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In order to improve the profitability and customer service management oforiginal equipment manufacturers (OEMs) in a market where full-service (FS) andon-call service (OS) co-exist, this article extends the optimizing modellingfor pricing FS repair contracts with the effects of dynamic learning andforgetting. Along with considering autonomous learning in maintenance practice,this study also analyses how induced learning and forgetting process in aworkplace put impact on the pricing optimizing model of FS contracts in theportfolio of FS and OS. A numerical analysis based on real data from a medicalindustry proves that the enhanced FS pricing model discussed here has two mainadvantages: (1) It could prominently improve repair efficiency, and (2) It helpOEMs gain better profits compared to the original FS model and the sole OSmaintenance. Sensitivity analysis shows that if internal failure rateincreases, the optimized FS price rises gradually until reaching the maximumvalue, and profitability to the OEM increases overall; if frequency of inducedlearning goes up, the optimal FS price rises after a short-term downward trend,with a stable profitability to the OEM.

The Effects of Dynamic Learning and the Forgetting Process on an Optimizing Modelling for Full-Service Repair Pricing Contracts for Medical Devices