DETERMINING THE FAULTY AND REFUND PRODUCTS IN MANUFACTURING SYSTEM: APPLICATION ON A TEXTILE FIRM

Year 2020, , 178 - 187, 30.09.2020

Ezgi Demır , Sait Erdal Dincer

https://doi.org/10.17261/Pressacademia.2020.1281

Abstract

Purpose- In this study, it is aimed to improve the production and quality control processes of a company operating in the textile industry. For this purpose predicting faulty and refund products by using simulation of oversampling and undersampling applications.
Methodology– In this study, there are 250 different variables and 72959 lines of data on the production line. These data have been taken from the last 1-year data of the firm. In this study, simulation has been done. New machine learning methods have been used by simulating. The reason for the simulation is that it was easy to detect the refund and faulty conditions made in a large lot group production in previous studies. However, the aim is to investigate whether the accuracy of the prediction algorithms will yield consistent results in terms of the increase in the number of refund and faulty products when production is made in a larger structure. In the simulation method, "oversampling" and "undersampling" methods have been used. While making simulation prediction, in the literature, boosting algorithms, which are used as ensemble machine learning techniques, have been used. In this study, simulation has been done as follows, while the number of production lots increased, refund and faulty products were increased within the same application. The reason for doing this is to investigate whether the prediction status in normal machine learning algorithms can be captured in a larger data stack. This process is called oversampling. Then, the "undersampling" method was applied. According to the “undersampling” method, it is aimed to determine the refund and defect situations in a smaller lot by taking samples of refund and defective products with less frequency. At the end of the study, the results were interpreted by applying boosting algorithms.
Findings- As a result of the study, it is concluded that "undersampling" and "oversampling" simulations predict better than usual machine learning methodology.
Conclusion- In this study, it has been observed that the ensemble machine learning algorithms (adaboost, xgboost, gradient boosting algorithms), which are one of the ensemble machine learning methods that emerged in 2016, were applied to the production data for the first time and showed success in the prediction of faulty and refund products.

