The UHCL credit union has a trouble in getting the correct staffing levels to match customer arrival patterns. On some days, the number of tellers was too high relative to the customer traffic, so that tellers were often idle. On other days, the opposite happens – the relatively few tellers could not keep up with the number of customers. The credit union manger, James, knew that there was a problem, but he had little of the quantitative training he believed that would be necessary to find a better staffing solution. James figures that the problem could be broken down into three parts. First, he needed a reliable forecast of each day’s number of customer arrivals under all conditions if the conditions are meaningful. Second, he needed to translate these forecasts into staffing levels that would make an adequate trade-off between teller idleness and customer waiting. Third, he needed to translate these staffing levels into individual teller work assignments – who should come to work when.
At this moment, James wants the answer for the first part. The file Credit Union Arrivals.xlsx(embedded below to see some data structure and potential variables) lists the number of customer entering this credit union branch each day of the past year. It also lists other information: day of the week, whether the day was a staff or faculty payday, and whether the day was the day before or after a holiday. Use this data set to develop one or more forecasting models that James could use to help solve his problem.
