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Engineering change management challenges and management cybernetics

机译:工程变革管理挑战和管理控制措施

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Competitive advantage in mature industries is very important for companies to thrive in the market. One possibility for attaining competitive advantage is pursuing systematic product development (PD) efforts, which keep the requirements of the customers in focus. Engineering change (EC) processes as part of PD efforts are present at the back end of almost all PD projects. EC processes usually change parts, drawings, software of the product in order to meet variations in internal and external requirements. Therefore, engineering change management (ECM) is crucial for the overall success of a PD project, as it is used to coordinate process activities which drive the maturity progress of the product. The main objective of this paper is to identify the main challenges and problems in managing engineering changes and to propose an approach that tackles these challenges from the systems perspective. To accomplish this objective, a thorough review of ECM literature was performed. In particular, topics such as ECM processes, organization and strategies to cope with ECs were analyzed. This gave the authors not only an overview of the currently most important challenges in ECM, but also ideas on how these challenges could be addressed. Because of the coordination and communication complexity that arises in ECM processes, a systemic approach is proposed. As a conclusion, the viable systems model stemming from management cybernetics is considered as the most appropriate approach to cope with such coordination and communication complexities.
机译:成熟行业的竞争优势对于公司在市场上茁壮成长非常重要。实现竞争优势的一种可能性正在追求系统的产品开发(PD)努力,这使客户的需求保持在焦点中。作为PD努力的一部分的工程变更(EC)流程存在于几乎所有PD项目的后端。 EC流程通常会改变产品的零件,图纸,软件,以满足内部和外部要求的变化。因此,工程变更管理(ECM)对于PD项目的总体成功至关重要,因为它用于协调推动产品成熟度进展的过程活动。本文的主要目标是确定管理工程变更方面的主要挑战和问题,并提出一种从系统角度来解决这些挑战的方法。为实现这一目标,对ECM文献进行了彻底审查。特别是,分析了诸如ECM过程,组织和应对ECS的战略等主题。这不仅给了作者不仅概述了ECM目前最重要的挑战,而且还有关于如何解决这些挑战的想法。由于ECM过程中出现的协调和通信复杂性,提出了一种系统方法。作为一个结论,来自管理网络通知的可行系统模型被认为是应对这种协调和通信复杂性的最合适的方法。

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