Abstract
Decision making is a very
important area of research. In the era of data, the efforts to support decision
making by powerful data processing tools are becoming intense. True and in-time
decision is of great importance. For this, decision makers need qualified data
and qualified processing. Simulation is the reconstruction of real world
scenarios in the virtual environment. It makes possible to analyze the
different scenarios that is hard or dangerous to accomplish with a trial and
error method. This is a simulation case study in which a hardware maintenance
service is the subject. The aim of the study is to illustrate how simulation
can be used as a valuable decision support tool for managers and directors by
first detecting the bottleneck and then find a solution by applying different
scenarios. In this study, a hardware maintenance unit of a hospital, which is
suffering for long fixing times, is modelled. Data is collected from the system
that the hospital uses. Results show that there is a problem in the firm
service unit and it can be overcome by increasing the number of personnel.
References
- Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J. (2013). Artificial neural networks in medical diagnosis. Journal of applied biomedicine, 11(2), 47-58.
- Cheng, M. M., Li, C., Hackett, R. D., & Lee-Chin, M. (2018). Simulation and big data: in search of causality in big data-related managial decision making.
- Costin, Y., O'Brien, M. P., & Slattery, D. M. (2018). Using Simulation to Develop Entrepreneurial Skills and Mind-Set: An Exploratory Case Study. International Journal of Teaching and Learning in Higher Education, 30(1), 136-145.
- Garalis, A., & Strazdiene, G. (2007). Entrepreneurial skills development via simulation business enterprise. Social Research/ Socialiniai tyrimai, 2(10), 39-48.
- Eppich, W., Howard, V., Vozenilek, J., & Curran, I. (2011). Simulation-based team training in healthcare. Simulation in Healthcare, 6(7), S14-S19.Laguna, M., & Marklund, J. (2013). Business process modeling, simulation and design. CRC Press. pp. 253.
- Jain, N., & Srivastava, V. (2013). DATA MINING TECHNIQUES: A SURVEY PAPER.IJRET: International Journal of Research in Engineering and Technology,2(11). Retrieved on 11 March 2018 from http://ijret.org/Volumes/V02/I11/IJRET_110211019.pdf
- Jensen, F.V., Nielsen, T.D., 2007. Bayesian Networks and Decision Graphs. London, Springer.
- Jiawei, H., & Kamber, M. (2001). Data mining: concepts and techniques.San Francisco, CA, itd: Morgan Kaufmann,5.
- LeBlanc, V. R., Manser, T., Weinger, M. B., Musson, D., Kutzin, J., & Howard, S. K. (2011). The study of factors affecting human and systems performance in healthcare using simulation. Simulation in Healthcare, 6(7), S24-S29.
- Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing,66(3), 247-259.
- Planas, M. E., García, P. J., Bustelo, M., Cárcamo, C., Ñopo, H. R., Martinez, S., ... & Morrison, A. (2014). Using standardized simulated patients to measure ethnic disparities in family planning services in Peru: Study protocol and pre-trial procedures of a crossover randomized trial. Inter-American Development Bank.
- Purniya, R., & Rai, D. (2018). A Comparatively Analysis of Various Manet Based Throughput Enhancement Techniques. International Journal of engineering sciences & Research Technology, 7(2),200-207.
- Rohleder, T. R., Lewkonia, P., Bischak, D. P., Duffy, P., & Hendijani, R. (2011). Using simulation modeling to improve patient flow at an outpatient orthopedic clinic. Health Care Management Science, 14(2), 135-145.
- Rogers, L. (2011). Developing simulations in multi‐user virtual environments to enhance healthcare education. British Journal of Educational Technology,42(4), 608-615.
- Sharma, J., Sharma, M. S., & Pandey, R. (2018). A Complete Review of Concept of Data Mining. International Journal For Technological Research In Engineering, 5(6), 3143-3146.
