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A dynamic supply chain BSC-based methodology to improve operations efficiency

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A dynamic supply chain BSC-based methodology to improve operations efficiency

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dc.contributor.author Rodríguez Rodríguez, Raúl es_ES
dc.contributor.author Alfaro Saiz, Juan José es_ES
dc.contributor.author Carot Sierra, José Miguel es_ES
dc.date.accessioned 2021-05-12T03:32:21Z
dc.date.available 2021-05-12T03:32:21Z
dc.date.issued 2020-11 es_ES
dc.identifier.issn 0166-3615 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166217
dc.description.abstract [EN] This paper presents how to objectively set up the process for activation of the future action plans from a supply chain Balanced Scorecard (BSC), aligning such an activation process to reach the main objectives and being able to save resources. Additionally, it also shows how to define supply chain business scenarios based on the future expected values of the Key Performance Indicators (KPI). Once the future business scenario has been chosen, the KPI values associated with this scenario will become the new KPI values of the BSC in order to align efforts and save resources Further, the strategic objectives associated with the KPIs may also be extensively retuned, thus redefining their target values. From the application carried out, there were four KPIs whose values needed, in absolute terms, to be increased and four other KPIs to be decreased. Additionally, the associated strategic objectives were retuned; for example, the value of the strategic objective "To reduce product development costs" was initially set in the BSC at 10 % but it was deduced to 8% as a consequence of the application of the proposal. As a result, this methodology has aligned all the future efforts of a whole supply chain in order to reach one point on a plane, which is a combination of interrelated supply chain KPI. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Computers in Industry es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Performance management es_ES
dc.subject Scenarios es_ES
dc.subject Principal component analysis es_ES
dc.subject Operations efficiency es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title A dynamic supply chain BSC-based methodology to improve operations efficiency es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.compind.2020.103294 es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.description.bibliographicCitation Rodríguez Rodríguez, R.; Alfaro Saiz, JJ.; Carot Sierra, JM. (2020). A dynamic supply chain BSC-based methodology to improve operations efficiency. Computers in Industry. 122:1-10. https://doi.org/10.1016/j.compind.2020.103294 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.compind.2020.103294 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 10 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 122 es_ES
dc.relation.pasarela S\420302 es_ES
dc.description.references Alfaro-Saiz, J.-J., Bas, M. C., Giner-Bosch, V., Rodríguez-Rodríguez, R., & Verdecho, M.-J. (2020). An evaluation of the environmental factors for supply chain strategy decisions using grey systems and composite indicators. Applied Mathematical Modelling, 79, 490-505. doi:10.1016/j.apm.2019.10.048 es_ES
dc.description.references Balfaqih, H., Nopiah, Z. M., Saibani, N., & Al-Nory, M. T. (2016). Review of supply chain performance measurement systems: 1998–2015. Computers in Industry, 82, 135-150. doi:10.1016/j.compind.2016.07.002 es_ES
dc.description.references Cai, J., Liu, X., Xiao, Z., & Liu, J. (2009). Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment. Decision Support Systems, 46(2), 512-521. doi:10.1016/j.dss.2008.09.004 es_ES
dc.description.references Chenhall, R. H., & Langfield-Smith, K. (2007). Multiple Perspectives of Performance Measures. European Management Journal, 25(4), 266-282. doi:10.1016/j.emj.2007.06.001 es_ES
dc.description.references Chytas, P., Glykas, M., & Valiris, G. (2011). A proactive balanced scorecard. International Journal of Information Management, 31(5), 460-468. doi:10.1016/j.ijinfomgt.2010.12.007 es_ES
dc.description.references Coughlan, P., & Coghlan, D. (2002). Action research for operations management. International Journal of Operations & Production Management, 22(2), 220-240. doi:10.1108/01443570210417515 es_ES
dc.description.references Cross, K. F., & Lynch, R. L. (1988). The «SMART» way to define and sustain success. National Productivity Review, 8(1), 23-33. doi:10.1002/npr.4040080105 es_ES
dc.description.references Denham, M. C. (1995). Implementing partial least squares. Statistics and Computing, 5(3), 191-202. doi:10.1007/bf00142661 es_ES
dc.description.references Derbyshire, J. (2017). Potential surprise theory as a theoretical foundation for scenario planning. Technological Forecasting and Social Change, 124, 77-87. doi:10.1016/j.techfore.2016.05.008 es_ES
dc.description.references Dey, P. K., & Cheffi, W. (2012). Green supply chain performance measurement using the analytic hierarchy process: a comparative analysis of manufacturing organisations. Production Planning & Control, 24(8-9), 702-720. doi:10.1080/09537287.2012.666859 es_ES
dc.description.references Dror, S. (2017). Linking operation plans to business objectives using QFD. Total Quality Management & Business Excellence, 30(1-2), 135-150. doi:10.1080/14783363.2017.1300053 es_ES
dc.description.references Geladi, P. (1988). Notes on the history and nature of partial least squares (PLS) modelling. Journal of Chemometrics, 2(4), 231-246. doi:10.1002/cem.1180020403 es_ES
dc.description.references Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2001). Performance measures and metrics in a supply chain environment. International Journal of Operations & Production Management, 21(1/2), 71-87. doi:10.1108/01443570110358468 es_ES
