Knowledge that Transforms

A study of the patterns underlying business profits and progress was touched off by loss of market position and profit. The problem was to discover patterns in our technologically advanced economy which would give top management a new basis for planning profit. Useful insights were gained, particularly on the acceleration in idea production and productivity and on the character of the innovation cycle and its relation to employment, investment, and profitability. A model is built which corrects certain errors in Parkinson's law and Keynes theory.

A growing number of case studies, describing the increasing number of applications of statistical sampling methods to accounting problems, have appeared in accounting literature. A substantial number, although not all, of these case studies have tended to emphasize the statistical aspects of the particular problem being described, at the neglect of accounting considerations. This is understandable in the sense that statistical techniques are relatively new to the accounting profession and new techniques, rather than the old, require exposition. However, the fruitful use of statistics in accounting requires full consideration of both statistical and accounting aspects of any particular problem. With this point of view, the following case study describing the statistical and accounting problems encountered in an adoption of the LIFO method of valuing inventory is presented.

The paper is concerned with the optimum routing of a fleet of gasoline delivery trucks between a bulk terminal and a large number of service stations supplied by the terminal. The shortest routes between any two points in the system are given and a demand for one or several products is specified for a number of stations within the distribution system. It is desired to find a way to assign stations to trucks in such a manner that station demands are satisfied and total mileage covered by the fleet is a minimum A procedure based on a linear programming formulation is given for obtaining a near optimal solution. The calculations may be readily performed by hand or by an automatic digital computing machine. No practical applications of the method have been made as yet. A number of trial problems have been calculated, however.

This is a new epistemological approach to the inexact sciences. The purpose of all science is to explain and predict in an objective manner. While in the exact sciences explanation and prediction have the same logical structure, this is not so in the inexact sciences. This permits various methodological innovations in the inexact sciences, e.g., expert judgment and simulation.

Considerable attention in recent years has been devoted to the problem of meeting known demands or requirements over some finite number of planning periods at minimum cost. Representative of these problems is a problem that was first solved in the fundamental paper by Modigliani and Hohn [Modigliani, F., F. E. Hohn. 1955. Production planning over time and the nature of the expectation and planning horizon. Econometrica 23 46–66.] (referred to as M-H). No new solutions to this problem are presented in this note. Rather the procedure is to utilize the results of the M-H paper and to investigate the behavior of the decision rule through time in a very special case which seems to be of considerable practical importance.

The problem considered in this paper is that of allocating a budget of resources among the links of a network for the purpose of increasing its flow capacity relative to given sources and sinks. On the assumption that the cost of increasing each link capacity is linear, a labeling algorithm is described that permits rapid calculation of optimal allocations for all budgets.

A new conceptual and analytical vehicle for problems of temporal planning under uncertainty, involving determination of optimal (sequential) stochastic decision rules is defined and illustrated by means of a typical industrial example. The paper presents a method of attack which splits the problem into two non-linear (or linear) programming parts, (i) determining optimal probability distributions, (ii) approximating the optimal distributions as closely as possible by decision rules of prescribed form.

A linear programming model is set forth for maximizing a vector of final demand deliveries under both regional production constraints and transportation network constraints. The model is essentially designed to study short-run effects, and hence no provision is made for the expansion of either industrial or transportation capacities. Within available capacities, however, the location of production is chosen so as to maximize final demand deliveries. A rough dynamic version of the model is presented as well as an algorithm for reaching an approximation of the optimum solution.

Our objective is to discuss the technique of digital system simulation. This procedure has already achieved a considerable stature in industrial and research organizations and promises to attain even greater importance in the future. Yet, with few exceptions (5) 1 , the published literature in the area consists of introductory expositions (10) or of descriptions of the solution of a particular problem (7) where the technique itself is correctly relegated to secondary position. The only publication devoted entirely to the topic (12) is a compendium of papers of the latter type with little attempt to summarize or generalize. Simulations are, of course, as varied as the systems which they represent but they do have certain common characteristics and problems. An identification of these problems would at least allow the investigator to anticipate them and plan accordingly. This paper consists of three sections. The first is an attempt to describe characteristics common to many system simulations; the second is a discussion of some problems involved in the construction of a digital simulator; and the third concerns problems that arise in the use of such a simulator.

The “off-the-top” findings of the air-defense experiments conducted in RAND's Systems Research Laboratory between 1952 and 1954 were the training principles that are the basis of the system training program being implemented by the System Development Corporation. There are, however, other theoretical and methodological implications. The desired performance of complex systems can be realized only through designing and managing them for operational flexibility. This can be done by harnessing the learning ability of men in organizations. Conditions for encouraging men to adapt system's resources to most efficient use follow from the organism analogy—a key notion for explaining the behavior of the four crews studied. In addition, the simulation techniques developed in the course of the research provide a means for continued “head-on” attacks on a crucial problem of the present day—that of better understanding the adaptation process in organizations, so that it can be used and controlled. The paper is in the form of a report of a scientific search. The story of experimental mistakes, modifications in method, and successive insights is presented to convey the essence of an ambitious investigation at the frontier of knowledge where the terrain is not well-mapped and the research tools are being developed as needed.