Forecasting project plan based on the learnings

Reference no: EM133708223

Data-driven Decision Making and Forecasting

Assessment - Forecasting for a Business Problem

Your Task

Develop a real-world forecasting project plan/proposal based on the learnings from the subject.

Assessment Description

This assessment seeks to simulate a real-world task that you may have to undertake in the future. Therefore, the assignment is non-prescriptive and requires you to pose a relevant, small, creative and significant problem to solve that could result in benefits to the organisation of choice.

In this assessment, you need to consider an organisation in an industry of your choice and articulate the steps this organisation needs to take to enable forecasting for data-driven decision making. You are required to analyse a sample data set to demonstrate expected forecasting outcomes.
You need to be familiar with the organisation and industry (e.g., where you have worked or are working, a future start-up company), NOT an organisation such as Amazon/Boeing/Qantas etc.

The report should address:
Why forecasting would help this organisation given their current operations.
The forecasting technique(s) you would recommend.
Justification of your recommended forecasting technique(s) by analysis of a relevant sample dataset.
The benefits of this forecasting project for the organisation (The benefit could be financial, such as Return on Investment (ROI) or societal benefits).

Assessment Instructions

By Week 9 identify a company and industry you are familiar with that would benefit from a forecasting application. Note:
The application needs to be based on forecasting (not some other aspect of analytics).
Focus on a single, well defined (small) application.
Sample datasets maybe sourced from:
an organisation if you work there,
public repositories such as kaggle.com
Open government data such as abs.gov.au.

By Week 12 draft some preliminary points pertaining to the report in class. You are encouraged to consider the current mode of operation, possible inefficiencies, available data and how this data may be used to provide efficiencies based on the concepts and techniques covered in the subject. Think of yourself as a consultant or a founder.

Your lecturer will advise on the appropriateness of your choice and proposed methodology regarding the requirements for the assessment.

Include a list of references that is directly related to the content. Each reference needs to be linked to at least one specific point in the content of your assessment. It is expected that you will have at least 10 relevant references.

Discuss or show how ChatGPT can be leveraged to enhance your solution.

Topic and Problem

Familiarity with topic (industry and organisation).
The topic is precise and not too general.

Clearly defined problem statement that is appropriate for forecasting:

Needs and goals for a forecasting model from a business perspective clearly articulated.

The problem statement is concise.

The relevance of forecasting is clearly stated.

Methodology

Identified and sourced appropriate sample data.

Identified and built appropriate forecasting model.

Determined appropriate parameters for using the model.

Output results in a manner suitable for interpretation.

Determined accuracy and other metrics pertaining to the model.

Report:

Structured such that the reader can grasp key points from the analysis.

Key headings are included.

Justification of assumptions and interpretations are clear and concise.

In-line referencing used and references are relevant and genuine.

Visualisations are used to convey key arguments.

Before you begin your analysis, make sure to address these four points
1. What industry are you focusing on?
2. Who is your chosen organization (target audience)?
3. Provide a brief overview of your research proposal and methodology.
4. Find a relevant and applicable time series dataset.

Minimum dataset requirements:
- Time series length: at least 2000 periods
- Number of features: at least 4 if you can
- Make sure that your data is a time series dataset.

Reference no: EM133708223

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