Abstract
There is legislation that makes manufacturers responsible for incorporating recycling of waste electric and electronic equipment (WEEE). The white appliances industry is one of these sectors and in many countries, particularly those that are members of the European Union, there are regulations to guarantee the recycling of white appliances. This paper aims to investigate the data analysis of the white appliances industry in terms of reverse logistics operations. The most important usage and logistics operation data of a white appliances manufacturer are identified and evaluated by using data-mining methods. Important factors for types of white appliances are analyzed with respect to the lifespans of products, regional data, transaction times, campaign period, and choice of new products. A neural network is applied for prediction importance and ANOVA and Pearson correlation tests for region, lifespan, and brand of new product data are performed using SPSS. The results demonstrated that customers are prone to buying the same brand when they are delivering waste white appliances. Besides analysis of the campaign time, important inferences for strategic planning could be drawn from the lifespan and regional data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akoka J, Comyn-Wattiau I, Laoufi N (2017) Research on big data—a systematic mapping study. Comput Stand Interfaces
De Mauro A, Greco M, Grimaldi M (2015) What is big data? A consensual definition and a review of key research topics. In: Giannakopoulos G, Sakas DP, Kyriaki-Manessi D (eds) AIP conference proceedings, vol 1644, no 1. AIP, pp 97–104
Jain ADS, Mehta I, Mitra J, Agrawal S (2017) Application of big data in supply chain management. Mater Today Proc 4(2):1106–1115
LaValle S, Lesser E, Shockley R, Hopkins MS, Kruschwitz N (2011) Big data, analytics and the path from insights to value. MIT Sloan Manag Rev 52(2):21
Ministry of Environment and Urbanization (2012) Regulation No: 28300 Regulatory control of waste electric and electronic equipment. Off J Turkish Repub
Muhtaroglu FCP, Demir S, Obali M, Girgin C (2013) Business model canvas perspective on big data applications. In: 2013 IEEE International Conference on Big Data, IEEE, pp 32–37
Najafi M, Eshghi K, Dullaert W (2013) A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transp Res Part E: Logist Transp Rev 49(1):217–249
Sarvari PA, Ustundag A, Takci H, Takci H (2016) Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis. Kybernetes 45(7):1129–1157
Tobergte DR, Curtis S (2013) Business intelligence. J Chem Inf Model 53. http://doi.org/10.1017/CBO9781107415324.004
Wamba SF, Akter S, Edwards A, Chopin G, Gnanzou D (2015) How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study. Int J Prod Econ 165:234–246
Wang G, Gunasekaran A, Ngai EW, Papadopoulos T (2016) Big data analytics in logistics and supply chain management: certain investigations for research and applications. Int J Prod Econ 176:98–110
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Bal, A., Sarvari, P.A., Satoglu, S.I. (2018). Analyzing the Recycling Operations Data of the White Appliances Industry in the Turkish Market. In: Calisir, F., Camgoz Akdag, H. (eds) Industrial Engineering in the Industry 4.0 Era. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-71225-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-71225-3_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71224-6
Online ISBN: 978-3-319-71225-3
eBook Packages: EngineeringEngineering (R0)