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IMA Journal of Management Mathematics Advance Access originally published online on January 21, 2008
IMA Journal of Management Mathematics 2008 19(2):207-218; doi:10.1093/imaman/dpm039
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© The authors 2008. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

Forecasting item-level demands: an analytical evaluation of top–down versus bottom–up forecasting in a production-planning framework

Handik Widiarta and S. Viswanathan{dagger}

Nanyang Business School, Nanyang Technological University, Singapore 639798, Republic of Singapore

Rajesh Piplani

School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Republic of Singapore

{dagger} Email: vish{at}pmail.ntu.edu.sg

Received on 12 January 2007. Accepted on 19 November 2007.

We compare the performance of top–down (TD) and bottom–up (BU) strategies for forecasting the demand of an item that belongs to a product family. The TD strategy forecasts the sum of the item demands and distributes it to the individual item based upon the historical demand proportion of each item in the family. The BU strategy forecasts each item demand individually using the historical demand data for the particular item. All the item demands, which may be correlated with each other, are assumed to follow a first-order univariate moving average process. As is common in a production-planning environment, the forecasting under both strategies is carried out using the exponential smoothing technique. We show that the performance of the two forecasting strategies is nearly identical, regardless of the coefficient of correlation between the item demands, the items' proportion in the family and the coefficient of the serial correlation term of the demand process. We further investigate the relative performance of the two strategies when a fixed (rather than the optimal) smoothing constant is used for forecasting the demand under both strategies.

Keywords: forecasting; stationary time series; moving average process; top–down forecasting; bottom–up forecasting; exponential smoothing


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