A Fuzzy Expert System to Select a Supply Chain Strategy: Lean, Agile or Leagile | ||
| Journal of Quality Engineering and Production Optimization | ||
| دوره 6، شماره 2، اسفند 2021، صفحه 201-218 اصل مقاله (671.88 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22070/jqepo.2021.14946.1202 | ||
| نویسندگان | ||
| Nima Esfandiari* 1؛ Mahmoud Moradi2؛ Amir-Mohammad Golmohammadi3 | ||
| 1Ph.D. candidate in industrial management, university of Guilan, Rasht, Iran. | ||
| 2Associate Professor, Management Department, Faculty of Literature and Humanities, University of Guilan, Rasht, Iran | ||
| 3Assistant Professor, Department of Industrial Engineering, Arak University, Arak, Iran | ||
| چکیده | ||
| Supply chain field has always been an aspiration for competitiveness in manufacturing organizations. Any organization’s conditions can be judged based on several criteria, such as robustness, rapid reconfiguration, lead time compression, etc. These criteria effectively determine the kind of supply chain strategy; it should be noted that this strategy varies in different markets and industries. Therefore, considering an appropriate strategy for the supply chain is an essential issue for most managers. Hence, this study, using a fuzzy expert system, shows how to select the best supply chain strategy. Three popular strategies, i.e., lean, agile, and leagile, are the main elements in this research. In addition, five applicable criteria are applied for selecting a supply chain strategy. A fuzzy expert system based on if-then rules was designed to connect these criteria to three strategies and select an appropriate supply chain strategy. Hence, criteria and supply chain strategies are taken to be input and output of this expert system, respectively, which lead to faster decision-making in the selection of supply chain strategy. | ||
| کلیدواژهها | ||
| Supply Chain Strategy؛ Lean؛ Agile؛ Leagile؛ Fuzzy Expert System | ||
| مراجع | ||
|
Agarwal, A., Shankar, R., & Tiwari, M. K. (2006). Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach. European journal of operational research, 173(1), 211-225.
Bhamra, R., Nand, A., Yang, L., Albregard, P., Azevedo, G., Corraini, D., & Emiliasiq, M. (2020). Is leagile still relevant? A review and research opportunities. Total Quality Management & Business Excellence, 1-25. DOI: 10.1080/14783363.2020.1750360
Bruce, M., Daly, L., & Towers, N. (2004). Lean or agile: a solution for supply chain management in the textiles and clothing industry?. International journal of operations & production management, 24(2), 151-170.
Centobelli, P., Cerchione, R., & Ertz, M. (2020). Agile supply chain management: where did it come from and where will it go in the era of digital transformation?. Industrial Marketing Management, 90, 324-345.
Childerhouse, P., & Towill, D. (2000). Engineering supply chains to match customer requirements. Logistics information management, 13(6), 337-345.
Christopher, M. and Towill, D.R. (2000), “Supply chain migration from lean and functional to agile and customized.” International Journal of Supply Chain Management, 5(4), 206-213.
Chtourou, H., Masmoudi, W., & Maalej, A. (2005). An expert system for manufacturing systems machine selection. Expert Systems with Applications, 28(3), 461-467
Fadaki, M., Rahman, S., & Chan, C. (2020). Leagile supply chain: design drivers and business performance implications. International Journal of Production Research, 58(18), 5601-5623.
Fasanghari, M., & Montazer, G. A. (2010). Design and implementation of fuzzy expert system for Tehran Stock Exchange portfolio recommendation. Expert Systems with Applications, 37(9),
Galasso, F., & Thierry, C. (2009). Design of cooperative processes in a customer–supplier relationship: An approach based on simulation and decision theory. Engineering Applications of Artificial Intelligence, 22(6), 865-881.
Ghaderi, H., Gitinavard, H., & Mehralizadeh, M. (2020). An Intuitionistic Fuzzy DEA Cross-Efficiency Methodology with an Application to Production Group Decision-Making Problems. Journal of Quality Engineering and Production Optimization, 5(2), 69-86.
Gitinavard, H. (2019). Strategic Evaluation of Sustainable Projects based on Hybrid Group Decision Analysis with Incomplete Information. Journal of Quality Engineering and Production Optimization, 4(2), 17-30.
