Processus Achat 5.0 et Acheteurs Augmentés : L’IA collective avec chat-bots dotés d’aversion au risque post-COVID-19
Cas d’un constructeur automobile Français
DOI :
https://doi.org/10.53102/2022.36.01.907Mots-clés :
Achat 5.0, acheteur augmenté, sélection multifournisseurs, analyse multicritères, chat-bots, apprentissage automatique par renforcementRésumé
A l’aube de la 5ème génération de la transformation digitale industrielle, le processus « Achat 5.0 » connait, lui aussi, une mutation profonde en passant d’abord par ses acheteurs, appelés « acheteurs augmentés ». Face aux défis de l’automatisation induite par cette transformation, les travaux s’accentuent et tentent de converger vers des techniques plus avancées de l’Intelligence Artificielle (IA) pour faire face au problème complexe de la sélection multifournisseurs. Les risques liés à la volatilité des fournisseurs, encore fragilisés par la crise pandémique COVID-19, ont fortement augmenté en conséquence. L’objectif de cet article est de palier à cette faiblesse. Il propose une nouvelle approche par hybridation d’analyse multicritères et des chat-bots dotés de capacité d’aversion au risque à l’aide de l’apprentissage par renforcement. Un cadre de validation d’un constructeur automobile Français nous sert de scénario préliminaire. Les premiers résultats sont prometteurs et nous encouragent à continuer dans la suite de ces travaux.
Références
Akram, M., & Shumaiza, S. (2021). Multi-criteria decision making based on q-rung orthopair fuzzy promethee approach. Iranian Journal of Fuzzy Systems, 18(5), 107-127. doi: https://10.22111/IJFS.2021.6258 DOI: https://doi.org/10.3233/JIFS-202646
Allal-Chérif, O. (2019, 08 10). Achats intelligents : quand l’intelligence artificielle redéfinit la fonction achats. Excellence HA, Conseil national des achats (CNA), 11. Récupéré sur theconversation.com: https://theconversation.com/
Arıoğlu, M. Ö., Sarkis, J., & Dhavale, D. G. (2020). Selection of suppliers using Bayesian estimators: a case of concrete ring suppliers to Eurasia Tunnel of Turkey. International Journal of Production Research, 1-12. doi: https://doi.org/10.1080/00207543.2020.1789236 DOI: https://doi.org/10.1080/00207543.2020.1789236
Azadfallah, M. (2017). Multi criteria supplier selection using PROMETHEE outranking procedures. Journal of Supply Chain Management Systems. 6(1), 24.
Baali, S., Hamzane, I., Moutachaouik, H., & Marzak, A. (2021). A Multi-Criteria Analysis and Advanced Comparative Study of Recommendation Systems. International Journal of Engineering Trends and Technology, 69(3), 69-75. doi: https://doi.org/10.14445/22315381/IJETT-V69I3P213 DOI: https://doi.org/10.14445/22315381/IJETT-V69I3P213
Bai, C., Kusi-Sarpong, S., Badri Ahmadi, H., & Sarkis, J. (2019). Social sustainable supplier evaluation and selection: a group decision-support approach. International Journal of Production Research, 57(22), 7046-7067. doi: https://doi.org/10.1080/00207543.2019.1574042 DOI: https://doi.org/10.1080/00207543.2019.1574042
BANA Consulting. (2020, 12 10). MACBETH. Récupéré sur MACBETH: http://m-macbeth.com/
Ben Jouida, S., & Krichen, S. (2020). A genetic algorithm for supplier selection problem under collaboration opportunities. Journal of Experimental & Theoretical Artificial Intelligence, 1-27. doi: https://doi.org/10.1080/0952813X.2020.1836031 DOI: https://doi.org/10.1080/0952813X.2020.1836031
Bera, A. K., Jana, D. K., Banerjee, D., & Nandy, T. (2021). A group evaluation method for supplier selection based on interval type-2 fuzzy TOPSIS method. International Journal of Business Performance and Supply Chain Modelling, 12(1), 1-26. DOI: https://doi.org/10.1504/IJBPSCM.2021.114722
Bottani, E., Centobelli, P., Murino, T., & Shekarian, E. (2018). A QFD-ANP method for supplier selection with benefits, opportunities, costs and risks considerations. International journal of information technology & decision making, 17(03), 911-939. doi: https://doi.org/10.1142/S021962201850013X DOI: https://doi.org/10.1142/S021962201850013X
