This paper explores the possibilities of making fairly complex multi-variable demand forecasts for life insurance sold by utilizing readily available published data, plus an existing computer program available to everyone. The thinking behind this experiment was that if a good demand forecast could be made in this manner, insurance companies could have a cheap, quick, yet sophisticated method of estimating demand in advance. This estimate could be used to check more complicated demand forecasts made at greater cost. The demand forecast presented in the paper used as dependent variables the relative price of life insurance; number of marriages; number of births; personal income; population; and employment. These variables, when used with the U.C.L.A. Biomedical 03RMultiple Regression with Case Combinations program, proved able to forecast insurance demand with reasonable accuracy in most cases. The authors experimented with various combinations of these and other variables, but this set proved the most accurate forecaster. All of the dependent variables are widely available and estimated for the near future by many competent forecasters. Much time was consumed preparing this forecast, but once it was set up, it proved to be very easy to utilize. It is estimated that less than one man day of skilled work would be required to make this estimate annually. Executives can judge whether or not such estimates would be worthwhile for their companies. The more accurate demand predictions for any firm or industry may be, the easier it becomes to manage the firm efficiently. Thus if an insurance company could predict sales for next year accurately, it would be in a position to control costs more precisely and to manage its investment accounts more carefully. Knowing that sales might rise by $33.7 million (plus George Mantis, M.B.A., is Chief, Data Control, U.S. Army Data Support Command. Mr. Mantis is presently serving a two year tour of duty as a commission officer in the Adjutant General Corps of the U.S. Army Reserve. Richard N. Farmer, Ph.D., is Professor of International Business in Indiana University. Dr. Farmer is author of Management in the Future, Incidents in International Business, and is coauthor of Comparative Management and Economic Progress, and International Business: An Operational Theory. An earlier article of his, "The Long Term Crisis in Life Insurance" appeared in this Journal. This article was submitted for publication in February, 1967. or minus 3 per cent) instead of having a vague feeling that they might go up, could prove most useful to any firm in its planning process. Until quite recently demand forecasting for major industries has been quite difficult. The major problem has always been that the number of factors which influence future demand are so large as to require extremely complex statistical manipulation techniques, and few firms had either the skilled manpower or the data necessary to do adequate forecasting. Although pioneering efforts in econometric analysis of demand go back to the 1930's, few firms or industries have advanced very far in this area.' There has been much recent work in econometric theory of demand, although to date many l Joel Dean, Managerial Economics (New York, New York: Prentice-Hall Inc., 1951), pp. 164172, 218-219.