Giysi Endüstrisinde Üretim Performansının Tahmininde Yapay Sinir Ağlarının Kullanılması

Öz

Digitalleşme çağında işletmeler kendilerini yeni teknolojilere adapte etmek istemektedirler. Bu yeni teknolojilere uyum sağlamak, verimlilik ve karlılığı arttırmak için verilerin işlenmesi ve akıllı karar verme sistemleri ile durumun analiz edilmesine ihtiyaç vardır. Özellikle büyük bir üretim hacmine sahip olan giysi endüstrisinde hem geleneksel işlemlerin devam etmesi hemde iş akışlarının insan performansına doğrudan bağlı olması verimliliği önemli bir ölçüde etkilemektedir. Böylece beklenen performans değerleri ile gerçek çıktılar arasında ciddi farklar görülmektedir. Bu çalışmada veri madenciliği teknikleri uygulanarak örnek bir giysi endüstrisinde yer alan bir işletmeye ait veriler üzerinde analizler yapılmıştır. Bu işletmede işçilerin çalışma durumları incelenerek gerçek üretim performansını tahmin etmeye yönelik bir yapay sinir ağı modeli oluşturulmuştur. Sonuçlar incelendiğinde %85 doğruluk değerine ulaşılmıştır. Modelin işletmelerin gerekli düzeltmeler ile üretim performanslarını ve verimliliklerini artırmasına ve aynı zamanda kayıpları minumum seviyeye indirmesine katkı sunacağı gösterilmiştir.

Anahtar Kelimeler

• Üretim Performansı
• Tahmin
• Yapay Sinir Ağları

Using Artificial Neural Networks in Prediction of Production Performance in the Garment Industry

Abstract

In the age of digitalization, businesses want to adapt themselves to new technologies. In order to adapt to these new technologies and increase efficiency and profitability, there is a need for data processing and analysis of the situation with intelligent decision-making systems. Especially in the garment industry, which has a large production volume, both the continuation of traditional processes and the direct dependence of work flows on human performance affect productivity significantly. Thus, there are serious differences between the expected performance values and the actual outputs. In this study, data mining techniques were applied and analyzes were made on the data of a business in a garment industry. In this enterprise, an artificial neural network model was created to predict the real production performance by examining the working conditions of the workers. When the results were examined, an accuracy value of 85% was reached. It has been shown that the model will contribute to increasing the production performance and efficiency of the enterprises with the necessary corrections and at the same time reducing the losses to the minimum level.

Keywords

• Production Performance
• Prediction
• Artificial Neural Networks

References

1. Aksu, G., Güzeller, C. O., Eser, M. T. (2019), “The Effect of the Normalization Method Used in Different Sample Sizes on the Success of Artificial Neural Network Model”, International Journal of Assessment Tools in Education, Vol. 6, No. 2, p. 170-192.
2. Arthur, C. K., Temeng, V. A., Ziggah, Y. Y. (2020), “Performance Evaluation of Training Algorithms in Backpropagation Neural Network Approach to Blast-Induced Ground Vibration Prediction”, Ghana Mining Journal, Vol. 20, No. 1, p. 20 – 33.
3. Bashimov, G. (2014), “Tekstil ve Hazır Giyim Sektörünün Karşılaştırmalı Avantajı: Türkiye ve Pakistan Örneği”, BEU.SBU.Derg., Cilt:3, Sayı:1.
4. Cottini, E., Kato, T., Westergaard-Nielsen, N. (2011), “Adverse Workplace Conditions, High-Involvement Work Practices And Labor Turnover: Evidence From Danish Linked Employer–Employee Data”, Labour Economics, Vol. 18, No. 6, p. 872-880.
5. Crowther, P. S., Cox, R. J. (2005), “A Method for Optimal Division of Data Sets for Use in Neural Networks”, 9th International Conference KES.
6. Ersoz, F., Ersoz, T., Guler, E. (2017), “Knowledge Discovery And Data Mining Techniques In Textile Industry”, World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Vol. 11, No. 7, p. 906–910.
7. Heaton, J. (2008), “Introduction to neural networks with Java”, Heaton Research, Inc.; 2 edition.
8. Hsu, C.H. (2009), “Data Mining To Improve Industrial Standards And Enhance Production And Marketing: An Empirical Study In Apparel Industry”, Expert Systems with Applications, Vol. 36, No. 3, p. 4185–4191.

9. Imran, A. A., Amin, M. N., Islam Rifat, M. R., Mehreen, S. (2019), “Deep Neural Network Approach for Predicting the Productivity of Garment Employees”, 6th International Conference on Control, Decision and Information Technologies.
10. Islam, M. S., Rakib, M. A., Adnan, A. T. M. (2016), “Ready-Made Garments Sector of Bangladesh: Its Contribution and Challenges Towards Development”, Journal of Asian development studies, Vol. 5, No. 2.
11. Kumar, S., Chong, I. (2018), “Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States”, International Journal of Environmental Research and Public Health, Vol. 15.
12. Lai, P.S. and Christiani, D.C. (2013), “Long Term Respiratory Health Effects In Textile Workers”, Current Opinion in Pulmonary Medicine, Vol. 19, No. 2, p.152.
13. Lee, C. K. H., Choy, K. L., Ho, G. T., Chin, K. S., Law, K. M. Y., Tse, Y. K. (2013), “A Hybrid OLAP-Association Rule Mining Based Quality Management System For Extracting Defect Patterns In The Garment Industry”, Expert Systems with Applications, Vol. 40, No. 7, p. 2435-2446.
14. Lee, C. K. H., Ho, G. T. S., Choy, K. L., Lam, C.H.Y. (2016), “A Slippery Genetic Algorithm-Based Process Mining System For Achievingbetter Quality Assurance In The Garment Industry”, Expert Systems With Applications, Vol. 46, p. 236–248.
15. Lee, C. K. H., Ho, G. T. S., Choy, K. L., Pang, G. K. H. (2014), “A RFID-Based Recursive Process Mining System For Quality Assurance In The Garment Industry”, International journal of production research, Vol. 52, No. 14, p. 4216-4238.
16. MATH,https://www.mathworks.com/help/deeplearning/ref/nntool.html, 22 Nisan 2021.
17. PPGE,https://archive.ics.uci.edu/ml/datasets/Productivity+Prediction+of+Garment+Employees, 22 Nisan 2021.
18. Rahim, M. S., Imran, A. A., Ahmed, T. (2021), “Mining The Productivity Data of Garment Industry”, International Journal of Business Intelligence and Data Mining.
19. Sheela, K. G., Deepa, S. N. (2013), "Review on Methods to Fix Number of Hidden Neurons in Neural Networks", Mathematical Problems in Engineering, vol. 2013, p. 11.
20. Tamilselvi, R., Kalaiselvi, S. (2013), “An Overview of Data Mining Techniques and Applications”, International Journal of Science and Research (IJSR), Vol. 2, No. 2.
21. Więcek, D., Burduk, A., Kuric, I. (2019), “The Use of ANN In Improving Efficiency and Ensuring The Stability of The Copper Ore Mining Process”, Acta Montanistica Slovaca, Vol. 24, No. 1, p. 1-14.
22. Zhiqiang, G., Zhihuan S., Steven, X. D., Biao, H. (2017), Data Mining and Analytics in the Process Industry: The Role of Machine Learning”, IEEE Access, Vol. 5, p. 20590 – 20616.