Rewards-Supply Aggregate Planning in the Management of Loyalty Reward Programs---A Stochastic Linear Programming Approach.

摘要

Loyalty reward programs (LRPs), initially developed as marketing programs to enhance customer retention, have now become an important part of customer-focused business strategies. One of the operational challenges faced by LRP managers is that of planning for the supply of rewards in a given period of time. We have developed three mathematical models for solving this problem under various settings. In each setting, the problem has been formulated as a two-stage stochastic linear programming model with recourse. A heuristic optimization procedure based on sample average approximation (SAA) is proposed for solving each of these models. We carried out extensive computational experiments to demonstrate the viability of the modeling and solution approaches for solving realistically sized (large-scale) problems as well as to evaluate the impacts of changes that internal dynamics and external uncertainties have on the performance of a loyalty reward program operating as a profit center. Findings from these computational studies have led to a number of managerial insights. Our results show that demand variability has negative impacts on LRP performance. As such, adopting an option contract provides good means for mitigation, especially when demand uncertainty is high. Our results have also shown that offering cooperative advertisement through bonus points is a double-edged sword. It may bring in higher LRP profitability, but it also results in higher liability. When demand variability is high, offering bonus points is not preferred in rewards-supply planning. Finally, our results indicate that budget tightness and liability control tightness have an impact on LRP performance to an extent that varies across different system settings.;This research contributes to the literature in several ways: it synthesizes and extends the concept of supply chain management in the context of LRPs and it enhances the understanding of LRPs and rewards-supply planning problems through quantitative modeling and stochastic programming. Our findings will help LRP managers to understand the roles of cooperative advertising through bonus points and option contract in planning for the supply of rewards as well as to evaluate the impact of changes in the internal dynamics and external uncertainties on the performance of loyalty reward program operations.