APM Webinar hosted by the North West Branch on 18 October 2023.
Speaker: Dr Michala Techau
Addressing the why, when, and how to use Reference Class Forecasting (RCF) as a method to predict uncertainty in major project planning and delivery. The key reason behind cost and schedule overrun is underestimation of risks, and the root causes of underestimation are optimism and political bias. This webinar was held on Wednesday 18 October 2023.
https://youtu.be/Shl5r1wTzxc
https://www.apm.org.uk/news/reference-class-forecasting-useful-method-or-random-number-generator-webinar/
Oxford Global Projects has vast experience of working on the risk assessment of complex and uncertain major capital projects. Our research shows that these projects are more likely to experience overruns in time and cost than underruns, and that the potential magnitude of overruns is likely to be greater than the magnitude of underruns.
Thus, for large capital projects, the norm is over budget, over time, under benefits, over and over again, The founder of OGP, Professor Bent Flyvbjerg, has named this the “iron law of megaprojects” - and we argue that it is crucial to build a strong evidence base to de-bias cost, schedule, and benefit estimates to derive clear, actionable recommendations for de-risking capital project delivery.
RCF is a method for systematically taking an outside-view on planned actions. The method is used to make explicit, empirically based adjustments to estimates that prevent estimation biases such as optimism bias. To be accurate, these adjustments should be based on data from past and similar projects calibrated for the unique characteristics of the project at hand.
The basic idea of RCF is that we answer the questions:
What was the actual cost and schedule performance of past similar projects?
How risky were they in terms of how much their cost and schedule changed in comparison to their original estimates?
How do the cost and schedule performance of past relevant projects compare to your project, and what is your project’s risk of overrun?
What contingency is required to provide the level of certainty you require that your project will not overrun?
The talk highlighted typical misconceptions about RCF, and put them to bed. Challenges, causes, and cures for major project performance will be illustrated with case studies from the transport, energy, and the built environment sectors. The talk concluded with a Q&A session and an open discussion on how RCF is potentially more important than ever, in relation to addressing our impact on the environment.
Applying risk factors in the strategic selection of portfolio projects
Presented by John MacGregor
Monday 10th October 2016
APM North West branch and Risk SIG conference
Alderley Park, Macclesfield
Public-Private- Partnership Projects - What, Why & How Is Risk Allocatedm_l_u
1) The document discusses risk allocation in public-private partnership (P3) infrastructure projects. It notes that while P3 projects are often touted as beneficial, some studies have found 30% of surveyed P3 projects in the US ended in default or bankruptcy.
2) Improper risk allocation between public and private sectors has been cited as a key reason for failures of some P3 transport projects in London. The document aims to further examine how risks are and should be allocated on P3 projects.
3) Large infrastructure projects inherently carry more risk than smaller projects. The document will analyze typical risks for P3 projects and how to properly assess, manage, and allocate those risks between public and private partners.
3 Critical Success Factors in the Delivery of Major Programmes - Jay ArmstrongOliviaParsons4
Jay Armstrong's presentation looks at the current situation of project success, how project complexity feeds into this and the three critical success factors in the delivery of major programmes.
3 Critical Success Factors in the Delivery of Major Programmes - Jay ArmstrongLogiKal Projects
Jay Armstrong's presentation takes a look at the 3 critical success factors in the delivery of major programmes. Using case studies from LogiKal's experience, Jay explores why projects are failing and how we can address this.
IRJET- Projects in Constructions due to Inadequate Risk ManagementIRJET Journal
This document discusses risks in construction projects due to inadequate risk management. It begins by introducing the topic and defining key terms like risk management. It then discusses sources of failure in construction projects when there is no initial risk assessment or risk management. Some of the major causes of project failure discussed include changes without documentation or tracking, incomplete status reports, and undefined parameters.
The document presents results from a questionnaire survey given to construction industry experts. The survey found that most companies have a poor understanding of managing troubled projects and not all project managers have the necessary skills. It then provides recommendations for recovering from failed projects, including conducting a review to identify lessons learned and causes of failure. Overall, the document advocates that systematic risk management can help construction projects
The document discusses scope creep, providing cases and preventative actions. It defines scope and scope creep, noting that scope creep is a common issue on many projects that can lead to going over budget and missing deadlines. The document then provides tips for controlling scope creep such as thoroughly understanding requirements, having a well-defined WBS, using change control forms, and expect some scope creep to occur. It encourages sharing lessons learned from experience to help improve projects and careers.
The document discusses the challenges facing the power and utilities sector in meeting the large infrastructure investment needs of over $7.2 trillion by 2025 due to population growth, GDP growth, and environmental challenges. It notes that many large capital projects in the sector suffer from cost overruns, delays, and suboptimal returns due to issues with financing, delivering assets, and managing assets long-term. It provides examples of average megaproject overspends of 35% and delays of two years and outlines five key questions organizations should ask to improve project performance in planning, analyzing, executing, and operating assets. EY offers its experience and tools to help organizations develop the right strategic approach, raise funding, control projects, manage risks,
We will cover:
• Definition of Risk
• Why Risk Management?
• Project Stakeholders
• Psychology of Risk
• Risks on Estimation
• Project Budget
About the presenter:
This webinar will be presented by Gilberto Costa (PMP, AGILE, PRINCE2, RMP), Senior Project Manager at GBC London.
Gilberto is a value-driven project manager who has extensive business experiences in the IT industry, telecom, utilities and government affairs.
Gilberto holds multiple professional certifications, including the PMP and RMP certifications from the Project Management Institute. He is also a PECB Certified Trainer for ISO 31000 Risk Management.
Applying risk factors in the strategic selection of portfolio projects
Presented by John MacGregor
Monday 10th October 2016
APM North West branch and Risk SIG conference
Alderley Park, Macclesfield
Public-Private- Partnership Projects - What, Why & How Is Risk Allocatedm_l_u
1) The document discusses risk allocation in public-private partnership (P3) infrastructure projects. It notes that while P3 projects are often touted as beneficial, some studies have found 30% of surveyed P3 projects in the US ended in default or bankruptcy.
2) Improper risk allocation between public and private sectors has been cited as a key reason for failures of some P3 transport projects in London. The document aims to further examine how risks are and should be allocated on P3 projects.
3) Large infrastructure projects inherently carry more risk than smaller projects. The document will analyze typical risks for P3 projects and how to properly assess, manage, and allocate those risks between public and private partners.
3 Critical Success Factors in the Delivery of Major Programmes - Jay ArmstrongOliviaParsons4
Jay Armstrong's presentation looks at the current situation of project success, how project complexity feeds into this and the three critical success factors in the delivery of major programmes.
3 Critical Success Factors in the Delivery of Major Programmes - Jay ArmstrongLogiKal Projects
Jay Armstrong's presentation takes a look at the 3 critical success factors in the delivery of major programmes. Using case studies from LogiKal's experience, Jay explores why projects are failing and how we can address this.
IRJET- Projects in Constructions due to Inadequate Risk ManagementIRJET Journal
This document discusses risks in construction projects due to inadequate risk management. It begins by introducing the topic and defining key terms like risk management. It then discusses sources of failure in construction projects when there is no initial risk assessment or risk management. Some of the major causes of project failure discussed include changes without documentation or tracking, incomplete status reports, and undefined parameters.
The document presents results from a questionnaire survey given to construction industry experts. The survey found that most companies have a poor understanding of managing troubled projects and not all project managers have the necessary skills. It then provides recommendations for recovering from failed projects, including conducting a review to identify lessons learned and causes of failure. Overall, the document advocates that systematic risk management can help construction projects
The document discusses scope creep, providing cases and preventative actions. It defines scope and scope creep, noting that scope creep is a common issue on many projects that can lead to going over budget and missing deadlines. The document then provides tips for controlling scope creep such as thoroughly understanding requirements, having a well-defined WBS, using change control forms, and expect some scope creep to occur. It encourages sharing lessons learned from experience to help improve projects and careers.
