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To introduce you to the core factors of managing operations and the concept of flow in operations.

• An introduction to manufacturing organisations and functions.

• The theory of operations, flow in manufacturing and what enables/inhibits it.

• Order winners, Order qualifiers, and competitive priorities.

• Key Performance Indicators in manufacturing.

• Product/Process matrix, facility layouts, production strategies, product families.

• Customer Demand and capacity planning, and standardization.

• Process flow diagrams, and value stream maps.

• S&OP, Master Production Scheduling, BOM, and scheduling rules.

• Push vs Pull production.

• Information systems; MRP, MPRll, ERP, and Kanban systems.

• Maintenance management strategies.

• Dimensions of Quality, Quality management frameworks, and the cost of quality.

• Roles of inventory; inventory management systems and measures.

• Lean Manufacturing.

• Class discussion of cases, exercises, and videos to support this syllabus.

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

1. Discuss the importance of the operations functions of an organisation and how operations performance can impact the success of the whole organisation.

2. Assess production and capacity management strategies that can be deployed to meet customer demand for products and services.

3. Assess the importance of inventory, maintenance, and quality management systems in achieving high levels of operational performance.

4. Determine the role of information in planning, control, and scheduling, including the role of IT systems.

5. Critique the different attributes of the Lean Production System and how they apply to contemporary operational contexts.

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 using examples from product and service development.

• Introduction to Digital Engineering concepts.

• Digital Engineering Tools and Methods.

• Internet of Things (IoT), Virtual and Augmented Reality (VR&AR).

• Digital Twins.

• Artificial intelligence and machine learning.

• Digital Engineering Industrial Case Studies.

On successful completion of this module, you will be able to:

1. Evaluate the principles of digital engineering, its applications and benefits in product and service development.

2. Critically evaluate the selection of digital engineering tools and methods.

3. Create digital engineering tools and techniques in areas such as product and service development.

4. Critically evaluate the process of developing and using Virtual and Augmented Reality (VR&AR) methods and tools.

5. Evaluate the challenges in digital engineering implementation in industry.

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.

• 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.

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.

To develop your understanding and practice of business data analytics to describe, predict, and inform business decisions.

• Information-based decisions.

• Basic Data Analytics.

• Usage of Tools for Data Modelling, Management and Analysis.

• Data quality and system interoperability.

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

• Introduction to modelling: taxonomy, overview of methods and techniques.

• Enterprise Modelling.

• Process description capture tools and techniques.

• Discrete-event simulation, Systems dynamics and Agent-based simulation techniques and methodologies.

• Case study analysis, use of industry-based software tools.

On successful completion of this module you should be able to:

1. Distinguish the concepts related to modelling approaches.

2. Analyse challenges and opportunities in the capture and representation of business knowledge through modelling.

3. Critically evaluate the opportunities in a business and industrial processes where modelling and simulation can add value.

4. Construct and apply different modelling & simulation approaches used in complex systems.

​​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 ​​

• 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.

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

1. Distinguish the principles of business functions, processes and information management.

2. Examine the concepts, tools and techniques of Enterprise Resource Planning (ERP) and the criteria for ERP selection.

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

4. Appraise Enterprise Systems Operations and Support and related subjects such as IoT and Cyber Security.

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

To introduce you to the wider issues surrounding the management and optimisation of supply chains.

• 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

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. Apply criteria in the sourcing, evaluation and validation of suitable providers of parts, design and manufacturing services for the supply of a complex engineered product.

5. Integrate procurement and supplier management for the supply chain to function effectively.

This module provides the skills to design and develop integrated data management approaches and systems to address data related challenges. This includes managing large volumes of data from disparate sources, identifying and resolving data quality issues, handling disparate data lacking integration and generating insights for agile decision making that are integrated in terms of their data, models and visualisation for agile decision making.

• Introduction to software programming with a view to developing data management systems.
• Evaluate existing standards related to data management.
• Apply methods for data needs analysis.
• Establish mechanisms for enabling connectivity of data acquired from alternative sources – e.g. people, sensors, 5G, IoT.
• Develop data structures and approaches to data modelling using ontologies and reference architectures.

On successful completion of this module you should be able to:

• Assess the system requirements for the integration and accessibility of data to deliver value in complex systems.
• Critically evaluate existing approaches to acquire data from fixed and mobile sources.
• Appraise strategies and techniques to measure and optimise the quality of data.
• Construct efficient data structures enabled by ontologies and reference architectures to allow continuous and standardised data flow.
• Justify mechanisms for allowing connectivity of data to enable links to models and visualisation platforms.