Boucher, X., Boudarel, M. R., & Poyard, D. (2014). Transition industrielle vers des offres intégrées «produits/services» . Revue française de gestion industrielle, 33(3), 89-113. DOI: https://doi.org/10.53102/2014.33.03.786
Cao, Q., Wu, J., & Liang, C. (2015). An intuitionsitic fuzzy judgement matrix and TOPSIS integrated multi-criteria decision making method for green supplier selection. Journal of Intelligent & Fuzzy Systems, 28(1), 117-126. doi: https://10.3233/IFS-141281 DOI: https://doi.org/10.3233/IFS-141281
Chehbi-Gamoura, S. (2019). A Cloud-Based Approach for Cross-Management of Disaster Plans: Managing Risk in Networked Enterprises. Dans S. Aljawarneh, & M. Malhotra, Critical Research on Scalability and Security Issues in Virtual Cloud Environments (pp. 240-268). New York USA: IGI Global. doi: https://10.4018/978-1-5225-6195-8.ch040
Chehbi-Gamoura, S. (2021). Predictive Reinforcement Learning Algorithm for Unstructured Business Process Optimization: Case of Human Ressources Process. International Journal of Spatio-Temporal Data Science, 1(2), 184-214. DOI: https://doi.org/10.1504/IJSTDS.2021.10040060
Chen, Z., Ming, X., Zhou, T., & Chang, Y. (2020). Sustainable supplier selection for smart supply chain considering internal and external uncertainty: An integrated rough-fuzzy approach. Applied Soft Computing, 87, 106004. doi: https://doi.org/10.1016/j.asoc.2019.106004 DOI: https://doi.org/10.1016/j.asoc.2019.106004
Chien, C. F., Lin, Y. S., & Lin, S. K. (2020). Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor. International Journal of Production Research, 58(9). doi: https://doi.org/10.1080/00207543.2020.1733125 DOI: https://doi.org/10.1080/00207543.2020.1733125
Costa, A. S., Govindan, K., & Figueira, J. R. (2018). Supplier classification in emerging economies using the ELECTRE TRI-nC method: A case study considering sustainability aspects. Journal of Cleaner Production, 201, 925-947. doi: https://doi.org/10.1016/j.jclepro.2018.07.285 DOI: https://doi.org/10.1016/j.jclepro.2018.07.285
Cuayáhuitl, H., Lee, D., Ryu, S., & Cho, Y. C. (2019). Ensemble-based deep reinforcement learning for chatbots. Neurocomputing, 366, 118-130. doi: https://doi.org/10.1016/j.neucom.2019.08.007 DOI: https://doi.org/10.1016/j.neucom.2019.08.007
Cui, R., Li, M., & Zhang, S. (2021). AI and Procurement. Manufacturing & Service Operations Management, 1(1). doi: https://doi.org/10.1287/msom.2021.0989 DOI: https://doi.org/10.1287/msom.2021.0989
CxO Advisory . (2021, 08 10). Modules d’analyse de la performance « Achats ». Récupéré sur cxo-advisory.com: https://12f6ff26-d705-08f3-3e10-0d7b237354a0.filesusr.com/ugd/1a54db_22f457696e2f47dfb279df4e325b0bd1.pdf
Dickson. (1966). An analysis of vendor selection systems and decisions. Journal of Purchasing, 5-17. doi: https://doi.org/10.1111/j.1745-493X.1966.tb00818.x DOI: https://doi.org/10.1111/j.1745-493X.1966.tb00818.x
Dobos, I., & Vörösmarty, G. (2019). Inventory-related costs in green supplier selection problems with Data Envelopment Analysis (DEA). International Journal of Production Economics, 209, 374-380. doi: https://doi.org/10.1016/j.ijpe.2018.03.022 DOI: https://doi.org/10.1016/j.ijpe.2018.03.022
Durmic, E. (2019). Evaluation of criteria for sustainable supplier selection using FUCOM method. Operational Research in Engineering Sciences: Theory and Applications, 2(1), 91-107. doi: https://10.31181/oresta1901085d DOI: https://doi.org/10.31181/oresta1901085d
Dzedek, L. R. (2018). Initiatives by subsidiaries of multinational corporations: An empirical study on the influence of subsidiary role context. Berlin, Germany: Springer. DOI: https://doi.org/10.1007/978-3-658-20950-6
El Hasnaoui, M., & El Haoud, N. (2020). L'Innovation par les outils Industrie 4.0: Analyse et Mesure de performance. International Journal of Innovation and Applied Studies, 28(2), 366-379.
