Re-engineering Project Budgeting and Management of Risk · 2014-12-11 · Re-engineering Project...

ICEC2014_Paper26_Re-engineeringProjectBudgetingandManagementofRiskRev1.doc 25 June 2014

ICEC IX World Congress

Re-engineering Project Budgeting and Management of Risk

Author name, Colin H Cropley1

1 Risk Integration Management Pty Ltd (RIMPL), 9 Aristoc Rd, Glen Waverley, Vic 3150, AUSTRALIA.

[email protected], Tel: (+613) 9560 2142 ABSTRACT Total Cost Management (TCM) is a systematic approach to managing cost throughout the life cycle of any enterprise, program, facility, project, product, or service. This paper focuses on projects created to make a return on assets created, from conception through to the end of their economic life. The paper sets out reasons why too many projects still fail to achieve their projected profitability targets or to be completed within their budgeted time and cost. It explains why deterministic planning and estimating lead to optimistic project schedules and budgets. Also, why separating planning, estimating, risk analysis and project economics analysis prevents a full and holistic assessment of the riskiness and viability of projects and what drives them. It explains how the use of Integrated Cost & Schedule Risk Analysis (IRA) can replace deterministic schedules (with single value task durations) by schedules with probability distributions for each uncertain duration task and incorporate probabilistic logic where appropriate. The probabilistic schedule can be overlaid with the project estimate, with costs (split into time-dependent and time-independent) spanning appropriate groups of tasks and each of the uncertain costs replaced by a probability distribution. In addition, the significant time and cost impact risks from the project risk register (also expressed in probability distributions) can be appropriately mapped into the cost-loaded schedule. The whole model can then be subject to Monte Carlo method simulation and the probabilistic time and cost forecasts for each milestone and cost component of the project analysed and reported. From the above process, realistic estimates of cost and time contingency can be assessed and drivers of project time and cost uncertainty can be measured by difference and ranked together as drivers of project cost. The above approach has been used on studies and projects ranging from a few million dollars to many billions. IRA is compared with conventional separate Serial Schedule and Cost Risk Analyses (Serial SRA&CRA) and IRA’s advantages highlighted. By extending the modelling to include asset operation and Revenue (IRRA), their associated uncertainties and risk events can be incorporated to generate probabilistic NPVs and IRRs. The balance between profit, break-even and loss can be explored and the drivers ranked for risk optimisation in fulfilment of TCM goals. Purpose of this paper The purpose of the paper is to demonstrate the capacity of Integrated Cost & Schedule Risk Analysis (IRA) and particularly Integrated Cost, Schedule & Revenue Risk Analysis (IRRA) to enable all the known risks and uncertainties in a proposed project and its resultant asset to be modelled and optimised before financial commitment is made, materially improving the probability of success in accordance with TCM Principles. Design/methodology/approach The paper sets out the deficiencies of conventional methods of planning and estimating projects, ranging from deterministic project planning and estimating through separated analyses of time and cost outcomes, and separate modelling of project and asset economics. It highlights the inability of separate time and cost analyses to model the cost consequences of delay risk and optimise their management, including consideration of the probabilistic effects of weather uncertainty on project cost and profitability. The deficiencies of separate analysis of time and cost uncertainty and risks are carried on through the project by separate management of risks, time and costs.

mailto:[email protected]

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The effects of operational and revenue uncertainties and risks on asset profitability are seldom modelled with the project uncertainties and risks. This lack of integration of all the uncertainties and risks to the project and its consequential assets is compared with the improved fulfilment of TCM goals of the integrated approaches of IRA and IRRA to defining all influences on time and cost outcomes of the project and its product assets achieved by producing an integrated Monte Carlo method model of the project and its assets. A topically appropriate example project of Floating LNG production (FLNG) is used to illustrate the value of this integrated approach. The closely related project type of Floating LNG regasification is amenable to similar analysis and is referenced. Findings and value The usefulness of IRA methodology has already been proven in a wide range of oil & gas, petrochemical and mining projects, ranging in scale from a few million to many billions of dollars. Some results from oil & gas exploration that have confirmed the value at the lower end of the cost scale are reported. Extension of the methodology to integrate revenue and operational cost uncertainties and risks makes complete sense in the Total Cost Management framework. The practicality of the IRRA methodology has been demonstrated by demonstration project models. Research limitations/implications IRRA is not yet proven apart from conceptually on a demonstration project and should be applied to a range of projects and assets to demonstrate its value on real projects. Practical implications The practical consequences of the IRA and IRRA approaches should be to adopt these holistic approaches to investigating, sensitivity analysing and proving the viability of projects under a sufficiently wide range of conditions of uncertainty and risk with time, cost and revenue impacts before the time and cost conditions are finalised for Financial Investment Decisions. It is through such rigorous and comprehensive evaluations that the resilience of projects will be sufficiently tested to enable them to withstand subsequent assaults on their viability from previously unknown unknowns or all but the most severe changes in market conditions. Originality/value of paper What is new in this paper is the demonstration of the viability of fully integrated time and cost risk analyses of complex project schedule and cost models previously considered impracticable and the extension of this approach to include the operating life of the asset produced by the project and the uncertainties and risks to operating costs and revenues. This IRRA methodology enables practicable analysis of probabilistic NPVs of projects and their created assets in the one time, cost and revenue analysis for the first time, enabling sensitivity testing of investment proposals against all manner of conditions, as the project moves over a fluctuating backdrop of seasonal weather variations and cyclical market uncertainties or even structural changes. Conclusions: A wide range of uncertainties and risk events can be applied to integrated time/cost/revenue models of complex projects and their asset investment proposals in a practicable way for the first time. Performed with due care over modelling inputs and analysis methodology, this should result in significantly improved investment decision success rates. Keywords (no more than 5): Integrated Costs Schedule Revenue Risk Analysis (cannot reduce to 5)



Contents 1 Introduction ....................................................................................................................................... 4

1.1 Poor track record of mega projects ......................................................................................... 4 1.2 Pessimism versus Activism ..................................................................................................... 4 1.3 Elements of a Successful Risk-based approach to project definition for sanction .................. 5

2 Why many projects fail ..................................................................................................................... 5 2.1 Pressure to comply with predetermined time and cost targets ............................................... 5 2.2 Single value estimates of time and cost tend to be optimistic ................................................. 5 2.3 Monte Carlo Method range analysis – a solution to single duration values ............................ 6 2.4 Failure to account for overlapping of converging logic pathways ............................................ 7 2.5 Failure to allow for risk events ................................................................................................. 8 2.6 Failure to allow for change in market conditions ..................................................................... 8

3 Integrated Cost & Schedule Risk Analysis ....................................................................................... 8 3.1 Definition of Integrated Cost & Schedule Risk Analysis .......................................................... 9 3.2 The IRA Process ..................................................................................................................... 9

3.2.1 Starting with the Schedule ................................................................................................... 9 3.2.2 Summarising the Estimate ................................................................................................... 9 3.2.3 Splitting Fixed & Variable Costs .......................................................................................... 9 3.2.4 Ranging and Reviewing the Uncertainty and Risk Inputs ................................................... 9 3.2.5 Correlation: a necessary input ........................................................................................... 10 3.2.6 Building the IRA Model ...................................................................................................... 10 3.2.7 Integrated Analysis ............................................................................................................ 10

3.3 The benefits of IRA ................................................................................................................ 10 3.4 The Challenges of IRA .......................................................................................................... 11 3.5 Mega Projects Exacerbate the Challenges ........................................................................... 11

4 conventional Serial Schedule Risk Analysis and Cost Risk Analysis ............................................ 12 4.1 SRA & CRA practitioners from different backgrounds........................................................... 12 4.2 SRAs and CRAs remain separate ......................................................................................... 12 4.3 Unsafe Assumptions .............................................................................................................. 12