- Slakey, D. P., Simms, E. R., Rennie, K. V., Garstka, M. E., & Korndorffer, J. R. (2014). Using simulation to improve root cause analysis of adverse surgical outcomes. International Journal for Quality in Health Care, 26(2), 144-150.
- Wager,K.A., Lee,F.W.,& Glaser,J.P.(2005). Managing Healthcare Information Systems: A Practical Approach for Healthcare Executives. San Francisco:Wiley. pp 92.
- Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. Knowledge and Data Engineering. IEEE Transactions on, 26(1), 97–107.
- Yardimci, A. (2009). Soft computing in medicine. Applied Soft Computing, 9(3), 1029-1043.
- Zuniga, C., Mujica Mota, M., & Herrera García, A. (2016). Analyzing airport capacity by simulation: a Mexican case study. In A. Ochoa-Zezzatti, J. Sanchez , & M. G. Cedillo-Campos (Eds.), Handbook of research on military, aeronautical, and maritime logistics and operations (pp. 115-150). Hershey, PA: IGI Global.
Abstract
Decision making is a very
important area of research. In the era of data, the efforts to support decision
making by powerful data processing tools are becoming intense. True and in-time
decision is of great importance. For this, decision makers need qualified data
and qualified processing. Simulation is the reconstruction of real world scenarios
in the virtual environment. It makes possible to analyze the different
scenarios that is hard or dangerous to accomplish with a trial and error
method. This is a simulation case study in which a hardware maintenance service
is the subject. The aim of the study is to illustrate how simulation can be
used as a valuable decision support tool for managers and directors by first
detecting the bottleneck and then find a solution by applying different
scenarios. In this study, a hardware maintenance unit of a hospital, which is
suffering for long fixing times, is modelled. Data are collected from the
system that the hospital uses. Results show that there is a problem in the firm
service unit and it can be overcome by increasing the number of personnel.
References
- Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J. (2013). Artificial neural networks in medical diagnosis. Journal of applied biomedicine, 11(2), 47-58.
- Cheng, M. M., Li, C., Hackett, R. D., & Lee-Chin, M. (2018). Simulation and big data: in search of causality in big data-related managial decision making.
- Costin, Y., O'Brien, M. P., & Slattery, D. M. (2018). Using Simulation to Develop Entrepreneurial Skills and Mind-Set: An Exploratory Case Study. International Journal of Teaching and Learning in Higher Education, 30(1), 136-145.
- Garalis, A., & Strazdiene, G. (2007). Entrepreneurial skills development via simulation business enterprise. Social Research/ Socialiniai tyrimai, 2(10), 39-48.
- Eppich, W., Howard, V., Vozenilek, J., & Curran, I. (2011). Simulation-based team training in healthcare. Simulation in Healthcare, 6(7), S14-S19.Laguna, M., & Marklund, J. (2013). Business process modeling, simulation and design. CRC Press. pp. 253.
- Jain, N., & Srivastava, V. (2013). DATA MINING TECHNIQUES: A SURVEY PAPER.IJRET: International Journal of Research in Engineering and Technology,2(11). Retrieved on 11 March 2018 from http://ijret.org/Volumes/V02/I11/IJRET_110211019.pdf
- Jensen, F.V., Nielsen, T.D., 2007. Bayesian Networks and Decision Graphs. London, Springer.
- Jiawei, H., & Kamber, M. (2001). Data mining: concepts and techniques.San Francisco, CA, itd: Morgan Kaufmann,5.
- LeBlanc, V. R., Manser, T., Weinger, M. B., Musson, D., Kutzin, J., & Howard, S. K. (2011). The study of factors affecting human and systems performance in healthcare using simulation. Simulation in Healthcare, 6(7), S24-S29.
- Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing,66(3), 247-259.
- Planas, M. E., García, P. J., Bustelo, M., Cárcamo, C., Ñopo, H. R., Martinez, S., ... & Morrison, A. (2014). Using standardized simulated patients to measure ethnic disparities in family planning services in Peru: Study protocol and pre-trial procedures of a crossover randomized trial. Inter-American Development Bank.