dc.description.references Gunasekaran, A., Patel, C., & McGaughey, R. E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, 87(3), 333-347. doi:10.1016/j.ijpe.2003.08.003 es_ES
dc.description.references Hald, K. S., & Mouritsen, J. (2018). The evolution of performance measurement systems in a supply chain: A longitudinal case study on the role of interorganisational factors. International Journal of Production Economics, 205, 256-271. doi:10.1016/j.ijpe.2018.09.021 es_ES
dc.description.references Reddy. K, J. M., Rao. A, N., & L, K. (2019). A review on supply chain performance measurement systems. Procedia Manufacturing, 30, 40-47. doi:10.1016/j.promfg.2019.02.007 es_ES
dc.description.references Jafari, M., Shahanaghi, K., & Tootooni, M. (2015). Developing a Robust Strategy Map in Balanced Scorecard Model Using Scenario Planning. Mathematical Problems in Engineering, 2015, 1-9. doi:10.1155/2015/102606 es_ES
dc.description.references Kaplan, R. S. (2009). Conceptual Foundations of the Balanced Scorecard. Handbooks of Management Accounting Research, 1253-1269. doi:10.1016/s1751-3243(07)03003-9 es_ES
dc.description.references Khakbaz, S. B., & Hajiheydari, N. (2015). Proposing a basic methodology for developing balanced scorecard by system dynamics approach. Kybernetes, 44(6/7), 1049-1066. doi:10.1108/k-12-2014-0287 es_ES
dc.description.references Lehr, T., Lorenz, U., Willert, M., & Rohrbeck, R. (2017). Scenario-based strategizing: Advancing the applicability in strategists’ teams. Technological Forecasting and Social Change, 124, 214-224. doi:10.1016/j.techfore.2017.06.026 es_ES
dc.description.references Melkonyan, A., Krumme, K., Gruchmann, T., Spinler, S., Schumacher, T., & Bleischwitz, R. (2019). Scenario and strategy planning for transformative supply chains within a sustainable economy. Journal of Cleaner Production, 231, 144-160. doi:10.1016/j.jclepro.2019.05.222 es_ES
dc.description.references Meredith, J. (1993). Theory Building through Conceptual Methods. International Journal of Operations & Production Management, 13(5), 3-11. doi:10.1108/01443579310028120 es_ES
dc.description.references Nielsen, S., & Nielsen, E. H. (2011). Discussing feedback system thinking in relation to scenario evaluation in a balanced scorecard setup. Production Planning & Control, 23(6), 436-451. doi:10.1080/09537287.2011.561816 es_ES
dc.description.references Rodriguez, R. R., Saiz, J. J. A., & Bas, A. O. (2009). Quantitative relationships between key performance indicators for supporting decision-making processes. Computers in Industry, 60(2), 104-113. doi:10.1016/j.compind.2008.09.002 es_ES
dc.description.references Rodriguez-Rodriguez, R., Alfaro Saiz, J. J., Ortiz Bas, A., Carot, J. M., & Jabaloyes, J. M. (2010). Building internal business scenarios based on real data from a performance measurement system. Technological Forecasting and Social Change, 77(1), 50-62. doi:10.1016/j.techfore.2009.07.006 es_ES
dc.description.references Shahin, A., & Mahbod, M. A. (2007). Prioritization of key performance indicators. International Journal of Productivity and Performance Management, 56(3), 226-240. doi:10.1108/17410400710731437 es_ES
dc.description.references Sanchez-Marquez, R., Albarracin Guillem, J. M., Vicens-Salort, E., & Jabaloyes Vivas, J. (2018). A statistical system management method to tackle data uncertainty when using key performance indicators of the balanced scorecard. Journal of Manufacturing Systems, 48, 166-179. doi:10.1016/j.jmsy.2018.07.010 es_ES
dc.description.references Thanki, S., & Thakkar, J. (2018). A quantitative framework for lean and green assessment of supply chain performance. International Journal of Productivity and Performance Management, 67(2), 366-400. doi:10.1108/ijppm-09-2016-0215 es_ES
dc.description.references Tsalis, A. T., Nikolaou, E. I., Grigoroudis, E., & Tsagarakis, P. K. (2015). A dynamic sustainability Balanced Scorecard methodology as a navigator for exploring the dynamics and complexity of corporate sustainability strategy. Civil Engineering and Environmental Systems, 32(4), 281-300. doi:10.1080/10286608.2015.1006129 es_ES
dc.description.references Verdecho, M.-J., Alfaro-Saiz, J.-J., Rodriguez-Rodriguez, R., & Ortiz-Bas, A. (2012). A multi-criteria approach for managing inter-enterprise collaborative relationships. Omega, 40(3), 249-263. doi:10.1016/j.omega.2011.07.004 es_ES
dc.description.references Visser, M. P., & Chermack, T. J. (2009). Perceptions of the relationship between scenario planning and firm performance: A qualitative study. Futures, 41(9), 581-592. doi:10.1016/j.futures.2009.04.010 es_ES
dc.description.references Wold, S., Ruhe, A., Wold, H., & Dunn, III, W. J. (1984). The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses. SIAM Journal on Scientific and Statistical Computing, 5(3), 735-743. doi:10.1137/0905052 es_ES
dc.description.references Wold, S., Trygg, J., Berglund, A., & Antti, H. (2001). Some recent developments in PLS modeling. Chemometrics and Intelligent Laboratory Systems, 58(2), 131-150. doi:10.1016/s0169-7439(01)00156-3 es_ES
dc.description.references Wiktorsson, M., Andersson, C., & Turunen, V. (2018). Leading towards high-performance manufacturing – Enabling indicators in early R&D phases ensuring future KPI outcome. Procedia Manufacturing, 25, 223-230. doi:10.1016/j.promfg.2018.06.077 es_ES
dc.description.references Xia, D., Yu, Q., Gao, Q., & Cheng, G. (2017). Sustainable technology selection decision-making model for enterprise in supply chain: Based on a modified strategic balanced scorecard. Journal of Cleaner Production, 141, 1337-1348. doi:10.1016/j.jclepro.2016.09.083 es_ES
dc.subject.ods 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación es_ES


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