Empirical study on learning curves in logistics management systems

Abstract

Learning curves have been frequently applied in production/operations management and various logistics processes in many manufacturing and service organizations. However, studies on their integral use in the supply chain are recent. This paper contributes to filling this knowledge gap by measuring the impact of learning on lead time in logistics management systems. The empirical study was used as a methodological tool to demonstrate this. The logarithmic-linear models, with their terminology and calculation equations, were applied to three case studies representatives of the logistics systems proposed by the Supply Chain Operations Reference (SCOR) model: make-to-order, make-to-stock, and engineer-to-order. As a result, the first two were adjusted to the Stanford model and the third to De Jong’s model. Their learning curve, mathematical equations, and a sensitivity analysis were determined. This approach demonstrated its relevance and difference compared to previous publications, which mainly analyze links or parts of the CS.