Much of the research in operations management deals with the study of events. These events take many different forms: the length of time until a job is completed, the time interval until a component breaks down or the time taken for the development and introduction of a new product. When studying events, we are often interested in examining how factors such as the introduction of a new dispatching rule or a change in the level of capacity utilization affect the resulting events (e.g., completion time for a job). Analyzing events is not an easy task. Events are often time‐varying (where the probability of the event taking place changes over the life of the event), seldom normally distributed (with the distributions often being highly skewed) and affected by censoring (observations lacking a beginning or ending point). Finally, information about events is frequently collected longitudinally. These traits create problems for the application of such commonly used procedures as ANOVA. To cope with such problems, new statistical tools are needed. This paper introduces such a statistical procedure, survival analysis. Survival analysis is a set of statistical techniques (non‐parametric, semi‐parametric and parametric) used to determine quantitatively the impact of independent variables on a dependent variable which represents the time interval between events. The usefulness of this procedure is illustrated by applying it to data taken from a study focusing on the blood donation process. The paper concludes by pointing out some of the problems encountered in operations management which can be readily analyzed using this procedure. Overall, the paper tries to make the reader aware of the capabilities offered by survival analysis.