Knowledge that Transforms

A simple model is presented which allows us to determine the optimal size, fillup, and drawdown rates for a Strategic Petroleum Reserve (SPR) under a variety of supply and demand conditions. The optimal policy variables are determined by minimizing an analytic expression which we derive for the expected insecurity cost rate to the U.S. due to uncertainty in supply of imported oil. The oil market is modeled in terms of an elastic demand curve and two levels of supply which alternate according to a stationary, continuous time Markov process. The SPR policy is characterized by a fixed fillup rate up to the reserve's capacity, when supply is at its normal level, and a fixed drawdown rate during shortages. The insecurity cost rate being minimized includes consumer welfare loss due to price hikes, reserve holding cost and capital appreciation of the reserve. Base case results and sensitivity analysis are presented and compared to results obtained by previous approaches. These comparisons suggest that the proposed model can reasonably approximate the more computationally demanding stochastic dynamic programming formulation. The main advantage of the new approach is that it permits extensive sensitivity analysis which is important given the quality of the data.

In their paper, “Economic Models for R&D Project Selection in the Presence of Project Interactions,” Fox, Baker, and Bryant (Fox, G. E., N. R. Baker, J. L. Bryant. 1984. Economic models for R and D project selection in the presence of project interactions. Management Sci. 30 (July) 890–902.) proposed a model that included benefit interactions, called present value (PV) interactions, between R&D projects. Several computational errors in this paper invalidated the illustrative example which sought to demonstrate that ignoring the PV interactions could result in both nonoptimal project selections and resource allocations. However, a single correction yields an example that supports the authors' central premise. Furthermore, we believe that the model itself can prove useful in real applications.

Recent interest in product quality suggests that effort devoted to improving the quality of manufactured products may reduce unit costs. This conjecture—that improving quality can lower costs—challenges the traditional assumption that unit costs increase with increased quality assurance activities and has significant implications for quality management. By introducing the idea of a quality-based learning curve, this paper links the previously disjoint literatures of quality control and learning curves to explain why high quality and low costs need not be inconsistent. When costs are affected by a quality-based learning curve, product quality favorably influences the rate of cost reduction due to learning. Thus, costs decline more rapidly with the experience of producing higher quality products. Two formulations of the quality-based learning phenomenon are presented. The first assumes that quality-based experience affects direct manufacturing costs. For this formulation, the optimal quality level is decreasing over time, but is always larger than the optimal base-case quality level. The optimal production quantity is constant if the interest rate is zero and increasing in time when the interest rate is positive. The second formulation assumes that quality-based experience affects quality control costs. In this case, the optimal quality level is always increasing over time. The optimal quantity behavior is qualitatively similar to the first formulation. A key feature of the second quality-based model is that it resolves the controversy between the economic conformance level model of Juran, which asserts that one should use cost-tradeoff analysis to find the optimal quality level, and the claims of Deming and Crosby, that zero defects is always the optimal quality level. For certain parameter values, the optimal quality policy in the second model conforms to the economic conformance level model but the dynamics of the model demonstrates the optimality of always pushing towards zero defects.

In this paper, we report on a computational experiment designed to assess the efficacy of 26 heuristic decision rules which group work tasks into work stations along an assembly line such that the number of work stations required is minimized. The heuristic decision rules investigated vary from simple list processing procedures that consider a single attribute of each work task for assignment, to procedures which are optimal seeking, but which have had their search terminated through the imposition of a limit on the amount of computation time that can be devoted to each search. Also included are heuristic decision rules which backtrack in an attempt to locate an improved solution, and decision rules which probabilistically search for improved solutions. Our investigation differs from those reported previously, in that the objective in balancing each line is to determine the minimum number of work stations for a given limit on the time available for assembly at each work station (the cycle time). Previous approaches have investigated the problem of determining the minimum cycle time for a given line length. We also compare the results obtained with the optimal solution for a subset of the problems investigated. Both problems which have appeared in the open literature and problems which have been solved for the first time are included. Because a portion of our results differ from those reported previously, we suggest why these differences have occurred. Guidelines are also given to those balancing industrial assembly lines on the choice of the heuristic decision rule to use whether one is attempting to obtain a minimum station balance given a limit on the time available for assembly at each work station, or whether one is attempting to minimize the time devoted to assembly at a work station given a limit on the number of work stations available.

