Develop different demand forecast models

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Reference no: EM133586705

Data-driven Decision Making and Forecasting

Assessment - A Forecasting Project

Your Task
Apply forecasting techniques to a given dataset and provide a business application of the forecasts. The assessment is worth 30 marks (see rubric for allocation of these marks).

Assessment Description

The data provided for the assessment represents two store's sales revenue. The objective of the assessment is to develop different demand forecast models for these stores and compare the forecast models in terms of accuracy, trend, and seasonality alignment with the historical data provided. To provide conclusions, students must use visual inspection, error metrics, and information criteria on the test data.

Assessment Instructions

In class: You will be presented with a dataset in class. As a group, analyse the dataset using Tableau and Exploratory.io.

You will provide an oral presentation of the group work in parts A to C during the third hour of the workshop.

Individually write a 500-word report, which briefly summarises the analysis and provides suggestions for further analysis. This component of the assessment is to be submitted via Turnitin, no more than 24 hours after class in Week 5.

As a group:

Part A

- Use descriptive analytics to compare the two stores in terms of sales revenue.
- Provide key insights or findings.

Part B

- Use Tableau to generate Holt-Winters forecasts of the next 5 months for the two stores.
- Analyse the results of the forecasts in terms of: (1) Accuracy, (2) Alignment with the historical trend, (3) Alignment with the historical seasonality.

Part C

- Use Exploratory to generate ARIMA forecasts of the next 5 months for the two stores.
- Analyse the results of the forecasts in terms of: (1) Accuracy, (2) Alignment with the historical trend, (3) Alignment with the historical seasonality.

Part D

- For each store, compare Holt-Winters and ARIMA forecasting models and advise which models to use.

Part E

Prepare a presentation:
• Include key findings.
• Highlight methodologies.
• Advise which methods to use for each store.
• Recommend improvements in terms of forecasting for the retailer.

Note: All members of the group should be involved in the presentation. The allocated time for the presentation will be decided by your lecturer.

As individuals:

• Briefly summarise the analysis performed as a group.
• Outline how the dataset, visualisations and forecasts could be improved.
• Describe how a retail organisation could use the forecast for its operation.

Reference no: EM133586705

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