Engineering Tripos Part IIA, 3E6: Organisational Behaviour, 2019-20
Module Leader
Lecturer
Dr Y J Kim
Lab Leader
Dr Y J Kim
Timing and Structure
Lent term. 16 lectures.
Aims
The aims of the course are to:
- Provide students with a broad and critical understanding of the key issues and concepts in Organisational Behavior.
- Stimulate both appreciation and critical consideration of current Organisational Behaviour theory and research.
- Allow students to reflect on their own experience, extrapolate and develop better people skills.
- Prepare students for future roles in which they need to work with individuals and groups in organisations.
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand the central issues in work organizations.
- Understand how these issues have changed over time.
- Understand how these link to practical situations.
- Understand the nature and problems of organizational change.
Content
The philosophy behind the course is that academic concepts can be used as an ‘intellectual tool kit’ - a collection of frameworks and ideas that can be used to critically analyse organizational situations, thereby gaining a better understanding of ‘what is going on’ in order to take appropriate action. The course will consider: Classical Perspectives on Organisational Behaviour (OB); Micro-Perspectives on OB; Macro-Perspectives on OB; Organizational Change.
- Introduction to Organisational Behaviour
- Perceptions and Personality
- Attitudes and Motivation I
- Motivation II, Moods and Emotions
- Groups and Teams
- Leadership and Communication
- Organizational Structure, Culture, and Climate
- Organizational Change
Coursework
Students may choose between the coursework topics motivation, teamwork, or change in organisations.
Learning objectives: After completing this coursework, students should be able to:
- Apply knowledge of relevant lecture material and related literature of your chosen topic
- Reflect upon your personal experience regarding your chosen topic
- Gain an awareness of how organisational behavior theory and research can help manage workplace situations
Practical information:
- Sessions will *provisionally* take place in Cambridge University Engineering Department, Trumpington Street Site, Lecture Room 12, on Thursdays, 3-5pm.
Full Technical Report:
There is no Full Technical Report (FTR) associated with this module..
Booklists
Please see the Booklist for Part IIA Courses for references for this module.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
S1
The ability to make general evaluations of commercial risks through some understanding of the basis of such risks.
S2
Extensive knowledge and understanding of management and business practices, and their limitations, and how these may be applied appropriately to strategic and tactical issues.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
Last modified: 10/12/2019 14:47
Engineering Tripos Part IIA, 3E6: Organisational Behaviour, 2023-24
Module Leader
Lecturer
Dr Y J Kim
Lab Leader
Dr Y J Kim
Timing and Structure
Michaelmas term. 8 lectures.
Aims
The aims of the course are to:
- Provide students with a broad and critical understanding of the key issues and concepts in Organisational Behavior.
- Stimulate both appreciation and critical consideration of current Organisational Behaviour research.
- Allow students to reflect on their own experience, extrapolate and develop better people skills.
- Prepare students for future roles in which they need to work with individuals and groups in organisations.
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand the central issues in work organizations.
- Understand how these issues have changed over time.
- Understand how these link to practical situations.
- Understand the nature and problems of organizational change.
Content
3E6: Organisational Behaviour is an eight-lecture course delivered in the Michaelmas term. Organisational behaviour (OB) studies the psychology and work-related activities of employees and workgroups in an organisational context. Employees in organisations experience various issues related to the field of OB and should be aware of how these issues affect their working lives. The topics in this course include organisational culture, attitudes, perceptions, motivations, leadership, team dynamics, creativity, innovation, understanding personalities and more.
Students enrolled in this course will become familiar with theories and research in OB and will learn to think critically about the research in OB. I encourage you to think of yourself not as a student but as a “manager in training” to get the most out of this course.
Because your organisational experience may be somewhat limited at this stage, I will do my best to create in-class activities that enable you to apply your learning to the real world. My goal is that you will learn as much as possible about organisational behaviour and will be able to exploit its practical applications.
1. Introduction to OB
2. Personality
3. Culture
4. Culture and Perception
5. Motivation
6. Creativity, Innovation, Innovation Diffusion
7. Group Dynamics
8. Leadership
Coursework
You may choose to submit coursework. This may be used to contribute to the coursework part of your portfolio; it does not form part of the assessment for this module. The coursework consists of an essay of minimum 2,000 words (excluding titles, footnotes, figures, references).
