On Acquiring Automation for Value Improvement under Learning Effects

Presents a dynamic programming model for studying the effects of automation acquisition on the value, cost, and quality control processes in an aggregate single product environment. The model provides the optimal automation acquisition policy, that is the optimal amount of automation to be acquired and the optimal timing for acquiring it, so that the accumulated net product value can be maximized. The model can be used with different sets of learning rates and cost data. It can also be used with non-uniform learning rates among the different processes, and non-uniform automation effects on the value, cost, and quality control learning curves. The cases both of unbounded and bounded learning curves are examined. Selective results demonstrate that the early acquisition of the optimal amount of automation enhances the accumulated net product value.