Strathclyde Business SchoolDepartment of Management Science

The class will explore the generic problem solving process which underpins the provision of decision support. In particular, it will consider the role of modelling in that process. The activities of problem structuring, data collection and analysis, identification and evaluation of options, communication and implementation of learning, findings and recommendations will each be discussed along with the issues pertaining to each of them. In addition, the links between each of these activities will be explored. Basic methodological issues will be considered and debated. Relevant and up-to-date case studies will be used to illustrate key points and to initiate debate. Time will be spent appreciating the role of problem structuring methods and a variety of other approaches to modelling will be briefly discussed in order to introduce students to key techniques and tools in the field.

Class aims

To introduce, and provide an overview of, the fields of Operational Research and Business Analysis. This will involve gaining an appreciation of the generic problem solving process, an exploration of basic methodological issues, plus an introduction to and overview of key modelling tools and techniques. The class will provide a framework for understanding how these modelling techniques, which will be explored in greater depth in other classes, fit together.

Learning outcomes

Subject specific knowledge and skills

• To develop an understanding of the problem solving process required when supporting management decision-making
• To gain an understanding of the different elements involved in the problem-solving process
• To appreciate the role of modelling in supporting each element of the problem- solving process
• To gain an appreciation of a variety of key modelling tools and techniques and how each can support the management decision-making process cognitive abilities and non- subject specific skills
• Critical thinking
• Written communication skills

This module provides an introduction to the basic theory and application of statistical modelling. Topics covered included data analysis, probability theory, distributions and moments, estimation and hypothesis testing. Throughout, there is an emphasis on the use of statistical analysis to help support decision-making and the management of business and industrial problems. Cases are used to illustrate topical issues.

Class aims

The aim of the course is to introduce statistical modeling to support business and management decisions.

Learning outcomes

Subject specific knowledge and skills

• To display and interpret data using appropriate visual displays.
• Select, construct and interpret summary statistics.
• Understand probabilistic reasoning and compute probabilities for simple problems.
• Use graphical methods to identify appropriate models and estimate parameters.
• Apply and interpret formal statistical estimation procedures and goodness-of-fit tests.

Cognitive abilities and non-subject specific skills

• Develop students ability to construct numerical argument
• Critical thinking with respect to quantitative analysis

Quantitative Business Data Analysis focuses mainly on two areas - regression modelling and multivariate analysis. While key background theory will be presented, the emphasis is on the generation and interpretation of output from commercially available software.

Throughout, there is an emphasis on the use of statistical analysis to help support decision- making and the management of business and industrial problems. Cases are used to illustrate topical issues.

Class aims

The aim of the course is to introduce statistical modeling to support business and management decisions.

Learning outcomes

Subject specific knowledge and skills

• To perform analysis of business data using modern multivariate analysis software
• To develop and validate appropriate simple and multiple linear regression models
• Understand the basic principles of classification methods
• Understand the basic principles of ANalysis Of VAriance (ANOVA).

Cognitive abilities and non-subject specific skills

• Develop students ability to construct numerical argument
• Critical thinking with respect to quantitative analysis

This module introduces the fundamental deterministic methods of OR. The course introduces students into the basic deterministic models and methods of OR. Unstructured problem descriptions are formulated using mathematical notation. These problems are classified and appropriate solution methods are selected where they exist. The solution methods are covered in detail for certain classes of problem.

Class aims

The aim of the class is to introduce the basic deterministic methods used in OR.

Learning outcomes

Subject specific knowledge and skills

• To understand the basic models, methods and techniques used in optimisation
• To be able to classify optimization problems according the following classes: combinatorial- (transportation-, network flow-, integer programming problems) and continuous optimization problems (linear programming-, quadratic programming-, linear complementarity-, convex programming- and general nonlinear programming problems)
• To understand methods for solving combinatorial- and continuous optimization problems
• To conduct sensitivity analysis of solutions, and report on this in a matter understandable and useful to a client.

Cognitive abilities and non-subject specific skills

• To write about models and analysis in a coherent manner
• To improve problem structuring skills
• To communicate using mathematical models and their solutions

This module introduces the fundamental stochastic methods of OR. Probabilistic modelling is fundamental to appreciating uncertainty and risk. This element of the class provides much of the foundations that underpin the tools and techniques that are taught on other classes of the MSc such as statistics, forecasting, risk and simulation.