The document discusses the challenges facing the power and utilities sector in meeting the large infrastructure investment needs of over $7.2 trillion by 2025 due to population growth, GDP growth, and environmental challenges. It notes that many large capital projects in the sector suffer from cost overruns, delays, and suboptimal returns due to issues with financing, delivering assets, and managing assets long-term. It provides examples of average megaproject overspends of 35% and delays of two years and outlines five key questions organizations should ask to improve project performance in planning, analyzing, executing, and operating assets. EY offers its experience and tools to help organizations develop the right strategic approach, raise funding, control projects, manage risks,
We will cover:
• Definition of Risk
• Why Risk Management?
• Project Stakeholders
• Psychology of Risk
• Risks on Estimation
• Project Budget
About the presenter:
This webinar will be presented by Gilberto Costa (PMP, AGILE, PRINCE2, RMP), Senior Project Manager at GBC London.
Gilberto is a value-driven project manager who has extensive business experiences in the IT industry, telecom, utilities and government affairs.
Gilberto holds multiple professional certifications, including the PMP and RMP certifications from the Project Management Institute. He is also a PECB Certified Trainer for ISO 31000 Risk Management.
52 a risk-management_approach_to_a_successful_infrastructure_projectEng. Mohamed Muhumed
This document discusses the need for effective risk management across the entire life cycle of large infrastructure projects in order to avoid costly failures and overruns. It notes that poor risk assessment and allocation early in the planning process can lead to higher costs and delays later on. The document advocates taking a comprehensive risk management approach that considers risks at each stage of project initiation, financing, construction, and operation. It also emphasizes the importance of allocating risks to the parties best able to manage them and of involving private financing perspectives early in the development process.
Risk analysis for project decision-making
Presented by Keith Gray
Monday 10th October 2016
APM North West branch and Risk SIG conference
Alderley Park, Macclesfield
The document discusses applying risk factors in the strategic selection of portfolio projects. It describes using the CIFTER analysis tool to assess risk across seven factors for a given project. A worked example involves conducting a CIFTER analysis for a project that involves decanting and demolishing a 1960s building with 330 occupants over 6 months, which has asbestos and is near a main road. The analysis rates each risk factor to determine an overall complexity score and recommended project manager level. The document advocates using such risk analysis to inform strategic project prioritization and scope decisions.
This document summarizes a study on project risks for rail projects in Brazil. It finds that rail projects regularly overrun budgets by 45% on average due to underestimating risks. Common risks include cost overruns, delays, subcontractor quality issues, and cultural/implementation challenges. The document recommends focusing on contract management, planning, scheduling and addressing implementation risks early in large projects to control costs.
AN INTEGRATED PROJECT EVALUATION TOOL FOR PFI SEAPORT PROJECTSFredy Kurniawan
The evaluation of the financial viability for seaport projects is a critical activity for bidders and governments under traditional procurement or through private finance initiative (PFI). The aim of this research is to assist government agencies in
evaluating bids and making decision efficiently for seaport development projects through the use of an integrated project evaluation tool. The proposed tool is expected to integrate the results of the financial model and the risk sharing strategy. The
integrated project evaluation tool can be mutually used by the government agency and the sponsor(s). This paper discusses the proposed tool to be tested in future study. The research strategy uses literature review, questionnaire surveys, interviews, and document analyses in order to develop the proposed tool. The tool will be tested through case studies and experts’ opinion to validate its applicability and effectiveness. The main conclusion of this paper is that the knowledge gap between the sponsor(s) and the government agency can be improved if the government agency is provided with efficient tools that consider both the financial and the risk factors
affecting a new project.
An Integrated Project Evaluation Tool for PFI Seaport ProjectFredy Kurniawan
The evaluation of the financial viability for seaport projects is a critical activity for bidders and governments under traditional procurement or through private finance initiative (PFI). The aim of this research is to assist government agencies in
evaluating bids and making decision efficiently for seaport development projects through the use of an integrated project evaluation tool. The proposed tool is expected to integrate the results of the financial model and the risk sharing strategy. The integrated project evaluation tool can be mutually used by the government agency and
the sponsor(s). This paper discusses the proposed tool to be tested in future study. The research strategy uses literature review, questionnaire surveys, interviews, and document analyses in order to develop the proposed tool. The tool will be tested
through case studies and experts’ opinion to validate its applicability and
effectiveness. The main conclusion of this paper is that the knowledge gap between
the sponsor(s) and the government agency can be improved if the government agency is provided with efficient tools that consider both the financial and the risk factors affecting a new project
A Best of Breed Approach to Accelerate Projects with High Reliability binozu
We discuss a harmonious mix of proven Best of Breed techniques from multiple leading methods—including Critical Chain, Agile, and Lean—integrated in an innovative way to boost performance by 2X or better with High Reliability. This approach provides leaders in project-centric organizations with a much higher-powered framework for unifying project teams and stakeholders at all levels, for enhancing the flow of productive work, and for aggregating risk for much higher portfolio reliability and for increasing the Return on Investment. Presented at PMI Pasadena Morning Meeting on Thursday, October 18, 2018
Judgement: Managing the Cognitive Factor to Improve the accuracy of Technolog...IFIS_org
Presentation made at Military Technology University (Warsaw) for PYTHIA project. The PYTHIA project aims to devise an innovative methodology for strategic technology foresight, able to deliver frequent “predictions” on technology-related matters. Project is granted by European Defence Agency.
P
A
P
E
R
S
72 September 2009 ■ Project Management Journal ■ DOI: 10.1002/pmj
INTRODUCTION ■
A
ccording to the United Kingdom’s Royal Academy of Engineering, bil-
lions of pounds are wasted every year on new information technology
(IT) systems. Troubled public-sector IT projects such as the National
Health Service (NHS) National Programme for IT, the Child Support
Agency systems, and HM Revenue and Customs’ Tax Credits IT system have
attracted considerable negative press. They have overrun, cost millions of
pounds more than was budgeted, and, in some cases, have been cancelled
before their costs spiral even further out of control. Terms such as “nightmare”
and “disaster” tend to be attached to such projects. IT projects (the provision
of a service to implement systems and solutions, including a variety of hard-
ware and software products; (Howard, 2001) seem to be more problematic
than other types of projects, with a particularly high rate of failure (McGrew &
Bilotta, 2000; The Standish Group International, 2007; Whittaker, 1999).
Despite well-established best practice project management processes, project
managers appear to be ineffective in the light of such failure.
Organizations such as the Project Management Institute (PMI) and the
United Kingdom’s Association for Project Management (APM) promote best-
practice project management standards. As part of these standards, project risk
management is defined as the systematic process of identifying, analyzing, and
responding to risks. Risk is any project-related event, or managerial behavior,
that is not definitely known in advance but has the potential of adverse conse-
quences on a project objective (PMI, 2004). Project risk management claims to
enable project managers to effectively manage risk and minimize the adverse
influence of risk on the project outcome. However, we have found that IT proj-
ect managers often do not apply a process to manage risks. The reasons for this
vary. Nevertheless, the evidence behind this phenomenon is very scarce, often
descriptive, and inchoate. The purpose of this study was to investigate whether
best practice standards are applied, and if they are not, what reasons led the IT
project manager to decide not to actively approach and manage project risks.
The results show that IT project managers primarily face the problem of
cost justification. Facing costs and time constraints and the uncertainty of
the success of project risk management, they often decided not to actively
manage risks. However, with the benefit of hindsight, we see that such a
decision often turns out to be fatal. Not surprisingly, in projects where proj-
ect risk management is not used, a greater degree of risks materialize than in
those projects where the IT project manager does actively manage risks.
Project Risk Management
Risks may potentially endanger the ability of the project manager to meet
the predefined project objectives, such as scope, time, and cost; tasks may
The .
IRJET- A Study on Factors Affecting Estimation of Construction Project : Conc...IRJET Journal
This document summarizes a study on factors affecting the estimation of construction project costs. It identifies 12 key factors that influence cost estimation accuracy based on a survey of experts. These include economic instability, quality of project planning/management, experience of estimators, and availability of management/finance plans. The study develops an artificial neural network model to predict cost variance based on these factors. Testing shows the model can predict cost variance with 80% accuracy. It recommends construction parties consider the 12 identified factors when preparing estimates and assigning qualified project managers, estimators, and planners to reduce cost variance. Further research expanding the model to different project types and using a more structured project database is suggested.
