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Research Papers: Design Automation

Simulation Modeling of Consumers' Participation in Product Take-Back Systems

[+] Author and Article Information
Ardeshir Raihanian Mashhadi

Department of Mechanical and
Aerospace Engineering,
University at Buffalo—SUNY,
Amherst, NY 14260
e-mail: ardeshir@buffalo.edu

Behzad Esmaeilian

Industrial and Systems Engineering,
Northern Illinois University,
DeKalb, IL 60115
e-mail: besmaeilian@niu.edu

Sara Behdad

Department of Mechanical and
Aerospace Engineering,
Industrial and Systems Engineering Department,
University at Buffalo—SUNY,
Amherst, NY 14260
e-mail: sarabehd@buffalo.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received April 14, 2015; final manuscript received February 10, 2016; published online March 21, 2016. Assoc. Editor: Gul E. Okudan Kremer.

J. Mech. Des 138(5), 051403 (Mar 21, 2016) (11 pages) Paper No: MD-15-1290; doi: 10.1115/1.4032773 History: Received April 14, 2015; Revised February 10, 2016

As electronic waste (e-waste) becomes one of the fastest growing environmental concerns, remanufacturing may be a promising solution. However, the profitability of take-back systems is hampered by several factors, including the lack of information on the quantity and timing of to-be-returned used products to a remanufacturing facility. Factors that contribute to this unpredictability in the waste stream include product design features, consumers' awareness of recycling opportunities, sociodemographic characteristics, peer pressure, and the tendency of consumers to keep used items in storage. A system that helps predict when the consumer will stop using a product and store, resell, recycle, or discard it could help manufacturers better estimate return trends. The objective of this paper is to develop an agent-based simulation (ABS) framework that integrates a discrete choice analysis (DCA) technique to predict consumer end-of-use (EOU) decisions. The proposed simulation tools examine the impact of design features, interaction among individual consumers, and sociodemographic criteria related to the number of e-product returns. A numerical example of a cellphone take-back system has been provided to show the application of the model.

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Figures

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Fig. 1

Simulation environment: agents and their networks. Squares represent products and circles represent consumers. Colors illustrate different states of the agents. Bright (red) circles represent the consumers who have not purchased a product yet, and bright (yellow) squares demonstrate the products that have not been assigned to a consumer. Turning dark (blue) indicates the change of state. (See online version for color.)

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Fig. 2

Different states for a consumer decision including product adoption (wantProduct), usage phase (useProduct) and EOU phase decisions (Store, Throw Away, Return, Sell). The dashed arrow indicates the default option in case of a tie.

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Fig. 3

Three subgroups of consumers with different levels of environmental concerns: Consumers may migrate from regular to green level (A) and from green level (A) to green level (B)

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Fig. 5

Scenario I, case II: the number of products stored, returned, sold, and thrown away over time. With consideration of interactions (peer pressure and awareness factors) between consumers.

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Fig. 4

Scenario I, case I: The number of products stored, returned, sold, and thrown away over time. No consideration of interactions (peer pressure and awareness factors) between consumers.

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Fig. 11

Comparison of the number of returns and trash for two cases within scenario II

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Fig. 6

Comparison of the number of returns and stored products for two cases within scenario I

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Fig. 7

Sensitivity analysis of the impact of awareness (mean value of the distribution of β) on the number of returns

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Fig. 8

Sensitivity analysis of the impact of peer pressure (mean value of the distribution of β) on the number of returns

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Fig. 9

Scenario II, case I: The number of products stored, returned, sold, and thrown away over time. No consideration of the impact of environmental friendliness level.

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Fig. 10

Scenario II, case II: The number of products stored, returned, sold, and thrown away over time. With consideration of the impact of different levels of environmental friendliness.

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Fig. 12

The effect of ratio of products with a data security feature on returns for the case that the buy-back price coefficient is μβReturn (buyback price) = 0.02

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Fig. 13

The effect of ratio of products with a data security feature on returns for the case that the buy-back price coefficient is μβReturn (buyback price) = 0.06

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Fig. 14

The effect of ratio of products with a data security feature on returns for the case that the buy-back price coefficient is μβReturn (buyback price)= 0.1

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