Gitinavard, H., Mousavi, S. M., Vahdani, B., & Siadat, A. (2020). Project safety evaluation by a new soft computing approach-based last aggregation hesitant fuzzy complex proportional assessment in construction industry. Scientia Iranica, 27(2), 983-1000.
Gitinavard, H., Shirazi, M. A., & Zarandi, M. H. F. (2020). Sustainable feedstocks selection and renewable products allocation: A new hybrid adaptive utility-based consensus model. Journal of environmental management, 264, 110428.
Gligor, D. M., Holcomb, M. C., & Feizabadi, J. (2016). An exploration of the strategic antecedents of firm supply chain agility: The role of a firm's orientations. International Journal of Production Economics, 179, 24-34.
Hilletofth, P. (2009). How to develop a differentiated supply chain strategy. Industrial Management & Data Systems, 109(1), 16-33.
Huang, S. H., Uppal, M., & Shi, J. (2002). A product driven approach to manufacturing supply chain selection. Supply Chain Management: An International Journal, 7(4), 189-199.
Hwang, G. J., Sung, H. Y., Chang, S. C., & Huang, X. C. (2020). A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors. Computers and Education: Artificial Intelligence, 1, 100003.
Kerr, R. M., & Ebsary, R. V. (1988). Implementation of an expert system for production scheduling. European Journal of Operational Research, 33(1), 17-29.
Khan, M. K. (1999). Development of an expert system for implementation of ISO 9000 quality systems. Total Quality Management, 10(1), 47-59.
Khozeimeh, F., Alizadehsani, R., Roshanzamir, M., Khosravi, A., Layegh, P., & Nahavandi, S. (2017). An expert system for selecting wart treatment method. Computers in biology and medicine, 81, 167-175.
Lamming, R. (1996). Squaring lean supply with supply chain management. International Journal of Operations & Production Management, 16(2), 183-196.
Liang, G. S., & Wang, M. J. J. (1994). Personnel selection using fuzzy MCDM algorithm. European journal of operational research, 78(1), 22-33.
Lim, M. K., & Zhang, Z. (2012). A multi-agent system using iterative bidding mechanism to enhance manufacturing agility. Expert systems with applications, 39(9), 8259-8273.
Malakouti, M., Rezaei, S., & Shahijan, M. K. (2017). Agile supply chain management (ASCM): a management decision-making approach. Asia Pacific Journal of Marketing and Logistics, 29(1), 171-182.
Mason‐Jones, R., Naylor, B., & Towill, D. R. (2000a). Engineering the leagile supply chain. International journal of agile management systems, 2(1), 54-61.
Mason-Jones, R., Naylor, B., & Towill, D. R. (2000b). Lean, agile or leagile? Matching your supply chain to the marketplace. International Journal of Production Research, 38(17), 4061-4070.
Moradi, M., Esfandiari, N., & Moghaddam, M. K. (2018). An integrated FLinPreRa-FQFD approach to leagility assessment:(case study of four industries). International Journal of Lean Six Sigma, 11(2), 331-358.
Moyano-Fuentes, J., Maqueira-Marín, J. M., Martínez-Jurado, P. J., & Sacristán-Díaz, M. (2020). Extending lean management along the supply chain: impact on efficiency. Journal of Manufacturing Technology Management, 32(1), 63-84.
Naylor, J. B., Naim, M. M., & Berry, D. (1999). Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain. International Journal of production economics, 62(1-2), 107-118.
Olhager, J., Persson, F., Parborg, B., & Linkoping, S. R. (2002). Supply Chain impacts at Ericsson-from production units to demand-driven supply units. International Journal of Technology Management, 23(1-3), 40-59.
Olugu, E. U., & Wong, K. Y. (2012). An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry. Expert Systems with Applications, 39(1), 375-384.
Qi, Y., Boyer, K. K., & Zhao, X. (2009). Supply chain strategy, product characteristics, and performance impact: evidence from Chinese manufacturers. Decision Sciences, 40(4), 667-695.
