{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T18:12:34Z","timestamp":1771006354729,"version":"3.50.1"},"reference-count":27,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2013,5,17]],"date-time":"2013-05-17T00:00:00Z","timestamp":1368748800000},"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":[[2013,5,17]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>Intermittent demand appears sporadically, with some time periods not even displaying any demand at all. Even so, such patterns constitute considerable proportions of the total stock in many industrial settings. Forecasting intermittent demand is a rather difficult task but of critical importance for corresponding cost savings. The current study aims to examine the empirical outcomes of three heuristics towards the modification of established intermittent demand forecasting approaches.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>First, optimization of the smoothing parameter used in Croston's approach is empirically explored, in contrast to the use of an a priori fixed value as in earlier studies. Furthermore, the effect of integer rounding of the resulting forecasts is considered. Lastly, the authors evaluate the performance of Theta model as an alternative of SES estimator for extrapolating demand sizes and\/or intervals. The proposed heuristics are implemented into the forecasting support system.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>The experiment is performed on 3,000 real intermittent demand series from the automotive industry, while evaluation is made both in terms of bias and accuracy. Results indicate increased forecasting performance.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>The current research explores some very simple heuristics which have a positive impact on the accuracy of intermittent demand forecasting approaches. While some of these issues have been partially explored in the past, the current research focuses on a complete in\u2010depth analysis of easy\u2010to\u2010employ modifications to well\u2010established intermittent demand approaches. By this, the authors enable the application of such heuristics in an industrial environment, which may lead to significant inventory and production cost reductions and other benefits.<\/jats:p><\/jats:sec>","DOI":"10.1108\/02635571311324142","type":"journal-article","created":{"date-parts":[[2013,5,15]],"date-time":"2013-05-15T12:20:28Z","timestamp":1368620428000},"page":"683-696","source":"Crossref","is-referenced-by-count":23,"title":["Empirical heuristics for improving intermittent demand forecasting"],"prefix":"10.1108","volume":"113","author":[{"given":"Fotios","family":"Petropoulos","sequence":"first","affiliation":[]},{"given":"Konstantinos","family":"Nikolopoulos","sequence":"additional","affiliation":[]},{"given":"Georgios P.","family":"Spithourakis","sequence":"additional","affiliation":[]},{"given":"Vassilios","family":"Assimakopoulos","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022013020154281400_b1","doi-asserted-by":"crossref","unstructured":"Ali, M.M., Boylan, J.E. and Syntetos, A.A. (2012), \u201cForecast errors and inventory performance under forecast information sharing\u201d, International Journal of Forecasting, Vol. 28, pp. 830\u2010841.","DOI":"10.1016\/j.ijforecast.2010.08.003"},{"key":"key2022013020154281400_b2","doi-asserted-by":"crossref","unstructured":"Assimakopoulos, V. and Nikolopoulos, N. (2000), \u201cThe theta model: a decomposition approach to forecasting\u201d, International Journal of Forecasting, Vol. 16, pp. 521\u2010530.","DOI":"10.1016\/S0169-2070(00)00066-2"},{"key":"key2022013020154281400_b3","doi-asserted-by":"crossref","unstructured":"Babai, M.Z., Ali, M.M. and Nikolopoulos, K. (2012), \u201cImpact of temporal aggregation on stock control performance of intermittent demand estimators: empirical analysis\u201d, Omega, Vol. 40, pp. 713\u2010721.","DOI":"10.1016\/j.omega.2011.09.004"},{"key":"key2022013020154281400_b4","unstructured":"Brown, R. (1959), Statistical Forecasting for Inventory Control, McGraw\u2010Hill, New York, NY."