ANALYSIS OF RISK CATEGORIES AND FACTORS FOR PPP PROJECTS USING ANALYTIC HIERA...A Makwana
Success of Public Private Partnership projects is greatly influenced by proper management of the risks associated with the project. All projects which are undertaken using conventional procurement method or using a PPP approach have known risks and unknown risks. Risk identification plays an important role in development of PPP framework. The participation and investment of Private sector has been the main stay of the Government of India policy toward infrastructural growth. In this study main risk categories and factors of Public Private Partnership projects have been recognized. A total of 7 risk categories and 31 risk sub-factors for each category were identified for PPP projects safety listed under subheads. The questionnaire was prepared on the basis of literature review and was filled by 100 Stakeholders namely Consultant/Client, Project Manager/ Contractor, Engineer. Generally Analytic Hierarchy Process (AHP) is widely used as multi criteria decision making. Normally it is very hard to meet the consistence need of a comparison matrix in analytic hierarchy process. In this study AHP is used to categories the risks of PPP projects in different levels and the impact of those risks on the PPP projects are identified.
IRJET- A Study on Factors Affecting Estimation of Construction Project : Conc...IRJET Journal
This document summarizes a study on factors affecting the estimation of construction project costs. It identifies 12 key factors that influence cost estimation accuracy based on a questionnaire survey of experts. These include economic instability, quality of project planning, experience of the estimating team, and accuracy of bidding documents. The study develops an artificial neural network model to predict cost variance based on these factors. Testing shows the model can predict cost variance with 80% accuracy. It recommends construction parties consider the 12 identified factors when preparing cost estimates and allow for contingency based on economic conditions and project location. Further research expanding the model to different project types and using more structured cost data is suggested.
Towards an integrated governance framework for infrastructure - Ian Hawkeswor...OECD Governance
This presentation was made by Ian Hawkesworth, OECD, Thailand, at the 10th OECD-Asian Senior Budget Officials Annual Meeting held in Bangkok, Thailand, on 18-19 December 2014.
Overcoming Optimism Bias in Portfolio PlanningDecision Lens
Hope is not a strategy.
Optimism Bias is one of the most common and detrimental biases in portfolio planning. Portfolio strategists routinely overvalue potential and underestimate risk.
Decision Lens is proud to welcome Professor Yael Grushka-Cockayne of The University of Virginia Darden School of Business to share the impact of Optimism Bias and the best methods to overcome it.
The document discusses various capital budgeting techniques. It defines net present value and internal rate of return, and discusses how they can sometimes provide contradictory results. It also provides short notes on certainty equivalent approach and sensitivity analysis, explaining how they handle risk in capital budgeting. Social cost benefit analysis is discussed in the context of evaluating industrial projects. The document also covers other topics like project appraisal under inflation, capital rationing, zero date of a project, simulation analysis, and the key contents of a project report.
Towards and Integrated Governance Framework for Infrastructure by Ian Hawkesw...OECD Governance
Presentation by Ian Hawkesworth at the 7th annual meeting of the MENA Senior Budget Officials held on 10-11 December 2014. Find more information at http://www.oecd.org/gov/budgeting
This document discusses the need for project management evolution in the infrastructure megaproject industry to improve economic success. It notes that infrastructure megaprojects regularly experience cost and schedule overruns of 10-12% on average, equivalent to $110 million in losses per $1 billion spent. As global infrastructure demand and spending is projected to greatly increase by 2025 and beyond, strengthened project management is needed to mitigate losses. The document advocates expanding project managers' skills and knowledge beyond traditional areas to include change leadership, innovation, and other areas to better deliver massive, complex infrastructure projects on time and on budget.
The governance of mega infrastructure projects - Juliane JANSEN, OECD Secreta...OECD Governance
This presentation was made by Juliane JANSEN, OECD Secretariat, at the 11th Annual Meeting of the OECD Network of Senior PPP & Infrastructure Officials held at the OECD, Paris, on 27 March 2018
A project is a temporary endeavor undertaken to create a unique product, service or result with a defined start and end date, and scope and resources. Project management involves applying knowledge, skills, tools and techniques to project activities to meet project requirements. It draws on 10 knowledge areas including scope, time, cost, quality and risk management. The project manager is responsible for managing the triple constraint of scope, time and cost to achieve project success.
RISK RESPONSE STRATEGIES AND PERFORMANCE OF PROJECTS IN KIRINYAGA .docxdaniely50
RISK RESPONSE STRATEGIES AND PERFORMANCE OF PROJECTS IN KIRINYAGA COUNTY, KENYA
JAMES KADEGHE WARUI
D53/OL/CTY/26217/15
A RESEARCH PROJECT SUBMITTED TO THE SCHOOL OF BUSINESS IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF DEGREE OF MASTER OF BUSINESS ADMINISTRATION (PROJECT MANAGEMENT) OF KENYATTA UNIVERSITY Comment by user: Proposal
MAY, 2019
DECLARATION
I declare that, this proposal is my own original work and has not been presented for award of any degree in any university. No part of this proposal should be reproduced without the authority of the author and/or Kenyatta University.
Signature Date .
James Kadeghe Warui,
D53/OL/CTY/26217/15.
This research proposal has been submitted for the course examination with my approval as the University supervisor.
Signature . Date.
Dr. Lucy Ngugi,
Department of Management Science,
Kenyatta University.
DEDICATION
This work is dedicated to my family for giving me a chance to pursue an education. I also wish to dedicate this proposal to my colleagues for the encouragement and support they gave me towards the completion of this work
ACKNOWLEDGEMENT
I am thankful to God for the good health and strength He installed upon me to pursue this project. I wish to most sincerely thank my entire family for their overwhelming support throughout this process, they have always been a source of inspiration from whom I get my strength. I also appreciate my friends and colleagues who shared this journey with me and encouraged me in this journey. Comment by user: Need to acknowledge supervisor
TABLE OF CONTENTS
DECLARATIONii
DEDICATIONiii
ACKNOWLEDGEMENTiv
LIST OF TABLESvii
LIST OF FIGURESviii
OPERATIONAL DEFINITION OF TERMSix
ABBREVIATIONS AND ACRONYMSx
ABSTRACTxi
CHAPTER ONE1 put chapter and its heading on same line
INTRODUCTION1
1.1Background of the Study1
1.1.1 Project Performance2
1.1.2 Risk Response Strategies3
1.1.3 Projects in Kirinyaga County5
1.2 Statement of the Problem5
1.3 Objectives of the Study6
1.3.1 General Objective of the Study6
1.3.1 Specific Objectives of the Study6
1.4 Research Questions7
1.5 Significance of the Study7
1.6 Scope of the Study8
1.7 Limitation of the Study8
1.8 Organization of the Study9
CHAPTER TWO10 put chapter and its heading on same line
LITERATURE REVIEW10
2.1 Introduction10
2.2 Theoretical Review10
2.2.1 Enterprise Risk Management Model10
2.2.2 Expectancy Theory11
2.2.3 Network Theory12
2.3 Empirical Literature Review12
2.3.1 Risk Avoidance and Project Performance13
2.3.2 Risk Acceptance and Project Performance14
2.3.3 Risk Monitoring and Project Performance15
2.3.4 Risk Mitigation and Project Performance16
2.3.5 Risk Transfer and Project Performance17
2.4 Summary of Literature Review and Research Gaps19
2.5 Conceptual Framework23
CHAPTER THREE24 put chapter and its heading on same line
RESEARCH METHODOLOGY24
3.1 Introduction24
3.2 Research Design24
3.3 Target Population24
3.4 Data Collection Instruments25
.
APM event hosted by the North West Branch on 5 December 2023.
Speaker: Katie Demain, Global Partner and Change Expert, iOpener Institute
There is compelling evidence that it pays to invest in your employees’ happiness. Research findings are clear that happier employees are more productive, which improves your bottom line. Your happiest employees are 65% more energised at work, spend twice as much time on-task, and intend to stay in their job 4 times longer, according to iOpener Institute. This event was held on 5 December 2023.
But what does being happy at work actually mean? Why is happiness considered the antidote to poor performance? Isn’t happiness just a fuzzy concept? Should workforce happiness be on your agenda in the current tough economic climate? What does making happy employees mean in real terms for business leaders and managers?
This event will bring participants up to speed with all aspects behind workforce happiness – its theory, its value and its challenges – for managers, teams and individuals. And to present practical solutions that you’ll be able to implement straight away with your teams.
https://www.apm.org.uk/news/does-happiness-pay/
-Project professionals: Ready for the future? Climate change, Leigh WoodcockAPMDonotuse
The APM South Wales and West of England Branch held yet another fantastic conference on 18 October 2023.
Speaker: Leigh Woodcock
In 2023, we as project professionals face a world of increasing uncertainty, with political and market conflicts, the continued impacts of COVID-19, and the climate crisis influencing the world we operate in. At the same time, domestic and international sustainability goals, diversity and inclusion targets and new, collaborative technologies are guiding us to build a better future. As a community, how can we ensure we’re ready?
Join us at the APM’s South Wales & West of England branch’s 2023 Conference to explore the ways in which our landscape is changing, and how we as project professionals can stay at the forefront of this ever-evolving world.
Más contenido relacionado
Similar a Reference Class Forecasting - useful method, or random number generator? webinar
52 a risk-management_approach_to_a_successful_infrastructure_projectEng. Mohamed Muhumed
This document discusses the need for effective risk management across the entire life cycle of large infrastructure projects in order to avoid costly failures and overruns. It notes that poor risk assessment and allocation early in the planning process can lead to higher costs and delays later on. The document advocates taking a comprehensive risk management approach that considers risks at each stage of project initiation, financing, construction, and operation. It also emphasizes the importance of allocating risks to the parties best able to manage them and of involving private financing perspectives early in the development process.
Risk analysis for project decision-making
Presented by Keith Gray
Monday 10th October 2016
APM North West branch and Risk SIG conference
Alderley Park, Macclesfield
The document discusses applying risk factors in the strategic selection of portfolio projects. It describes using the CIFTER analysis tool to assess risk across seven factors for a given project. A worked example involves conducting a CIFTER analysis for a project that involves decanting and demolishing a 1960s building with 330 occupants over 6 months, which has asbestos and is near a main road. The analysis rates each risk factor to determine an overall complexity score and recommended project manager level. The document advocates using such risk analysis to inform strategic project prioritization and scope decisions.
This document summarizes a study on project risks for rail projects in Brazil. It finds that rail projects regularly overrun budgets by 45% on average due to underestimating risks. Common risks include cost overruns, delays, subcontractor quality issues, and cultural/implementation challenges. The document recommends focusing on contract management, planning, scheduling and addressing implementation risks early in large projects to control costs.
AN INTEGRATED PROJECT EVALUATION TOOL FOR PFI SEAPORT PROJECTSFredy Kurniawan
The evaluation of the financial viability for seaport projects is a critical activity for bidders and governments under traditional procurement or through private finance initiative (PFI). The aim of this research is to assist government agencies in
evaluating bids and making decision efficiently for seaport development projects through the use of an integrated project evaluation tool. The proposed tool is expected to integrate the results of the financial model and the risk sharing strategy. The
integrated project evaluation tool can be mutually used by the government agency and the sponsor(s). This paper discusses the proposed tool to be tested in future study. The research strategy uses literature review, questionnaire surveys, interviews, and document analyses in order to develop the proposed tool. The tool will be tested through case studies and experts’ opinion to validate its applicability and effectiveness. The main conclusion of this paper is that the knowledge gap between the sponsor(s) and the government agency can be improved if the government agency is provided with efficient tools that consider both the financial and the risk factors
affecting a new project.
An Integrated Project Evaluation Tool for PFI Seaport ProjectFredy Kurniawan
The evaluation of the financial viability for seaport projects is a critical activity for bidders and governments under traditional procurement or through private finance initiative (PFI). The aim of this research is to assist government agencies in
evaluating bids and making decision efficiently for seaport development projects through the use of an integrated project evaluation tool. The proposed tool is expected to integrate the results of the financial model and the risk sharing strategy. The integrated project evaluation tool can be mutually used by the government agency and
the sponsor(s). This paper discusses the proposed tool to be tested in future study. The research strategy uses literature review, questionnaire surveys, interviews, and document analyses in order to develop the proposed tool. The tool will be tested
through case studies and experts’ opinion to validate its applicability and
effectiveness. The main conclusion of this paper is that the knowledge gap between
the sponsor(s) and the government agency can be improved if the government agency is provided with efficient tools that consider both the financial and the risk factors affecting a new project
A Best of Breed Approach to Accelerate Projects with High Reliability binozu
We discuss a harmonious mix of proven Best of Breed techniques from multiple leading methods—including Critical Chain, Agile, and Lean—integrated in an innovative way to boost performance by 2X or better with High Reliability. This approach provides leaders in project-centric organizations with a much higher-powered framework for unifying project teams and stakeholders at all levels, for enhancing the flow of productive work, and for aggregating risk for much higher portfolio reliability and for increasing the Return on Investment. Presented at PMI Pasadena Morning Meeting on Thursday, October 18, 2018
Judgement: Managing the Cognitive Factor to Improve the accuracy of Technolog...IFIS_org
Presentation made at Military Technology University (Warsaw) for PYTHIA project. The PYTHIA project aims to devise an innovative methodology for strategic technology foresight, able to deliver frequent “predictions” on technology-related matters. Project is granted by European Defence Agency.
P
A
P
E
R
S
72 September 2009 ■ Project Management Journal ■ DOI: 10.1002/pmj
INTRODUCTION ■
A
ccording to the United Kingdom’s Royal Academy of Engineering, bil-
lions of pounds are wasted every year on new information technology
(IT) systems. Troubled public-sector IT projects such as the National
Health Service (NHS) National Programme for IT, the Child Support
Agency systems, and HM Revenue and Customs’ Tax Credits IT system have
attracted considerable negative press. They have overrun, cost millions of
pounds more than was budgeted, and, in some cases, have been cancelled
before their costs spiral even further out of control. Terms such as “nightmare”
and “disaster” tend to be attached to such projects. IT projects (the provision
of a service to implement systems and solutions, including a variety of hard-
ware and software products; (Howard, 2001) seem to be more problematic
than other types of projects, with a particularly high rate of failure (McGrew &
Bilotta, 2000; The Standish Group International, 2007; Whittaker, 1999).
Despite well-established best practice project management processes, project
managers appear to be ineffective in the light of such failure.
Organizations such as the Project Management Institute (PMI) and the
United Kingdom’s Association for Project Management (APM) promote best-
practice project management standards. As part of these standards, project risk
management is defined as the systematic process of identifying, analyzing, and
responding to risks. Risk is any project-related event, or managerial behavior,
that is not definitely known in advance but has the potential of adverse conse-
quences on a project objective (PMI, 2004). Project risk management claims to
enable project managers to effectively manage risk and minimize the adverse
influence of risk on the project outcome. However, we have found that IT proj-
ect managers often do not apply a process to manage risks. The reasons for this
vary. Nevertheless, the evidence behind this phenomenon is very scarce, often
descriptive, and inchoate. The purpose of this study was to investigate whether
best practice standards are applied, and if they are not, what reasons led the IT
project manager to decide not to actively approach and manage project risks.
The results show that IT project managers primarily face the problem of
cost justification. Facing costs and time constraints and the uncertainty of
the success of project risk management, they often decided not to actively
manage risks. However, with the benefit of hindsight, we see that such a
decision often turns out to be fatal. Not surprisingly, in projects where proj-
ect risk management is not used, a greater degree of risks materialize than in
those projects where the IT project manager does actively manage risks.
