Forecasting product returns for remanufacturing systems
Tóm tắt
One of the major challenges that a remanufacturer faces at strategic planning level today is to match its supply (returned items) with demand due to the inherited uncertainties and variations on both sides. Forecasting product returns is one of the most important tasks of this matching process. Unlike forecasting for traditional manufacturing systems, both quantity and quality forecasts are critical since return timing, quantity, and the quality of returned products can all vary dramatically. This research develops a forecasting method which incorporates knowledge from related sales, product usage, customer return behavior, and product life expectancy information to provide a more accurate prediction of product returns. The models are validated using Monte Carlo simulations. Numerical cases are also presented to illustrate its usage and some important insights.
Tài liệu tham khảo
Lund RT: Remanufacturing. Technol. Rev. 1984,87(2):18.
Sutherland JW, Adler DP, Haapala KR, Kumar VA: Comparison of manufacturing and remanufacturing energy intensities with application to diesel engine production. CIRP. Annals–Manuf. Technol. 2008, 57: 5–8. 10.1016/j.cirp.2008.03.004
Guide VDR: Production planning and control for remanufacturing: industry practice and research needs. J. Oper. Manag. 2000,18(4):467–483. 10.1016/S0272-6963(00)00034-6
Westkamper E, Arndt G: Life cycle management and assessment: approaches and visions towards sustainable manufacturing. CIRP. Annals- Manuf. Technol. 2000,49(2):501–526. 10.1016/S0007-8506(07)63453-2
Hauser W, Lund RT: The Remanufacturing Industry: Anatomy of a Giant. Department of Manufacturing Engineering. Boston University, Boston, USA; 2003.
Srivastava SK, Srivastava RK: Managing product returns for reverse logistics. Int. J. Physical. Distri. Logist. Manage. 2006,36(7):524–546. 10.1108/09600030610684962
Buxcey I: Remanufacturing in the Automotive Industry. [http://www.remanufacturing.org.uk/pdf/remanufacturing_in_automotive_industry_ian_buxcey_automotive_parts_rebuilders_associatio.pdf?-session=RemanSession:42F940711b84a138FAnHU1EE5043], Automotive Parts Remanufacturers Association 2003., Buxcey I: Remanufacturing in the Automotive Industry. , Automotive Parts Remanufacturers Association 2003. [http://www.remanufacturing.org.uk/pdf/remanufacturing_in_automotive_industry_ian_buxcey_automotive_parts_rebuilders_associatio.pdf?-session=RemanSession:42F940711b84a138FAnHU1EE5043]
Hammond R, Amezquita T, Bras B: Issues in the automotive parts remanufacturing industry: a discussion of results from surveys performed among remanufacturers. Eng. Des. Autom. 1998, 4: 27–46.
Marx-Gómez J, Rautenstrauch C, Nürnberger A, Kruse R: Neuro-fuzzy approach to forecast returns of scrapped products to recycling and remanufacturing. Knowl.-Based Syst. 2002,15(1):119–128. 10.1016/S0950-7051(01)00128-9
Guide VDR, Jayaraman V, Srivastava R: Production planning and control for remanufacturing: a state-of-the-art survey. Robot. Comput. Integr. Manuf. 1999, 15: 221–230. 10.1016/S0736-5845(99)00020-4
Ghoreishi N, Mark JJ, Nekouzadeh A: A cost model for optimizing the take back phase of used product recovery. J. Remanuf. 2011,1(1):1–15. 10.1186/2210-4690-1-1
Jin X, Hu SJ, Ni J, Xiao G: Assembly strategies for product remanufacturing with variable quality returns. IEEE Trans. Autom. Sci. Eng. 2013,10(1):76–85. 10.1109/TASE.2012.2217741
Jin X, Ni J, Koren Y: Optimal control of reassembly with variable quality returns in a product remanufacturing system. CIRP. Annals-Manuf. Technol. 2011,60(1):25–28. 10.1016/j.cirp.2011.03.133
Toktay B: Forecasting product returns, in Business Aspects of Closed-Loop Supply Chains. Carnegie Mellon University Press, Pittsburgh, PA; 2003.
Nahmias S: Production and Operations Analysis. Irwin Professional Publishing, Glencoe, IL; 1993.
Toktay, B, van der Laan, E, de Brito, MP: Managing Product Returns: The Role of Forecasting (22 2003 4,). ERIM Report Series Reference No. ERS-2003–023-LIS
Clottey T, Benton WC, Srivastava R: Forecasting product returns for remanufacturing operations. Decis. Sci. 2012,43(4):589–614. 10.1111/j.1540-5915.2012.00362.x
Kelle P, Silver EA: Forecasting the returns of reusable containers. J. Oper. Manag. 1989, 8: 17–35. 10.1016/S0272-6963(89)80003-8
Goh T, Varaprasad N: A statistical methodology for the analysis of the life-cycle of reusable containers. IIE Trans. 1986, 18: 42–47. 10.1080/07408178608975328
Toktay B, Wein LM, Zenios SA: Inventory management of remanufacturable products. Manag. Sci. 2000, 46: 1412–1426. 10.1287/mnsc.46.11.1412.12082
Hanafi J, Sami K, Hartmut K: Generating fuzzy coloured petri net forecasting model to predict the return of products. In Electronics & the Environment, Proceedings of the 2007 IEEE International Symposium on. IEEE, Orlando, FL; 2007:245–250. 10.1109/ISEE.2007.369402
Urban GL, Weinberg BD, Hauser JR: Premarket forecasting of really-new products. J. Mark. 1996,60(1):47–60. 10.2307/1251887
Mahajan V, Muller E, Bass F: Diffusion of new products: empirical generalizations and managerial uses. Mark. Sci. 1995,14(3):79–88. 10.1287/mksc.14.3.G79
Lilien, GL, Rangaswamy, A: Marketing engineering: computer-assisted marketing analysis and planning. Decision Pro. 253–261 (2004)
Rinne, H: The Weibull distribution: a handbook, 1st edn. Chapman and Hall/CRC, (2008)
Pinder JE III, Wiener JG, Smith MH: The Weibull distribution: a new method of summarizing survivorship data. Ecology 1978, 59: 175–179. 10.2307/1936645
Nawaz SM, Nazrul Islam M: The Weibull distribution as a general model for forecasting technological change. Technol. Forecast Soc. Change 1980, 18.3: 247–256.
Smith SW: The scientist and engineer’s guide to digital signal processing. California Technical Publishing 1997, 15: 277.