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Enterprise Resilience Assessment A Quantitative Approach

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Enterprise Resilience Assessment A Quantitative Approach

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dc.contributor.author Sanchis, R. es_ES
dc.contributor.author Poler, R. es_ES
dc.date.accessioned 2020-05-22T03:03:03Z
dc.date.available 2020-05-22T03:03:03Z
dc.date.issued 2019-08-10 es_ES
dc.identifier.uri http://hdl.handle.net/10251/144103
dc.description.abstract [EN] Enterprise resilience is a key capacity to guarantee enterprises¿ long-term continuity. This paper proposes a quantitative approach to enhance enterprise resilience by selecting optimal preventive actions to be activated to cushion the impact of disruptive events and to improve preparedness capability, one of the pillars of the enterprise resilience capacity. The proposed algorithms combine the dynamic programming approach with attenuation formulas to model real improvements when a combined set of preventive actions is activated for the same disruptive event. A numerical example is presented that shows remarkable reductions in the expected annual cost due to potential disruptive events. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sustainability es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Enterprise resilience es_ES
dc.subject Preventive actions es_ES
dc.subject Attenuation formulas es_ES
dc.subject Dynamic programming es_ES
dc.subject Optimization algorithm es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title Enterprise Resilience Assessment A Quantitative Approach es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/su11164327 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.description.bibliographicCitation Sanchis, R.; Poler, R. (2019). Enterprise Resilience Assessment A Quantitative Approach. Sustainability. 11(16):1-13. https://doi.org/10.3390/su11164327 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/su11164327 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 13 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 11 es_ES
dc.description.issue 16 es_ES
dc.identifier.eissn 2071-1050 es_ES
dc.relation.pasarela S\392533 es_ES
dc.description.references Baghersad, M., & Zobel, C. W. (2015). Economic impact of production bottlenecks caused by disasters impacting interdependent industry sectors. International Journal of Production Economics, 168, 71-80. doi:10.1016/j.ijpe.2015.06.011 es_ES
dc.description.references Cagliano, A. C., De Marco, A., Grimaldi, S., & Rafele, C. (2012). An integrated approach to supply chain risk analysis. Journal of Risk Research, 15(7), 817-840. doi:10.1080/13669877.2012.666757 es_ES
dc.description.references Vanpoucke, E., Boyer, K. K., & Vereecke, A. (2009). Supply chain information flow strategies: an empirical taxonomy. International Journal of Operations & Production Management, 29(12), 1213-1241. doi:10.1108/01443570911005974 es_ES
dc.description.references Chaudhuri, A., Boer, H., & Taran, Y. (2018). Supply chain integration, risk management and manufacturing flexibility. International Journal of Operations & Production Management, 38(3), 690-712. doi:10.1108/ijopm-08-2015-0508 es_ES
dc.description.references Oliva, F. L. (2016). A maturity model for enterprise risk management. International Journal of Production Economics, 173, 66-79. doi:10.1016/j.ijpe.2015.12.007 es_ES
dc.description.references Hendry, L. C., Stevenson, M., MacBryde, J., Ball, P., Sayed, M., & Liu, L. (2019). Local food supply chain resilience to constitutional change: the Brexit effect. International Journal of Operations & Production Management, 39(3), 429-453. doi:10.1108/ijopm-03-2018-0184 es_ES
