Multi-product inventory modeling with demand forecasting and Bayesian optimization

The complexity of supply chains requires advanced methods to schedule companies’ inventories. This paper presents a comparison of model forecasts of demand for multiple products, choosing the best among the following: autoregressive integrated moving average (ARIMA), exponential smoothing (ES), a Ba...

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Autores:
Valencia Cárdenas, Marisol
Díaz Serna, Francisco Javier
Correa Morales, Juan Carlos
Tipo de recurso:
Article of investigation
Fecha de publicación:
2016
Institución:
Tecnológico de Antioquia
Repositorio:
Repositorio Tdea
Idioma:
eng
OAI Identifier:
oai:dspace.tdea.edu.co:tdea/4015
Acceso en línea:
https://dspace.tdea.edu.co/handle/tdea/4015
Palabra clave:
Cadenas de suministro
Chaîne d'approvisionnement
Supply chains
Prognosis
Pronóstico
Prognóstico
Pronostic
Modelos dinámicos lineales
Dynamic linear models
Estadística bayesiana
Bayesian statistics
Modelos de inventario
Inventory models
Rights
openAccess
License
https://creativecommons.org/licenses/by-nc-nd/4.0/
id RepoTdea2_c11982a8a32a7b077c0d4d20dce24702
oai_identifier_str oai:dspace.tdea.edu.co:tdea/4015
network_acronym_str RepoTdea2
network_name_str Repositorio Tdea
repository_id_str
dc.title.none.fl_str_mv Multi-product inventory modeling with demand forecasting and Bayesian optimization
dc.title.translated.none.fl_str_mv Modelo de inventario multi-producto, con pronósticos de demanda y optimización Bayesiana
title Multi-product inventory modeling with demand forecasting and Bayesian optimization
spellingShingle Multi-product inventory modeling with demand forecasting and Bayesian optimization
Cadenas de suministro
Chaîne d'approvisionnement
Supply chains
Prognosis
Pronóstico
Prognóstico
Pronostic
Modelos dinámicos lineales
Dynamic linear models
Estadística bayesiana
Bayesian statistics
Modelos de inventario
Inventory models
title_short Multi-product inventory modeling with demand forecasting and Bayesian optimization
title_full Multi-product inventory modeling with demand forecasting and Bayesian optimization
title_fullStr Multi-product inventory modeling with demand forecasting and Bayesian optimization
title_full_unstemmed Multi-product inventory modeling with demand forecasting and Bayesian optimization
title_sort Multi-product inventory modeling with demand forecasting and Bayesian optimization
dc.creator.fl_str_mv Valencia Cárdenas, Marisol
Díaz Serna, Francisco Javier
Correa Morales, Juan Carlos
dc.contributor.author.none.fl_str_mv Valencia Cárdenas, Marisol
Díaz Serna, Francisco Javier
Correa Morales, Juan Carlos
dc.subject.agrovoc.none.fl_str_mv Cadenas de suministro
Chaîne d'approvisionnement
Supply chains
topic Cadenas de suministro
Chaîne d'approvisionnement
Supply chains
Prognosis
Pronóstico
Prognóstico
Pronostic
Modelos dinámicos lineales
Dynamic linear models
Estadística bayesiana
Bayesian statistics
Modelos de inventario
Inventory models
dc.subject.decs.none.fl_str_mv Prognosis
Pronóstico
Prognóstico
Pronostic
dc.subject.proposal.none.fl_str_mv Modelos dinámicos lineales
Dynamic linear models
Estadística bayesiana
Bayesian statistics
dc.subject.spines.none.fl_str_mv Modelos de inventario
Inventory models
description The complexity of supply chains requires advanced methods to schedule companies’ inventories. This paper presents a comparison of model forecasts of demand for multiple products, choosing the best among the following: autoregressive integrated moving average (ARIMA), exponential smoothing (ES), a Bayesian regression model (BRM), and a Bayesian dynamic linear model (BDLM). To this end, cases in which the time series is normally distributed are first simulated. Second, sales predictions for three products of a gas service station are estimated using the four models, revealing the BRM to be the best model. Subsequently, the multi-product inventory model is optimized. To define the policies for ordering, inventory, costs, and profits, a Bayesian search integrating elements of a Tabu search is used to improve the solution. This inventory model optimization process is then applied to the case of a gas service station in Colombia. Keywords: Dynamic Linear Models, Inventory Models, Forecasts, Bayesian Statistics.
publishDate 2016
dc.date.issued.none.fl_str_mv 2016
dc.date.accessioned.none.fl_str_mv 2023-10-31T22:28:49Z
dc.date.available.none.fl_str_mv 2023-10-31T22:28:49Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.language.iso.spa.fl_str_mv eng
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dc.relation.references.spa.fl_str_mv Simchi-Levi, D., Kaminski, P. and Simchi-Levi, E., Designing and managing the supply chain. 3rd ed. New York: McGraw-Hill; 2008.
