A successful industry strategy should be managed to integrate the decisions, such as pricing, production, and customer services, in order to maximize profits. In fact, some research has been carried out to cope with the multiple considerations for the case in which sufficient historical data are available. However, if sufficient historical data cannot be gathered to confidently estimate the deterioration of a new product, then the solution may not be assertively reliable. In dealing with such a problem for the situation of scarce historical data, a Bayesian analysis should be suitable because it can effectively assess the deterioration based on experts' opinions and possibly few relevant data. In this paper, we employed a mathematical programming approach along with a Bayesian updating process to tackle such a complex decision problem, and the optimal prior and posterior decisions of pricing scheme, production plan, and warranty policy can thus be determined simultaneously. In addition, we provided a computerized architecture to help decision makers in implementing the proposed approach. Finally, a practical application case was used to demonstrate the usefulness of the proposed model.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence