EN7068 Leadership, Stakeholders And Data Analytics Coursework Brief 2026

EN7068 Coursework Brief

Please read the entire coursework brief to ensure you understand all the requirements before you begin.

NOTE: Master’s degree learners are required to read the entire coursework brief-question paper. Effort and engagement with the material presented in this coursework question paper is essential. Learners must take responsibility for your learning and ask questions to your lecturer teaching this module for further clarifications if necessary.

Task A

This coursework requires you to do critical assessment of an industrial scenario related to an engineering management project of your choice. The focus is on how client requirements, scope definition, and the involvement of external stakeholders influence the formulation of a technical plan and decision-making processes. You will analyse the impact of these factors on value management, decision-making, and project outcomes, particularly in the face of uncertainty, dynamic variables, and incomplete data. Additionally, you will evaluate the role of leadership, data analysis, and data visualization in driving innovation and effective communication in project management.

Based on an industrial case study of your choice, you are required to:

• Critically evaluate how client requirements, scope, and external stakeholder involvement influence the technical planning and decision-making in an engineering project.
• Discuss the impact of these factors on value management, project performance, and the handling of uncertainty, dynamic variables, and incomplete data.
• Examine tools and methodologies used to address these complexities in real-world projects, particularly in value management and decision support.
• Analyse the trade-offs between technical and socio-economic factors in project, finance, and personnel management.
• Evaluate the role of leadership and management styles in guiding decisions under uncertainty, integrating ethical, legal, economic, and risk considerations into the decision-making process.
• Investigate how data analysis and visualization impact leadership behaviour, strategic planning, and communication in project management.

Task 1: Client Requirements, Scope Definition, and Stakeholder Influence (15 Marks)

i. In the context of an industrial engineering project, critically evaluate how client requirements and scope definition shape the technical plan.

ii. Discuss the complexities involved in aligning the scope with client expectations, and how the technical team translates these into actionable plans.

iii. Assess how external stakeholders (e.g., suppliers, regulatory bodies, local communities, government agencies) influence the scope, timeline, and technical decisions in real-world engineering projects.

iv. Analyse how their influence can shape both project outcomes and the technical approach.

v. Discuss how client and external stakeholder expectations influence decision-making, particularly in situations involving uncertainty, scope changes, or incomplete data. 

Task 2: Value Management and Uncertainty in Industrial Engineering Projects (15 Marks)

Tools and Methodologies for Value Management: Discuss the tools and methodologies used in industry to manage value in engineering projects. Specifically, evaluate frameworks such as Value Engineering (VE), Monte Carlo simulations, and Cost-Benefit Analysis (CBA) in the context of managing uncertainty and incomplete data in an industrial project.

i. Critically assess how industrial engineering projects systematically handle uncertainty in areas such as cost estimation, scheduling, and performance measurement. How can engineering teams creatively respond to dynamic variables and incomplete data?

ii. Analyse how uncertainty in project parameters (e.g., technology changes, market conditions, or regulatory shifts) affects project outcomes such as delivery time, quality, and cost. Provide examples from industrial scenarios where these factors have played a significant role.

Task 3: Balancing Technical and Socio-Economic Factors in Industrial Projects (15 Marks)

i. In the context of an industrial project, critically assess how project managers balance technical excellence with socio-economic factors such as cost control, social impact, and environmental sustainability. How do these trade-offs influence decision-making and project planning?

ii. Analyse how financial constraints, resource management, and personnel capabilities shape project decisions in real-world industrial scenarios. What strategies should managers employ to align technical goals with budgetary and resource limitations?

iii. Managing Socio-Economic Impacts: Propose practical approaches to managing trade-offs between technical objectives and socio-economic considerations, ensuring that value is optimised, and project objectives are met.

Task 4: Leadership, Decision-Making, and Ethical Considerations in Industrial Engineering (10 Marks)

i. Reflect on how different leadership styles (e.g., transformational, transactional, or adaptive leadership) impact decision-making processes in industrial engineering projects, especially under uncertainty. How do leaders ensure that decisions are defensible, timely, and aligned with project goals?

ii. Discuss the role of ethics, legal issues, and risk management in industrial engineering projects. How should these considerations be integrated into decision-making frameworks to ensure project success while minimizing legal and ethical risks?

iii. Critically evaluate how leaders foster innovation in industrial projects, especially in environments characterized by uncertainty and dynamic change. How does leadership style affect the ability to adapt to new technologies or market conditions while managing risk?

