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DETERMINING SUBJECTIVE WEIGHTS IN DISCRETE EVENT SIMULATION (DES) IMPLEMENTATION CHALLENGES IN MANUFACTURING ENTERPRISES

Year 2023, Volume: 7 Issue: 1, 1 - 11, 28.12.2023

Abstract

In the Industry 4.0 era, manufacturing enterprises need various digital optimization tools to adapt to digital transformation. This study aims to identify the subjective weights of the challenges faced by manufacturing enterprises when implementing Discrete Event Simulation (DES), one of the prominent optimization tools of the Industry 4.0 era. The challenges examined in this empirical research are derived from the existing literature. In this study, three experts responsible for DES implementation and production in their organizations were consulted. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) was used to reflect the subjective weights of the decision criteria and the cause-effect relationships between them. The DEMATEL method is a method used in the Multi-Criteria Decision Making (MCDM) literature to determine the subjective weights of decision criteria. As a result of the study, the importance ranking of the challenges faced by manufacturing enterprises in DES implementation was obtained and the causal relationship between these challenges was determined. Identifying the causal relationship between these challenges can provide decision makers with a competitive advantage in overcoming these challenges. Adoption of the findings of the study will facilitate the adoption of DES, resulting in reduced costs, increased customer satisfaction and competitive advantage for industry professionals.

References

  • Ademujimi, T. and Prabhu, V. (2022). Digital twin for training bayesian networks for fault diagnostics of manufacturing systems. Sensors, 22(4), 1430. https://doi.org/10.3390/s22041430
  • Bokrantz, J., Skoogh, A., Lämkull, D., Hanna, A., & Perera, T. (2017). Data quality problems in discrete event simulation of manufacturing operations. Simulation, 94(11), 1009-1025. https://doi.org/10.1177/0037549717742954
  • Çavdarcı, S. (2017). Geri dönüşüm sektörüne ilişkin sorun alanlarının dematel-gri dematel yöntemiyle önceliklendirilip değerlendirilmesi. Fen Bilimleri Enstitüsü, Erciyes Üniversitesi. Kayseri. Comuzzi, M. (2018). Optimal paths in business processes: framework and applications., 107-123. https://doi.org/10.1007/978-3-319-74030-0_7
  • Fernández, M., Herrera, M., Trejos, C., & Romero, O. (2021). Resources allocation in service planning using discrete-event simulation. Ingenieria Y Universidad, 25. https://doi.org/10.11144/javeriana.iued25.rasp
  • Flores-García, E., Wiktorsson, M., Bruch, J., & Jackson, M. (2018). Revisiting challenges in using discrete event simulation in early stages of production system design., 534-540. https://doi.org/10.1007/978-3-319-99704-9_65
  • Hwang, C. L., & Lin, M. J. (2012). Group decision making under multiple criteria: methods and applications (Vol. 281). Springer Science & Business Media.
  • Jacobson, S. and Yücesan, E. (1999). On the complexity of verifying structural properties of discrete event simulation models. Operations Research, 47(3), 476-481. https://doi.org/10.1287/opre.47.3.476
  • Jianlin, F., Zhang, J., Ding, G., Qin, S., & Jiang, H. (2021). Determination of vehicle requirements of agv system based on discrete event simulation and response surface methodology. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 235(9), 1425-1436. https://doi.org/10.1177/0954405421995602
  • Keleş, M. K., Işildak, B., & Özdağoğlu, A. Pazarlama Karmasi Bileşenlerinin Dematel Yöntemiyle Analiz Edilmesi: Havayolu Sektöründe Bir Uygulama. Uygulamalı Sosyal Bilimler ve Güzel Sanatlar Dergisi, 5(11), 16-28.
  • Khin, S. and Kee, D. (2022). Factors influencing industry 4.0 adoption. Journal of Manufacturing Technology Management, 33(3), 448-467. https://doi.org/10.1108/jmtm-03-2021-0111
  • Kobryń, A. (2017). DEMATEL as a weighting method in multi-criteria decision analysis. Multiple Criteria Decision Making, 12, 153-167.
  • Kshatra, D. (2019). A methodology to adapt and understand a manufacturing system and operations using discrete-event simulation. International Journal of Engineering and Advanced Technology, 8(6), 4998-5003. https://doi.org/10.35940/ijeat.f9206.088619
  • Li, C. W., & Tzeng, G. H. (2009). Identification of a threshold value for the DEMATEL method using the maximum mean de-entropy algorithm to find critical services provided by a semiconductor intellectual property mall. Expert Systems with Applications, 36(6), 9891-9898.
  • Li, H., Li, J., & Dietl, H. (2020). A novel decision making approach for benchmarking the service quality of smart community health centers. Ieee Access, 8, 209904-209914. https://doi.org/10.1109/access.2020.3037769
  • Nutaro, J., Kuruganti, P., Shankar, M., Miller, L., & Mullen, S. (2008). Integrated modeling of the electric grid, communications, and control. International Journal of Energy Sector Management, 2(3), 420-438. https://doi.org/10.1108/17506220810892955
  • Schriber, T. and Brunner, D. (2007). Inside discrete-event simulation software: how it works and why it matters.. https://doi.org/10.1109/wsc.2007.4419594
  • Sinha, R. and Noble, C. (2008). The adoption of radical manufacturing technologies and firm survival. Strategic Management Journal, 29(9), 943-962. https://doi.org/10.1002/smj.687
  • Tiwari, A. (2011). Rapid modeling of field maintenance using discrete event simulation.. https://doi.org/10.1109/wsc.2011.6147792
  • Tsai, W. H., & Chou, W. C. (2009). Selecting management systems for sustainable development in SMEs: A novel hybrid model based on DEMATEL, ANP, and ZOGP. Expert systems with applications, 36(2), 1444-1458.
  • Tsai, W. H., & Chou, W. C. (2009). Selecting management systems for sustainable development in SMEs: A novel hybrid model based on DEMATEL, ANP, and ZOGP. Expert systems with applications, 36(2), 1444-1458.
  • Utama, D., Maharani, B., & Amallynda, I. (2021). Integration dematel and anp for the supplier selection in the textile industry: a case study. Jurnal Ilmiah Teknik Industri, 20(1), 119-130. https://doi.org/10.23917/jiti.v20i1.13806
  • Xie, X. and Verbraeck, A. (2018). A particle filter-based data assimilation framework for discrete event simulations. Simulation, 95(11), 1027-1053. https://doi.org/10.1177/0037549718798466

