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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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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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/166217

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Título: A dynamic supply chain BSC-based methodology to improve operations efficiency
Autor: Rodríguez Rodríguez, Raúl Alfaro Saiz, Juan José Carot Sierra, José Miguel
Entidad UPV: Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses
Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
Fecha difusión:
Resumen:
[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 ...[+]
Palabras clave: Performance management , Scenarios , Principal component analysis , Operations efficiency
Derechos de uso: Cerrado
Fuente:
Computers in Industry. (issn: 0166-3615 )
DOI: 10.1016/j.compind.2020.103294
Editorial:
Elsevier
Versión del editor: https://doi.org/10.1016/j.compind.2020.103294
Tipo: Artículo

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

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

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 [+]
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

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

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

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

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

Coughlan, P., & Coghlan, D. (2002). Action research for operations management. International Journal of Operations & Production Management, 22(2), 220-240. doi:10.1108/01443570210417515

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

Denham, M. C. (1995). Implementing partial least squares. Statistics and Computing, 5(3), 191-202. doi:10.1007/bf00142661

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

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

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

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

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

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

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

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

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

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

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

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

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

Meredith, J. (1993). Theory Building through Conceptual Methods. International Journal of Operations & Production Management, 13(5), 3-11. doi:10.1108/01443579310028120

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

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

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

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

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

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

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

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

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

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

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

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

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

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