Module manager: Antonino Sgalambro
Email: a.sgalambro@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2025/26
This module is not approved as a discovery module
In today's global economy, the number of businesses and organizations exploring and relying upon the adoption of intelligent technologies to gain a competitive advantage and enhance efficiency is growing exponentially. A good understanding and appropriate utilisation of intelligent technologies has therefore become an essential skill in the workplace at all levels of responsibilities. This module is aimed at providing students with the fundamental knowledge pillars around methods and tools from optimisation and artificial intelligence, which underpin data-driven intelligent technologies. Drawing upon real-world success cases in a range of productive sectors including manufacturing, healthcare, logistics and service management the students will learn how to lead and manage a knowledgeable adoption of intelligent technologies in the context of businesses and organisations. The module will allow students to acquire a critical understanding and hands-on experiences on how intelligent technologies can be successfully adopted in Business Management.
The module aims to:
Provide an introduction to basic concepts in optimisation modelling, exact, heuristic and multi-objective solution algorithms, decision support systems, machine learning, artificial neural networks and language processing tools.
Develop practical skills in optimisation and artificial intelligence tools and data-driven business analysis.
Utilise an array of real-world success cases to explore different approaches to building, solving, applying and calibrating optimisation and AI models for business management.
Encourage critical understanding and thinking about the broad impact of adopting technologies based on optimisation and AI, including ethical considerations and future trends.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
LO1: Understand the basic concepts underpinning prescriptive and predictive modelling in optimisation and artificial intelligence
LO2: Critically evaluate an appropriate adoption of optimisation and AI tools in business management, and assess their effectiveness.
LO3: Select and utilise a range of tools from optimisation and AI for increasing efficiency in data-analysis and problem-solving in business management.
LO4: Actively and creatively design and apply optimisation and AI solutions to specific real-world business problems.
LO5: Show awareness of emerging intelligent technologies and trends in business applications.
On successful completion of the module students will have demonstrated the following skills learning outcomes:
SLO1 - Reflection & presentation skills & research: Effectively understand and articulate optimisation and artificial intelligence concepts, also engaging with related literature
SLO2 - Problem Solving & critical thinking & creativity: Develop a critical understanding and creative approach on how problems in business management can be modelled and solved by adopting tools from optimisation and artificial intelligence
SLO3 - Technical, Programming and IT skills: Acquire technical skills on optimisation modelling languages and tools, spreadsheet-based optimisation solvers, Python, OpenAI API.
SLO4 - Digital creation & problem solving & innovation: Analyse specific challenges in Business Operations, Decision-making and Strategic planning, identify elements of complexity and design tailored Optimisation and AI driven solutions
SLO5 - Creativity & analytical skills & commercial awareness: Assess real-world decision-making challenges at different levels (strategic, tactical and operational) and develop data-driven approaches to provide insightful and actionable recommendations, understanding impact and benefits.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lectures | 11 | 1 | 11 |
| Practicals | 5 | 2 | 10 |
| Private study hours | 79 | ||
| Total Contact hours | 21 | ||
| Total hours (100hr per 10 credits) | 100 | ||
Formative feedback will be offered on a voluntary piece of work related to the final assessment.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | 2,000 words | 100 |
| Total percentage (Assessment Coursework) | 100 | |
The resit for this module will be 100% by 2,000 word coursework.
The reading list is available from the Library website
Last updated: 30/04/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team