İMALAT İŞLETMELERİNDE AYRIK OLAY SİMÜLASYONU (DES) UYGULAMA ZORLUKLARININ ÖZNEL AĞIRLIKLARININ BELİRLENMESİ

Year 2023, Volume: 7 Issue: 1, 1 - 11, 28.12.2023

Abstract

Endüstri 4.0 döneminde, üretim işletmeleri dijital dönüşüme uyum sağlamak için çeşitli dijital optimizasyon araçlarına ihtiyaç duymaktadırlar. Bu çalışma, Endüstri 4.0 döneminin öne çıkan optimizasyon araçlarından biri olan Ayrık Olay Simülasyonunu (DES) uygularken üretim işletmelerinin karşılaştığı zorlukların öznel ağırlıklarını belirlemeyi amaçlamaktadır. Bu ampirik araştırmada incelenen zorluklar mevcut literatürden türetilmiştir. Bu çalışmada, işletmelerinde DES uygulaması ve üretiminden sorumlu üç uzmanın görüşlerine başvurulmuştur. Karar kriterlerinin öznel ağırlıklarını ve bunlar arasındaki neden-sonuç ilişkilerini yansıtmak için Karar Verme Deneme ve Değerlendirme Laboratuvarı (DEMATEL) kullanılmıştır. DEMATEL yöntemi, Çok Kriterli Karar Verme (ÇKKV) literatüründe karar kriterlerinin sübjektif ağırlıklarını belirlemek için kullanılan bir yöntemdir Çalışma sonucunda, imalat işletmelerinin DES uygulamasında karşılaştıkları zorlukların önem sıralaması elde edilmiş ve bu zorluklar arasındaki nedensellik ilişkisi belirlenmiştir. Bu zorluklar arasındaki nedensel ilişkinin belirlenmesi, karar vericilere bu zorlukların üstesinden gelmede rekabet avantajı sağlayabilir. Çalışmanın bulgularının benimsenmesi, DES'in benimsenmesini kolaylaştırarak endüstri profesyonelleri için maliyetlerin düşmesi, müşteri memnuniyetinin artması ve rekabet avantajı ile sonuçlanacaktır.