4.3.1 When ................................................................................................................................. 12 4.3.2 Where ................................................................................................................................ 13 4.3.3 What / Why / How .............................................................................................................. 13

4.4 The latest version of linked SRA & CRA ............................................................................... 13 5 Summary Comparison of IRA with Serial SRA / CRA .................................................................... 13 6 Extension of IRA to include full project and asset lifecycle ............................................................ 14

6.1 Incorporation of the Asset Life Cycle ..................................................................................... 14 6.2 Modelling Functionality required to incorporate the Asset Life Cycle.................................... 14 6.3 Benefits of modelling Asset Life Cycle .................................................................................. 14 6.4 Demonstration IRRA models produced ................................................................................. 15

6.4.1 Features of FLNG Conceptual IRRA Model ...................................................................... 15



6.4.2 Outputs from FLNG Conceptual IRRA Model ................................................................... 15 6.5 Analyses possible from IRRA modelling ............................................................................... 16

7 Dealing with Scepticism toward MCM ............................................................................................ 17 7.1 Lack of published evidence of value of project quantitative risk analysis ............................. 17 7.2 Scepticism toward Quantitative Risk Analyses ..................................................................... 18 7.3 Immaturity of QRA Discipline leads to poor specifications and performance ....................... 18 7.4 Examples of improved project forecasting using quantitative risk analysis .......................... 19

7.4.1 Australian LNG Train Completion to RFSU - SRA ............................................................ 19 7.4.2 Middle Eastern LNG Train Completion to RFSU - SRA .................................................... 19 7.4.3 Oil Refinery Enhancement Project - SRA .......................................................................... 19 7.4.4 Coal Seam Gas LNG Project Key Milestones Forecast – SRA & IRA .............................. 20 7.4.5 Talisman Energy Papua New Guinea O&G Exploration 2013/14 - IRAs .......................... 20

8 Conclusions .................................................................................................................................... 21 9 References ..................................................................................................................................... 22

1 INTRODUCTION Total Cost Management (TCM) is definedi as “the effective application of professional and technical expertise to plan and control resources, costs, profitability and risks systematically throughout the life cycle of any enterprise, program, facility, project, product, or service.” It is to be accomplished through the application of cost engineering and cost management principles, proven methodologies and the latest technology in support of the management process. This paper is about applying the principles of TCM to projects and their deliverable assets, using proven methodology and the latest evolution of technology in managing risk and defining project budgets, to achieve materially better success rates in the definition, sanction and execution of major and mega projects and the profitable operation and eventual closure of the assets.

1.1 Poor track record of mega projects Around the world, projects that cost more than a billion dollars or equivalent, whether they are for infrastructure, energy, resources or industrial purposes, seem to end in overruns of time and cost more often than not. The examples range from Olympic venues through Hydro-electric dams, high-rise buildings, high-speed rail projects, defence projects to mining and energy projects such as greenfield LNG projects. In the Australian region, there have been a number of mining projects completed over time and budget and many have been cancelled since the decline of the very favourable prices the mined products could secure, driven by reduced demand from China. No LNG project completed since the Darwin LNG project in 2003 has been completed on time or within budget, with the recent exception of Exxon Mobil’s PNG LNG Project, which delivered its first cargo in May 2014, several months early, but $3 billion or nearly 20% above its 2009 budget of $16 billion. There are a number of LNG Projects currently under construction, of which the largest, Chevron’s Gorgon project was originally announced as a $43 billion project. It is now more than a year late and more than 25% over budget.

1.2 Pessimism versus Activism Bent Flyvbjerg (Professor of Major Programme Management at Oxford University's Saïd Business School) has been studying and writing about mega projects and their propensity to fail for a number of years. In December 2013 he wrote in New Scientist about “The Curse of the Megaproject” (FLYVBJERG, 2013)ii. In March 2014, a vigorous debate took place between Professor Flyvbjerg (BF) and John K Hollman (JH, author of AACEI’s TCM Framework Recommended Practice), as well as others, in the LinkedIn



“Risk Managers” Forum on the topic “Does bad risk management contribute to bad performance of large dams?” JH asserted that BF did not allow for the use of proper Project Management tools and methodologies, notably better scope definition before sanction, including tendency to scope growth (as quantified in AACEI Recommended Practice 42R-08iii), awareness of risk events and conditions that may occur, that BF seemed to assume that bias could not be overcome and that overruns were inevitable. This writer agrees with JH that failure to achieve the objectives of mega projects is not inevitable, but success is dependent on the comprehensive estimation of the fullest possible scope of the project using methods and tools capable of modelling the project realistically enough to identify what success is likely to require.

1.3 Elements of a Successful Risk-based approach to project definition for sanction This paper identifies a number of reasons for project failures inherent in approaches used in time and cost estimating and argues that a risk-based approach to estimating time and cost to complete projects will improve materially the success rate of major and complex projects. This is not a new observation, but identification of the elements of a risk based approach that are very important to achieving success helps improve our understanding of this modelling challenge. The paper explores why Monte Carlo Method quantitative risk analysis is widely recognised as useful for forecasting project outcomes, without consensus about the methodology to follow and little public evidence of successful outcomes of such forecasts. It argues for certain key characteristics to be present in analyses prior to project sanction with reasons for those features being included.

2 WHY MANY PROJECTS FAIL There are of course many reasons for failure of projects, but we will focus on those related to project time, cost and profitability.

2.1 Pressure to comply with predetermined time and cost targets Many projects are planned to fit in with predetermined dates of completion, rather than to provide durations required to complete the scope. This situation is often exacerbated by slippage of the timing of project commencement without corresponding delay to the project completion date. Whether due to fear of loss of face for company management or due to politically imposed deadlines, such schedule compression reduces the probability of timely completion, although it is unlikely to be apparent from viewing the project plan.

2.2 Single value estimates of time and cost tend to be optimistic Project planning tools such as Primavera P6 or Microsoft Project require the user to input single durations for each task defined as part of the project plan. Connecting up all the single-duration tasks with appropriate logic determines the end date through application of the rules of the critical path method to find the longest path through the project schedule, along with all the early and late dates for the rest of the tasks in the schedule. This is why such software is described as deterministic – there is only one date able to be determined for the project completion once the start date is defined. When there is pressure to produce an expected date that may have been pre-announced it is hard to avoid incorporating optimism in the durations. In reality the duration of a task is rarely able to be known exactly. In most cases, it is more like being asked how long it takes to travel from home to work each day. Because it is a regularly repeated activity, most people are likely to answer in a way similar to the following: “If it is a fine day when most people are away on holidays, it only takes me 20 minutes. In winter, when the weather is bad and everyone is trying to get to work, it may take 50 minutes. If there is an accident or the road is being repaired, it can take 75 minutes. Most of the time it takes about 30 minutes.” Project plans contain hundreds or even thousands of activities like this. But deterministic project planning only allows one duration value for each task. The project planners cannot represent the inherent uncertainty of each task in their single duration values. Consequently, when the pressures of



producing the desired end date are factored in, the likelihood of the schedule being inherently optimistic is high. If the project cost estimate is aligned with the schedule durations, it also is likely to be optimistic.

