Evaluating the integration of leading edge pervasive technologies to improve service-based performance

Within retail banking making decisions related to staff allocation to deliver services is complex in nature. It is often the case that certain services are either over or under staffed. Over staffing incurs a cost to the bank while under staffing impedes existing services and typically offers a poor consumer experience. Thus, making these sizing decisions is important in terms of both short and long term impact. In this paper, traditional approaches to measuring branch performance are compared with a new approach leveraging leading edge technologies. While current performance measurement protocols are based on an audit scenario, with a group of experts travelling from branch to branch to perform scheduled monitoring of key processes. This paper intends to demonstrate that such evaluation and auditing activities can be performed in near real-time using for example live customer location tracking feeds at the branch level. When implemented correctly the integration of such a technology should ensure service based activities can be executed in a far more efficient manner. The effectiveness of this approach from a performance measurement perspective is assessed well beyond simple cost factors in order to deliver a higher level of service quality and improve overall efficiency levels e.g. average customer waiting times and staff utilisation at various points throughout the day. This paper presents its findings from two perspectives: 1) Existing Data Gathering Approach (comprehensive analysis): To help evaluate the existing data gathering approach, a queue and staffing resource simulation model has been developed. 2) Automated Data Gathering Approach (initial and projected impact): Further use of leading edge technologies for data representation, in the shape of dashboards of management information, lead to a complete decision support solution. The granular level of detail attainable from such location tracking devices is immense, providing the dual function of both enhancing staff allocation solutions which can provide near real-time data, as well as new analytical capabilities to branch managers and regional managers alike. Branch metrics have been extensively evaluated in the past from a variety of viewpoints through the application of different business processes and technology integration. In this paper the Predictive Customer Queue-Resource Simulation Model (PCQ-RSM) architecture is proposed. Using simulation tools, an extensive evaluation of an existing manual data collection approach is contrasted with the application of a location tracking technology. It is evident from initial experiments that new dynamic staffing approaches can help improve the overall performance of service based businesses.