Galo, N. R., Calache, L. D., & Carpinetti, L. C. (2018). A group decision approach for supplier categorization based on hesitant fuzzy and ELECTRE TRI. International Journal of Production Economics, 202, 182-196. doi: https://doi.org/10.1016/j.ijpe.2018.05.023 DOI: https://doi.org/10.1016/j.ijpe.2018.05.023
Gama, N., Alves, C. A., & Oliveira, P. S. (2020). Suppliers Selection in Restaurants: Application of Delphi and Fuzzy AHP Methods. Journal of Hospitality, 2(3-4), 94-106.
Garg, R., Kiwelekar, A. W., Netak, L. D., & Bhate, S. S. (2021). Potential Use-Cases of Natural Language Processing for a Logistics Organization. Modern Approaches. Dans G. V. K., & J. M. Zurada, Machine Learning and Cognitive Science: A Walkthrough: Latest Trends in AI, volume 2 (p. 157.). LouisVille, KY, USA: Springer. doi: https://doi.org/10.1007/978-3-030-68291-0_13 DOI: https://doi.org/10.1007/978-3-030-68291-0_13
Gelderman, C. J., Semeijn, J., & Vluggen, R. (2017). Development of sustainability in public sector procurement. Public Money & Management, 37(6), 435-442. doi: https://doi.org/10.1080/09540962.2017.1344027 DOI: https://doi.org/10.1080/09540962.2017.1344027
Ghadge, A., Jena, S. K., Kamble, S., Misra, D., & Tiwari, M. K. (2020). Impact of financial risk on supply chains: a manufacturer-supplier relational perspective. International Journal of Production Research, 1-16. doi: https://doi.org/10.1080/00207543.2020.1834638 DOI: https://doi.org/10.1080/00207543.2020.1834638
Ghariani, R., & Younes, B. (2019). Orientation à l'innovation, intégration de la chaîne logistique et avantage concurrentiel. Cas des entreprises industielles tunisiennes. Revue française de gestion industrielle, 1(1), 1-19.
Giannakis, M., Dubey, R., Vlachos, I., & Ju, Y. (2020). Supplier sustainability performance evaluation using the analytic network process. Journal of cleaner production, 247, 119439. doi: https://doi.org/10.1016/j.jclepro.2019.119439 DOI: https://doi.org/10.1016/j.jclepro.2019.119439
Guo, C., Thompson, R. G., Foliente, G., & Peng, X. (2021). Reinforcement learning enabled dynamic bidding strategy for instant delivery trading. Computers & Industrial Engineering, 160, 107596. doi: https://doi.org/10.1016/j.cie.2021.107596 DOI: https://doi.org/10.1016/j.cie.2021.107596
Hadian, H., Chahardoli, S., Golmohammadi, A. M., & Mostafaeipour, A. (2020). A practical framework for supplier selection decisions with an application to the automotive sector. International Journal of Production Research, 58(10), 2997-3014. doi: https://doi.org/10.1080/00207543.2019.1624854 DOI: https://doi.org/10.1080/00207543.2019.1624854
Hedhili, N., & Aouadi, H. (2013). Tableau de bord qualité: processus approvisionnement. Revue Française de Gestion Industrielle, 32(2), 27-47. doi: https://doi.org/10.53102/2013.32.02.694 DOI: https://doi.org/10.53102/2013.32.02.694
Ho, J. Y., Ooi, J., Wan, Y. K., & Andiappan, V. (2021). Synthesis of wastewater treatment process (WWTP) and supplier selection via Fuzzy Analytic Hierarchy Process (FAHP). Journal of Cleaner Production, 314, 128104. doi: https://doi.org/10.1016/j.jclepro.2021.128104 DOI: https://doi.org/10.1016/j.jclepro.2021.128104