2.3 Monte Carlo Method range analysis – a solution to single duration values By replacing single durations with duration probability distributions we can represent the inherent uncertainty of activities in the project schedule. For example, the following two probability distributions represent a task of between 58 Minimum and 77 days Maximum duration, with a Most Likely (highest probability) duration of 64 days:

Figure 1 Task 3-point probability duration distributions

The two probability distributions – Triangular and Beta Pert – represent different ways of expressing how likely the task duration is to be around the Most Likely value (more likely using Beta Pert) or alternatively, spread more toward the extremities (Triangular). Other shapes can be used, but the value of replacing the single durations with probability distributions is realised by applying software using the Monte Carlo Method (MCM), a mathematical technique for simulating running the project hundreds or even thousands of times, randomly sampling the duration of each uncertain duration task in the schedule for each critical path calculation of the project schedule. The early and late dates and float for each task are stored for each critical path calculation or “iteration” and the software samples the duration values so that over the pre-determined number of iterations of the MCM simulation, from say 500 to 5,000, the sampling frequency for each task matches its predefined probability distribution. The object is to simulate as many possible combinations of task durations to produce the full range of project time outcomes as could realistically be expected to occur. Through this method, probability distributions are generated for the dates of all uncertain duration activities in the project schedule, including the end date. An example output is shown below, produced from Oracle’s Primavera Risk Analysis™ (“PRA”, formerly known as Pertmaster Risk Expert):

Figure 2 Typical output from MCM Schedule Range Analysis

The above histogram plots the date outcomes for the completion milestone of the project of all the 5,000 iterations in the simulation, from earliest (left edge of the horizontal date axis) to the latest (right



edge). The height of each orange bar represents the number of iterations (“Hits”) out of the 5,000 comprising the simulation, for which the PRA critical path software calculated the finish date between the left and right edges of the bar, shown on the left hand vertical axis. The cumulative curve overlaid on the histogram adds each successive bar’s iterations to the sum of the bars preceding it, such that the right hand vertical axis represents the cumulative number of iterations out of the total. So a horizontal intercept from the cumulative curve represents the probability that the project will be completed on or before the date vertically below the intercept on the cumulative curve. From this we can learn a lot more than we previously knew from the deterministic plan, including: • The 50% probable date of 24Mar07, expressed as P50 - a date for completion of the project as

likely to be exceeded as it is to be achieved; • An optimistic date, say P10, of 26Feb07; • A pessimistic date, say P90, of 26Apr07, by which there is only a 10% chance the project will

finish later. In addition, we can examine how probable the deterministic finish date is of being achieved. The horizontal red arrow near the bottom of the histogram and the Statistics tabulated on the right side show us that achieving the planned finish of 25Feb07 is only 9% probable. So through using Monte Carlo Range Analysis we can answer questions, based on substituting duration distributions for single values, such as “How likely is the project to be completed by the planned date?” Or “What is a likely date for completing the project?” If we also overlay the schedule with the project cost estimate, dividing the costs appropriately into fixed (time-independent) and variable (time-dependent) costs, which are ranged in an analogous way to the durations, similar cost histograms and cumulative probability curves are produced at the same time. So we can overcome the tendency for optimism inherent in using single durations for project planning by using MCM Range Analysis and careful assessments of duration ranges used as inputs. But there are two other significant contributors to schedule delay and cost overruns not apparent from deterministic planning.

2.4 Failure to account for overlapping of converging logic pathways All but the simplest project plans consist of multiple logic pathways that necessarily converge through milestone logic nodes leading to project completion. The probability of completing a given logic node milestone by the planned date is the determined by the probabilities of each pathway to that convergent node. How those probabilities are related to each other can be illustrated by the following example. Two identical activities are to be performed in parallel using identical but separate resources. If the probability of each activity A & B being completed by the planned finish date is 20%, what is the probability of both being completed by that date?

Figure 3 Merge Bias Effect of converging parallel logic

As shown above the probability of both tasks A & B being completed by the planned finish date is the product of the probabilities of each path being finished by that date. This is analogous to the probability of throwing two dice and getting the same value on each. It is known as the Merge Bias Effect (MBE) and explains why it becomes progressively harder to complete a project on time as more converging parallel logic paths overlap. It is also a reason for it being hard to complete complex projects on time, due to the convergence of many parallel logic strands. So when a project is delayed in starting, one effect is to reduce the probability of each logic pathway being completed by the planned date. Due to the MBE, the reduction in probability is not linear with time delayed but geometric, due to the probability of timely completion being the product of all converging probabilities.



2.5 Failure to allow for risk events

Earlier we discussed the time required to get to work from home and referred to the occasional traffic accident or road repair or malfunctioning traffic lights. Each of these is a Risk Event that has a low probability of occurrence, but in a project with thousands of activities, some risk events are virtually certain of happening. It is therefore essential to take risk events into account in planning such projects, to increase the resilience of the project, not only to such “known unknowns” that are identified and managed in the project risk register, but also to the inevitable “unknown unknowns” that occur in almost all projects and for which projects require a buffer of time and cost contingency. Projects that are planned with no allowance for risk events have no protection against unplanned threats to the project objectives. This can be seen as naïve optimism. MCM simulation of the project can be broadened to allow for risk events as well as range uncertainties. The significant risks, both threats and opportunities with cost and schedule impacts in the project risk register can be mapped into the MCM model of the project and their effects taken into account through the product of their probabilities and impacts. By assessing the cost and schedule impacts of the risk events as three-point distributions, the uncertainty of the risk events can be allowed for both in their probabilities of occurrence and their impacts if they do occur. For construction projects, we can also group weather events and uncertainties under risk events because the impacts of weather modelling are often not modelled well in deterministic planning and estimating. Historical weather data can be analysed and seasonal probability distributions modelled in MCM simulations to take account of complex weather effects such as: • Variability of weather causing random fluctuating downtime due to wind or rain year-round; • Seasonal weather events such as hurricanes or cyclones; and • Weather windows of uncertain start and finish times such as winter freeze and thaw cycles,

monsoons and wet seasons.

2.6 Failure to allow for change in market conditions The final reason why many projects fail can be external to the project, which may be executed on time and within budget, but still fail to meet its objectives, due to changes in market conditions. Or the converse may be true: the project may go over time and budget yet rising market conditions still enable the created asset to be profitable to operate. It may be that the product to be produced by the asset being created by the project falls in value below the required threshold for the project to make its required NPV or even break even. This reason has recently ended the resources boom in Australia, due to the combination of falling commodity prices and rising exchange rate. A number of planned iron ore and coal projects have been cancelled due to reduction in demand by China as well as increased supply coming onto the market. State owned production facilities producing subsidised aluminium have depressed the global price of aluminium to the point that smelters and alumina refineries are being shut down. Sometimes the change may be in the price of feedstock for a plant. A plant to produce diesel fuel from palm oil was built in Darwin in Australia in the mid-2000s when the price of palm oil had fluctuated between USD180 and 630/tonne over the previous 20 years, averaging around USD350/t, but demand from China pushed the price up over USD1,000/t in 2007 and it has averaged about USD800/t since. This destroyed the economics of the diesel plant.

3 INTEGRATED COST & SCHEDULE RISK ANALYSIS Cost Risk Analyses (CRAs) and Schedule Risk Analyses (SRAs) have been performed for many years. But integrating cost and schedule risk analyses into one analysis is not fully accepted in the project quantitative risk analysis community, partly due to differences in the origins of the species of personnel performing CRAs versus SRAs and partly due to perceived limitations in software tools. Consequently it is worth setting out the reasons why cost and schedule uncertainties and risks should be analysed together and what the best practice process looks like. Note that this section of the paper makes extensive use of an article by the author and a colleague published in our April 2014 Newsletter “RiskIntegral”iv



3.1 Definition of Integrated Cost & Schedule Risk Analysis

Integrated Cost & Schedule Risk Analysis (IRA) is the Monte Carlo Method simulation of a project using a schedule representation of the project overlaid by a cost estimate or control budget of the project and with treated risk events with cost and schedule impacts appropriately mapped into the cost-loaded schedule. All significant sources of time and cost uncertainty are incorporated into the model and simultaneous cost and schedule uncertainty distributions are calculated by the simulation, together with cost and schedule driver sensitivities.

3.2 The IRA Process It is assumed that this process is being performed in the latter stages of a Feasibility Study prior to Financial Investment Decision (FID) when well-developed detailed schedule, estimate and risk register are available. This does not preclude the process being performed at other times, including during pre-feasibility or after execution has started, but if so, the inputs are likely to differ.

