Application of machine learning techniques for cost estimation of engineer to order products.
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| Title: | Application of machine learning techniques for cost estimation of engineer to order products. |
|---|---|
| Authors: | Rapaccini, Mario1 (AUTHOR), Cadonna, Veronica Loew1 (AUTHOR), Leoni, Leonardo1 (AUTHOR) leonardo.leoni@unifi.it, De Carlo, Filippo1 (AUTHOR) |
| Source: | International Journal of Production Research. Oct2023, Vol. 61 Issue 20, p6978-7000. 23p. 3 Diagrams, 7 Charts, 5 Graphs. |
| Subjects: | Engineers, Machine learning, Feature selection, Solid dosage forms, Engineering, Cost |
| Abstract: | Cost engineering capabilities are becoming increasingly important for the competitiveness of industrial firms, especially for engineer to order products (ETOPs). Despite this relevance, the literature on the use of advanced data-driven methodologies, such as machine learning (ML), for early cost estimation (CE) of ETOPs is quite sparse. Furthermore, ML has still seen little use in real industrial applications due to several challenges. Accordingly, the objective of this paper is threefold: (a) to develop a solid early CE approach for ETOPs, including feature selection; (b) to investigate the benefits of adopting ML for ETOPs' CE; (c) to identify how ML can be introduced into real industrial context with little knowledge on ML. Long action research has been carried out with a large industrial company that produces Oil & Gas ETOPs. We observed how ML facilitates the exploration of the relationships between the choices of early design stages and the CE. ML algorithms also allowed to both capture the high variability of the data and test different combinations of cost drivers in very effective ways. The project resulted in an accurate CE framework with an iterative feature selection process and an approach for introducing ML into a real industrial context. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 170023613 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Application of machine learning techniques for cost estimation of engineer to order products. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rapaccini%2C+Mario%22">Rapaccini, Mario</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cadonna%2C+Veronica+Loew%22">Cadonna, Veronica Loew</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Leoni%2C+Leonardo%22">Leoni, Leonardo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> leonardo.leoni@unifi.it</i><br /><searchLink fieldCode="AR" term="%22De+Carlo%2C+Filippo%22">De Carlo, Filippo</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Oct2023, Vol. 61 Issue 20, p6978-7000. 23p. 3 Diagrams, 7 Charts, 5 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Engineers%22">Engineers</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Solid+dosage+forms%22">Solid dosage forms</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering%22">Engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Cost%22">Cost</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Cost engineering capabilities are becoming increasingly important for the competitiveness of industrial firms, especially for engineer to order products (ETOPs). Despite this relevance, the literature on the use of advanced data-driven methodologies, such as machine learning (ML), for early cost estimation (CE) of ETOPs is quite sparse. Furthermore, ML has still seen little use in real industrial applications due to several challenges. Accordingly, the objective of this paper is threefold: (a) to develop a solid early CE approach for ETOPs, including feature selection; (b) to investigate the benefits of adopting ML for ETOPs' CE; (c) to identify how ML can be introduced into real industrial context with little knowledge on ML. Long action research has been carried out with a large industrial company that produces Oil & Gas ETOPs. We observed how ML facilitates the exploration of the relationships between the choices of early design stages and the CE. ML algorithms also allowed to both capture the high variability of the data and test different combinations of cost drivers in very effective ways. The project resulted in an accurate CE framework with an iterative feature selection process and an approach for introducing ML into a real industrial context. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2022.2141907 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 6978 Subjects: – SubjectFull: Engineers Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Solid dosage forms Type: general – SubjectFull: Engineering Type: general – SubjectFull: Cost Type: general Titles: – TitleFull: Application of machine learning techniques for cost estimation of engineer to order products. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rapaccini, Mario – PersonEntity: Name: NameFull: Cadonna, Veronica Loew – PersonEntity: Name: NameFull: Leoni, Leonardo – PersonEntity: Name: NameFull: De Carlo, Filippo IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 10 Text: Oct2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 61 – Type: issue Value: 20 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