Project Risk Management
Risks may potentially endanger the ability of the project manager to meet
the predefined project objectives, such as scope, time, and cost; tasks may
The .
IRJET- A Study on Factors Affecting Estimation of Construction Project : Conc...IRJET Journal
This document summarizes a study on factors affecting the estimation of construction project costs. It identifies 12 key factors that influence cost estimation accuracy based on a survey of experts. These include economic instability, quality of project planning/management, experience of estimators, and availability of management/finance plans. The study develops an artificial neural network model to predict cost variance based on these factors. Testing shows the model can predict cost variance with 80% accuracy. It recommends construction parties consider the 12 identified factors when preparing estimates and assigning qualified project managers, estimators, and planners to reduce cost variance. Further research expanding the model to different project types and using a more structured project database is suggested.
ANALYSIS OF RISK CATEGORIES AND FACTORS FOR PPP PROJECTS USING ANALYTIC HIERA...A Makwana
Success of Public Private Partnership projects is greatly influenced by proper management of the risks associated with the project. All projects which are undertaken using conventional procurement method or using a PPP approach have known risks and unknown risks. Risk identification plays an important role in development of PPP framework. The participation and investment of Private sector has been the main stay of the Government of India policy toward infrastructural growth. In this study main risk categories and factors of Public Private Partnership projects have been recognized. A total of 7 risk categories and 31 risk sub-factors for each category were identified for PPP projects safety listed under subheads. The questionnaire was prepared on the basis of literature review and was filled by 100 Stakeholders namely Consultant/Client, Project Manager/ Contractor, Engineer. Generally Analytic Hierarchy Process (AHP) is widely used as multi criteria decision making. Normally it is very hard to meet the consistence need of a comparison matrix in analytic hierarchy process. In this study AHP is used to categories the risks of PPP projects in different levels and the impact of those risks on the PPP projects are identified.
IRJET- A Study on Factors Affecting Estimation of Construction Project : Conc...IRJET Journal
This document summarizes a study on factors affecting the estimation of construction project costs. It identifies 12 key factors that influence cost estimation accuracy based on a questionnaire survey of experts. These include economic instability, quality of project planning, experience of the estimating team, and accuracy of bidding documents. The study develops an artificial neural network model to predict cost variance based on these factors. Testing shows the model can predict cost variance with 80% accuracy. It recommends construction parties consider the 12 identified factors when preparing cost estimates and allow for contingency based on economic conditions and project location. Further research expanding the model to different project types and using more structured cost data is suggested.
Towards an integrated governance framework for infrastructure - Ian Hawkeswor...OECD Governance
This presentation was made by Ian Hawkesworth, OECD, Thailand, at the 10th OECD-Asian Senior Budget Officials Annual Meeting held in Bangkok, Thailand, on 18-19 December 2014.
Overcoming Optimism Bias in Portfolio PlanningDecision Lens
Hope is not a strategy.
Optimism Bias is one of the most common and detrimental biases in portfolio planning. Portfolio strategists routinely overvalue potential and underestimate risk.
Decision Lens is proud to welcome Professor Yael Grushka-Cockayne of The University of Virginia Darden School of Business to share the impact of Optimism Bias and the best methods to overcome it.
The document discusses various capital budgeting techniques. It defines net present value and internal rate of return, and discusses how they can sometimes provide contradictory results. It also provides short notes on certainty equivalent approach and sensitivity analysis, explaining how they handle risk in capital budgeting. Social cost benefit analysis is discussed in the context of evaluating industrial projects. The document also covers other topics like project appraisal under inflation, capital rationing, zero date of a project, simulation analysis, and the key contents of a project report.
Towards and Integrated Governance Framework for Infrastructure by Ian Hawkesw...OECD Governance
Presentation by Ian Hawkesworth at the 7th annual meeting of the MENA Senior Budget Officials held on 10-11 December 2014. Find more information at http://www.oecd.org/gov/budgeting
This document discusses the need for project management evolution in the infrastructure megaproject industry to improve economic success. It notes that infrastructure megaprojects regularly experience cost and schedule overruns of 10-12% on average, equivalent to $110 million in losses per $1 billion spent. As global infrastructure demand and spending is projected to greatly increase by 2025 and beyond, strengthened project management is needed to mitigate losses. The document advocates expanding project managers' skills and knowledge beyond traditional areas to include change leadership, innovation, and other areas to better deliver massive, complex infrastructure projects on time and on budget.
The governance of mega infrastructure projects - Juliane JANSEN, OECD Secreta...OECD Governance
This presentation was made by Juliane JANSEN, OECD Secretariat, at the 11th Annual Meeting of the OECD Network of Senior PPP & Infrastructure Officials held at the OECD, Paris, on 27 March 2018
A project is a temporary endeavor undertaken to create a unique product, service or result with a defined start and end date, and scope and resources. Project management involves applying knowledge, skills, tools and techniques to project activities to meet project requirements. It draws on 10 knowledge areas including scope, time, cost, quality and risk management. The project manager is responsible for managing the triple constraint of scope, time and cost to achieve project success.
RISK RESPONSE STRATEGIES AND PERFORMANCE OF PROJECTS IN KIRINYAGA .docxdaniely50
RISK RESPONSE STRATEGIES AND PERFORMANCE OF PROJECTS IN KIRINYAGA COUNTY, KENYA
JAMES KADEGHE WARUI
D53/OL/CTY/26217/15
A RESEARCH PROJECT SUBMITTED TO THE SCHOOL OF BUSINESS IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF DEGREE OF MASTER OF BUSINESS ADMINISTRATION (PROJECT MANAGEMENT) OF KENYATTA UNIVERSITY Comment by user: Proposal
MAY, 2019
DECLARATION
I declare that, this proposal is my own original work and has not been presented for award of any degree in any university. No part of this proposal should be reproduced without the authority of the author and/or Kenyatta University.
Signature Date .
James Kadeghe Warui,
D53/OL/CTY/26217/15.
This research proposal has been submitted for the course examination with my approval as the University supervisor.
Signature . Date.
Dr. Lucy Ngugi,
Department of Management Science,
Kenyatta University.
DEDICATION
This work is dedicated to my family for giving me a chance to pursue an education. I also wish to dedicate this proposal to my colleagues for the encouragement and support they gave me towards the completion of this work
ACKNOWLEDGEMENT
I am thankful to God for the good health and strength He installed upon me to pursue this project. I wish to most sincerely thank my entire family for their overwhelming support throughout this process, they have always been a source of inspiration from whom I get my strength. I also appreciate my friends and colleagues who shared this journey with me and encouraged me in this journey. Comment by user: Need to acknowledge supervisor
TABLE OF CONTENTS
DECLARATIONii
DEDICATIONiii
ACKNOWLEDGEMENTiv
LIST OF TABLESvii
LIST OF FIGURESviii
OPERATIONAL DEFINITION OF TERMSix
ABBREVIATIONS AND ACRONYMSx
ABSTRACTxi
CHAPTER ONE1 put chapter and its heading on same line
INTRODUCTION1
1.1Background of the Study1
1.1.1 Project Performance2
1.1.2 Risk Response Strategies3
1.1.3 Projects in Kirinyaga County5
1.2 Statement of the Problem5
1.3 Objectives of the Study6
1.3.1 General Objective of the Study6
1.3.1 Specific Objectives of the Study6
1.4 Research Questions7
1.5 Significance of the Study7
1.6 Scope of the Study8
1.7 Limitation of the Study8
1.8 Organization of the Study9
CHAPTER TWO10 put chapter and its heading on same line
LITERATURE REVIEW10
2.1 Introduction10
2.2 Theoretical Review10
2.2.1 Enterprise Risk Management Model10
2.2.2 Expectancy Theory11
2.2.3 Network Theory12
2.3 Empirical Literature Review12
2.3.1 Risk Avoidance and Project Performance13
2.3.2 Risk Acceptance and Project Performance14
2.3.3 Risk Monitoring and Project Performance15
2.3.4 Risk Mitigation and Project Performance16
2.3.5 Risk Transfer and Project Performance17
2.4 Summary of Literature Review and Research Gaps19
2.5 Conceptual Framework23
CHAPTER THREE24 put chapter and its heading on same line
RESEARCH METHODOLOGY24
3.1 Introduction24
3.2 Research Design24
3.3 Target Population24
3.4 Data Collection Instruments25
.