Keywords

Machine learning, boosting algorithms, production planning, fault detection

References

• Adam, E. E., & Ebert, R. J. (1986). Production and Operations Management: Concepts, Models and Behaviour. (3. Edition). UK: Prentice-Hall International Editions.
• Afentakis, P., & Gavish, B. (1986). Optimal Lot-sizing Algorithms for Complexproduct Structures. Operations Research , 237–249.
• Afentakis, P., Gavish, B., & Karmarkar, U. (1984). Computationally Efficient Optimal Solutions to the Lot-sizing Problem in Multistage Assembly Systems. Management Science, 222–239.
• Anthony, R. (1965). Planning and Control Systems: A Framework for Analysis . Cambridge, Mass: Harvard University Press.
• Bamford, D., & Wystouri, T. (2005). A Case Study of Service Failure and Recovery within an International Airline. Managing Service Quality: An International Journal, 15(3), 306-322.
• Bell, C. R., & Zemke, R. E. (1987). Service Breakdown: The Road to Recovery (Cilt 76). New York.
• Butler, J. K. (1991). Toward Understanding and Measuring Conditions of Trust: Evolution of a Conditions of Trust Inventory. Journal of Management, 17(3), 643-663.
• Chang, J., Khan, M. A., & Tsai, C.-T. (. (2011). Dining Occasions, Service Failures and Customer Complaint Behaviours: an Empirical Assessment. International Journal of Tourism Research, 6(14).
• Chase, R. B., Aquilano, N. J., & Jacobs, F. R. (2001). Operations Management For Competitive Advantage (9. Edition). New York: The McGraw-Hill Companies.
• Crowston, W. B., Wagner, M. H., & W. J. (1973). Economic Lot Size Determination in multi Stage Assembly Systems. Management Science, 517–527.
• Demir, E., & Dinçer, S. E. (2020). Place and solution proposals of data mining in production planning and control processes: a business application. Pressacademia, 189-193.
• Demiröğen, O., & Güzel, D. ( 2009). Üretim Planlama Ve İş Yükleme Metotları. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, Cilt: 23, Sayı: 4, 43-67.
• Ekmekçi, N. (2015). SANAYİ İŞLETMELERİNDE ÜRETİM PLANLAMASI VE DOĞRUSAL PROGRAMLAMA İLE BİR SANAYİ İŞLETMESİNDE OPTİMİZASYON UYGULAMASI. Konya: T.C. SELÇUK ÜNİVERSİTESİ SOSYAL BİLİMLER ENSTİTÜSÜ İŞLETME ANABİLİM DALI ÜRETİM YÖNETİMİ VE PAZARLAMA BİLİM DALI-YÜKSEK LİSANS TEZİ.
• Elmaghraby, S. (1978). The Economic Lot-scheduling Problem (ELSP): Reviews Andextensions. Management Science, 587–598.
• Ennew, C., & Schoefer, K. (2003). Service Failure and Service Recovery in Tourism: A Review. The Tourist: A Psychological Perspective.
• Eppen, G. D., & Martin, R. (1987). Solving Multi-item Lot-sizing Problems Using Variable Definition. Operations Research, 832–848.
• Fleischmann, B. (1990). The Discrete Lotsizing and Scheduling Problem. European Journal of Operational Research, 337–348.
• Fleischmann, B. (1994). The Discrete Lotsizing and Scheduling Problem with Sequence-dependent Setup Costs . European Journal of Operational Research, 395-404.
• Florian, M., & Klein, M. (1971). Deterministic Production Planning with Concave Costs and Capacity Constraints. Management Science 18, 12–20.
• Harris, F. W. (1913). How Many Parts to Make at once . Factory, The Magazine ofManagemen, 135-136.
• Hax, A. C., & Meal, H. C. (1975). Hierarchical Integration of Production Planning and Scheduling. M. G. editor içinde, TIMS studies in Management Science (s. Chapter 1). New York: North Holland/American Elsevier.
• Heizer, J., & Render, B. (2011). Operations Management (Global Edition / 10. Edition). Kanada: Pearson Publishing.
• Hess, R. L., Ganesan, S., & Klein, N. M. (2003). Service Failure and Recovery: The Impact of Relationship Factors on Customer Satisfaction. Journal of the Academy of Marketing Science, 31(2), 125-145.
• Hoffman, K. D., & Kelley, S. W. (2003). A CIT Investigation of Servicescape Failures and Associated Recovery Strategies. Journal of Services Marketing, 17(4).
• İkizoğlu, S., Demir, E., & Atasoy, B. (2019). Machine Learning Based Feature Extraction for Determination of Balance Disorders.International Statistics Congress (s. 164). Bodrum, Muğla: Turkish Statistical Association.
• Jacobs, F. R., & Chase, R. B. (2008). Operations and Supply Management: The Core. Indiana: McGraw-Hill/Irwin.
