Steady state analysis for improving manufacturing productivity

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Author: 
Abid Thyab Al Ajeeli
Abstract: 

This paper describes techniques that simulate a number of statistical distribution models in order to understand the behavior of manufacturing system processes and to identify irregularities. These simulation techniques provide top management with clear understanding of the flow of manufacturing activities. Manufacturing productivity can dramatically be improved by setting an efficient management plan. A management plan is the process of assessing an organization's goals and creating a realistic, detailed plan of action for meeting the organization's goals. A management plan takes into consideration short- and long-term corporate strategies. The basic steps in the management planning process involve creating a road map that outlines each task the company must accomplish to meet its overall objectives. To achieve an effective and productive plan one need to study and investigate bottleneck problems associated with manufacturing processes. One approach that could identify and detect bottleneck is the understanding of the steady state of a manufacturing system. A steady state implies that the System State is independent of its initial start-up conditions. The steady state provides top managements with clear picture of how to make their production line more effective. A steady state is essentially required in order to show any inconsistencies in the environment of manufacturing process. For example, steady state furnishes top management with sufficient items of information regarding the efficiency and skill of their employees and whether they properly trained in order to maximize efficiency of production lines. The steady state confirms that the better trained employees the better productivity and effectiveness be achieved. This step alone could increase the efficiency of production process and make manufacturing plant more profitable. The author has designed, developed, and implemented the simulation models. These simulation models are more flexible, adjustable, robust, and reusable.

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   Vol. 07, Issue 03, March 2017

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