📖Program Curriculum

Course modules
Compulsory modules
All the modules in the following list need to be taken as part of this course.
Enterprise Systems
Aim
The module aims to provide a systematic understanding and knowledge of the enterprise systems principles and how to use these systems to manage an enterprise. The course will also provide hands-on experience using SAP as a leading industry-standard software application.

Syllabus
• Introduction to business functions, processes and data requirements within an enterprise.
• Enterprise wide IT systems. Managing Enterprise through ERP.
• Enterprise Resource Planning (ERP): concepts, techniques and tools.
• ERP selection and implementation issues.
• An Introduction to IoT and Cyber Security.
• SAP based hands-on case studies.

Intended learning outcomes
On successful completion of this module you will be able to:

1. Describe the principles of business functions, processes and data infrastructure.

2. Explain the concepts, tools and techniques of Enterprise Resource Planning (ERP) and its related subjects such as IoT and Cyber Security.

3. Evaluate issues and challenges in ERP implementation and the importance of Enterprise-wide systems to business operations.

4. Identify the various criteria for ERP selection.

5. Demonstrate working/application knowledge on the use of SAP tool through hands-on case studies.

Operations Management
Aim
To introduce you to core factors of managing operations.

Syllabus
An introduction to manufacturing and service activities.
Capacity, demand and load; identifying key capacity determinant; order-size mix problem; coping with changes in demand.
Standard times, and how to calculate them; process analysis and supporting tools; process simplification.
What quality is; standards and frameworks; quality tools; quality in the supply chain.
Scheduling rules; scheduling and nested set-ups.
Roles of inventory; dependent and independent demand; Economic Order Quantity; uncertain demand; inventory management systems and measures.
Information systems – at operational, managerial, and strategic levels; bills of material; MRP, MPRll and ERP systems.
Ohno’s 7 wastes; Just-in-Time systems (including the Toyota Production System, and Kanbans).
Class discussion of cases, exercises, and videos to support this syllabus.
Intended learning outcomes On successful completion of this module you will be able to:

1. Assess the key capacity determinant in an operation, and carry out an analysis to develop the most appropriate approach in response to changes in demand.
2. Select and apply appropriate approaches and tools to determine standards and improve processes.
3. Determine the information needed to support businesses, in particular manufacturing operations.
4. Assess and select appropriate Just-in-Time (JIT) tools to improve operations.
5. Develop appropriate quality systems for the whole of their supply chain – from supplier, through operations to customers – and ensure these systems are sustained and a culture of continuous improvement prevails.
Data Analytics
Aim
To develop your understanding and practice of business data analytics to describe, predict, and inform business decisions.
Syllabus
Big Data and Business decisions.
Basic Data Analytics.
Usage of Tools for Data Modelling, Management and Analysis.
Data quality and system interoperability.

Intended learning outcomes
On successful completion of this module a student should be able to:

1. Distinguish the types of data typical in business management.

2. Construct data models from datasets representative of data extracted from business IT systems.

3. Evaluate reliability of datasets and devise ways to enhance trustworthiness of data extracted from business IT systems.

4. Analyse and investigate patterns from datasets representative of data extracted from business IT systems.

5. Present data analysis results to support management decisions.

Enterprise Modelling
Aim
To extend your ability to evaluate integrated knowledge systems within the context of the wider enterprise environment through the application of modelling and simulation tools, techniques and methodologies.

Syllabus
• Introduction to modelling: taxonomy, overview of methods and techniques;
• Enterprise Modelling and lean concepts and architecture
• Structured Systems Analysis methodology, Process description capture tools and techniques, Object state transition network;
• Discrete-event simulation, Systems dynamics and Agent-based simulation techniques and methodologies;
• Case study analysis, use of industry-based software tools
Intended learning outcomes On successful completion of this module you should be able to:
1. Distinguish the concepts of modelling approaches and architecture.
2. Analyse challenges in the capture and representation of business knowledge for the purpose of modelling.
3. Critically evaluate the opportunities in a business where modelling and simulation can add value.
4. Construct and apply different modelling & simulation tools used in producing enterprise models.
Supply Chain Management
Aim
To introduce you to the wider issues surrounding the management and optimisation of supply chains.

Syllabus
Supply chain concepts

Supply chain strategy

Relationship management

Supplier Selection and Evaluation

Supplier Sustainability

Supply chain Planning

Design & Operating SC

Outsourcing Product Design and Manufacturing

Intended learning outcomes
On successful completion of this module you will be able to:

1. Evaluate issues surrounding the development of the right supply chain strategy for the business / product groups.
2. Create strategies for managing the information flows in a supply network in order to reduce the bullwhip effect and the challenges of accurate demand and forecast planning.
3. Evaluate the challenges with improving performance of supply networks and gain familiarity with the application of a variety of supply chain tools to help in the re-design of the SC.
4. Organize the complexities in managing and designing distribution centres so that they support the overall SC strategy and customer value proposition in the market place.
5. Integrate procurement and supplier management for the supply chain to function effectively.