Assessment criteria for essays are:
- clear, accurate and relevant to the question set and supported by appropriate use of a business case;
- effective organisation and prioritisation of material; usually, on the basis of a theme or argument (a collage of information with no coherent argument should be avoided);
- clear and logical analyses with theory and a business case used to advance the analysis;
- knowledge of relevant lecture material and related literature;
- creativity in discussion and analyses.
You must submit your essay by Friday 9th December, 2022, 4pm via Moodle. Late submissions will be penalized.
The Topic of Course Work (Case Study)
In these days, innovation developed by one company is rapidly diffused to competitors due to the development of technology (e.g., communication tools such as internet). Explain why innovation diffusion is important to the extent that it determines a company’s survival in a market. Note that the main purpose of this course work is to help you understand the importance of innovation diffusion through a real business case (see the examples of the cellphone market in my lecture). Therefore, it is vital that you find a relevant business case to prepare your course work successfully. In your answer, you should also answer the following sub-questions (your case should be able to answer the following sub-questions as well).
- Provide a business case showing that companies failing to adopt innovation failed in a market, whereas companies successfully adopting innovation survive in the market. Exclude cases of the cellphone market in your essay as my lecture already explains various cases of the cellphone market.
- Innovation diffusion involves patent issues. Using patents, original innovators attempt to inhibit innovation diffusion. Explain why inhibiting innovation diffusion is beneficial to the original innovator. In addition, explain strategies other than patenting that original innovators use to protect their innovation.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
S1
The ability to make general evaluations of commercial risks through some understanding of the basis of such risks.
S2
Extensive knowledge and understanding of management and business practices, and their limitations, and how these may be applied appropriately to strategic and tactical issues.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
Last modified: 30/05/2023 15:21
Engineering Tripos Part IIA, 3E6: Organisational Behaviour, 2022-23
Module Leader
Lecturer
Dr Y J Kim
Lab Leader
Dr Y J Kim
Timing and Structure
Michaelmas term. 8 lectures.
Aims
The aims of the course are to:
- Provide students with a broad and critical understanding of the key issues and concepts in Organisational Behavior.
- Stimulate both appreciation and critical consideration of current Organisational Behaviour research.
- Allow students to reflect on their own experience, extrapolate and develop better people skills.
- Prepare students for future roles in which they need to work with individuals and groups in organisations.
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand the central issues in work organizations.
- Understand how these issues have changed over time.
- Understand how these link to practical situations.
- Understand the nature and problems of organizational change.
Content
3E6: Organisational Behaviour is an eight-lecture course delivered in the Lent term. Organisational behaviour (OB) studies the psychology and work-related activities of employees and workgroups in an organisational context. Employees in organisations experience issues related to the field of OB and should be aware of how these concerns affect our working lives. The topics in this course include organisational culture, attitudes, perceptions, motivations, leadership, team dynamics, creativity, innovation, understanding personalities and more.
Students enrolled in this course will become familiar with theories and research in OB and will learn to think critically about organisations and their effectiveness. I encourage you to think of yourself not as a student in a course but as a “manager in training” to get the most out of this course.
Because your organisational experience may be somewhat limited at this stage, I will do my best to create in-class activities that enable you to apply your learning to the real world. My goal is that you will learn as much as possible about organisational behaviour and will be able to exploit its practical applications.
1. Introduction to OB
2. Personality
3. Culture
4. Culture and Perception
5. Motivation
6. Creativity, Innovation, Innovation Diffusion
7. Group Dynamics
8. Leadership
Coursework
You may choose to submit coursework. This may be used to contribute to the coursework part of your portfolio; it does not form part of the assessment for this module. The coursework consists of an essay of minimum 2,000 words (excluding titles, footnotes, figures, references).
Assessment criteria for essays are:
- clear, accurate and relevant to the question set and supported by appropriate use of a business case;
- effective organisation and prioritisation of material; usually, on the basis of a theme or argument (a collage of information with no coherent argument should be avoided);
- clear and logical analyses with theory and a business case used to advance the analysis;
- knowledge of relevant lecture material and related literature;
- creativity in discussion and analyses.