Class aims

The aim of the class is to introduce the basic stochastic processes used in OR.

Learning outcomes

Subject specific knowledge and skills

• To demonstrate probabilistic modelling
• To understand the key results for discrete and continuous Markov chains, queuing models and Poisson point processes
• To be able to apply stochastic models to a variety of operations problems

Cognitive abilities and non-subject specific skills

• To read mathematical texts to broaden and deepen understanding of probability and its applications
• To write about models and analysis in a coherent manner
• To improve problem structuring skills
• To communicate using mathematical models and their solutions

The class will provide a background to system dynamics. A complete approach to system dynamics modelling is then covered in detail. The behaviour of various systems, in particular complex business problems, are examined through the construction of causal loop diagrams. The class then goes on to introduce computer software used specifically for system dynamics modelling. After familiarisation with the package, students will be expected to use it to model and investigate a variety of systems. In particular, the class will explore how such models can be used to investigate complex business problems and how they can be used to decide upon managerial actions that should be taken to help alleviate the problems.

Class aims

To introduce students to the system dynamics simulation method. This technique is used to help provide understanding about complex systems through the construction of qualitative diagrams and quantitative simulation models.

Learning outcomes

Subject specific knowledge and skills

• To be able to determine the type of systems whose behaviour can be investigated using system dynamics
• To develop an understanding of the elements involved in the basic construction of a causal loop diagram;
• To appreciate how a verbal description of a system can be translated into a causal loop diagram and used to examine the system’s behaviour;
• To appreciate how a causal loop diagram, representing a given system, can be translated into a quantitative system dynamics model;
• To develop an understanding of the procedures used to validate a system dynmaics model;
• To appreciate the process by which system dynamics models can be used to investigate systems behaviours so that practical recommendations can be made to help improve the system;

Cognitive abilities and non-subject specific skills

• The learning activities are designed within the class to develop the students within the following areas:
• Problem structuring skills
• Express problems in forms conducive for the software support available

The class will start with an introduction to simulating operations aiming to familiarise students with the concept and its use as well as specific features of a typical simulation tool. The class continues with discussing a rational approach to simulation using a number of examples from manufacturing and service operations. The link between simulation and performance measurement is discussed at this part. This is followed by a detailed discussion on the use of probabilistic distributions in simulation where the emphasis is on ‘relevance for use’ rather than the underlined statistical theories and techniques. The issues of certainty of out put and validation of the model are discussed in the next stage where students understand the importance of trials in simulation. While the main emphasis is on simulation, a number of basic and advanced techniques of Simul8 (a popular discrete event simulation software) are explained and demonstrated for students. The discussion on simulation concludes with addressing some practical issues with regard to doing a simulation project for a business client.

Class aims

To introduce students to a widely used simulation tool in business. The objective is to make students competent in applying discrete event simulation in any operation system and specifically to enable them to use a popular discrete event simulation software (Simu8) for this purpose.

Learning outcomes

Subject specific knowledge and skills

• To understand specific features of discrete event simulation
• To be able to approach a discrete event simulation project in a rational way, using performance measures
• To be able to investigate certainty of output and validation of a discrete event simulation model
• To understand how to use features like trials and warm up period to make the model and its outcome more reliable
• To be able to work with Simul8 software and some of its advanced tools
• To understand and appreciate practical issues involved with a typical discrete event simulation project

Cognitive abilities and non-subject specific skills

The learning activities are designed within the class to develop the students within the following areas:

• Problem structuring skills
• Written Communication skills
• Presenting and reporting skills

Trade-offs between different desirable attributes are covered through multiattribute value theory and multiattribute utility theory. A short introduction is given to game theory in order to highlight the differences in approach required when making decisions to work against an intelligent opponent as opposed to an unknown and undirected “state of nature”.

Class aims

To make students aware of and able to apply models that enable them to support decisions involving trade-offs or which have the character of strategic games.

Learning outcomes

Subject specific knowledge and skills

• Learn about value functions and trade-offs.
• Be able to apply the even swaps method to determine trade offs.
• Understand the concept of Pareto optimality
• Use of different criteria and sensitivity analysis to explore the impact of different stakeholder viewpoints
• Be aware of the basic types of games and be able to perform calculations involving simple non-cooperative games.
• Be able to use state of the art software for modeling multicriteria problems.