dc.description.references Prior, T., & Hagmann, J. (2013). Measuring resilience: methodological and political challenges of a trend security concept. Journal of Risk Research, 17(3), 281-298. doi:10.1080/13669877.2013.808686 es_ES
dc.description.references Holling, C. S. (1973). Resilience and Stability of Ecological Systems. Annual Review of Ecology and Systematics, 4(1), 1-23. doi:10.1146/annurev.es.04.110173.000245 es_ES
dc.description.references Haimes, Y. Y. (2009). On the Definition of Resilience in Systems. Risk Analysis, 29(4), 498-501. doi:10.1111/j.1539-6924.2009.01216.x es_ES
dc.description.references Doorn, N. (2015). Resilience indicators: opportunities for including distributive justice concerns in disaster management. Journal of Risk Research, 20(6), 711-731. doi:10.1080/13669877.2015.1100662 es_ES
dc.description.references Scholz, R. W., Blumer, Y. B., & Brand, F. S. (2012). Risk, vulnerability, robustness, and resilience from a decision-theoretic perspective. Journal of Risk Research, 15(3), 313-330. doi:10.1080/13669877.2011.634522 es_ES
dc.description.references Reyes Levalle, R., & Nof, S. Y. (2015). Resilience by teaming in supply network formation and re-configuration. International Journal of Production Economics, 160, 80-93. doi:10.1016/j.ijpe.2014.09.036 es_ES
dc.description.references Kamalahmadi, M., & Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International Journal of Production Economics, 171, 116-133. doi:10.1016/j.ijpe.2015.10.023 es_ES
dc.description.references Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management, 20(1), 124-143. doi:10.1108/09574090910954873 es_ES
dc.description.references Comfort, L. K., Sungu, Y., Johnson, D., & Dunn, M. (2001). Complex Systems in Crisis: Anticipation and Resilience in Dynamic Environments. Journal of Contingencies and Crisis Management, 9(3), 144-158. doi:10.1111/1468-5973.00164 es_ES
dc.description.references Ayyub, B. M. (2013). Systems Resilience for Multihazard Environments: Definition, Metrics, and Valuation for Decision Making. Risk Analysis, 34(2), 340-355. doi:10.1111/risa.12093 es_ES
dc.description.references Cox Jr., L. A. T. (2012). Community Resilience and Decision Theory Challenges for Catastrophic Events. Risk Analysis, 32(11), 1919-1934. doi:10.1111/j.1539-6924.2012.01881.x es_ES
dc.description.references Schmitt, A. J., & Singh, M. (2012). A quantitative analysis of disruption risk in a multi-echelon supply chain. International Journal of Production Economics, 139(1), 22-32. doi:10.1016/j.ijpe.2012.01.004 es_ES
dc.description.references Dabhilkar, M., Birkie, S. E., & Kaulio, M. (2016). Supply-side resilience as practice bundles: a critical incident study. International Journal of Operations & Production Management, 36(8), 948-970. doi:10.1108/ijopm-12-2014-0614 es_ES
dc.description.references Dormady, N., Roa-Henriquez, A., & Rose, A. (2019). Economic resilience of the firm: A production theory approach. International Journal of Production Economics, 208, 446-460. doi:10.1016/j.ijpe.2018.07.017 es_ES
dc.description.references Polyviou, M., Croxton, K. L., & Knemeyer, A. M. (2019). Resilience of medium-sized firms to supply chain disruptions: the role of internal social capital. International Journal of Operations & Production Management, 40(1), 68-91. doi:10.1108/ijopm-09-2017-0530 es_ES
dc.description.references The Ripple Effect—How Manufacturing and Retail Executives View the Growing Challenge of Supply Chain Risk www2.deloitte.com/us/en/pages/operations/articles/supply-chain-risk-ripple-effect.html es_ES
dc.description.references Risk Ranking 2013–2015 http://www.ey.com/GL/en/Services/Advisory/Business-Pulse--top-10-risks-and-opportunities es_ES
