Learning, unlearning and re-learning curves:
Learning, Unlearning and Re-learning Curves (Volume IV of the Working Guides to Estimating & Forecasting series) focuses in on Learning Curves, and the various tried and tested models of Wright, Crawford, DeJong, Towill-Bevis and others. It explores the differences and similarities between the v...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Abingdon, Oxon
Routledge
2018
|
| Schriftenreihe: | Working guides to estimating & forecasting
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| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Learning, Unlearning and Re-learning Curves (Volume IV of the Working Guides to Estimating & Forecasting series) focuses in on Learning Curves, and the various tried and tested models of Wright, Crawford, DeJong, Towill-Bevis and others. It explores the differences and similarities between the various models and examines the key properties that Estimators and Forecasters can exploit. A discussion about Learning Curve Cost Drivers leads to the consideration of a little used but very powerful technique of Learning Curve modelling called Segmentation, which looks at an organisation's complex learning curve as the product of multiple shallower learning curves. Perhaps the biggest benefit is that it simplifies the calculations in Microsoft Excel where there is a change in the rate of learning observed or expected. The same technique can be used to model and calibrate discontinuities in the learning process that result in setbacks and uplifts in time or cost. This technique is compared with other, better known techniques such as Anderlohr's. Equivalent Unit Learning is another, relative new technique that can be used alongside traditional completed unit learning to give an early warning of changes in the rates of learning. Finally, a Learning Curve can be exploited to estimate the penalty of collaborative working across multiple partners. Supported by a wealth of figures and tables, this is a valuable resource for estimators, engineers, accountants, project risk specialists, as well as students of cost engineering |
| Beschreibung: | Description based on print version record and CIP data provided by publisher |
| Beschreibung: | 1 online resource |
| ISBN: | 9781351661478 1351661477 9781351661461 1351661469 9781315160092 1315160099 9781351661454 1351661450 |
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| 520 | |a Learning, Unlearning and Re-learning Curves (Volume IV of the Working Guides to Estimating & Forecasting series) focuses in on Learning Curves, and the various tried and tested models of Wright, Crawford, DeJong, Towill-Bevis and others. It explores the differences and similarities between the various models and examines the key properties that Estimators and Forecasters can exploit. A discussion about Learning Curve Cost Drivers leads to the consideration of a little used but very powerful technique of Learning Curve modelling called Segmentation, which looks at an organisation's complex learning curve as the product of multiple shallower learning curves. Perhaps the biggest benefit is that it simplifies the calculations in Microsoft Excel where there is a change in the rate of learning observed or expected. The same technique can be used to model and calibrate discontinuities in the learning process that result in setbacks and uplifts in time or cost. This technique is compared with other, better known techniques such as Anderlohr's. Equivalent Unit Learning is another, relative new technique that can be used alongside traditional completed unit learning to give an early warning of changes in the rates of learning. Finally, a Learning Curve can be exploited to estimate the penalty of collaborative working across multiple partners. Supported by a wealth of figures and tables, this is a valuable resource for estimators, engineers, accountants, project risk specialists, as well as students of cost engineering | ||
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| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
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| id | DE-604.BV047011397 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T15:58:10Z |
| indexdate | 2024-07-10T09:00:05Z |
| institution | BVB |
| isbn | 9781351661478 1351661477 9781351661461 1351661469 9781315160092 1315160099 9781351661454 1351661450 |
| language | English |
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| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Routledge |
| record_format | marc |
| series2 | Working guides to estimating & forecasting |
| spelling | Jones, Alan 1953- Verfasser aut Learning, unlearning and re-learning curves Alan Jones Abingdon, Oxon Routledge 2018 1 online resource txt rdacontent c rdamedia cr rdacarrier Working guides to estimating & forecasting Description based on print version record and CIP data provided by publisher Learning, Unlearning and Re-learning Curves (Volume IV of the Working Guides to Estimating & Forecasting series) focuses in on Learning Curves, and the various tried and tested models of Wright, Crawford, DeJong, Towill-Bevis and others. It explores the differences and similarities between the various models and examines the key properties that Estimators and Forecasters can exploit. A discussion about Learning Curve Cost Drivers leads to the consideration of a little used but very powerful technique of Learning Curve modelling called Segmentation, which looks at an organisation's complex learning curve as the product of multiple shallower learning curves. Perhaps the biggest benefit is that it simplifies the calculations in Microsoft Excel where there is a change in the rate of learning observed or expected. The same technique can be used to model and calibrate discontinuities in the learning process that result in setbacks and uplifts in time or cost. This technique is compared with other, better known techniques such as Anderlohr's. Equivalent Unit Learning is another, relative new technique that can be used alongside traditional completed unit learning to give an early warning of changes in the rates of learning. Finally, a Learning Curve can be exploited to estimate the penalty of collaborative working across multiple partners. Supported by a wealth of figures and tables, this is a valuable resource for estimators, engineers, accountants, project risk specialists, as well as students of cost engineering Learning curve (Industrial engineering) Industrial productivity / Statistical methods https://www.taylorfrancis.com/books/9781315160092 Verlag URL des Erstveroeffentlichers Volltext |
| spellingShingle | Jones, Alan 1953- Learning, unlearning and re-learning curves Learning curve (Industrial engineering) Industrial productivity / Statistical methods |
| title | Learning, unlearning and re-learning curves |
| title_auth | Learning, unlearning and re-learning curves |
| title_exact_search | Learning, unlearning and re-learning curves |
| title_exact_search_txtP | Learning, unlearning and re-learning curves |
| title_full | Learning, unlearning and re-learning curves Alan Jones |
| title_fullStr | Learning, unlearning and re-learning curves Alan Jones |
| title_full_unstemmed | Learning, unlearning and re-learning curves Alan Jones |
| title_short | Learning, unlearning and re-learning curves |
| title_sort | learning unlearning and re learning curves |
| topic | Learning curve (Industrial engineering) Industrial productivity / Statistical methods |
| topic_facet | Learning curve (Industrial engineering) Industrial productivity / Statistical methods |
| url | https://www.taylorfrancis.com/books/9781315160092 |
| work_keys_str_mv | AT jonesalan learningunlearningandrelearningcurves |