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International Journal of Production Research Volume 45, 2007 - Issue 10
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Original Articles

Demand planning approaches to aggregating and forecasting interrelated demands for safety stock and backup capacity planning

Argon Chen Graduate Institute of Industrial Engineering, National Taiwan University, 1 Roosevelt Road, Sec. 4, Taipei, Taiwan 106Correspondence[email protected] [email protected]
,
C.-H. Hsu Graduate Institute of Industrial Engineering, National Taiwan University, 1 Roosevelt Road, Sec. 4, Taipei, Taiwan 106
&
J. Blue Graduate Institute of Industrial Engineering, National Taiwan University, 1 Roosevelt Road, Sec. 4, Taipei, Taiwan 106
Pages 2269-2294 | Received 01 Mar 2006, Published online: 27 Apr 2007

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Read on this site (5)

Chen-Fu Chien, Hsuan-An Kuo & Yun-Siang Lin. (2022) Smart semiconductor manufacturing for pricing, demand planning, capacity portfolio and cost for sustainable supply chain management. International Journal of Logistics Research and Applications 0:0, pages 1-24.
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M. Zied Babai, John E. Boylan & Bahman Rostami-Tabar. (2022) Demand forecasting in supply chains: a review of aggregation and hierarchical approaches. International Journal of Production Research 60:1, pages 324-348.
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Ying-Jen Chen & Chen-Fu Chien. (2018) An empirical study of demand forecasting of non-volatile memory for smart production of semiconductor manufacturing. International Journal of Production Research 56:13, pages 4629-4643.
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Sanjay Sharma & Aditya Malhotra. (2015) Safety stock calculations and inventory analysis: a practical approach for the FMCG case in a South-East Asian country. International Journal of Advanced Logistics 4:3, pages 131-144.
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Alex J. Ruiz-Torres & Farzad Mahmoodi. (2010) Safety stock determination based on parametric lead time and demand information. International Journal of Production Research 48:10, pages 2841-2857.
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Articles from other publishers (7)

Marina Meireles Pereira & Enzo Morosini Frazzon. (2021) A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains. International Journal of Information Management 57, pages 102165.
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Kittiwat Sirikasemsuk & Huynh Trung Luong. (2017) Measure of bullwhip effect in supply chains with first-order bivariate vector autoregression time-series demand model. Computers & Operations Research 78, pages 59-79.
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M.M. Monsreal, J.A. Royo & M.P. Lambán. (2014) Order Variability Decomposition: A New Variability Measure on Real Data. Journal of Applied Research and Technology 12:4, pages 695-703.
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Bahman Rostami-Tabar, M. Zied Babai, Aris Syntetos & Yves Ducq. (2013) Demand forecasting by temporal aggregation. Naval Research Logistics (NRL) 60:6, pages 479-498.
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B. Rostami Tabar, M.Z. Babai, A.A. Syntetos & Y. Ducq. (2013) The Impact of Temporal Aggregation on Demand Forecasting of ARMA(1,1) Process: Theoretical Analysis. IFAC Proceedings Volumes 46:9, pages 1696-1701.
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James J. Cochran, Louis A. CoxJr.Jr., Pinar Keskinocak, Jeffrey P. Kharoufeh & J. Cole SmithMelike Baykal‐Gürsoy & Nesim K. Erkip. 2011. Wiley Encyclopedia of Operations Research and Management Science. Wiley Encyclopedia of Operations Research and Management Science.
Argon Chen & Jakey Blue. (2010) Performance analysis of demand planning approaches for aggregating, forecasting and disaggregating interrelated demands. International Journal of Production Economics 128:2, pages 586-602.
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