动态学习和遗忘过程对医疗器械全服务维修定价合同优化模型的影响
The Effects of Dynamic Learning and the Forgetting Process on an Optimizing Modelling for Full-Service Repair Pricing Contracts for Medical Devices
摘要：
为了提高原始设备制造商（OEM）在全方位服务（FS）和随叫随到服务（OS）并存的市场中的盈利能力和客户服务管理水平，本文扩展了具有动态学习和遗忘效应的FS维修合同定价优化模型。在考虑维护实践中的自主学习的同时，本研究还分析了工作场所中的诱导学习和遗忘过程如何影响FS和OS组合中FS合同的定价优化模型。基于医疗行业真实数据的数值分析证明，本文讨论的增强FS定价模型具有两个主要优点：（1）可以显著提高维修效率;（2）与原有的FS模式和单一的OS维护相比，可以帮助OEM获得更好的利润。敏感性分析表明，当内部故障率增加时，最优FS价格逐渐上升，直至达到最大值，OEM的盈利能力总体增加;当诱导学习频率增加时，最优FS价格在短期下降后上升，OEM的盈利能力稳定。
英文摘要：
In order to improve the profitability and customer service management of original equipment manufacturers (OEMs) in a market where full-service (FS) and on-call service (OS) co-exist, this article extends the optimizing modelling for pricing FS repair contracts with the effects of dynamic learning and forgetting. Along with considering autonomous learning in maintenance practice, this study also analyses how induced learning and forgetting process in a workplace put impact on the pricing optimizing model of FS contracts in the portfolio of FS and OS. A numerical analysis based on real data from a medical industry proves that the enhanced FS pricing model discussed here has two main advantages: (1) It could prominently improve repair efficiency, and (2) It help OEMs gain better profits compared to the original FS model and the sole OS maintenance. Sensitivity analysis shows that if internal failure rate increases, the optimized FS price rises gradually until reaching the maximum value, and profitability to the OEM increases overall; if frequency of induced learning goes up, the optimal FS price rises after a short-term downward trend, with a stable profitability to the OEM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
