A Seasonal Index for Business

doi:10.1111/j.1540-5915.1997.tb01314.x

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Wiley InterScience

Decision Sciences

Volume 28 Issue 2, Pages 335 - 355

Published Online: 7 Jun 2007

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A Seasonal Index for Business

Peter T. Ittig ¹ , ¹

¹ Department of Management Science and Information Systems, University of Massachusetts, Boston, MA 02125–3393

¹ Peter T. Ittig is an associate professor of management science in the Department of Management Science and Information Systems, College of Management, University of Massachusetts, Boston. He is also chairman of the Graduate Program Committee for the College of Management. He earned his Ph.D. degree from Cornell University, an M.S. degree from Purdue University, and a B.S. degree from the State University of New York at Buffalo. He has previously held faculty appointments in the Graduate School of Public Administration, New York University and in the School of Management, State University of New York at Buffalo. His professional experience includes employment as a program analyst with the U.S. Public Health Service (Office of Planning, Evaluation and Legislation, Health Services Administration) and employment as an operations research analyst with the Research Analysis Corporation (a Federal contract research center). His work has been published in Decision Sciences, Management Science, Socio-Economic Planning Sciences, and elsewhere. His primary research interests are in service operations and management science.

KEYWORDS

Subject Area: Forecasting.

ABSTRACT

This paper explores the problem of obtaining a multiplicative seasonal index for forecasting sales from a small set of historical data (as is common in business applications) and in the presence of a trend. It is shown that the standard method for generating a seasonal index (from a centered moving average) contains a systematic error. This error is transmitted through to forecasts that use the seasonal index and causes higher than necessary safety stocks and other consequences. The paper presents two alternative consistent methods for estimating the seasonal index in the presence of a trend, one for a multiplicative (nonlinear) trend and one for an additive (linear) trend. These methods may be run easily on a spreadsheet program or on statistical software. The nonlinear method is suggested as a convenient alternative to the standard method in many circumstances.

Received: October 29, 1994. Accepted: May 28, 1996.

DIGITAL OBJECT IDENTIFIER (DOI)

10.1111/j.1540-5915.1997.tb01314.x About DOI