Cognitive abilities and non-subject specific skills

• Improved problem structuring skills
• Report writing
• Work effectively in a team

Graphical modelling using Bayesian Nets (Influence diagrams and Belief nets) are used to structure models. The basic notions of probability, utility and value of information are covered and are operationalised through the use of decision analysis modelling software.

Class aims

To enable students to build models that support decision making under uncertainty, using decision analysis software.

Learning outcomes

Subject specific knowledge and skills

• Understand the notion of subjective probability and the axioms for rational decision making
• Learn how to structure a model with decisions, uncertainties and consequences
• Build graphical models of decision problems and exploit them to solve problems, including the use of sensitivity analysis
• Understand and apply Expected Monetary Value and Expected Utility criteria to support a decision maker
• Apply value of information techniques to support a decision maker in spending resources to reduce uncertainties
• Be able to use state of the art software for modeling decisions under uncertainty problems.

Cognitive abilities and non-subject specific skills

• Improved problem structuring skills
• Report writing
• Work effectively in a team

Throughout this module we will explore fundamental models used in the analysis of operational risk. We will cover topics from structuring expert judgment to developing a model and data analysis to estimate the rate of occurrence of hazards as well as indentifying the key drivers of undesirable events.

Class aims

The aim of this module is to develop an understanding of the fundamental techniques used with risk analysis and explore bow these are used within practice.

Learning outcomes

Subject specific knowledge and skills

• Understand theory that underpins standard approaches to elicitation of expert judgment
• Understand basic theory of fault and event tree modelling
• Understand the standard approaches to modelling dependency between random variables
• Develop the ability to assess the robustness of a risk model

Cognitive abilities and non-subject specific skills

• Develop students ability to construct rational arguments
• Critical thinking

Risk management is linked with decision analysis in so far as we explore decision making under uncertainty and it has links with quantitative business analysis as we explore the use of statistics in understanding risk. However, the topic has some unique attributes such as risk communication and the role that experts play in risk assessment.

Class aims

The aim of this module is to develop an understanding of the fundamental techniques used with risk management and explore bow these are used within practice.

Learning outcomes

Subject specific knowledge and skills

• Understand the standard methods used in ALARP decision making
• Appreciate the consequences of choosing specific measures for risk
• Appreciate the implications of different communication strategies

Cognitive abilities and non-subject specific skills

• Develop students ability to construct rational arguments
• Critical thinking

The class will firstly explore Problem Structuring Methods at a general level i.e. what are they and what their purpose is and how they can contribute to other forms of modelling. It will then move on to focus at a more detailed level two specific Problem Structuring Methods paying particular attention to each method’s underlying theories, techniques and processual considerations. It is intended that at the end of the class, students will be able to determine when a Problem Structuring Method should be used, which one and how best to apply it to the organizational situation.

Class aims

To introduce, and provide an overview of, Problem Structuring Methods along with providing guidance in their use in organisations, complementarities with other modelling techniques, and insights into supporting facilitating groups when employing such methods.

Learning outcomes

Subject specific knowledge and skills

• To develop an understanding of what problem structuring methods are and where they fit within a modelling and operational research framework
• To gain competence in the use of two problem structuring methods (SODA and SSM) – focusing upon each method’s techniques, applications and process considerations.

Cognitive abilities and non-subject specific skills

• Critical, holistic and systemic thinking
• Team working skills through group work – with a particular emphasis on facilitation
• Communication skills, both verbal and written

This class teaches essential principles, tools and techniques of Operations Management.

Class aims

• Students should understand the principles of Operations Management as a management function and as a set of management decisions concerning the design, planning and control, and improvement of operations.
• Students should understand the key concepts of Operations Management and be able to apply relevant analytical tools, models and quantitative methods.