Chen, X. and Simchi-Levi, D., Coordinating inventory control and pricing strategies with random demand and fixed ordering cost: The finite horizon case. Operations Research, 2004, 52(6), pp. 887-896. DOI: 10.1287/opre.1040.0127
Hillier, F. y Hillier, M., Métodos cuantitativos para administración. Third Ed. City: México. McGraw-Hill; 2007.
Garcia, C.A., Ibeas, A., Vilanova, R. and Herrera, J., Inventory control of supply chains: Mitigating the bullwhip effect by centralized and decentralized internal model control approaches. European Journal of Operational Research, 224(2), pp. 261-272, 2013. DOI: 10.1016/J.EJOR.2012.07.029
Sarimveis, H., Patrinos, P., Tarantilis, C.D. and Kiranoudis, C.T., Dynamic modeling and control of supply chain systems: A review. Computers and Operations Research. 35(11), pp. 3530-3561, 2008. DOI: 10.1016/J.COR.2007.01.017
Braun, M.W., Rivera, D.E., Flores, M.E., Carlyle, W.M. and Kempf, K.G., A model predictive control framework for robust management of multi-product, multi-echelon demand networks. Annual Reviews in Control, 27(2), pp. 229-245, 2003. DOI: 10.1016/j.arcontrol.2003.09.006
Pole, A., West, M. and Harrison, J., Nonnormal and nonlinear dynamic Bayesian modeling. In Bayesian analysis of time series and dynamic linear models. New York: Marcel Dekker; 1988, pp. 167-198.
West, M. and Harrison, J., Bayesian forecasting and dynamic models. Second ed. New York: Springer Series in Statistics; 1997.
Petris, G., An R package for dynamic linear models. Journal of Statistical Software [online], 36(12), pp. 1-16, 2010. Available at: http://www.jstatsoft.org/
Bermúdez, J.D., Segura, J.V. and Vercher, E., Bayesian forecasting with the Holt–Winters model. Journal of the Operational Research Society, 61(1), pp. 164-171, 2009. DOI: 10.1057/jors.2008.152.
Harrison, J. and Stevens, C., Bayesian forecasting. Journal of the Royan Statistical Society, 38(3), pp. 205-247, 1976.
Petris, G., Petrone, S. and Campagnoli, P., Dynamic linear models with R [online]. 2009. Available at: http://www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-77237-0
Kociecki, A., Kolasa, M. and Rubaszek, M., A Bayesian method of combining judgmental and model-based density forecasts. Economic Modelling, 29, pp. 1349-1355, 2012. DOI: 10.1016/j.econmod.2012.03.004
Coelho, C., Pezzulli, S, Balmaseda, M., Doblas-Reyes, F. and Stephenson, D., Forecast calibration and combination: A simple Bayesian approach for ENSO. Journal of Climate. 17(7), pp. 1504-1516, 2004. DOI: DOI: 10.1175/1520-0442(2004)017<1504:FCACAS>2.0.CO;2
Andersson, M. and Karlson, S., Bayesian forecast combination for VAR models. Sveriges Riskbanc-working Papers [online]. pp. 1-17, 2007. Available at: http://www.riksbank.se/Upload/Dokument_riksbank/Kat_publicerat/WorkingPapers/2007/wp216.pdf
Bijak, J., Bayesian methods in international migration forecasting. CEFMR Working Papers. Warsaw: Central European Forum for Migration Research, 2005.
Clements, M.P. and Hendry, D.F.H., Forecasting non-stationary economic time series. Cambridge: MIT Press; 2000, pp. 1-6.
Craig, P., Goldstein, M., Rougier, J. and Seheult, A.H., Bayesian forecasting for complex systems using computer simulators. Journal of the American Statistical Association, 96(454), pp. 717-729, 2001.