You must submit your essay by Wed 14th December, 2022, 5pm via Moodle. Late submissions will be penalized.
The Topic of Course Work (Case Study)
In these days, innovation developed by one company is rapidly diffused to competitors due to the development of technology (e.g., communication tools such as internet). Explain why innovation diffusion is important to the extent that it determines a company’s survival in a market. Note that the main purpose of this course work is to help you understand the importance of innovation diffusion through a real business case (see the examples of the cellphone market in my lecture). Therefore, it is vital that you find a relevant business case to prepare your course work successfully. In your answer, you should also answer the following sub-questions (your case should be able to answer the following sub-questions as well).
- Provide a business case showing that companies failing to adopt innovation failed in a market, whereas companies successfully adopting innovation survive in the market. Exclude cases of the cellphone market in your essay as my lecture already explains various cases of the cellphone market.
- Innovation diffusion involves patent issues. Using patents, original innovators attempt to inhibit innovation diffusion. Explain why inhibiting innovation diffusion is beneficial to the original innovator. In addition, explain strategies other than patenting that original innovators use to protect their innovation.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
S1
The ability to make general evaluations of commercial risks through some understanding of the basis of such risks.
S2
Extensive knowledge and understanding of management and business practices, and their limitations, and how these may be applied appropriately to strategic and tactical issues.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
Last modified: 06/12/2022 08:49
Engineering Tripos Part IIA, 3E6: Organisational Behaviour, 2022-23
Module Leader
Lecturer
Dr Y J Kim
Lab Leader
Dr Y J Kim
Timing and Structure
Michaelmas term. 8 lectures.
Aims
The aims of the course are to:
- Provide students with a broad and critical understanding of the key issues and concepts in Organisational Behavior.
- Stimulate both appreciation and critical consideration of current Organisational Behaviour research.
- Allow students to reflect on their own experience, extrapolate and develop better people skills.
- Prepare students for future roles in which they need to work with individuals and groups in organisations.
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand the central issues in work organizations.
- Understand how these issues have changed over time.
- Understand how these link to practical situations.
- Understand the nature and problems of organizational change.
Content
3E6: Organisational Behaviour is an eight-lecture course delivered in the Michaelmas term. Organisational behaviour (OB) studies the psychology and work-related activities of employees and workgroups in an organisational context. Employees in organisations experience various issues related to the field of OB and should be aware of how these issues affect their working lives. The topics in this course include organisational culture, attitudes, perceptions, motivations, leadership, team dynamics, creativity, innovation, understanding personalities and more.
Students enrolled in this course will become familiar with theories and research in OB and will learn to think critically about the research in OB. I encourage you to think of yourself not as a student but as a “manager in training” to get the most out of this course.
Because your organisational experience may be somewhat limited at this stage, I will do my best to create in-class activities that enable you to apply your learning to the real world. My goal is that you will learn as much as possible about organisational behaviour and will be able to exploit its practical applications.
1. Introduction to OB
2. Personality
3. Culture
4. Culture and Perception
5. Motivation
6. Creativity, Innovation, Innovation Diffusion
7. Group Dynamics
8. Leadership
Coursework
You may choose to submit coursework. This may be used to contribute to the coursework part of your portfolio; it does not form part of the assessment for this module. The coursework consists of an essay of minimum 2,000 words (excluding titles, footnotes, figures, references).
Assessment criteria for essays are:
- clear, accurate and relevant to the question set and supported by appropriate use of a business case;
- effective organisation and prioritisation of material; usually, on the basis of a theme or argument (a collage of information with no coherent argument should be avoided);
- clear and logical analyses with theory and a business case used to advance the analysis;
- knowledge of relevant lecture material and related literature;
- creativity in discussion and analyses.
You must submit your essay by Friday 9th December, 2022, 4pm via Moodle. Late submissions will be penalized.