Hosseini, S., & Al Khaled, A. (2019). A hybrid ensemble and AHP approach for resilient supplier selection. Journal of Intelligent Manufacturing, 30(1), 207-228. doi: https://doi.org/10.1007/s10845-016-1241-y DOI: https://doi.org/10.1007/s10845-016-1241-y
Hsu, C. W., & Hu, A. H. (2009). Applying hazardous substance management to supplier selection using analytic network process. Journal of cleaner production, 17(2), 255-264. doi: https://doi.org/10.1016/j.jclepro.2008.05.004 DOI: https://doi.org/10.1016/j.jclepro.2008.05.004
Hwang, I., & Jang, Y. J. (2020). Q (λ) learning-based dynamic route guidance algorithm for overhead hoist transport systems in semiconductor fabs. International Journal of Production Research, 58(4), 1199-1221. doi: https://doi.org/10.1080/00207543.2019.1614692 DOI: https://doi.org/10.1080/00207543.2019.1614692
Javad, M. O., Darvishi, M., & Javad, A. O. (2020). Green supplier selection for the steel industry using BWM and fuzzy TOPSIS: A case study of Khouzestan steel company. Sustainable Futures, 2, 100012. doi: https://doi.org/10.1016/j.sftr.2020.100012 DOI: https://doi.org/10.1016/j.sftr.2020.100012
Javaid, M., Haleem, A., Singh, R. P., Haq, M. I., Raina, A., & Suman, R. (2020). Industry 5.0: Potential applications in COVID-19. Journal of Industrial Integration and Management, 5(4). doi: https://doi.org/10.1142/S2424862220500220 DOI: https://doi.org/10.1142/S2424862220500220
Jin, F., Ni, Z., Chen, H., Langari, R., Zhu, X., & Yuan, H. (2018). Single-valued neutrosophic entropy and similarity measures to solve supplier selection problems. Journal of Intelligent & Fuzzy Systems, 35(6), 6513-6523. doi: https://doi.org/10.3233/JIFS-18854 DOI: https://doi.org/10.3233/JIFS-18854
Kaggle (Google). (2021, 08 10). kaggle datasets. Récupéré sur https://www.kaggle.com/datasets: https://www.kaggle.com/datasets
Khaldi, R., Afia, A. E., & Chiheb, R. (2019). Performance prediction of pharmaceutical suppliers: comparative study between DEA-ANFIS-PSO and DEA-ANFIS-GA. International Journal of Computer Applications in Technology, 60(4), 317-325. DOI: https://doi.org/10.1504/IJCAT.2019.101172
Kim, T., Bilsel, R. U., & Kumara, S. (2008). Supplier selection in dynamic competitive environments. International Journal of Services Operations and Informatics, 3(3-4), 283-293. DOI: https://doi.org/10.1504/IJSOI.2008.021340
Klinmalee, S., Naenna, T., & Woarawichai, C. (2020). Application of a genetic algorithm for multi-item inventory lot-sizing with supplier selection under quantity discount and lead time. International Journal of Operational Research, 38(3), 403-421. DOI: https://doi.org/10.1504/IJOR.2020.107540
Kuberkar, S., & Singhal, T. K. (2020). Factors Influencing Adoption Intention of AI Powered Chatbot for Public Transport Services within a Smart City. International Journal of Emerging Technologies in Learning, 11(3), 948-958.
Kucharavy, D., Damand, D., Chehbi Gamoura, S., & Barth, M. (2020). Supporting Strategic Decision-Making in Manufacturing 4.0 with mix of qualitative and quantitative data analysis. Dans 13ème Conf. Int.de Modélisation, Optimisation et Simulation (MOSIM'20), Rabat (Maroc): MOSIM.
