Cluster-based hierarchical demand forecasting for perishable goods

[1]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

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

[3]  Everette S. Gardner,et al.  Exponential smoothing: The state of the art , 1985 .

[4]  G. C. Tiao,et al.  Estimation of time series parameters in the presence of outliers , 1988 .

[5]  Jeffrey Sohl,et al.  Disaggregation methods to expedite product line forecasting , 1990 .

[6]  Andrew B. Whinston,et al.  Decision Support Systems: A Knowledge Based Approach : , 1996 .

[7]  Hau L. Lee,et al.  Information distortion in a supply chain: the bullwhip effect , 1997 .

[8]  Kenneth B. Kahn.  Revisiting Top-Down versus Bottom-Up Forecasting , 1998 .

[9]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[10]  Dotun Adebanjo,et al.  Identifying problems in forecasting consumer demand in the fast moving consumer goods sector , 2000 .

[11]  Hau L. Lee,et al.  Information sharing in a supply chain , 2000, Int. J. Manuf. Technol. Manag..

[12]  Katia Campo,et al.  Towards understanding consumer response to stock-outs , 2000 .

[13]  W. Zinn,et al.  CONSUMER RESPONSE TO RETAIL STOCKOUTS , 2001 .

[14]  M. Barratt,et al.  Exploring the experiences of collaborative planning initiatives , 2001 .

[15]  Siddhartha Bhattacharyya,et al.  Data mining on time series: an illustration using fast-food restaurant franchise data , 2001 .

[16]  Daniel Corsten,et al.  Desperately seeking shelf availability: an examination of the extent, the causes, and the efforts to address retail out‐of‐stocks , 2003 .

[17]  Giulio Zotteri,et al.  The impact of aggregation level on forecasting performance , 2005 .

[18]  Kenneth Gilbert,et al.  An ARIMA Supply Chain Model , 2005, Manag. Sci..

[19]  J. Holmström,et al.  Supply chain collaboration: making sense of the strategy continuum , 2005 .

[20]  Clyde W. Holsapple,et al.  ERP plans and decision-support benefits , 2005, Decis. Support Syst..

[21]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[22]  Jc Jan Fransoo,et al.  Inventory control of perishables in supermarkets , 2006 .

[23]  Roberto Verganti,et al.  Forecasting demand from heterogeneous customers , 2006 .

[24]  Riikka Kaipia,et al.  Planning nervousness in a demand supply network: an empirical study , 2006 .

[25]  Rob J Hyndman,et al.  25 years of time series forecasting , 2006 .

[26]  E. S. Gardner.  EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II , 2006 .

[27]  Werner Kaiser.  Fast Moving Consumer Goods , 2007 .

[28]  Jc Jan Fransoo,et al.  Consumer responses to shelf out‐of‐stocks of perishable products , 2007 .

[29]  P. Danese.  Designing CPFR collaborations: insights from seven case studies , 2007 .

[30]  W. Zinn,et al.  A COMPARISON OF ACTUAL AND INTENDED CONSUMER BEHAVIOR IN RESPONSE TO RETAIL STOCKOUTS , 2008 .

[31]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .

[32]  Daniel J. Power,et al.  Decision Support Systems: A Historical Overview , 2008 .

[33]  Jayanthi Ranjan,et al.  Real time business intelligence in supply chain analytics , 2008, Inf. Manag. Comput. Secur..

[34]  Olli-Pekka Hilmola,et al.  Forecasting and Risk Analysis in Supply Chain Management: GARCH Proof of Concept , 2008 .

[35]  Héctor Pomares,et al.  Soft-computing techniques and ARMA model for time series prediction , 2008, Neurocomputing.

[36]  Víctor Leiva,et al.  An R Package for a General Class of Inverse Gaussian Distributions , 2008 .

[37]  Sven F. Crone,et al.  Forecasting and operational research: a review , 2008, J. Oper. Res. Soc..

[38]  Hasso Plattner,et al.  A common database approach for OLTP and OLAP using an in-memory column database , 2009, SIGMOD Conference.

[39]  Hugh J. Watson,et al.  Tutorial: Business Intelligence - Past, Present, and Future , 2009, Communications of the Association for Information Systems.

[40]  R. Fildes,et al.  Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning , 2009 .

[41]  Dotun Adebanjo,et al.  Understanding demand management challenges in intermediary food trading: a case study , 2009 .

[42]  Rajesh Piplani,et al.  Forecasting aggregate demand: An analytical evaluation of top-down versus bottom-up forecasting in a production planning framework , 2009 .

[43]  Herbert Kotzab,et al.  Forty years of Out-of-Stock research – and shelves are still empty , 2010 .

[44]  Jan Fransoo,et al.  Ordering Behavior in Retail Stores and Implications for Automated Replenishment , 2010, Manag. Sci..

[45]  John E. Boylan,et al.  Judging the judges through accuracy-implication metrics: The case of inventory forecasting , 2010 .

[46]  Surajit Chaudhuri,et al.  An overview of business intelligence technology , 2011, Commun. ACM.

[47]  Brent D. Williams,et al.  Top‐Down Versus Bottom‐Up Demand Forecasts: The Value of Shared Point‐of‐Sale Data in the Retail Supply Chain , 2011 .

[48]  Pamela Danese,et al.  Towards a contingency theory of collaborative planning initiatives in supply networks , 2011 .

[49]  Gülçin Büyüközkan,et al.  Analyzing of CPFR success factors using fuzzy cognitive maps in retail industry , 2012, Expert Syst. Appl..

[50]  Guoqiang Peter Zhang,et al.  Neural Networks for Time-Series Forecasting , 2012, Handbook of Natural Computing.

[51]  Wolfgang Lehner,et al.  Efficient transaction processing in SAP HANA database: the end of a column store myth , 2012, SIGMOD Conference.

[52]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[53]  Herbert Endres,et al.  Explaining costumer reactions to real stockouts , 2013 .

[54]  Sanjay Jharkharia,et al.  Applicability of ARIMA Models in Wholesale Vegetable Market: An Investigation , 2013, Int. J. Inf. Syst. Supply Chain Manag..

[55]  Joachim C.F. Ehrenthal,et al.  An examination of the causes for retail stockouts , 2013 .

[56]  Usha Ramanathan,et al.  Performance of supply chain collaboration - A simulation study , 2014, Expert Syst. Appl..

[57]  Antonio Márcio Tavares Thomé,et al.  Research synthesis in collaborative planning forecast and replenishment , 2014, Ind. Manag. Data Syst..

[58]  S. S. Mahapatra,et al.  An improved demand forecasting method to reduce bullwhip effect in supply chains , 2014, Expert Syst. Appl..

[59]  Michael Bourlakis,et al.  Collaborative forecasting in the food supply chain: A conceptual framework , 2014 .

[60]  Gerd J. Hahn,et al.  A perspective on applications of in-memory analytics in supply chain management , 2015, Decis. Support Syst..

[61]  Hau L. Lee,et al.  The bullwhip effect in supply chains , 2015, IEEE Engineering Management Review.

[62]  Antônio Márcio Tavares Thomé,et al.  Collaborative planning, forecasting and replenishment: a literature review , 2015 .

[63]  Konstantinos Nikolopoulos,et al.  Supply chain forecasting: Theory, practice, their gap and the future , 2016, Eur. J. Oper. Res..

[64]  David F. Pyke,et al.  Style Goods and Perishable Items , 2016 .