Learning, Unlearning and Re-Learning Curves

Learning is an empirical phenomenon whereby people or organisations undergo a level of efficiency improvement with recurring tasks. Alan Jones pragmatic guide to this important element within estimating introduces two key learning curve models: Wright and Crawford and explains where, how and when to apply them.

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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