Waste Management xxx (2014) xxx–xxx
Contents lists available at ScienceDirect
Waste Management journal homepage: www.elsevier.com/locate/wasman
An investigation of used electronics return flows: A data-driven approach to capture and predict consumers storage and utilization behavior Mostafa Sabbaghi a,1, Behzad Esmaeilian b,2, Ardeshir Raihanian Mashhadi c,1, Sara Behdad a,c,⇑, Willie Cade d,3 a
Industrial and Systems Engineering Department, State University of New York, University at Buffalo, 437 Bell Hall, Buffalo, NY, USA Healthcare Systems Engineering Institute, Northeastern University, Boston, MA 02115, USA c Mechanical and Aerospace Engineering, State University of New York, University at Buffalo, 437 Bell Hall, Buffalo, NY, USA d PC Rebuilder and Recyclers, 4734 W Chicago Ave, Chicago, IL 60651-3322, USA b
a r t i c l e
i n f o
Article history: Received 2 September 2014 Accepted 27 November 2014 Available online xxxx Keywords: Electronic waste Consumer behavior Design characteristics Machine learning
a b s t r a c t 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 providing 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. Ó 2014 Elsevier Ltd. All rights reserved.
1. E-waste stream: Current and upcoming challenges The rapid technological change, especially in electronic market leads consumers to purchase more products while creating consequences such as decreasing products usage time (Babbitt et al., 2009), progression of products obsolescence and accelerating Elec⇑ Corresponding author at: Industrial and Systems Engineering Department, State University of New York, University at Buffalo, Buffalo, NY, USA. Tel.: +1 716 645 5914. E-mail addresses:
[email protected] (M. Sabbaghi),
[email protected] (B. Esmaeilian),
[email protected] (A. Raihanian Mashhadi), sarabehd@buffalo. edu (S. Behdad),
[email protected] (W. Cade). 1 Tel.: +1 7166452426. 2 Tel.: +1 617 373 8140. 3 Tel.:+1 773545 7575.
tronic Waste (e-waste) generation. E-waste is the fastest growing waste stream in the US and many other countries. According to the Solving the E-Waste Problem (StEP) initiative, the amount of e-waste generated worldwide was 48 million tons in 2012. This volume will increase by 33% in five years, reaching 65 million tons in 2017. In the U.S., 171.4 million units of used electronics were collected for recovery in 2010 which was 66.4% of the total generated e-waste (Duan and H., 2013). The term ‘e-waste’ includes all discarded used electronics including both reusable and non-reusable ones. In fact, a large amount of what is called as ‘e-waste’ is not waste at all, but are reusable, remarketable or can be recycled for materials recovery. Although some countries started building infrastructure to facilitate End-of-Use (EOU) products recovery, in most of the developing countries, e-waste is dumped in landfill sites or recy-
http://dx.doi.org/10.1016/j.wasman.2014.11.024 0956-053X/Ó 2014 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Sabbaghi, M., et al. An investigation of used electronics return flows: A data-driven approach to capture and predict consumers storage and utilization behavior. Waste Management (2014), http://dx.doi.org/10.1016/j.wasman.2014.11.024
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M. Sabbaghi et al. / Waste Management xxx (2014) xxx–xxx
cled with low efficiency such as being burnt for valuable metals which results in releasing of hazardous toxics into the environment and harmful effects on human health (Kolias et al., 2014). Xu et al. (2012) reported how exposure to informal e-waste recycling in South China intensifies the levels of cord blood lead resulted to poor birth outcomes such as stillbirth, low weight, and low Apgar score. Hibbert and Ogunseitan (2014) explain the link between exposure to hazardous chemicals, which diffuse from open burning of used cellphones, and developing cancer and noncancer diseases. Even in the developed countries, illegal exports of used electronics to developing countries is still considered as an alternative to formal recycling and recovery (Sthiannopkao and Wong, 2013). To overcome the e-waste challenges, international environmental protection agencies and governments have explored different solutions such as enacting rules and regulations, incentivizing manufactures to design greener products (Yu et al., 2014), and increasing public awareness (EPA). Despite all these efforts to control transboundary and domestic stream of e-waste, there remains still a growing concern for global management. OEMs and recycling coalitions play a key role in the appropriate management of e-waste flow. They can lessen the severity of ewaste crisis through different actions such as design products for end-of-life practices (Rose, 2000), design for multiple lifecycles (van Nes and Cramer, 2005), commitment to take-back laws, participation in launched initiatives (Short, 2004), improve metallurgical recovery of metals (Tuncuk et al., 2012), and facilitating collection and reuse of discarded items. Companies use several methods to collect the used products from consumers. For instance, they might take charge of collecting products themselves, provide incentives for retailers who collect the used products, or engage third parties to collect them (Ali and Chan, 2008). A large number of studies have analyzed the effectiveness of e-waste recovery systems (Xu et al., 2013; Kang and Schoenung, 2006; Gregory and Kirchain, 2008; Low et al., 2014). Consumers often keep the electronics in storage and do not return them immediately to recyclers after stopping usage. Regardless of functionality, the obsolete used products are not likely to be reusable (Babbitt et al., 2011). In general, the sooner a used product is processed, the higher value recovered. Sometimes the time delay in collecting and processing products makes them unusable, obsolete and even completely unsalvageable. Specifically, technological obsolescence intensifies this problem dramatically (Guiltinan, 2009). There are several reasons that make product recovery in the U.S. unprofitable including cost of collection (Kang and Schoenung, 2005), lack of information about consumer purchase and usage behavior toward electronic products (Kwak et al., 2011), and the uncertainties dealt with quality, quantity and return time of ewaste (Brown-West et al., 2010). Understanding the nature of the used electronics in terms of quality, quantity