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| Break—even Analyses for Random production and Demand Processes | |
| 引用本文： | Dr. Marcus Schweitzer University of Saarbr(?)cken,Germany. Break—even Analyses for Random production and Demand Processes[J]. 系统科学与系统工程学报(英文版), 2002, 11(2): 224-233 |
| 作者姓名： | Dr. Marcus Schweitzer University of Saarbr(?)cken Germany |
| 作者单位： | University of Saarbrücken , Germany |
| 摘 要： | Break-even analyses are often used as controlling instruments. Typically, they are applied to support decision processes or to gain information for the control of profits and sales. Firstly, the study gives an overview of the basic accounting systems. Secondly, the study shows possible ways of performing breakeven analyses for a single-stage, make-to-order production in the case of random production and demand structures. To model these structures, queueing systems are employed. As a general result, we see that break-even analyses must always be performed taking into account an existing planning system. Under practical aspects, GI/G/1 systems turn out to map complex real situations realistically. From the examples given it can be concluded that one achieves different results compared with using a deterministic model even in the case of a simple, random effects approach. In particular it is shown that stochastic modelling in general is helpful in avoiding incorrect decisions. |
| 关 键 词： | 决策支持系统 随机生产 无盈亏分析 |
Break-even Analyses for Random Production and Demand Processes |
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| Marcus Schweitzer. Break-even Analyses for Random Production and Demand Processes[J]. Journal of Systems Science and Systems Engineering, 2002, 11(2): 224-233 | |
| Authors: | Marcus Schweitzer |
| Affiliation: | University of Saarbrücken , Germany |
| Abstract: | Break-even analyses are often used as controlling instruments. Typically, they are applied to support decision processes or to gain information for the control of profits and sales. Firstly, the study gives an overview of the basic accounting systems. Secondly, the study shows possible ways of performing breakeven analyses for a single-stage, make-to-order production in the case of random production and demand structures. To model these structures, queueing systems are employed. As a general result, we see that break-even analyses must always be performed taking into account an existing planning system. Under practical aspects, GI/G/1 systems turn out to map complex real situations realistically. From the examples given it can be concluded that one achieves different results compared with using a deterministic model even in the case of a simple, random effects approach. In particular, it is shown that stochastic modelling in general is helpful in avoiding incorrect decisions. |
| Keywords: | break-even analyses support decision system random production |
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