3.2.1 Starting with the Schedule IRA starts with the project schedule. For maximum realism, the schedule should represent the project strategy and how the project is to be executed. The schedule should include sufficient detail to reveal the critical and near-critical paths of the project. It should also retain the characteristic complexity of the project to ensure that the Merge Bias Effect (MBE) realistically constrains the potential for early completion. For major and complex projects including construction, the schedule used to organise and control the overall project without becoming too detailed and unwieldy is often referred to as the Level 3 Integrated Master Control Schedule. This is usually a good basis for an IRA model.

3.2.2 Summarising the Estimate The project estimate is overlaid on the project schedule in a series of hammock activities (tasks that change in duration according to the durations of the tasks to which they are linked without taking part in critical path calculations). The estimate / control budget may consist of thousands of line items. The goal is to set up the cost breakdown structure so that it aligns with the schedule breakdown structure and enables costs to be summarised to differentiate between significantly different risk profiles; for example, in costing of equipment and materials packages, separating procurement and fabrication from construction.

3.2.3 Splitting Fixed & Variable Costs The line item costs are a mixture of variable (time-dependent) and fixed (time-independent) costs, all of which are uncertain at the start of the project. The proportions (“splits”) of fixed and variable costs must be accurately known or estimated for each line item so that the summarised variable costs, when spread over the applicable groups of tasks in the schedule can vary realistically due to duration changes. Variable costs can vary due to task duration changes and also due to uncertainty in their rates. For example, labour and equipment hire rates may be uncertain at the start of the project.

3.2.4 Ranging and Reviewing the Uncertainty and Risk Inputs Subject Matter Experts (SMEs) can provide range and rate inputs to workshops or interviews to develop consensus inputs for the IRA model schedule and cost ranges. In addition, risk events from the project risk registers with time and cost impacts incorporate the known risks that could affect the project goals. The risk events could be opportunities or threats and could affect multiple activities in the project. They may also be mapped into the cost-loaded schedule as risk factors that affect groups of activities to reflect such things as productivity, quantity uncertainty or market conditions.



3.2.5 Correlation: a necessary input Correlation models are developed to ensure that groups of activities and resources that behave in related ways are represented realistically in the IRA model. This extends to risk factors that vary in related ways, such as productivity risk factors for different disciplines. Correlation inputs are essential instructions to the MCM modelling tool that correct the inherent assumption of complete independence between all inputs and enable the model to forecast realistic probabilistic spreads of schedule and cost.

3.2.6 Building the IRA Model The IRA model is built from: • the carefully reviewed and technically corrected schedule; • overlaid by the summarised estimate; • inputting the schedule and cost ranges; • schedule and cost correlation models; • mapping in the treated cost and schedule impact risk events and risk factors; and • assigning the probabilistic weather calendars (usually derived from historical weather data) to the

appropriate construction tasks where the project involves weather-exposed construction.

3.2.7 Integrated Analysis The analysis is usually performed at least twice and sometimes three or more times, depending on the complexity of the model and how far the client wants to go in optimising the risk profile of the IRA model and thus the project. The key point is that changes to inputs to the model depend on the wishes of the project team in reviewing the results and what the sensitivity rankings reveal about the schedule drivers and the underlying logic in particular.

3.3 The benefits of IRA IRA enables the simultaneous analysis of probabilistic schedule and cost distributions for IRA models incorporating all known and possible sources of time and cost uncertainty, such as shown in the example distributions below. It reflects the project reality that “time is money”. By modelling all time and cost uncertainties together, IRA enables the analysis to calculate the cost consequences of schedule changes based on where and when they occur in each iteration and thus probabilistically from the complete simulation.

Figure 4 Simultaneous Schedule and Cost Risk Analysis Results Produced

In addition, IRA enables the quantification and ranking of all driving sources of uncertainty in the IRA model, providing insight into the what, why, and how of assessing model behaviours and outputs.



This is done by a technique that can be called Quantitative Exclusion Analysis (QEA), which entails the systematic removal of classes of uncertainty or individual tasks or costs or groups of tasks or costs. A full simulation without each class, group or individual contributor enables the effect of the missing element to be measured by difference at selected probability levels. This provides valuable information on the priorities to assign to optimising project schedule and/or cost risk. Examples are shown below of the results of QEA, graphically expressed:

Figure 5 Ranked Classes of Cost and Schedule Uncertainty by QEA

More detailed analyses are possible, down to individual tasks, cost line items and risk events if needed to understand project risk better and optimise it: IRA provides the means to do it.

3.4 The Challenges of IRA IRA has pitfalls that must be avoided to produce reliable results. These include: • The schedule and estimate must be aligned (produced on the same set of assumptions) for the

combined analysis to be valid. For example if the schedule assumes a different sequence or rate of performing work from the estimate, the analysis will be wrong.

• Obtaining accurate splits between cost types - time-dependent (variable) and time-independent (fixed).

• Avoiding “double-dipping” between duration and cost ranges versus risk events or risk factors • Spreading the costs correctly over the rights groups of activities to represent the true time-

variability of the costs. • Dealing with mismatches in level of detail between the estimate and the schedule, such as where

the estimate does not split out procurement costs from installation costs, preventing different risk profiles from being measured and differentiated.

3.5 Mega Projects Exacerbate the Challenges IRA modelling done well is a demanding environment, especially for mega projects (capex >$1bn). Large L3 Integrated Master Control Schedules and large complex estimates are to be expected. The bigger the project the more difficult the task of developing a well-structured and responsive schedule that is technically acceptable for SRA and IRA. In addition, ensuring that the project scope is fully and properly represented is another challenge of increasing difficulty as the project grows in size. Already noted is the need for the complexity of the project to be adequately represented in the schedule model to ensure the MBE is appropriately represented. As the size of the IRA model increases, the effects of the Central Limit Theorem become more significant and the importance and difficulty of adequately correlating the IRA model increase. Weather may be a major uncertainty factor for projects including construction. Being able to input the probabilistic weather calendars and model the weather realistically in all its complexity is another



demanding IRA modelling element that becomes more challenging as the model size increases. But for some projects weather may be the dominant risk that must be modelled and only IRA provides the means to measure and rank the cost consequences reliably. As the scale of the IRA model increases, the difficulty of measuring meaningful and accurate risk driver information also increases. This is because the increasingly sophisticated correlation models distort the use of correlation sensitivities as risk outputs from the analyses to rank the drivers of schedule completion and schedule cost. The correlation models behave as undeclared independent variables to bias the crucialities and cost sensitivities, making the use of QEA vital for realistic ranking of risk drivers of the IRA model.

4 CONVENTIONAL SERIAL SCHEDULE RISK ANALYSIS AND COST RISK ANALYSIS

4.1 SRA & CRA practitioners from different backgrounds Estimators and cost controllers have traditionally come from the ranks of quantity surveyors and cost engineers, while project planners and schedulers have tended to emerge from the ranks of design and construction engineers. The first group is concerned with counting and quantities while the second group is more concerned with how things are put together. This has carried through to the application of Monte Carlo simulation: Cost Risk Analysis tends to be performed by practitioners with estimating or cost control backgrounds, using spreadsheet based tools like @Risk and Crystal Ball. The practitioners may compile Trend Registers in which all uncertain items with cost impact are listed, including those driven by schedule. In the latter category, cost controllers assume some time rate of cost expenditure to forecast cost ranges in the Trend Register. On the other hand, project planners who have transitioned into Schedule Risk Analysis tend to use Monte Carlo simulation tools built on project planning software. When costs have been included in such modelling, it has often been through working with resource-loaded schedules. Functionality for costs has not been as well-incorporated in such SRA tools, further deterring SRA practitioners from including costs in their analyses.