Raj, S. A., Jayakrishna, K., & Vimal, K. E. K. (2018). Modelling the metrics of leagile supply chain and leagility evaluation. International Journal of Agile Systems and Management, 11(2), 179-202.
Razmi, J., Seifoory, M., & Pishvaee, M. S. (2011). A fuzzy multi-attribute decision making model for selecting the best supply chain strategy: Lean, agile or leagile. Advances in Industrial Engineering, 45(Special Issue), 127-142.
Rehman, M.A. (2017). Determination of relative importance of agility enablers for agile manufacturing companies by analytical hierarchy process. International Journal of Agile Systems and Management, 10(1), 49-72.
Reichhart, A., & Holweg, M. (2007). Lean distribution: concepts, contributions, conflicts. International journal of production research, 45(16), 3699-3722.
Sari, K. (2017). Modeling of a fuzzy expert system for choosing an appropriate supply chain collaboration strategy. Intelligent Automation & Soft Computing, 1-8.
Sharma, P., & Kulkarni, M. S. (2016). Framework for a dynamic and responsive. International Journal of Productivity and Performance Management, 65(2), 207-222.
Silveira, G.D, Borenstein, D., & Fogliatto, F. S. (2001). Mass customization: Literature review and research directions. International journal of production economics, 72(1), 1-13.
Soni, G., & Kodali, R. (2009). Performance value analysis for the justification of the leagile supply chain. International Journal of Business Performance Management, 11(1-2), 96-133.
Tang, M., Chen, X., Hu, W., & Yu, W. (2012). Generation of a probabilistic fuzzy rule base by learning from examples. Information Sciences, 217, 21-30.
Tortorella, G. L., Miorando, R., & Tlapa, D. (2017). Implementation of lean supply chain: an empirical research on the effect of context. The TQM Journal, 29(4), 610-623.
Tortorella, G., Giglio, R., Fettermmann, D. C., & Tlapa, D. (2018). Lean supply chain practices: an exploratory study on their relationship. The International Journal of Logistics Management, 29(3), 1049-1076.
Udokporo, C., Anosike, A., & Lim, M. (2020). A decision-support framework for Lean, Agile and Green practices in product life cycle stages. Production Planning & Control, 1-22.
Um, J. (2017). Improving supply chain flexibility and agility through variety management. The International Journal of Logistics Management, 28(2), 464-487.
Vahdani, B. (2016). Solving robot selection problem by a new interval-valued hesitant fuzzy multi-attributes group decision method. International Journal of Industrial Mathematics, 8(3), 231-240.
Villarreal, B., Garza-Reyes, J. A., Kumar, V., & Lim, M. K. (2017). Improving road transport operations through lean thinking: A case study. International Journal of Logistics Research and Applications, 20(2), 163-180.
Vinodh, S., & Chintha, S. K. (2011). Leanness assessment using multi-grade fuzzy approach. International Journal of Production Research, 49(2), 431-445.
Vitasek, K. L., Manrodt, K. B., & Abbott, J. (2005). What makes a lean supply chain?. Supply chain management review, 9(7), 39-45.
Vonderembse, M. A., Uppal, M., Huang, S. H., & Dismukes, J. P. (2006). Designing supply chains: Towards theory development. International Journal of production economics, 100(2), 223-238.
Wang, W. J., & Luoh, L. (2000). Simple computation for the defuzzifications of center of sum and center of gravity. Journal of Intelligent & Fuzzy Systems, 9(1, 2), 53-59.
Wieland, A. (2013). Selecting the right supply chain based on risks. Journal of Manufacturing Technology Management, 24(5), 652-668.
Zarandi, M. H. F., Mansour, S., Hosseinijou, S. A., & Avazbeigi, M. (2011). A material selection methodology and expert system for sustainable product design. The International Journal of Advanced Manufacturing Technology,57(9-12), 885-903.
Zhang, D. Z. (2011). Towards theory building in agile manufacturing strategies—Case studies of an agility taxonomy. International Journal of Production Economics, 131(1), 303-312.
Zhang, Y., Wang, Y., & Wu, L. (2012). Research on demand-driven leagile supply chain operation model: a simulation based on anylogic in system engineering. Systems Engineering Procedia, 3, 249-258. | ||
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