Duncan, G., Gorr, W. and Szczypula, J., Bayesian unrelated time forecasting series: For seemingly to local forecasting application government revenue. Management Science, 39(3), pp. 275-293, 1993.
Li, G., Shi, J. and Zhou, J., Bayesian adaptive combination of short-term wind speed forecasts from neural network models. Renewable Energy, 36(1), pp. 352-359, 2011. DOI: 10.1016/j.renene.2010.06.049
Meinhold, R.J. and Singpurwalla, N.D., Understanding the Kalman Filter. The American Statistician, 37(2), pp. 123-127, 1983. DOI: 10.2307/2685871
Neelamegham, R. and Chintagunta, P., A Bayesian model to forecast new product performance in domestic and international markets. Marketing Science [online]. 18(2), pp. 115-136, 1999. Available at: http://bear.warrington.ufl.edu/centers/mks/articles/684541.pdf
Oracle, Inc., The Bayesian Approach to Forecasting [online], 2006. Available at: http://www.oracle.com/us/products/applications/057028.pdf
Pedroza, C., A Bayesian forecasting model: Predicting U.S. male mortality. Biostatistics, 7(4), pp. 530-550, 2006.
Pezzulli, S., Frederic, P., Majithia, S., Sabbagh, S, Black, E, Sutton, R, et al., The seasonal forecast of electricity demand: A simple Bayesian model with climatological weather generator. Applied Stochastic Models in Business and Industry, 22(2), pp. 1-16, 2006. Available at: http://empslocal.ex.ac.uk/people/staff/dbs202/publications/2005/pezzullib.pdf
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Valencia, M., González, D. y Cardona, J., Metodología de un modelo de optimización para el pronóstico y manejo de inventarios usando el metaheurístico Tabu. Revista de Ingeniería. 24(1), pp. 13-27, 2014. DOI: 10.15517/ring.v24i1.13771
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Jeyanthi, N. and Radhakrishnan, P., Optimizing multi product inventory using genetic algorithm for efficient supply chain management involving lead time. International Journal of Computer Science and Network Security [online], 10(5), pp. 231-239, 2010. Available at: http://paper.ijcsns.org/07_book/201005/20100534.pdf
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Valencia, M., Dynamic model for the multiproduct inventory optimization with multivariate. PhD. Thesis. Department of Engineering, Facultad de Minas, Universidad Nacional de Colombia, Sede Medellín, Colombia, 2016.
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Wang, S., Exponential smoothing for forecasting and bayesian validation of computer models [online]. Thesis, Georgia Institute of Technology, [Online]. 2006. Available at: https://smartech.gatech.edu/bitstream/handle/1853/19753/wang_shuchun_200612_phd.pdf
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spelling Valencia Cárdenas, Marisol7d8db2ff-5466-4ebb-a8b6-daf0bdc169d8Díaz Serna, Francisco Javierc3741378-d19d-4e48-901e-28d36d7e22a7Correa Morales, Juan Carlos80dd1582-58bc-447f-85ba-6aaadd85c30a2023-10-31T22:28:49Z2023-10-31T22:28:49Z20160012-7353https://dspace.tdea.edu.co/handle/tdea/40152346-2183The complexity of supply chains requires advanced methods to schedule companies’ inventories. This paper presents a comparison of model forecasts of demand for multiple products, choosing the best among the following: autoregressive integrated moving average (ARIMA), exponential smoothing (ES), a Bayesian regression model (BRM), and a Bayesian dynamic linear model (BDLM). To this end, cases in which the time series is normally distributed are first simulated. Second, sales predictions for three products of a gas service station are estimated using the four models, revealing the BRM to be the best model. Subsequently, the multi-product inventory model is optimized. To define the policies for ordering, inventory, costs, and profits, a Bayesian search integrating elements of a Tabu search is used to improve the solution. This inventory model optimization process is then applied to the case of a gas service station in Colombia. Keywords: Dynamic Linear Models, Inventory Models, Forecasts, Bayesian Statistics.La complejidad de las cadenas de suministro exige mejores métodos para programar los inventarios de una empresa. En