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LUBS5308M01

(August Re-Assessment 2023)

Assessed Coursework for the degree of MA/MSc

Business Analytics and Decision Science

This assignment contains two parts which are both equally weighted:

Part 1 (50% marks – 1,500 words maximum):

As a consultant for a major international car manufacturing company, you have been asked to help decide on which prototype technology to develop for a new car powertrain. You have been approached by the car company to help model the decision using the Analytic Hierarchy Process (AHP) and TOPSIS based on five criteria; development cost, expected carbon emissions, engine efficiency, car acceleration, and the maximum speed in miles per hour. There are four alternatives of powertrain technology: electric, hydrogen, hybrid, and petrol.

Using this information model this decision using AHP and TOPSIS. You will have to make assumptions based on the information provided to create the decision tables for AHP and TOPSIS. Your report has two objectives:

1. Summarise the data used and outputs for the AHP and TOPSIS analyses, including your workings in a single Excel file.

2. Respond to the car company on how to proceed with their decision. Provide justification for your response using your analyses.

Your (1,500 word maximum) report should use tables and figures as appropriate as well as text to present your findings. In addition to the report, you must submit all your workings/calculations in a single Excel file. This Excel file must contain no annotations, all written work should be in the report. No appendices are required, and any material provided in appendices will not contribute to the final mark. This section will be marked using the following marking scheme:

Note: Presentation includes structure/format of the report, clarity of expression, grammar, and spelling. Your report will be rated on each success criterion using a six-point scale from 0 (very poor) to 5 (excellent). The overall mark will be a weighted average of the ratings.

Part 2 (50% marks - 1,500 words maximum):

A computer part retailer has approached you to find an innovative way of recommending central processing unit (CPU). They have provided you with data on 74 different CPUs which have the following columns (Q2Data.csv):

Name: CPU’s Name

Core Count: Number of Processor Cores

Core Clock: Base Speed of Processor Cores

Boost Clock: Boost Speed of Processor Cores

TDP: Thermal design power (watts)

Integrated Graphics: Does it have integrated graphics? (Yes/No)

Simultaneous multithreading:   Does it have ability to do simultaneous multithreading?

Price:   The price of the CPU

The retailer has asked you to group the 74 processors into unique clusters that can be recommended to customers based on the attributes provided. The company will use the cluster information for marketing similar CPUs to their customers. They are also missing a price for some of the CPUs.  Identify the missing values, use imputation to replace the missing values and then a suitable clustering algorithm to cluster the CPUs.

Your report has two objectives:

1. Show how you can use imputation to replace the missing values and then evaluate the imputed values.

2. Utilise a suitable clustering algorithm to cluster the CPUs. Provide and explain the information in a dendrogram.

Your (1,500 word maximum) report should use tables and figures as appropriate as well as text to present your findings. No appendices are required, and any material provided in appendices will not contribute to the final mark. This section will be marked using the following marking scheme:

Note: Presentation includes structure/format of the report, clarity of expression, grammar and spelling. Your report will be rated on each success criterion using a six-point scale from 0 (very poor) to 5 (excellent). The overall mark will be a weighted average of the ratings.

Assignments should be a maximum of 3,000 words in length.

All coursework assignments that contribute to the assessment of a module are subject to a word limit, as specified in the online module handbook in the relevant module area of the MINERVA. The word limit is an extremely important aspect of good academic practice, and must be adhered to. Unless stated specifically otherwise in the relevant module handbook, the word count includes EVERYTHING (i.e. all text in the main body of the assignment including summaries, subtitles, contents pages, tables, supportive material whether in footnotes or in-text references) except the main title, reference list and/or bibliography and any appendices.  It is not acceptable to present matters of substance, which should be included in the main body of the text, in the appendices (“appendix abuse”).  It is not acceptable to attempt to hide words in graphs and diagrams; only text which is strictly necessary should be included in graphs and diagrams.

You are required to adhere to the word limit specified and state an accurate word count on the cover page of your assignment brief.  Your declared word count must be accurate, and should not mislead. Making a fraudulent statement concerning the work submitted for assessment could be considered academic malpractice and investigated as such.  If the amount of work submitted is higher than that specified by the word limit or that declared on your word count, this may be reflected in the mark awarded and noted through individual feedback given to you.

The deadline date for this assignment is 12:00:00 noon on Wednesday 30^th August 2023.

An electronic copy of the assignment must be submitted to the Assignment Submission area within the module resource on the Blackboard MINERVA website no later than 12 noon on the deadline date.

Failure to meet this initial deadline will result in a reduction of marks, details of which can be found at the following place:

https://lubswww.leeds.ac.uk/TSG/coursework/

SUBMISSION

Please ensure that you leave sufficient time to complete the online submission process, as upload times can vary. Accessing the submission link before the deadline does NOT constitute completion of submission. You MUST click the ‘CONFIRM’ button before 12 noon for your assignment to be classed as submitted on time, if not you will need to submit to the Late Area and your assignment will be marked as late.  It is your responsibility to ensure you upload the correct file to the MINERVA, and that it has uploaded successfully.

It is important that any file submitted follows the conventions stated below:

FILE NAME

The name of the file that you upload must be your student ID only.

ASSIGNMENT TITLE

During the submission process the system will ask you to enter the title of your submission. This should also be your student ID only.

FRONT COVER

The first page of your assignment should always be the Assessed Coursework Coversheet (individual), which is available to download from the following location:

http://lubswww.leeds.ac.uk/TSG/index.php?id=27

STUDENT NAME

You should NOT include your name anywhere on your assignment