The Topic of Course Work (Case Study)
In these days, innovation developed by one company is rapidly diffused to competitors due to the development of technology (e.g., communication tools such as internet). Explain why innovation diffusion is important to the extent that it determines a company’s survival in a market. Note that the main purpose of this course work is to help you understand the importance of innovation diffusion through a real business case (see the examples of the cellphone market in my lecture). Therefore, it is vital that you find a relevant business case to prepare your course work successfully. In your answer, you should also answer the following sub-questions (your case should be able to answer the following sub-questions as well).
- Provide a business case showing that companies failing to adopt innovation failed in a market, whereas companies successfully adopting innovation survive in the market. Exclude cases of the cellphone market in your essay as my lecture already explains various cases of the cellphone market.
- Innovation diffusion involves patent issues. Using patents, original innovators attempt to inhibit innovation diffusion. Explain why inhibiting innovation diffusion is beneficial to the original innovator. In addition, explain strategies other than patenting that original innovators use to protect their innovation.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
S1
The ability to make general evaluations of commercial risks through some understanding of the basis of such risks.
S2
Extensive knowledge and understanding of management and business practices, and their limitations, and how these may be applied appropriately to strategic and tactical issues.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
Last modified: 30/09/2022 17:18
Engineering Tripos Part IIA, 3E6: Organisational Behaviour, 2024-25
Module Leader
Lecturer
Dr Y J Kim
Lab Leader
Dr Y J Kim
Timing and Structure
Michaelmas term. 8 lectures.
Aims
The aims of the course are to:
- Provide students with a broad and critical understanding of the key issues and concepts in Organisational Behavior.
- Stimulate both appreciation and critical consideration of current Organisational Behaviour research.
- Allow students to reflect on their own experience, extrapolate and develop better people skills.
- Prepare students for future roles in which they need to work with individuals and groups in organisations.
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand the central issues in work organizations.
- Understand how these issues have changed over time.
- Understand how these link to practical situations.
- Understand the nature and problems of organizational change.
Content
3E6: Organisational Behaviour is an eight-lecture course delivered in the Michaelmas term. Organisational behaviour (OB) studies the psychology and work-related activities of employees and workgroups in an organisational context. Employees in organisations experience various issues related to the field of OB and should be aware of how these issues affect their working lives. The topics in this course include organisational culture, attitudes, perceptions, motivations, leadership, team dynamics, creativity, innovation, understanding personalities and more.
Students enrolled in this course will become familiar with theories and research in OB and will learn to think critically about the research in OB. I encourage you to think of yourself not as a student but as a “manager in training” to get the most out of this course.
Because your organisational experience may be somewhat limited at this stage, I will do my best to create in-class activities that enable you to apply your learning to the real world. My goal is that you will learn as much as possible about organisational behaviour and will be able to exploit its practical applications.
Dr. Yeun Joon Kim is the module leader. There will be a guest lecturer, Dr. Jungmin Choi, who is a post-doctoral researcher at Cambridge Judge Business School. She will cover three topics in which she has expertise.
1. Introduction to OB
2. Personality
3. Culture
4. Decision Making
5. Motivation
6. Group Dynamics
7. Creativity, Innovation, Innovation Diffusion
8. Leadership
Note 1: The topical order of the eight lectures may change. The order is not important at all because each topic is independent of the others.
Note 2: Week 7’s topic could change to “Artificial Intelligence and Creativity at Work” depending on the number of academic publications available by the end of Michaelmas. AI is a nascent topic in Organizational Behaviour, and thus academic publications are currently limited. However, this status is rapidly changing as researchers increasingly focus on publishing papers addressing AI issues, resulting in a fast accumulation of scientific evidence. I will evaluate whether the accumulated evidence is sufficient to deliver a lecture on this topic later in Michaelmas and will make an announcement if I decide to proceed with it.
Further notes
Important Announcement
This course will not be recorded. All students are required to attend the lectures in person.
Coursework
You may choose to submit coursework. This may be used to contribute to the coursework part of your portfolio; it does not form part of the assessment for this module. The coursework consists of an essay of minimum 2,000 words (excluding titles, footnotes, figures, references).
Assessment criteria for essays are:
- clear, accurate and relevant to the question set and supported by appropriate use of a business case;
- effective organisation and prioritisation of material; usually, on the basis of a theme or argument (a collage of information with no coherent argument should be avoided);
- clear and logical analyses with theory and a business case used to advance the analysis;
- knowledge of relevant lecture material and related literature;
- creativity in discussion and analyses.