References

  • Ademujimi, T. and Prabhu, V. (2022). Digital twin for training bayesian networks for fault diagnostics of manufacturing systems. Sensors, 22(4), 1430. https://doi.org/10.3390/s22041430
  • Bokrantz, J., Skoogh, A., Lämkull, D., Hanna, A., & Perera, T. (2017). Data quality problems in discrete event simulation of manufacturing operations. Simulation, 94(11), 1009-1025. https://doi.org/10.1177/0037549717742954
  • Çavdarcı, S. (2017). Geri dönüşüm sektörüne ilişkin sorun alanlarının dematel-gri dematel yöntemiyle önceliklendirilip değerlendirilmesi. Fen Bilimleri Enstitüsü, Erciyes Üniversitesi. Kayseri. Comuzzi, M. (2018). Optimal paths in business processes: framework and applications., 107-123. https://doi.org/10.1007/978-3-319-74030-0_7
  • Fernández, M., Herrera, M., Trejos, C., & Romero, O. (2021). Resources allocation in service planning using discrete-event simulation. Ingenieria Y Universidad, 25. https://doi.org/10.11144/javeriana.iued25.rasp
  • Flores-García, E., Wiktorsson, M., Bruch, J., & Jackson, M. (2018). Revisiting challenges in using discrete event simulation in early stages of production system design., 534-540. https://doi.org/10.1007/978-3-319-99704-9_65
  • Hwang, C. L., & Lin, M. J. (2012). Group decision making under multiple criteria: methods and applications (Vol. 281). Springer Science & Business Media.
  • Jacobson, S. and Yücesan, E. (1999). On the complexity of verifying structural properties of discrete event simulation models. Operations Research, 47(3), 476-481. https://doi.org/10.1287/opre.47.3.476
  • Jianlin, F., Zhang, J., Ding, G., Qin, S., & Jiang, H. (2021). Determination of vehicle requirements of agv system based on discrete event simulation and response surface methodology. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 235(9), 1425-1436. https://doi.org/10.1177/0954405421995602
  • Keleş, M. K., Işildak, B., & Özdağoğlu, A. Pazarlama Karmasi Bileşenlerinin Dematel Yöntemiyle Analiz Edilmesi: Havayolu Sektöründe Bir Uygulama. Uygulamalı Sosyal Bilimler ve Güzel Sanatlar Dergisi, 5(11), 16-28.
  • Khin, S. and Kee, D. (2022). Factors influencing industry 4.0 adoption. Journal of Manufacturing Technology Management, 33(3), 448-467. https://doi.org/10.1108/jmtm-03-2021-0111
  • Kobryń, A. (2017). DEMATEL as a weighting method in multi-criteria decision analysis. Multiple Criteria Decision Making, 12, 153-167.
  • Kshatra, D. (2019). A methodology to adapt and understand a manufacturing system and operations using discrete-event simulation. International Journal of Engineering and Advanced Technology, 8(6), 4998-5003. https://doi.org/10.35940/ijeat.f9206.088619
  • Li, C. W., & Tzeng, G. H. (2009). Identification of a threshold value for the DEMATEL method using the maximum mean de-entropy algorithm to find critical services provided by a semiconductor intellectual property mall. Expert Systems with Applications, 36(6), 9891-9898.
  • Li, H., Li, J., & Dietl, H. (2020). A novel decision making approach for benchmarking the service quality of smart community health centers. Ieee Access, 8, 209904-209914. https://doi.org/10.1109/access.2020.3037769
  • Nutaro, J., Kuruganti, P., Shankar, M., Miller, L., & Mullen, S. (2008). Integrated modeling of the electric grid, communications, and control. International Journal of Energy Sector Management, 2(3), 420-438. https://doi.org/10.1108/17506220810892955
  • Schriber, T. and Brunner, D. (2007). Inside discrete-event simulation software: how it works and why it matters.. https://doi.org/10.1109/wsc.2007.4419594
  • Sinha, R. and Noble, C. (2008). The adoption of radical manufacturing technologies and firm survival. Strategic Management Journal, 29(9), 943-962. https://doi.org/10.1002/smj.687
  • Tiwari, A. (2011). Rapid modeling of field maintenance using discrete event simulation.. https://doi.org/10.1109/wsc.2011.6147792
  • Tsai, W. H., & Chou, W. C. (2009). Selecting management systems for sustainable development in SMEs: A novel hybrid model based on DEMATEL, ANP, and ZOGP. Expert systems with applications, 36(2), 1444-1458.
  • Tsai, W. H., & Chou, W. C. (2009). Selecting management systems for sustainable development in SMEs: A novel hybrid model based on DEMATEL, ANP, and ZOGP. Expert systems with applications, 36(2), 1444-1458.
  • Utama, D., Maharani, B., & Amallynda, I. (2021). Integration dematel and anp for the supplier selection in the textile industry: a case study. Jurnal Ilmiah Teknik Industri, 20(1), 119-130. https://doi.org/10.23917/jiti.v20i1.13806
  • Xie, X. and Verbraeck, A. (2018). A particle filter-based data assimilation framework for discrete event simulations. Simulation, 95(11), 1027-1053. https://doi.org/10.1177/0037549718798466
There are 22 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section İşletme
Authors

Gülper Basmacı Aktuna 0000-0002-8038-9639

Onur Özveri 0000-0001-9203-917X

Publication Date December 28, 2023
Submission Date December 16, 2023
Acceptance Date December 27, 2023
Published in Issue Year 2023 Volume: 7 Issue: 1

Cite

APA Basmacı Aktuna, G., & Özveri, O. (2023). DETERMINING SUBJECTIVE WEIGHTS IN DISCRETE EVENT SIMULATION (DES) IMPLEMENTATION CHALLENGES IN MANUFACTURING ENTERPRISES. Kapadokya Akademik Bakış, 7(1), 1-11.