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• http://dx.doi.org/10.17093/alphanumeric.541307

• Research Article

• English

• OR

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Using Machine Learning Algorithms For Forecasting Rate of Return Product In Reverse Logistics Process

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Ayşe Nur Adıgüzel Tüylü, Ph.D.

Ergün Eroğlu, Ph.D.

Abstract

Many textile products are in reverse logistics network due to mistakes made in activities such as sales forecasting, inventory planning and distribution. In order to reduce resource usage and cost at first step, in addition to producing the correct quantity, these products must be sent to branches, in correct properties (amount, color, size, model…) and transportation planning and stock planning should be done correctly. Statistical methods, artificial intelligence and machine learning methods are used because of the difficulty of establishing mathematical models in multi-parameter and multi-variable problems. In general, all these activities are based on demand forecasts by time series, but there are important differences between these demand predictions and the actual demands because of fashion and consumers’ requests change very quickly. Artificial intelligence and machine learning methods provide faster and more accurate results in complex data sets. The difference of this study from other studies is to estimate the product return rates in Reverse Logistics with Machine Learning. In this direction, it is aimed to predict the claims accurately by concentrating on the customers' preferences, their reasons and the replies of the products which are sold to the customers. Thus, the consumer information obtained as a result of these analyzes can provide us with more accurate planning in terms of avoiding unnecessary production, transportation and storage activities, and sending the products with the correct properties; amount, color, size and model, to the branches. Best results (the correlation coefficient value is 82.35% and lowest error metrics) of this study are obtained with M5P algorithms of machine learning techniques

Keywords: Forecasting Rate of Return Product, Machine Learning, Reverse Logistics, Textile

Jel Classification: C01

Tersine Lojistik Sürecinde İade Oranlarının Tahmini İçin Makine Öğrenme Algoritmalarının Kullanılması

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Öz

Satış tahmini, stok planlama ve dağıtım gibi faaliyetlerde yapılan hatalar nedeni ile birçok tekstil ürünü tersine lojistik ağına girmektedir. Kaynak kullanımını ve maliyeti en başta azaltmak için doğru sayıda üretimin yanı sıra bu ürünlerin doğru şubelere doğru sayıda, renkte, bedende ve modelde gönderilmesi, nakliyesinin ve stok planlamasının doğru bir şekilde yapılması gerekmektedir. Çok parametreli ve çok değişkenli problemlerde matematiksel model kurmanın zorluğu nedeniyle istatistiksel yöntemler, yapay zeka yöntemleri ve makine öğrenme yöntemleri kullanılmaktadır. Genel olarak tüm bu faaliyetler zaman serisine dayalı talep tahminleri baz alınarak yapılır, fakat moda ve tüketicilerin çok çabuk değişen istekleri nedeniyle talep tahminleri ile gerçekleşen talepler arasında önemli farklılıklar doğmaktadır. Son dönemde yapılan çalışmalar gösteriyor ki bu şekilde karmaşık yapılı büyük veri setlerinde yapay zeka ve makine öğrenme yöntemleri diğer tahmin yöntemlerine göre doğruluğu daha yüksek sonuçlar vermektedir. Bu çalışmada diğer çalışmalardan faklı olarak Tersine Lojistikte ürün iade oranlarının ilk defa Makine Öğrenme yöntemleri ile tahmin edilmesi yapılmıştır. Bu kapsamda müşterilerin tercihleri ile birlikte satışa çıkan ürünlerin iadeleri ve nedenleri üzerinde yoğunlaşılıp iadelerin daha doğru bir şekilde tahmin edilmesi amaçlanmıştır. Elde edilen analizler sonucunda şubelere doğru beden, renk ve modelde ürünlerin gitmesi; gereksiz üretim, nakliye ve depolama faaliyetlerinden kaçınılması; maliyetin, kaynak kullanımının ve çevre kirliliğinin azaltılması; kaçınılamayan nakliye ve depolama maliyetlerinin tahmin edilmesi konularında daha doğru bir planlama yapılması sağlanmıştır. Makine Öğrenme tekniklerinden M5P algoritması ile en iyi tahmin performansına (% 82,35 korelasyon katsayısı ve en düşük hata ölçütleri) ulaşmıştır.