and timing diminishes the risk of unprofitability of recovery system. However, this cannot be achieved unless adequate information about consumers’ behavior is gathered. Due to the importance of understanding the consumer behavior, various empirical studies have investigated the households and corporates green behavior and debated that these behaviors are influenced by both internal and external factors such as identities, social norms, religious, cultural beliefs, values, habits, sociodemographical characteristics, pro-environmental attitude, as well as mediating and moderating variables such as social pressure, rules, and behavioral imitation (Lin and Huang, 2012; Jansson, 2011; Jansson et al., 2010). Milovantseva and Saphores (2013) propose a multinomial digit model to explain the disposal behavior of household in discarding cell phones and TVs. They find that the presence of e-waste ban such as California’s Cell Phone Recycling Act has a positive impact on disposal intention of households. In
another study, Saphores et al. (2012) claimed that the socio-economic characteristics have the least effect on the consumers’ willingness toward drop-off recycling. Despite the existing empirical studies on finding the factors that influence consumer green behavior, the following research gaps have been identified in the prior studies. First, the consumer behavior in storage of used electronics has not been explored as an anti-green behavior so far. Second, the impacts of design features such as product age, brand and capacity (for example hard drive capacity in personal computers) on consumers disposal behavior have not been examined in prior studies. Finally, no study exists on using the real world industry data on predicting the actual consumer behavior in storage of used electronics. In addition to storage behavior, assessing the electronic devices’ utilization time provides recyclers, OEMs, and electricity market suppliers with remarkable insights on energy consumption, ewaste quality and purchasing behavior. For instance, an empirical study done by Beauvisage (2009) in 2010 reveals that although household’ Personal Computers (PCs) are on for 574.49 min per day on average, they are used only for 215.12 min. Underrating consumer usage behavior not only helps designers in modifying the product design based on the actual consumer needs, but also helps recyclers and manufacturers to identify potentials for reuse of these products in future lifecycles. To overcome some of these challenges, the main objective of this paper is to investigate the trend of consumer behavior toward usage and storage of used electronics and link these behaviors to the products design features and to the best of our knowledge, the end-of-use reaction of consumers to design features has not been studied in the literature so far. Specifically, the research seeks to address the following questions: How long have the retuned electronic products been used? What is the trend of lifespan and the average age of different products retuned back over time? What are the relations between products design features and consumer behavior toward storage of end-of-use products? How different is the electronics usage behavior of households and corporates? Has consumer storage behavior changed over time and why? What will be the future trends of storage time of electronic products? 2. E-waste stream assessment This section gives an overview of the e-waste stream through analysis of a dataset collected by a PC remanufacturing company located in Chicago, Illinois. Because the recycling strategies by recyclers and policies implications by government are different in most of the states in the U.S., a brief comparison of e-waste laws and electronics recycling regulations passed in Illinois with other states has been provided in Table 1. Considering the existing differences in the recycling programs and laws assists decision makers in measuring the performance of their current recovery system and planning for future. 2.1. Data collection, cleaning and preprocessing Data collection and cleaning is a tedious process in almost any data-based analyses. Especially in the e-waste stream data collection, it would be a big challenge since a part of the data cannot be retrieved from used products due to physical damages and the significant lapse of time between purchasing and discarding dates. Recyclers are parts of the electronics reverse logistics who can participate in data gathering. PC Rebuilders and Recyclers (PCRR) is one of these companies collaborating with the City of
Please cite this article in press as: Sabbaghi, M., et al. An investigation of used electronics return flows: A data-driven approach to capture and predict consumers storage and utilization behavior. Waste Management (2014), http://dx.doi.org/10.1016/j.wasman.2014.11.024
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M. Sabbaghi et al. / Waste Management xxx (2014) xxx–xxx Table 1 Comparison of different aspects of e-waste recycling laws in Illinois with other states (U.S. State & Local Legislation, 2012). Illinois
Other states
E-waste Laws
Electronic Products Recycling and Reuse Act Electronic disposal ban Producer Responsibility Law
Advanced Recycling Fee (ARF) is another policy enacted only by California to persuade consumers to take back the end of life electronic devices to recyclers. Based on ARF, consumers are required to pay a recycling fee at the time of their purchase. These fees are collected to afford the recycling cost Some states do not have disposal ban laws, e.g. Washington
Date law passed
2008
California passed the e-waste laws in 2003 as the first state. Until 2013, 25 states (AR, CA, CT, HI, IL, IN, ME, MD, MI, MN, NH, NJ, NY, NC, OK, OR, PA, SC, TX, UT, VT, VA, WA, WV, and WI) have passed recycling laws. A few states have already proposed laws (AZ, NE, OH, KY, and MA). The other states have conducted related studies or not passed the waste legislation yet
List of electronics covered by laws
CRTs, TVs, Desktops, Laptops, Monitors, Computer Peripherals, Printers, Fax Machines, Cell phones, PDAs, MP3 Players, Video Game Consoles, CD/DVD Players, Zip Drive
Most states have considered fewer types of electronic devices compared to Illinois in the waste laws. For instance, desktops, laptops, and monitors, are only items included in e-waste recycling law of Virginia
Who pays for recycling services?
