Parametric and neural methods for cost estimation of process vessels
International Journal of Production Economics, ISSN: 0925-5273, Vol: 112, Issue: 2, Page: 934-954
2008
- 53Citations
- 120Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
In this paper, a comparison is made between artificial neural networks and parametric functions for estimating the manufacturing cost of large-sized and complex-shaped pressure vessels in engineer-to-order manufacturing systems. In the case of large equipment built to customer's design, in fact, it is hard to estimate the production cost owing to the wide variability of vessel's size and configuration and the often scarce previous experience with similar units. However, when cost estimates are to be used for bidding purposes, a poor accuracy may have detrimental financial consequences. A cost overestimation bears the risk of making the firm uncompetitive and losing a customer, while underestimating the cost leads to winning a contract but incurring a financial loss. Furthermore, a precise knowledge of prospective resources utilization is critical for project management purposes when passing to the actual manufacture phase.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0925527307002824; http://dx.doi.org/10.1016/j.ijpe.2007.08.002; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=39749160333&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0925527307002824; https://api.elsevier.com/content/article/PII:S0925527307002824?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S0925527307002824?httpAccept=text/plain; https://dx.doi.org/10.1016/j.ijpe.2007.08.002
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know