You must submit your essay by Wednesday 11th December, 2024, 5pm via Moodle. Late submissions will be penalized.
The Topic of Course Work (Case Study)
In these days, innovation developed by one company is rapidly diffused to competitors due to the development of technology (e.g., communication tools such as internet). Explain why innovation diffusion is important to the extent that it determines a company’s survival in a market. Note that the main purpose of this course work is to help you understand the importance of innovation diffusion through a real business case (see the examples of the cellphone market in my lecture). Therefore, it is vital that you find a relevant business case to prepare your course work successfully. In your answer, you should also answer the following sub-questions (your case should be able to answer the following sub-questions as well).
- Provide a business case showing that companies failing to adopt innovation failed in a market, whereas companies successfully adopting innovation survive in the market. Exclude cases of the cellphone market in your essay as my lecture already explains various cases of the cellphone market.
- Innovation diffusion involves patent issues. Using patents, original innovators attempt to inhibit innovation diffusion. Explain why inhibiting innovation diffusion is beneficial to the original innovator. In addition, explain strategies other than patenting that original innovators use to protect their innovation.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
S1
The ability to make general evaluations of commercial risks through some understanding of the basis of such risks.
S2
Extensive knowledge and understanding of management and business practices, and their limitations, and how these may be applied appropriately to strategic and tactical issues.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
Last modified: 19/07/2024 11:46
Engineering Tripos Part IIA, 3E3: Modelling Risk, 2023-24
Leader
Lecturer
Lab Leader
Timing and Structure
Lent term. 2 lectures/week. 16 lectures.
Prerequisites
Basic probability theory and statistics and basic knowledge of using Excel of Microsoft.
Aims
The aims of the course are to:
- Provide an understanding of a range of management science modelling methods involving randomness, such as statistics, decision analysis, behavioral factors, portfolio management, process analysis, queueing theory, forecasting, and regression.
- For each of the modelling areas, students will become familiar with the types of situations in which the method is useful.
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand basic concepts of probability and the rationale behind statistical reasoning.
- Be able to calculate statistical measures like mean and variance, and interpret these in realistic situations.
- Use confidence intervals to quantify risk.
- Conduct hypothesis testing.
- Be able to understand decision trees and how to apply them in decision making.
- Identify and manage the bottleneck in a serial process, calculate the throughput of the entire system and utilisation at each step.
- Understand and use simple formulas for queues in which arrivals occur as a Poisson process.
- Understand the role of behavioral biases in decision making.
- Forecast data using short range extrapolative techniques such as exponential smoothing.
- Know how to take account of seasonality when forecasting.
- Apply regression techniques to estimate the way in which two variables are related.
- Be able to understand investment strategies for portfolios.
- Be able to incorporate risk into investment and decision making.
Content
"There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. These are things we don't know we don't know."
- Donald Rumsfeld
Note: The content covered across all lectures and example papers will be as listed below. However, elements of the content may be re-sequenced to achieve a better flow.
Mathematical Analysis of Deterministic and Stochastic Processes (4L)
- Process Analysis: Identify and manage the bottleneck in a serial process, calculate the throughput of the entire system and utilisation at each step, evaluate the impact of improvements to different steps in a process.
- Queueing theory: Poisson arrival processes, classification of queueing systems, steady state, performance measures, Little's formula, benefits and limitations of queueing theory.
Regression Analysis and Forecasting (4L)
- Simple linear regression analysis, least squares estimates, significance of regression, multiple regression, multi-collinearity.
- Different methods for forecasting: moving average, exponential smoothing, modelling seasonality and trends.
Inventory Management (2L)
- Basic concepts in inventory management: inventory management under stochastic demand.
Portfolio Management (2L)
- Basic portfolio concepts
- Risk and expected return on a portfolio, and the efficient frontier.
Decision Analysis (4L)
- Events and decisions, decision trees, expected monetary value, sensitivity analysis, expected value of perfect information, expected value of sample information.