Consumers receive free recycling services and manufactures pay for recycling fee
In some states (e.g. New York), consumers are categorized into several groups: Households, NonProfit Organizations, Businesses, Schools, and so on. They may (or not) included in free recycling services. They may limit the number of returned items or the size of company for free recycling services. For instance, New Jersey’s law considers free services for businesses with less than 50 employees. In Maine and Maryland, municipalities and manufactures are responsible together in managing the e-waste recycling programs. In California, there is no manufacturer responsibility. Instead, consumers should pay for the recycling program.
Consumers’ characteristics: ID, number of computers donated by each consumer, type (commercial versus household). Design and technology characteristics: Model, brand, manufacturing date, serial number, firmware revision, computer bus interface, number of cylinders, heads and sectors, and capacity. Return and Usage Information: Return or donation date, last used date, Power-on Time (the accumulated amount of time that computers have been on). After dealing with missing data and correcting inaccurate data, the size of dataset has been reduced to 10063 HDDs. In addition to the existing information, a set of auxiliary variables is derived from the original dataset. For instance, Usage Time Upper Bound representing the maximum usage time of the hard drive. The upper bound is added to the usage time since we do not know when consumer purchased HDD and turned it on for the first time. In fact, it has been assumed that the manufacturing time is the same as the purchase date and the first time that the product has been used. Storage time and age are derived from manufacturing date, last used date, and return date. Fig. 1 demonstrates the above definitions and the derived variables as well. 2.2. E-waste stream dynamics: analysis of age and number of returns The first analysis conducted on the dataset is the descriptive statistics of product age, usage time, and storage time. The param-
Age Usage Time Upper Bound
Storage Time Time Last Used Date
Manufacturing Date
Return Date
Fig. 1. Demonstration of life cycle characteristics.
7.3 7.2
Age (years)
Chicago in the US to achieve a sustainable environment by refurbishing, recycling and recovery of collected used items. The used items are returned by consumers to local waste collection centers without any reward or charge. PCRR records the information of every single used electronic device donated to their collection site. Among the different types of electronics, a big dataset of Hard Disk Drives (HDD) is chosen for the purpose of this study. The main reason for choosing HDD is having access to the life cycle characteristics such as manufacturing year and the last time that the computer was used. These HDDs have been part of different types of computers such as desktop, notebook, or other computers. This dataset contains information of 44,617 HDDs returned to the PCRR collection site during 2011–2013. This information can be divided into three categories:
7.1 7.0 6.9 6.8 6.7 2011
2012
2013
Return Year Fig. 2. 95% Confidence intervals for age parameter.
eters which describe e-waste stream are likely to be non-stationary. An evidence for this claim is a slightly decreasing trend in the average age of returned HDDs over time. As seen in Fig. 2, the 95% confidence intervals for the average age of products returned back in 2011, 2012 and 2013 does not show any overlap. Also Levene’s test rejects the equality of variances for different return years (p-value = 0.000). The average age of HDDs has decreased by 5.5% in three years from 7.2 years in 2011 to 6.8 years in 2013. Regardless of time, the average age of HDDs is 6.96 years and consumers tend to store the unwanted HDDs for 1.11 years on average (Table 2). In addition to the analysis of the time, we also have observed a dynamic behavior in the number of HDDs manufactured in different years and donated in each return year (Fig. 3). These quantities are normalized through dividing them by the total number of HDDs returned in the corresponding year. For instance, among those HDDs returned in 2011, 5% was manufactured in 2008. Using the results of Figs. 2 and 3, it is concluded that the returned items in 2011 were mainly manufactured in 2004 on
Please cite this article in press as: Sabbaghi, M., et al. An investigation of used electronics return flows: A data-driven approach to capture and predict consumers storage and utilization behavior. Waste Management (2014), http://dx.doi.org/10.1016/j.wasman.2014.11.024
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M. Sabbaghi et al. / Waste Management xxx (2014) xxx–xxx
Table 2 Descriptive statistics: age, Usage Time Upper Bound and Storage Time (years). Read year
Mean
St. dev.
Min.
Median
Max.
