Effects of imperfect IoT-enabled diagnostics on maintenance services : a system design perspective

Abstract

Although many firms have deployed Internet of Things (IoT)-based diagnostics to predict equipment failures and maintenance requirements, they receive imperfect demand signals due to inadequate data quality and the use of ineffective predictive tools. A strategic queuing model was developed in this study to investigate a maintenance service provider's optimal capacity allocation and pricing decisions in the presence of imperfect IoT-based diagnostics. In addition, we considered the heterogeneous service rates resulting from the accelerating effect of IoT-based diagnostics. The results of the study revealed that, on the one hand, the optimal service rate always increases with the diagnostic quality; on the other hand, if customer error cost is higher than that of the service provider, the optimal price increases with the diagnostic quality; otherwise, it decreases with the diagnostic quality. Furthermore, we found that the error costs for the two major stakeholders—the service provider and the customer—may affect the equilibrium in different ways. Finally, in the presence of the accelerating effect, we found that although the accelerating effect can improve the average service rate, system congestion actually increases if the effect becomes increasingly obvious; however, the service provider's profit improves in this case.