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

TL;DR

This paper enhances full-service repair pricing models for medical devices by incorporating dynamic learning and forgetting effects, demonstrating improved efficiency and profitability through numerical analysis.

Contribution

It introduces a novel optimization model that accounts for learning and forgetting processes, providing better pricing strategies for OEMs in medical device maintenance.

Findings

Enhanced pricing model improves repair efficiency

OEM profits increase with optimized pricing

Sensitivity analysis reveals effects of failure rates and learning on pricing

Abstract

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.