Imperfect production process with learning and forgetting effects

被引:9
作者
Jaber M.Y. [1 ]
Givi Z.S. [1 ]
机构
[1] Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, M5B 2K3, ON
关键词
Forgetting; Imperfect production; Learning; Production; Quality; Rework;
D O I
10.1007/s10287-014-0205-y
中图分类号
学科分类号
摘要
Wright’s learning curve (WLC) assumes every unit of production has an acceptable level of quality, which is not the case in many production environments. Many studies reported that a production process may go out-of-control therefore generating defective items requiring rework. Jaber and Guiffrida (Int J Prod Econ 127(1):27–38, 2004) have modified the WLC by accounting for rework time. In a later study, Jaber and Guiffrida (Eur J Oper Res 189(1):93–104, 2008) allowed for production interruption to restore the quality of the production process to reduce the number of defective items per lot. Although these works were the first analytical models that linked learning to quality, their results cannot be generalized as they considered a single (first) production cycle. This assumption ignores the transfer of learning that occurs between cycles in intermittent production environments. This paper addresses this limitation and considers the knowledge transferred to deteriorate because of forgetting. The results indicate that the performance function of the process has a convex form under certain conditions. The performance of the system improves with faster learning in production and rework, frequent process restorations, and transfer of learning between cycles. © 2014, Springer-Verlag Berlin Heidelberg.
引用
收藏
页码:129 / 152
页数:23
相关论文
共 61 条
  • [1] Argote L., Organizational learning: creating, retaining and transferring knowledge, (2013)
  • [2] Badiru A.B., Multivariate analysis of the effect of learning and forgetting on product quality, Int J Prod Res, 33, 3, pp. 777-794, (1995)
  • [3] Badiru A.B., Ijaduola A.O., Half-life theory of learning curves for system performance analysis, IEEE Syst J, 3, 2, pp. 154-165, (2009)
  • [4] Bapna R., Langer N., Mehra A., Gopal R., Gupta A., Human capital investments and employee performance: an analysis of IT services industry, Manag Sci, 59, 3, pp. 641-658, (2013)
  • [5] Bjork E.L., Bjork R., Making things hard on yourself, but in a good way: creating desirable difficulties to enhance learning, (2011)
  • [6] Burr W., Pearne N., Learning curve theory and innovation, Circuit World, 39, 4, pp. 169-173, (2013)
  • [7] Choo A.S., Linderman K.W., Schroeder R.G., Method and psychological effects on learning behaviors and knowledge creation in quality improvement projects, Manag Sci, 53, 3, pp. 437-450, (2007)
  • [8] El Saadany A., Inventory management in reverse logistics with imperfect production, learning, lost sales, subassemblies, and price/quality considerations, (2009)
  • [9] Feldman L.S., Cao J., Andalib A., Fraser S., Fried G.M., A method to characterize the learning curve for performance of a fundamental laparoscopic simulator task: defining “learning plateau” and “learning rate, Surgery, 146, 2, pp. 381-386, (2009)
  • [10] Ferioli F., Schoots K., Van der Zwaan B., Use and limitations of learning curves for energy technology policy: a component-learning hypothesis, Energy policy, 37, 7, pp. 2525-2535, (2009)