📖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