How complex a learning curve model need we use?

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Abstract
In using learning curves for management control in a typical industrial environment, we seek to identify a number of patterns in the basic data, each of which is an important source of information to be fed into the decision-making machinery. These patterns may beclassified as follows:(a) A trend-line, which in some ‘best’ sense, can be used for predicting future output. This trend-line can be influenced by proper design and planning of the product line.(b) ‘Normal’ scatter about the trend-line, which constitutes a natural and acceptable variation, and which can be used for setting upper and lower bounds predicted output.(c) ‘Abnormal’ scatter about the trend-line, which results in an unacceptable variation. It indicates an avoidable loss in production which can be traced to an assignable cause and hence eliminated by management control.(d) ‘Deterministic’ changes in the trend-line. These may be long or short term, and have an assignable cause. An example of a management-induced cause is a planned change in the size or constitution of the direct labour force.To derive a learning curve model which will cope with these four patterns simultaneously is a complex problem. The author believes there are considerable advantages in selecting the simplest model which is adequate for the purpose of efficient management control of a particular enterprise and will review a procedure for doing so. We are, after all, dealing with huge cost savings if we properly plan this activity. Understanding and implementing a simple model derived on the back of an envelope can be often more profitable for management than a sophisticatedcomputerized model, the significance of which is difficult to grasp. The paper concentrates attention on the time constant model, and its variants, as found appropriate to ‘industry learning’.

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