Systematic continuous improvement model for variation management of key characteristics running with low capability

Gabriela Estrada, Dan Shunk, Feng Ju

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

A systematic continuous improvement model (SCIM) is described in this paper. This model responds to improvements opportunities that were identified in the literature and aerospace companies to aim in variation management of KCs and for developing solutions to improve issues in KCs. This approach helps to identify and improve key characteristics (KCs) in products that most influence in rework and scrap costs, especially in material removal processes. SCIM complies with two purposes; a mathematical method to calculate the rework cost for KCs as a variable in function of expected amount of material to be removed. This cost plus scrap cost is used to prioritise KCs running with low capability; this prioritisation is performed by predicting rework and scrap costs based on historical data of manufacturing processes performance, costs associated to rework and scrap parts out of specification and forecast for product demand. Once critical KCs are identified, the second purpose of this model helps engineers to develop solutions to eliminate what is causing KCs running with low capability; this is possible using knowledge management methodologies to capture, structure and storage solutions developed, in order to reuse them in future similar issues. A case study is presented in this paper to apply this model.

Original languageEnglish (US)
Pages (from-to)2370-2387
Number of pages18
JournalInternational Journal of Production Research
Volume56
Issue number6
DOIs
StatePublished - Mar 19 2018

Keywords

  • key characteristics (KCs)
  • knowledge management
  • material removal processes
  • rework and scrap cost
  • variation management of KCs

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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