Digital Engineering
Aim
This module aims to provide a systematic understanding and knowledge of key concepts and principles for digital engineering and its current practices, tools and processes and future development. The course will also provide hands-on experience using digital engineering tools and methods to facilitate product and service development.
Syllabus
Introduction to digital engineering concepts.
Digital engineering tools and methods to support zero physical prototyping.
Internet of Things (IoT), Virtual and Augmented Reality (VR & AR).
Digital twins for product development.
Artificial intelligence and machine learning.
Digital engineering industrial case studies.
Intended learning outcomes
On successful completion of this module, you will be able to:

Evaluate the principles of digital engineering, its applications and benefits in product and service development
Critically evaluate the selection of digital engineering tools and methods.
Evaluate the application of digital engineering tools and techniques to support product and service development.
Manage the application of using Virtual and Augmented Reality (VR & AR) tools to support zero physical prototyping.
Evaluate the challenges in digital engineering implementation in industry.

Data Analytics and Artificial Intelligence​
Aim
This module will provide the processes to design and develop artificial intelligence (AI) based approaches to be trained for data analytics on a spectrum of data types (e.g. messy data, data gaps or big data), whilst also considering the ethical implications.
Syllabus
Theory of data analytics, AI, ML, data mining, statistics and supervised learning, e.g., probability, decision trees, regression and classification.
Experience of real-world AI/ML applications, in areas such as engineering, business, social media, medical data and financial data
Evaluate alternative ethical considerations including human-machine collaboration that are related to the use of AI/ML.
The opportunity to work on industry problems that can benefit from AI/ML approaches.
Intended learning outcomes
On successful completion of this module you should be able to:

Compare and contrast data analytics methods including machine learning (ML) in terms of its current and future concepts, principles and theories.
Construct ML concepts and methods to impart innovative problem-solving skills in a variety of data maturity scenarios.
Evaluate value creation opportunities from ML, develop value propositions and revenue models for businesses and organisations
Construct data analytics-based methods for real world problems with the changing nature of digital technology infrastructure and varying volume and quality of data;
Appraise ethical responsibility considering human-machine collaboration in data analytics by reflecting on intelligent systems that benefit society.
Integrated Data Management

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🏠 Accommodation

You will need to book the accommodation after you have been accepted.

You can choose to live on campus or off campus in private accommodation.

How to book:

  • Make a booking online after you have been accepted (in this case please let us know your choice when you apply).
  • Register when you arrive - its not possible to reserve a room before arriving. You can arrive a few days before and book it
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💰 Fees

Application Fee:

$0 USD

Tuition fee:

26,580 GBP per year

26,580 GBP in total

Entry Requirements

You are not eligible to apply to this program because:

The minimum age is 18.

English fluency is required.
You need to be either:
- A native English speaker
- Studied in English at high school or a degree
- Have passed IELTS level 6.5 or TOEFL 95 or above.

Minimum education level: Bachelor's.

The program is competitive, you need to have a high grades of Average A, 70%, or a high GPA.

All students from all countries are eligible to apply to this program.

Is this not correct? You can edit your profile or contact us.
Or see the list of programs you are eligible for here .
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📬 Admissions Process

3 Steps to Apply to a University

Application step 1

Application step 2

Application step 3

Please choose the programs here , "You are advised to select 2-3 programs to increase your chances of getting accepted.

Required Documents:

  • Passport
  • Graduation certificate
  • Passport size photo
  • Official transcript
  • Personal statement
  • English certificate (You can take the English test online)
  • Guarantor letter
  • 2 Recommendation letters

Preparing documents:

You can start your application now and send the application documents during your application. Some documents you can send later if you don’t have them right away. Some more info about preparing application documents is here

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Application process:

Applying Online is simple in just a few steps. More information is available here.

The first steps are to choose the programs, pay the application fee and upload the application documents.

Once submitted to Global Admissions, we will review your application within 2-3 days and proceed to the university or ask you for further clarification

After it has been processed to the university you will receive your unique application ID from each university.

The university may contact you directly for further questions.

We will then follow up each week with the university for updates. As soon as there is any update we will let you know. If you have made other plans, decide to withdraw / change address at any time please let us know.

After you have been accepted you will receive your admissions letter electronically and asked to pay the non-refundable deposit to the university.

Once you have paid the deposit the university will issue you the admissions letter and visa form to your home country.

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Here is some more information about the enrollment process after you have been accepted.

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