Competitive Intelligence and Forecasting Systems: Strategic Marketing Planning Tool for SME\u27s

One of the drivers of both strategic planning and success in the marketplace is the role of competitive intelligence systems (CIS). CIS activity and its value to consumer/competitive intelligence are well established. In a survey of a broad cross section of firms, it was found that two thirds of the companies indicated a dramatic increase in level of activity and nearly three fifths (54%) said the impact of marketing intelligence systems (MI) contribute heavily to tactical and strategic decision making (Lackman, Lanasa, and Saban, 2000). Small and medium enterprises (SME’s) traditionally have neglected CIS partly because of the cost and the complexity. However, a low cost, less complex tool is operative in an SME as illustrated in this paper. An integral part of CIS is forecasting capability which needs to be built into a CIS in order to effectively serve strategic planning. This paper specifies the basic elements of a CIS and the forecasting modules required for the system’s effectiveness, including the external and internal modules of a basic CIS and the forecasting models needed to support each CIS module in an integrated format all feasible for an SM

Raising interest rates: IOER vs. OMO: Interest on excess reserves vs open market operations

We demonstrate that IOER should make the excess reserves even larger, continuing the problem of monetary policy control and rewarding the banks for their policy errors fostering the Great Recession by giving them risk free returns on the $2.5 trillion of idle funds that are benefiting no one except the banks themselves, or having the banks invest those idle funds in some useful manner such as helping finance the government deficit and fix our roads and bridges. The number 1 priority should be to get rid of the troublesome excess reserves and utilizing open market operations (OMO)

Forecasting commercial paper rates

A model previously developed by Lackman (C. L. Lackman, Forecasting commercial paper rates. Journal of Business Finance and Accounting 15 (1988) 499-524) for the period 1960 to 1985 is updated to include the 1990s and incorporate statistical techniques relating to tests for stationary conditions not available in 1988. As in the previous model, the demand for commercial paper by each institution (Households (HH), Life Insurance Companies (LIC), Non-Financial Corporations (CRP) and Finance Corporations (FC)) and the total demand is simulated. Simulations of the commercial paper rate are also generated-using just the demand equations (total supply exogenous) and then employing the entire model (supply endogenous) to determine the rate. Simulation periods are from 1960:2 to 2001:4 for all demand simulations. The dynamic simulation of the total demand for commercial paper performs well. The resulting root mean square error, 3.485, compares favourably with the Federal Reserve Boston-Massachusetts Institute of Technology (FRB-MIT) estimate of the commercial paper rate (deLeeuw and Granlich, 1968). Copyright © 2004 John Wiley & Sons, Ltd.