Makinelerin Performanslarının Tercih Seçim Endeksi Yöntemi ile Ölçülmesi ve Toplam Ekipman Etkinliği Değerleri ile Karşılaştırılması

Year 2019, Volume: 34 Issue: 4, 573 - 581, 31.12.2019

Emre Bilgin Sarı

https://doi.org/10.24988/ije.2019344859

Cited By: 6

Abstract

İmalat işletmeleri operasyonlarını yönetmek ve iyileştirme faaliyetlerini önceliklendirmek amacıyla makine/ekipman performanslarının belirlenmesine önem vermektedir. Toplam Ekipman Etkinliği (OEE), üretim yöneticilerinin makine performanslarını incelemelerinde sıklıkla kullanılan bir araç olup, performans, kalite ve kullanılabilirlik değerleri göz önünde bulundurularak hesaplanmaktadır. Ancak makinelerin performanslarının etkilendiği farklı ölçütler de mevcuttur. Bu durumda makine performans değerlerinin ele alınması konusunda çok kriterli karar verme problemleri yaklaşımı uygun olmakta ve çözüm sunmaktadır. Tercih Seçim Endeksi (Preference Selection Index / PSI) yöntemi, alternatiflerin değerlendirilmesi için belirlenen kriterleri kendi içinde ağırlıklandıran bir yöntemdir. Bu çalışma, PSI yönteminin makinelerin performans endeksini oluşturmasında kullanılması amacıyla yapılmıştır. OEE hesaplaması sırasında kullanılan, ölçütlerin yanında, literatürce desteklenen faktörler de göz önünde bulundurularak ÇKKV problemi oluşturulmuştur, PSI yöntemi ile makinelerin performans sıralaması yapılmıştır. Bu çalışmanın bulguları, performans değerlendirme çalışmalarına daha fazla kriter dahil edildiğinde, sonuçların değerindeki farklılıkların daha yakın aralıkta ve hassasiyette görülebileceğini göstermektedir. Bu nedenle, makine performansı değerlendirmesi için OEE'den farklı kriterleri de göz önünde bulunduran modelleri uygulamak faydalı olacaktır.

Keywords

PSI, Toplam Ekipman Etkinliği, OEE, ÇKKV

Measuring The Performances of the Machines Via Preference Selection Index (PSI) Method and Comparing Them with Values of Overall Equipment Efficiency (OEE)

Abstract

Manufacturing enterprises give importance to determining machine performances in order to manage their operations and prioritize improvement activities. Overall Equipment Efficiency (OEE) is a tool that is frequently used by production managers to examine machine performances and is calculated by considering performance, quality and availability values. However, there are many different criteria that affect the performance of the machines. In this case, a multi-criteria decision-making (MCDM) approach is appropriate for handling machine performance values. The Preference Selection Index (PSI) is a MCDM method that is used for the evaluation of alternatives while weighting the criteria determined. This study was carried out to purpose of using the PSI method to generate the performance index of the machines. In addition to factors used in OEE calculation, MCDM problem with multiple criteria that supported by the literature was developed. The performance ranking of the machines was performed by PSI method with these criteria. The findings of this study suggest that when the more criteria are included in the studies for performance evaluation, the differences in the value of the results can be seen in the closer range and sensitivity. So, it would be useful to implement models that consider different criteria from OEE for machine performance evaluation.

