Knowledge that Transforms

Using Williamson's (1971) control loss model, the relative merits of dual hierarchies (P. B. Evans [Evans, P. B. 1975. Multiple hierarchies and organizational control. Admin. Sci. Quart. 20 250–259.), strong vertical dyadic linkages, and mentoring for reducing overall control loss are explored. The potential impact for each organizational structure or process is assessed. Simulations are performed to examine the impact of each of these organizational arrangements for organizations with hierarchical levels ranging from 3 to 11 levels. In addition, the effects of deteriorating control at each level are investigated. In the discussion of appropriate control strategies, a number of other organizational characteristics are discussed. I conclude that: Under conditions of sequential or reciprocal interdependence, building uniformly high control efficiency throughout the organization is essential. In small organizations (and those with high turnover) dual hierarchies and cadre relations are appropriate mechanisms for reducing control loss. In large organizations (and those with low turnover) mentoring and cadres are appropriate.

This paper focuses upon the maximization of labor productivity through selection of the optimal number, duration, and placement of rest breaks. It is demonstrated that resolution of the limited validity associated with previous models leads to a mixed-integer quadratic programming formulation of this problem. Efficient analytic procedures are developed which allow solutions to be obtained in minutes using a hand-held calculator. Then, the characteristics, properties, and potential effectiveness of optimal solutions are examined through an illustration from an existing work environment. Issues relevant to the implementation of the models are presented in a summary of an initial experiment in which one of the models in this paper was used to select rest break policy. The results of this experiment indicated that the output achieved using the model's policy was 13% higher than the output achieved when the subjects used their own rest break policies. Several future research topics are also identified.

The classical confidence interval estimator commonly used in simulation is compared with four new estimators based on standardization of a time series presented in a previous paper. These new interval estimators are shown to have asymptotic properties that strictly dominate the classical estimator when used with data from independent simulation replications or means of batched observations from a single simulation run. Two of the new estimators also can be used with a single unbatched replication of a simulation program, a situation where the classical estimator is not defined.

This investigation draws upon concepts from prospect theory (Kahneman and Tversky [Kahneman, D., A. Tversky. 1979. Prospect theory: an analysis of decisions under risk. Econometrica 47 263–291.]) and multiattribute utility theory (Keeney and Raiffa [Keeney, R. L., H. Raiffa. 1976. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Wiley, New York.]) in an examination of the multiattribute risky choice behavior of 128 managers. The questions of how managers edit multiattribute prospects and how editing relates to various independence assumptions were explored. The major result is that managers appear to violate attribute independence in its general form, and especially in the form of the marginality assumption. The most common form of behavior observed was multiattribute risk aversion for prospects involving only gains and multiattribute risk seeking for prospects involving only losses. This result reinforces the importance of a target, reference point, or aspiration level that has been found in earlier studies of single attribute risky choice. Furthermore, the result casts doubt on such commonly used multiattribute utility functions as the additive, multiplicative, and multilinear forms. The implications of the results for the development of multiattribute risky decision aids are discussed.

This paper first presents a brief reveiw of the multicriteria problems faced by the United States Army, Navy, and Marine Corps in making enlisted personnel assignment decisions. Then for the most complex of these military assignment problems, the Marine Corps problem, a new modeling and solution approach is presented with computational results for a problem having eleven criteria and approximately 10,000 constraints and 780,000 variables. Other contributions of this paper include: New insights into the effects on model structure and solution efficiency of handling preemptive multiple criteria sequentially versus simultaneously. Derivation of an excellent convex proportional fill function. Providing multicriteria researchers with a class of large-scale test problems.

This paper is concerned with the development of an optimal budget holdback policy for a multi-item procurement process. An annual budget is made available for the purchase and resupply of n items. The issue is to determine how much of the budget to commit initially and how much to hold back for spot purchases later in the year. An analytic solution to the problem is developed for the single item case and compared to the unconstrained “newsboy” solution. In the multi-item case we develop stochastic linear programming with resource models under two views of the organization's procurement process. The first view is that the overall budget is allocated to the individual item managers and spot purchase monies earmarked for one item cannot be used for another. The computational and data requirements for solution of this model are reasonable. The second view is that spot buy monies are pooled and allocated to individual items on an “as needed” basis. The linear programming model of this procurement process is much more complex and not computationally feasible for more than a few items. However, the budget allocation model of the first view is shown to be a good heuristic for determining the percent of funds to hold back for the pooled process. A numerical example and sensitivity analysis are included.

We consider the problem of smoothing production in a job shop in which all production is to customer order and the demand process is a stationary stochastic process. We present an approach to production smoothing based on the concept of a planning window. A planning window is the difference between the promised delivery time and the planned production time for a product. It represents the degree of flexibility available for planning the production of committed orders. We characterize the production smoothing benefits for a range of planning windows by means of an approximate analytic model and a simulation study. These analyses show that substantial smoothing benefits result from small changes in the length of the planning window. We discuss the implementation of the production smoothing approach and illustrate this implementation with an industrial case study that was the motivation for this work.