Kulkarni, C. S., Bhavsar, A. U., Pingale, S. R., & Kumbhar, S. S. (2017). BANK CHAT BOT–An Intelligent Assistant System Using NLP and Machine Learning. International Research Journal of Engineering and Technology, 4(05).
Labbi, O., Ahmadi, A., Ouzizi, L., & Douimi, M. (2020). A non-dominant sorting genetic algorithm for optimization of a product design and selection of its suppliers. Journal of Advanced Manufacturing Systems, 19(01), 167-188. doi: https://doi.org/10.1142/S0219686720500092 DOI: https://doi.org/10.1142/S0219686720500092
Lazzeri, J., & Fabbe-Costes, N. (2014). La traçabilité totale des supply chains: concept et modèle théorique de mise en œuvre. Revue française de gestion industrielle, 33(1), 55. DOI: https://doi.org/10.53102/2014.33.01.718
Lee, C. Y., Chou, B. J., & Huang, C. F. (2022). Data science and reinforcement learning for price forecasting and raw material procurement in petrochemical industry. Advanced Engineering Informatics, 51, 101443. doi: https://doi.org/10.1016/j.aei.2021.101443 DOI: https://doi.org/10.1016/j.aei.2021.101443
Lei, F., Wei, G., Gao, H., Wu, J., & Wei, C. (2020). TOPSIS method for developing supplier selection with probabilistic linguistic information. International Journal of Fuzzy Systems, 22(3), 749-759. doi: https://doi.org/10.1016/j.jclepro.2019.03.070 DOI: https://doi.org/10.1007/s40815-019-00797-6
Li, J., Fang, H., & Song, W. (2019). Sustainable supplier selection based on SSCM practices: A rough cloud TOPSIS approach. Journal of cleaner production, 222, 606-621. DOI: https://doi.org/10.1016/j.jclepro.2019.03.070
Li, Y., Ding, K., Wang, L., Zheng, W., Peng, Z., & Guo, S. (2018). An optimizing model for solving outsourcing supplier selecting problem based on particle swarm algorithm. Journal of Industrial and Production Engineering, 35(8), 526-534. doi: https://doi.org/10.1080/21681015.2018.1533893 DOI: https://doi.org/10.1080/21681015.2018.1533893
Lu, L., Peng, J. W., & Lu, Y. (2021). Perceived impact of the Covid-19 crisis on SMEs in different industry sectors: Evidence from Sichuan, China. International Journal of Disaster Risk Reduction, 55, 102085. doi: https://doi.org/10.1016/j.ijdrr.2021.102085 DOI: https://doi.org/10.1016/j.ijdrr.2021.102085
Luzon, B., & El-Sayegh, S. M. (2016). Evaluating supplier selection criteria for oil and gas projects in the UAE using AHP and Delphi. International Journal of Construction Management, 16(2), 175-183. doi: https://doi.org/10.1080/15623599.2016.1146112 DOI: https://doi.org/10.1080/15623599.2016.1146112
Makkar, S., Devi, G. N., & Solanki, V. K. (2019). Applications of machine learning techniques in supply chain optimization. Dans V. K., G. G., D. M., C. V., K. S., & S. V. N., proceedings of the International Conference on Intelligent Computing and Communication Technologies (pp. 861-869). Springer, Singapore: Springer. doi: https://doi.org/10.1007/978-981-13-8461-5_98 DOI: https://doi.org/10.1007/978-981-13-8461-5_98
Memari, A., Dargi, A., Jokar, M. R., Ahmad, R., & Rahim, A. R. (2019). Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of Manufacturing Systems, 50, 9-24. doi: https://doi.org/10.1016/j.jmsy.2018.11.002 DOI: https://doi.org/10.1016/j.jmsy.2018.11.002
