Optimal joint design of two-dimensional warranty and preventive maintenance policies for new products considering learning effects

• Published in: Computers & industrial engineering Vol. 166; p. 107958
• Main Authors: Ruan, Yuanpeng, Wu, Guochang, Luo, Xinggang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2022
• Subjects: Learning effects; Preventive maintenance planning; Profit maximization; Two-dimensional warranty; Warranty period optimization; Two-dimensional warranty; Learning effects; Warranty period optimization; Preventive maintenance planning; Profit maximization
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2022.107958

•A novel model jointly considering 2D warranty design and PM planning is established.•Two learning effects are creatively considered in the 2D warranty design process.•A heuristic optimization method faster than the grid search method is proposed. Providing free preventive maintenance (PM) for products during the warranty period is a prevalent type of combinational warranty design in the market, particularly in the automobile industry. The characteristics of certain new products, such as short life cycles, frequent updates, and complex functions require customers and manufacturers to continuously learn during the use and maintenance process, which leads to learning effects of both parties in the warranty period. In this study, we investigate the “PM–two-dimensional (2D) warranty design” problem with the aim of maximizing the profit of the manufacturer. Profit is affected by the number of PMs as well as the age- and usage-based PM intervals. Moreover, we explore the influence of the learning effects on the 2D warranty design by introducing two scenarios. A heuristic algorithm is applied to solve the optimization models for various policies under different scenarios. A comparison of the results of the studied models reveals that simultaneous consideration of PM planning and 2D warranty design is a win-win strategy for the manufacturer and customers, and learning effects are critical elements in the design process, which can improve the accuracy and rationality of the design. A few management recommendations are provided based on the sensitivity analysis results.