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.

Bo Li is a faculty member at Ningbo China Institute for Supply Chain Innovation (NISCI) and a research affiliate at MIT Center for Transportation and Logistics. He was a visiting scholar at MIT in 2017 and 2020. His research centers on managing supply chain risks in areas such as retail, healthcare, chemicals, and cloud services. He has published in premier academic journals such as Production and Operations Management, European Journal of Operational Research, IISE Transactions, and Naval Research Logistics. He has received a general program grant from the National Natural Science Foundation of China. Dr. Li obtained his Ph.D. in Business Administration from Texas A&M University.