Anahtar Kelimeler: Makine Öğrenme, Tekstil, Tersine Lojistik, Ürün İade Oran Tahmini

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Suggested citation

Adıgüzel Tüylü, A. N. & Eroğlu, E. (2019). Using Machine Learning Algorithms For Forecasting Rate of Return Product In Reverse Logistics Process. Alphanumeric Journal, 7(1), 143-156. http://dx.doi.org/10.17093/alphanumeric.541307

References

• Agrawal, S., Singh, R.K., Murtaza, Q., (2014), “Forecasting product returns for recycling in Indian electronics industry”, J Adv Manag Res., C.11, No:1, 102–14.
• Aha, D. W., Kibler, D., Albert, M. K., (1991), “Instance-based learning algorithms”, Machine learning, C.6, No:1, 37-66.
• Akküçük, U., (2011), Veri Madenciliği: Kümeleme ve Sınıflama Algoritmaları, Yalın Yayıncılık, İstanbul.
• Alpaydın, E., (2014), Introduction to machine learning, MIT press.
• Anyanwu, M. N., Shiva, S. G., (2009), “Comparative analysis of serial decision tree classification algorithms”, International Journal of Computer Science and Security, C.3, No:3, 230-240.
• Ayaz, Y., Kocamaz, A. F., Karakoç, M. B., (2015), “Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers”, Construction and Building Materials, C.94, 235-240.
• Aydogmus, H. Y., Erdal, H. İ., Karakurt, O., Namli, E., Turkan, Y. S., Erdal, H., (2015), “A comparative assessment of bagging ensemble models for modeling concrete slump flow”, Computers and Concrete, C.16, No:5, 741-757.
• Chen, H., He, H., (2010), “Reverse logistics demand forecasting under demand uncertainty”, IntConf Logist Eng Manag ASCE Conf Proc., 1:343.
• Chou, J. S., Ngo, N. T., Pham, A. D., (2015), “Shear strength prediction in reinforced concrete deep beams using nature-inspired metaheuristic support vector regression”, Journal of Computing in Civil Engineering, C.30, No:1, 04015002.
• Cleary, J. G., Trigg, L. E., (1995), “K*: An instance-based learner using an entropic distance measure”, In Proceedings of the 12th International Conference on Machine learning, C.5, 108-114.
• Clottey, T., Benton, W.C., Jr, Srivastava, R., (2012), “Forecasting product returns for remanufac-turing operations”, Decis Sci, C.43, No:4, 589–614.
• Deng, S., Yeh, T. H., (2011), “Using least squares support vector machines for the airframe structures manufacturing cost estimation”, International Journal of Production Economics, C.131, No:2, 701-708.
• Efendigil, T., Onut, S., Kahraman, C., (2009), “A decision support system for demand forecastingwith artificial neural networks and neuro-fuzzy models: a comparative analysis”, Expert Syst Appl, C.36, No:3, 6697–707.
• Erpolat, S., Öz, E., (2010), “Kanser Verilerinin Sınıflandırılmasında Yapay Sinir ağları ile Destek Vektör Makineleri’nin Karşılaştırılması”, İstanbul Aydın Üniversitesi Fen Bilimleri Dergisi, C.2, No:5, 71-83.
• Fleischmann, M, Van Wassenhove, L.N., Van Nunen, J.A.E.E., van der Laan, E.A., Dekker, R., Bloemhof-Ruwaard, J.M., (1997), “Quantitative models for reverse logistics: a review”, European Journal of Operation Research, C.103, No:1, 1–17.
• Frank, E., Hall, M., Holmes, G., Kirkby, R., Pfahringer, B., Witten, I. H., Trigg, L., (2009), “Weka-a machine learning workbench for data mining”, In Data mining and knowledge discovery handbook, Springer, ABD, 1269-1277.
• Garner, S. R., (1995), “Weka: The waikato environment for knowledge analysis”, In Proceedings of the New Zealand computer science research students conference, 57-64.
• Han, J., Pei, J., Kamber, M., (2011), Data mining: concepts and techniques, Elsevier, 2011.
• Holmes, G., Donkin, A., Witten, I. H., (1994), “Weka: A machine learning workbench. In Intelligent Information Systems”, Proceedings of the 1994 Second Australian and New Zealand IEEE Conference on, 357-361.
