دانلود مقاله ISI انگلیسی شماره 7026
عنوان فارسی مقاله

ابزار پشتیبانی تصمیم گیری مبتنی بر گسترش کارکرد کیفیت QFD و FMEA برای انتخاب فن آوری های اتوماسیون تولید

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
7026 2008 7 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A decision support tool based on QFD and FMEA for the selection of manufacturing automation technologies
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Robotics and Computer-Integrated Manufacturing, Volume 24, Issue 4, August 2008, Pages 501–507

کلمات کلیدی
تصمیم گیری - سیستم های تولید - گسترش کارکرد کیفیت
پیش نمایش مقاله
پیش نمایش مقاله ابزار پشتیبانی تصمیم گیری مبتنی بر گسترش کارکرد کیفیت QFD و FMEA برای انتخاب فن آوری های اتوماسیون تولید

چکیده انگلیسی

With the advent of the new challenge to design a more lean and responsive computer-integrated manufacturing system, firms have been striving to achieve a coherent interaction between technology, organisation, and people to meet this challenge. This paper describes an integrated approach developed for supporting management in addressing technology, organisation, and people at the earliest stages of manufacturing automation decision-making. The approach uses both the quality function deployment (QFD) technique and the failure mode and effects analysis (FMEA) technique. The principal concepts of both applications are merged together to form a decision tool; QFD in its ability to identify the most suitable manufacturing automation alternative and FMEA in its ability to identify the associated risk with that option to be addressed in the manufacturing system design and implementation phases. In addition, this paper presents the results of a practical evaluation conducted in industry.

مقدمه انگلیسی

Today the manufacturing world is facing major pressures due to the globalisation of markets. Internal and external organisational pressures have led to increased competition, market complexity, and new customer demands. It has been noted how organisations adopt lean or agile manufacturing strategies to overcome this problem [1]. These strategies have different approaches and elements to address in the design of the manufacturing system, but they all depend on two common things: acquiring technology and the effective operation of this technology by humans. Developments in computer-integrated manufacturing systems and the methods by which they are designed have induced firms to shift their emphasis towards human factors, particularly man–machine interaction, and to consider people as assets instead of costs. In the manufacturing systems design literature, emphasis is directed towards producing a coherent interaction between technology, organisation, and people to overcome new competitive challenges. Various authors have pointed out the importance of addressing human factors generally in the evaluation and design of manufacturing systems, calling specifically for the adoption of a balanced method based on technology, organisation, and people [2], [3] and [4]. Furthermore, the literature on investment evaluation is continuously being updated to accommodate the new market demands and manufacturing technology [5]. The changes in the market environment and justification of new manufacturing technologies have caused management to shift away from relying on traditional economic justification to the incorporation of intangible benefits and organisational strategy [6]. However, there continue to be reports of investment failures and difficulties in computer-integrated manufacturing systems implementation, due to the lack of addressing man–machine interaction appropriately [2] and [7]. Moreover, an investigation into human factors and manufacturing automation clearly illustrated that despite managers’ interest in having a balanced consideration of both technology and humans in the planning and designing of their manufacturing system, and their efforts in placing more emphasis on the importance of human elements in the manufacturing environment; in practice they were still not appropriately considering man–machine interaction in their manufacturing automation decision-making [8]. In addition, it was noticed that management needed to be supported in improving man–machine interaction at the earliest stage of their manufacturing automation decision-making process, in order for them to avoid the pitfalls of over-automation which can lead to the failure of computer-integrated manufacturing systems to deliver cost-effective and flexible operations. In an attempt to respond to this, a decision tool for the integration of technology, organisation, and people during the automation decision-making process has been developed. The decision tool uses the quality function deployment (QFD) technique to link management's automation investment objectives with technology, organisation, and people evaluation to determine the best alternative. Thereafter, the failure mode and effects analysis (FMEA) technique is deployed to draw attention to any problems that might be associated with that option in terms of design and implementation. This paper describes the approach and the results of an evaluation in industry. It is organised into five sections. Section 2 contains a general view of the developed method while Section 3 describes the technique in detail; the methodology is applied in a real case in Section 4 followed by a discussion in Section 5 and conclusions in Section 6.

نتیجه گیری انگلیسی

The objectives of this paper were to highlight the importance of having a balanced consideration of technology, organisation, and people issues in manufacturing automation investment, and to present a decision methodology that addressed this issue. This paper has described the development of a manufacturing automation decision support tool that is intended to support management not only in improving their decision by addressing the right proportions of technical, organisational, and people issues, but also to be prepared for implementation and operation unforeseen problems. Furthermore, the results from a practical application in industry were presented. Overall, the results demonstrated the feasibility, usability, and usefulness of the proposed methodology. Future work needs to be done to further assess the benefits and weakness of the method proposed. Another interesting extension for this research would be the application of this methodology in other areas of the manufacturing decision-making process, such as manufacturing process selection.

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