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Title: An investigation of used electronics return flows: A data-driven approach to capture and predict consumers storage and utilization behavior

Highlights: • We analyzed a data set of HDDs returned back to an e-waste collection site. • We studied factors that affect the storage behavior. • Consumer type, brand and size are among factors which affect the storage behavior. • Commercial consumers have stored computers more than household consumers. • Machine learning models were used to predict the storage behavior. - Abstract: Consumers often have a tendency to store their used, old or un-functional electronics for a period of time before they discard them and return them back to the waste stream. This behavior increases the obsolescence rate of used still-functional products leading to lower profitability that could be resulted out of End-of-Use (EOU) treatments such as reuse, upgrade, and refurbishment. These types of behaviors are influenced by several product and consumer-related factors such as consumers’ traits and lifestyles, technology evolution, product design features, product market value, and pro-environmental stimuli. Better understanding of different groups of consumers, their utilization and storage behavior and the connection of these behaviors with product design features helps Original Equipment Manufacturers (OEMs) and recycling and recovery industry to better overcome the challenges resulting from the undesirable storage of used products. This paper aims at providingmore » insightful statistical analysis of Electronic Waste (e-waste) dynamic nature by studying the effects of design characteristics, brand and consumer type on the electronics usage time and end of use time-in-storage. A database consisting of 10,063 Hard Disk Drives (HDD) of used personal computers returned back to a remanufacturing facility located in Chicago, IL, USA during 2011–2013 has been selected as the base for this study. The results show that commercial consumers have stored computers more than household consumers regardless of brand and capacity factors. Moreover, a heterogeneous storage behavior is observed for different brands of HDDs regardless of capacity and consumer type factors. Finally, the storage behavior trends are projected for short-time forecasting and the storage times are precisely predicted by applying machine learning methods.« less
Authors:
 [1] ;  [2] ;  [3] ;  [1] ;  [4] ;  [5]
  1. Industrial and Systems Engineering Department, State University of New York, University at Buffalo, 437 Bell Hall, Buffalo, NY (United States)
  2. Healthcare Systems Engineering Institute, Northeastern University, Boston, MA 02115 (United States)
  3. Mechanical and Aerospace Engineering, State University of New York, University at Buffalo, 437 Bell Hall, Buffalo, NY (United States)
  4. (United States)
  5. PC Rebuilder and Recyclers, 4734 W Chicago Ave, Chicago, IL 60651-3322 (United States)
Publication Date:
OSTI Identifier:
22472505
Resource Type:
Journal Article
Resource Relation:
Journal Name: Waste Management; Journal Volume: 36; Other Information: Copyright (c) 2014 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
12 MANAGEMENT OF RADIOACTIVE WASTES, AND NON-RADIOACTIVE WASTES FROM NUCLEAR FACILITIES; INDUSTRY; MAGNETIC DISKS; MATERIALS RECOVERY; PERSONAL COMPUTERS; RECYCLING; WASTE STORAGE