- Behavioural Factors in Decision Making
Examples papers
In this course, we will have examples classes for all students at the same time, rather than supervisions for small groups.
- Class 1: Process Analysis and Queuing theory.
- Class 2: Regression, forecasting, and inventory management.
- Class 3: Portfolio and decision analysis.
Coursework
To be announced in lectures.
There is no Full Technical Report (FTR) associated with this module.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
Last modified: 30/10/2023 14:58
Engineering Tripos Part IIA, 3E3: Modelling Risk, 2022-23
Leader
Lecturer
Lab Leader
Timing and Structure
Lent term. 2 lectures/week. 16 lectures.
Prerequisites
Basic probability theory and statistics and basic knowledge of using Excel of Microsoft.
Aims
The aims of the course are to:
- Provide an understanding of a range of management science modelling methods involving randomness, such as statistics, decision analysis, behavioral factors, portfolio management, process analysis, queueing theory, forecasting, and regression.
- For each of the modelling areas, students will become familiar with the types of situations in which the method is useful.
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand basic concepts of probability and the rationale behind statistical reasoning.
- Be able to calculate statistical measures like mean and variance, and interpret these in realistic situations.
- Use confidence intervals to quantify risk.
- Conduct hypothesis testing.
- Be able to understand decision trees and how to apply them in decision making.
- Identify and manage the bottleneck in a serial process, calculate the throughput of the entire system and utilisation at each step.
- Understand and use simple formulas for queues in which arrivals occur as a Poisson process.
- Understand the role of behavioral biases in decision making.
- Forecast data using short range extrapolative techniques such as exponential smoothing.
- Know how to take account of seasonality when forecasting.
- Apply regression techniques to estimate the way in which two variables are related.
- Be able to understand investment strategies for portfolios.
- Be able to incorporate risk into investment and decision making.
Content
"There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. These are things we don't know we don't know."
- Donald Rumsfeld
Note: The content covered across all lectures and example papers will be as listed below. However, elements of the content may be re-sequenced to achieve a better flow.
Mathematical Analysis of Deterministic and Stochastic Processes (4L)
- Process Analysis: Identify and manage the bottleneck in a serial process, calculate the throughput of the entire system and utilisation at each step, evaluate the impact of improvements to different steps in a process.
- Queueing theory: Poisson arrival processes, classification of queueing systems, steady state, performance measures, Little's formula, benefits and limitations of queueing theory.
Regression Analysis and Forecasting (4L)
- Simple linear regression analysis, least squares estimates, significance of regression, multiple regression, multi-collinearity.
- Different methods for forecasting: moving average, exponential smoothing, modelling seasonality and trends.
Inventory Management (2L)
- Basic concepts in inventory management: inventory management under stochastic demand.
Portfolio Management (2L)
- Basic portfolio concepts
- Risk and expected return on a portfolio, and the efficient frontier.
Decision Analysis (4L)
- Events and decisions, decision trees, expected monetary value, sensitivity analysis, expected value of perfect information, expected value of sample information.
- Behavioural Factors in Decision Making
Examples papers
In this course, we will have examples classes for all students at the same time, rather than supervisions for small groups.
- Class 1: Process Analysis and Queuing theory.
- Class 2: Regression, forecasting, and inventory management.
- Class 3: Portfolio and decision analysis.
Coursework
To be announced in lectures.
There is no Full Technical Report (FTR) associated with this module.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
Last modified: 23/11/2022 09:56
Engineering Tripos Part IIA, 3E3: Modelling Risk, 2024-25
Leader
Lecturer
Lab Leader
Timing and Structure
Lent term. 2 lectures/week. 16 lectures.
Prerequisites
Basic probability theory and statistics and basic knowledge of using Excel of Microsoft.
Aims
The aims of the course are to:
- Provide an understanding of a range of management science modelling methods involving randomness, such as statistics, decision analysis, behavioral factors, portfolio management, process analysis, queueing theory, forecasting, and regression.
- For each of the modelling areas, students will become familiar with the types of situations in which the method is useful.
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand basic concepts of probability and the rationale behind statistical reasoning.
- Be able to calculate statistical measures like mean and variance, and interpret these in realistic situations.
