Cross-training with Imperfect training Schemes
Burak Buke (School of Mathematics, The University of Edinburgh), Ozgur, M. Araz (College of Public Health, Department of Health Promotion, Social and, Behavioral Health, University of Nebraska Medical Center), John W. Fowler, (W. P. Carey School of Business

TL;DR
This paper analyzes how variations in worker productivity affect cross-training policies in manufacturing, emphasizing the importance of consistency in productivity levels over the timing of cross-training and examining impacts of demand and productivity variability.
Contribution
It introduces a two-stage model to study the impact of productivity variability on cross-training decisions, highlighting the significance of consistency in worker productivity levels.
Findings
Consistency in productivity levels reduces the impact of timing on cross-training decisions.
Higher demand or productivity variability influences the timing and extent of cross-training investments.
Decision makers may delay or accelerate cross-training based on productivity consistency and variability levels.
Abstract
Cross-training workers is one of the most efficient ways to achieve flexibility in manufacturing and service systems to increase responsiveness to demand variability. However, it is generally the case that cross-trained employees are not as productive as employees who are originally trained on a specific task. Also, the productivity of the cross-trained workers depend on when they are cross-trained. In this work, we consider a two-stage model to analyze the affect of variations in productivity levels of workers on cross-training policies. Our results indicate that the most important factor determining the problem structure is the consistency in productivity levels of workers trained at different times. As long as cross-training can be done in a consistent manner, the productivity differences between cross-trained workers and workers originally trained on the task plays a minor role. We…
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Taxonomy
TopicsSupply Chain and Inventory Management · Scheduling and Optimization Algorithms · Auction Theory and Applications