este trabajo se presenta una comparación entre modelos de pronósticos de demanda de múltiples productos, eligiendo el mejor entre: ARIMA, Suavización exponencial, Regresión Lineal Bayesiana y un Modelo Lineal Dinámico Bayesiano. Para ello, primero se realiza una simulación de casos donde no hay una Distribución Normal en las series de tiempo, segundo, se estiman las predicciones de ventas de tres productos de una estación de servicios de gasolina con los cuatro modelos, encontrando los mejores resultados para la Regresión Lineal Bayesiana. Seguido a esto, se presenta la optimización de un Modelo de Inventarios Multi-Producto. Para definir la política de pedidos, inventarios, costos y ganancias, se utiliza una búsqueda bayesiana, que integra elementos de búsqueda Tabú para mejorar la solución. Dicha Optimización del Modelo de Inventarios se aplica a un caso de una estación de combustibles en Colombia. Palabras clave: Modelos Dinámicos Lineales, Modelos de Inventarios, Pronósticos, Estadística Bayesiana.9 páginasapplication/pdfengUniversidad Nacional de ColombiaColombiahttps://creativecommons.org/licenses/by-nc-nd/4.0/Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2https://revistas.unal.edu.co/index.php/dyna/article/view/51310Multi-product inventory modeling with demand forecasting and Bayesian optimizationModelo de inventario multi-producto, con pronósticos de demanda y optimización BayesianaArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Colombia24319823583DynaSimchi-Levi, D., Kaminski, P. and Simchi-Levi, E., Designing and managing the supply chain. 3rd ed. New York: McGraw-Hill; 2008.Chen, X. and Simchi-Levi, D., Coordinating inventory control and pricing strategies with random demand and fixed ordering cost: The finite horizon case. Operations Research, 2004, 52(6), pp. 887-896. DOI: 10.1287/opre.1040.0127Hillier, F. y Hillier, M., Métodos cuantitativos para administración. Third Ed. City: México. McGraw-Hill; 2007.Garcia, C.A., Ibeas, A., Vilanova, R. and Herrera, J., Inventory control of supply chains: Mitigating the bullwhip effect by centralized and decentralized internal model control approaches. European Journal of Operational Research, 224(2), pp. 261-272, 2013. DOI: 10.1016/J.EJOR.2012.07.029Sarimveis, H., Patrinos, P., Tarantilis, C.D. and Kiranoudis, C.T., Dynamic modeling and control of supply chain systems: A review. Computers and Operations Research. 35(11), pp. 3530-3561, 2008. DOI: 10.1016/J.COR.2007.01.017Braun, M.W., Rivera, D.E., Flores, M.E., Carlyle, W.M. and Kempf, K.G., A model predictive control framework for robust management of multi-product, multi-echelon demand networks. Annual Reviews in Control, 27(2), pp. 229-245, 2003. DOI: 10.1016/j.arcontrol.2003.09.006Pole, A., West, M. and Harrison, J., Nonnormal and nonlinear dynamic Bayesian modeling. In Bayesian analysis of time series and dynamic linear models. New York: Marcel Dekker; 1988, pp. 167-198.West, M. and Harrison, J., Bayesian forecasting and dynamic models. Second ed. New York: Springer Series in Statistics; 1997.Petris, G., An R package for dynamic linear models. Journal of Statistical Software [online], 36(12), pp. 1-16, 2010. Available at: http://www.jstatsoft.org/Bermúdez, J.D., Segura, J.V. and Vercher, E., Bayesian forecasting with the Holt–Winters model. Journal of the Operational Research Society, 61(1), pp. 164-171, 2009. DOI: 10.1057/jors.2008.152.Harrison, J. and Stevens, C., Bayesian forecasting. Journal of the Royan Statistical Society, 38(3), pp. 205-247, 1976.Petris, G., Petrone, S. and Campagnoli, P., Dynamic linear models with R [online]. 2009. Available at: http://www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-77237-0Kociecki, A., Kolasa, M. and Rubaszek, M., A Bayesian method of combining judgmental and model-based density forecasts. Economic Modelling, 29, pp. 1349-1355, 2012. DOI: 10.1016/j.econmod.2012.03.004Coelho, C., Pezzulli, S, Balmaseda, M., Doblas-Reyes, F. and Stephenson, D., Forecast calibration and combination: A simple Bayesian approach for ENSO. Journal of Climate. 