IQR
Age Usage Time Upper Bound Storage Time
6.96 5.85
0.36 1.83
0.36 0.11
7.06 6.95
15.13 12.49
2.71 2.50
1.11
1.14
0.005
6.77
9.57
1.20
Return Date: 2011 Return Date: 2012 Return Date: 2013
Normalized Quantity
0.20
Usage Time Upper Bound on Storage Time is evaluated in Section 2. The focus of this section is on understanding the impact of design and technology evolution. In regard to this matter, we choose brand and capacity of HDDs as two factors representing design characteristics. To be more accurate, the data has been labeled into two groups based on consumer type (commercial versus household). The purpose is to statistically compare the storage and usage behavior of two types of consumers toward different brands and various HDDs capacities. Since storage, Usage Time Upper Bound and Power-on Time are age-dependent, we define three new parameters: Storage, Usage and Utilization Ratios. These ratios are defined as below:
Storage Ratio ¼ Storage Time=Age
ð1Þ
Usage Ratio ¼ Usage Time Upper Bound=Age
ð2Þ
Utilization Ratio ¼ Power-on Time=Usage Time Upper Bound
ð3Þ
Storage Ratio þ Usage Ratio ¼ 1
ð4Þ
0.15
0.10
0.05
0.00 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 19 19 19 19 19 20 20 20 20 20 20 20 20 20 20 20 20 20 20
Manufacturing Date
10000
100
3.1. Brand and design differentiation
8000
80
6000
60
4000
40
2000
20
Data storage devices including hard disk drives have been technologically advanced over the last five decades. Capacity is a primary characteristic for an HDD. The capacity of HDDs has been increased exponentially. Since the first magnetic hard disk with 5 MB capacity introduced by IBM in 1956 (Ibm.com), a remarkable capacity of 60 TB is promised to achieve by 2016 (Hall, 2012). HDDs are differentiated based on Average Seek Time, Revolutions per Minute, Data Transfer Time, Average Rotational Latency, Internal Data Rate, Reliability and Energy Consumption (Deng, 2011). Emerging new technologies, Hybrid Disks and Solid-state Disks are the main competitive products for traditional HDDs (Deng, 2011). The analysis starts with an investigation of the variety of brands returned back to the collection site. Fig. 4 represents the Pareto chart of different brands. As illustrated, 77% of returns belonged to brand H and J. This portion of returns somehow represents the market shares of these two brands. A quick side-study of the market has revealed that brands A, B, D, E, and G have been purchased by brands H, J and I over the last decade. Therefore, they no longer exist in the market. The 95% confidence intervals of HDDs life characteristics shows that brands D and E have been used and stored for a longer period of time compared to other brands (Fig. 5). Our dataset shows that these items were mostly manufactured before 2005. In addition to brand, the HDD capacity has been studied as one of the HDD primary design characteristics. For both brands H and J (From now on, the rest of analysis is done only for these two brands), the most frequent capacities are 20 (GB), 40 (GB), 80 (GB), 160 (GB) and 250 (GB) (Fig. 6). The manufacturing date distributions of different capacities are depicted in Fig. 7. The rough comparison of distributions implies the existence of market competition between these two brands in releasing the new technology into the market. After setting design characteristics, we are now able to capture consumer behavior toward storing e-waste and utilizing electronics.
0 Brand Quantity Percent Cum %
Percent
Return Quantity
Fig. 3. Returned HDDs stream based on manufacturing and return dates.
It should be noted that the Usage Ratio is the complement of Storage Ratio, therefore distinct analysis of Usage ratio is redundant. In the first part of this section, the nature of HDDs data set is explored in terms of design characteristics variety. Then, the effects of mentioned factors on Storage Ratio with each pair of interactions are evaluated through non-parametric statistical analysis. Finally, electronics utilization behavior is investigated as a supplementary analysis for consumer behavior characterization study.
0 J H B 4249 3494 703 42.2 34.7 7.0 42.2 76.9 83.9
E 650 6.5 90.4
G 494 4.9 95.3
D 224 2.2 97.5
C I A F 215 19 10 5 2.1 0.2 0.1 0.0 99.7 99.9 100.0100.0
Fig. 4. Pareto chart of brand of returned HDDs.
average. Interestingly, newly manufactured items were returned in 2012 and 2013. 3. Consumer behavior characterization Assessing consumer behavior toward storing used electronics is an important step toward developing a successful e-waste recovery system. Dynamic consumer behavior over time regarding products time-in-storage brings some fluctuations into the supply side of the recovery system. The uncertainty in the quality and quantity of return flows resulted from the unpredictable consumer storage behavior challenges strategic planners in selecting the best recovery options. Characterizing the effects of consumer behavior is often very difficult since a well-organized assessment framework does not exist and there is a lack of accurate and adequate e-waste data set. Consumers’ tendency to store the e-waste is an ambiguous behavior dependent on several factors such as consumer sociodemographic information, risk attitude, the length of the time product has been used for, manufacturing date, product design features and many other internal and external factors. The effect of
Please cite this article in press as: Sabbaghi, M., et al. An investigation of used electronics return flows: A data-driven approach to capture and predict consumers storage and utilization behavior. Waste Management (2014), http://dx.doi.org/10.1016/j.wasman.2014.11.024
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2.0 1.5 1.0 0.5 A
B
D
E
G
H
J
7.5
10
7.0
Age (years)
2.5
Usage Time Upper Bound (years)
Storage Time (years)
M. Sabbaghi et al. / Waste Management xxx (2014) xxx–xxx
6.5 6.0 5.5 5.0
9 8 7 6
A
B
Brand
D
E
G
H
A
J
B
D
E
G
H
J
Brand
Brand Fig. 5. 95% CI of HDDs life characteristics for each brand.
