Kartläggning av kundinflöde - En fallstudie om schemaläggning på en svensk telefonbank

Abstract

The banking industry is currently going through a big change where the traditional bank branches shut down in favour of meetings on digital platforms. In today's society, customers require fast service with high flexibility allowing them to conduct their banking business wherever and whenever they want. This development results in a greater burden on telephone banks that need to plan their capacity to meet the higher demand. Many telephone banks are open 24 hours, have multi-skilled personnel and great variation in demand. Hence, this puts a high requirement on the capacity planning in order to create satisfied customers. The purpose of the study is to map the customer inflow of a telephone bank and based on its selected scheduling methods examine how well the relationship between capacity and demand is balanced. The study has been conducted through a qualitative case study where a Swedish telephone bank has acted as the case company. The study is mainly based on primary sources from two interviews but also secondary data in terms of scientific journals, books and reports. Some quantitative data has also been obtained from the case company. The collected data have been analysed and constitutes the basis for the study´s conclusions. The customer inflow of a telephone bank varies significantly during the day. On a normal day, two distinct peaks occur, the first before lunch and the second during the afternoon. The inflow follows a repeating pattern where it reaches its high on Monday. The inflow then decreases during the week. The study shows a discrepancy between the methods suggested by the literature compared to the methods used by the case company. The case company bases its scheduling on its software programme together with the gut feeling of the scheduler, rather than the models advocated by the theory. In today’s situation, the relationship between the case company’s capacity and demand is unbalanced. During the first peak, the queue increases rapidly and the planned staffing cannot handle the inflow. This impacts the queue situation during the rest of the day. However, the case company is using a service target that does not consider the queue time, therefore its target can still be reached regardless of the current situation.