TY - GEN
T1 - Varying lifecycle lengths within a portfolio for product take-back
AU - Zhao, Yuan
AU - Pandey, Vijitashwa
AU - Kim, Harrison Hyung Min
AU - Thurston, Deborah L
PY - 2010/6/25
Y1 - 2010/6/25
N2 - Product take back and reuse is sometimes at odds with the rapidly evolving desires of some customers. For other customers, the environmental benefits of reuse more than compensate for minor drawbacks. "Selling a service" (rather than a product) through leasing enables the manufacturer to control the timing and quality of product take-back, but current methods assume a fixed leasing period. What is needed is a method for fine-tuning the time span of the customer's life cycle in order to provide each market segment the combination of features it most desires. This paper presents a new method for performing long range product planning so that the manufacturer can determine optimal take-back times, end-of-life design decisions, and number of lifecycles. The method first determines a Pareto optimal frontier over price, environmental impact and reliability using a genetic algorithm. Then, a multiattribute utility function is employed to maximize utility across different segments of the market, and also across different lifecycles within each segment. The proposed methodology is illustrated through an example involving personal computers.
AB - Product take back and reuse is sometimes at odds with the rapidly evolving desires of some customers. For other customers, the environmental benefits of reuse more than compensate for minor drawbacks. "Selling a service" (rather than a product) through leasing enables the manufacturer to control the timing and quality of product take-back, but current methods assume a fixed leasing period. What is needed is a method for fine-tuning the time span of the customer's life cycle in order to provide each market segment the combination of features it most desires. This paper presents a new method for performing long range product planning so that the manufacturer can determine optimal take-back times, end-of-life design decisions, and number of lifecycles. The method first determines a Pareto optimal frontier over price, environmental impact and reliability using a genetic algorithm. Then, a multiattribute utility function is employed to maximize utility across different segments of the market, and also across different lifecycles within each segment. The proposed methodology is illustrated through an example involving personal computers.
UR - http://www.scopus.com/inward/record.url?scp=77953786497&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77953786497&partnerID=8YFLogxK
U2 - 10.1115/DETC2009-87625
DO - 10.1115/DETC2009-87625
M3 - Conference contribution
AN - SCOPUS:77953786497
SN - 9780791849057
T3 - Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2009, DETC2009
SP - 323
EP - 335
BT - Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2009, DETC2009
T2 - 2009 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC2009
Y2 - 30 August 2009 through 2 September 2009
ER -