یک روش تجزیه برای طراحی سیستم تولید
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|10638||2001||19 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 9435 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
- تولید محتوا با مقالات ISI برای سایت یا وبلاگ شما
- تولید محتوا با مقالات ISI برای کتاب شما
- تولید محتوا با مقالات ISI برای نشریه یا رسانه شما
پیشنهاد می کنیم کیفیت محتوای سایت خود را با استفاده از منابع علمی، افزایش دهید.
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Manufacturing Systems, Volume 20, Issue 6, 2001–2002, Pages 371–389
Successful manufacturing system designs must be capable of satisfying the strategic objectives of a company. There exist numerous tools to design manufacturing systems. Most frameworks, however, do not separate objectives from means. As a result, it is difficult to understand the interactions among different design objectives and solutions and to communicate these interactions. The research described in this paper develops an approach to help manufacturing system designers: (1) clearly separate objectives from the means of achievement, (2) relate low-level activities and decisions to high-level goals and requirements, (3) understand the interrelationships among the different elements of a system design, and (4) effectively communicate this information across a manufacturing organization. This research does so by describing a manufacturing system design decomposition (MSDD). The MSDD enables a firm to simultaneously achieve cost, quality, delivery responsiveness to the customer, and flexibility objectives. The application section illustrates how the MSDD can be applied in conjunction with existing procedural manufacturing engineering.
Designing a manufacturing system to achieve a set of strategic objectives involves making a series of complex decisions over time (Hayes and Wheelwright 1979). Making these decisions in a way that supports a firm’s high-level objectives requires an understanding of how detailed design issues affect the interactions among various components of a manufacturing system. This paper presents an axiomatic design-based decomposition of a general set of functional requirements and design parameters for a manufacturing system and explains how this decomposition can be used as an approach to aid engineers and managers in the design and operation of manufacturing systems.In practice, designing the details of manufacturing systems (equipment design and specification,layout, manual and automatic work content, material and information flow, etc.) in a way that is supportive of a firm’s business strategy has proven to be a difficult challenge. Because manufacturing systems are complex entities involving many interacting elements, it can be difficult to understand the impact of detailed, low-level deficiencies and change the performance of a manufacturing system as a whole.Shingo (1988) discusses the problem of optimizing individual operations as opposed to the overall process (referred to as the manufacturing system herein). Hopp and Spearman (1996) describe the same problem, calling it a reductionist approach where the focus is on breaking a complex system into its more simple components and then analyzing each component separately. They go on to point out that “too much emphasis on individual components can lead to a loss of perspective for the overall system,” and that a more holistic approach can lead to better overall system performance.The framework presented in this paper develops a tool to help manufacturing system designers (1) clearly separate objectives from the means of achieving them, (2) relate low-level activities and decisions to high-level goals and requirements, (3)understand the interrelationships among the different elements of a system design, and (4) effectively communicate this information across the organization.The structure of the framework is based onaxiomatic design.The decomposition framework for manufacturing system design and control integrates several different disciplines, such as plant layout design and operation,human work organization, ergonomics, equipment design, material supply, use of information technology, and performance measurement. The target industries of the framework are medium to highvolume repetitive manufacturing companies.
نتیجه گیری انگلیسی
This paper has presented an axiomatic designbased decomposition of a general set of functional requirements and design parameters for a manufacturing system. This decomposition applies to a wide variety of manufacturing systems in different competitive environments. It is particular suitable for medium to high-volume repetitive manufacturing. Other similar frameworks reviewed do not match objectives to means when relating low-level design decisions to higher-level system objectives. The use of the principles of axiomatic design was also reviewed with an emphasis on the structured decomposition process it provides. The resulting decomposition has been found to be a useful approach for: 1.Understanding the relationships between highlevel system objectives (increasing customer satisfaction, reducing system throughput time, and so on) and lower-level design decisions (equipment design and selection, system layout, etc.) 2.Understanding the interrelations, precedence,and dependencies among various elements of a system design that determine its ability to meet high-level requirements and objectives. Future work must combine the approach presented with existing manufacturing system design tools such as those discussed in the integration section. Because the MSDD covers many different aspects of manufacturing systems, a foundation has been developed to integrate a wide diversity of systems engineering design tools. While the MSDD states interrelationships between design solutions and design objectives, it is also desirable to quantify these interrelationships. Additional work has been done to associate performance measurables with each functional requirement of the MSDD (Cochran et al. 2000).