BUS352 Operations Analytics Group-Based Assignment (GBA) Predictive Modeling & Aggregate Planning Analysis
University | Singapore University of Social Science (SUSS) |
Subject | Operations Analytics |
GROUP-BASED ASSIGNMENT
This assignment is worth 20% of the final mark for BUS352 Operations Analytics.
The cut-off date for this assignment is 09 March 2025, 2355 hrs.
This is a group-based assignment. You should form a group of 4 members from your seminar group. Each group is required to upload a single report via your respective seminar group site in Canvas. Please elect a group leader. The responsibility of the group leader is to upload the report on behalf of the group.
It is important for each group member to contribute substantially to the final submitted work. All group members are equally responsible for the entire submitted assignment. If you feel that the work distribution is inequitable to either yourself or your group mates, please highlight this to your instructor as soon as possible. Your instructor will then investigate and decide on any action that needs to be taken. It is not necessary for all group members to be awarded the same mark.
Note to Students:
Compose your report using Microsoft Office Word, and save either as .doc or .docx (preferred).
You are to include the following particulars in your submission: Course Code, Title of the GBA, SUSS PI No., Your Name, and Submission Date.
Use of Generative AI Tools (Allowed)
The use of generative AI tools is allowed for this assignment.
• You are expected to provide proper attribution if you use generative AI tools while completing the assignment, including appropriate and discipline-specific citation, a table detailing the name of the AI tool used, the approach to using the tool (e.g. what prompts were used), the full output provided by the tool, and which part of the output was adapted for the assignment;
• To take note of section 3, paragraph 3.2 and section 5.2, paragraph 2A.1 (Viva Voce) of the Student Handbook;
• The University has the right to exercise the viva voce option to determine the authorship of a student’s submission should there be reasonable grounds to suspect that the submission may not be fully the student’s own work.
• For more details on academic integrity and guidance on responsible use of generative AI tools in assignments, please refer to the TLC website for more details;
• The University will continue to review the use of generative AI tools based on feedback and in light of developments in AI and related technologies.
Important Note: Grading of TMA/GBA/ECA Submissions
Marks awarded to your assignment are based on the following guidelines:
1. 80% of the marks are allocated to the content of your answers:
The marks awarded to what your answers cover depend on the extent to which they cover the key points that correctly and comprehensively address each question.
The key points should be supported by evidence drawn from course materials and, wherever relevant, from other credible sources.
2. 20% of the marks are allocated to the presentation of your answers:
Wherever applicable, the marks awarded to how your answers are presented depend on the extent to which your answers:
form a sound reasoning by developing those key points in a clear, logical and succinct manner;
provide proper and adequate in-text citations and referencing to content drawn from course materials and other credible sources;
strictly follow APA formatting and style guidelines1 , in particular for:
• in-text citations and end-of-report references;
• the identification of figures and tables;
use, wherever relevant, the specialised vocabulary and terminology commonly used in discussions about the topic(s) covered by each question;
provide a reference or bibliography at the end of the main report;
include the less relevant details in an Appendix;
use sentence constructions that are grammatically and syntactically correct;
are free from spelling mistakes;
present the workings, numerical formulations and results in a logical manner that follows the APA formatting and style guidelines;
design and present graphs, diagrams and plots that follow the APA formatting and style guidelines;
are highly original;
have proper formatting, which may:
• include a properly formatted cover page;
• respect the answer length/word count set out in the assignment guidelines, if
any is prescribed;
• present answers in paragraphs with proper spacing and page margins;
• include page numbers and appendices, if necessary.
You can find a short tutorial on the APA formatting and style guidelines here: https://apastyle.apa.org/index. Additional details (pertaining to tables and figures) can be found here: https://is.gd/O4vDdT .
Instructions
Students need to apply essential knowledge and skills learnt in this course to address the questions, and apply interpersonal skills to work effectively as a team.
Students should work together in a group and leverage on the knowledge of and resources from each team member to produce the report.
Students must demonstrate written proficiency by producing a type-written report in Microsoft Word 2013 or later (*.docx) format. Any key illustrations and diagrams should be shown in the main report with proper in-text citations.
Please note that:
The details and names used in the questions are fictional. Any resemblance to real-life situation is purely coincidental.
Students should not include the questions in the report.
An ability to (i) conduct proper research, provide the appropriate referencing of sources (students must provide proper in-text citations and an end-text reference list), and (ii) demonstrate originality, critical thinking and creativity are all essential skills that our students must develop. Hence, marks will also be awarded for good research effort and proper referencing.
Fewer marks will be awarded to students who merely extract information from their reference sources without demonstration of critical analysis and creativity.
Answers that are simply reproduced from the course materials will be given very low marks or no marks.
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Question 1
EverydayBuy.com is an online B2C platform that helps manufacturers market and sell their products directly to end consumers. If a consumer purchases a good from a seller in EverydayBuy.com and finds quality problem within 7 days after receiving it, the platform provides return service at seller’s charge, so that the faulty good can be returned to the seller.
The platform is interested in understanding the characteristics of sellers with low return rates. The platform collects the following information of 87 sellers for analysis. The information is collected for the four-month period of September 2024 to December 2024.
• Number of returns: Number of units of transacted goods being returned from
customers.