- Use confidence intervals to quantify risk.
- Conduct hypothesis testing.
- Be able to understand decision trees and how to apply them in decision making.
- Identify and manage the bottleneck in a serial process, calculate the throughput of the entire system and utilisation at each step.
- Understand and use simple formulas for queues in which arrivals occur as a Poisson process.
- Understand the role of behavioral biases in decision making.
- Forecast data using short range extrapolative techniques such as exponential smoothing.
- Know how to take account of seasonality when forecasting.
- Apply regression techniques to estimate the way in which two variables are related.
- Be able to understand investment strategies for portfolios.
- Be able to incorporate risk into investment and decision making.
Content
"There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. These are things we don't know we don't know."
- Donald Rumsfeld
Note: The content covered across all lectures and example papers will be as listed below. However, elements of the content may be re-sequenced to achieve a better flow.
Mathematical Analysis of Deterministic and Stochastic Processes (4L)
- Process Analysis: Identify and manage the bottleneck in a serial process, calculate the throughput of the entire system and utilisation at each step, evaluate the impact of improvements to different steps in a process.
- Queueing theory: Poisson arrival processes, classification of queueing systems, steady state, performance measures, Little's formula, benefits and limitations of queueing theory.
Regression Analysis and Forecasting (4L)
- Simple linear regression analysis, least squares estimates, significance of regression, multiple regression, multi-collinearity.
- Different methods for forecasting: moving average, exponential smoothing, modelling seasonality and trends.
Inventory Management (2L)
- Basic concepts in inventory management: inventory management under stochastic demand.
Portfolio Management (2L)
- Basic portfolio concepts
- Risk and expected return on a portfolio, and the efficient frontier.
Decision Analysis (4L)
- Events and decisions, decision trees, expected monetary value, sensitivity analysis, expected value of perfect information, expected value of sample information.
- Behavioural Factors in Decision Making
Examples papers
In this course, we will have examples classes for all students at the same time, rather than supervisions for small groups.
- Class 1: Process Analysis and Queuing theory.
- Class 2: Regression, forecasting, and inventory management.
- Class 3: Portfolio and decision analysis.
Coursework
To be announced in lectures.
There is no Full Technical Report (FTR) associated with this module.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
Last modified: 29/01/2025 11:35
Engineering Tripos Part IIA, 3E3: Modelling Risk, 2025-26
Leader
Lecturer
Lab Leader
Timing and Structure
Lent term. 2 lectures/week. 16 lectures.
Prerequisites
Basic probability theory and statistics and basic knowledge of using Excel of Microsoft.
Aims
The aims of the course are to:
- Provide an understanding of a range of management science modelling methods involving randomness, such as statistics, decision analysis, behavioral factors, portfolio management, process analysis, queueing theory, forecasting, and regression.
- For each of the modelling areas, students will become familiar with the types of situations in which the method is useful.
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand basic concepts of probability and the rationale behind statistical reasoning.
- Be able to calculate statistical measures like mean and variance, and interpret these in realistic situations.
- Use confidence intervals to quantify risk.
- Conduct hypothesis testing.
- Be able to understand decision trees and how to apply them in decision making.
- Identify and manage the bottleneck in a serial process, calculate the throughput of the entire system and utilisation at each step.
- Understand and use simple formulas for queues in which arrivals occur as a Poisson process.
- Understand the role of behavioral biases in decision making.
- Forecast data using short range extrapolative techniques such as exponential smoothing.
- Know how to take account of seasonality when forecasting.
- Apply regression techniques to estimate the way in which two variables are related.
- Be able to understand investment strategies for portfolios.
- Be able to incorporate risk into investment and decision making.
Content
"There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. These are things we don't know we don't know."
- Donald Rumsfeld
Note: The content covered across all lectures and example papers will be as listed below. However, elements of the content may be re-sequenced to achieve a better flow.
Mathematical Analysis of Deterministic and Stochastic Processes (4L)
- Process Analysis: Identify and manage the bottleneck in a serial process, calculate the throughput of the entire system and utilisation at each step, evaluate the impact of improvements to different steps in a process.
- Queueing theory: Poisson arrival processes, classification of queueing systems, steady state, performance measures, Little's formula, benefits and limitations of queueing theory.
Regression Analysis and Forecasting (4L)
- Simple linear regression analysis, least squares estimates, significance of regression, multiple regression, multi-collinearity.
- Different methods for forecasting: moving average, exponential smoothing, modelling seasonality and trends.
Inventory Management (2L)
- Basic concepts in inventory management: inventory management under stochastic demand.
Portfolio Management (2L)
- Basic portfolio concepts
- Risk and expected return on a portfolio, and the efficient frontier.
Decision Analysis (4L)
- Events and decisions, decision trees, expected monetary value, sensitivity analysis, expected value of perfect information, expected value of sample information.
- Behavioural Factors in Decision Making
Examples papers
In this course, we will have examples classes for all students at the same time, rather than supervisions for small groups.
- Class 1: Process Analysis and Queuing theory.
- Class 2: Regression, forecasting, and inventory management.
- Class 3: Portfolio and decision analysis.
Coursework
To be announced in lectures.
There is no Full Technical Report (FTR) associated with this module.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
Last modified: 04/06/2025 13:19
Engineering Tripos Part IIA, 3E3: Modelling Risk, 2021-22
Leader
Lecturer
Dr N Taneri
Lab Leader
Timing and Structure
Lent term. 2 lectures/week. 16 lectures.
Prerequisites
Basic probability theory and statistics and basic knowledge of using Excel of Microsoft.
Aims
The aims of the course are to:
- Provide an understanding of a range of management science modelling methods involving randomness, such as statistics, decision analysis, behavioral factors, portfolio management, process analysis, queueing theory, forecasting, and regression.
- For each of the modelling areas, students will become familiar with the types of situations in which the method is useful.
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand basic concepts of probability and the rationale behind statistical reasoning.
- Be able to calculate statistical measures like mean and variance, and interpret these in realistic situations.
- Use confidence intervals to quantify risk.
- Conduct hypothesis testing.
- Be able to understand decision trees and how to apply them in decision making.
- Identify and manage the bottleneck in a serial process, calculate the throughput of the entire system and utilisation at each step.
- Understand and use simple formulas for queues in which arrivals occur as a Poisson process.
- Understand the role of behavioral biases in decision making.
- Forecast data using short range extrapolative techniques such as exponential smoothing.
- Know how to take account of seasonality when forecasting.
- Apply regression techniques to estimate the way in which two variables are related.
- Be able to understand investment strategies for portfolios.
- Be able to incorporate risk into investment and decision making.
Content
"There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. These are things we don't know we don't know."
- Donald Rumsfeld
Note: The content covered across all lectures and example papers will be as listed below. However, elements of the content may be re-sequenced to achieve a better flow.
Mathematical Analysis of Deterministic and Stochastic Processes (4L)
- Process Analysis: Identify and manage the bottleneck in a serial process, calculate the throughput of the entire system and utilisation at each step, evaluate the impact of improvements to different steps in a process.
- Queueing theory: Poisson arrival processes, classification of queueing systems, steady state, performance measures, Little's formula, benefits and limitations of queueing theory.
Regression Analysis and Forecasting (4L)
- Simple linear regression analysis, least squares estimates, significance of regression, multiple regression, multi-collinearity.
- Different methods for forecasting: moving average, exponential smoothing, modelling seasonality and trends.
Inventory Management (2L)
- Basic concepts in inventory management: inventory management under stochastic demand.
Portfolio Management (2L)
- Basic portfolio concepts
- Risk and expected return on a portfolio, and the efficient frontier.
Decision Analysis (4L)
- Events and decisions, decision trees, expected monetary value, sensitivity analysis, expected value of perfect information, expected value of sample information.
- Behavioural Factors in Decision Making
Examples papers
In this course, we will have examples classes for all students at the same time, rather than supervisions for small groups.
- Class 1: Process Analysis and Queuing theory.
- Class 2: Regression, forecasting, and inventory management.
- Class 3: Portfolio and decision analysis.
Coursework
To be announced in lectures.
There is no Full Technical Report (FTR) associated with this module.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
Last modified: 02/02/2022 12:33
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