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A popular ordering management system is used to emulate the behavior of the system when the game is played with human players.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>\u2013 FTS is tested against some other well-known forecasting systems and it proves to be the best of the lot. It is also shown that it is better to go for higher order FTS for higher tiers, to match auto regressive integrated moving average.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Research limitations\/implications<\/jats:title><jats:p>\u2013 This study fills an important research gap by proving that FTS forecasting system is the best for a supply chain during disruption scenarios. This is important because the forecasting performance deteriorates significantly and the effect is more pronounced in the upstream tiers because of bullwhip effect.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title><jats:p>\u2013 Having a system which works best in all scenarios and also across the tiers in a chain simplifies things for the practitioners. The costs related to acquiring and training comes down significantly.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>\u2013 This study contributes by suggesting a forecasting system which works best for all the tiers and also for every scenario tested and simultaneously significantly improves on the previous studies available in this area.<\/jats:p><\/jats:sec>","DOI":"10.1108\/imds-07-2014-0199","type":"journal-article","created":{"date-parts":[[2015,4,8]],"date-time":"2015-04-08T05:04:10Z","timestamp":1428469450000},"page":"419-435","source":"Crossref","is-referenced-by-count":20,"title":["Fuzzy time series forecasting for supply chain disruptions"],"prefix":"10.1108","volume":"115","author":[{"given":"Felix T.S.","family":"Chan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Avinash","family":"Samvedi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.H.","family":"Chung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020122500240879900_b1","unstructured":"Aiyer, S. and Ledesma, G. (2004), \u201cWaste not, want not, logistics today\u201d, available at: http:\/\/outsourced-logistics.com (accessed April 12, 2013)."},{"key":"key2020122500240879900_b2","doi-asserted-by":"crossref","unstructured":"Asbj\u00f8rnslett, B.E. (2009), \u201cAssessing the vulnerability of supply chains\u201d, Supply Chain Risk , Vol. 124 No. 1, pp. 15-33.","DOI":"10.1007\/978-0-387-79934-6_2"},{"key":"key2020122500240879900_b3","doi-asserted-by":"crossref","unstructured":"Bandyopadhyay, S. and Bhattacharya, R. (2013), \u201cA generalized measure of bullwhip effect in supply chain with ARMA demand process under various replenishment policies\u201d, International Journal of Advance Manufacturing Technology , Vol. 68 No. 6, pp. 963-979.","DOI":"10.1007\/s00170-013-4888-y"},{"key":"key2020122500240879900_b4","unstructured":"Box, G.E.P. , Jenkins, G.M. , Reinsel, G.C. and Jenkins, G. (1994), Time Series Analysis: Forecasting and Control , 3rd ed., Prentice-Hall, Englewood Cliffs, NJ."},{"key":"key2020122500240879900_b5","unstructured":"Business Continuity Institute (2011), \u201cBusiness Continuity Institute survey reveals the high levels and deep-rooted nature of supply chain failure\u201d, available at: www.thebci.org\/index.php?option=com_content&view=article&id=168&Itemid=256 (accessed September 27, 2012)."},{"key":"key2020122500240879900_b154","doi-asserted-by":"crossref","unstructured":"Chen, S.M. (1996), \u201cForecasting enrollments based on fuzzy time series\u201d, Fuzzy Sets and Systems , Vol. 81 No. 3, pp. 311-319.","DOI":"10.1016\/0165-0114(95)00220-0"},{"key":"key2020122500240879900_b7","unstructured":"Chen, H.S. and Chang, W.C. (1998), \u201cA study of optimal grey model GM (1, 1)\u201d, Journal of China Grey System Association , Vol. 1 No. 2, pp. 141-145."},{"key":"key2020122500240879900_b10","doi-asserted-by":"crossref","unstructured":"Craighead, C.W. , Blackhurst, J. , Rungtusanatham, M.J. and Handfield, R.B. (2007), \u201cThe severity of supply chain disruptions: design characteristics and mitigation capabilities\u201d, Decision Sciences , Vol. 38 No. 1, pp. 131-156.","DOI":"10.1111\/j.1540-5915.2007.00151.x"},{"key":"key2020122500240879900_b11","doi-asserted-by":"crossref","unstructured":"Croson, R. and Donohue, K. (2002), \u201cExperimental economics and supply chain management\u201d, Interfaces , Vol. 32 No. 5, pp. 74-82.","DOI":"10.1287\/inte.32.5.74.37"},{"key":"key2020122500240879900_b12","doi-asserted-by":"crossref","unstructured":"Croson, R. and Donohue, K. (2006), \u201cBehavioral causes of the bullwhip effect and the observed value of inventory information\u201d, Management Science , Vol. 52 No. 3, pp. 323-336.","DOI":"10.1287\/mnsc.1050.0436"},{"key":"key2020122500240879900_b13","doi-asserted-by":"crossref","unstructured":"Duc, T.T.H. , Luong, H.T. and Kim, Y.D. (2008), \u201cA measure of bullwhip effect in supply chains with a mixed autoregressive-moving average demand process\u201d, European Journal of Operational Research , Vol. 187 No. 2, pp. 243-256.","DOI":"10.1016\/j.ejor.2007.03.008"},{"key":"key2020122500240879900_b14","doi-asserted-by":"crossref","unstructured":"Fu, T.C. (2011), \u201cA review on time series data mining\u201d, Engineering Applications of Artificial Intelligence , Vol. 24 No. 1, pp. 164-181.","DOI":"10.1016\/j.engappai.2010.09.007"},{"key":"key2020122500240879900_b15","doi-asserted-by":"crossref","unstructured":"Geary, S. , Disney, S.M. and Towill, D.R. (2006), \u201cOn bullwhip in supply chains \u2013 historical review, present practice and expected future impact\u201d, International Journal of Production Economics , Vol. 101 No. 1, pp. 2-18.","DOI":"10.1016\/j.ijpe.2005.05.009"},{"key":"key2020122500240879900_b16","doi-asserted-by":"crossref","unstructured":"Hendricks, K.B. and Singhal, V.R. (2005), \u201cAssociation between supply chain glitches and operating performance\u201d, Management Science , Vol. 51 No. 5, pp. 695-711.","DOI":"10.1287\/mnsc.1040.0353"},{"key":"key2020122500240879900_b19","doi-asserted-by":"crossref","unstructured":"Hsu, L.C. and Wang, C.H. (2007), \u201cForecast the output of integrated circuit industry using a grey model improved by the bayesian analysis\u201d, Technological Forecasting and Social Change , Vol. 74 No. 6, pp. 843-853.","DOI":"10.1016\/j.techfore.2006.02.005"},{"key":"key2020122500240879900_b21","doi-asserted-by":"crossref","unstructured":"Huarng, K. 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