4.2 SRAs and CRAs remain separate Consequently, CRAs tend to be performed by one set of practitioners using their favoured software and SRAs by another group of practitioners using different software. Even when the same analysts do both SRA and CRA, they commonly use different software due to the evolved specialisations. Where the decision has been made to perform combined schedule and cost risk analyses, analyses will be conducted separately, often by different personnel or contractors. The assessed SRA contingency allowance is fed into the CRA as a “schedule risk allowance” using an assumed “cost burn rate” over the contingency period.

4.3 Unsafe Assumptions The problem with this approach is that it takes no consideration of where and when schedule changes occur. To demonstrate this point, consider the following scenario: A schedule risk analysis reveals that 30 days of contingency are required in addition to the planned duration to be 90% confident of achieving project completion on time. It also reveals that the bulk of the duration uncertainty for the project is distributed across its construction phase. The results of the schedule risk analysis are then input to the cost risk analysis at an assumed cash burn rate of $1 million dollars a day based on the conservative assumption of peak construction manning levels. There are three clear issues which we will identify as the ‘when’, ‘where’, and ‘why’ of the traditional approach to combining cost and schedule risk which fail to accurately characterise the true project uncertainty:

4.3.1 When The first issue relating to the combining of separate cost and schedule risk analyses lies in the assumed cash burn rate per day. For the example above, because the bulk of the duration uncertainty was identified as coming from the construction phase, the assumed cash burn rate per day



was calculated based on peak construction manning levels. However, this assumption is overly pessimistic, as only a proportion of the construction period will actually run at peak manning levels. What if delay occurred before all contractors had been mobilized to site? The capital cost impact of the delay would understandably be significantly reduced. Similarly, critical path delays affecting pre-execution engineering or approvals would have drastically different cost impact profiles. The calculated cost of delay is clearly dependent on when the delay occurs.

4.3.2 Where The second issue relates to where in the program a delay occurs. It is likely that the schedule will consist of multiple parallel paths of tasks that ultimately converge on one completion milestone (either directly or through other connected tasks). Some of these paths will be dominant in determining the completion date of the project, occurring frequently on the critical path, whereas others will not. It is entirely possible for a chain of tasks to be significantly delayed, but never impact on the overall project critical path. However, even though they’re not impacting on the end date of the project, prolongation costs will still be incurred associated with the delays, due to longer use of hired equipment, labour, etc. Calculating the cost of a schedule allowance based on delay to project completion fails to account for these non-critical delay cost uncertainties.

4.3.3 What / Why / How The final issue with the traditional approach to cost and schedule risk analysis deals with why a particular answer was given, what was driving it, and the assumptions and methodology of how it was derived. Separate cost and schedule risk analyses will almost always have different assumptions that underlie their inputs. A cost risk analysis that draws from the result of a schedule risk analysis must take account of the schedule assumptions underlying the schedule answers to accurately portray forecast cost of delay over-runs. Further, because the schedule is analysed separately from cost, the visibility of individual schedule elements as drivers of delay cost is lost. We can indeed attribute a certain amount of cost contingency requirement to schedule, but why it is required, what drives it, and how it has been derived is very difficult to express through such a methodology.

4.4 The latest version of linked SRA & CRA A more sophisticated serial SRA/CRA approach is described by Yuri Raydugin in his useful recent book “Project Risk Management”v. By this method a project duration probability distribution from a SRA is transferred into a matching CRA, representing the project variable cost uncertainty. All other cost line items in the CRA represent fixed cost uncertainties and risks, and exclude variable costs. While this approach provides a variable cost probability distribution, it is still based on an assumed rate of expenditure of variable cost per unit time and divorces the schedule drivers from the cost drivers. The approach also advocates using a highly summarised schedule (<100 tasks), ignoring the importance of the Merge Bias Effect in producing realistic schedule forecasts.

5 SUMMARY COMPARISON OF IRA WITH SERIAL SRA / CRA The comparison between IRA and serial SRA/CRA can be summarised in the following table:

Table 1 IRA versus Serial SRA/CRA

Integrated Cost & Schedule Risk Analysis (IRA) Schedule Risk Analysis (SRA) to Cost Risk Analysis (CRA) More rigorous if done correctly Less rigorous Harder to do correctly Easier to perform Can provide key driver information, unifying schedule and cost drivers in one ranking as influences on project cost

Cannot reveal cost consequences of schedule delays – schedule drivers are separate from cost drivers

The practicality of IRA depends on being able to handle large scale inputs and analysis of large models in a timely and accurate way. Increasingly, practitioners and the tools to do this are appearing. Apart from PRA, Booz Allen Hamilton’s Polaris is becoming increasingly capable and is significantly faster than PRA. Other contenders include Intaver Institute’s RiskyProject and Barbecana’s Full Monte, along with Deltek’s Acumen Risk.



6 EXTENSION OF IRA TO INCLUDE FULL PROJECT AND ASSET LIFECYCLE

6.1 Incorporation of the Asset Life Cycle While IRA methodology is now well-proven on projects ranging from a few million dollars to more than $15 billion, the methodology does not risk-assess a major part of the Total Cost Management framework: the Asset being created by the project. The goal of the project is to create an asset that will produce products or services to realise benefits set out as project objectives and in so doing, generate target financial returns from that asset. When the focus is on the project the ultimate profitability of the asset is not risk-assessed. If our Integrated Cost & Schedule Risk Analysis is continued beyond project completion, represented by startup of the Asset, to consider operation of the Asset through to the end of the economic life of the asset, or at least to the end of the economic analysis period, risk analysis can be broadened to include effects on the Asset such as: • Operating cost uncertainties and risks over time; • Revenue uncertainties and risks over time; • Operating uncertainties and risks including to inputs to the asset; • Maintenance uncertainties and risks; • Changes to the regulatory environment; and • Asset closure uncertainties and risks

6.2 Modelling Functionality required to incorporate the Asset Life Cycle To include operational and revenue uncertainties through the Asset life in IRA modelling requires the ability to model a range of parameter changes, including: • Fluctuations in market pricing affecting revenue, whether cyclical or structural; • Changing production / operating costs and efficiencies; • Occurrence of maintenance shutdowns; and • Changes in regulations and compliance costs. Operational Risk factors to be modelled could include: • Operating cost inputs such as labour, utilities and raw materials/feedstock; • Market conditions for various inputs including

o Maintenance costs; o Shipping of product and raw materials; and o Cost of borrowing money / interest rates.

Risk events to be modelled affecting revenue and operating conditions could include: • Threat or opportunity of major change in product pricing beyond normal cyclical variation; • Threat of extended industrial dispute by operational workforce; • Opportunities for premium pricing of product sold on the spot market rather through long term

contract commitments; • Threat of increasingly stringent environmental regulations increasing operating and maintenance

costs; and • Risks of changes to government taxes, charges and subsidies.

6.3 Benefits of modelling Asset Life Cycle The pre-eminent advantage of modelling operating and revenue uncertainty and risks is the capability to model the whole project and asset life cycle from conception to asset closure and remediation. Through generation of probabilistic cash flow curves and consequently, of probabilistic NPV and IRR distributions, the susceptibility of the project and asset to change from being healthily profitable to breaking even and then to making an unacceptable loss can be explored, including their resilience and ability to withstand various sensitivity “tests” while still producing an acceptable return on investment. Because all this can be done on the one model integrating all development and execution costs and risks through operation and revenue uncertainties and costs, and all drivers of profitability explored and sensitivities assessed, for the first time, such integrated analysis provides an unprecedented opportunity to improve understanding of a project before financial commitment is made. We call this extension to IRA Integrated Costs, Schedule and Revenue Risk Analysis, or IRRA.



