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Quantitative supply chain segmentation model for dynamic alignment

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Quantitative supply chain segmentation model for dynamic alignment

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dc.contributor.author Alves Ferreira, Rafael es_ES
dc.contributor.author Espôsto, Kleber F. es_ES
dc.contributor.author Santos, Lucas A. S.
dc.date.accessioned 2022-09-08T07:16:39Z
dc.date.available 2022-09-08T07:16:39Z
dc.date.issued 2022-07-29
dc.identifier.uri http://hdl.handle.net/10251/185593
dc.description.abstract [EN] Companies deal with different customer groups, requirements differ among them, which makes it important to define the service level precisely and improve customer service through different supply chain strategies for each group. An alternative to deal with imprecision related to the segmentation processes suggested by either the Leagile or the Dynamic Alignment Schools is the application of fuzzy set theory. The objective of this work is to develop a quantitative model that uses the fuzzy set theory and, based on sales data, assess the company s supply chain(s). The model's aim is to facilitate managers' decision-making processes to achieve the dynamic alignment. It was possible to identify the supply chains that serve the client groups evaluated, providing answers faster than the analysis proposed by the models found in the literature. The application in two real situations validated the model since the results obtained were consistent with the reality pointed out by the experts of the companies assessed. The model indicates possible actions for the realignment of the supply chain by their managers. Results obtained should improve practice, preparing managers to cope with the organizations` multiple supply chains. This study is the first one that aims to segment quantitatively supply chains on a company applying fuzzy set theory, providing a novel approach to align operations and supply chain strategy dynamically. es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof International Journal of Production Management and Engineering es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Supply chain segmentation es_ES
dc.subject Supply chain management es_ES
dc.subject Fuzzy inference system es_ES
dc.title Quantitative supply chain segmentation model for dynamic alignment es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/ijpme.2022.16494
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Alves Ferreira, R.; Espôsto, KF.; Santos, LAS. (2022). Quantitative supply chain segmentation model for dynamic alignment. International Journal of Production Management and Engineering. 10(2):99-113. https://doi.org/10.4995/ijpme.2022.16494 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/ijpme.2022.16494 es_ES
dc.description.upvformatpinicio 99 es_ES
dc.description.upvformatpfin 113 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.description.issue 2 es_ES
dc.identifier.eissn 2340-4876
dc.relation.pasarela OJS\16494 es_ES
dc.description.references Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I. E., & Omid, M. (2016). Green Supplier Selection Using Fuzzy Group Decision Making Methods: A Case Study from the Agri-Food Industry. Computers & Operations Research, 1–11. https://doi.org/10.1016/j.cor.2016.02.015 es_ES
dc.description.references Bertrand, J. W. M., & Fransoo, J. C. (2002). Operations management research methodologies using quantitative modeling. In International Journal of Operations & Production Management (Vol. 22, Issue 2). https://doi.org/Doi 10.1108/01443570210414338 es_ES
dc.description.references Bojadziev, G., & Bojadziev, M. (2007). Fuzzy logic for business, finance, and management. (2 ed.). World Scientific Publishing Co. Pte. Ltd. https://doi.org/10.1142/6451 es_ES
dc.description.references Celik, E., Gul, M., Aydin, N., Gumus, A. T., & Guneri, A. F. (2015). A comprehensive review of multi criteria decision making approaches based on interval type-2 fuzzy sets. Knowledge-Based Systems, 85, 329–341. https://doi.org/10.1016/j.knosys.2015.06.004 es_ES
dc.description.references Christopher, M., & Gattorna, J. (2005). Supply chain cost management and value-based pricing. Industrial Marketing Management, 34(2 SPEC. ISS.), 115–121. https://doi.org/10.1016/j.indmarman.2004.07.016 es_ES
dc.description.references Christopher, M., & Towill, D. R. (2000). Marrying the lean and agile paradigms. Proceedings of the EUROMA 2000 Conference, 114–121. es_ES
dc.description.references Christopher, M., & Towill, D.R. (2002). Developing Market Specific Supply Chain Strategies. The International Journal of Logistics Management, 13(1), 1–14. https://doi.org/10.1108/09574090210806324 es_ES
dc.description.references Christopher, M., Towill, D.R., Aitken, J., & Childerhouse, P. (2009). Value stream classification. Journal of Manufacturing Technology Management, 20(4), 460–474. https://doi.org/10.1108/17410380910953720 es_ES
dc.description.references Fichtinger, J., Chan, C. (Wan C., & Yates, N. (2019). A joint network design and multi-echelon inventory optimisation approach for supply chain segmentation. International Journal of Production Economics, 209(August 2017), 103–111. https://doi.org/10.1016/j.ijpe.2017.09.003 es_ES
dc.description.references Fu, X., Zeng, X.J., Luo, X., Wang, D., Xu, D., & Fan, Q.L. (2017). Designing an intelligent decision support system for effective negotiation pricing: A systematic and learning approach. Decision Support Systems, 96, 49–66. https://doi.org/10.1016/j.dss.2017.02.003 es_ES
dc.description.references Ganga, G. M. D., & Carpinetti, L. C. R. (2011). A fuzzy logic approach to supply chain performance management. International Journal of Production Economics, 134(1), 177–187. https://doi.org/10.1016/j.ijpe.2011.06.011 es_ES