• Type: Category A, B and C of goods.
• Inventory turnover: the average inventory turnover.
• Sales size: the number of units of goods transacted.
• Year: the number of years for which a seller has engaged with the platform.
Data are included in Appendix 1. The platform considers a return rate of 6% or below being acceptable. The platform intends to construct a predictive model for product return analysis and use Logistic regression to examine the sample data.
(a) Define Return Rate as the percentage of goods being return from end consumers. Use the information in the data set to calculate the return rate for each seller. Use the calculated return rate information in the following analysis.
(5 marks)
(b) Use the “Return Rate” information and the platform’s criterion of an acceptable return rate to construct a binary dependent variable. Treat “Type”, “Inventory Turnover”, “Sales Size”, and “Year” as explanatory variables. Note that you need to transform the categorial value “Type” into numerical variables. Provide details how these variables are constructed if necessary. Assign a notation to each variable and use these notations to explicitly write out the logistic response function.
(13 marks)
(c) Use Microsoft Excel add-in (or online tools) to run Logistic regression on the sample data. Show the regression output. Construct the Logistic regression function based on the regression output. Which variable(s) is(are) statistically significant at the 0.05 level? What do the regression results suggest?
(Word count: 250) 17 marks)
(d) Based on the Logistic regression function in Q1(c), compute the predicted probability of a seller with an acceptable return rate given that the seller sells category A good, has an inventory turnover of 7.2, a sales volume of 1,400 units, and has engaged with the platform for 6.8 years. Show your calculation details. (5 marks)
(e) Given a threshold of 0.8, what are the number of True Negatives (TN), False Positives (FP), False Negatives (FN) and True Positives (TP)? What is the accuracy of the predictive model? Show your calculation details. (10 marks)
Question 2
A firm produces portable electricity generators and makes the following demand forecasts in a 6-month period as shown in the table below. “Demand” refers to the quantities of product ordered by customers in a month, and “days” refer to the number of working days in a month.
Month | Jan | Fab | Mar | Apr | May | Jun |
Demand (units) | 750 | 1210 | 950 | 890 | 910 | 790 |
Number of working days | 23 | 20 | 21 | 22 | 22 | 21 |
The firm currently has nine (9) full-time workers in its production site. A productivity analysis shows that on average, one worker can produce four (4) units of product per working day.
On average, one unit of product generates a revenue of $390. The average material cost of one unit of product is $127. The average salary for a worker is $6,700/month. If overtime is used,the average overtime payment is $500 per day for each worker. HR policy mandates that the total overtime of 11 workers cannot exceed 13 days per month.
The firm can consider using subcontractors. However, subcontractors can only accept orders in multiples of 50 units, i.e., the order size should be of 50, 100, etc. The maximum order that subcontractors can accept is 200 units per month. Subcontractors charge the firm the following unit price based on the order size. Order size Unit Price
Order size | Unit Price |
50 units | $370 |
100 units | $347 |
150 units | $332 |
200 units | $324 |
Unfulfilled demands are considered as backorders, and the estimated backorder cost is $117 per unit of product per month. Unsold finished products are stored in the warehouse and incur inventory holding cost of $31 per unit per month. The warehouse can accommodate a maximum of 300 units of finished product inventory. Assume that the initial inventory of finished products at the beginning of January is 150 units.
As an operational analyst, you are expected to develop an aggregate plan to guide the firm’s production and subcontracting decisions. Note that in the following analysis, you are NOT supposed to follow either Chase or Level strategies.
(a) Formulate the above planning problem into an optimisation problem. You can take reference from Appendix 4.1, Study Unit 4. You should do the following:
(i) State the assumptions and provide justifications if necessary. Note that assumptions are needed for revenue/cost calculations and for the construction of constraints. (Word count: 200) (6 marks)
(ii) State the decision variables, objective and constraints. Use proper notations to denote decision variables and related parameters.
(Word count: 400) (18 marks)
(iii) Use notations in (ii) to construct the objective function and constraints according to the standard optimisation problem formulation.
(Word count: 200) (9 marks)
(b) Use Microsoft Excel Solver to solve the optimisation problem and answer the following questions.
(i) Select the appropriate solving method and provide justification. Show the Solver setup and solve the optimisation problem. Show Excel Solver output as the solution. (Word count: 150) 9 marks)
(ii) Based on the output, what is the company’s aggregate plan (which should look like Table 4.9 in Appendix 4.1, Study Unit 4) for the 6-month period? Are there any months with overtime, inventory holding cost, backorder cost, and subcontract cost? What is the objective function value? (8 marks)
Note: For details of data analysis related to Questions 1 and 2, you are required to embed one (1) Microsoft Excel spreadsheet (not screenshots, but the *.xlsx file) at the end of the Word document as an appendix for verification purpose. Please test and make sure the embedded spreadsheet can be opened properly. A mark deduction penalty may be applied if the Excel
worksheet is not included or cannot be opened properly.
If you want to know how to embed a file in WORD, please find the following link for helpful information:
https://support.microsoft.com/en-us/office/insert-a-chart-from-an-excel-spreadsheet-intoword-0b4d40a5-3544-4dcd-b28f-ba82a9b9f1e1
Appendix 1. Data for Question 1
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