Integrated Risk-Based and Economic-Based (IRBEB) Methodology for Selection of Project Alternatives

Selection of a most optimal project alternative in early phases of project development is paramount for overall project success. A standard practice is to make the selection based on economic considerations that overlook risk exposure of a selected alternative. Standalone risk evaluation of alternatives cannot ensure that a most optimal alternative is selected either as economic considerations may be overlooked. Moreover, both economic-based and risk-based alternative's selection methodologies cannot guaranty that all viable alternatives have been considered. This chapter introduces integrated risk-based and economic-based (IRBEB) alternative's selection methodology that includes an algorithm to generate a comprehensive set of all viable project alternatives to choose from.

Handling Uncertainty in Project Management and Business Development

This chapter reports a study into the management of uncertainty in the disciplines of business development and project management. The first part of the chapter analyses the disciplines by looking at the process, the planning, uncertainty and risk and the measurement of success. Based on our analysis of these two disciplines, we conclude that they differ substantially in the perception and handling of uncertainty and how this is included in the overall process. We found that business development uses additional methods, for example scenario planning, to manage the uncertainty that is inherent to the business development process. The second part of the chapter reports an explorative study into the potential application of scenario planning in project management. This study shows that scenario planning may benefit project management in creating a shared understanding of the project as well as the provision and consideration of different information. In the planning processes of the project, this information is considered useful in risk management and milestone planning.

Integration of Project Risk Management (PRM) into Enterprise Risk Management (ERM)

Integrating Project Risk Management (PRM) into Enterprise Risk Management (ERM) is a multi-year journey and has long term value. ERM provides a holistic view to existing risks and overcomes the disadvantage of risk being managed in silos in PRM. The main aim of integration of two approaches is to mange risk from both macro and micro perspectives by exploiting opportunities while balancing the downside of risk. The chapter provides a fundamental understanding of what ERM is and its components and shows how PRM is a subset of ERM. Issues and opportunities in integrating PRM into ERM are discussed using real life examples. Furthermore, the chapter brings attention to formal and informal ways of integration and concludes by making six recommendations.

Improving Objectivity in Project Risk Management

Project risk management requires subject matter expertise to identify and assess relevant and sometimes unique risks. Insufficient experience data and fast evolvement of emerging risks in the field of project risk management make qualitative analysis more prevalent in project risk assessment. Therefore, expert knowledge and experience play a critical role in project risk management. On the other hand, the resulting subjectivity often leads to inconsistent risk assessment. Undesired consequences include cost underestimation, risk underestimation and resource misallocation. This chapter discusses the causes and adverse impact of subjectivity in project risk management and methods to improve objectivity. It covers common human biases in project risk management and introduces measures to improve objectivity in project risk management using expert diversification, risk culture, process mining, fuzzy logic models, and back testing.

Getting to Grips with Behavioural Bias

The author discusses whether the impressive progress achieved by technical advances in project management have been stalled by failure to make similar advances in addressing the Human Factors. This imbalance may, he believes, be contributing to challenges being widely experienced in dealing with a residual ‘rump' of workplace safety incidents, for example. He argues that ever tightening the controls and micro-managing workplace behaviour or pursuing zero safety incidents can be counterproductive both for compliance and for the bottom line. Professional, regulatory and standards bodies increasingly emphasise the importance of employee participation and risk leadership in achieving the mutual trust and respect necessary for objectives to be fully realised. He advises that project managers need to appreciate distinctive and deeply rooted individual differences in the behavioural dispositions of individual employees and proposes that readily available assessment techniques that address these issues should be added to their toolkit.