dc.description.references García, N., Puente, J., Fernández, I., & Priore, P. (2013). Supplier selection model for commodities procurement. Optimised assessment using a fuzzy decision support system. Applied Soft Computing Journal, 13(4), 1939–1951. https://doi.org/10.1016/j.asoc.2012.12.008 es_ES
dc.description.references Gattorna, J. (2015). Dynamic Supply Chains: How to design, build and manage people-centric value networks (3rd ed.). Pearson Education Limited. es_ES
dc.description.references Gattorna, J., & Walters, D. W. (1996). Managing the Supply Chain: A Strategic Perspective (1st editio). Palgrave. https://doi.org/10.1007/978-1-349-24841-4_1 es_ES
dc.description.references Giri, B.C., Molla, M.U., & Biswas, P. (2022). Pythagorean fuzzy DEMATEL method for supplier selection in sustainable supply chain management. Expert Systems with Applications, 193. https://doi.org/10.1016/j.eswa.2021.116396 es_ES
dc.description.references Godsell, J., Diefenbach, T., Clemmow, C., Towill, D., & Christopher, M. (2011). Enabling supply chain segmentation through demand profiling. International Journal of Physical Distribution & Logistics Management, 41(3), 296–314. https://doi.org/10.1108/09600031111123804 es_ES
dc.description.references Hjort, K., Lantz, B., Ericsson, D., & Gattorna, J. (2016). Customer Segmentation Based on Buying and Returning Behaviour: Supporting Differentiated Service Delivery in Fashion E-Commerce. In Developments in Logistics and Supply Chain Management (pp. 153–169). Palgrave Macmillan UK. https://doi.org/10.1057/9781137541253_14 es_ES
dc.description.references Kumar, D., Singh, J., Singh, O. P., & Seema. (2013). A fuzzy logic based decision support system for evaluation of suppliers in supply chain management practices. Mathematical and Computer Model-ling, 57(11–12), 2945–2960. https://doi.org/10.1016/j.mcm.2013.03.002 es_ES
dc.description.references Lima Junior, F.R., Osiro, L., & Carpinetti, L.C. R. (2013). A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules. Applied Soft Computing Journal, 13(10), 4133–4147. https://doi.org/10.1016/j.asoc.2013.06.020 es_ES
dc.description.references Lima Junior, F. R., Osiro, L., & Carpinetti, L. C. R. (2014). A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Applied Soft Computing Journal, 21, 194–209. https://doi.org/10.1016/j.asoc.2014.03.014 es_ES
dc.description.references Lima Junior, F.R., & Carpinetti, L.C. R. (2020). An adaptive network-based fuzzy inference system to supply chain performance evaluation based on SCOR® metrics. Computers and Industrial Engineering, 139(May 2019), 106191. https://doi.org/10.1016/j.cie.2019.106191 es_ES
dc.description.references Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13. https://doi.org/10.1016/S0020-7373(75)80002-2 es_ES
dc.description.references Mason-Jones, R., Naylor, B., & Towill, D. R. (2000). Engineering the leagile supply chain. International Journal of Agile Management Systems, 2, 54–61. https://doi.org/10.1108/14654650010312606 es_ES
dc.description.references Pedrycz, W., & Gomide, F. (2007). Fuzzy Systems Engineering—Toward Human-Centric Computing. Wiley. https://doi.org/10.1002/9780470168967 es_ES
dc.description.references Renganath, K., & Suresh, M. (2017). Supplier selection using fuzzy MCDM techniques: A literature review. 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016. https://doi.org/10.1109/ICCIC.2016.7919590 es_ES
dc.description.references Routroy, S. a, & Shankar, A. b. (2015). Performance analysis of agile supply chain. International Journal of Manufacturing Technology and Management, 29(3–4), 180–210. https://doi.org/10.1504/IJMTM.2015.069255 es_ES
dc.description.references Santos, L.F. de O.M., Osiro, L., & Lima, R.H. P. (2017). A model based on 2-tuple fuzzy linguistic representation and Analytic Hierarchy Process for supplier segmentation using qualitative and quantitative criteria. Expert Systems with Applications, 79, 1339–1351. https://doi.org/10.1016/j.eswa.2017.02.032 es_ES
dc.description.references Simchi-Levi, D., Clayton, A., & Raven, B. (2013). When One Size Does Not Fit All. MIT Sloan Management Review, 54(2), 15–17. es_ES
dc.description.references Simões, M. G., & Shaw, I. S. (2007). Controle e modelagem fuzzy (2 ed.). Editora Blucher. es_ES
dc.description.references Von Altrock, C. (1997). Fuzzy Logic & Neurofuzzy Applications in Business & Finance. Prentice Hall. es_ES
dc.description.references Wen, X., Choi, T.M., & Chung, S.H. (2019). Fashion retail supply chain management: A review of operational models. International Journal of Production Economics, 207, 34–55. https://doi.org/10.1016/J.IJPE.2018.10.012 es_ES
dc.description.references Yusuf, Y. Y., Gunasekaran, A., Musa, A., Dauda, M., El-Berishy, N. M., & Cang, S. (2014). A relational study of supply chain agility, competitiveness and business performance in the oil and gas industry. International Journal of Production Economics, 147(Part B), 531–543. https://doi.org/10.1016/j.ijpe.2012.10.009 es_ES
dc.description.references Zadeh, L. a. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X es_ES
dc.description.references Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst., 3, 28–44. https://doi.org/10.1109/TSMC.1973.5408575 es_ES
dc.description.references Zimmermann, H.J. (1987). Fuzzy Sets, Decision Making and Expert Systems. Kluwer Academic Publishers. https://doi.org/10.1007/978-94-009-3249-4 es_ES
dc.description.references Zimmermann, H.J. (2001). Fuzzy Set Theory and Its Applications. In Kluwer Academic Publishers, USA (4th ed.). Kluwer Academic. https://doi.org/10.1007/978-94-010-0646-0 es_ES


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