Learning outcomes

Subject specific knowledge and skills

• Describe the design, planning and control, and improvement decisions to be taken in the Operations Management function, and explain the link between operations strategy and business strategy.
• Analyse and explain the interrelationships between the performance objectives in Operations Management and the design of an operating system, for any given organisation.
• Identify the main aspects of process and layout design, and evaluate the trade-offs between these aspects, and use relevant quantitative methods in support of layout design.
• Explain key concepts in supply chain planning and control, and evaluate the trade- offs between performance objectives in this area, and apply independent-demand inventory control systems to minimise inventory.
• Use appropriate network planning methods for effective project planning and control.
• Explain key concepts in quality planning and control, and statistical process control and acceptance sampling to effectively manage quality costs.

Cognitive abilities and non-subject specific skills

• Present a reasoned argument in writing based on a thorough knowledge and understanding of the relevant academic literature and supported, where required, by analytical discussion and/or appropriate quantitative analysis.
• Being able to use conceptual modelling techniques to study and analyse factors affecting business operations.

The class aims to provide a broad coverage of strategy modelling ranging from theories and techniques underpinning the rational/analytical school to those espoused by the emergent and ‘processual’ schools. The class therefore concentrates on covering two main thrusts of strategy making – undertaking analysis and negotiating direction.

The 'rational analysis' thrust of the class will include coverage of tools and techniques developed to help in examining the environment (including considering market forces, competitors, and product mixes etc), appreciation of the theories underpinning tools for assessing internal strengths and determining resources (tapping into both SWOT and the Resource Based View) and monitoring threats and opportunities through forecasting and scenario modelling. Through understanding these analysis students should be able to produce a strategy model that is robust and coherent. The ‘negotiating direction’ element of the class will introduce concepts of procedural rationality and procedural justice, and will touch on political feasibility. It will also pay attention to stakeholder identification and management both within the strategy making group and those affected by the resultant strategy.

Class aims

To provide students with an appreciation of a number of analytical tools (as noted above)
To enable students to select appropriate tools depending on context, client, and resources
To give them skills to manage the processual elements of strategy making and increase the likelihood of successful implementation

Learning outcomes

Subject specific knowledge and skills

• The ability to conduct a range of business/organisational analyses
• The ability to identify and utilise alternative frameworks to categorise the position of organisation’s and their products / services in their appropriate market and competitive space
• The ability to analyse an organisation’s resource base to reveal competencies within existing organisational routines and activities that provide competitive advantage
• The ability to determine who are the organization’s stakeholders and which ones matter
• The ability to recognise the political and social considerations necessary when making strategy
• The ability to understand the language of strategy and strategic management that is rooted in economic theory;

Cognitive abilities and non-subject specific skills

• The ability to understand the processes that influence the way in which individuals and groups approach strategic problem identification and decision making in ambiguous and complex environments; and
• The ability to present and communicate strategy to varying audiences

This module will demonstrate how spreadsheets can be used to support the analytical techniques whose theory is taught primarily within other modules, for example, simulation, optimisation, data analysis and forecasting, as well as being used to support technical report writing. This module also extends the basics of using a spreadsheet to explore the principles of effective computer programming through the development of macros to automate the functionality available within spreadsheets. In order to provide core methods for which a meaningful spreadsheet tool can be developed, we include coverage of demand forecasting, in particular, simple projective forecasting methods such as Exponential Smoothing and Holt-Winters. These methods can be easily implemented using spreadsheets and provide the basis for the development of a spreadsheet tool for demand forecasting that integrates the principles of sound spreadsheet modelling and effective computer programming.

Class aims

The aim of this module is to develop an understanding of how spreadsheets can be used to effectively support business modelling, particularly in relation to the development of demand forecasting systems.

Learning outcomes

Subject specific knowledge and skills:

• Create simple but appropriately organised spreadsheet models for complex problems;
• Use the spreadsheet to support traditional operational research techniques such as forecasting, optimisation and simulation;
• Describe the main categories of projective forecasting technique, their data requirements and applicability to different operational situations;
• Construct and interpret forecasts using smoothing methods and Holt-Winters;
• Compute and interpret forecast errors to track accuracy of forecasts;
• Understand the nature of effective demand forecasting systems;
• Understand basic principles of computer programming;
• Understand the basic elements of VBA for developing macros within Excel.

Cognitive abilities and non-subject specific skills:

• Further develop ability to see patterns in numerical data;
• Develop students ability to express modelling process algorithmically;
• Develop students ability to express problems in forms conducive for the software support available.