TY - JOUR T1 - TEKNOLOJİK GELİŞMELER IŞIĞINDA ENDÜSTRİ MÜHENDİSLİĞİNİN GELECEĞİ TT - THE FUTURE OF INDUSTRIAL ENGINEERING WITH KNOWLEDGE OF TECHNOLOGICAL ADVANCEMENTS AU - Aktar Demirtaş, Ezgi AU - Sağır Özdemir, Müjgan AU - Alpay, Şerafettin AU - Özkan, N. Fırat AU - Hasgül, Servet AU - Sipahioğlu, Aydın PY - 2023 DA - December Y2 - 2023 DO - 10.31796/ogummf.1401960 JF - Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi JO - ESOGÜ Müh Mim Fak Derg PB - Eskişehir Osmangazi Üniversitesi WT - DergiPark SN - 2630-5712 SP - 1094 EP - 1111 VL - 31 IS - 4 LA - tr AB - Endüstri Mühendisliği insan, makine ve malzemeden oluşan bütünleşik sistemlerin tasarımı, kurulması ve geliştirilmesi ile ilgilenir. Kaynakların verimli kullanımının gittikçe daha önemli olduğu küresel dünyada, tesislerin yer seçimi ve yerleşiminden, hammadde ve diğer girdilerin tedariğine, üretim süreçlerinin planlanması ve çizelgelenmesinden stok ve lojistik süreçlerinin yönetimine, standart süre ve kapasitelerin belirlenmesinden ürün, süreç ve hizmet kalitesinin iyileştirilmesine kadar pek çok aşamada eniyi kararların verilmesinde rol oynamaktadır. Çok çeşitli çalışma alanlarına sahip olan Endüstri Mühendisliği, son yıllarda hızla gelişen Yapay Zekâ teknikleri ve diğer teknolojik gelişmelerden oldukça etkilenmektedir. Bu makalede, son yıllarda Endüstri Mühendisliği alanındaki gelişme ve yenilikler, içerdiği bilim dalları temelinde literatüre dayandırılarak açıklanmaktadır. Çalışmanın bulguları Cumhuriyetimizin 100. yılında yeni mezun olan Endüstri Mühendisleri ve mühendis adayları için de bir farkındalık oluşturacaktır. KW - Üretim ve Servis Sistemleri KW - Yöneylem Araştırması KW - Kalite Yönetimi KW - Ergonomi KW - Bilgi Sistemleri ve Yapay Zekâ KW - Endüstri Mühendisliği’nin tarihçesi N2 - Industrial Engineering is concerned with the design, installation, and improvement of integrated systems comprising human, machine, and materials. In a globalized world where efficient resource utilization is increasingly crucial, Industrial Engineering plays a pivotal role in decision-making at various stages, from site selection and layout of facilities to procurement of raw materials and other inputs, planning and scheduling of production processes, management of inventory and logistics processes, determination of standard times and capacities, and enhancement of product, process, and service quality. With diverse areas of application, Industrial Engineering has rapidly evolved in recent years, significantly influenced by emerging artificial intelligence techniques and other technological advancements. This article explores the developments and innovations in the field of Industrial Engineering in the context of various scientific disciplines, relying on literature-based evidence. The findings of this study aim to create awareness for newly graduated Industrial Engineers and engineering candidates, particularly in the centennial year of Republic of Turkey. CR - Abualsauod, E. H. (2023). Machine learning based fault detection approach to enhance quality control in smart manufacturing. Production Planning & Control, 1-9. doi: https://doi.org/10.1080/09537287.2023.2175736 CR - Ackoff, R.,1972, A Note on Systems Science, Interfaces, 2,4. doi: https://doi.org/10.1287/inte.2.4.40 CR - Adhikari, A., Joshi, R., & Basu, S. (2023). Collaboration and coordination strategies for a multi-level AI-enabled healthcare supply chain under disaster. International Journal of Production Research, 1-27. doi: https://doi.org/10.1080/00207543.2023.2252933 CR - Ahmed, S., Alshater, M. M., El Ammari, A., & Hammami, H. (2022). Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61, 101646. doi: https://doi.org/10.1016/j.ribaf.2022.101646 CR - Al-Refaie, A., Abbasi, G., & Ghanim, D. (2021). Proposed α-cut CUSUM and EWMA control charts for fuzzy response observations. International Journal of Reliability, Quality and Safety Engineering, 28(02), 2150012. doi: https://doi.org/10.1142/S0218539321500121 CR - Alwan, W., Ngadiman, N. H. A., Hassan, A., Saufi, S. R., & Mahmood, S. (2023). Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns. Machines, 11(1), 115. doi: https://doi.org/10.3390/machines11010115 CR - Aouag, H., Soltani, M., & Mouss, M. D. (2021). Enhancement of value stream mapping application process through using fuzzy DEMATEL and fuzzy QFD approaches: a case study considering economic and environmental perspectives. Journal of Modelling in Management, 16(3), 1002-1023. doi: http://dx.doi.org/10.1108/JM2-01-2020-0007 CR - Apaydin-Özkan, H. (2022). Appliance-Level Anomaly Detection by Using Control Charts and Artificial Neural Networks with Power Profiles. Sensors, 22(17), 6639. doi: https://doi.org/10.3390/s22176639 CR - Aslam, M., AL-Marshadi, A. H., & Khan, N. (2019). A new X-bar control chart for using neutrosophic exponentially weighted moving average. Mathematics, 7(10), 957. doi: https://doi.org/10.3390/math7100957 CR - Azmat, S., Sabir, Q. U. A., Tariq, S., Shafqat, A., Rao, G. S., & Aslam, M. (2023). Monitoring Air Quality using the Neural Network based Control Chart. MAPAN, 1-9. doi: http://dx.doi.org/10.1007/s12647-023-00663-9 CR - Bahroun, Z., Tanash, M., As’ad, R., & Alnajar, M. (2023). Artificial Intelligence Applications in Project Scheduling: A Systematic Review, Bibliometric Analysis, and Prospects for Future Research. Management Systems in Production Engineering, 31(2), 144-161. doi: https://doi.org/10.2478/mspe-2023-0017 CR - Bai, R., Chen, X., Chen, Z. L., Cui, T., Gong, S., He, W., ... & Zhang, H. (2023). Analytics and machine learning in vehicle routing research. International Journal of Production Research, 61(1), 4-30. doi: https://doi.org/10.48550/arXiv.2102.10012 CR - Balasubramanian, S., Shukla, V., Islam, N., Upadhyay, A., & Duong, L. (2023). Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic. International Journal of Production Research, 1-34. doi: https://doi.org/10.1080/00207543.2023.2263102 CR - Bayraktar, C., & Gökçen, H. (2020). Yüksek raflı depolama sistemlerinin enerji optimizasyonunda anomali tespiti için sınıflama algoritmalarının karşılaştırılması, Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi, 4(2), 89-109. doi: https://doi.org/10.33461/uybisbbd.790369 CR - Behnia, F., Ahmadabadi, H. Z., Schuelke-Leech, B. A., & Mirhassani, M. (2023). Developing a Fuzzy Optimized Model for Selecting Maintenance Strategy in Paper Industry: An Integrated FGP-ANP-FMEA approach. Expert Systems with Applications, 120899. doi: https://doi.org/10.1016/j.eswa.2023.120899 CR - Beseiso, M., & Kumar, G. (2021). A fuzzy computational approach for selecting interdependent projects using prioritized criteria. Journal of Intelligent & Fuzzy Systems, 40(6), 11341-11354. doi: https://doi.org/10.3233/JIFS-202506 CR - Bhambri, P., & Rani, S. (2024). Challenges, Opportunities, and the Future of Industrial Engineering with IoT and AI. Integration of AI-Based Manufacturing and Industrial Engineering Systems with the Internet of Things, 1-18. CR - Blanc, J., & Deb, K. (2020), Pymoo: Multi-Objective Optimization in Python, 2020, IEEE Access, 8, 89497-89509. doi: http://dx.doi.org/10.1109/ACCESS.2020.2990567 CR - Boff Medeiros, N., Fogliatto, F. S., Karla Rocha, M., & Tortorella, G. L. (2023). Predicting the length-of-stay of pediatric patients using machine learning algorithms. International Journal of Production Research, 1-14. doi: http://dx.doi.org/10.1080/00207543.2023.2235029 CR - Bolatan, G. İ. S. (2019). Kalite 4.0. Iğdır Üniversitesi Sosyal Bilimler Dergisi, 21, 437-454. Erişim Adresi: https://dergipark.org.tr/tr/download/article-file/2154486 CR - Buchmeister, B., Palcic, I., & Ojstersek, R. (2019). Artificial intelligence in manufacturing companies and broader: an overvıew. Chapter 07 in DAAAM International Scientific Book, 081-098. doi: http://dx.doi.org/10.2507/daaam.scibook.2019.07 CR - Carl May, M., Nestroy, C., Overbeck, L., & Lanza, G. (2023). Automated model