|University||The Royal Melbourne Institute of Technology (RMIT)|
|Subject||OMGT2228: Operations Management|
Case study: Bank Account Forecast
A well-known commercial bank in Australia is interested in estimating the number of new bank account opened by customers in each year. The number of new accounts opened in this bank has increased slowly over years even during the global financial crisis (2008–2009). Top management strongly believes that they need a long-term strategic plan for the bank which is a 5-year forecast for the number of new accounts opened. To achieve this aim, the bank operations manager examined past account data and also extracted the employment rate over 30 years (1991-2020). The resulting data are shown in below table:
|Year||No of new account(000)||Employment Rate (%)||Year||No of new account(000)||Employment Rate (%)|
Using the following forecast methods discuss which method fits best for the bank’s strategic plan. You need to justify the selection of one method over another.
- Moving average (you need to find the best value for n)
- Linear trend (trend projection)
- Linear regression
Can you exclude a portion of the data for the analysis? If yes, why?
The bank operations manager also collected the data on Australia’s GDP Per Capita (Gross Domestic Product) and believes that GDP Per Capita can also affect the number of a bank accounts. Below table shows the data for the same period:
|Year||GDP Per Capita (000A$)||Year||GDP Per Capita (000A$)|
Between GDP Per Capita and employment rate which one do you think can better estimate the number of new bank accounts?
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