Knowledge that Transforms

All capital investment decisions are made under varying degrees of risk and uncertainty. Evaluations of the desirability of capital investments require estimates of present conditions as well as forecasts of future events which involve these risks and uncertainties. Several recently developed models for capital investment decision-making have involved the random variable treatment of anticipated cash flows. When present worth calculations are made in these cases, a fixed interest rate is usually assumed. When internal rate of return calculations are made, the minimum acceptable rate of return is given a fixed value. This paper treats the minimum attractive rate of return (used with present worth calculations) and the minimum acceptable internal rate of return (used with internal rate of return calculations) as stochastic variables having subjective probability distributions. This procedure takes explicit account of the uncertainty of future investment opportunities. It is shown that, under certain circumstances, significant differences in the values of the mean and the variance of the criteria for making the investment decision are obtained when the results of this procedure are compared with results using the usual deterministic interest rate or minimum acceptable rate of return.

In PERT-type networks, where activity times are random variables, the expected duration of the total project can be approximated by first substituting the expected values of the individual activities, and then evaluating the length of the critical path. This paper develops two improved estimates. Both estimates approach the true expected duration from below, i.e., they are both optimistic estimates.

Languages are important in any discussion of information systems. It is through the medium of a machine-oriented (as opposed to natural) language that the user must communicate with the computer. In order to use the earliest computers, it was necessary to describe the problem in the detailed computer hardware language. Then higher level languages were evolved so that instead of describing a solution in machine language, it was possible to describe it in a more “natural” language. These languages (FORTRAN, COBOL, etc.) are general in the sense that it is possible to solve almost any problem that is solvable in machine language using them. Now we have a group of languages in which it is possible to solve only the specific problem that each was intended to solve. The following letter describes such a language and its use in a simulated management environment. At the end, Editor's notes are presented.

This paper describes and discusses the results of a national survey [1] on the use of mathematical models in plant maintenance decision making. The major points considered are: (1) The extent to which such models are used, (2) their potential financial benefits, and (3) the determinants of their use. Of special interest is the finding that these determinants are similar to those involved in the general problem of OR models in imiplementation, despite the technological character of maintenance decisions.

Most inventory depletion analysis has been concerned with the case of a deterministic field life function. Since it is rarely the case that the exact field life function of a stock of items would be known, it is more reasonable to treat the case where the field life of an item is a random variable, X(S), which depends only on the age, S, of the item upon issuance. In this stochastic case, it is shown that the policy of issuing the oldest item first (FIFO) is optimal provided E[X(S)] = aS + b where 0 ≧ a > − 1, b > 0, i.e., where the items have a linearly decreasing mean value function, and where some additional, but behaviorally not restrictive, assumptions are made.

In the first part of this paper we will review and modify the cluster analysis procedure presented by Green, Frank and Robinson [Green, P. E., R. E. Frank, P. J. Robinson. 1967. Cluster analysis in test market selection. Management Sci. 13 (8, April) B-387–B-400.] in a recent issue of this journal. In revising their procedure, we will raise some very fundamental questions with respect to cluster analysis in particular and multivariate statistics in general. The scale that we use in measuring the input variables (e.g., age, income, education, etc.) will affect the results. The question is: “How do we scale the input variables so that the results are ‘meaningful’?” We will see that some of the usual methods that are used to give statistically meaningful results will not assure us of managerially meaningful results. Finally, a possible “solution” along with its advantages and disadvantages over standard techniques will be presented.

The scientific exploration of the moon requires that a number of experiments be conducted for the purpose of investigating the many questions and hypotheses posed by nearly every discipline in the scientific community. The broad interest in this effort, the limitations of time and resources for conducting the program, and the complex engineering constraints imposed by the logistics system justify the development of an effective method of making optimum selection of experiments according to the most meaningful criteria. The developed method provides a determination of alternate optimum solutions from which scientific authority may choose. Basic lunar scientific objectives, as determined by NASA interdisciplinary panels, serve as inputs to the system. The relative worth of each objective according to scientific merit is determined by a stratified, intradisciplinary sample of the scientific community using a modified majority-rule technique. The experiments are then ordered according to their contribution to the ordered objectives. A subjective programming method provides alternate payload choices that optimize the scientific merit of the experiments within the engineering constraints of a lunar IMjdoad. An example is presented that simulates the use of an algorithm for sdecting an optimum experiment pajdoad.