Chained cross-training of workers for robust performance

Abstract

Training workers to perform multiple tasks can improve workforce agility for dealing with variations in workload. However, cross-training can be costly, time consuming to implement, is limited by worker learning capacity, and can lead to ambiguity about work responsibilities. Therefore, it is important to implement cross-training in the most efficient way and especially, due to the training time required, in a way that is robust to system changes. We use queueing and simulation analysis to investigate cross-training in the context of maintenance in a manufacturing plant. The tasks are independent and can be represented as a set of parallel queues that are served by dedicated and cross-trained workers. We propose a cross-training strategy called chaining, in which a few workers are strategically cross-trained, and show that it yields most of the benefits of cross-training all workers, with much less effort. Most importantly, we demonstrate that cross-training workers to form a “complete chain” is extremely robust in the following ways: (i) it is insensitive to the variety of ways a complete chain can be formed; (ii) it performs well even if there are major changes to or uncertainty in system parameters (such as mean task arrival rates); and (iii) performance is insensitive to control decisions that, without complete chaining, can significantly harm performance.