This module is designed to provide students with an in-depth knowledge of key areas of supply chain management. It is designed to explain and evaluate the current thinking in supply chain management. On completion of the module students will have gained an in-depth knowledge of this field and be able to apply theories and concepts in a practical setting. The module also covers strategic management issues, such as supply chain performance, drivers and metrics; coordination in supply chains; outsourcing; sustainability concerns, and future trends in supply chain management.

1. Critique and contextualise the pivotal role of supply chain management within a business and how it can impact business performance .
2. Justify and rationalise how to approach the complexity of supply chains both from a tactical as well as a strategic perspective and formulate a supply chain management strategy.
3. Critically discuss the concepts, structure, and strategic roles of the supply chain relationships within the contemporary organisation .
4. Acquire a set of tools and frameworks that represent state-of-the-art , scientifically based management thinking and apply them in case settings .
5. Synthesise information to draw conclusions and develop effective solutions within the constraints of limited or incomplete data .
6. Research and analyse current practice and concepts in supply chain management.

This module will provide the student with an in-depth knowledge of the theoretical and applied concepts underpinning business intelligence. It will examine conceptual and logical data modelling whereby the student will gain the practical skills in schema development and be able to extract data on single and multiple tables using SQL. The student will explore the tools and techniques available to capture and analyse data to allow them to create a data science solution to meet business requirements. This course is designed to evaluate and explain how to critically appraise the appropriateness of business intelligence for industry. At the end of this module, the student should be able to collect, process and query data with a view to how they provides insight into managerial decision making.

1. Critically discuss the area of business intelligence .
2. Perform data schema design and critically appraise modelling techniques.
3. Define, create, alter and delete tables and data.
4. Effectively complete data manipulation using SQL commands.
5. Appraise data categories (nominal and ordinal) and how data can be processed to solve a real-world problem or address a business requirement.
6. Critically appraise data analysis techniques available to ingest raw unstructured data, transform, clean and manipulate this data for further analysis.
7. Apply best practice for data gathering, data cleaning, and data formatting to a specific use case or business requirement.

Operations Management deals with the creation, management and improvement of operations strategies, processes and practices, so that they are effectively executed across the entire company. This module is designed to explain and evaluate current thinking in operations management. It reviews operations management skills and techniques, and explores the analytical methods that are useful in operation management. It is expected that students will gain a strategic knowledge of this discipline and be able to apply the operations management theories and concepts in a practical setting.

1. Present and defend operations management theory.
2. Determine the key decisions in operations and illustrate their application in product and service contexts.
3. Synthesise and critically analyse operations management concepts and theories.
4. Analyse the principles of operations management in their own industry.
5. Manage and work as part of a team and demonstrate written and presentation skills.
6. Engage in deeper learning using different learning strategies and reflect on the process of learning.

As a primary business function, along with marketing and finance, the logistics function plays a vital role in achieving a company's strategic plans. Since the logistics function distributes the goods/services, it typically involves the external operations of a company, but also integrates the internal operations. It has a major impact on cost, service and quality, and is often the visible face of the company in terms of dealing with the customer. This module applies key concepts of logistics to provide students with knowledge of the strategic and operational roles of logistics in the global business environment.

1. Critically reflect on the contribution of global logistics to organisational performance .
2. Examine the main aspects of global logistics ranging from strategy design to its execution, stressing its importance in shaping competitive advantage
3. Evaluate and apply methods and techniques to facilitate decision-making in the field of global logistics
4. Synthesise and integrate logistics management concepts which are appropriate to sustainability
5. Assess and develop appropriate logistics strategies in the context of overall business objectives
6. F orm a strategic focus and apply global logistics concepts to practical settings/cases individually and in groups .

This module aims to provide the student with a comprehensive insight into data analysis and visualisation techniques that can be used to gain business insights from data in a variety of formats. The student will learn how to critically appraise the appropriateness of these techniques for industry.

1. Critically discuss notions of intelligence in Supply Chain Management.
2. Examine the data analysis pipeline with respect to business needs in Operation Management.
3. Justify the selection of complex queries to gain specific business insights.
4. Critically appraise the needs of a given business sector in the selection of the appropriate KPIs for application in data analytics.
5. Analyse complex problems resultant from the use of large-scale data sources for business analytics and defend appropriate approaches to solving the business problem.
6. Apply research, information gathering, critical analysis, design and implementation techniques appropriately and effectively.

This module will provide the student with a detailed knowledge of the steps needed to prepare, statistically analyse, and evaluate supply chain data before it is used to build relevant predictive/analytical models. Students will examine the procedures needed to pre-process data before analysis begins. They will gain an in-depth knowledge of the steps needed to identify, implement, and examine descriptive and statistical analysis methods and interpret relevant outputs. The student will acquire detailed knowledge of the processes required to implement a predictive model for analysis and interpretation. Finally, the students will visualise their data for presentation and prepare reports for a non-technical audience.

1. Explain, analyse, and examine the key components of statistical analysis to facilitate data science processes.
2. Prepare and implement key Data Science methods and techniques on Supply Chain data.
3. Critically evaluate the choice of Data Science techniques and tools for a particular scenario with consideration for data and regulatory constraints.
4. Critically evaluate data structure, type, and distribution, and implement relevant hypothesis and statistical tests.
5. Critique and analyse statistical test outputs using relevant analytical metrics.
6. Critically appraise the outputs from predictive/analytical models using evaluation techniques and visualise the results for presentation to a non-technical audience.