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Case Study: Forecasting and Analysis Assignment

Case 12.1 Arrivals at the Credit Union (from your Albright & Winston textbook)

Union 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 idle. On other days, the opposite

occurred. Long customer waiting lines formed because the

relatively few tellers could not keep 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 a 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

(queueing and scheduling) that we have not covered. However,

you can help James with the first part—forecasting.

The file C12_01.xlsx lists the number of customers entering

this credit union branch each day of the past year. It

also lists other information: the 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. Based on your model(s),

make any recommendations about staffing that appear


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