Operations Analytics

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.

Full module specification

Module title:Operations Analytics
Module code:BEM3062
Module level:3
Academic year:2020/1
Module lecturers:
Module credit:15
ECTS value:



BEM1024 Programming for business analytics

BEM1025 Statistics business 



Duration of module: Duration (weeks) - term 1:


Module aims

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.

ILO: Module-specific skills

  • 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

  • 4. critically analyse the use of data within a business context, identifying strengths and limitations.

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching Activity30Lectures, workshops and labs
Guided Independent Study50Preparatory reading prior to workshops and lectures
Guided Independent Study70Practice use of software and concepts from additional exercises and examples

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Review of individual performance on group exercisesDuring workshops / tutorialsn/aVerbal
Outline plan for assessed reportOne pagen/aWritten/Verbal

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Practical (take-home) coursework exercise302 hours duration 1-4Written comments
Individual report703,000 words1-4Written comments

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Practical (take-home) coursework exercisePractical (take-home) coursework exercise (30%)1-4August/September Reassessment Period
Individual reportIndividual report (70%)1-4August/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.

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

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:

  • Download page:

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Indicative learning resources - Web based and electronic resources

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