0.17
er 0 0 th 40 80 16 20 25 10 O Brand = J
Quantity
4000 3000 2000 1000 0
0.16 Capacity (GB) 40 80 160 20 250 10 Other
Storage Ratio
Brand = H
0.15 0.14 0.13 0.12
Brand Consumer Type 40 80 160 20 250 10 ther O
H
J
Commercial
H
J
Household
Fig. 8. 95% CI for Storage Ratio parameter for Brand and Consumer Type.
Capacity (GB) Fig. 6. Pareto chart of the different capacities of HDDs for brand H and J.
3.2. Storage behavior and consumer type: Commercial versus household Does consumer type influence the storage behavior? To answer this question, commercial consumers are separated from household ones (53.23% versus 46.77%). Fig. 8 is the interaction plot between two factors: consumer type and brand. This plot shows
a negligible interaction between these factors. In addition to this plot, rough comparison of similar curves in Fig. 9 also shows a weak interaction among consumer type and capacity factors. Therefore, the effects of these main factors can be analyzed separately through statistical methods. To compare the storage ratios of different consumer types, we apply two non-parametric tests: The Kruskal–Wallis Analysis of Variance and Mood’s Median tests. The main difference between them is the equality assumption of variances for the Kruskal–Wallis test, which should be verified separately by Levene’s test. The equality assumption of variances is
600
400
450
300
300
200
30
150
100
0
0
0
90
Quantity
80 (GB)*Manufacturing Year
40 (GB)*Manufacturing Year
20 (GB)*Manufacturing Year
120
60
98 00 02 04 06 08 10 12 19 20 20 20 20 20 20 20
200
160 (GB)*Manufacturing Year
98 00 02 04 06 08 10 12 19 20 20 20 20 20 20 20
60
250 (GB)*Manufacturing Year
98 00 02 04 06 08 10 12 19 20 20 20 20 20 20 20
Brand H
150
45
100
30
50
15
J
0
0
98 00 02 04 06 08 10 12 19 20 20 20 20 20 20 20
98 00 02 04 06 08 10 12 19 20 20 20 20 20 20 20
Fig. 7. Manufacturing date distribution plots of returned HDDs with different capacities.
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M. Sabbaghi et al. / Waste Management xxx (2014) xxx–xxx
20
40
Commercial
80
160
250
Household
0.35
Storage Ratio
0.30
0.25
0.20
0.15
0.10 20
40
80
160
250
Capacity (GB) Fig. 9. 95% CI for Storage Ratio parameter for different consumers and capacities.
Table 3 Equal variances test: Storage Ratio versus Consumer Type.
Table 5 Equal variances test: Storage Ratio versus Brand.
Consumer type
N
Lower
St. dev.
Upper
Brand
N
Lower
St. dev.
Upper
Commercial Household Overall
3888 3415 7303
0.135023 0.122482
0.138459 0.125807
0.142067 0.129311
H J Overall
4039 3264 7303
0.129034 0.129328
0.132255 0.132920
0.135635 0.136709
Levene’s test (any continuous distribution). Test statistic = 7.70, p-value = 0.006.
Levene’s test (any continuous distribution). Test statistic = 2.49, p-value = 0.115.
Table 4 Mood’s Median Test: Storage Ratio versus Consumer Type.
Table 6 Kruskal–Wallis test: age versus Brand.
Consumer type
N6
N>
Median
Q3–Q1
Brand
N
Median
Ave rank
Z
Commercial Household
1877 1775
2011 1640
0.1110 0.0977
0.1767 0.1659
H J Overall
4039 3264 7303
0.094 0.116
3456.3 3894.2 3652.0
8.82 8.82
Chi-Square = 9.95, DF = 1, P = 0.002, Overall median = 0.104.
H = 77.86 DF = 1 P = 0.000. H = 77.86 DF = 1 P = 0.000.
rejected according to the results of Table 3 (p-value = 0.006). Following that, Table 4 shows a significant difference between the medians of two groups and discloses a stronger tendency of commercial consumers toward storing e-waste compared with individual ones (p-value = 0.002). We repeat this statistical analysis in Section 3.3 to assess brand and capacity effects.
3.3. Storage behavior and design characteristics In the previous section, it was observed that consumer type and capacity effects can be additive. Here, additive means that there is no significant interaction between these factors. A similar situation
20
H
40
80
160
250
J
Storage Ratio
0.30
0.25
0.20
0.15
0.10 20
40
80
160
250
Capacity (GB) Fig. 10. 95% CI for Storage Ratio parameter for each brand and capacity.
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M. Sabbaghi et al. / Waste Management xxx (2014) xxx–xxx Table 7 Equal variances test: Storage Ratio versus Capacity.
Table 9 Correlation analysis between Power-on Time, Usage Time Upper Bound and Storage Time.
Capacity
N
Lower
St. dev.
