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

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 hel...

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Bibliographic Details
Published in:International journal of production research 2018-03, Vol.56 (6), p.2370-2387
Main Authors: Estrada, Gabriela, Shunk, Dan L., Ju, Feng
Format: Article
Language:English
Subjects:
Continuous improvement
Costs
Economic forecasting
Knowledge management
Mathematical models
Performance prediction
Scrap
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Summary: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.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2017.1369599