A systemic view of the ADIDA framework

This paper is an attempt to gain mathematical insight into the aggregate–disaggregate intermittent demand approach (ADIDA) forecasting framework, by formulating it as a multi-rate signal processing system. After a brief synopsis of the framework's background, an alternative way to perceive ADIDA from a systemic viewpoint is derived by breaking down its managerial steps into fundamental and well-studied components. Mathematical properties stemming from each separate system block are thoroughly explored and their practical effects are then exemplified through simulated paradigms of common time series patterns. Subsequently, theoretical and practical evidence are combined to draw useful conclusions about the framework's performance and make suggestions on its application. Finally, guidelines for further research are proposed.

[1]  L.W.G. Strijbosch,et al.  Calculating the accuracy of hierarchical estimation , 2010 .

[2]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[3]  J. D. Croston Forecasting and Stock Control for Intermittent Demands , 1972 .

[4]  J. Sandee,et al.  Derivation of Quarterly Figures from Annual Data , 1964 .

[5]  Steven C. Wheelwright,et al.  Forecasting: Methods and Applications, 3rd Ed , 1997 .

[6]  Carolyn R. Bertozzi,et al.  Methods and Applications , 2009 .

[7]  H. Akaike A new look at the statistical model identification , 1974 .

[8]  J. Boylan,et al.  On the bias of intermittent demand estimates , 2001 .

[9]  Rajesh Piplani,et al.  Forecasting aggregate time series with intermittent subaggregate components: top-down versus bottom-up forecasting , 2007 .

[10]  Alan V. Oppenheim,et al.  Discrete-time signal processing (2nd ed.) , 1999 .

[11]  L.W.G. Strijbosch,et al.  Hierarchical Estimation as Basis for Hierarchical Forecasting , 2006 .

[12]  A. W. M. van den Enden,et al.  Discrete Time Signal Processing , 1989 .

[13]  Fotios Petropoulos,et al.  Improving the Performance of Popular Supply Chain Forecasting Techniques , 2011 .

[14]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[15]  Daniel O. Stram,et al.  Disaggregation of Time Series Models , 1990 .

[16]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .

[17]  Spyros Makridakis,et al.  The M3-Competition: results, conclusions and implications , 2000 .

[18]  Wai-Sum Chan,et al.  Disaggregation of annual time‐series data to quarterly figures: A comparative study , 1993 .

[19]  Fotios Petropoulos,et al.  An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis , 2011, J. Oper. Res. Soc..

[20]  J. Boot,et al.  Further Methods of Derivation of Quarterly Figures from Annual Data , 1967 .

[21]  S. Biyiksiz,et al.  Multirate digital signal processing , 1985, Proceedings of the IEEE.

[22]  Rajesh Piplani,et al.  Forecasting item-level demands: an analytical evaluation of top–down versus bottom–up forecasting in a production-planning framework , 2008 .

[23]  K. Nikolopoulos,et al.  The theta model: a decomposition approach to forecasting , 2000 .

[24]  Santiago Rodríguez Feijoó,et al.  Methods for quarterly disaggregation without indicators; a comparative study using simulation , 2003, Comput. Stat. Data Anal..

[25]  David Veredas,et al.  Temporal Aggregation of Univariate and Multivariate Time Series Models: A Survey , 2008 .

[26]  Aris A. Syntetos,et al.  Spare parts management : a review of forecasting research and extensions , 2010 .

[27]  M. Z. Babai,et al.  Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis , 2012 .