{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T18:10:22Z","timestamp":1754158222313,"version":"3.41.2"},"reference-count":39,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2016,2,1]],"date-time":"2016-02-01T00:00:00Z","timestamp":1454284800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,2,1]]},"abstract":"\n Purpose<\/jats:title>\n \u2013 The purpose of this paper is to establish a combined model for selecting key indexes of complex equipment, and then improve the cost forecasting precision of the model. The problem how to choose the key elements of complex products has always been concerned on many fields, such as cost assessment, investment decision making, etc. <\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n \u2013 Using Grey System Theory to establish a cost estimation model of complicated equipment is more reasonable under the few data and poor information. Therefore, this paper constructs cost index\u2019s system of complex equipment, and then quantitative and qualitative analysis methods are utilized to calculate the grey entropy between the characteristic parameter and the behavior parameters. Further, establish the grey relational clustering matrix of the behavior sequences by using the grey relative incidence analysis. Finally, the authors select key indicators according to the grey degree. <\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \u2013 The experiment demonstrates that the cost key parameters of complex equipment can be successfully screened out by the proposed approach, and the cost estimation accuracy of complicated products is improved. <\/jats:p>\n <\/jats:sec>\n \n Practical implications<\/jats:title>\n \u2013 The method proposed in this paper could be utilized to solve some practical problems, particularly the selection of cost critical parameters for complex products with few samples and poor information. Taking the cost key indexes of civil aircraft as an example, the results verified the validity of the GICM model. <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n \u2013 In this paper, the authors develop the method of GICM model. Taking the data of civil aircraft as an example, the authors screen the key indicators of complex products successfully, and improve the prediction accuracy of the GM (1, N) model by using the selected parameters, which provides a reference for some firms.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/gs-03-2016-0008","type":"journal-article","created":{"date-parts":[[2016,5,9]],"date-time":"2016-05-09T06:43:08Z","timestamp":1462776188000},"page":"110-123","source":"Crossref","is-referenced-by-count":9,"title":["Key indexes choosing approach of complex equipment\u2019s development cost based on grey incidence cluster model"],"prefix":"10.1108","volume":"6","author":[{"given":"Naiming","family":"Xie","sequence":"first","affiliation":[]},{"given":"Chuanzhen","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Songming","family":"Yin","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2020121804494643900_b27","doi-asserted-by":"crossref","unstructured":"Chang, P.-C.\n , \n Lin, J.-J.\n and \n Dzan, W.-Y.\n (2012), \u201cForecasting of manufacturing cost in mobile phone products by case-based reasoning and artificial neural network models\u201d, \n Journal of Intelligent Manufacturing\n , Vol. 23 No. 1, pp. 517-531.","DOI":"10.1007\/s10845-010-0390-7"},{"key":"key2020121804494643900_b31","doi-asserted-by":"crossref","unstructured":"Chang, Y.K.\n , \n Kim, H.\n and \n Kang, J.S.\n (2013), \u201cDevelopment of reliability-corrected cost model for small earth observation satellites\u201d, \n Acta Astronautica\n , Vol. 88 No. 1, pp. 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