Implementation of a demand planning system using advance order information

被引:15
作者
Haberleitner, Helmut [1 ]
Meyr, Herbert [2 ]
Taudes, Alfred [1 ]
机构
[1] Vienna Univ Econ & Business Adm, Dept Prod Management, A-1090 Vienna, Austria
[2] Tech Univ Darmstadt, Dept Prod & Supply Chain Management, D-64289 Darmstadt, Germany
关键词
Demand forecasting; Supply chain management; Industrial application; Software integration; PARTIAL ACCUMULATION; SEASONAL PATTERNS; BAYESIAN METHOD; SHORT SERIES;
D O I
10.1016/j.ijpe.2010.07.003
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In times of demand shocks, when quantitative forecasting based on historical time series becomes obsolete, the only information about future demand is "advance demand information", i.e. interpreting early customer bookings as an indicator of not yet known demand. This paper deals with a forecasting method which selects the optimal forecasting model type and the level of integration of advance demand information, depending on the patterns of the particular time series. This constitutes the applicability of the procedure within an industrial application where a large number of time series is automatically forecasted in a flexible and data-driven way. The architecture of such a planning system is explained and using real-world data from a make-to-order industry it is shown that the system is flexible enough to cover different demand patterns and is well-suited to forecast demand shocks. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:518 / 526
页数:9
相关论文
共 18 条
[1]  
BODILY SE, 1988, J OPER RES SOC, V39, P833, DOI 10.1057/palgrave.jors.0390905
[2]   Forecasting an accumulated series based on partial accumulation: A Bayesian method for short series with seasonal patterns [J].
de Alba, E ;
Mendoza, M .
JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 2001, 19 (01) :95-102
[3]  
ELLIOT G, 2006, HDB EC FORECASTING
[4]   A TYPOLOGY OF PRODUCTION CONTROL SITUATIONS IN-PROCESS INDUSTRIES [J].
FRANSOO, JC ;
RUTTEN, WGMM .
INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT, 1994, 14 (12) :47-57
[5]  
GANGLOFF C, 2008, REFRACTORIES APPL NE, V13, P6
[6]   Forecasting a cumulative variable using its partially accumulated data [J].
Guerrero, VM ;
Elizondo, JA .
MANAGEMENT SCIENCE, 1997, 43 (06) :879-889
[7]  
HABLA C, 2009, INTELLIGENTE SYSTEME, P113
[8]  
Jones P., 2008, SAP Business Information Warehouse Reporting
[9]   FORECASTING USING PARTIALLY KNOWN DEMANDS [J].
KEKRE, S ;
MORTON, TE ;
SMUNT, TL .
INTERNATIONAL JOURNAL OF FORECASTING, 1990, 6 (01) :115-125
[10]  
LEE Y, 2006, INFORMS INT C JUN 25