Optimizing Maintenance Contract Pricing Through Comprehensive Risk Assessment

This study develops a risk-informed pricing framework for maintenance contracts in the trucking industry. We apply a comprehensive methodology combining statistical segmentation, cost analysis, and Value at Risk (VaR) modeling to a dataset of nearly 2000 contracts. Contracts were grouped by duration and truck usage, and distributions were fitted to estimate costs and compute risk premiums. Two pricing models are proposed: a traditional VaR-based approach and an adaptive model that incorporates distribution tail heaviness. Results show that the adaptive model resolves the counterintuitive decline in prices at higher risk levels and yields more stable, flexible premiums. These findings underscore the importance of tail-risk metrics in contract pricing to better capture cost uncertainty. The approach supports more accurate risk management and sustainable pricing strategies for maintenance services.