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There is compelling evidence that it pays to invest in your employees’ happiness. Research findings are clear that happier employees are more productive, which improves your bottom line. Your happiest employees are 65% more energised at work, spend twice as much time on-task, and intend to stay in their job 4 times longer, according to iOpener Institute. This event was held on 5 December 2023.
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it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
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Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
How to Create a More Engaging and Human Online Learning Experience
Reference Class Forecasting - useful method, or random number generator? webinar
1. Reference class forecasting -
useful method, or random
number generator?
1
18th of October 2023
Dr Michala Techau, Head of Resilience and
Sustainability at Oxford Global Projects
CONFIDENTIAL & PROPRIETARY
Any use of this material without specific permission of Oxford Global Projects is strictly prohibited
For the Association for Project Management
2. Using mega projects as a force for good
The why, when, and how to use Reference Class
Forecasting (RCF) as a method for systematically taking
an outside view to predict uncertainty in mega project
planning and delivery.
Wider context - mega projects are key to the green
transitioning and getting estimates (more) right, is
important in mapping how to reach net zero targets.
CONFIDENTIAL & PROPRIETARY
Any use of this material without specific permission of Oxford Global Projects is strictly prohibited
2
3. What will we cover?
• Example of mega project performance
• What are the main causes of mega project risk?
• What are the cures for mega project risk?
• How do you apply reference class forecasting?
• What are typical misconceptions about reference class
forecasting?
• Q&A
3
CONFIDENTIAL & PROPRIETARY
Any use of this material without specific permission of Oxford Global Projects is strictly prohibited
5. In mega project performance – there is room for improvement…
5
CONFIDENTIAL & PROPRIETARY
Any use of this material without specific permission of Oxford Global Projects is strictly prohibited
6. 6
Edinburgh tram - delivery can be doomed before you
start
…if your forecast is wrong
• Estimate: £498m + a £37m contingency = £535m (2007, Phase 1a only), opening 2011.
• Reality: £776m, opening 2014 ~ 52% over budget (and reduced scope).
• RCF estimate: £697-£781m
CONFIDENTIAL & PROPRIETARY
Any use of this material without specific permission of Oxford Global Projects is strictly prohibited
Source: The City of Edinburgh Council, 2013.
Report for the Edinburgh Tram Inquiry, 2018, B. Flyvbjerg and A. Budzier
7. 7
Hong Kong express rail link – using the “wrong anchor”
can set you up to fail
• Estimate: HK$39.5bn and 4 years to build.
• Reality: Four years in, no end in sight and ballooning
budget.
• RCF estimate: HK$85bn (high confidence, low risk) and a
six-year project schedule.
• XLR completed within budget and time.
CONFIDENTIAL & PROPRIETARY
Any use of this material without specific permission of Oxford Global Projects is strictly prohibited
8. Poll
CONFIDENTIAL & PROPRIETARY
Any use of this material without specific permission of Oxford Global Projects is strictly prohibited
8
Which of these five industries do you think perform best?
9. How reliably do we deliver our promises across industries?
Ø duration, years
Cost overrun
Benefits
overrun
Frequency of
cost overrun
Schedule
overrun
Cost
Black Swans
Olympics
7.1
157%
n/a
10 of 10
0%
57%
IT-led
change
3.3
73%
-28%
4 of 10
43%
18%
Dams
7.9
74%
-11%
8 of 10
45%
23%
Roads
4.9
15%
-4%
6 of 10
36%
3%
8.1
Rail
32%
-23%
7 of 10
31%
9%
Buildings
57%
-5%
7 of 10
29%
18%
6.7 6.8
238%
-23%
9 of 10
70%
43%
Nuclear
waste
storage
Solar
power
2.2
1%
n/a
4 of 10
2%
0%
Source: Oxford Global Projects Database (Q4 2022)
Note: Measured from date of decision to build, in constant prices
Black Swans (outliers) are defined as 1.5 times the Inter-Quartile Range (IQR) IQR=difference difference between the third quartile (Q3) and the first quartile (Q1).
9
10. 100.0%
47.9%
8.5%
0.5%
All projects
(n=16,357)
On-budget (or
better)
On-budget &
on-time
(or better)
On-budget &
on-time &
on-benefits
(or better)
Iron Law of (Mega)-Projects
“Over budget, over time, under benefits, over
and over again” (Professor Bent Flyvbjerg)
The iron law of mega projects
10
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11. 100.0%
47.9%
8.5%
0.5%
All projects
(n=16,357)
On-budget (or
better)
On-budget &
on-time
(or better)
On-budget &
on-time &
on-benefits
(or better)
11
This is the likelihood of success
if we deliver projects as we have
always done!
The iron law of mega projects
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13. CONFIDENTIAL & PROPRIETARY
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The root cause of overruns – the main risks are
internal, not external
Why do we
underestimate risk?
Risk is external All risks are internal
Overrun Underestimation
15. 15
Optimism bias: “The cognitive predisposition
found with most people to judge future events in
a more positive light than is warranted by actual
experience.” (Flyvbjerg 2006)
“The planning fallacy is a consequence of the tendency to
neglect distributional data, and to adopt what may be
termed an 'internal approach' to prediction, where one
focuses on the constituents of the specific problem rather
than on the distribution of outcomes in similar cases.”
(Kahneman & Tversky 1977)
Psychological explanation – what are the most
important biases in projects management?
16. 16
How optimism bias influence our decision making -
experimental demonstration
• Almost all newlyweds in a US study expected their marriage to
last a lifetime, even while aware of the divorce statistics
• Professional financial analysts consistently overestimated
corporate earnings
• Second-year MBA students overestimated the number of job
offers they would receive and their starting salary
• Most smokers believed they are less at risk of developing
smoking-related diseases than others who smoke
18. A planner on cost underestimation
“…as a planner, you will often know the real
costs. You know that the budget is too low, but it
is difficult to pass such a message to the
counsellors [politicians] and the private actors.
They know that high costs reduce the chances
of national funding.”
18
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19. Poll
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Which of these two impact your project most?
21. The Big Idea
Reference Class Forecasting (RCF) is an alternative
forecasting method that uses data from past similar projects to
forecast the range of possible outturn costs and / or schedule.
The best predictor of performance in a planned project
is actual performance in a class of implemented,
comparable projects. Reference Class Forecasts do not
guarantee accuracy, just most accurate forecasts.
The method is based on theories that won the Nobel Prize in
Economics (planning fallacy, optimism bias).
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22. Reference Class
Forecasting
Our reference class forecasting tool has been cited by Nobel
Laureate Daniel Kahneman as “the single most important
piece of advice regarding how to increase accuracy in
forecasting through improved methods.”
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23. What reference class forecasting does
23
1 2
2
Outside View
Reference
Class
Inside View
Experts’ Forecast
Regresses best guess toward
the mean of the reference class
1
Expands estimate of interval
to interval of the reference class
2
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24. Unknown-unknowns
RCF takes into account “unknown unknowns”.
How? By incorporating in the reference class ALL effects on
performance, including “unknown unknowns”.
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25. How do you Apply Reference Class
Forecasting to Mega Projects?
26. The three steps of RCF
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We show the cost overruns experienced
by the projects in the reference class as a
cumulative distribution....
50% of projects had an
overrun of 25%
1. Build a reference class of similar projects
2. Establish probability distribution for the reference class
3. Compare your project with the distribution
26
Cost overrun is calculated as
𝐴𝑐𝑡𝑢𝑎𝑙 𝑝𝑟𝑜𝑗𝑒𝑐𝑡 𝑐𝑜𝑠𝑡
𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝑝𝑟𝑜𝑗𝑒𝑐𝑡 𝑐𝑜𝑠𝑡
P80 = 85%
…which - provided your project performs
no better or worse than projects in the
reference class - can be translated into
probability.
