Workforce Cross‐Training in Call Centers



Queueing models have been developed in order to experiment on the effects of workforce cross training on call center performance. Simulation results show that the impact of cross‐training seems to be stronger in large multi‐type call centers as compared of small contact centers. 2‐type models have been deployed in order to show the impact of cross‐training in view of misclassified calls. Accelerating the time to identify misclassification events by adequate technology can be a significant cost saver, since similar performance can be achieved by partial cross‐training as compared of a completely cross‐trained workforce. Call centers with lower usage of cross‐training are more reluctant to variation in the fraction of a second adjacent service time in a cross‐trained pool, when identification of a misclassification event preceded. Thus, partial cross‐training hedges against fluctuations in this rate and relieves psychological pressure from call agents. Also, if only third of the workforce is cross‐trained, high increases in model performance can be confirmed, whereas little benefit is added by higher amounts of cross‐training. Further, in terms of customer abandonment, partial cross‐trained systems were found to outperform a completely flexible workforce. We conclude, that partial cross‐training outperforms full cross‐training from different perspectives, if the cross‐trained pool size is chosen with care. The assignment policy of calls in partially cross‐trained systems strongly influences customer waiting times. Similarly, this result was achieved for a Markovian loss model with no waiting space. When accounting for misclassification events, we show that a proof of preferred call admission to dedicated servers will most likely fail; since simulation results display significant performance improvement for primary admission to the cross‐trained agent pool, when assuming descent misclassification probabilities. It is suggested to find probability bounds, below and above which preferred admission to the dedicated and cross‐trained station may be optimal, respectively. Research in the field of workforce cross‐training, when misclassification events persist, is encouraged by means of prevalent trends towards multi‐type call centers, that we believe to be a main driver of misclassified calls.

This event is organised by the Econometric Institute.
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