Module Name: Finance, Sustainability and Performance Management
Module Code: 7008SOM
Assignment Title: Individual report, supported by a group management simulation
Assignment Due: Mar 2026,18:00 UK time
Assignment Credit: 30 credits
Word Count (or equivalent): 3,000 words. There will be a penalty of a deduction of 10% of the mark (after internal moderation) for work exceeding the word limit by 10% or more. The word limit includes quotations and citations but excludes the references list.
Assignment Type: Percentage Grade (Applied Core Assessment). You will be provided with an overall grade between 0% and 100%. To pass the assignment you must achieve a grade of 40% or above.
This assignment is an individual assignment supported by an online management simulation.
This assignment requires you to run 3 to 4 rounds of the management simulation and to write an individual report based on your simulation activities or outcomes.
Management simulation (group work)
The simulation is a mandatory activity that will serve as the basis for writing your final individual report.
You will run 3-4 rounds of the simulation in groups of 4 to 6 students during workshops. Throughout the simulation, you will apply your learning from the module, exchange ideas, and receive feedback from the tutor.
Individual report, 3,000 words (weight 100%)
You are required to write a report that analyses performance and proposes a set of performance management tools, all of which reflect your management simulation activities and results.
Your report is to include the following:
You are expected to use insight obtained from the core textbook, relevant additional reading from the lectures (available on Aula) and your own research using the University’s library facilities (locate.coventry.ac.uk).
Your report should follow the below structure:
(calculation, tables, diagrams, charts, illustrations, etc.)
This assessment is in the Amber category for use of Artificial Intelligence (AI), which means that the use of AI is permitted to assist you in the development of the assessment in line with the student guidance. Please note that if using AI tools, you must reference which tools you have used and for what purposes you have used them. This information must be acknowledged in your final submission. The below format is recommended:
| Tool | How used in this assignment |
| e.g. ChatGPT-3.5 | Eg Key word search on topics xxx |
| e.g. Microsoft Copilot | Eg Request for a suggested structure for xxx |
| Etc. |
Submission Instructions:
You must submit your work through Aula by the submission deadline. You can access the submission link through the module web.
How will my assignment be marked?
Your assignment will be marked by the module team.
How will I receive my grades and feedback?
Provisional marks will be released once internally moderated.
Feedback will be provided by the module team alongside grades release.
Your provisional marks and feedback should be available within 2 weeks (10 working days).
What will I be marked against?
Details of the marking criteria for this task can be found at the bottom of this assignment brief.
The Learning Outcomes for this module align to the marking criteria which can be found at the end of this brief. Ensure you understand the marking criteria to ensure successful achievement of the assessment task. The following module learning outcomes are assessed in this task:
If you have any questions about this assignment please see the Student Guidance on Coursework for more information.
Spelling, Punctuation, and Grammar:
You are expected to use effective, accurate, and appropriate language within this assessment task.
Academic Integrity:
The work you submit must be your own, or in the case of groupwork, that of your group. All sources of information need to be acknowledged and attributed; therefore, you must provide references for all sources of information and acknowledge any tools used in the production of your work. We use detection software and make routine checks for evidence of academic misconduct.
It is your responsibility to keep a record of how your thinking has developed as you progress through to submission. Appropriate evidence could include: version controlled documents, developmental sketchbooks, or journals. This evidence can be called upon if we suspect academic misconduct.
If using Artificial Intelligence (AI) tools in the development of your assignment, you must reference which tools you have used and for what purposes you have used them. This information must be acknowledged in your final submission.
Definitions of academic misconduct, including plagiarism, self-plagiarism, and collusion can be found on the Student Portal. All cases of suspected academic misconduct are referred for investigation, the outcomes of which can have profound consequences to your studies. For more information on academic integrity please visit the Academic and Research Integrity section of the Student Portal.
Support for Students with Disabilities or Additional Needs:
If you have a disability, long-term health condition, specific learning difference, mental health diagnosis or symptoms and have discussed your support needs with health and wellbeing you may be able to access support that will help with your studies.
If you feel you may benefit from additional support, but have not disclosed a disability to the University, or have disclosed but are yet to discuss your support needs it is important to let us know so we can provide the right support for your circumstances. Visit the Student Portal to find out more.
Unable to Submit on Time?
The University wants you to do your best. However, we know that sometimes events happen which mean that you cannot submit your assessment by the deadline or sit a scheduled exam. If you think this might be the case, guidance on understanding what counts as an extenuating circumstance, and how to apply is available on the Student Portal.