Complexity Theory and System Dynamics for Project Risk Management

As a consequence of the consensus that projects are growing in complexity from ever ambitious goals there is a perpetual search for methods aimed at pinpointing and describing the source of complexity with the objective of subsequently reducing uncertainty, managing risk and improving project performance. An area of study that has engaged enquiring minds for over fifty years but has not yet been accepted into mainstream project management is the study and application of systems thinking and system dynamics. The purpose of this chapter is to promulgate the view that the mapping of projects as systems should be re-examined as a means of articulating and responding to complexity. The chapter examines general systems theory, systems thinking and systems dynamics with examples of causal loop diagrams as an aid to describe and respond to risk exposure. It includes simple causal loop diagrams as a means of illustrating how risks may be identified and addressed. The emphasis is on seeing the ‘big picture' to avoid gaps and omissions in the management of risk and uncertainty.

Risk Based Contingency in Project Cost Estimates

Project Cost estimation is carried out for making investment decisions. Cost estimation is carried out during different phases of the project. Contingency in cost estimation is an important factor before releasing final cost estimate for formal approval of the project by senior management. Major Petrochemical companies use risk-based contingency calculation instead of following a standard practice of adding a certain fixed percentage to the final project cost estimate. In this chapter, cost contingency calculation methodology has been elaborated by conducting case study of a sample project. The methodology described here uses famous tool of Monte Carlo for simulation. It is pragmatic approach to calculate required cost contingency in the project cost estimate, based upon the particular project risks as compared to simply following rule of adding fixed percentage of the estimate as cost contingency in overall project cost estimate.

The Case for Truly Integrated Cost and Schedule Risk Analysis

Time and cost outcomes of large and complex projects are forecast poorly across all sectors. Over recent years, Monte Carlo (MC) simulation has increasingly been adopted to forecast project time and cost outcomes more realistically. It is recognised that the simultaneous analysis of time and cost impacts makes sense as a modelling objective, due to the well-known relationship of time and money in projects. But most MC practitioners advocate the use of Schedule Risk Analysis (SRA) feeding into Cost Risk Analysis (CRA) because they believe it is too hard to perform Integrated Cost & Schedule Risk Analysis (IRA) realistically. This chapter elaborates an IRA methodology that produces realistic forecasts without relying on questionable assumptions and enables identification and ranking of all sources of cost uncertainty for risk optimisation as part of the process. It also describes an extension of IRA methodology to include assessment of the assets produced by the project as well as the project itself, thus enabling the analysis of business risks as well as project risks.

Modeling Risk and Opportunity Reality

The author of this chapter worked for 42 years in the construction industry. Employed by a company with great leadership, vision and values. The author was able to evolve from the pencil and paper applications to the latest in data acquisition, analysis and modeling. Simulation modeling and business science were successfully applied to many business functions with significant results in predicting risk and uncertainty ranges before bids, projects and investment decisions were complete. Included in this chapter are samples of Strategic Bidding, Cost (Range) Estimating, Scheduling, Project Management/Control, and Corporate Resource Management. Business processes can be measured for Stability, Capability and Predictability. Consistently balancing decisions within the upper and lower control limits, using valid reasoning for taking advantage of risk or opportunity will usually guarantee long term success. Reality can be a friend if it is measured and applied with the right expectations. Modeling creates the environment for tapping into substance and understanding.

A Fuzzy Group Decision-Making Approach to Construction Project Risk Management

Implementation of the risk management concepts into construction practice may enhance the performance of project by taking appropriate response actions against identified risks. This research proposes a multi-criteria group decision making approach for the evaluation of different alternative response scenarios. To take into account the uncertainties inherent in evaluation process, fuzzy logic is integrated into the revaluation process. To evaluate alternative response scenarios, first the collective group weight of each criterion is calculated considering opinions of a group consisted of five experts. As each expert has its own ideas, attitudes, knowledge and personalities, different experts will give their preferences in different ways. Fuzzy preference relations are used to unify the opinions of different experts. After computation of collective weights, the best alternative response scenario is selected by the use of proposed fuzzy group decision making methodology which aggregates opinions of different experts. To evaluate the performance of the proposed methodology, it is implemented in a real project and the best alternative responses scenario is selected for one of the identified risks.