generation framework for material flow simulations of production systems. International Journal of Production Research, 1-16. doi: https://doi.org/10.1080/00207543.2023.2284833 CR - Castañé, G., Dolgui, A., Kousi, N., Meyers, B., Thevenin, S., Vyhmeister, E., & Östberg, P. O. (2023). The ASSISTANT project: AI for high level decisions in manufacturing. International Journal of Production Research, 61(7), 2288-2306. doi: https://doi.org/10.1080/00207543.2022.2069525 CR - Cavallo, F., Sinigaglia, S., Megali, G., Pietrabissa, A., Dario, P., Mosca, F., & Cuschieri, A. (2014). Biomechanics–machine learning system for surgical gesture analysis and development of technologies for minimal access surgery. Surgical Innovation, 21(5), 504-512. doi: https://doi.org/10.1177/1553350613510612 CR - Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58. doi: http://dx.doi.org/10.1145/1541880.1541882 CR - Chaouch, F., Ben Khalifa, A., Zitoune, R., & Zidi, M. (2023). Modeling and multi-objective optimization of abrasive water jet machining process of composite laminates using a hybrid approach based on neural networks and metaheuristic algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. doi: https://doi.org/10.1177/09544054231191816 CR - Charalambous O., & Hindi, K. S. (1991). A Review a of Artificial Intelligence Based Job Shop Scheduling Systems, Information and Decisions Technologies, 17,3,189-202. CR - Chiarini, A. (2020). Industry 4.0, quality management and TQM world. A systematic literature review and a proposed agenda for further research. The TQM Journal. 32(4), 603-616. doi: http://dx.doi.org/10.1108/TQM-04-2020-0082 CR - Chukhrova, N., & Johannssen, A. (2018). Inspection tables for single acceptance sampling with crisp and fuzzy formulation of quality limits. International Journal of Quality & Reliability Management, 35(9), 1755-1791. doi: http://dx.doi.org/10.1108/IJQRM-03-2017-0034 CR - Ciccone, F., Bacciaglia, A., & Ceruti, A. (2023). Optimization with Artificial Intelligence in Additive Manufacturing, a systematic review. Journal of Brazilian Society of Mechanical Sciences of Engineering, 45, 6. doi: https://doi.org/10.1007/s40430-023-04200-2 CR - Čisar, P., & Maravić-Čisar, S. (2019). EWMA statistics and fuzzy logic in function of network anomaly detection. Facta universitatis-series: Electronics and Energetics, 32(2), 249-265. doi: http://dx.doi.org/10.2298/FUEE1902249C CR - Clark, C. (1957). The Conditions of Economic Progress, 3.Edition, London, Macmillan. CR - CoReceptionist (2023), Industry 4.0-What is it? History and Current Applications & Future. Retrieved from https://coreceptionist.co/industry-4-0-what-is-it-history-current-applications-future CR - Davenport, T. H., & Short, J. E. (2003). The new industrial engineering: Information technology and business process redesign. Operations management: critical perspectives on business and management, 97-123. CR - Decker, L., Leite, D., Giommi, L., & Bonacorsi, D. (2020, July). Real-time anomaly detection in data centers for log-based predictive maintenance using an evolving fuzzy-rule-based approach. In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-8. IEEE. doi: http://dx.doi.org/10.1109/FUZZ48607.2020.9177762 CR - Dehghan Shoorkand, H., Nourelfath, M., & Hajji, A. (2023). A deep learning approach for integrated production planning and predictive maintenance. International Journal of Production Research, 1-20. doi: http://dx.doi.org/10.1080/00207543.2022.2162618 CR - Desoutter (2023), Industrial Revolution - From Industry 1.0 to Industry 4.0. Erişim adresi: https://www.desouttertools.com/your-industry/news/503/industrial-revolution-from-industry-1-0-to-industry-4-0 CR - Doumpos, M., Zopounidis, C., Gounopoulos, D., Platanakis, E., & Zhang, W. (2023). Operational research and artificial intelligence methods in banking. European Journal of Operational Research, 306(1), 1-16. doi: https://doi.org/10.1016/j.ejor.2022.04.027 CR - Du, P., He, X., Cao, H., Garg, S., Kaddoum, G., & Hassan, M. M. (2023). AI-based energy-efficient path planning of multiple logistics UAVs in intelligent transportation systems. Computer Communications, 207, 46-55. doi: http://dx.doi.org/10.1016/j.comcom.2023.04.032 CR - Du‐Harpur, X., Watt, F. M., Luscombe, N. M., & Lynch, M. D. (2020). What is AI? Applications of artificial intelligence to dermatology. British Journal of Dermatology, 183(3), 423-430. doi: https://doi.org/10.1111/bjd.18880 CR - Esteso, A., Peidro, D., Mula, J., & Díaz-Madroñero, M. (2023). Reinforcement learning applied to production planning and control. International Journal of Production Research, 61(16), 5772-5789. doi: http://dx.doi.org/10.1080/00207543.2022.2104180 CR - Ever, D., Demircioğlu, E. N. (2022). Yapay Zekâ Teknolojilerinin Kalite Maliyetleri Üzerine Etkisi. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 31(1), 59-72. doi: https://doi.org/10.35379/cusosbil.1023004 CR - Feizabadi, J. (2022). Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 25(2), 119-142. doi: http://dx.doi.org/10.1080/13675567.2020.1803246 CR - García-Alcaraz, J. L., Díaz Reza, J. R., Villalon Turrubiates, I. E., Lopez Herrera, R., Soto Cabral, A., Ganzalez Lazalde, I., & Rodriguez Alvarez, J. L. (2022). A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE. Instituto de Ingeniería y Tecnología. doi: https://doi.org/10.3390/math10163000 CR - Geramian, A., Abraham, A., & Ahmadi Nozari, M. (2019). Fuzzy logic-based FMEA robust design: a quantitative approach for robustness against groupthink in group/team decision-making. International Journal of Production Research, 57(5), 1331-1344. doi: https://doi.org/10.1080/00207543.2018.1471236 CR - Geramian, A., Shahin, A., Minaei, B., & Antony, J. (2020). Enhanced FMEA: An integrative approach of fuzzy logic-based FMEA and collective process capability analysis. Journal of the Operational Research Society, 71(5), 800-812. doi: http://dx.doi.org/10.1080/01605682.2019.1606986 CR - Ghislieri, C., Molino, M., & Cortese, C. G. (2018). Work and organizational psychology looks at the fourth industrial revolution: how to support workers and organizations? Frontiers in psychology, 9, 2365. doi: https://doi.org/10.3389/fpsyg.2018.02365 CR - Gholizadeh, H., Javadian, N., & Fazlollahtabar, H. (2020). An integrated fuzzy-genetic failure mode and effect analysis for aircraft wing reliability. Soft Computing, 24, 13401-13412. doi: https://link.springer.com/article/10.1007/s00500-020-04757-3 CR - Giudici, P., & Raffinetti, E. (2023). SAFE artificial intelligence in finance. Finance Research Letters, 104088. doi: https://doi.org/10.1016/j.frl.2023.104088 CR - Gojković, R., Đurić, G., Tadić, D., Nestić, S., & Aleksić, A. (2021). Evaluation and selection of the quality methods for manufacturing process reliability improvement—Intuitionistic fuzzy sets and genetic algorithm approach. Mathematics, 9(13), 1531. doi: https://doi.org/10.3390/math9131531 CR - Gomez, C., Guardia, A., Mantari, J. L., Coronado, A. M., & Reddy, J. N. (2022). A contemporary approach to the MSE paradigm powered by Artificial Intelligence from a review focused on Polymer Matrix Composites. Mechanics of Advanced Materials and Structures, 29(21), 3076-3096. doi: https://doi.org/10.1080/15376494.2021.1886379 CR - Gupta, S., Modgil, S., Bhattacharyya, S., & Bose, I. (2022). Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research, 1-60. doi: https://link.springer.com/article/10.1007/s10479-020-03856-6 CR - Gümüşoğlu, Ş. (2018). Bilimsel yaklaşımlarla değişim, dönüşüm ve kalite 4.0. