When to swing into high gear? A time-limit approach to problem escalation
研究了制造和服务中随机问题升级的时间限制策略，通过随机动态规划模型找到最优时间限制，以最小化问题处理成本，对运营管理者有参考价值。
In manufacturing and services, random problems arise that disrupt normal operations. Organizations must resolve these problems in a timely and cost-efficient manner—which can be a daunting task. A typical management response is assigning escalation routes whereby the lowest tier initially owns a problem that may later be escalated to higher tiers. To minimize the costs associated with these problems, we consider a management policy that specifies a time limit beyond which problems must move up a tier; the setting is formulated as a stochastic dynamic program. For scenarios involving a single problem type, we find that optimal time limits can exist when the problem service times are generally distributed or correlated and when the marginal delay cost increases with time; we also characterize the single–problem type, multi-tier optimal solution when the marginal delay cost is constant. When multiple types of problems are pooled together, we show that a time-limit–based approach is robust to various probability distributions of service times and performs reasonably well even when the problem type is unidentified ex ante. Finally, we derive comparative statics on how parameter values, problem characteristics, and the composition of problems each affect the optimal time limit.