},{"key":"key2022013020154281400_b5","doi-asserted-by":"crossref","unstructured":"Croston, J.D. (1972), \u201cForecasting and stock control for intermittent demands\u201d, Operational Research Quarterly, Vol. 23, pp. 289\u2010303.","DOI":"10.1057\/jors.1972.50"},{"key":"key2022013020154281400_b6","doi-asserted-by":"crossref","unstructured":"De Gooijer, J.G. and Hyndman, R.J. (2005), \u201c25 years of time series forecasting\u201d, International Journal of Forecasting, Vol. 22, pp. 443\u2010473.","DOI":"10.1016\/j.ijforecast.2006.01.001"},{"key":"key2022013020154281400_b7","doi-asserted-by":"crossref","unstructured":"Fildes, R., Nikolopoulos, K., Crone, S.F. and Syntetos, A.A. (2008), \u201cForecasting and operational research: a review\u201d, Journal of the Operational Research Society, Vol. 59, pp. 1150\u20101172.","DOI":"10.1057\/palgrave.jors.2602597"},{"key":"key2022013020154281400_b8","doi-asserted-by":"crossref","unstructured":"Huang, L.T., Hsieh, I.C. and Farn, C.K. (2011), \u201cOn ordering adjustment policy under rolling forecast in supply chain planning\u201d, Computers & Industrial Engineering, Vol. 60, pp. 397\u2010410.","DOI":"10.1016\/j.cie.2010.07.018"},{"key":"key2022013020154281400_b9","doi-asserted-by":"crossref","unstructured":"Hummel, J.W. and Jesse, R.R. (1990), \u201cA spreadsheet heuristic approach for the stocking and retention of slow\u2010moving, obsolescent items\u201d, Computers & Industrial Engineering, Vol. 18, pp. 163\u2010173.","DOI":"10.1016\/0360-8352(90)90027-J"},{"key":"key2022013020154281400_b10","doi-asserted-by":"crossref","unstructured":"Hyndman, R.J. and Koehler, A.B. (2006), \u201cAnother look at measures of forecast accuracy\u201d, International Journal of Forecasting, Vol. 22, pp. 679\u2010688.","DOI":"10.1016\/j.ijforecast.2006.03.001"},{"key":"key2022013020154281400_b11","doi-asserted-by":"crossref","unstructured":"Johnston, F.R. and Boylan, J.E. (1996), \u201cForecasting intermittent demand: a comparative evaluation of Croston's method\u201d, International Journal of Forecasting, Vol. 12, pp. 297\u2010298.","DOI":"10.1016\/0169-2070(95)00642-7"},{"key":"key2022013020154281400_b12","doi-asserted-by":"crossref","unstructured":"Johnston, F.R., Boylan, J.E. and Shale, E.A. (2003), \u201cAn examination of the size of orders from customers, their characterization and the implications for inventory control of slow moving items\u201d, Journal of the Operational Research Society, Vol. 54, pp. 833\u2010837.","DOI":"10.1057\/palgrave.jors.2601586"},{"key":"key2022013020154281400_b14","doi-asserted-by":"crossref","unstructured":"Makridakis, S. and Hibon, M. (2000), \u201cThe M3\u2010competition: results, conclusions and implications\u201d, International Journal of Forecasting, Vol. 16, pp. 451\u2010476.","DOI":"10.1016\/S0169-2070(00)00057-1"},{"key":"key2022013020154281400_b13","unstructured":"Makridakis, S., Wheelwright, S.C. and Hyndman, R.J. (1998), Forecasting: Methods and Applications, 3rd ed., Wiley, New York, NY."},{"key":"key2022013020154281400_b15","unstructured":"Nikolopoulos, K., Syntetos, A.A. and Babai, M.Z. (2007), \u201cA new intermittent demand approach via combining Croston's method and the theta model\u201d, paper presented at the 22nd European Conference on Operational Research EURO XXII, Prague, Czech Republic, July 8\u201011."},{"key":"key2022013020154281400_b16","doi-asserted-by":"crossref","unstructured":"Nikolopoulos, K., Syntetos, A.A., Boylan, J.E., Petropoulos, F. and Assimakopoulos, V. (2011), \u201cAn aggregate disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis\u201d, Journal of the Operational Research Society, Vol. 62, pp. 544\u2010554.","DOI":"10.1057\/jors.2010.32"},{"key":"key2022013020154281400_b17","doi-asserted-by":"crossref","unstructured":"Snyder, R. (2002), \u201cForecasting sales of slow and fast moving inventories\u201d, European Journal of Operational Research, Vol. 140, pp. 684\u2010699.","DOI":"10.1016\/S0377-2217(01)00231-4"},{"key":"key2022013020154281400_b19","unstructured":"Syntetos, A.A. (2001), \u201cForecasting for intermittent demand\u201d, unpublished PhD thesis, Brunel University, Uxbridge."