6.4 Demonstration IRRA models produced

Originally the concept was explored using a mining project model.vi The latest modelling is based on a Floating LNG project and asset development, or FLNG IRRA model. Apart from it being the newest type of energy project being developed, with at multiple FLNG Projects being studied or project managed in Perth, Western Australia alone, the FLNG project is conceptually very appropriate to study using IRRA methodology because it incorporates the approach of being able to take the main revenue producing facility – the FLNG vessel - elsewhere after exhausting the gas field assets on which its design and construction has been based, in a relatively short time, and redeploying it elsewhere. The project economics are likely to be sensitive to the residual value of the FLNG vessel and the ease with which it can be reconfigured and redeployed to a different field, although that sensitivity will be reduced as the DCF rate is increased and the life of the field(s) supplying the FLNG vessel increases. This type of project is similar to that for a Floating Production, Storage and Offtake (FPSO) vessel for offshore oil production, although the FLNG facility is likely to be much more expensive.

6.4.1 Features of FLNG Conceptual IRRA Model The FLNG project and asset schedule starts from the Concept Phase and progresses through Pre-FEED, FEED, Financial Closure and Execution Phases of the project, to First Cargo. The Operations phase is assumed to continue for 25 years, with maintenance of the FLNG vessel stopping production every 5 years. The first 15 years are shown in the summary view of the schedule below:

Figure 6 Section of FLNG Project & Asset Schedule for IRRA Modelling

About 200 activities were used to describe the project and operation of the assets over the 5 years leading to FID, the four years of project execution and the 25 years of operation followed by field closure and remediation with FLNG Vessel notional sale to another project (a “balloon payment”) and transfer for refit and reconfiguration. The tasks leading up to First Cargo were subject to duration uncertainty ranging and the maintenance shutdowns of the FLNG vessel every 5 years were also duration ranged. Notional fixed and variable costs were assigned to the various phases of the project and subject to ranging by duration (Variable costs) and cost distributions (Fixed costs). LNG revenue assigned to fixed monthly production blocks was ranged to represent uncertainty and 6-monthly maintenance tasks every 5 years (subject to duration uncertainty ranging) interrupted the production revenue stream. Notional Opex costs were assigned for maintenance, operations and remediation on field closure.

6.4.2 Outputs from FLNG Conceptual IRRA Model The following probabilistic cash flow was produced for the project and asset. Note the assumed balloon payment for the FLNG Vessel at the end of the cash flows.



The three curves shown are P10 (top curve), Pmean and P90 (bottom curve). The DCF discount rates are built into the histogram values. The 5-yearly maintenance shutdown costs and breaks in revenue cause the “ripples” in the histogram bars and the cumulative curves.

Figure 6 FLNG Project & Asset Probabilistic Cash Flow from IRRA Modelling

The analysis enables probabilistic Net Present Values (NPVs) and Internal Rates of Return (IRRs) to be produced. In the IRRA modelling, we included a 10% probability risk that there would be an LNG price drop of around 20% (10% minimum, 30% maximum) that would affect the LNG revenue from year 6 onwards. This causes the bimodal distribution in the Probabilistic NPV analysis below:

Figure 7 FLNG Project & Asset Probabilistic NPV Distribution from IRRA modelling

$10,000,000,000 $15,000,000,000 $20,000,000,000

Distribution (start of interval)

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

Hits

0% $22,682,599,320

5% $21,546,742,945

10% $21,327,006,283

15% $21,027,451,715

20% $20,783,567,115

25% $20,555,613,561

30% $20,374,353,800

35% $20,208,699,587

40% $20,065,624,114

45% $19,877,373,854

50% $19,706,352,200

55% $19,522,468,702

60% $19,333,157,880

65% $19,161,684,629

70% $18,959,792,604

75% $18,716,400,325

80% $18,441,679,772

85% $18,056,386,527

90% $14,000,951,939

95% $12,446,007,146

100% $9,848,856,808Cu

mul

ativ

e Fr

eque

ncy

FLNG Project (5% DCF, 3% project finance interest)Entire Plan : NPV

In addition to these financial analyses which take into account revenue, capex and opex uncertainties and risks, it is possible to produce the usual cost and schedule probability distributions for First Cargo and any intermediate milestones and key subsidiary costs, along with the schedule and cost drivers.

6.5 Analyses possible from IRRA modelling What this integrated approach allows is the evaluation of a full range of uncertainties and risks that may affect capital cost, development or construction timing, startup and operating costs and conditions or revenue. The project and the revenue generated from the resultant asset may be sensitive to weather uncertainties and fluctuations, market conditions such as changes in supply and demand balances or regulatory changes that may change operating costs or product pricing.



Once the time/cost/revenue model developed meets stakeholder needs, it can be used to model any uncertainty or risk event that has incremental or sudden effects on timing or costs, in any phase of the project and asset. Such an integrated model enables sensitivity analyses for all kinds of combinations of conditions that may be envisaged as possible. The model can also be used to model different combinations of risk treatments to optimise the treated risks and the overall project for best possible outcomes and cost-effectiveness. Optimisation can extend to modelling operational reliability and availability, taxation and depreciation, to produce the most realistic and informative analyses. Thus the IRRA model can integrate project estimating, planning and risk management together with operations and maintenance modelling and economic analysis.

7 DEALING WITH SCEPTICISM TOWARD MCM One of the problems this paper is intended to address in re-engineering project budgeting and management of risk is the wide-spread scepticism toward Monte Carlo Method simulation which results in hostility toward its use by those who have encountered poor or deliberately misleading usage of quantitative risk analysis in projects. This is believed to be due to multiple factors: • Lack of reliable published evidence that project quantitative risk analysis is helpful; • Immaturity of the discipline of quantitative risk analysis in projects, resulting in a wide range of

capabilities being offered in the market or practised within organisations, often producing results of little value, resulting in disillusionment with the idea of MCM being useful;

• Lack of understanding of what is required to produce high quality and reliable quantitative risk analyses in projects and thus an inability to specify the process adequately;

• Misuse of quantitative risk analysis by users who have deliberately skewed ranges to produce desired results, sometimes described by the slang term “cooking the books”.

These factors are discussed in more detail below.

7.1 Lack of published evidence of value of project quantitative risk analysis Published information on the outcomes of quantitative risk analyses or critically assessing the effectiveness of quantitative risk analysis techniques is hard to find. A Working Paper published by RAND Corporation over ten years ago surveyed the use of quantitative risk analysis and management on the planning and execution of complex projects (Galway, February 2004vii). The survey found that there was a reasonable consensus on the need for and usefulness of project QRA but also that it is not well-understood, not well-integrated into project management and not easily explicable to senior decision makers. There is a lack of hard data on the accuracy of forecasting by quantitative risk analysis, despite the apparent widespread use of it on large and complex projects and the growth of computing power. This was ascribed to several reasons: • The risk analysis community of practice is fragmented in nature, including academics, project

professionals, risk consultants and software developments, making sharing of information harder. • Projects change as they progress, outdating the results of QRA and calling into question the

criteria for evaluating their effectiveness. • Most projects are kept confidential and project owners and contractors are reluctant to share

information about their progress or commercial outcomes. This is particularly applicable to high technology projects or those with an R&D component.

• Finding out about what quantitative risk analysis techniques work and what do not depends on publication of successes and failures and there is little incentive to do this, apart from the desire of the practitioners to have their successes recognised.

• Learning lessons from quantitative risk analyses on a project depends on a full report being prepared on the brief, inputs, assumptions and exclusions, analysis methodology, range results, rankings of drivers, actions arising and, after the actual results are in, recording the actuals for comparison. Unless the organisation takes Project Closeout seriously enough to fund such an exercise as part of a “Lessons Learned” Report, it is unlikely to happen, even if reports were prepared at the time of the risk analyses.

Other more recent papers provide listings of techniques without any evaluation of their effectiveness beyond opinions on their applicability. For example, “A Review of Quantitative Analysis Techniques for Construction Project Risk Management” (Thaheem et al, 2012)viii



A review more specifically focused on Oil & Gas “The Application of Quantitative Risk Analysis Techniques in the Oil and Gas Industry” (Hartke, 2012)ix looks more incisively at quantitative risk analyses and the use of different types of Monte Carlo simulation at different stages of a project, envisaging use of CRA, SRA and even IRRA in assessing the probability distribution of the project’s NPV and the value of identifying key risk drivers to mitigate risks. However, apart from stating that “investment projects in the oil and gas industry… offer an abundance of examples of successful application of risk analysis”, Hartke provides no evidence of effectiveness in forecasting outcomes.

