{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T20:46:27Z","timestamp":1759092387236,"version":"3.41.2"},"reference-count":23,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2015,4,7]],"date-time":"2015-04-07T00:00:00Z","timestamp":1428364800000},"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":[[2015,4,7]]},"abstract":"\n Purpose<\/jats:title>\n \u2013 Accurate forecasting of intermittent demand is very important since parts with intermittent demand characteristics are very common. The purpose of this paper is to bring an easier way of handling the hard work of intermittent demand forecasting by using commonly used Excel spreadsheet and also performing parameter optimization. <\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n \u2013 Smoothing parameters of the forecasting methods are optimized dynamically by Excel Solver in order to achieve the best performance. Application is done on real data of Turkish Airlines\u2019 spare parts comprising 262 weekly periods from January 2009 to December 2013. The data set are composed of 500 stock-keeping units, so there are 131,000 data points in total. <\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \u2013 From the results of implementation, it is shown that using the optimum parameter values yields better performance for each of the methods. <\/jats:p>\n <\/jats:sec>\n \n Research limitations\/implications<\/jats:title>\n \u2013 Although it is an intensive study, this research has some limitations. Since only real data are considered, this research is limited to the aviation industry. <\/jats:p>\n <\/jats:sec>\n \n Practical implications<\/jats:title>\n \u2013 This study guides market players by explaining the features of intermittent demand. With the help of the study, decision makers dealing with intermittent demand are capable of applying specialized intermittent demand forecasting methods. <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n \u2013 The study brings simplicity to intermittent demand forecasting work by using commonly used spreadsheet software. The study is valuable for giving insights to market players dealing with items having intermittent demand characteristics, and it is one of the first study which is optimizing the smoothing parameters of the forecasting methods by using spreadsheet in the area of intermittent demand forecasting.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/k-03-2015-0062","type":"journal-article","created":{"date-parts":[[2015,6,8]],"date-time":"2015-06-08T08:55:27Z","timestamp":1433753727000},"page":"576-587","source":"Crossref","is-referenced-by-count":5,"title":["Parameter optimization of intermittent demand forecasting by using spreadsheet"],"prefix":"10.1108","volume":"44","author":[{"given":"Gamze","family":"Ogcu Kaya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omer Fahrettin","family":"Demirel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020122504120895400_b1","doi-asserted-by":"crossref","unstructured":"Al-Saba, T.\n and \n El-Amin, I.M.\n (1999), \u201cArtificial neural networks as applied to long-term demand forecasting\u201d, \n Journal of Artificial Intelligence in Engineering\n , Vol. 13 No. 2, pp. 189-197.","DOI":"10.1016\/S0954-1810(98)00018-1"},{"key":"key2020122504120895400_b2","doi-asserted-by":"crossref","unstructured":"Bartezzaghi, E.\n , \n Verganti, R.\n and \n Zotteri, G.\n (1999), \u201cA simulation framework for forecasting uncertain lumpy demand\u201d, \n International Journal of Production Economics\n , Vol. 59 Nos 1-3, pp. 499-510.","DOI":"10.1016\/S0925-5273(98)00012-7"},{"key":"key2020122504120895400_b3","doi-asserted-by":"crossref","unstructured":"Croston, J.F.\n (1972), \u201cForecasting and stock control for intermittent demands\u201d, \n Operational Research Quarterly\n , Vol. 23 No. 3, pp. 289-304.","DOI":"10.1057\/jors.1972.50"},{"key":"key2020122504120895400_b4","doi-asserted-by":"crossref","unstructured":"Eaves, A.H.C.\n and \n Kingsman, B.G.\n (2004), \u201cForecasting for the ordering and stock-holding of spare parts\u201d, \n Journal of the Operational Research Society\n , Vol. 55 No. 4, pp. 431-437.","DOI":"10.1057\/palgrave.jors.2601697"},{"key":"key2020122504120895400_b5","doi-asserted-by":"crossref","unstructured":"Ghobbar, A.A.\n and \n Friend, C.H.\n (2003), \u201cEvaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model\u201d, \n Computers and Operations Research\n , Vol. 30 No. 14, pp. 2097-2114.","DOI":"10.1016\/S0305-0548(02)00125-9"},{"key":"key2020122504120895400_b6","doi-asserted-by":"crossref","unstructured":"Hua, Z.\n and \n Zhang, B.\n (2007), \u201cA hybrid support vector machines and logistic regression approach for 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and stock control for intermittent demands\u201d, \n Operational Research Society\n , Vol. 24 No. 4, pp. 639-640.","DOI":"10.1057\/jors.1973.120"},{"key":"key2020122504120895400_b13","unstructured":"Segerstedt, A.\n and \n Lev\u00e9n, E.\n (2014), \u201cA study of different Croston-like forecasting methods\u201d, working paper, Lule\u00e5 University of Technology, Lule\u00e5."},{"key":"key2020122504120895400_b201","doi-asserted-by":"crossref","unstructured":"Segura, J.V.\n and \n Vercher, V.\n (2011), \u201cA spreadsheet modeling approach to the Holt-Winters optimal forecasting\u201d, \n European Journal of Operational Research\n , Vol. 131, pp. 375-388.","DOI":"10.1016\/S0377-2217(00)00062-X"},{"key":"key2020122504120895400_b14","unstructured":"Smart, C.N.\n (2001), \u201cForecasting \u2013 as important as ever\u201d, \n APICS \u2013 The Performance Advantage\n , Vol. 11, pp. 32-37."},{"key":"key2020122504120895400_b15","doi-asserted-by":"crossref","unstructured":"Syntetos, A.A.\n and 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