Review and Novel Framework with Hui–Walter Method and Bayesian Approach for Estimation of Uncertain Remaining Value in Refurbished Products

Ieva Dundulienė and Robertas Alzbutas ()
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Ieva Dundulienė: Department of Applied Mathematics, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, Studentų Str. 50, LT-51368 Kaunas, Lithuania
Robertas Alzbutas: Department of Applied Mathematics, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, Studentų Str. 50, LT-51368 Kaunas, Lithuania

Sustainability, 2025, vol. 17, issue 12, 1-20

Abstract: Consumers’ growing interest in sustainability and the consideration of purchasing second-hand products present conditions for developing and improving a new method for Remaining Value (RV) estimation. The remaining value refers to the value of an end-of-life product that has been inspected, repaired, if necessary, and prepared for resale. Through the literature review, the main blockers, trustworthiness, price, and quality, were identified as preventing consumers from purchasing used products. Trustworthiness could be ensured by evaluating used products in an automated and model-based manner. To enhance consumers’ confidence, this study proposes a novel framework to assess the remaining value of non-new products by incorporating the diagnostic test results, even in the absence of a gold standard for model comparison and evaluation. This research expands the application of the Hui–Walter method beyond medical diagnostics by adapting it to sustainability-focused estimation. The proposed framework is designed to assist consumers in making data-informed purchase decisions and support retailers in assessing the market price while contributing to the environmental pillar of sustainability by reducing waste and resource consumption and extending the product lifetime. This work aligns with the United Nations Sustainable Development Goals 12 (Responsible Consumption and Production) and 13 (Climate Action) by providing quantifiable methods to extend the product lifecycle and minimize electronic waste. While this study focuses on developing the theoretical framework, future work will apply and validate this framework using empirical case studies and compare it with the remaining value estimation models.

Keywords: sustainability; refurbished products; remaining value; latent variables; Hui–Walter method; Bayesian approach; uncertainty estimation; machine learning; big data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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