EJOR review paper-manuscript
暂无分享,去创建一个
[1] O. D. Anderson,et al. On a lemma associated with Box, Jenkins and Granger , 1975 .
[2] A. A. Weiss. Systematic sampling and temporal aggregation in time series models , 1984 .
[3] Helmut Lütkepohl,et al. Forecasting Contemporaneously Aggregated Vector ARMA Processes , 1984 .
[4] F. R. Johnston,et al. Modelling and the estimation of changing relationships , 1986 .
[5] Richard Withycombe,et al. Forecasting with combined seasonal indices , 1989 .
[6] Adamantios Diamantopoulos,et al. Judgemental revision of sales forecasts: The relative performance of judgementally revised versus non‐revised forecasts , 1992 .
[7] Luiz Koodi Hotta,et al. The Effect of Overlapping Aggregation on Time Series Models: An Application to the Unemployment Rate in Brazil , 1992 .
[8] Byron J. Dangerfield,et al. Top-down or bottom-up: Aggregate versus disaggregate extrapolations , 1992 .
[9] T. Willemain,et al. Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method , 1994 .
[10] J. Boylan,et al. Forecasting for Items with Intermittent Demand , 1996 .
[11] Stefano Ronchi,et al. Clustering customers to forecast demand , 2005 .
[12] Clive W. J. Granger,et al. Time series modeling and interpretation , 2001 .
[13] Layth C. Alwan,et al. Stochastic characterization of upstream demand processes in a supply chain , 2003 .
[14] Srinivasan Raghunathan,et al. Impact of demand correlation on the value of and incentives for information sharing in a supply chain , 2003, Eur. J. Oper. Res..
[15] Vineet Padmanabhan,et al. Comments on "Information Distortion in a Supply Chain: The Bullwhip Effect" , 1997, Manag. Sci..
[16] K. V. Donselaar,et al. How to use aggregation and combined forecasting to improve seasonal demand forecasts , 2004 .
[17] J. Holmström,et al. Supply chain collaboration: making sense of the strategy continuum , 2005 .
[18] J. Boylan,et al. On the stock control performance of intermittent demand estimators , 2006 .
[19] Rob J Hyndman,et al. Minimum Sample Size requirements for Seasonal Forecasting Models , 2007 .
[20] Robert Fildes,et al. Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting , 2007, Interfaces.
[21] Rajesh Piplani,et al. Forecasting aggregate time series with intermittent subaggregate components: top-down versus bottom-up forecasting , 2007 .
[22] Giulio Zotteri,et al. A model for selecting the appropriate level of aggregation in forecasting processes , 2007 .
[23] David Veredas,et al. Temporal Aggregation of Univariate and Multivariate Time Series Models: A Survey , 2008 .
[24] Aris A. Syntetos,et al. Classification for forecasting and stock control: a case study , 2008, J. Oper. Res. Soc..
[25] J. Boylan,et al. Empirical evidence on individual, group and shrinkage seasonal indices , 2008 .
[26] Rommert Dekker,et al. An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods , 2008, Eur. J. Oper. Res..
[27] Mohammad M. Ali,et al. Centralised demand information sharing in supply chains , 2008 .
[28] Philip Hans Franses,et al. Do experts' adjustments on model-based SKU-level forecasts improve forecast quality? , 2009 .
[29] Stephen M. Disney,et al. Forecasting for inventory planning: a 50-year review , 2009, J. Oper. Res. Soc..
[30] R. Fildes,et al. The effects of integrating management judgement into intermittent demand forecasts , 2009 .
[31] R. Fildes,et al. Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning , 2009 .
[32] L.W.G. Strijbosch,et al. Calculating the accuracy of hierarchical estimation , 2010 .
[33] Aris A. Syntetos,et al. Spare parts management : a review of forecasting research and extensions , 2010 .
[34] P. Yelland. Bayesian forecasting of parts demand , 2010 .
[35] John E. Boylan,et al. Choosing levels of aggregation for supply chain forecasts , 2010 .
[36] John E. Boylan,et al. Feasibility principles for Downstream Demand Inference in supply chains , 2011, J. Oper. Res. Soc..
[37] Fotios Petropoulos,et al. Improving the Performance of Popular Supply Chain Forecasting Techniques , 2011 .
[38] Rob J. Hyndman,et al. Optimal combination forecasts for hierarchical time series , 2011, Comput. Stat. Data Anal..
[39] Fotios Petropoulos,et al. An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis , 2011, J. Oper. Res. Soc..
[40] John E. Boylan,et al. Forecast horizon aggregation in integer autoregressive moving average (INARMA) models , 2012 .
[41] M. Z. Babai,et al. Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis , 2012 .
[42] Andrea Silvestrini,et al. Forecasting Aggregate Demand: Analytical Comparison of Top-Down and Bottom-Up Approaches in a Multivariate Exponential Smoothing Framework , 2013 .
[43] Nicola Saccani,et al. Forecasting by cross-sectional aggregation , 2014 .
[44] Yves Ducq,et al. A note on the forecast performance of temporal aggregation , 2014 .
[45] Reza Zanjirani Farahani,et al. New forecasting insights on the bullwhip effect in a supply chain , 2014 .
[46] P. Goodwin,et al. The challenges of pre-launch forecasting of adoption time series for new durable products , 2014 .
[47] Fotios Petropoulos,et al. Improving forecasting via multiple temporal aggregation , 2014 .
[48] Sérgio Luis da Silva,et al. Mitigation of the bullwhip effect considering trust and collaboration in supply chain management: a literature review , 2015 .
[49] Aris A. Syntetos,et al. The effects of integrating management judgement into OUT levels: In or out of context? , 2016, Eur. J. Oper. Res..