Application of Learning Curves in Operations Management Decisions

  • Alexandra Tamás Department of Management and Business Economics, Faculty of Economic and Social Sciences, Budapest University of Technology and Economics, H-1521 Budapest, P.O.B. 91, Hungary
  • Tamás Koltai Department of Management and Business Economics, Faculty of Economic and Social Sciences, Budapest University of Technology and Economics, H-1521 Budapest, P.O.B. 91, Hungary

Abstract

In the time of industry 4.0 and big data, methods which are based on the collection and the processing of a large amount of data in order to support managerial decisions have outstanding significance. The learning curve theory pertains to these methods. The purpose of this paper is to explore some application possibilities of the classical learning curve in manufacturing and service operations. The learning effect assumes that as the quantity of units manufactured increases, the time needed to produce an individual unit decreases. The function describing this phenomenon is the learning curve. Various learning curves have been developed and applied in the area of production economics and much research studies the significance of the learning effect in management decisions. This study summarizes the main learning curve models and demonstrates how learning can be considered in three classical areas of operations management. First, the calculation of economic manufacturing quantity in the presence of learning is studied. Next, the effect of learning in break-even analysis and assembly line balancing is explored. The results show that with the consideration of the learning effect, calculations become more complex and require greater efforts, but the application of the learning curve concept can provide valuable insight both at operational and strategic levels.

Keywords: learning curve, operations management, break-even analysis, economic production quantity, assembly line balancing
Published online
2019-12-08
How to Cite
Tamás, A. and Koltai, T. (2020) “Application of Learning Curves in Operations Management Decisions”, Periodica Polytechnica Social and Management Sciences, 28(1), pp. 81-90. https://doi.org/10.3311/PPso.14136.
Section
Articles