The following is a part of a work, “Definition of Management Information Systems,” which was written as support for a Ph.D. dissertation at the University of Illinois by Hartmut J. Will: “A Critical Analysis of the Assumptions Underlying Management Accounting Models in Relation to Budgeting and Simulation: An Information Systems Approach.”
Large data systems are expensive and difficult to construct even where the inputs are well defined. In the following paper, “The Computer—New Partner in Investment Management,” Dr. Arnold E. Amstutz of the MIT Sloan School of Management describes work done on a system, where, although the data was well defined, the effort of understanding how it would be utilized and organized spread over seven years. Essential to the success of the work that Dr. Amstutz describes was the emphasis upon both measurement and structure.
Within the past two years a number of services have been offered that make a large central computer available on a time shared basis to anyone who has a terminal. In general these services provide the user with at least one language (usually FORTRAN) that is especially tailored to on-line communications. The following letter describes an application that was programmed in FORTRAN using one of these services. —“To the Editor” by T. E. Hlavac, Jr., Interactive Systems, Inc., Statler Office Building, Boston, Massachusetts 02126.
Numerous alternatives are available in the kinds of studies which can be elected in the market research which should precede the introduction of a new product. This gives rise to a possible network interpretation and treatment by an associated chance-constrained programming characterization and analysis. The resulting model, called DEMON (Decision Mapping Via Optimum GO-NO Networks), which was discussed in [Charnes, A., W. W. Cooper, J. K. DeVoe, D. B. Learner. DEMON: Decision mapping via optimum GO-NO networks—A model for marketing new products. Management Sci. 12(11) 865–877.], is here given a new formulation in terms of an extremal equation. A general analytic characterization is achieved and then replaced by more special ones. Means for effecting study decisions and inferences are discussed along with the sensitivity analyses that we associate with GO and NO preemptions, relative to profit and payback constraints. Payback (in a constraint) is here distinguished from its use as a criterion for choice (or objective), the latter being here oriented toward MEMP (maximizing expected maximum profit). A chart is provided for interpreting these and other aspects of policy which bear on the problems of marketing a new product.
A machine is to be bought, used for productive purposes for a length of time, and then sold. It is possible to do preventive maintenance while the machine is being used, if desired, in order to slow down the degradation of the machine's capability. The value of owning the machine is the sum of the discounted value of its production while it is owned plus its discounted value when sold. In this paper a modification of a model due to B. Naslund for the problem is discussed. Using Pontryagin's maximum principle, the problem is treated as one of optimal control with the control variables being the use or nonuse of preventive maintenance and the selection of the sale date. It is shown that the optimal maintenance policy is bang-bang, and methods for finding the optimal sale date are determined. Simply examples show the various kinds of solutions that can be obtained with varying assumptions concerning the actual functions used in the model.
In the March, 1968, issue of Management Science, Ansoff and Slevin discuss industrial dynamics [2]. Although the authors acknowledge writing without having used industrial dynamics, their paper contributes by raising important questions which need discussion. The rapidly increasing domestic and international interest in industrial dynamics makes especially timely their paper and an opportunity to respond. The authors base their viewpoint on information available from the published literature. I respond from the viewpoint of one familiar with much additional work in the field which has not yet reached the public press. This may account for some of our differences. Between glimpsing early results and seeing these in print, five or more years can elapse. The work must be consolidated and improved, articles written, and publication delays awaited. Access through publication is also impeded by proprietary restrictions placed on some of the more important and exciting applications of industrial dynamics in industry. Although space limits discussion to those points where I differ, the authors have developed many important ideas in their paper. They have performed a valuable service for industrial dynamics. The comments that follow are grouped by subject and not in the sequence of their article.
Part I of this paper has described a new theory for the analysis of games with incomplete information. It has been shown that, if the various players' subjective probability distributions satisfy a certain mutual-consistency requirement, then any given game with incomplete information will be equivalent to a certain game with complete information, called the “Bayes-equivalent” of the original game, or briefly a “Bayesian game.” Part II of the paper will now show that any Nash equilibrium point of this Bayesian game yields a “Bayesian equilibrium point” for the original game and conversely. This result will then be illustrated by numerical examples, representing two-person zero-sum games with incomplete information. We shall also show how our theory enables us to analyze the problem of exploiting the opponent's erroneous beliefs. However, apart from its indubitable usefulness in locating Bayesian equilibrium points, we shall show it on a numerical example (the Bayes-equivalent of a two-person cooperative game) that the normal form of a Bayesian game is in many cases a highly unsatisfactory representation of the game situation and has to be replaced by other representations (e.g., by the semi-normal form). We shall argue that this rather unexpected result is due to the fact that Bayesian games must be interpreted as games with “delayed commitment” whereas the normal-form representation always envisages a game with “immediate commitment.”
The problem considered in this paper deals with the optimal policies for keeping cash in anticipation of future net expenses. It is an inventory problem in which the inventory level can either decrease or increase, and the decision maker is allowed to change the inventory level in any direction at the beginning of each period. In addition to the usual holding and shortage costs, the problem involves fixed and proportional costs for deciding to change the inventory level. This paper shows the form of the optimal policy if the expected holding and shortage cost is convex and if deciding either to increase or to decrease the inventory level does not involve a fixed cost.