dc.description.references Global Risk Management Survey—Executive Summary www.aon.com/2017-global-risk-management-survey/pdfs/2017-Aon-Global-Risk-Management-Survey-Full-Report-062617.pdf es_ES
dc.description.references The State of Enterprise Resilience Survey 2016/2017 www.controlrisks.com/our-thinking/insights/reports/the-state-of-enterprise-resilience-survey-2016-2017 es_ES
dc.description.references 20th CEO Survey www.pwc.com/gx/en/ceo-survey/2017/pwc-ceo-20th-survey-report-2017.pdf es_ES
dc.description.references BCI Supply Chain Resilience Report 2018 www.thebci.org/uploads/assets/uploaded/c50072bf-df5c-4c98-a5e1876aafb15bd0.pdf es_ES
dc.description.references The global risks report 2019 www.weforum.org/reports/the-global-risks-report-2019 es_ES
dc.description.references Madni, A. M., & Jackson, S. (2009). Towards a Conceptual Framework for Resilience Engineering. IEEE Systems Journal, 3(2), 181-191. doi:10.1109/jsyst.2009.2017397 es_ES
dc.description.references Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). ENSURING SUPPLY CHAIN RESILIENCE: DEVELOPMENT OF A CONCEPTUAL FRAMEWORK. Journal of Business Logistics, 31(1), 1-21. doi:10.1002/j.2158-1592.2010.tb00125.x es_ES
dc.description.references Bellman, R. (1954). The theory of dynamic programming. Bulletin of the American Mathematical Society, 60(6), 503-516. doi:10.1090/s0002-9904-1954-09848-8 es_ES
dc.description.references Cord, J. (1964). A Method for Allocating Funds to Investment Projects when Returns are Subject to Uncertainty. Management Science, 10(2), 335-341. doi:10.1287/mnsc.10.2.335 es_ES
dc.description.references Weingartner, H. M. (1966). Capital Budgeting of Interrelated Projects: Survey and Synthesis. Management Science, 12(7), 485-516. doi:10.1287/mnsc.12.7.485 es_ES
dc.description.references Weingartner, H. M., & Ness, D. N. (1967). Methods for the Solution of the Multidimensional 0/1 Knapsack Problem. Operations Research, 15(1), 83-103. doi:10.1287/opre.15.1.83 es_ES
dc.description.references Nemhauser, G. L., & Ullmann, Z. (1969). Discrete Dynamic Programming and Capital Allocation. Management Science, 15(9), 494-505. doi:10.1287/mnsc.15.9.494 es_ES
dc.description.references Boyer, V., Baz, D. E., & Elkihel, M. (2010). Solution of multidimensional knapsack problems via cooperation of dynamic programming and branch and bound. European J. of Industrial Engineering, 4(4), 434. doi:10.1504/ejie.2010.035653 es_ES
dc.description.references Skiena, S. S. (1999). Who is interested in algorithms and why? ACM SIGACT News, 30(3), 65-74. doi:10.1145/333623.333627 es_ES
dc.description.references Chou, T.-C., & Talalay, P. (1983). Analysis of combined drug effects: a new look at a very old problem. Trends in Pharmacological Sciences, 4, 450-454. doi:10.1016/0165-6147(83)90490-x es_ES
dc.description.references Chou, T.-C., & Talalay, P. (1984). Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors. Advances in Enzyme Regulation, 22, 27-55. doi:10.1016/0065-2571(84)90007-4 es_ES
dc.description.references Belen’kii, M. S., & Schinazi, R. F. (1994). Multiple drug effect analysis with confidence interval. Antiviral Research, 25(1), 1-11. doi:10.1016/0166-3542(94)90089-2 es_ES
dc.description.references Glossary of Terms and Symbols Used in Pharmacology. Pharmacology and Experimental Therapeutics Department at Boston University School of Medicine http://www.bumc.bu.edu/busm-pm/academics/resources/glossary/ es_ES
dc.description.references Foucquier, J., & Guedj, M. (2015). Analysis of drug combinations: current methodological landscape. Pharmacology Research & Perspectives, 3(3), e00149. doi:10.1002/prp2.149 es_ES
dc.description.references Tallarida, R. J. (2011). Quantitative Methods for Assessing Drug Synergism. Genes & Cancer, 2(11), 1003-1008. doi:10.1177/1947601912440575 es_ES


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