Modules

Core Module Information
Module title: Prescriptive Analytics
SCQF level: 11:
SCQF credit value: 20.00
ECTS credit value: 10
Module code: SOE11168
Module leader: Max Chipulu
School The Business School
Subject area group: Management
Prerequisites

There are no pre-requisites for this module to be added
Description of module content:

Organisations face complex operational and strategic decisions in uncertain environments. This module develops the skills required to design and use prescriptive analytics models to support effective, evidence-based decision-making.You will learn how to conceptualise real-world systems, translate them into formal models, and analyse performance using a range of analytical approaches. Core topics include simulation modelling (Monte Carlo, discrete-event simulation, agent-based modelling, and system dynamics), optimisation using linear programming, and the treatment of uncertainty and risk in decision contexts. Emphasis is placed on understanding when and why different modelling paradigms are appropriate.The module adopts a strongly applied approach, requiring you to integrate simulation, optimisation, and sensitivity analysis within a single decision-support framework, and to communicate insights through management-focused dashboards. You will also critically assess the role of Generative AI as a modelling aid within the analytics modelling cycle.By the end of the module, you will be equipped to frame complex decision problems, build and validate analytic models, and translate quantitative results into actionable strategies for organisations.
Learning Outcomes for module:

Upon completion of this module you will be able to

LO1: Appraise and formulate an observed real-world problem and translate it into a structured conceptual model.

LO2: Critically evaluate the appropriateness of optimisation techniques and software to model and solve diverse business problems.

LO3: Demonstrate a critical understanding of the different types of simulation techniques and have insight into the domains in which we can usefully apply each.

LO4: Model and solve business decision problems using the appropriate simulation techniques and software.

LO5: Explain the results of your prescriptive model to non-technical business users in a way that is easy to understand.
Full Details of Teaching and Assessment
2026/7, Trimester 1, In Person,
VIEW FULL DETAILS
Occurrence: 001
Primary mode of delivery: In Person
Location of delivery: CRAIGLOCKHAR
Partner:
Member of staff responsible for delivering module: Max Chipulu
Module Organiser:


Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)NESH Description
Face To Face Lecture 20 Lectures will introduce new topics and provide an overview of key concepts. In-class debate and discussion: we will encourage in-class debate and discussion to help you develop your critical thinking skills and to learn from each other.
Face To Face Practical classes and workshops 20 Computer labs will give you hands-on experience in modelling with the relevant software tools. Interactive case studies based, as far as possible, on real organisations and real datasets: to help you apply what they have learned to real-world situations.
Online Guided independent study 160 Directed reading: we assign directed readings to help you learn more about specific topics in depth. Additional independent learning: we expect you to conduct further additional independent learning to address any additional weaknesses and gaps you identify to achieve the required depth of learning. Use audio, video and online materials: to help you learn at your own pace and explore topics in more depth.
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words Description
Project - Practical 20 1~4~5 Week 8 HOURS= 1 page This component assesses your ability to develop an early‑stage conceptual model of a real‑world system. You will translate a system description into a structured visual representation, identifying key elements and relationships to clearly define how the system operates.Further details and submission requirements are provided on the assessment brief and Moodle page.
Project - Practical 80 1~2~3~4~5 Exam Period HOURS= 500-1000 words This component assesses your ability to apply intermediate and advanced analytics modelling techniques to support managerial decision‑making. You will build on the conceptual model from Component 1 to develop, integrate, and analyse quantitative models, evaluating system performance under different scenarios. Outputs should be synthesised into a concise, decision‑focused dashboard.Further details and submission requirements are provided on the assessment brief and Moodle page.
Component 1 subtotal: 20
Component 2 subtotal: 80
Module subtotal: 100

Indicative References and Reading List - URL:
Prescriptive Analytics
 

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