Judgement based forecasts are widely used in practice either alone or in conjunction with computer prepared forecasts. This study empirically examines the improvement in accuracy which can be gained from combining judgemental forecasts, either with other judgemental or with quantitatively derived forecasts. Two judgemental forecasting approaches are used by each of two different groups in a laboratory setting to give four sets of judgemental forecasts for the 68 monthly time series of the M-competition. These are combined either with each other or with forecasts from deseasonalised single exponential smoothing. Combined forecasts are found to be more accurate than single forecasts with the greatest benefit realised at short forecast horizons and for easier (as opposed to harder) forecast series. Averaging was observed to be a far better way of combining judgemental forecasts than a judgemental, nonsystematic combination.

Some authors have argued that research on organizational effectiveness should cease. This study demonstrates why organizational effectiveness studies are crucial in certain types of organizations, and it points out how many of the weaknesses and criticisms of past investigations can be addressed. The results of this study of 29 organizations indicate that certain managerial strategies are strongly associated with high static scores and with improving effectiveness over time. Managerial strategies, in fact, were found to be more important than structure, demographics, finances, and other factors. Proactive strategies and those with an external emphasis are more successful than internal and reactive strategies. Managerial strategies that are multifaceted are more likely to lead to effectiveness than monolithic strategies.

Concern with the effectiveness, productivity, efficiency or excellence of organizations is a subject that has motivated the writings of economists, organization theorists, management philosophers, financial analysts, management scientists, consultants, and practitioners. It has served as a unifying theme for over a century of research on the management and design of organizations, yet the empirical research has not contributed to the development of a universal theory of organizational effectiveness. In this paper we review the extent to which the components of a contingent behavioral theory of organizational effectiveness already exist, one that incorporates the paradoxes and tradeoffs inherent in real life organizations. We consider the problem of effectiveness measurement, and we propose a research approach utilizing a strategy of engineering organizational effectiveness which could lead to an inductive, applied, empirically-based theory of contingent organization design. In this context the engineering of organizational effectiveness has a dual purpose. For the organizations and their managers participating in the research, it refers to understanding, constructing and managing organizational activities in given contexts so as to achieve and maintain improved performance as measured by one or more situationally-determined criteria. In addition, to provide the empirical basis from which induction can proceed, the engineered events must be structured and implemented in such a way so as to facilitate theory construction and testing.

Each of n members of a finite customer population must weigh the rewards of service completion against the cost of waiting in a single exponential server queuing system. After completing service, a customer may re-enter the system. In attempting to maximize average return per unit time over an infinite horizon each customer must make a single decision, a choice of arrival rate which must then be used to determine the time until re-entry whenever the customer finishes being served. Equilibrium behavior in the resulting n person game is investigated. Equilibria exist involving a subset of the players who re-enter instantaneously after service while all others abstain from entry. In instances where all customers re-enter instantaneously, everyone could be made better off if it were possible to impose a non-instantaneous arrival rate.

Cost-benefit and risk analysis studies that model tradeoffs between the present and the distant future by means of present value discounting have been criticized for according the future, and thus future generations, far too little importance. This paper presents an alternative means of modeling tradeoffs between different periods that accords the future far more importance than present value discounting, and that is no more difficult to apply. The paper also presents a means of modeling the preference issue of concern for equity or stability between different periods.

Attention to the subject of organizational effectiveness has been increasing in the last several years as popular management books have extolled management excellence, almost two million jobs have been lost due to poor U.S. competitiveness, and economic conditions have put pressure on organizations to become more accountable with their resources. However, despite its popularity, much confusion continues in the organizational literature regarding the definition, circumscription, and appropriate criteria for assessing effectiveness. In this paper, I summarize what areas are becoming consensual among most writers on effectiveness, and I point out continuing areas of disagreement and conflict. The five statements summarizing consensual characteristics of effectiveness and the three statements summarizing areas of continuing conflict point out that agreement about effectiveness is mainly an agreement to disagree. Conflicts center mainly on the incompatibility and inappropriateness of commonly selected criteria. The main theme of the paper, however, is a discussion of an inherent, but largely ignored, characteristic of effectiveness in organizations—the paradoxical nature of effectiveness criteria. This discussion illustrates that the most effective organizations are also those characterized by paradoxes—i.e., contradictions, simultaneous opposites, and incompatibilities. Taking account of this characteristic helps explain one reason why so much confusion and disagreement continues to surround effectiveness, and it uncovers a new set of research questions that can guide future investigations. Some suggestions are provided for how research on paradoxes in effectiveness might be pursued in the future.