Short interval forecasting of emergency phone call (911) work loads
研究了六种预测技术，用于预测印第安纳波利斯警察局通信中心的每日紧急电话工作量，发现简单模型在复杂服务环境中表现良好，且需针对服务组织特点定制预测模型。
Abstract There has been an increasing emphasis over the last 5 to 10 years to improve productivity in the Service Sector of the U.S. economy. Much of the improvement obtained by these managers has come about through better scheduling of the work force in these organizations. Effective scheduling of this personnel requires good estimates of demand, which may exhibit substantial variations between days for certain times of the year. The Indianapolis Police Department (IPD) Communications area is one such organization that exhibits varying workloads and is interested in improving staff scheduling of dispatch operators. This article explores the use of six different forecasting techniques for predicting daily emergency call workloads for the IPD's communications area. Historical call volume data are used to estimate the model parameters. A hold‐out sample of five months compares forecasts and actual daily call levels. The forecast system utilizes a rolling horizon approach, where daily forecasts are made for the coming month from the end of the prior month. The forecast origin is then advanced to the end of the month, where the current month's actual call data are added to the historical database, new parameters are estimated, and then the next month's daily estimates are generated. Error measures of residual standard deviation, mean absolute percent error, and bias are used to measure performance. Statistical analyses are conducted to evaluate if significant differences in performance are present among the six models. The research presented in this article indicates that there are clearly significant differences in performance for the six models analyzed. These models were tailored to the specific structure and this work suggests that the short interval forecasting problems faced by many service organizations has several structural differences compared to the typical manufacturing firm in a made‐to‐stock environment. The results also suggests two other points. First, simple modeling approaches can perform well in complex environments that are present in many service organizations. Second, special tailoring of the forecasting model is necessary for many service firms. Historical data patterns for these organizations tend to be more complex than just trend and seasonal elements, which are normally tracked in smoothing models. These are important conclusions for both managers of operating systems and staff analysts supporting these operating systems. The design of an appropriate forecasting system to support effective staff planning must consider the nature, scope, and complexity of these environments.