Okwu, M. O., & Tartibu, L. K. (2020). Sustainable supplier selection in the retail industry: A TOPSIS-and ANFIS-based evaluating methodology. International journal of engineering business management, 12, 1847979019899542. doi: https://doi.org/10.1177/1847979019899542 DOI: https://doi.org/10.1177/1847979019899542
Pantha, R. P., Islam, M., Akter, N., & Islam, E. (2020). Sustainable supplier selection using integrated data envelopment analysis and differential evolution model. Journal of Applied Research on Industrial Engineering, 7(1), 25-35. doi: https://doi.org/10.22105/JARIE.2020.213449.1115
Permatasari, D. A., & Maharani, D. A. (2021). Combination of Natural Language Understanding and Reinforcement Learning for Booking Bot. Journal of Electrical, Electronic, Information, and Communication Technology, 3(1), 12-17. doi: https://doi.org/10.20961/jeeict.3.1.49818 DOI: https://doi.org/10.20961/jeeict.3.1.49818
Philippart, M. (2014). Mesurer la performance de l’entreprise étendue pour piloter la création de valeur: une approche par l’immatériel. Revue Française de Gestion Industrielle, 33(4), -. DOI: https://doi.org/10.53102/2014.33.04.793
Pishchulov, G., Trautrims, A., Chesney, T., Gold, S., & Schwab, L. (2019). The Voting Analytic Hierarchy Process revisited: A revised method with application to sustainable supplier selection. International Journal of Production Economics, 211, 166-179. doi: https://doi.org/10.1016/j.ijpe.2019.01.025 DOI: https://doi.org/10.1016/j.ijpe.2019.01.025
Prabhu, T. P., Chaudhari, H. B., Pathak, A. G., & Rajhans, N. R. (2018). Ideation Selection of a New Product Using Fuzzy Multi Criteria Decision Making and Promethee. Industrial Engineering Journal, 10(7). DOI: https://doi.org/10.26488/IEJ.10.7.49
Pradana, A. D., Goh, O. S., & Kumar, Y. J. (2018). Intelligent conversational bot for interactive marketing. . Journal of Telecommunication, Electronic and Computer Engineering, 10(1-7), 1-4.
Pratap, S., Daultani, Y., Dwivedi, A., & Zhou, F. (2021). Supplier selection and evaluation in e-commerce enterprises: a data envelopment analysis approach. Benchmarking: An International Journal. doi: https://doi.org/10.1108/BIJ-10-2020-0556 DOI: https://doi.org/10.1108/BIJ-10-2020-0556
Qu, G., Zhang, Z., Qu, W., & Xu, Z. (2020). Green supplier selection based on green practices evaluated using fuzzy approaches of TOPSIS and ELECTRE with a case study in a Chinese Internet company. International journal of environmental research and public health, 17(9), 3268. doi: https://doi.org/10.3390/ijerph17093268 DOI: https://doi.org/10.3390/ijerph17093268
Rahimi, M., Kumar, P., Moomivand, B., & Yari, G. (2021). An intuitionistic fuzzy entropy approach for supplier selection. Complex & Intelligent Systems, 1-8. DOI: https://doi.org/10.1007/s40747-020-00224-6
Ricciardelli, E., & Biswas, D. (2019). Self-improving Chatbots based on Reinforcement Learning. In Proceeding of the 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making.
Rouquet, A., & Vega, D. (2015). l’integration de la logistique dans l’organisation aux organisations orientees logistiques. Revue Française de Gestion Industrielle, 34(3), 39-53. DOI: https://doi.org/10.53102/2015.34.03.834
Sagar, M. K., & Singh, D. (2012). Supplier selection criteria: Study of automobile sector in India . International Journal of Engineering Research and Development, 4(4), 34-39.
