Operations Analytics
Module title | Operations Analytics |
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Module code | BEM3062 |
Academic year | 2023/4 |
Credits | 15 |
Module staff | Dr Stuart So (Lecturer) |
Duration: Term | 1 | 2 | 3 |
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Duration: Weeks | 12 | 0 | 0 |
Number students taking module (anticipated) | 60 |
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Module description
This module focuses on analytics from an operations management perspective. Operations Management covers the design, optimisation and management of products, processes, services and supply chains. It uses analytics to make decisions regarding product and service quality and cost, and as well as decisions regarding acquisition, development, and utilization of resources. You will learn about the value of analytics when applied to different types of data such as: machine data, sensor data, and other forms of data generated by operational systems.
Module aims - intentions of the module
The module aims to impart knowledge and skills in optimisation and decision-making algorithms where students can apply to a variety of fields, including business, education, and research. Graduates of this module would be equipped to frame and analyse decisions through an optimisation framework, leading to employment as technical staff members in business or industry, government planners, and private consultants.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. identify and apply appropriate analytics methods and tools to a range of business situations;
- 2. demonstrate knowledge and understanding of fundamental, and domain-specific, analytics methods and tools;
- 3. create, manage, interrogate, interpret and visualise data from a wide range of different sources, types and including structured and unstructured forms.
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 4. critically analyse the use of data within a business context, identifying strengths and limitations.
ILO: Personal and key skills
On successfully completing the module you will be able to...
Syllabus plan
The module showcases analytical techniques and tools to problems involving the operations of a system, which includes:
- Formulating operational and business problems as linear programs
- Apply solution methods of linear optimisation in operational problems
- Solving supply and demand issues through the assignment problem
- Apply network algorithms to solve a broad variety of operational problem
- Apply decision making algorithms under uncertainty
Learning activities and teaching methods (given in hours of study time)
Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
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30 | 120 | 0 |
Details of learning activities and teaching methods
Category | Hours of study time | Description |
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Scheduled Learning and Teaching Activity | 30 | Lectures, workshops and labs |
Guided Independent Study | 50 | Preparatory reading prior to workshops and lectures |
Guided Independent Study | 70 | Practice use of software and concepts from additional exercises and examples |
Formative assessment
Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
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Review of individual performance on group exercises | During workshops / tutorials | n/a | Verbal |
Outline plan for assessed report | One page | n/a | Written/Verbal |
Summative assessment (% of credit)
Coursework | Written exams | Practical exams |
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100 | 0 | 0 |
Details of summative assessment
Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
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Practical (take-home) coursework exercise | 30 | 2 hours duration | 1-4 | Written comments |
Individual report | 70 | 3,000 words | 1-4 | Written comments |
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0 |
Details of re-assessment (where required by referral or deferral)
Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
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Practical (take-home) coursework exercise | Practical (take-home) coursework exercise (30%) | 1-4 | August/September Reassessment Period |
Individual report | Individual report (70%) | 1-4 | August/September Reassessment Period |
Re-assessment notes
Re-assessment will be in nature to the original assessment, but the topic, data, and materials must be new.
Indicative learning resources - Basic reading
The following resources may be useful throughout the course:
- Winston (2004). Operations research: Applications and algorithms. Belmont, CA: Thomson/Brooks/Cole.
- Bertsimas, D., Allison, K. O. & Pulleyblank, W. R. (2016). The analytics edge. Dynamic Ideas LLC.
Official page for R: http://www.r-project.org
- Download page: http://www.cran.r-project.org
Indicative learning resources - Web based and electronic resources
Some helpful websites:
• http://www.statmethods.net
• www.rseek.org
• http://www.ats.ucla.edu/stat/r/
• http://finzi.psych.upenn.edu/search.html
Credit value | 15 |
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Module ECTS | 7.5 |
Module pre-requisites | BEM1024 OR BEE1022 OR BEE1025 OR BEA1012 AND BEM1025 |
Module co-requisites | None |
NQF level (module) | 6 |
Available as distance learning? | No |
Origin date | 06/01/2020 |
Last revision date | 21/09/2022 |