A staff utilisation-resource simulation model: Towards a hybrid decision support system in retail banking

Within a retail environment the scheduling of staff to accommodate the dynamic nature of customer throughput is of utmost importance. However, in outlets that face unpredictable patterns of customer activity, suboptimal staff schedules are a common occurrence. This event adversely affects the staffing rota, service time, and queue length thereby affecting the level of customer satisfaction. At present the vast majority of retail environments make use of a number of estimation techniques based on past experiences and historical data, sometimes supported by the ad-hoc observation of customer throughput. However, the lack of pervasive monitoring technologies may hide weaknesses within current staffing rotas and standard service times. A Decision Support System (DSS) with a real-time data logging architecture, referred to as the Staff Utilisation-Resource Simulation Model (SU-RSM) is the subject of this paper. The SU-RSM is designed to assist retail managers in assessing the efficiency of their staffing rotas to a high degree of granularity. In this paper, design, implementation and evaluation of SU-RSM in a single retail bank is presented. This research offers the opportunity to perform a back-to-back comparison between the existing paper-based estimation models used in the bank and the SURSM. Findings provided in this paper reveal a lack of awareness on the actual customer flow by managers and staff, and as a result a suboptimal allocation of resources within the branch. This knowledge gap provides a strong justification for the use of real-time data logging technologies combined with a simulation model (i.e. DSS). Such an approach will assist retail mangers in achieving near optimal staff resource allocation.