},{"key":"key2022013020154281400_b18","doi-asserted-by":"crossref","unstructured":"Syntetos, A.A. and Boylan, J.E. (2001), \u201cOn the bias of intermittent demand estimates\u201d, International Journal of Production Economics, Vol. 71, pp. 457\u2010466.","DOI":"10.1016\/S0925-5273(00)00143-2"},{"key":"key2022013020154281400_b20","doi-asserted-by":"crossref","unstructured":"Syntetos, A.A. and Boylan, J.E. (2005), \u201cThe accuracy of intermittent demand estimates\u201d, International Journal of Forecasting, Vol. 21, pp. 303\u2010314.","DOI":"10.1016\/j.ijforecast.2004.10.001"},{"key":"key2022013020154281400_b22","doi-asserted-by":"crossref","unstructured":"Syntetos, A.A. and Boylan, J.E. (2010), \u201cOn the variance of intermittent demand estimates\u201d, International Journal of Production Economics, Vol. 128, pp. 546\u2010555.","DOI":"10.1016\/j.ijpe.2010.07.005"},{"key":"key2022013020154281400_b21","doi-asserted-by":"crossref","unstructured":"Syntetos, A.A., Boylan, J.E. and Croston, J.D. (2005), \u201cOn the categorization of demand patterns\u201d, Journal of the Operational Research Society, Vol. 56, pp. 495\u2010503.","DOI":"10.1057\/palgrave.jors.2601841"},{"key":"key2022013020154281400_b23","doi-asserted-by":"crossref","unstructured":"Syntetos, A.A., Nikolopoulos, K. and Boylan, J.E. (2010), \u201cJudging the judges through accuracy\u2010implication metrics: the case of inventory forecasting\u201d, International Journal of Forecasting, Vol. 26, pp. 134\u2010143.","DOI":"10.1016\/j.ijforecast.2009.05.016"},{"key":"key2022013020154281400_b24","doi-asserted-by":"crossref","unstructured":"Teunter, R. and Sani, B. (2009), \u201cOn the bias of Croston's forecasting method\u201d, European Journal of Operational Research, Vol. 194, pp. 177\u2010183.","DOI":"10.1016\/j.ejor.2007.12.001"},{"key":"key2022013020154281400_b25","doi-asserted-by":"crossref","unstructured":"Teunter, R.H., Syntetos, A.A. and Babai, M.Z. (2011), \u201cIntermittent demand: linking forecasting to inventory obsolescence\u201d, European Journal of Operational Research, Vol. 214, pp. 606\u2010615.","DOI":"10.1016\/j.ejor.2011.05.018"},{"key":"key2022013020154281400_b26","doi-asserted-by":"crossref","unstructured":"Willemain, T.R., Smart, C.N., Shockor, J.H. and DeSautels, P.A. (1994), \u201cForecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method\u201d, International Journal of Forecasting, Vol. 10, pp. 529\u2010538.","DOI":"10.1016\/0169-2070(94)90021-3"},{"key":"key2022013020154281400_b27","doi-asserted-by":"crossref","unstructured":"Williams, T.M. (1984), \u201cStock control with sporadic and slow\u2010moving demand\u201d, Journal of the Operational Research Society, Vol. 35, pp. 939\u2010948.","DOI":"10.1057\/jors.1984.185"}],"container-title":["Industrial Management &amp; Data Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.emeraldinsight.com\/doi\/full-xml\/10.1108\/02635571311324142","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/02635571311324142\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/02635571311324142\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T23:38:52Z","timestamp":1753400332000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/imds\/article\/113\/5\/683-696\/176587"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,5,17]]},"references-count":27,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2013,5,17]]}},"alternative-id":["10.1108\/02635571311324142"],"URL":"https:\/\/doi.org\/10.1108\/02635571311324142","relation":{},"ISSN":["0263-5577"],"issn-type":[{"value":"0263-5577","type":"print"}],"subject":[],"published":{"date-parts":[[2013,5,17]]}}}