- Purniya, R., & Rai, D. (2018). A Comparatively Analysis of Various Manet Based Throughput Enhancement Techniques. International Journal of engineering sciences & Research Technology, 7(2),200-207.
- Rohleder, T. R., Lewkonia, P., Bischak, D. P., Duffy, P., & Hendijani, R. (2011). Using simulation modeling to improve patient flow at an outpatient orthopedic clinic. Health Care Management Science, 14(2), 135-145.
- Rogers, L. (2011). Developing simulations in multi‐user virtual environments to enhance healthcare education. British Journal of Educational Technology,42(4), 608-615.
- Sharma, J., Sharma, M. S., & Pandey, R. (2018). A Complete Review of Concept of Data Mining. International Journal For Technological Research In Engineering, 5(6), 3143-3146.
- Slakey, D. P., Simms, E. R., Rennie, K. V., Garstka, M. E., & Korndorffer, J. R. (2014). Using simulation to improve root cause analysis of adverse surgical outcomes. International Journal for Quality in Health Care, 26(2), 144-150.
- Wager,K.A., Lee,F.W.,& Glaser,J.P.(2005). Managing Healthcare Information Systems: A Practical Approach for Healthcare Executives. San Francisco:Wiley. pp 92.
- Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. Knowledge and Data Engineering. IEEE Transactions on, 26(1), 97–107.
- Yardimci, A. (2009). Soft computing in medicine. Applied Soft Computing, 9(3), 1029-1043.
- Zuniga, C., Mujica Mota, M., & Herrera García, A. (2016). Analyzing airport capacity by simulation: a Mexican case study. In A. Ochoa-Zezzatti, J. Sanchez , & M. G. Cedillo-Campos (Eds.), Handbook of research on military, aeronautical, and maritime logistics and operations (pp. 115-150). Hershey, PA: IGI Global.
Details
| Primary Language | English |
|---|---|
| Journal Section | Makaleler |
| Authors | |
| Publication Date | June 13, 2018 |
| Submission Date | March 13, 2018 |
| Acceptance Date | April 24, 2018 |
| Published in Issue | Year 2018 Volume: 4 Issue: 1 |
Cited By
ARAŞTIRMA VE YAYIN ETİĞİNE İLİŞKİN DERGİ POLİTİKAMIZ
Etik Kurul izni gerektiren çalışmalar (anket ya da ölçek uygulamayı gerektiren, görüşme ve gözlem içeren; doküman, resim, anket vb. diğerleri tarafından geliştirilen ve kullanım izni gerektiren çalışmalar) için etik kurullardan ya da komisyonlardan gerekli izinlerin araştırma yapılmadan önce alınmış olması, bunların makale içeriğinde belirtilmesi ya da ek olarak sunulması gerekmektedir. Bu izinlerin olmaması durumunda yayın ön inceleme safhasında yazara iade edilir.
Diğer yandan, Araştırma Makalelerinde verisi 2020 yılından önce toplanmış makaleler için ETİK KURUL İZNİ istenmemektedir. Ancak yine bu makalelerde de yöntem kısmında verilerin toplanma tarihlerine ilişkin bilgilere yer verilmesi gerekmektedir.
Hakem değerlendirmelerinde olan araştırmalara ilişkin ham verilerin hakemler tarafından talep edildiğinde sunulması şarttır. Verilerin makalenin yayımı sonrasında da gerektiğinde sağlanması zorunludur.
MAKALE BENZERLİK RAPORU VE BENZERLİK ORANI HAKKINDA DERGİ POLİTİKAMIZ
Aday makaleler akademik intihal engelleme programından (Örneğin: Turnitin, Ithenticate, intihal.net vb.) alınmış orijinallik raporu ile birlikte gönderilmelidir. Bu oran %20'ye kadar kabul edilmektedir. %20'nin üzerinde benzerlik oranı olan çalışmalar ön kontrol aşamasında iade edilecektir.
ISSN: 2149 - 5823