The Japanese "just-in-time with kanban" technique reduces in-process inventory to absolute minimal levels, in concert with the Japamse belief that inventory is an unnecessary evil. Due to the succas of Japanese firms that employ this type of system. American firms would like to import this technique and emulate Japanese successes. But this Japanese succus may be attributable not only to the just-in-time with kanban technique but also to the production environment in which the technique is employed. This paper simulates the just-in-time with kanban technique for a multiline, multistage production system in order to determine its adaptability to an American production environment that might include such characteristics as variable procasing tima, variable master production scheduling, and imbalances between production stages. The results have practical implications for those firms considering adoption of the Japanese technique.Stb1-t A m * lk&i!tbn/@mtionr Marmgment, ShuMon, Inventory Management, and Netwarb.
Machine-learning methods can assist with the medical decision-making processes at the both the clinical and diagnostic levels. In this article, we first review historical milestones and specific applications of computer-based medical decision support tools in both veterinary and human medicine. Next, we take a mechanistic look at 3 archetypal learning algorithms—naive Bayes, decision trees, and neural network—commonly used to power these medical decision support tools. Last, we focus our discussion on the data sets used to train these algorithms and examine methods for validation, data representation, transformation, and feature selection. From this review, the reader should gain some appreciation for how these decision support tools have and can be used in medicine along with insight on their inner workings.
The desirability of a merger/acquisition alternative depends in part on the perceptions of the decision maker. What sources of information are "useful" to the decision maker?Does the set of useful information remain constant for all decision makers; if not, do individuals using similar information sets have similar information processing characteristics? Do these sets vary as feedback is obtained during the decision process? To answer these questions, graduate students participated in a modified Delphi experiment, and the resulting data were analyzed by the two-way aligned-ranks nonparametric test. These test results affirm that in a merger/acquisition scenario, decision makers with different cognitive styles prefer different sets of information and these sets vary dynamically as feedback is incorporated in the decision-making process. Furthermore, information that contains worker and community welfare considerations is identified as "useful" five times more frequently by decision makers with a "feeling" cognitive style than those with a "thinking" style.
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