.png){kind=icon}
返回
Developing due dates in an engineer-to-order engineering environment
.png){kind=icon}
PRE
AI
DOI：10.1080/00207543.2014.940072.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Engineer-to-order (ETO) firms comprise approximately one-fourth of all North American manufacturing, and the number is growing. These firms produce complex one-of-a-kind products and, like most firms, desire shorter lead times as a key component to cost competitiveness. In ETO firms, the engineering process is the largest controllable consumer of lead time using one-half of the total. Hence, one critical process is to accurately determine the engineering due date. However, unlike other manufacturing models such as Make to Stock or Make to Order, the design for an ETO product is not realised until after the engineering process has been completed; therefore, the only information available does not include data normally required by most due date-setting algorithms. The question then becomes how does one accurately determine the engineering due date in a complex transactional process when the job has not even been designed yet? This paper investigates this issue in the context of the engineering process within the ETO model. Analytical research is conducted in conjunction with multiple ETO firms. Several common factors are identified which drive complexity in the ETO engineering environment. A new framework and algorithm are then presented for using these factors to predict ETO engineering flow times in the absence of normally assumed information. Comparison of the performance of this new algorithm with that reported in the literature shows it to be a statistically significant improvement.
Keyword:
due dates
job complexity
Engineer-to-order
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.3
论文数:
1.1W
被引数:
3.7W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容