Evaluating design implementation strategies using enterprise simulation
Section snippets
Introductory remarks
In the competitive era of modern manufacturing, it is no longer sufficient to have the most elegant product design. Competitive firms must also have an established means of converting design ideas into cost effective manufacturing. Design for manufacture and assembly (DFMA) and concurrent engineering (CE) techniques are among the most consequential of cost reduction strategies to emerge in the past quarter century. These strategies force product designers to consider process issues during the
Background information and supporting literature
As indicated above, the purpose of this paper is to evaluate the enterprise level effects of using capacity-based DFX strategies (DFEE in particular). The objectives for the paper are twofold:(1) to examine the enterprise level effects of the DFEE strategy, which we feel is fundamentally new and different, (2) to showcase the capabilities of WBS in comparison to results obtained from static machine utilisation models and discrete-event simulation models presented in [2], [3].
Neither of the latter
Experimental plan
The DFEE algorithm presented in [2] suggests three means of dealing with capacity problems. The first means is to consider a product re-design to take advantage of slack manufacturing capacity. The second considers the possibility of flexibility in build schedules to allow introduction of new parts to a more favourable mix either earlier or later than originally planned. Finally, DFEE suggests adding capital equipment as a last resort. The experimental plan in this paper includes scenarios
Tools and validation efforts
Two types of model validation efforts have been undertaken:(1) The first validation effort verifies that the critical task of determining maximum production levels for the various scenarios is accurate. (2) The second means of validation is brought about by using some identical scenarios to those published in [2]. The original publication was based upon actual industrial data and thus some measure of validation, or at least of practicality of results, is assured.
The task of determining and
Results of experimentation
Previously published results indicate that DFEE methods can lead to significant improvements relative to manufacturing metrics. The 40% throughput improvement for the re-designed product in [2] is a case in point. Even so, the effect of DFEE methods is unknown at the enterprise level. It is possible that very counterintuitive results could exist. Consider, for example, a very interesting paper by Sterman et al. [15] that examines the connection between quality improvement systems and financial
Implications and future work
The primary objectives of the research presented herein are to demonstrate that more holistic product design strategies are needed to fully exploit process capabilities and to illustrate the efficacy of enterprise simulation in evaluating the financial effects of design alternatives. These objectives have been fully met.
The DFEE design methodologies consistently produce the highest cash flows, have the highest total profit, and the highest ROE values in all scenarios examined by statistically
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