PERFORMANCE ANALYTICS - 2026/7

Module Overview

This module is designed to introduce students to how mathematical and econometric methods can be used to model diverse transformation processes, to establish benchmarks of efficiency and productivity for organisations, and to carry out a benchmarking exercise using such methods. The students will gain valuable hands-on experience in implementing an efficiency and productivity assessment with real case studies using appropriate software.

Module provider

Surrey Business School

Module Leader

ZHOU Xun (SBS)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

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

Module Availability

Semester 2

Prerequisites / Co-requisites

None

Module content

• Introduction to the notions of comparative performance measurement, units of assessment, and measures of efficiency
• Principles of Linear Programming
• Basics of Data Envelopment Analysis (DEA)
• Extensions to the basic DEA models considering variable returns to scale 
• Production Theory
• Regression models for Inference
• Econometric models for transformation functions
• Parametric methods for efficiency measurement

The module will utilise a number of case studies as appropriate, drawn from areas where comparative performance assessments have been extensively applied, such as Healthcare, Banking, Education, Utility Regulation, etc.

Assessment pattern

Alternative Assessment

Not applicable.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate that they:

• Understand how to carry out an efficiency assessment exercise by formulating appropriate models for the data at hand; 
• Can advise decision makers on appropriate action to increase the efficiency and/or productivity of the business;
• Can present solutions to business managers in a way they can understand and act upon.

The summative assessment for this module consists of:

• A final written report demonstrating the ability to follow the interpretation of the results from end-to-end, to take a real-world business problem and produce realistic, robust, and understandable options and insights. This combines the technical skills above with the softer skills of process and communication that transform raw data into actionable, decision-making insights.

Formative assessment and feedback:

Via SurreyLearn, individualised written feedback, meetings with students if required, and general comments in classes.

Module aims

• Enable students to understand how analytical methods relying on linear programming and statistical methods can be used to derive benchmarks, targets, and measures of efficiency and productivity for decision-making units.
• Understand the theories of performance evaluation and apply them in complex multi-output multi-input contexts in the production of goods and services.
• Provide students with hands-on experience in implementing an efficiency and productivity assessment using these methods with the help of specialised software.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to cultivate an understanding of the Analytics for comparative assessment of decision-making units.

Students will learn how to apply the theoretical concepts using data from real-world applications.

The learning and teaching methods include:

Synthesising theories of data envelopment analysis and related areas:

• Hands-on-approach by evaluating several software tools relevant to measuring efficiency and productivity of decision-making units.
• Demonstrating evidence of background reading and research of the academic and practitioner literature relevant to performance evaluation of homogeneous decision-making units.
• This module is delivered as a mix of lectures and lab classes.
• Web-based learning support and electronic resources will be provided.

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: MANM529

Other information

Employability: The module is highly applied and directly teaches key skills needed for Business Analytics graduates going into analytical careers.

Digital capabilities: Students develop digital capabilities specifically by using problem-solving and analytical skills to use data to make decisions on how to transform data into useful insights by understanding how data gets turned into knowledge and eventually into insight.

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.