• Jones, M. A., Reynolds, K. E., Mothersbaugh, D. L., & Beatty, S. E. (2007). The Positive and Negative Effects of Switching Costs on Relational Outcomes. Journal of Service Research, 9, 335-355.
• Jünger, M., & Naddef, D. (2001). Mathematical Programming Models and Formulations for Deterministic Production Planning Problems. Y. Pochet içinde, Computational Combinatorial Optimization (s. 57-111). Berlin, Heidelberg: Springer.
• Karmarkar, U., & Schrage, L. (1985). The Deterministic Dynamic Product Cycling Problem. Operations Research , 326–345.
• Krajewski, L. J., Ritzman, L. P., & Malhotra, M. K. (2013). Operations Managemen: Processes and Supply Chains. Üretim Yönetimi: Süreçler ve Tadarik Zincirleri. Çeviri Editörü: Semra Birgün. 9. Baskıdan Çeviri. İstanbul: Nobel Akademik.
• Kumar, S. A., & Suresh, N. ( 2008). Production and Operations Management (With Skill Development, Caselets and Cases) . India: (2. Edition). New Delhi: New Age International..
• Levesque, T. J., & McDouggall, G. H. (2000). Service Problems and Recovery Stratégies: An Experiment. Canadian Journal of Administrative Sciences, 17(1), 20-37.
• Lewis, B. R., & McCann, P. (2004). Service Failure and Recovery: Evidence from the Hotel Industry. International Journal of Contemporary Hospitality Management, 16(1).
• Lewis, B. R., & Spyrakopoulos, S. (2001). Service Failure and Recovery in Retail Bankimg: The Customers' Perspective. International Journal of Bank Marketing, 19(1), 37-48.
• Loo, P. T., Boo, H. C., & Khoo-Lattimore, C. (2013). Profiling Service Failure and Customer Online Complaint Motives in the Case of Single Failure and Double Deviation. Journal of Hospitalit Marketing & Management, 22(7).
• Lorenzoni, N., & Lewis, B. R. (2004). Service Recovery in the Airline Industry: A cross- Cultural Comparison of the Attitudes and Behavirours of British and Italian Front-Line Personnel. Managing Service Quality: An International Journal, 14(1), 11-25.
• Love, S. (1972). A Facilities in Series Inventory Model with Nested Schedules . Management Science, 327–338.
• Nguyen, D. T., & McColl-Kennedy, J. R. (2003). Diffusing Customer Anger in Service Recovery: A Conceptual Framework. Australasian Marketing Journal, 11(2), 46-55.
• Orlicky, J. (1975). Material Requirements Planning . New York: McGraw-Hill.
• Özgen, H. (1987). Üretim Yönetimi. Ankara: Bizim Büro Basımevi.
• Roy, R. N. (2005). A Modern Approach To Operations Management. India: New Age International.
• Russell, R. S., & Taylor, B. W. (2011). Operations Management: Creating Value Along the Supply Chain (7. Edition). United States: John Wiley.
• Salomon, M. (1990). Deterministic Lotsizing Models for Production Planning. PhD Thesis. Erasmus Universiteit Rotterdam, The Netherlands.
• Salomon, M., Kroon, L., Kuik, R., & Van Wassenhove, L. (1991). Some Extensions of the Discrete Lotsizing and Scheduling Problem. Management Science, 801–812.
• Slack, N., Brandon, -J. A., & Johnston, R. (2013). Operations Management (7. Edition). Kanada: Pearson Publishing.
• Stevenson, W. J. (1996). Production / Operations Management (5. Edition). Kanada: Irwin Publishing.
• Tekin, M. (2012). Üretim Yönetimi . Konya: Günay Ofset Cilt 1 (Yenilenmiş 8. Baskı).
• Trigeiro, W., L.J., T., & McClain, J. (1989). Capacitated Lot Sizing with Setuptimes . Management Science, 353–366.
• Veinott, A. (1969). Minimum Concave Cost Solution of Leontief Substitution Models of Multi-facility Inventory Systems. Operations Research , 262–291.
• Vollman, T., & Berry, W. L. (1997). Manufacturing Planning and Control Systems . New York: Richard D. Irwin, Third Edition.
• Wagner, H. M., & Whitin, T. M. (1958). Dynamic Version of the Economic Lot Size Model. Management Science 5, 89–96.
• Weun, S., Beatty, S. E., & Jones, M. A. (2004). The Impact of Service Failure Severity on Service Recovery Evaluations and Post-Recovery Relationships. Journal of Services Marketing, 18(2), 133-146.
• Wilson, R. H. (1934). A Scientific Routine for Stock Control. Harvard Business Review 1934, 116-128.
• Zangwill, W. I. (1969). A backlogging Model and a Multi-Echelon Model of a Dynamic Economic Lot Size Production System – a Network Approach. Management Science , 506–527