Task 5: The Role of Data Analysis and Data Visualization in Industrial Leadership (10 Marks)

i. Critically assess the influence of data analysis on leadership behaviour and decision-making in industrial engineering projects. How can leaders use data-driven insights to drive innovation, mitigate risks, and improve project outcomes?

ii. Discuss the role of data analysis in shaping long-term vision and strategic planning in industrial projects. How does effective data analysis align project goals with organizational strategy, and what challenges do leaders face in using data to make strategic decisions?

iii. Examine how data visualization tools (e.g., dashboards, performance metrics, and risk analysis charts) improve leadership communication and decision-making in industrial engineering projects. How do these tools enhance clarity, transparency, and understanding of complex technical or financial data?

iv. Explore how effective data governance supports innovation in engineering management. What role does reliable, high-quality data play in fostering innovative behaviours and improving project outcomes in industrial settings?

Task B

DELIVERABLES TO BE UPLOADED in LSBF Canvas submission link

i A report in MS Word Document format

ii Turnitin Report

Please note that your lecturer may ask for a presentation and ask Q&A.

Learning Outcomes

Knowledge

1. Critically evaluate the impact of client requirements, scope and external stakeholders upon value and management decisions whilst formulating a technical plan using the most appropriate tools.

2. Demonstrate systematic understanding and critical awareness of advanced and current issues in data analysis and decision support involving uncertainty and new insights related to engineering projects.

3. Critically solve complex engineering management problems involving uncertainty both systematically and creatively, making rationale and defensible decisions in the presence of uncertainty (dynamic variables) and incomplete information/ data.

Subject-based practical skills

4. Analyse the overall facets of project value and evaluate approaches to value management that are applicable to project management.

5. Demonstrate accountability for project, finance and personnel management and managing trade-offs between technical and socio-economic factors.

Skills for life and work (general skills)

6. Develop and engage in logical arguments concerning engineering management data analysis within a technical report balancing technical, ethical, legal, economic and risk considerations. (UGB)

7. Understand their own management/ leadership styles whilst making decision.

8. Creatively use appropriate project and engineering management tools for achieving higher yields.

Report Text Formatting Instructions

i. Spelling: Use British English spellings

ii. Font: Use a plain, easy-to-read font style, such as Calibri. Use font size 11 for the body of the report.

ii. Be consistent with the size of headings, for example:

• Title (font size 16, Bold)
• Heading 1 (size 14, Bold)
• Heading 2 (size 12, Bold)
• Heading 3 (size 12, Italics)

Ideally, use the facility for headings in Microsoft Word (Home tab ► Styles) because this allows for consistency and generation of a table of contents, if required. However, if choosing to use the sizes outlined above, you will need to update the Styles settings to match them. Whatever headings you decide to use, be consistent. Only the first letter of the first word of each heading or subheading is capitalised (except in the case of proper nouns).

iv. Line spacing Numbers: The recommended academic standard is 1.5. Single line spacing is not normally used in academic work except for quotations over twenty-five words. Only put a single line space between paragraphs and be consistent throughout the report.

v. Page numbers should be centred at the bottom of the page

vi. Remember that paragraphs consist of more than one sentence. A paragraph should focus on a single idea, theme or argument. Linking sentences from one paragraph to another ensures coherence.

vii. Tables should be numbered and have a heading, for example: Table 1: Literature Framework. The format should be consistent throughout the report.

viii. References: Support your arguments with relevant academic literature, industry case studies, and reports from engineering practice. All references must be correctly cited using a standard citation style (Harvard).

ix. Presentation: The coursework should be presented in a professional format, with clear headings, subheadings, and appropriate formatting for readability. Ensure the text is wellorganized and free from grammatical errors.

x. Word Count Guidelines

Maximum 3000 words ( Introduction to Conclusion). Excludes executive summary, cover page, Table of Content, References, and Annexes.

The purpose of a word limit is to provide you as learners with a clear indication of the maximum length for an assessed written piece, helping to set expectations for the scope of work, the level of detail required, and how to manage time effectively for different assignments. Adhering to word limits is not only an academic skill but also a valuable professional competency.

Word limits are established based on the assessment objectives. For all coursework assignments, the maximum word count is 3,000 words. Anything beyond this limit will not be marked. The word count includes any allowed tolerance (e.g., a +20% margin). If an executive summary or abstract is required, its word count will be specified separately.

For more information on word count guidelines, read again this coursework question paper or consult with your lecturer for clarification

-End Of Coursework Brief-

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