{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T12:50:10Z","timestamp":1753879810116,"version":"3.41.2"},"reference-count":36,"publisher":"ASME International","issue":"2","content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2012,6,1]]},"abstract":"<jats:p>Detailed evaluation of a proposed engineering change (EC) or its effects is a time-consuming process requiring considerable user experience and expertise. Therefore, enterprises plan detailed evaluation of only those EC effects that might have a significant impact. Since similar ECs are likely to have similar effects and impacts, past EC knowledge can be utilized for determining whether the proposed EC effect has significant impact. This paper presents an approach for predicting the impact of proposed EC effect based on past ECs that are similar to it. Our approach accounts for the differences in context of impact between attribute values in two changes. The Bayes\u2019 rule is utilized to determine differences in impact value based on the differences in attribute values. The probability values required in Bayes\u2019 rule are determined based on the principle of minimum cross entropy. An example EC knowledge base is created and utilized to evaluate our approach against two state-of-the-art approaches, namely k-nearest neighbor (NN) and regularized local similarity discriminant analysis (SDA). The success rate in predicting impact is used as an evaluation metric. The results show that there is a statistically significant improvement in success rate obtained using our approach as compared to those obtained using the two state-of-the-art approaches. The results also show that for a very large number of proposed ECs, i.e., N\u2009&gt;\u2009100, the success rate in predicting impact using our approach shall be greater than that obtained using the two state-of-the-art approaches.<\/jats:p>","DOI":"10.1115\/1.4005593","type":"journal-article","created":{"date-parts":[[2012,3,19]],"date-time":"2012-03-19T18:06:15Z","timestamp":1332180375000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":3,"title":["An Approach to Predict Impact of Proposed Engineering Change Effect"],"prefix":"10.1115","volume":"12","author":[{"given":"Chandresh","family":"Mehta","sequence":"first","affiliation":[{"name":"Mechanical Engineering,University of Michigan, Ann Arbor, MI 48105"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lalit","family":"Patil","sequence":"additional","affiliation":[{"name":"Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Debasish","family":"Dutta","sequence":"additional","affiliation":[{"name":"Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"33","published-online":{"date-parts":[[2012,5,14]]},"reference":[{"key":"2019100512082181400_c1","doi-asserted-by":"crossref","DOI":"10.1115\/DETC2005-85267","article-title":"Systematic Decision Support for Engineering Change Management in PLM","volume-title":"Proceedings of Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference","author":"Joshi"},{"key":"2019100512082181400_c2","first-page":"40","article-title":"Managing Obsolescence: Value Engineering Change Proposal Proves Its Worth","volume":"XXXIII","author":"Gunther","journal-title":"Defense AT&L"},{"key":"2019100512082181400_c3","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1016\/S0924-0136(03)00524-7","article-title":"Current Practice of Engineering Change Management in Hong Kong Manufacturing Industries","volume":"139","author":"Huang","journal-title":"J. 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