7.2 Scepticism toward Quantitative Risk Analyses Experience of the author with various clients has revealed mistrust of the validity and reliability of QRA, due in many cases to prior experience of poor or misleading analyses. This is particularly pronounced toward CRAs among the financial community. It seems clear that these have been misused in the past to produce favourable forecasts. The author’s own experience of intervention by clients to produce desired outcomes is exemplified by the following (for which the practitioner’s only protection apart from declining to do the work is to be explicit in listing input assumptions and exclusions): (i) A tender specification for a naval defence contract called for a SRA to be submitted with the

tender to demonstrate the bid was achievable – Client SRA inputs were adjusted until they gave the “right” answer;

(ii) A CRA for a petrochemical plant was required to be kept below $450m and input ranges were constrained with this in mind. (Some years later the plant was completed for a cost that was more than double this limit…)

(iii) A major construction contractor carefully adjusted the inputs to an LNG design & construct SRA requested by the client during the tender process so that the contract period planned by the contractor was equivalent to a P51. (This contract period was considered by an LNG plant construction expert on the client’s team to be around a realistic P80. The contract was to include a sliding scale bonus for early completion and penalty for late completion.)

These examples are outweighed by many more engagements where no such influence was apparent.

7.3 Immaturity of QRA Discipline leads to poor specifications and performance Because knowledge is poor of what are the necessary and desirable ingredients of effective QRAs, especially involving SRA, specifications for QRAs are rarely prescriptive concerning methodology and where they are, it is usually to specify particular software tools. The situation described in the 2004 RAND Working Paper remains relatively unchanged in the author’s experience: There is a lack of available evidence of successful methodologies and techniques – the practitioner has to rely on: • Limited useful reference books – see for example both excellent books by Hulett: “Practical

Schedule Risk Analysis” (2009)x and “Integrated Cost-Schedule Risk Analysis” (2011)xi • Discussions on specialist forums, such as the following on LinkedIn (most of which require

application for admission, which is not unreasonably withheld): o Schedule and Cost Risk Analysis o Oracle Primavera Risk Analysis (PertMaster) o Oracle's Primavera Risk Analysis - a subgroup of Primavera o Monte Carlo Simulation and Risk Modelling o Mining risk and uncertainty analysis

While contributors to these forums include many of the most knowledgeable and skilled practitioners, there is understandable reluctance to share commercially sensitive evidence of QRA performance, due to client confidentiality, but also to protect the business interests of the practitioners. Requirements to perform QRAs usually do not specify the methodology, presumably because: (i) Organisations may not know how to specify what is required; (ii) Industry is not risk-mature enough to know definitively what are the best techniques; and (iii) There is a dearth of data on QRA performance in forecasting project outcomes.



7.4 Examples of improved project forecasting using quantitative risk analysis In an attempt to provide some specific examples of projects where QRA can be assessed to have been successful, based on comparison of probabilistic forecasts versus actual outcomes, the following are offered.

7.4.1 Australian LNG Train Completion to RFSU - SRAxii The project was about 5 months from planned plant startup and the client was concerned that the schedule was slipping each week. The current completion schedule of ~2,700 tasks was converted to an hourly schedule to provide more granularity for the hundreds of 1-3 day remaining duration tasks. Duration ranges were obtained by interviews with the various discipline superintendents. Several SRA iterations were required, using cruciality tornado diagrams to identify schedule drivers and twice remove delaying equipment not necessary for startup (a flare awaiting a missing part; a let-down gas turbine with a faulty bearing) until the schedule was driven by the main remaining work, to complete installation of the refrigerant modules. The third simulation for plant RFSU was within 3 days of the actual RFSU by the client’s criteria.

7.4.2 Middle Eastern LNG Train Completion to RFSU - SRAxiii About 4 months from planned startup, the project owner was concerned completion would be late as contractor progress was slipping and shipment of LNG in the month after planned startup was in doubt. The construction schedule was high-level and did not show systems to enable handover linkages to be made to the detailed and consistent commissioning schedule. The final steps before construction handover included pipeline pressure testing, which enabled “pseudo tasks” to be created between the data date and the forecast final test pack pressure test date for each system, extracted from a testing database. The ~1,000 remaining tasks schedule was also converted to hours to increase ranging granularity. Conservative ranging assumptions based on experience to date were made for System Mechanical Completion and Intermediate RFSU Punch Listing and Clearing (MCPL & IRFSUPL) tasks when Owner’s Team and Contractor discipline supervisors met to range the schedule. Initial SRA results were considered pessimistic so further changes were made to the schedule to add an air blowing nightshift and reduce pessimism in the MCPL & IRFSUPL tasks. Optimistic and pessimistic scenarios were modelled. These reduced the probabilistic dates, but the cruciality tornado diagram showed the Punchlist & Clearing tasks were driving. The Commissioning Manager then refined the punch list clearing logic, replacing the single 15 day task with one 5 day punch listing owner task and three categories of clearing tasks by the contractor: A – Must clear for system MC (5 days), B – Clear in parallel with commissioning work by IRFSU (10 days), C – Clear after startup (20 days). Ranges were assigned pro-rata. These changes were made to all systems and added to the previous changes. These brought the deterministic date back within the contractual date of 31Oct. Results from the progressive analyses are shown below:

Table 2 RFSU Dates from SRA Analyses

Deterministic P50 P80 Initial Analysis 08Nov 14Dec 20Dec Conservative Scenario 07Nov 07Dec 12Dec Optimistic Scenario 07Nov 29Nov 04Dec Refined Punch Clearing 31Oct 14Nov 19Nov

The final report recommended tracking actual performance versus the refined P3 schedule. An email was later received showing RFSU was signed off on 13Nov, the day before the refined P50 date.

7.4.3 Oil Refinery Enhancement Project - SRA About 4 months into an EPC contract to enhance the quality of diesel produced from an Australian refinery, a SRA was commissioned by the project owner through the EPC contractor. The project schedule of about 700 activities was being used to manage engineering, procurement, construction and commissioning. The first intermediate milestone whose timing was to be forecast was “95% Engineering Completed”, about 4 months after the start of the engagement, along with later milestones and the project completion. After a day spent ranging the schedule durations with the



Chief Planner and the Project Manager, analysis was performed and a report submitted. The analysis showed the following for “95% Engineering Completed”: Planned Date – 16Sep; P50 26Oct; P90 5Nov. After the report was submitted the client requested a meeting to review the results, accompanied by the EPC contractor stakeholders. The client Project Director was very scathing about the 95% EC milestone forecast, stating that he was negotiating with the EPC Contractor to bring that date forward but the SRA was forecasting it would finish at least a month late. The fact that the forecast was based on the EPC Contractor’s own duration ranges and schedule did not deflect the Project Director from “shooting the messenger” and stating that the report was not credible. Yet, in mid-November, the author was called in to perform another SRA due to delays to procured pressure vessels from Korea. In the course of collecting the new inputs and ranges, the author learned that the 95% EC milestone had actually been achieved on 5Nov, the P90 date. The author was engaged two more times on the project after the initial work.