Keywords

Preference Selection Index, PSI, Overall Equipment Efficiency, OEE, MCDM

References

• Akyüz, G., and Aka, S. (2015). İmalat Performansı Ölçümü için Alternatif Bir Yaklaşım: Tercih Indeksi (PSI) Yöntemi. Business and Economics Research Journal, 6(1), 63.
• Attri, R., and Grover, S. (2015). Application of Preference Selection Index Method for Decision Making over The Design Stage of Production System Life Cycle. Journal of King Saud University-Engineering Sciences, 27(2), 207-216.
• Cooke, F.L. (2000). Implementing TPM in Plant Maintenance: Some Organizational Barriers. International Journal of Quality & Reliability Management, 17 (9), 1003–1016.
• Dağdeviren, M. (2008). Decision Making in Equipment Selection: An Integrated Approach with AHP and PROMETHEE. Journal of Intelligent Manufacturing, 19(4), 397-406.
• Dal B., Tugwell P., and Greatbanks R., (2000). Overall Equipment Effectiveness as a Measure of Operational Improvement – a Practical Analysis. International Journal of Operations & Production Management, 20 (12), 1488-1502.
• Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., and Antucheviciene, J. (2018). A New Hybrid Fuzzy MCDM Approach for Evaluation of Construction Equipment with Sustainability Considerations. Archives of Civil and Mechanical Engineering, 18(1), 32-49.
• Jain, S., Triantis, K. P., and Liu, S. (2011). Manufacturing Performance Measurement and Target Setting: A Data Envelopment Analysis Approach. European Journal of Operational Research, 214(3), 616-626.
• Kutucuoglu, K. Y., Hamali, J., Irani, Z., & Sharp, J. M. (2001). A framework for managing maintenance using performance measurement systems. International Journal of Operations & Production Management, 21(1/2), 173-195.
• Madu, C. (2000). Competing Through Maintenance Strategies. International Journal of Quality & Reliability Management 17(9), 937–948.
• Madu, C. (1999). Reliability & Quality Interface, International Journal of Quality & Reliability Management, 16(7), 691–698.
• Maniya, K. and Bhatt, M.G. (2010). A Selection of Material Using a Novel Type Decision-Making Method: Preference Selection Index Method. Materials and Design, 31, 1785-1789.
• Muchiri, P., and Pintelon, L. (2008). Performance Measurement Using Overall Equipment Effectiveness (OEE): Literature Review and Practical Application Discussion. International Journal of Production Research, 46(13), 3517-3535.
• Muchiri, P., Pintelon, L., Gelders, L., and Martin, H. (2011). Development of Maintenance Function Performance Measurement Framework and Indicators. International Journal of Production Economics, 131(1), 295-302.
• Mufazzal, S., and Muzakkir, S. M. (2018). A New Multi-Criterion Decision Making (MCDM) Method Based On Proximity Indexed Value for Minimizing Rank Reversals. Computers & Industrial Engineering, 119, 427-438.
• Nguyen, H. T., Dawal, S. Z. M., Nukman, Y., Rifai, A. P., and Aoyama, H. (2016). An Integrated MCDM Model for Conveyor Equipment Evaluation and Selection in an FMC Based on A Fuzzy AHP and Fuzzy ARAS in The Presence of Vagueness. PloS one, 11(4), 1 – 26.
• Noryani, M. I., Sapuan, S. M., and Mastura, M. T. (2018). Multi-Criteria Decision-Making Tools for Material Selection of Natural Fibre Composites: A Review. Journal of Mechanical Engineering and Sciences Malaysia, 12(1), 3330-3353.
• Parida, A. and Chattopadhyay, G. (2007). Development of A Multi‐Criteria Hierarchical Framework for Maintenance Performance Measurement (MPM). Journal of Quality in Maintenance Engineering, 13(3), 241-258,
• Samanta, B., Sarkar, B., and Mukherjee, S. K. (2002). Selection of Opencast Mining Equipment by A Multi-Criteria Decision-Making Process. Mining Technology, 111(2), 136-142.
• Sawant, V. B., Mohite, S. S., and Patil, J. (2011), A Decision-Making Framework Using a Preference Selection Index Method for Automated Guided Vehicle Selection Problem. International Conference on Technology Systems and Management (ICTSM) (12-16).
• Sucu A., Atasoy, E., and Temiz, İ. (2010). Toplam Ekipman Etkinliği ve Bir Uygulama. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 12(4), 49-60.
• Wang, T. Y., Shaw, C. F., and Chen, Y. L. (2000). Machine Selection in Flexible Manufacturing Cell: A Fuzzy Multiple Attribute Decision-Making Approach. International Journal of Production Research, 38(9), 2079-2097.
• Yan, J., Koc, M., and Lee, J. (2004). A Prognostic Algorithm for Machine Performance Assessment and Its Application. Production Planning & Control, 15(8), 796-801.
• Yaşin, M. F., and Daş, G. S. (2017). KOBİ’lerde Ekipman Etkinliğinin İyileştirilmesinde TEE Tabanlı Yeni Bir Yaklaşım: Bir Ahşap İşleme Kuruluşunda Uygulama. Journal of the Faculty of Engineering & Architecture of Gazi University, 32(1), 45 – 52.