1. 2. 3.
27. High Speed 2 - Phase One cost estimate provided by
OGP in 2019
Cost heading Cost GBP million*
Contracts & Delivery team 1,150
Tunnels 2,910
Civils 3,990
Stations 2,545
Depots and stabling 720
Railway systems 1,560
On-network works 480
Land & property 1,630
Corporate overheads 1,265
Total cost 15,650
27
HS2 Ltd’s Phase one base cost estimate @ OBC
Source: UK Department for Transport
*2013 cost year
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28. HS2 Ltd’s Outline Business Case estimate of cost risk*
applying Quantitative Risk Analysis (QRA)
28
P95 = £5.75bn
= +37%
P50 = £3.75bn =
+24%
Risk
identification
workshop
Expert
assessment
Monte Carlo
simulation
Limitations
ignored
* Size of a potential cost
overrun over the £15.65bn
base cost estimate in real
terms, Outline Business Case
(OBC)
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29. • Selecting past similar projects, based on statistical similarity
• Testing whether average, median (P50), P80, P90, P95 are statistically
significantly different from high-speed rail
• Only fixed links are comparable for the full range of estimates from
P50-P95
• Final selected reference class included 39 high-speed rail projects
and 132 fixed links = 171 projects
Is this project type a
suitable comparator for
HSR projects? (n=39)
Average P50 P80 P90 P95
Conv. rail (n=113) ✔ ✔ ✔
Fixed link (n=132) ✔ ✔ ✔ ✔ ✔
Metro (n=196) ✔ ✔ ✔
Road (n=658) ✔ ✔
✔ No statistically
significant difference
✗ Statistically
significant
difference
Step 1: Building a reference class for HS2
29
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30. Step 2: Establishing the cost risk distribution for the
reference class
30
• Cost overruns in 3 out of 4
projects but also cost
underruns
• High-cost overruns not
unlikely: 1 in 8 projects more
than doubled in cost
50% of projects had
an overrun of 23%
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31. Step 3: Comparing the inside view estimate with the
RCF “outside view”
31
• P50 risk (+24%) in conventional forecast = P51 in reference class
• P95 risk (+37%) in conventional forecast = P70 in reference class!
• P95 in reference class = +136%
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33. 33
Institute for Government publication “HS2: costs and controversies. How much has the HS2 project cost since its inception?” 5th October 2023
Comparing RCF estimate with current status of HS2 –
Phase 1 only estimated to cost around £40bn
Recent abandonment of Phase 2 limits estimated spend to £35-45bn (2019 prices) for Phase 1.
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35. 35
Examples of typical misconceptions about RCF
• Contingency upon contingency
• Where we have data without contingency, the resulting RCF provides suggested uplift to add to deterministic baseline cost
– do you know how much padding is already added to your estimate?
• History repeating
• No – set out to beat the odds. RCF makes you aware of how projects performed in the past, set out to understand why, so
you can mitigate and outperform previous projects.
• Comparing apples with rocks
• RCF is not about exact similarity of technology, but about similarity of risk distributions, e.g., for FoaK projects types, it is
still insightful to see how other FoaK projects performed.
• It is too subjective
• Adjustments to where your project sits on the RCF curve needs to be made as objectively AND consistently as possible.
• Our project will never be approved
• Inflating project cost / schedule using RCF suggested uplifts does look scary – but there to inform discussions on risk
appetite and affordability. More projects should go back to the drawing board!
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36. Key take home message on RCF
Enhances accuracy of
estimates by leveraging
full distributions of
historical data - to provide
a more realistic and
objective basis for decision
making
36
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37. 37
So how do we beat the odds and use mega projects as a
force for good?
• Informing policy for better incentivisation?
• Optimising resource allocation
• Project appraisal – selection effect
• Often the worst projects are approved, because they look
best on paper (highest BCR)
• Require more efficient delivery of projects
• Experience – people and technology
• Avoid the eternal beginner syndrome
• Modularity
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40. When to apply RCF and how it complements QRA
Estimate adjustments are needed before there is enough
certainty for QRA* - the IPA recommends using RCF
first, and then transitioning to QRA
Source: Infrastructure and Projects authority (2021).
Project Routemap: Risk Management UK Module. London.
• At early planning stages when little is known
about the project (SOC), RCF provides a top-
down overall risk estimate.
• As more is known about the project and risk
becomes clearer (OBC), the QRA becomes
more reliable.
• As the project matures to FBC the RCF is used
as a benchmark to check the QRA is realistic
and correct for any biases and unknowns.
Low
Certainty
Business case
stages
SOC OBC FBC
Increasing
certainty
Medium
Certainty
High
Certainty
*QRA – Quantitative Risk Assessment
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41. How risk distribution for historical data compares to
an example of a current estimate in the nuclear industry
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Clear difference between the Monte Carlo risk model (green) and the historical data, both inclusive (blue) and exclusive (orange) of the North
American projects. Especially for values greater than P50.
42. Bad data +
bad models
→ Error
Error means
• Overestimation as likely
as underestimation
• Overestimation similar
size as underestimation
• In the long run they
average out
Transport
infrastructure
(n=1,505)
Average
overrun
Standard
deviation
Level of significance
(p)
Cost 28% 61% < 0.001
Schedule 37% 53% < 0.001
Benefits -6% 43% < 0.001
The project performance data reject the error hypothesis.
Technical explanation
42
43. • Within an industry, programmes or projects
are statistically very similar, in terms of cost,
schedule and benefit risks.
• The assumption of uniqueness does not stand
up to hard, statistical test, it’s a myth.
• Truly unique programs are rare; even projects
like the Greenlandic Arctic Circle Road have
similarities with other projects.
• Statistical similarity is all we need for RCF and
better risk management.
Uniqueness bias – unique projects are rarer than you think
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44. 44
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• Risk appetite reflects decision-makers’ willingness to make investments
with uncertain outcomes
• Risk appetite depends not only on the risks involved in a specific capital
project but also on the organisations general attitude to risk as well as the
size of other risks the organisation carries
• In the context of projects’ forecasts, risk appetite determines the level of
certainty sought in a forecast, or inversely, the acceptable chance of an
overrun for the project
• More risk averse organisations seeking higher level of confidence in their
estimates have lower acceptable chance of overruns
• However, there is a trade-off between economic viability, affordability, and
target setting that must be negotiated
Project appraisal
question
Risk appetite
level
Level of certainty
of estimates
Economic
viability for
organisation
Risk neutral at
portfolio level
P40-P60
Affordability in
worst case
scenario
Very low risk P80-P95
Target setting to
incentivise
performance
Moderate to low
risk
P30-P50
OGP guides projects to navigate around complex risk appetite
discussions on economic viability, affordability &
performance incentives
Applying the findings to your project
45. How RCFs are applied to your project
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The S-curve is used to calculate the uplifts that need to be
added to the baseline estimate to provide different levels
of certainty of preventing an overrun
• You can then select the appropriate level of
probability for your risk appetite and apply
the corresponding uplift.
• Or you can compare your existing total
contingency to find out what level of
probability your current contingency
provides.