Upper
20 40 80 160 250 Overall
396 3315 2775 654 163 7303
0.136122 0.119008 0.123014 0.150087 0.156503
0.148666 0.122781 0.127278 0.160831 0.179080
0.163554 0.126783 0.131826 0.173106 0.208601
Usage Time Upper Bound Power-on Time
N6
N>
Median
Q3–Q1
130 1716 1477 288 41
266 1599 1298 366 122
0.163 0.101 0.0.94 0.122 0.164
0.238 0.166 0.164 0.190 0.230
0.367 (0.000)
occurs for the interaction between brand and capacity factors (Fig. 10). Looking at the results of Levene’s test (Table 5), the hypothesis of equal variances for the two sample capacities is accepted (p-value = 0.115). Thus, Kruskal–Wallis test is applied and it is found that the medians of two groups are different (Table 6). Therefore brand J’s buyers tend to store HDDs for a longer period of time. We evaluate the effect of capacity by Mood Median test since the variances equality assumption for Kruskal–Wallis test is rejected (p-value = 0.000, Table 7). Table 8 shows that the medians of different groups are not identical. To summarize, consumer types, brand and capacity are among the factors which affect the storage behavior. It should be noted that there might be some other factors which may impact the consumer storage behavior. Purchase price of electronic devices, accessibility to collection centers,
Table 8 Mood’s Median Test: Storage Ratio versus Capacity (GB).
20 40 80 160 250
Usage Time Upper Bound
0.182 (0.000) 0.155 (0.000)
Cell contents: Pearson correlation coefficient (p-values).
Levene’s test (any continuous distribution). Test statistic = 18.13, p-value = 0.000.
Capacity
Storage Time
Chi-Square = 111.94, DF = 4, P = 0.000, overall median = 0.104.
Commercial
Household
Usage Time Upper Bound (years)
Power-on Time (years) 5.96
2.9
5.92
2.8
5.88
2.7
5.84
2.6
5.80
2.5
Age (years)
Storage Time (years) 6.95
1.08 1.04
6.90
1.00 6.85
0.96 0.92
6.80 Commercial
Household
Fig. 11. 95% CI for HDDs life characteristics for different consumers.
Power-on Time, commercial
Power-on Time, Household 6 4 2 0
Percent
8
6
4
2
0 6 4 2 0
0
8
6
4
2
10
8
6
4
2
0
Usage Time Upper Bound, commercial
Usage Time Upper Bound, Household
0
8
6
4
2
Storage Time, commercial
10
Storage Time, Household 20 15 10 5 0
0
2
4
6
0
2
Age, commercial
4
6
Age, Household
6 4 2 0
0
2
4
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Fig. 12. Histogram of HDDs life characteristics for different consumers.
Please cite this article in press as: Sabbaghi, M., et al. An investigation of used electronics return flows: A data-driven approach to capture and predict consumers storage and utilization behavior. Waste Management (2014), http://dx.doi.org/10.1016/j.wasman.2014.11.024
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M. Sabbaghi et al. / Waste Management xxx (2014) xxx–xxx Power-on Time (years)*Usage Time Upper Bound (years)
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Fig. 13. Scatter plots of HDDs life characteristics.
99 00 01 02 03 04 05 06 07 08 09 10 11 12 19 20 20 20 20 20 20 20 20 20 20 20 20 20
Average Storage Ratio
H
J
0.48 0.42 0.36 0.30 0.24 0.18 0.12
99 00 01 02 03 04 05 06 07 08 09 10 11 12 19 20 20 20 20 20 20 20 20 20 20 20 20 20
Manufacturing Year Fig. 14. The trend of storage ratio over the manufacturing time for brand factor. Left part shows the actual and predicted trend for brand H. The predicted trend equation is a polynomial curve with equation SR = 0.0005t3 0.0044t2 0.0243t + 0.3155 (R2 = 0.9725). This equation for brand J (right part) is SR = 0.0002t3 + 0.0105t2 0.1186t + 0.5103 (R2 = 0.9603). Here, time starts from 1, which is equivalent to 1999.
99 00 01 02 03 04 05 06 07 08 09 10 11 12 19 20 20 20 20 20 20 20 20 20 20 20 20 20
Commercial
Household
Average Storage Ratio
0.56 0.48 0.40 0.32 0.24 0.16 0.08
99 00 01 02 03 04 05 06 07 08 09 10 11 12 19 20 20 20 20 20 20 20 20 20 20 20 20 20
Manufacturing Year Fig. 15. The trend of storage ratio over the manufacturing time for consumer type factor. Left part shows the actual and predicted trend for commercial consumers. The predicted trend equation is a polynomial curve with equation SR = 0.0068t2 0.0992t + 0.489 (R2 = 0.985). This equation for households (right part) is SR = 0.0001t3 + 0.0062t2 0.1037t + 0.4859 (R2 = 0.9152). Here, time starts from 1 which is equivalent to 1999.