• Hsieh, S. L., Hsieh, S. H., Cheng, P. H., Chen, C. H., Hsu, K. P., Lee, I. S., Lai, F., (2012), “Design ensemble machine learning model for breast cancer diagnosis”, Journal of medical systems, C.36, No:5, 2841-2847.
• Izenman, A. J., (2008), Modern multivariate statistical techniques, 1. Baskı, Springer, New York, ABD.
• Kohavi, R., (1995), Wrappers For Performance Enhancement And Oblıvıous Decısıon Graphs, Carnegie Mellon University, Department of Computer Science, Pittsburgh.
• Krapp, M., Nebel, J., Sahamie, H., (2013), “Forecasting product returns in closed-loop supplychains”, Int J Phys Distrib Logist Manag, C.43, No:8, 614–37.
• Krapp, M., Nebel, J., Sahamie, H., (2013), “Using forecasts and managerial accountinginformation to enhance closed-loop supply chain management”, OR Spectr, C.35, No:4, 975–1007.
• Kumar, D.T., Soleimani, H., Kannan, G., (2014), “Forecasting return products in an integratedforward/reverse supply chain utilizing an ANFIS”, Int J Appl Math Comput Sci., C.24, No:3, 669–82.
• Lamrini, B., Della Valle, G., Trelea, I. C., Perrot, N., & Trystram, G., (2016), “A new method for dynamic modelling of bread dough kneading based on artificial neural network”, Food control, C.26, No:2, 512-524.
• Marqués, A. I., García, V., Sánchez, J. S., (2012), “Exploring the behaviour of base classifiers in credit scoring ensembles”, Expert Systems with Applications, C.39, No:11, 10244-10250.
• Marqués, A. I., García, V., Sánchez, J. S., (2012), “Two-level classifier ensembles for credit risk assessment”, Expert Systems with Applications, C.39, No:12, 10916-10922.
• Namlı, E., (2012), Proje yönetimi kapsamında risk tabanlı ve yapay zeka destekli bir maliyet tahmin modeli, İstanbul Üniversitesi Fen Bilimleri Enstitüsü (Doktora Tezi), İstanbul.
• Nikoo, M. R., Karimi, A., Kerachian, R., Poorsepahy-Samian, H., Daneshmand, F., (2013), “Rules for optimal operation of reservoir-river-groundwater systems considering water quality targets: application of M5P model”, Water resources management, C.27, No:8, 2771-2784.
• Olivas, E. S., Guerrero, J. D. M., Sober, M. M., Benedito, J. R. M., Lopez, A. J. S., (2009), Handbook Of Research On Machine Learning Applications and Trends: Algorithms, Methods and Techniques, Information Science Reference, New York, ABD.
• Rogers, D.S., Tibben-Lembke, R.S., (2001), “An examination of reverse logistics practices”, J BusLogist, C.22, No:2, 129–48.
• Rogers, D.S., Melamed, B., Lembke, R.S., (2012), “Modeling and analysis of reverse logistics”, J BusLogist, C.33, No:2, 107–17.
• Srivastava, S.K., Srivastava, R.K., (2006), “Managing product returns for reverse logistics”, Int JPhys Distrib Logist Manag, C.36, No:7, 524–46.
• Temur, G.T., Balcilar, M., Bolat, B., (2014), “A fuzzy expert system design for forecastingreturn quantity in reverse logistics network”, J Enterp Inf Manag, C.27, No:3, 316–28.
• Toktay, B., (2003), “Forecasting Product Returns”, in Business Aspects of Closed-Loop Supply Chains, Ed: D. Guide, Jr. L.N. Van Wassenhove, Carnegie Bosch Institute, International Management Series: Cilt:2.
• Toktay, B., van der Laan, E.A. de Brito, M.P., (2004), “Managing Product Returns: Informational Issues and Forecasting Methods”, in Reverse Logistics: Quantitative Methods for Closed-Loop Supply Chains, Ed: R. Dekker, Fleischmann, M., K. Inderfurth, L.N. Van Wassenhove, Springer Verlag.
• Xiaofeng, X., Tijun, F., (2009), “Forecast for the amount of returned products based on wavefunction”, Int Conf Inf Manag Innov Manag Ind Eng, C.2, 324–7.

2019.07.01.OR.05

alphanumeric journal

Volume 7, Issue 1, 2019

Pages 143-156

Received: March 18, 2019

Accepted: June 20, 2019

Published: June 30, 2019

Full Text [587.9 KB]

2019 Adıgüzel Tüylü, AN., Eroğlu, E.

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

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