DPBS1110/BMGT1310 Evidence-Based Problem Solving

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DPBS1110/BMGT1310
Evidence-Based Problem Solving
Assessment 2b: Case Part 2 (40%)

Assessment Guide

Project Overview

In Assessment 2a, your client, Fresh1Grocer, a well-known retail supermarket chain, needed assistance with a notable rise in food waste, especially in the fresh food section. You employed techniques like the 5Ws and logic tree to organize information and develop logical problem solving strategies. Additionally, you conducted basic Excel data analysis and initiated an ethical analysis on the problem.

Now, we'll conduct a more in-depth analysis to address the increased food waste issue at Fresh1Grocer. This assessment mirrors the case project we encounter in real life. We will apply all the knowledge we have gained (statistical, ethics, and analytical skills) to solve the problem.

You will use the same dataset as Assessment 2a.

Assessment 2: Case Part 2 (40%)

Your main task and objective in this assessment are to prepare a business analysis report to provide final recommendations to Fresh1Grocer. Use analytical, statistical, and ethical toolbox to justify your recommendations.

Statistical Toolbox: Analyse Fresh1Grocer's data to identify the statistically significant and economically significant factors that contribute to food waste problem at Fresh1Grocer.

Ethics Toolbox: Apply the 7 steps ethical decision-marking framework to analyse the ethical dilemma.

Analytical Toolbox: Develop your arguments and provide recommendations to Fresh1Grocer on how to reduce their food waste problems.
  • Word Limit: 1,800 words with a 10% buffer. There's no minimum word requirement, so any word count below 1,800 is acceptable.
  • The word count rule is straightforward – everything in your report, such as headings, subheadings, and in-text citations, contribute to the word count, except for the reference list (bibliography), and any inserted screenshots or images. Use your Word document's built in word count feature to check your word count accurately.
  • Structure and Format: No need to have cover page, introduction, or executive summary.
  • Simply begin your report directly with section 1. Write in a business report style (e.g., using an essay format, formal language, headings, and subheadings to make your report easy to read).
  • Referencing Style: While referencing is optional, if you choose to cite external information, adhere to the Harvard referencing style for any sources cited in your report. (see the guideline link - How to Cite Different Sources with Harvard Referencing | UNSW Australia

Academic Integrity in DPBS1110/BMGT1310 and UNSW College

You may use AI software for brainstorming ideas initially, but your final submission should primarily be your own work. Avoid relying on AI tools to generate your report. UNSW College employs a tool to detect AI-generated writing with a high AI score. C

Always ensure your assignments are completed independently, without asking others, such as paid academic cheating companies, to complete them for you.

Potential cases of academic misconduct will be investigated. If confirmed, students will receive a grade of 0 for the course and an academic cheating record, or face exclusion from UNSW College. Please don't take the risk.

Section 1 - Advanced Statistical Excel Data Analysis (40%)

This section is approximately 700 words (guide only, not a word limit).
1) Confidence Intervals Analysis
Confidence Intervals for Food Waste: Based on what we learnt, choose a confidence level (e.g., 95%) and calculate confidence intervals for the quantity wasted in meat and seafood separately. Explain what these intervals imply and their significance/relevance in terms of food wastage.
2) Correlation Analysis and Simple Linear Regression
a) Correlation Analysis: Calculate the correlation between “Days_on_Shelf” and “Quantity_Wasted” for meat and seafood separately. Use suitable visualization tools such as tables or line charts to display these correlations in your report. Explain the direction and strength of each correlation and discuss how “Days_on_Shelf” influences food waste.
b) Simple Linear Regression Analysis: Perform a simple linear regression between “Days_on_Shelf” and “Quantity_Wasted”, treating “Quantity_Wasted” as the dependent variable. Conduct this analysis for either meat or seafood. Present your finding regarding the relationship between “Days_on_Shelf” and “Quantity_Wasted”. Explain how the finding from your analysis can provide insights to help address the food waste issue.
3) Multiple Linear Regression Analysis
Let’s conduct a Multiple Linear Regression for the whole dataset.
a) Advantages of Multiple Linear Regression: Explain the advantages of a Multiple Linear Regression over a Simple Linear Regression.
b) Select suitable variables: Select at least 2 or 3 variables you would like to include in your Multiple Linear Regression model. Provide a clear and concise justification for each variable you want to add to your regression model, explaining its relevance and potential contribution in addressing the Food Waste issue.
c) Multiple Linear Regression Analysis: Conduct a Multiple Linear Regression analysis for the whole dataset. Write down the equation of the line and interpret the slopes. Discuss whether the independent variables are statistically and economically significant. Highlight the implications for Fresh1Grocer’s food waste and identify the most influential variable identified by the regression model. Include screenshots of the key parts of your regression model in the report, accompanied by your explanation of the findings.
Section 2 - In-depth Ethical Analysis on Ethical Dilemma (25%)
This section is approximately 500 words (guide only, not a word limit).
Apply the “7-step ethical decision-making framework” to analyse the following ethical dilemma.
“Should Fresh1Grocer switch to smaller packaging, like 1 kg per package, or opt for larger packaging, considering potential contributions to plastic pollution and consumer preferences?”
Section 3 - Analytical Problem-Solving for Developing Solutions (35%)
This section is approximately 600 words (guide only, not a word limit).
1) Structuring the Argument

Fresh1Grocer wants to understand how you structure your arguments to develop practical problem-solving solutions. Please insert the provided table below directly into your report to answer this question. All content within this table contributes to the word count, so ensure your responses are concise and clear to read and avoid using screenshot.

We organise our arguments to develop practical solutions as follows:
Situation
Observation
Resolution

Instruction:

For Situation: Identify and summarize the current situation regarding the food waste issue, explaining it to provide any reader who may read your business report with a clear understanding of the report's purpose and context.

For Observations: Highlight key insights from your business analysis work, including insights from your logic tree in Assessment 2a, all Excel data analysis you have done from both Assessment 2a and 2b, and ethical considerations discussed in this report.

For Resolution: the key is to provide well-reasoned general directions, supported by all analysis you conducted in this assessment, to address the food waste issue. These directions should not be overly specific, as you will offer detailed recommendations in part (2) below.

2) Recommendations

Based on the argument structure you outlined in (1) above, you now need to present your final recommendations and solutions on how Fresh1Grocer should address the food waste issue. Ensure that your recommendations are supported by your data analysis and logical reasoning. Clearly explain the rationale behind each recommendation and how it is expected to mitigate the food waste problem.

3) Assumptions and Limitations

Explicitly identify any assumptions made during your analyses and acknowledge the limitations of the data analysis in your business report. Explain the potential impact of these assumptions and limitations on your findings and recommendations, ensuring that the reader is fully informed of the context and constraints of your analysis.

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