Please cite this article in press as: Sabbaghi, M., et al. An investigation of used electronics return flows: A data-driven approach to capture and predict consumers storage and utilization behavior. Waste Management (2014), http://dx.doi.org/10.1016/j.wasman.2014.11.024
M. Sabbaghi et al. / Waste Management xxx (2014) xxx–xxx
recycling incentives offered to consumers, data security, and second-hand selling price are among those factors. Milovantseva and Saphores (2013) showed other factors such as age, household size, education, and marital status that have meaningful statistical impact on the broken (or obsolete) TVs storage decision by U.S. households but nothing mentioned about the storage time. 3.4. Utilization behavior How do consumers utilize electronic products? Is the storage behavior correlated with utilization time? To give a brief overview of utilization behavior and its effect on storage behavior, some analyses have been done on ‘‘Power-on Time’’ and ‘‘Usage Time’’ data. The results show that the effects of consumer type, brand and capacity factors on utilization behavior contrast with the results of storage behavior analysis. For instance, Mood’s Median test results in a significant difference between utilization ratio Medians of different groups of consumers (0.475 versus 0.414, pvalue = 0.000). Household consumers have utilized computers more than the commercial group regardless of brand and capacity factors (it is ensured that there are no significant interactions between each pair of factors, as in the previous part). Also, HDDs manufactured by brand H have been utilized more compared with brand J (0.461 versus 0.414, p-value = 0.000). Fig. 11 summarizes the usage, utilization and storage behavior for different types of consumers. Also, the histogram of HDDs lifespan characteristics are depicted in the presence of consumer types in Fig. 12. The results of this section also provide useful insights in terms of energy consumption behavior. To generally measure the relationships between Power-on Time, Usage Time Upper Bound and Storage Time, Pearson correlation coefficients are calculated for each pair of parameters (Table 9). Based on the results, there are negative correlations between Storage Time and the other parameters. Furthermore, Usage Time Upper Bound is positively correlated with Power-on Time. According to the results, the consumers who utilize the electronic devices more keep them in storage less frequently. They may think that their electronic devices have been sufficiently depreciated and should not be stored longer. On the contrary, the electronic devices that have been utilized less than the normal life, may be stored longer. The quantities of these parameters are scattered in Fig. 13. 4. E-waste storage behavior prediction So far we have used observational data to evaluate the effects of design characteristics and consumer type on the e-waste storage. The focus of this section is on predicting storage behavior. Previous studies mainly focused on predicting the number of stored e-waste by consumers (Saphores et al., 2009), e-waste generation (Breivik et al., 2014) or the collection rate of e-waste (Bouvier and Wagner, 2011) and neglect the importance of storage behavior for the recovery system planning. To predict this behavior, it is necessary to aggregate all those effects with the time parameter in the appropriate prediction models with the ability to distinguish complex nonlinear patterns. In the first part of this section, static time series models are fitted to the average storage ratio. Then, the storage time is predicted using machine learning methods. Consumer Type, Brand, Capacity, Usage Time Upper Bound, Power-on Time and Manufacturing Date are predictors included in the prediction model. 4.1. Trend projection Fig. 14 displays the trend of storage ratio for brands H and J over time. Here, time is the manufacturing date of HDDs. The left part of this figure shows the trend for brand H. The best polynomial curve
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is fitted to the actual average storage ratios calculated from historical data. Then the corresponding storage ratio for Manufacturing Year 2012 is forecasted using the fitted curve equation. The predicted storage ratio for brand H is 0.48. To show how this prediction can be applied, assume that an HDD was manufactured at the end of 2012 and will be returned at the end of 2014. On average, this HDD has been probably used for 380 days and stored for the rest of time. The same analysis has been repeated for Consumer Type factor (Fig. 15). According to these figures, the minimum storage time belongs to devices with a normal life between 4 and 6 years. It reveals the point when consumers use the electronic devices for a normal lifetime (the average useful lifetime for the computers is estimated to be between 4 and 6 years), they keep them in storage as minimum as possible. For the devices with most recent manufacturing years, the consumers may think that the electronic devices have not been sufficiently enough and can be reused in future. Sometime, the initial purchase price paid by consumers was high, therefore they have the tendency to keep it in storage. Finding the actual reasons behind storage behavior requires a survey of consumers’ sustainability behavior. 4.2. Storage behavior prediction using machine learning methods To capture the complexity of relationships between storage time and the predictors such as Usage Time Upper Bound, machine learning methods are applied to predict the storage time. Multilayer Perceptron (MLP) Network and Support Vector Machine (SVM) methods are trained, validated and finally tested by the partitioned data. MLP is an artificial neural network method consisting of input, hidden and output layers of nodes with activation functions. In this directed graph, the weighted inputs are passed through the connected nodes to reach an output. By applying a learning method called backpropagation, the weights will be adjusted until the accurate output earned (Bishop, 2006). SVM is an optimization process to fit the best predictor function. It can be achieved through a sequential algorithm to minimize the error function and then find the parameters of the predictor function. To distinguish the differences between points easier, it applies kernel functions to transform the points into higher dimensional space (Murphy, 2012). The dataset has been divided into three subsets. 75% of data is allocated to train the models. 15% is used to avoid over-fitting of models and set their parameters as validation set. Finally, the performance of the best trained models has been tested by comparing the common accuracy measures including: Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), Mean Square Error (MSE), R2 that measures how well the model explains the response variability, and statistical t-test to compare the actual and fitted values. The formulas of accuracy parameters are defined as follows:
MAPE ¼
ð5Þ
n 1X jAi Pi j n i¼1
ð6Þ
n 1X ðAi Pi Þ2 n i¼1
ð7Þ
MAD ¼
MSE ¼
n 100 X jAi Pi j n i¼1 Ai
where Ai is actual storage time, Pi is the prediction for storage time and n is the total number of test sample. Table 10 summarizes the performance of MLP and SVM prediction models. Fitted MLP model offers lower values for MAPE, MAD and MSE even though the differences between two models are not significant. The R2 is 0.876 means that the fitted model can predict the storage time sufficiently
Please cite this article in press as: Sabbaghi, M., et al. An investigation of used electronics return flows: A data-driven approach to capture and predict consumers storage and utilization behavior. Waste Management (2014), http://dx.doi.org/10.1016/j.wasman.2014.11.024
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M. Sabbaghi et al. / Waste Management xxx (2014) xxx–xxx
Table 10 Performance analysis of trained prediction models. MAPE
MAD
MSE
R2
St. dev. (residual)
t-Value
p-Value
ANN SVM
MLP with 20 sigmoid hidden neurons and linear output neuron C = 10, Epsilon = 0.1, Kernel type = RBF, RBF gamma = 0.1
76.26 93.03
0.262 0.311
0.116 0.161
0.876 0.831
0.341 0.4016
0.04 0.05
0.966 0.957
7 6 5 4 3 2 1 0 0
1
2
3
4
5
7
6
8
Actual Storage Time (years)
Predicted Storage Time (years) by ANN
Parameters
Predicted Storage Time (years) by SVM
Method
8 7 6 5 4 3 2 1 0 0
1
2
3
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5
6
7
8
Actual Storage Time (years)
Fig. 16. Demonstration of actual storage time amounts (years) and the corresponding predicted values for ANN and SVM methods.
Table 11 Sensitivity analysis for the average Storage Time variation. Percentage change in Usage Time Upper Bound (%)
Average storage time (years) Commercial
Household
Brand H
Brand J
20 15 10 5 0 5 10 15 20
1.94 1.70 1.45 1.20 0.97 0.75 0.57 0.43 0.32
1.94 1.69 1.44 1.19 0.95 0.74 0.57 0.45 0.36
1.94 1.68 1.42 1.18 0.93 0.71 0.54 0.42 0.32
1.94 1.70 1.46 1.22 1.00 0.79 0.61 0.47 0.36
sensitivity analysis. To see the contribution of input factors to Storage Time, we change the values of Usage Time Upper Bound in the test data (we assume that the Power-on Time values are changed equally.) In Table 11, the variations of Storage Time mean values are presented for different types of consumers and brands. For instance, if the Usage Time Upper Bound values increase by 5% from the origin point, then Storage Time mean value is expected to decrease by 22.13% (from 0.95 to 0.74) for the household consumers. Fig. 17 shows the complete simulation results for this type of consumers. Knowing the consumer storage behavior, the manufacturers and recyclers can manipulate the take-back program more efficiently. According to the fitted line, household consumers tend to keep electronic waste longer in storage when they use them less than the normal time. So, they should be motivated by the manufacturers and recyclers to return the e-waste.
Average Storage Time (years)
2.0
5. Conclusion 1.5
1.0
0% 0.5
0%
0.0 5.0
5.5
6.0
6.5
7.0
Average Usage Time Upper Bound (years) Fig. 17. The fitted line represents the relationship between the mean values of Usage Time Upper Bound and Storage Time for household consumers obtained from simulation. The founded equation is Average Storage Time = 5.104–0.6857 Average Usage Time Upper Bound (R2 = 0.98).
accurate. The actual and prediction storage time values are plotted in Fig. 16 with a linear fitted line. The performance is also statistically tested by 2-sample T test and according to the large p-values; there is no evidence for dissimilarity between the actual values and the predicted ones. Now, the main question is how to use these models in managerial decisions. One way to provide more insight is by conducting a
This paper characterized consumers’ behavior toward electronic products utilization and end-of-use storage by analyzing a data set of 10,063 HDDs retuned back to an e-waste collection site in Chicago, IL. The effects of several factors including brand, capacity, and consumer type on storage time have been investigated. In addition, the storage behavior has been predicted through trend projection and machine learning models. In the future, this paper could be enhanced by learning the actual reasons behind stopping electronics usage and storing them by consumers. Further data collection from consumers will bring more insights on how to control the storage time and optimize the EOU recovery system in terms of profitability and sustainability. The study can be further extended to examine and compare other used electronics such as TV, iPad and mobile phone. The results would be helpful to different entities including OEMs, electricity market suppliers, recyclers and policy-makers. Acknowledgements This material is based upon work supported by the National Science Foundation – USA under grant # CMMI-1435908. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Please cite this article in press as: Sabbaghi, M., et al. An investigation of used electronics return flows: A data-driven approach to capture and predict consumers storage and utilization behavior. Waste Management (2014), http://dx.doi.org/10.1016/j.wasman.2014.11.024
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