TY - JOUR
T1 - Machine Learning Algorithms for Pricing End-of-Life Remanufactured Laptops
AU - Turkolmez, Gokce Baysal
AU - El Hathat, Zakaria
AU - Subramanian, Nachiappan
AU - Kuppusamy, Saravanan
AU - Sreedharan, V. Raja
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024
Y1 - 2024
N2 - Due to the growing volume of e-waste in the world and its environmental impact, it is important to understand how to extend the useful life of electronic items. In this paper, we examine the remanufacturing process of end-of-life laptops for third-party remanufacturers and consider their pricing problem, which involves issues like a lack of reliable datasets, fluctuating costs of new components, and difficulties in benchmarking laptop prices, to name a few. We develop a unique approach that uses machine learning algorithms to help price remanufactured laptops. Our methodology involves a variety of techniques, which include an additive model, CART analysis, Random Forest, and Polynomial Regression. We consider depreciation and discount factors to account for the varying ages and conditions of laptops when estimating remanufactured laptop prices. Finally, we also compare our estimated prices to traditional prices. In summary, we leverage data-driven decision-making and develop a robust methodology for pricing remanufactured laptops to extend their lifespan.
AB - Due to the growing volume of e-waste in the world and its environmental impact, it is important to understand how to extend the useful life of electronic items. In this paper, we examine the remanufacturing process of end-of-life laptops for third-party remanufacturers and consider their pricing problem, which involves issues like a lack of reliable datasets, fluctuating costs of new components, and difficulties in benchmarking laptop prices, to name a few. We develop a unique approach that uses machine learning algorithms to help price remanufactured laptops. Our methodology involves a variety of techniques, which include an additive model, CART analysis, Random Forest, and Polynomial Regression. We consider depreciation and discount factors to account for the varying ages and conditions of laptops when estimating remanufactured laptop prices. Finally, we also compare our estimated prices to traditional prices. In summary, we leverage data-driven decision-making and develop a robust methodology for pricing remanufactured laptops to extend their lifespan.
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U2 - 10.1007/s10796-024-10515-9
DO - 10.1007/s10796-024-10515-9
M3 - Article
AN - SCOPUS:85199980864
SN - 1387-3326
JO - Information Systems Frontiers
JF - Information Systems Frontiers
ER -