Sharma, M., Luthra, S., Joshi, S., & Kumar, A. (2020). Developing a framework for enhancing survivability of sustainable supply chains during and post-COVID-19 pandemic. International Journal of Logistics Research and Applications, 1-21. doi: https://doi.org/10.1080/13675567.2020.1810213 DOI: https://doi.org/10.1080/13675567.2020.1810213
Sheehan, B., J. H., & Gottlieb, U. (2020). Customer service chatbots: Anthropomorphism and adoption. Journal of Business Research, 115, 14-24. doi: https://doi.org/10.1016/j.jbusres.2020.04.030 DOI: https://doi.org/10.1016/j.jbusres.2020.04.030
Shukla, R. K., Garg, D., & Agarwal, A. (2018). Modelling supply chain coordination for performance improvement using analytical network process-based approach. International Journal of Business Excellence, 14(1), 18-48. DOI: https://doi.org/10.1504/IJBEX.2018.088313
Singh, A., & Kumar, S. (2021). Picture fuzzy set and quality function deployment approach based novel framework for multi-criteria group decision making method. Engineering Applications of Artificial Intelligence, 104, 104395. doi: https://doi.org/10.1016/j.engappai.2021.104395 DOI: https://doi.org/10.1016/j.engappai.2021.104395
Song, X., Yang, S., Huang, Z., & Huang, T. (2019). The Application of Artificial Intelligence in Electronic Commerce. Journal of Physics: Conference Series, 1302(3), 032030. DOI: https://doi.org/10.1088/1742-6596/1302/3/032030
Su, C. J., & Chen, Y. A. (2018). Risk assessment for global supplier selection using text mining. Computers & Electrical Engineering, 68, 140-155. doi: https://doi.org/10.1016/j.compeleceng.2018.03.042 DOI: https://doi.org/10.1016/j.compeleceng.2018.03.042
Sultana, I., Ahmed, I., & Azeem, A. (2015). An integrated approach for multiple criteria supplier selection combining Fuzzy Delphi, Fuzzy AHP & Fuzzy TOPSIS. Journal of Intelligent & Fuzzy Systems, 29(4), 1273-1287. DOI: https://doi.org/10.3233/IFS-141216
Tavana, M., Yazdani, M., & Di Caprio, D. (2017). An application of an integrated ANP–QFD framework for sustainable supplier selection. International Journal of Logistics Research and Applications, 20(3), 254-275. doi: https://doi.org/10.1080/13675567.2016.1219702 DOI: https://doi.org/10.1080/13675567.2016.1219702
Tavassoli, M. A., Darestani, S. A., & Tavassoli, S. A. (2018). Supplier selection and evaluation using QFD and ELECTRE in quality management system environment (case study: Faravari & Sakht Company). International Journal of Productivity and Quality Management, 24(1), 84-100. DOI: https://doi.org/10.1504/IJPQM.2018.091171
Tsai, C. K., & Phumchusri, N. (2021). Fuzzy Analytical Hierarchy Process for Supplier Selection: A Case Study in An Electronic Component Manufacturer. Engineering Journal, 25(8), 73-86. doi: https://doi.org/10.4186/ej.2021.25.8.73 DOI: https://doi.org/10.4186/ej.2021.25.8.73
Tusnial, A., Sharma, S. K., Dhingra, P., & Routroy, S. (2020). Supplier selection using hybrid multicriteria decision-making methods. International Journal of Productivity and Performance Management. doi: https://doi.org/10.1108/IJPPM-04-2019-0180 DOI: https://doi.org/10.1108/IJPPM-04-2019-0180
Vahidi, F., Torabi, S. A., & Ramezankhani, M. J. (2018). Sustainable supplier selection and order allocation under operational and disruption risks. Journal of Cleaner Production, 174, 1351-1365. doi: https://doi.org/10.1016/j.jclepro.2017.11.012 DOI: https://doi.org/10.1016/j.jclepro.2017.11.012
Vaidyam, A. N., Wisniewski, H., Halamka, J. D., Kashavan, M. S., & Torous, J. B. (2019). Chatbots and conversational agents in mental health: a review of the psychiatric landscape. The Canadian Journal of Psychiatry, 64(7), 456-464. doi: Vaidyam, A. N., Wisniewski, H., Halamka, J. D., Kashavan, M. S., & Torous, J. B. (2019). Chatbots and conversational agents in mental health: a review of the psychiatric landscape. . The Canadian Journal of Psychiatry, 64(7), 456-464. DOI: https://doi.org/10.1177/0706743719828977
Van Pinxteren, M. M., Pluymaekers, M., & Lemmink, J. G. (2020). Human-like communication in conversational agents: a literature review and research agenda. Journal of Service Management. doi: https://doi.org/10.1108/JOSM-06-2019-0175 DOI: https://doi.org/10.1108/JOSM-06-2019-0175
Vijayakumar, Y., Rahim, S. A., Ahmi, A., & Rahman, N. A. (2019). Investigation of supplier selection criteria that leads to buyer-supplier long term relationship for semiconductor industry. . International Journal of Supply Chain Management, 8(3), 982-993.
