A statistical process control approach to business activity monitoring.

1. Introduction

Activity monitoring is an automated process that applies operational intelligence and application integration technologies to alert individuals to changes in complex systems that may require action (see Fawcett and Provost (1999)). It has been widely implemented in business

and information industries to audit business processes and business process management systems to reduce revenue loss and enhance efficient resource allocation. For example, service companies use activity monitoring in a continuous, iterative effort to fine tune operations ranging from claims processing to inventory replenishment. Government agencies now use it to identify and thwart money laundering schemes and bioterrorist attacks. Other examples of activity monitoring include churn detection, credit card or insurance fraud detection, computer intrusion detection, network performance monitoring, and monitoring of news stories (Fawcett and Provost, 1997; DuMouchel and Schonlau, 1998), where thousands or even millions of time series streams have to be monitored simultaneously.

Although activity monitoring has recently begun to receive significant attention from information industries, similar problems and solutions were identified and developed in manufacturing industries, a long time ago, where they are referred to as Statistical Process Control (SPC). SPC methods classify the root causes of process variations as either a common cause or a special cause, and its basic objective is to quickly detect the occurrence of special cause variation (or process shifts) so that the process can be investigated and corrective action taken before the quality deteriorates and defective units are produced. SPC techniques are routinely used for on-line process control and monitoring and they are highly successful in manufacturing applications (Woodall et al., 1997; Montgomery, 2001). Montgomery and Woodall (1997) provide a comprehensive panel discussion on SPC and multivariate methods are reviewed by Hayter and Tsui (1994) and Mason et al. (1997). Recently, Jiang et al. (2003) successfully applied univariate and multivariate control chart techniques to monitor the stability of market segmentations.

Although the principles of SPC can be applied to service industry tasks such as business process monitoring, little research has been done on the application of SPC methods to the monitoring of customer activities so that appropriate marketing campaigns and service customizations can be developed. This paper develops a SPC framework for activity monitoring to allow business planning and forecasting in telecommunications industries. The proposed SPC approach monitors customer profile evolution through a set of state space equations to capture dynamic changes in profiles and detects abnormal events that deviate significantly from a customer's historical profile based on statistical testing principles.