45
46. Edinburgh tram extension – taking an outside view
from the beginning for a better forecast
• For the extension of the Edinburgh tram, from St Andrew Square to the waterfront at Ocean Terminal, OGP provided
support with budget and schedule estimation
• The decision was to use P50 – and the extension was completed on time and on budget
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https://www.railtech.com/all/2023/02/21/edinburgh-trams-extension-opening-finally-announced/
Report for the Edinburgh Tram Inquiry, 2018, B. Flyvbjerg and A. Budzier
46
47. 47
Top 10 behavioural biases in project planning and
management
Top Ten Behavioral Biases in Project Management: An Overview https://journals.sagepub.com/doi/full/10.1177/87569728211049046
48. Other methods
48
Appropriate budget contingency
determination for construction projects:
State-of-the-art
Author links open overlay
panelTaher Ammar, Mohamed Abdel-
Monem, Karim El-Dash
49. The External View: 20+ Years of Megaproject Research
13
Optimism bias
Political bias
Escalation of
commitment
Decision
making
Builder-buyer-user-funder
federation
Governance
Delivery model
Formal authority Informal authority
Strategy &
governance
Socio-politics
Leadership
Capabilities
Capacity
Manufacturing
integration and
coordination
Programatics
Commercials
System
integration
Systems
Procedures
Tools
Novelty
Scale &
pace
Emergence &
change
Predict &
provide
Predict &
prevent
Contingency
Buffer
Risk &
uncertainty
Institutional
context
Structures
Rules
Informal
norms
Fragmentation
Community
engagement
Stakeholder
engagement
Megaproject
performance
Adapted from:
Denicol, J., Davies, A. and Krystallis, I., 2020. What are the causes and cures of poor megaproject performance? A systematic literature review and research agenda. Project Management Journal, 51(3), pp.328-345.
49
50. Iron Law of (Mega)-Projects
“Over budget, over time, under
benefits, over and over again”
Summary
2 Key Challenges to Overcome the Iron Law (of many)
Decision Quality
Value Tracking
+
• Reference Class Forecasting
• Decision checklists
• Early-Warning Sign Systems
• Real-time tracking
Build
er
User Buyer
• Builder-Buyer-User Federation
• Shared values, behaviors, culture
Optimism bias
Political bias
Escalation of
commitment
Decision
making
Builder-buyer-user-funder
federation
Governance
Delivery model
Formal authority Informal authority
Strategy &
governance
Socio-politics
Leadership
Capabilities
Capacity
Manufacturing
integration and
coordination
Programatics
Commercials
System
integration
Systems
Procedures
Tools
Novelty
Scale &
pace
Emergence &
change
Predict &
provide
Predict &
prevent
Contingency
Buffer
Risk &
uncertainty
Institutional
context
Structures
Rules
Informal
norms
Fragmentation
Community
engagement
Stakeholder
engagement
Megaproject
performance
50
52. CONFIDENTIAL & PROPRIETARY
Outcomes.
“How can we beat the
odds and outperform
history?”
Our RCF approach provides objective findings
that can answer practical questions.
52
53. Independent Studies
1. Sydney Water Corporation, 11 infrastructure projects with RCF showed a significantly increased likelihood of completing under budget
(Napier and Liu, 2008).
2. Australian State Road and Traffic Authority, 44 projects with RCF showed increased forecast accuracy (Liu, Wehbe, and Siscovic, 2010).
3. Bridge construction forecast based on RCF and Bayesian updating produced more accurate forecasts (Kim and Reinschmidt, 2011)
4. RCF and Bayesian forecast of healthcare cost in 8 car manufacturing plants produced more accurate forecasts (Bordley, 2014)
5. A study of 56 construction projects shows that RCF outperforms conventional techniques, i.e. bottom-up estimation EVM and Monte Carlo
simulations (Batselier and Vanhoucke, 2016)
6. Application of RCF to Bujagali hydropower dam project increased accuracy of the cost-benefit analysis (Awojobi and Jenkins, 2016)
7. A study of 399 political forecasters shows that those trained and using RCF, taking different perspectives, and post-mortem analyses
produced more accurate forecasts (Chang, Chen, Mellers, and Tetlock, 2016)
8. Application of RCF on 369 Turkish public works projects resulted in reasonably accurate predictions (Bayram and Al-Jibouri, 2017)
9. Integrating RCF into EVM on 23 construction projects produced more accurate predictions of schedule performance (Batselier and
Vanhoucke, 2017)
10. RCF on 222 chemical industry projects proved effective for large homogenous projects but useless for less homogenous projects with little
historical data (Walcak and Majchrzak, 2018)
11. RCF improved the estimates of the cost of contaminated spoil removal in 3 nuclear projects (Devine, 2019)
12. A re-signaling project in Denmark applied RCF but still overran the budget because managers re-introduced optimism when selecting the
reference class (Themsen, 2019)
13. A modified RCF approach to estimate the remaining schedule to complete was accurate to 5 percentage points in 4 offshore oil and gas
projects (Dehghan et al., 2020)
14. RCF led to less optimistic forecasts in 322 project forecasts conducted as part of an experimental study (Friesdorf, 2020)
15. Providing RCF information to guide schedule forecasts in 139 experimental estimates improved the forecast accuracy, but more detailed
information about the task did not (Lorko et al., 2020)
16. The application of RCF to oil and gas production rates for the Norwegian Continental Shelf reduced optimism bias and increased forecast
accuracy
from 33% to 77% (at P80) (Jehan and Storsveen, 2020)
17. The first-ever RCF application in Germany, on Stuttgart 21, would have predicted the current cost overrun (Steininger et al., 2021)
18. The average cost overrun of UK major projects has reduced from 50% to 5% following the introduction of RCF (Park, 2021).
53
A typical conclusion was drawn by
Batselier and Vanhoucke (2016),
who said: "The conducted
evaluation is entirely based on
real-life project data and shows
that RCF indeed performs best, for
both cost and time forecasting, and
therefore supports the practical
relevance of the technique.”
• Introduced as Government Policy in major projects in the UK in 2004;
updated in 2021 for Department for Transport* and currently by Treasury
• Hong Kong’s DEVB introduced RCF starting in 2012**
• In 2019 adopted as policy in Ireland’s Spending Code and first adopted by Transport
Infrastructure Ireland in 2020
54. We help clients
navigate the UK
Government’s
business case
and approval
process for
projects & and
programmes.
55. CONFIDENTIAL & PROPRIETARY
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Our definition of risk
Risk = Negative uncertainty (downside)
Uncertainty = More than one possible outcome, i.e., “more things can happen than will
happen”
Positive uncertainty = Opportunity (upside)
Avg
Cost, schedule
or benefit
1sd 2sd
55
56. 56
M+ Museum in Hong Kong
• Hong Kong's M+ Museum (set opening date at RCF80;
managed to hit that target).
• From an initial HK$21.6 billion ($2.75 billion) to more
than HK$47.1 billion ($6 billion) after a reevaluation in
2013 and almost HK$70 billion ($8.91 billion) in 2021.
(M+ itself had an initial cost of HK$5.9 billion [$750
million] that city authorities confirmed has been
exceeded). On a broader scale, despite the district’s built
progress, there is still a lack of clear proposed public
policy that would foster local art production, a void
which paints the scheme as tourism-oriented rather than
for the benefit of the city’s arts scene.
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https://www.scmp.com/news/hong-kong/hong-kong-economy/article/3103507/head-hong-kongs-west-kowloon-cultural-district
57. Cost and schedule overrun
Cost overrun is calculated as 𝐴𝑐𝑡𝑢𝑎𝑙 𝑝𝑟𝑜𝑗𝑒𝑐𝑡 𝑐𝑜𝑠𝑡
𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝑝𝑟𝑜𝑗𝑒𝑐𝑡 𝑐𝑜𝑠𝑡
Schedule overrun is calculated as 𝐴𝑐𝑡𝑢𝑎𝑙 𝑝𝑟𝑜𝑗𝑒𝑐𝑡 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛
𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝑝𝑟𝑜𝑗𝑒𝑐𝑡 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛
Actual project cost refers to total project cost, which
includes all costs that materialised in the project.
Estimated project cost is the most likely project cost
estimate. Same for schedule.
As such, the overrun RCF data represent how much
additional budget or time projects needed to complete.
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
70%
5% 15% 25% 35% 45% 55% 65% 75% 85% 95%
57
58. Productivity 1995-Today
(GDP created by 1 hour of work in each industry baselined to 1995 = 100)
Note: 27 EU countries 58
100
125
150
175
200
1995 2000 2005 2010 2015 2020
Year
Productivity
per
hour
worked
(1996=100) Construction
Finance
ICT
Industry
Manufacturing
Prof. services
Trade
ICT
Manufacturing
Industry
Finance
Trade
Construction
Professional services