MIS771 - Business - Descriptive Analytics and Visualisation - Mitchies Superstore Case Study - Assessment Answer

MIS771: Descriptive Analytics and Visualisation Mitchie's Superstore Case Study Assessment Answer
 

Assessment Task:

Case Study (Background to Mitchie's Superstore)

Mitchies Superstore is one of Australia's leading supermarket chains. There are 500 stores in the chain. Originating from a family-based chain of general stores, Mitchie's Superstore now has stores all over Australia, with the first one being established 55 years ago. Regarding operation, individual store management has wide-ranging powers about the day-to-day operations of their stores. However, Mitchie's Superstore’s strategic planning and direction take place in the company Head Office in
Dandenong. Despite their successful operations and strong financial turnovers in the last two years, Mitchie's Superstore is forecasting a shift in the business climate within the next five years. This is a result of ever-increasing competition in the grocery supermarket sector. Now more than ever, Mitchie's Superstore management team feels the need to ensure a good understanding of their business performance. The Mitchie's Superstore Head Office is slightly confused about the lack of enthusiasm of store managers to open their stores on Sundays or to open an online sales channel given that Mitchie's Superstore Head office has invested heavily in digital platforms, self-checkout machines, and staff. Also, they are planning to put in place a formal procedure to forecast their Sales. Subsequently, Mitchie's Superstore has approached BEAUTIFUL-DATA (a market research company) and asked them to conduct a study to understand the characteristics of Mitchie's Superstore’s stores and their business performance.

The Data
For this study, BEAUTIFUL-DATA has collected two sets of Data:
1. The data related to stores were extracted from the company’s data marts. It is a random sample of 150 stores in the supermarket chain. A complete listing of variables, their definitions, and an explanation of their coding are provided in Working Sheet “Stores-Variable Description.

2. Time-Series data is available on Working Sheet “Quarterly Sales”.

Task 1 – Summarising Dependent Variables
The purpose of this task is to analyze and explore key features of these variables individually. At the very least, you should thoroughly investigate relevant summary measures/charts and graphs of these variables. Proper visualizations should be used to illustrate key features. Your technical report should describe ALL key aspects of each variable.

Task 2. – Model building (predicting Sales)
You should follow an appropriate model building process. All steps (including pre and post model diagnostics) of the model building process should be included in your analysis. You can have as many Excel worksheets (tabs) as you require to demonstrate different iterations of your regression model (i.e., 2.2.a., 2.2.b., 2.2.c., etc.).

Your technical report should clearly explain why the model might have undergone several iterations. Also, you must provide a detailed interpretation of ALL elements of the final model/regression output.

Task 3. – Interaction effect
To accomplish this task, you need to develop a regression model using ONLY the factors discussed in the meeting (Task 3). In other words, this section of analysis is separate from the regression model constructed in Task 2.

Your technical report should clearly explain the role of each variable included in the model. A proper visualization technique should be used. Make sure you interpret all relevant outputs in detail and provide managerial recommendations based on the results of your analysis.

Task 4 – Model building (likelihood of a store opening on Sunday)
You should follow an appropriate model building process. All steps (including pre and post model diagnostics) of the model building process should be included in your analysis. You can have as many Excel worksheets (tabs) as you require to demonstrate different iterations of your regression model.

Task 5.1 – Model building (likelihood of a store opening an online sales channel)
You should start building the predictive model by including ONLY the variables listed in the ‘minutes of the meeting – Task 5.1’. You must make reasonable/realistic/practical assumptions about the parameters mentioned in Task 5. You are required to discuss all details of your predictive model.

Task 5.2. – Visualising and interpreting predicted probabilities
Your technical report must include the predicted probability visualisation and be supplemented by practical recommendations to Superstore’s Management. These recommendations should answer the following question:

“How changes in Experience, Age and the Gender of the manager may affect the predicted probability of opening an online sales channel.”

Task 6 – Forecasting Sales
Mitchies Superstore’s past quarterly Sales are given in the Excel file. Your job is to develop a proper forecasting model to predict turnover for the next four quarters.
In your technical report, you must explain the reason for selecting the forecasting method to predict future sales. The report also must include a detailed interpretation of the final model (e.g. a practical interpretation of the time-series model, choices about smoothing techniques, etc.).

Task 7. – Technical report
Your technical report must be as comprehensive as possible. ALL aspects of your analysis and final outputs must be described/interpreted in detail. Remember, your audience are experts in analytics and expect a very high standard of work from your report. High standards mean quality content (demonstrated attention to details) as well as an aesthetically appealing report.

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