Unformatted text preview: Case‐09 Arrivals at the credit union The Indiana University Credit Union Eastland Plaza Branch was having trouble 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 ikle. On other days, the opposite occurred. Long customer waiting lines formed because the relatively few tellers could not kiip up with the number of customers. The credit union manager, James Chilton, knew that there was a problem, but he had little of the quantitative training he believed would be necessary to find better staffing solution. James figured that the problem could be broken down into three parts. First, he needed a reliable forecast of each day’s number of customer arrivals. 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. The last two parts of the problem require analysis tools that we have not covered. However, you can help James with the first part‐forecasting. Use dataset to develop one or more forecasting model that James could use to solve his problem. Based on your model(s), make any recommendations about staffing that appear reasonable. ...
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- Spring '10
- Math, James Chilton, staffing levels, correct staffing levels