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 33(2), 543-568. doi: https://doi.org/10.24988/deuiibf.2018332773 CR - Gürsoy, M. Ü., Çolak, U.C., Gökçe, M. H., Akkulak, C., & Ötleş, S. (2019). Endüstri için kestirimci bakım. International Journal of 3D Printing Technologies and Digital Industry, 3(1), 56-66. Erişim adresi: https://dergipark.org.tr/tr/download/article-file/706015 CR - Hassan, A., Purnomo, M. R. A., & Anugerah, A. R. (2020). Fuzzy-analytical-hierarchy process in failure mode and effect analysis (FMEA) to identify process failure in the warehouse of a cement industry. Journal of Engineering, Design and Technology, 18(2), 378-388. doi: http://dx.doi.org/10.1108/JEDT-05-2019-0131 CR - Hassouna, M., El-henawy, I., & Haggag, R. (2022). A Multi-Objective Optimization for supply chain management using Artificial Intelligence (AI), International Journal of Advanced Computer Science and Applications, 13,8, 140-149. doi: https://dx.doi.org/10.14569/IJACSA.2022.0130817 CR - Hatami, M., & Franz, B. (2021), Using Deep Learning Artificial Intelligence Foresight Method in the Optimization of Planning and Scheduling of Construction Processes, Computing in Civil Engineering, 1171-1178. doi: http://dx.doi.org/10.1061/9780784483893.143 CR - Hsieh, Y. C., You, P. S., & Chen, C. S. (2021). Scheduling the periodic delivery of liquefied petroleum gas tank with time window by using artificial intelligence approaches: An example in Taiwan. Science Progress, 104(3_suppl), 00368504211040355. doi: https://doi.org/10.1177/00368504211040355 CR - Ivančan, J., & Lisjak, D. (2021). New FMEA risks ranking approach utilizing four fuzzy logic systems. Machines, 9(11), 292. doi: https://doi.org/10.3390/machines9110292 CR - Jafarzadeh, H., Akbari, P., & Abedin, B. (2018). A methodology for project portfolio selection under criteria prioritisation, uncertainty and projects interdependency–combination of fuzzy QFD and DEA. Expert Systems with Applications, 110, 237-249. doi: https://doi.org/10.1016/j.eswa.2018.05.028 CR - Jiang, J. (2023). A survey of machine learning in additive manufacturing technologies. International Journal of Computer Integrated Manufacturing, 1-23. doi: https://doi.org/10.1080/0951192X.2023.2177740 CR - Kang, X., & Wang, N. (2022). A hybrid model to develop aesthetic product design of customer satisfaction. Concurrent Engineering, 1063293X221138650. doi: https://doi.org/10.1177/1063293X221138650 CR - Kara, İ. 1985, Yöneylem Araştırmasının Yöntembilimi, Anadolu Üniversitesi Yayınları 96, Anadolu Üniversitesi Basımevi, 117 s. CR - Kaya, İ., ve Engin, O. (2005). Kalite İyileştirme Sürecinde Yapay Zekâ Tekniklerinin Kullanımı. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 11(1), 103-114. Erişim adresi: https://dergipark.org.tr/tr/download/article-file/191103 CR - Kaya, İ., İlbahar, E., & Karaşan, A. (2023). A design methodology based on two dimensional fuzzy linguistic variables for attribute control charts with real case applications. Engineering Applications of Artificial Intelligence, 126, 106792. doi: http://dx.doi.org/10.1016/j.engappai.2023.106792 CR - Kesici, B. ve Yıldız, M. S. (2016). Kalite kontrol faaliyetlerinde Yapay Zekâ kullanımı ve bir otomotiv yan sanayisinde uygulanması. Yalova Sosyal Bilimler Dergisi, 6(12), 307-323. Erişim adresi: https://dergipark.org.tr/tr/download/article-file/272287 CR - Kousi, N., Dimosthenopoulos, D., Matthaiakis, A. S., Michalos, G., & Makris, S. (2019). AI based combined scheduling and motion planning in flexible robotic assembly lines. Procedia CIRP, 86, 74-79. doi: https://doi.org/10.1016/j.procir.2020.01.041 CR - Kula, U., Torkul, O. ve Taşkın, H. (2006). Endüstri ve sistem mühendisliğine giriş. Sakarya: Değişim Yayınları. CR - Kurt, R. (2022). Control of system parameters by estimating screw withdrawal strength values of particleboards using artificial neural network-based statistical control charts. Journal of Wood Science, 68(1), 64. doi: https://doi.org/10.1186/s10086-022-02065-y CR - Lee, S. M., Lee, D., & Kim, Y.S. (2019). The quality management ecosystem for predictive maintenance in the Industry 4.0 era. International Journal of Quality Innovation, 5(1), 1-11. doi: https://doi.org/10.1186/s40887-019-0029-5 CR - Li, B. H., Hou, B. C., Yu, W. T., Lu, X. B., & Yang, C. W. (2017). Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering, 18(1), 86-96. doi: http://dx.doi.org/10.1631/FITEE.1601885 CR - Liu, X., He, X., Wang, M., & Shen, H. (2022). What influences patients' continuance intention to use AI-powered service robots at hospitals? The role of individual characteristics. Technology in Society, 70, 101996. doi: https://doi.org/10.1016/j.techsoc.2022.101996 CR - Lolli, F., Balugani, E., Ishizaka, A., Gamberini, R., Rimini, B., & Regattieri, A. (2019). Machine learning for multi-criteria inventory classification applied to intermittent demand. Production Planning & Control, 30(1), 76-89. doi: https://doi.org/10.1080/09537287.2018.1525506 CR - Ma, G., & Wu, M. (2019). A Big Data and FMEA-based construction quality risk evaluation model considering project schedule for Shanghai apartment projects. International Journal of Quality & Reliability Management, 37(1), 18-33. doi: http://dx.doi.org/10.1108/IJQRM-11-2018-0318 CR - Maiti, C., & Muthuswamy, S. (2023). Classification of materials in cylindrical workpieces using image processing and machine learning techniques. International Journal of Production Research, 1-18. doi: https://doi.org/10.1080/00207543.2023.2219344 CR - Majumder, M. (2016). Technology as work and work as technology. International Journal of Human Capital and Information Technology Professionals (IJHCITP), 7(1), 20-34. doi: https://doi.org/10.4018/IJHCITP.2016010102 CR - Mariajayaprakash, A., Senthilvelan, T., & Gnanadass, R. (2015). Optimization of process parameters through fuzzy logic and genetic algorithm–A case study in a process industry. Applied Soft Computing, 30, 94-103. doi: https://doi.org/10.1016/j.asoc.2015.01.042 CR - Murad, C. A., Melani, A. H. D. A., Michalski, M. A. D. C., Caminada Netto, A., de Souza, G. F. M., & Nabeta, S. I. (2020). Fuzzy-FMSA: Evaluating Fault Monitoring and Detection Strategies Based on Failure Mode and Symptom Analysis and Fuzzy Logic. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 6(3), 031001. doi: https://doi.org/10.1115/1.4045974 CR - Na’amnh, S., Salim, M. B., Husti, I., & Daróczi, M. (2021). Using artificial neural network and fuzzy inference system based prediction to improve failure mode and effects analysis: A case study of the busbars production. Processes, 9(8), 1444. doi: https://doi.org/10.3390/pr9081444 CR - Nitnara, C., & Tragangoon, K. (2023). Simulation-Based Optimization of Injection Molding Process Parameters for Minimizing Warpage by ANN and GA. International Journal of Technology, 14(2). doi: https://doi.org/10.14716/ijtech.v14i2.5573 CR - Norzelan, N. A., Mohamed, I. S., & Mohamad, M. (2024). Technology acceptance of artificial intelligence (AI) among heads of finance and accounting units in the shared service industry. Technological Forecasting and Social Change, 198, 123022. doi: https://doi.org/10.1016/j.techfore.2023.123022 CR - Öztemel, E. (2020). Yapay Zekâ ve İnsanlığın Geleceği, Ankara: Türkiye Bilimler Akademisi. doi: https://doi.org/10.53478/TUBA.2020.011 CR - Panda, S. K., Mishra, V., Balamurali, R., & Elngar, A. A. (Eds.). (2021). Artificial Intelligence and Machine Learning in Business Management: Concepts, Challenges, and Case Studies (1st ed.). CRC Press. doi: https://doi.org/10.1201/9781003125129 CR - Patil, C. K., Husain, M., & Halegowda, N. V. (2018). Study of quality function deployment model based on artificial neural network with optimization techniques. Journal of Advanced Manufacturing Systems, 17(01), 119-136. doi: https://doi.org/10.1142/S0219686718500087 CR - Petrat, D. (2021). Artificial intelligence in human factors and ergonomics: an overview of the current state of research. Discover Artificial Intelligence, 1(1), 3. doi: http://doi.org/10.1007/s44163-021-00001-5 CR - Priore, P., Ponte, B., Rosillo, R., & de la Fuente, D. (2019). Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments. International Journal of Production Research, 57(11), 3663-3677. doi: http://dx.doi.org/doi:10.1080/00207543.2018.1552369 CR - Rasheed, H. M. W., Chen, Y., Khizar, H. M. U., & Safeer, A. A. (2023). Understanding the factors affecting AI services adoption in hospitality: The role of behavioral reasons and emotional intelligence. Heliyon. doi: https://doi.org/10.1016/j.heliyon.2023.e16968 CR - Reda, H., & Dvivedi, A. (2022). Decision-making on the selection of lean tools using fuzzy QFD and FMEA approach in the manufacturing industry. Expert Systems with Applications, 192, 116416. doi: https://doi.org/10.1016/j.eswa.2021.116416 CR - Sabahno, H., & Niaki, S. T. A. (2023). New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification. Mathematics, 11(16), 3566. doi: https://doi.org/10.3390/math11163566 CR - Sharma, A., Zhang, Z., & Rai, R. (2021). The interpretive model of manufacturing: a theoretical framework and research agenda for machine learning in manufacturing. International Journal of Production Research, 59(16), 4960-4994. doi: https://doi.org/10.1080/00207543.2021.1930234 CR - 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 CR - Singh, R., & Mishra, V. K. (2023). Machine learning based fuzzy inventory model for imperfect deteriorating products with demand forecast and partial backlogging under green investment technology. Journal of the Operational Research Society, 1-16. doi: https://doi.org/10.1080/01605682.2023.2239868 CR - Siskon (2023), Endüstri Devriminin Tarihsel Gelişimi. Erişim adresi: https://www.siskon.com.tr/haberler/endustri-devriminin-tarihsel-gelisimi CR - Souza, F. F., Corsi, A., Pagani, R. N., Balbinotti, G., & Kovaleski, J. L. (2021). Total quality management 4.0: adapting quality management to Industry 4.0. The TQM Journal, 1-21. doi: https://doi.org/10.1108/TQM-10-2020-0238 CR - Stancheva-Todorova, E.P. (2018). How artificial intelligence is challenging accounting profession. “Journal of International Scientific Publications" Economy & Business, 12, 126-141. Erişim adresi: https://www.scientific-publications.net/get/1000031/1536783976137495.pdf CR - Swamidass, P.M. (Ed.), (2000), Moving assembly line, Encyclopedia of production and manufacturing management. Springer Science & Business Media, Boston, MA. doi: https://doi.org/10.1007/1-4020-0612-8_596 CR - Swarnkar, A., Swarnkar, A. (2020). Artificial Intelligence based optimization techniques: A Review. Intelligent Computing Techniques for Smart Energy Systems, 95-103. doi: http://dx.doi.org/10.1007/978-981-15-0214-9_12 CR - Şahan, A.N. (2020). Stratejik yönetim perspektifinden sigortacılık sektöründe Makine Öğrenmesi algoritmaları ile anomali tespiti [Doktora Tezi, İstanbul Teknik Üniversitesi]. CR - Tamasiga, P., Onyeaka, H., Bakwena, M., Happonen, A., & Molala, M. (2023). Forecasting disruptions in global food value chains to tackle food insecurity: The role of AI and big data analytics–A bibliometric and scientometric analysis. Journal of Agriculture and Food Research, 14, 100819. doi: https://doi.org/10.1016/j.jafr.2023.100819 CR - Teksen, H. E., & Anagün, A. S. (2018). Type 2 fuzzy control charts using likelihood and deffuzzification methods. In Advances in Fuzzy Logic and Technology 2017: Proceedings of: EUSFLAT-2017–The 10th Conference of the European Society for Fuzzy Logic and Technology, September 11-15, 2017, Warsaw, Poland IWIFSGN’2017–The Sixteenth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, September 13-15, 2017, Warsaw, Poland, Volume 3 10 (pp. 405-417). Springer International Publishing. doi: http://dx.doi.org/10.1007/978-3-319-66827-7_37 CR - Testik, O. M., & Unlu, E. T. (2023). Fuzzy FMEA in risk assessment for test and calibration laboratories. Quality and Reliability Engineering International, 39(2), 575-589. doi: http://doi.org/10.1002/qre.3198 CR - Tseng, C. Y., Li, J., Lin, L. H., Wang, K., White III, C. C., & Wang, B. (2023). Deep reinforcement learning approach for dynamic capacity planning in decentralised regenerative medicine supply chains. International Journal of Production Research, 1-16. doi: https://doi.org/10.1080/00207543.2023.2262043 CR - Wan, (2020). Economic-statistical design of integrated model of VSI control chart and maintenance incorporating multiple dependent state sampling. IEEE Access, 8, 87609-87620. doi: https://doi.org/10.1109/ACCESS.2020.2993024 CR - Wan, Q., Chen, L., & Zhu, M. (2023). A reliability-oriented integration model of production control, adaptive quality control policy and maintenance planning for continuous flow processes. Computers & Industrial Engineering, 176, 108985. doi: https://doi.org/10.1016/j.cie.2023.108985 CR - Wang, W., Li, R., Chen, Y., Diekel, Z. M., & Jia, Y. (2018). Facilitating human–robot collaborative tasks by teaching-learning-collaboration from human demonstrations. IEEE Transactions on Automation Science and Engineering, 16(2), 640-653. doi: https://doi.org/10.1109/TASE.2018.2840345. CR - Wiers, V. C. S. (1997), A Review of the Applicability of OR and AI scheduling techniques in Practice, OMEGA-International Journal of Management Science, 25,2,145-153. doi: https://doi.org/10.1016/S0305-0483(96)00050-3 CR - Xia, H., Muskat, B., Li, G., & Prayag, G. (2023). Ai-based counterfactual reasoning for tourism research, 101, 103617. doi: https://doi.org/10.1016/j.annals.2023.103617 CR - Yaiprasert, C., & Hidayanto, A. N. (2023). AI-driven ensemble three machine learning to enhance digital marketing strategies in the food delivery business. Intelligent Systems with Applications, 18, 200235. doi: https://doi.org/10.1016/j.iswa.2023.200235 CR - Yamamura, C. L. K., Santana, J. C. C., Masiero, B. S., Quintanilha, J. A., & Berssaneti, F. T. (2022). Forecasting New Product Demand Using Domain Knowledge and Machine Learning: A proposed method uses machine learning and an expert’s domain knowledge to enhance the accuracy of new product predictions. Research-Technology Management, 65(4), 27-36. doi: https://doi.org/10.1080/08956308.2022.2062553 CR - Yasir, M., Ansari, Y., Latif, K., Maqsood, H., Habib, A., Moon, J., & Rho, S. (2022). Machine learning–assisted efficient demand forecasting using endogenous and exogenous indicators for the textile industry. International Journal of Logistics Research and Applications, 1-20. doi: https://doi.org/10.1080/13675567.2022.2100334 CR - Yeganeh, A., Abbasi, S. A., Shongwe, S. C., Malela-Majika, J. C., & Shadman, A. R. (2023). Evolutionary support vector regression for monitoring Poisson profiles. Soft Computing, 1-25. doi: https://doi.org/10.1007/s00500-023-09047-2 CR - Yeganeh, A., Johannssen, A., Chukhrova, N., Abbasi, S. A., & Pourpanah, F. (2023). Employing machine learning techniques in monitoring autocorrelated profiles. Neural Computing and Applications, 1-20. doi: https://doi.org/10.1007/s00521-023-08483-3 CR - Yildirim, S. (2023), Python Libraries You Need to Know in 2023. Erişim adresi: https://learnpython.com/blog/python-libraries/. CR - Yoo, S. D., Kim, J. Y., Han, S. K., Lee, B. H., Choi, D. H., & Park, E. S. (2023). Development of prediction model with machine learning in continuous twin screw granulation. Journal of Pharmaceutical Investigation, 1-16. doi: https://doi.org/10.1007/s40005-023-00625-y CR - Zhang, HF., Ge, HW., Tong, YB. (2022). Review of Vehicle Routing Problems: Models, Classification and Solving Algorithms. Archievs of Computational Methods in Engineering, 29,1 195-221. doi: https://doi.org/10.1007/s11831-021-09574-x CR - Zhang, Y., Peng, P., Liu, C., & Zhang, H. (2019). Anomaly detection for industry product quality inspection based on Gaussian restricted Boltzmann machine. Ekim 2019, IEEE International Conference On Systems, Man And Cybernetics, 1-6. Erişim adresi: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8914524 CR - Zhang, Y., Zhu, H., Tang, D., Zhou, T., & Gui, Y. (2022). Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems. Robotics and Computer-Integrated Manufacturing, 78, 102412. doi: https://doi.org/10.1016/j.rcim.2022.102412 UR - https://doi.org/10.31796/ogummf.1401960 L1 - http://dergipark.org.tr/tr/download/article-file/3586670 ER -