MGMT316 Decision modelling for managers MGMT316 Assignment 1 Brief 40% of course grade, CLOs 2, 3, 4, 6 Electronic Card Transaction Data
MGMT316 Decision modelling for managers MGMT316 Assignment 1 Brief 40% of course grade, CLOs 2, 3, 4, 6
Electronic Card Transaction Data
A high net-worth foreign individual is contemplating introducing a new electronic payment system in the New Zealand market.
It is the first week of August 2025. The investor is trying to understand the electronic payments situation in New Zealand. You have recently completed your BCom at Victoria University, where you studied MGMT 316. You have met the investor at a social gathering and are keen to impress with your new knowledge. The investor is also keen to size you up for possible employment. So you have been given the attached spreadsheet, sourced from Statistics New Zealand data, giving total electronic card transaction data from October 2002 to June 2025, in millions of dollars, broken down by credit and debit card sales, and by industry sector (Consumables, Durables, Hospitality, Services, Apparel, Motor Vehicles (excluding fuel) and Non-Retail transactions (excluding services).
Part A
Prepare a summary report for the investor (one page, plus graphs and tables) explaining your assessment of the reliability of the data and the patterns you see in each of the nine time series.
Part B
In your discussions with the investor, you have learned that they are interested in the total potential electronic card sales, but especially the potential for customizing the new offering for the Consumables and Hospitality sectors. The investor has suggested you prepare a forecast for the July total card, consumables and hospitality spending using a four-period moving average and exponential smoothing (with weights (α) suggested of 0.8 and 0.4). However, you suspect this might be a test and that a regression model using seasonal dummies will yield a better forecast, at least for some of the time series. Prepare another report providing the forecasts using each method for the Total, Consumables and Hospitality sectors and explain to the manager which you would recommend, based on the strengths and weaknesses of each method.
Part C
The investor now asks you to prepare a forecast for the three series for the next two years (July 2025 to June 2027). Would you change the method used for the forecast used in Part B above for any of the time series? If so, why? Calculate the forecasts for the extended period and report them in a further memo to the Investor. How confident are you in the accuracy of these forecasts?
Part D
Finally, the investor asks you to explain the usefulness of ARIMA forecasting for these data. You are shown the attached plots and correlograms of the Total electronic card data and Consumables data from July 2020, and the first order 12 period seasonal differences. Provide a brief explanation of the role of stationarity in time series forecasting, the key differences between seasonal regression-based and ARIMA forecasting, and whether (in your view) the additional effort required for ARIMA forecasting of this series is likely to be justified by providing a substantially more accurate forecast than provided in Part C for this series. (Note – you are not required to calculate the ARIMA forecasts for this part– just discuss the theoretical implications and interpretations from the plots provided).
MGMT316 Assignment Rubric Component Evaluation components Marks
Available
Memo presentation Introduction Structure, style and flow
Clarity and conciseness
Spelling, punctuation, appropriate language and grammar
Figures, graphs and tables
Correct referencing
10
Part A Appropriate graphs selected and presented Correct identification of levels, trends, seasonality, steps etc
Identification of outliers
Other factors
15
Part B Description and justification of chosen forecasting model(s) Correct analytical procedures followed to derive the forecasts
Discussion of strengths/limitations/accuracy of forecasts
Assumptions made explicit
30 Part C Correct analytical procedures followed to derive the forecasts Critical evaluation of method chosen.
Assessment of accuracy.
20 Part D Discussion of stationarity and application to selected series Critical comparison of methods (theoretical elements)
Critical comparison of methods (case context)
15
Spreadsheet modelling Face validity of models Correctness of formulae
Clear and helpful layout/naming conventions
Explanations/documentation of formulae and calculations performed for the analysis
10
Total Score 100
