Imperfect production process with learning and forgetting effects

M. Jaber and Z. Givi ()

Computational Management Science, 2015, vol. 12, issue 1, 129-152

Abstract: 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. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Learning; Forgetting; Production; Rework; Quality; Imperfect production (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s10287-014-0205-y (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:comgts:v:12:y:2015:i:1:p:129-152

Ordering information: This journal article can be ordered from
http://www.springer. ... ch/journal/10287/PS2

DOI: 10.1007/s10287-014-0205-y

Access Statistics for this article

Computational Management Science is currently edited by Ruediger Schultz

More articles in Computational Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().