7.4.4 Coal Seam Gas LNG Project Key Milestones Forecast – SRA & IRA As part of performing SRAs for the Santos GLNG project before Financial Investment Decision (FID), the author was required to prepare and analyse an integrated schedule of all work leading up to FID and then through the Execution Phase, the Upstream field development, the Pipeline, LNG Plant and Port work to First LNG Cargo. At the time of preparation of the integrated schedule (Data Date 18Dec09), FID was scheduled to occur at the beginning of September 2010 and First Cargo to occur at the end of July 2014. A modified Level 1 Integrated Project Schedule of 410 activities was developed using summary schedules for all the above aspects of the work. A more detailed schedule was impractical because of time pressures. 25 meetings, workshops and review sessions were held over a month covering all aspects of the project scope and including risk register reviews. 27 risk events were mapped into the integrated schedule. Duration correlation was applied to various groups of tasks in the SRA model, at around 50-60%. FID occurred in January 2011. At the time of writing this paper, First Cargo was forecast to occur “in 2015”. The comparison of planned, probabilistic and actual/current forecast dates was as follows:

Table 3 Planned, Forecast & Actual GLNG Dates

Milestone Planned P50 P90 Actual/ Current Forecast

FID 2Sep10 21Dec10 15Apr11 Jan11 First LNG Cargo 31Jul14 20May15 29Sep15 “2015”

An IRA was performed for the whole project in late 2010, simultaneously analysing probabilistic dates and costs for the whole project. The report was included in the FID Data Room for the GLNG partners. Further IRA work continues to be done by RIMPL for GLNG on Upstream FEED projects.

7.4.5 Talisman Energy Papua New Guinea O&G Exploration 2013/14 - IRAsxiv Talisman Energy (TE), a Canadian based Oil & Gas Exploration and production company has been exploring for natural gas and condensate in the Western Province of Papua New Guinea (PNG) since 2009, aiming to develop an LNG project. In the extremely challenging environment of marshy or steep and unstable terrain, heavy rain, low cloud, mandatory use of supply river boats, helicopters and long logistics chains, deterministic planning and estimating proved unworkably optimistic over the first couple of years. RIMPL offered IRA for risk-based forecasting of time and cost outcomes. TE’s Operations VP took the creative approach of breaking exploration down into “Unit Operations”: “Seismic Surveys”, “Seismic Interpretation”, “Drilling Site Preparation”, “Drilling Rig Move and Assembly” and “Drilling”. Generic Schedules for each of these stages (except Seismic Interpretation and Rig Move) were developed, along with estimates. Duration and cost ranges and risk events were workshopped for each Unit Operation. IRA models were produced and adapted as real seismic campaigns and drilling projects were proposed. Examples of rig moves and drilling and recent seismic campaigns are shown below. The rig moves were planned and estimated deterministically while the drilling and seismic campaigns were forecast and budgeted probabilistically. The differences in outcomes between rig moves and drilling, particularly financially, are significant.



Table 4 Planned/Forecast vs. Actual Rig Move & Drilling Duration & Cost Outcomes

Kupio-1 Well Rig Move (Un-risked) Act/Plan Drilling (Risked) Act/Plan Actual

Plan Actual % Plan Actual % cf Forecast Total Days 21d 40d 190% 51d 53.8d 105% P87 Total Cost $5.79m $7.90m 136% $16.2m $15.4m 95% P45 Manta-1 Well

Rig Move (Un-risked) Act/Plan Drilling (Risked) Act/Plan Plan Actual % Plan Actual % Total Days 35d 45d 129% 31d 29.5d 95% Total Cost $8.23m $9.63m 117% $10.2m $9.7m 95%

The seismic planning and estimating were based on probabilistic forecasting. In both cases, the actual survey costs were slightly higher than planned, but still within capital governance tolerances. Durations in both cases were higher than planned P90 values:

Table 5 Planned vs. Actual Seismic Surveys Duration & Cost OutcomesSouthern Blocks

Seismic Survey Act/Plan Plan Actual % Total Days 152d 168d 111% Total Cost $39.5m $39.8m 101%

PPL 239 (Highlands 2013) Seismic Survey Act/Plan

Plan Actual % Total Days 69d 80d 116% Total Cost $15.1m $15.9m 105%

RIMPL continues to perform IRAs for Talisman Energy for their PNG exploration program.

8 CONCLUSIONS 1. While project QRA has limitations, it has utility where:

• It is being used on projects that have enough similarities to others previously completed that sufficient informed expert opinion is available to provide consensus time and cost range and risk information; and

• It is being used at a stage of project development where enough definition is available for meaningful QRA to be performed, particularly Schedule Risk Analysis.

2. The quality of results produced from IRAs or IRRAs depends, like project success, on a number of elements being done well:

• The practitioners performing the work must be capable and experienced and using good tools. • The inputs to the analysis – schedule, estimate and risk register – must be technically good

and aligned to each other. • The project / study team and other stakeholders must be capable and experienced so that the

schedule and cost ranges and reviewed risks are built on well-founded, consensus values. 3. IRA & IRRA methodologies have a rigorous foundation based on using the Integrated Master

Control Schedule (IMCS) or suitably equivalent comprehensive and technically acceptable project schedule.

4. All IRA / IRRA inputs are transparent and auditable, available for review with the final report. If any attempt is made to manipulate or distort the results, the inputs causing the distortion should be explicitly reported in the Assumptions and Exclusions section of the report or be auditable from the analysis inputs.

5. IRRA methodology can be used to optimise risk treatments, operational availability and reliability, depreciation and taxation arrangements to maximise project objectives.

6. Use of IRA is scalable: it has been used on projects of only a few million dollars up to billions of dollars.

7. Overruns or failures of complex major and mega projects are not inevitable; using IRA and especially IRRA, the probability of construction and operational success of an asset can be materially improved.



9 REFERENCES i Hollmann, John K et al, 2012, “Total Cost Management Framework: An Integrated Approach to Portfolio, Program and Project Management”, First Edition, Revised, AACE International. ii Flyvbjerg, Bent, 2013, “Mega Delusional: The Curse of the Megaproject”, New Scientist, December,

pp 28-29 iii Hollmann, John K, 2009, “AACE International Recommended Practice No. 42R-08 Risk Analysis and

Contingency Determination using Parametric Estimating” iv Cropley, Colin & Dodds, Matthew, April 2014 RiskIntegral Newsletter “Integration of Project Cost &

Schedule Risk Analyses”, www.riskinteg.com/newsletters/2014_04_newsletter.aspx v Raydugin, Yuri, 2013, “Project Risk Management – Essential Methods for Project Teams and

Decision Makers”, Chapter 12 – Applications of Monte Carlo Methods in Project Risk Management – Integrated Cost and Schedule Risk Analysis, Wiley

vi Cropley, Colin, September 2013, “Assessing the profit and loss balance in capital projects by risk analysis”, Australian Risk Management Today, 2013. No 34, Lexis Nexis (copy available from Author)

vii Galway, Lionel, February 2004, “Quantitative Risk Analysis for Project Management – A Critical Review”, RAND Corporation RAND Working Paper WR-112-RC, February 2004, Page 19

viii Thaheem, M.J. et al, July 2012, “A Review of Quantitative Analysis Techniques for Construction Project Risk Management”, Proceedings of Creative Construction Conference, pp656-666, Budapest

ix Hartke, Rafael, December 2012, “The Application of Quantitative Risk Analysis Techniques in the Oil and Gas Industry”, Oil & Gas Monitor

x Hulett, D.T., 2009, “Practical Schedule Risk Analysis”, Gower xi Hulett, D.T., 2011, “Integrated Cost-Schedule Risk Analysis”, Gower xii Cropley, C., April 2006, “Forecast your Project Completion Timing Better using Schedule Risk

Analysis”, myPrimavera06 Conference, Canberra, Australia (copy available from Author) xiii Cropley, C., April 2006, “Forecast your Project Completion Timing Better using Schedule Risk

Analysis”, myPrimavera06 Conference, Canberra, Australia (copy available from Author) xiv Cropley, C. et al, June 2014, “Planning and Estimating Risky Projects: Oil and Gas Exploration”,

AACE International Annual Meeting, New Orleans

http://www.riskinteg.com/newsletters/2014_04_newsletter.aspx

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