HOTEL OPERATIONS ANALYSIS - 2026/7

Module Overview

This module deepens postgraduate-level operational analysis for hotels, combining a competitive computer-based hotel simulation (REVsim/HotelSim) with advanced analytics and AI-enabled techniques. Students will manage simulated hotels, analyse operational and financial outcomes, and apply theory to improve performance. The module emphasises use of AI and data tools for insight generation while embedding processes to ensure assessments are authentic, evidence-based and resistant to misuse of generative AI.

Module provider

Surrey Hospitality & Tourism Management

Module Leader

CIRAULO Marco (Hosp & Tour)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

None

Module content

Indicative content includes:

• Operations strategy and operations synergy
• Business and operational planning
• Control and performance measurement
• Challenging current strategies and operational practice
• Horizon scanning to delivery high performance and growth
• Developing competitive strategies
• Performance analysis, evaluation and using key performance indicators to make decisions / allocate resources
• Data analytics and AI applications in revenue management, demand forecasting, and guest experience

Assessment pattern

Alternative Assessment

In place of the group analysis, an individual analysis of a set of hotel accounts covering a twelve month period (1500 word report)

Assessment Strategy

The assessment strategy is designed to draw on the student's experiences through the exercise to build up a picture of the rationale and principles used to manage the business and explain the results achieved. In addition, students are asked to comment critically on the performance of their business as a whole and identify where alternative actions could have been taken. The final element of the individual work requires the student to explore the theoretical frameworks underlying operations analysis and propose an applied operational control measure.

The summative assessment methods include:

•  Simulation Presentation (oral) Summary Simulation Presentation 
  Team presentation of annual/period results, strategic rationale and alternative actions. Presentations include on-stage Q&A to probe understanding of decisions and constraints. AI use must be disclosed; presenters must explain how outputs were generated and validated.
   
• Individual Analysis Report  3,000 words + 500-word executive summary 
  Evidence-based individual report analysing a chosen performance dimension (e.g., pricing, distribution, staffing, guest satisfaction), linking simulation data to literature, and proposing tested interventions. Required appendices: data extracts, dashboard screenshots, model code/queries, and a documented AI-use log (tools, prompts, outputs, verification steps).
   

Alternative Assessment Instrument

Where students fail the presentation this will be replaced with the following individual assignment for August resits

Analysis of Results

You will be provided with a set of results for a single hotel business covering a given period of operation. From your analysis of these data, submit a report summarising these results providing an explanation of your understanding of the key operational rationale for the business, identifying and explaining the key factors that you feel have affected the performance of the operation and where you feel the management team were successful and unsuccessful. The report should be 1500 words in length, excluding tables, graphs and references as appropriate.

Formative assessment

Students will be required to contribute to running their simulated hotel through the module. In doing this a series of formative planning activities will take place, weekly operating decisions will be made, subsequent weekly results will be analysed and interactive digital data dashboards completed. A series of applied management exercises will also be undertaken on a weekly basis to explore operational tools and results in greater depth. All of these formative activities provide students with the necessary tools, techniques and models to build their assessed reports from.

Formative Feedback

During the first workshop, the assignments and the feedback process is explained;

Feedback is also provided during and after guided activities; as the online activities are built around topic-specific exercises in a group setting, students do not only benefit from lecturer feedback but also receive peer evaluations in their teams;

A pre-assignment feedback session is an integral part of this module. During this session, students present the annual results of their simulated hotel; an authentic task which reflects the requirements of the assignment and receive feedback on their presentation;

Once marking is completed, students are provided with feedback, which contains detailed and generic feedback as well as a breakdown of marks. This enables students to assess their own performance compared to their peers.

Module aims

• Provide students with the opportunity to apply the skills and knowledge gained at undergraduate level to the analysis of a simulated business operation,
• Make decisions about the future of an operation and evaluate
• Analyse and manage a simulated hotel operation, make data-driven strategic and tactical decisions, and critically evaluate outcomes using contemporary AI and analytics tools while maintaining academic integrity and professional ethics.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to allow students to experience the pressure of managing a business operation using a business simulation exercise and to link these experiences through a series of inputs and exercises to a series of  underlying theories, concepts and models.

The teaching and learning methods include the use of the HotelSim business simulation which is a competitive management exercise approximating a real world environment in which several free-standing organisations are competing for business in a closed, but elastic, economic system. Each exercise runs optimally with eight competing teams and is fully interactive, so that no two years can experience exactly the same results, although the underlying economic model ensures that the key drivers can be identified. The simulation will be run in group based workshops.

In addition to the simulation exercise there will be a series of lectures and seminars to expand on some of the key underlying principles and the issues discovered through the discussion of the simulation exercise. Students will also be provided through SurreyLearn with a series of guided study activities to extend their knowledge of the subject.

AI integration and Academic integrity measures
¿    AI-enabled tasks: forecasting, clustering of guest segments, sentiment analysis of guest feedback, automated dashboarding and scenario simulation are explicitly taught and permitted when transparently documented.
¿    Mandatory AI-use disclosure: Every submission must include an ¿AI and Tools Disclosure¿ appendix listing tools, versions, prompts/queries, code snippets, and how outputs were validated or corrected.
¿    Reproducibility requirement: Key analyses must be reproducible from provided data extracts and code (or step-by-step method); non-reproducible outputs will be penalised.
¿    Source-verification: Students must cite primary data sources and include raw extracts; suspiciously generic text or unreferenced AI outputs will be investigated.
¿    Authentic assessment design: Emphasis on individual reflection, justificatory reasoning, explanation of decision process, live Q&A, and interpretation of simulation-specific anomalies that cannot be generated by generic AI responses.
¿    Plagiarism and AI-detection workflows: Submissions are subject to standard academic integrity checks and metadata/code review; unexplained inconsistencies trigger follow-up (viva or code demonstration).
¿    Training and guidance: Module includes sessions on ethical AI use, prompt engineering best practices, and how to validate and document AI outputs.
 

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MANM320

Other information

This module adopts the University curriculum framework, which aims to develop learners with strong capabilities in Digital Capabilities, Employability, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module contributes to the development of the following capabilities: 

Digital Capabilities: This module focuses on developing students¿ capabilities in analysing operational data and capturing business insights for and from managerial decision making. Students will develop their skills in using descriptive, predictive, and prescriptive analytics and data visualisation. They will learn how to use software to create a management information dashboard. Students will use the virtual learning environment (VLE),SurreyLearn, REVsim, a hotel simulation product and microsoft software to facilitate learning. These include accessing teaching and learning materials and engaging with their instructors and peers. Module assessments require students to work collaboratively with peers to analyse datasets and create a periodic dashboard to present insights from the datasets. Meaningful use of AI technology relevant to hotel management and revenue analysis. 

Employability: The assessments in this module require students to extract business insights from datasets and to create a dashboard, to present the managerial implications of these insights. These business analytics and data-driven decision making skills will prepare students to be successful managers in the digital age.  

Global and Cultural Capabilities: Students will learn how to interpret results of operations analytics and their implications to service business in a global context by extracting, comparing, and contrasting individual and group behaviours, as well as sectoral, national, and regional differences captured from STR datasets. 

Sustainability: Students will learn how to reflect on how to interpret business intelligence to support sustainability in the services industry. 

Resourcefulness and Resilience: Students will be required to work collaboratively with peers to manage simulated hotels and identify relationships, extract patterns and critical insights from periodic datasets to help with this. Finding solutions through unstructured problems is the key learning aspect of this module that will develop students¿ resourcefulness and resilience.   

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2026/7 academic year.