Module Leader Name: Dr Liliani, Dr Shazib Shaikh
Module Leader Email: ae2917@coventry.ac.uk, ae3262@coventry.ac.uk
Assignment Category: Composite
Attempt Type: Standard
Component Code: CW
| Theory, concepts and models (30%) | Analysis, evaluation and application (40%) | Critique, conclusions and recommendations (30%) | |
| Tasks relevant to each Criteria (if unspecified, assume weight is equally divided among given tasks) | Main tasks:
– Demonstrate understanding of relevant models. – Apply theory to explain decisionmaking logic. – Integrate financial and sustainability frameworks. |
Main tasks:
– Analyse how decisions impacted simulation outcomes. – Apply financial and sustainability metrics to assess performance. – Evaluate alternative decisions and consider trade-offs. – Use data to support arguments. |
Main tasks:
– Critical reflection on decision-making process and performance. – Propose appropriate performance management tools. – Draw well-supported and insightful conclusions and viable recommendations. |
| 80 to 100%
Exceptional |
Exceptional understanding of relevant financial and non-financial models.
Exceptional integration of finance and sustainability frameworks to justify and underpin logic behind decisions, critical evaluations, and proposed performance tools. Exceptional use of theory with originality to support arguments and critiques.
|
Exceptional application of financial and sustainability metrics with high precision.
Insightful analysis of how decisions influenced performance across simulation rounds. Evidence of creative application of techniques to dynamic scenarios. Exceptional interpretation of simulation outcomes. Exceptional evidence of in-depth analyses supported by rich data and references from legitimate sources with precise referencing practice. |
Exceptional and in-depth critical reflection on decision-making and performance, recognising complexity and uncertainty. Well-developed arguments with outstanding integration of theoretical framework and analysis.
Exceptional and innovative proposed performance management tools, well-justified and demonstrate professional-level thinking. Exceptional conclusions and highly insightful takeaways from the work. Exceptionally wellargued innovative recommendations.
|
Assessment Marking Criteria
| 70 to 79%
Excellent |
Excellent understanding of relevant financial and non-financial models.
Excellent application of theory to support decision-making. Excellent integration of finance and sustainability frameworks to justify and underpin logic behind decisions, critical evaluations and proposed performance tools. |
Excellent application of financial and sustainability metrics, leading to excellent interpretation of the simulation outcomes.
Robust analysis linking decisions and performance across simulation rounds. Excellent evaluation with critical engagement, considering trade-offs across alternatives. Excellent evidence of in-depth analyses supported by relevant data and references from legitimate sources with excellent referencing practice. |
Excellent and in-depth critical reflection on decision-making and performance. Welldeveloped arguments with excellent integration of theoretical framework and analysis.
Excellent proposed performance management tools, well-articulated to improve future performance. Excellent conclusions with insightful takeaways and viable recommendations.
|
| 60 to 69%
Very good |
Very good understanding of relevant financial and non-financial models.
Very good application of most theories to support decision-making. Very good integration of most finance and sustainability frameworks with some room for stronger integration across analysis. |
Very good application of financial and sustainability metrics leading to very good interpretation outcomes, with minor issues.
Very good evaluation linking decisions and performance across simulation rounds, demonstrating critical thinking about what could have been done differently. Very good and robust analyses supported by data and references with very good referencing practice. |
Very good critical reflection on decision-making and performance, with minor issues. Relevant arguments with some integration of theoretical framework and critical awareness.
Very good and sensible performance management tools are proposed with minor issues in the contextual adaptation. Very good conclusions, logical and based on evidence, with relevant recommendations.
|
50 to 59% Good understanding of the core Good application of financial and sustainability Good critical reflection on decision-making and
Good financial and non-financial models with metrics with some issues or quite descriptive. performance, with relevant arguments, but some
good application to support decision- parts lack depth or specificity.
Good evaluation linking decisions and making, some issues in practice.
performance across simulation rounds, Good and functional performance management Good integration of the core finance demonstrating good logic of what could have tools are proposed with sufficient justifications or and sustainability frameworks, been done differently. contextualisation. showing good logic with some generic
Good analyses supported by data and Good conclusions, based on evidence with parts. references, good referencing practice with some generic insights and recommendations. some inaccuracy.
40 to 49% Outcomes met. Basic understanding Outcomes met. Basic application of financial Outcomes met.
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