OPERATIONAL ANALYTICS - 2024/5

Module Overview

Operational Analytics is a core module for Business Analytics.  Students will learn how to apply Operational Research techniques - the cornerstone of Management Science for the past 70 years - in a digital world rich with data.  Students will learn various quantitative techniques (linear programming, Risk Analysis, Simulation Modelling) that are commonly used within OA.  Importantly, these techniques will be studied in the context of the overall decision-making process, so that they are aware of why and how we turn data into actionable insights.  Thus the module also covers more qualitative approaches such as problem-structuring and data visualisation.

Module provider

Surrey Business School

Module Leader

TOLOO Mehdi (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

The module content will focus on a selected set of critical areas in Operational Analytics. As a guide to the kind of issues that will be covered, please find below an indicative set of topics.

• Decision Analysis (e.g. Utility theory)


• Linear Programming (e.g. transportation optimisations)


• Risk Analysis (e.g. Monte Carlo simulation of costs)


• Inventory Systems (e.g. Economic Order Quantity model)


• Simulations and Waiting Line Models (e.g. Multiple Server Waiting Line)

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate an understanding of the main issues and challenges faced by business operations and a critical awareness of the various approaches of operational research in business firms.

There are two critical aspects to mastery of the subject matter that must be assessed.  First, a student must be capable of applying quantitative OR techniques.  This must be assessed as mastery of the content is not possible without this understanding.  Crucially, an employer would expect such a student coming off a Business Analytics programme to understand the common OR techniques and their mark on the OA module should reflect their level.  To this end, the exam is a critical component of testing a student's mastery of the mathematical methods to be applied in OR fields.

Second, students must also understand how to ensure their analysis provides useful insights.  As such, much of the module will be spent teaching students how to apply the quantitative techniques they learn in the context of real business operational problems.  The analytical project therefore tests these skills through the delivery of both a working model and a presentation where the student can explain the results and the fit-for-purpose business conclusions.  The project is generally based on a real-life project, and as such, the coursework is a very authentic assessment, testing not only quantitative skills, but also the ability of a student to solve a real-world problem and translate quantitative results into useful insights.

To achieve the threshold standard for the award of credits for this module, the student must meet the following criteria related to the learning outcomes:

• Apply the theories, conceptual frameworks and methodologies that underpin operational research;


• Prove the ability to synthesise Management Science concepts within an analytical context;


• Demonstrate evidence of background reading and research of the academic and practitioner literature relevant to Management Science.

Thus, the summative assessment for this module consists of:

• An Analytical Project (1 piece of coursework, and one oral presentation).


• 1 exam at end of module

 

The project is typically based on a real-life analytical project similar to what the module leader has carried out in industry.

The student must submit a working model of their analysis and present their findings, with recommendations on possible actions.

Exam 

This will directly test the ability of a student to identify and apply a relevant OR quantitative technique. 

 

Module aims

• Solve operational problems analytically
• Develop timely, useful solutions to business problems
• Understand how quantitative analysis is used to make business decisions

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to encourage deep learning and critical review through the use of case studies linked to conceptual frameworks presented in the lectures.  In particular, each week follows a set structure.  First, a lecture introduces new concepts through practical examples (often including group exercises), which are then translated into quantitative techniques and demonstrated using simple walkthrough examples.  The lab session then gets students to apply those techniques for themselves in relevant software.  The exercise sheets give ample questions so that students can continue to apply the techniques and embed the skills. 

Team working will be encouraged through the use of case studies and active learning (e.g. operational efficiency is demonstrated through a Lego Learning activity).

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: MANM304

Other information

The module covers all pillars to some degree, but in particular the following:

Employability

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

Digital capabilities

Students will develop digital capabilities by using problem-solving and analytical skills to use data to make decisions), and how to transform data into useful insights.

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 2024/5 academic year.