Risk-Based Dynamic Pricing via Failure Prediction

Equipment-as-a-service is gaining popularity in industries as it provides the customers with high flexibility and scalability and allows the service provider to optimize operations so that both the service provider and the customers are benefited. However, pricing modeling is a critical challenge in EaaS due to the change of business objectives from selling products to providing services. Particularly, EaaS faces severe high-cost issues as unexpected equipment failures can result in expensive downtime costs. In this paper, we take advantage of Failure Prediction (FP) in Predictive Maintenance (PdM) domain and Dynamic Pricing to build a hybrid model that balances cost and benefit for EaaS. First, we build a Gradient Boosting based FP model to predict failure probabilities. Then a Risk-based Dynamic Pricing (RBDP) model uses the confusion matrix of FP to estimate the cost effects of predicted failures with a constrained objective function. Finally, the FP model and RBDP model are trained simultaneously to minimize the objective function so that it compensates for the failure cost and maximize the overall profit gain. The proposed hybrid modeling approach is compared with traditional FP without pricing policy approach, and FP with a static pricing policy approach to show significant improvements in profit increases.