Wang, C. N., Yang, C. Y., & Cheng, H. C. (2019). A fuzzy multicriteria decision-making (MCDM) model for sustainable supplier evaluation and selection based on triple bottom line approaches in the garment industry. Processes, 7(7), 400. doi: https://doi.org/10.3390/pr7070400 DOI: https://doi.org/10.3390/pr7070400
Wang, H., Sarker, B. R., Li, J., & Li, J. (2020). Adaptive scheduling for assembly job shop with uncertain assembly times based on dual Q-learning. International Journal of Production Research, 1-17. doi: https://doi.org/10.1080/00207543.2020.1794075 DOI: https://doi.org/10.1080/00207543.2020.1794075
Wang, R., Li, X., & Li, C. (2021). Optimal selection of sustainable battery supplier for battery swapping station based on Triangular fuzzy entropy-MULTIMOORA method. Journal of Energy Storage, 34, 102013. doi: https://doi.org/10.1016/j.est.2020.102013 DOI: https://doi.org/10.1016/j.est.2020.102013
Weber Current et Benton. (1991). Vendor Selection criteria and methods. European Journal of Operational Research, 50, 2-18. doi: https://doi.org/10.1016/0377-2217(91)90033-R DOI: https://doi.org/10.1016/0377-2217(91)90033-R
Winarso, W., Syarif, F., & Untari, D. T. (2021). Supplier Selection Of 40th Container in PT Tribudhi Pelita Indonesia Using Analytical Hierarchy Process (AHP) Method. Academy of Strategic Management Journal, 20, 1-6.
Wu, C., Lin, C., Barnes, D., & Zhang, Y. (2020). Partner selection in sustainable supply chains: A fuzzy ensemble learning model. Journal of Cleaner Production, 275, 123165. doi: https://doi.org/10.1016/j.jclepro.2020.123165 DOI: https://doi.org/10.1016/j.jclepro.2020.123165
Xu, X., & Ding, Y. (2014). Supplier selection in manufacturing innovation chain-oriented public procurement based on improved PSO method. Journal of Industrial Engineering and Management, 7(1), 276-293. doi: http://dx.doi.org/10.3926/jiem.898 DOI: https://doi.org/10.3926/jiem.898
Yoon, J., Talluri, S., Yildiz, H., & Ho, W. (2018). Models for supplier selection and risk mitigation: a holistic approach. International Journal of Production Research, 56(10), 3636-3661. doi: https://doi.org/10.1080/00207543.2017.1403056 DOI: https://doi.org/10.1080/00207543.2017.1403056
Yu, D., Li, D. F., & Merigo, J. M. (2016). Dual hesitant fuzzy group decision making method and its application to supplier selection. International Journal of Machine Learning and Cybernetics, 7(5), 819-831. doi: https://doi.org/10.1007/s13042-015-0400-3 DOI: https://doi.org/10.1007/s13042-015-0400-3
Zaoui, S., Hamou-ou-Brahim, S. A., Zhou, H., Omrane, A., & Huang, D. (2021). Consumer Purchasing Behaviour Towards Strategic Innovation Management Practices in Morocco During COVID-19 Health Crisis. FIIB Business Review, 10(2), 158-171. doi: https://doi.org/10.1177/23197145211020714 DOI: https://doi.org/10.1177/23197145211020714
Numéro
Publiée
Soumis
Comment citer
Rubrique
Licence
(c) Tous droits réservés Revue Française de Gestion Industrielle 2021
Ce travail est disponible sous licence Creative Commons Attribution - Pas d’Utilisation Commerciale 4.0 International.
Statistiques