17(7), pp. 1504-1516, 2004. DOI: DOI: 10.1175/1520-0442(2004)017<1504:FCACAS>2.0.CO;2Andersson, M. and Karlson, S., Bayesian forecast combination for VAR models. Sveriges Riskbanc-working Papers [online]. pp. 1-17, 2007. Available at: http://www.riksbank.se/Upload/Dokument_riksbank/Kat_publicerat/WorkingPapers/2007/wp216.pdfBijak, J., Bayesian methods in international migration forecasting. CEFMR Working Papers. Warsaw: Central European Forum for Migration Research, 2005.Clements, M.P. and Hendry, D.F.H., Forecasting non-stationary economic time series. Cambridge: MIT Press; 2000, pp. 1-6.Craig, P., Goldstein, M., Rougier, J. and Seheult, A.H., Bayesian forecasting for complex systems using computer simulators. Journal of the American Statistical Association, 96(454), pp. 717-729, 2001.Duncan, G., Gorr, W. and Szczypula, J., Bayesian unrelated time forecasting series: For seemingly to local forecasting application government revenue. Management Science, 39(3), pp. 275-293, 1993.Li, G., Shi, J. and Zhou, J., Bayesian adaptive combination of short-term wind speed forecasts from neural network models. Renewable Energy, 36(1), pp. 352-359, 2011. DOI: 10.1016/j.renene.2010.06.049Meinhold, R.J. and Singpurwalla, N.D., Understanding the Kalman Filter. The American Statistician, 37(2), pp. 123-127, 1983. DOI: 10.2307/2685871Neelamegham, R. and Chintagunta, P., A Bayesian model to forecast new product performance in domestic and international markets. Marketing Science [online]. 18(2), pp. 115-136, 1999. Available at: http://bear.warrington.ufl.edu/centers/mks/articles/684541.pdfOracle, Inc., The Bayesian Approach to Forecasting [online], 2006. Available at: http://www.oracle.com/us/products/applications/057028.pdfPedroza, C., A Bayesian forecasting model: Predicting U.S. male mortality. Biostatistics, 7(4), pp. 530-550, 2006.Pezzulli, S., Frederic, P., Majithia, S., Sabbagh, S, Black, E, Sutton, R, et al., The seasonal forecast of electricity demand: A simple Bayesian model with climatological weather generator. Applied Stochastic Models in Business and Industry, 22(2), pp. 1-16, 2006. 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New York: Springer-Verlag, 2005.Cadenas de suministroChaîne d'approvisionnementSupply chainsPrognosisPronósticoPrognósticoPronosticModelos dinámicos linealesDynamic linear modelsEstadística bayesianaBayesian statisticsModelos de inventarioInventory modelsTHUMBNAILMulti-product inventory modeling with demand forecasting and Bayesian optimization.pdf.jpgMulti-product inventory modeling with demand forecasting and Bayesian optimization.pdf.jpgGenerated Thumbnailimage/jpeg15238https://dspace.tdea.edu.co/bitstream/tdea/4015/4/Multi-product%20inventory%20modeling%20with%20demand%20forecasting%20and%20Bayesian%20optimization.pdf.jpg873500c3ebb1702560b0a4afb38f23baMD54open accessTEXTMulti-product inventory modeling with demand forecasting and Bayesian optimization.pdf.txtMulti-product inventory modeling with demand forecasting and Bayesian optimization.pdf.txtExtracted texttext/plain45835https://dspace.tdea.edu.co/bitstream/tdea/4015/3/Multi-product%20inventory%20modeling%20with%20demand%20forecasting%20and%20Bayesian%20optimization.pdf.txt6b27f9934386c930f003d59a2d8ff277MD53open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://dspace.tdea.edu.co/bitstream/tdea/4015/2/license.txt2f9959eaf5b71fae44bbf9ec84150c7aMD52open accessORIGINALMulti-product inventory modeling with demand forecasting and Bayesian optimization.pdfMulti-product inventory modeling with demand forecasting and Bayesian optimization.pdfapplication/pdf653159https://dspace.tdea.edu.co/bitstream/tdea/4015/1/Multi-product%20inventory%20modeling%20with%20demand%20forecasting%20and%20Bayesian%20optimization.pdf16891d289a1a75cace885f337565fb1dMD51open accesstdea/4015oai:dspace.tdea.edu.co:tdea/40152023-11-01 03:01:29.431An error occurred on the license name.|||https://creativecommons.org/licenses/by-nc-nd/4.0/open accessRepositorio Institucional Tecnologico de 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 incorporada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.


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