence the urban land rent and its capitalization in Spain and Florida, and also how fiscal policies and the communal services should be improved to increase the value of transactions or the net present value of housing rents. The main results show that the land rent and the value of transactions in the area where communal services for older persons are more developed could nearly double. In the USA they have not introduced the real estate transaction taxes while in Spain the transaction tax is determined by region and is equal to 7 % - 8 % (At Murcia region is equal to 8 %). We have found out that the proper fiscal policy enables to adapt the housing market in case of volatile economic growth. Therefore this financial flow could be more stable and the time windows in a period of economic decline and crises which influence badly on housing market can be reduced substantially. This conclusion is important also for industrial engineering sector which can contribute to better communal services by organizational schemes which reduce the prices of facilities and other services to older inhabitants at the same costs of logistics for seniors, which could contribute to the higher attractiveness of locations for new buyers and tenants