project risk management phd thesis

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Risk-based Framework for Management of Construction Projects

Well-developed risk management tools provide critical support for successful delivery of construction projects. Considerable research has been conducted towards integration of risk management in front-end planning and in execution phases of this class of projects. The accuracy of these tools relies heavily on their respective assumptions and on the data used in their application. Consideration of risk in these tools utilizes two types of data: actual past records and estimated future data related to completion of projects under consideration. The literature reveals that most published work in this area utilized these data either in bidding phase or in one of individual project execution phases to minimize the negative impact of risk on project cost and duration at completion. However, there is a lack of a comprehensive framework that employs both types of data in different phases of construction projects. This prevents construction practitioners from implementing an efficient risk management program. In this research, a new risk-based framework is developed, addressing limitations of existing models for different management functions over project lifecycle. The developed framework employs past performance data of construction organizations and projects in the bidding phase for risk maturity evaluation, contingency estimation, markup estimation, planning and scheduling, and progress reporting. The framework has five developed models. The first introduces a decision support model for risk maturity evaluation of construction organizations to identify their strengths and weaknesses in risk management processes, employing the Analytic Network Process (ANP) and fuzzy set theory. It enables construction organizations to assess and continuously improve their risk management capabilities. The second model introduces a new cost contingency estimation model considering correlations among project cost items, subjectively and objectively. It is also capable of modeling project cost contingency with and without the use of Monte Carlo simulation, which is deemed particularly useful when using subjective correlations. The third model introduces new pattern recognition techniques for estimating project markup. It utilizes Multiple Regression (MR), Artificial Neural Network (ANN) and Adaptive Nero-Fuzzy Inference System (ANFIS) techniques for that purpose, considering five factors: need for work, job uncertainty, job complexity, market condition, and owner capability. The fourth model introduces a newly developed multi-objective optimization model for scheduling of repetitive projects under uncertainty. The model considers the estimated cost contingency and the project markup in the total project cost and conducts, simultaneously, trade-offs between project duration, project cost, crew work interruptions, and interruption costs. It safeguards against assignment of unnecessary costly resources and provides a reliable project baseline. The fifth model presents a newly developed risk-based earned duration management model (RBEDM) that utilizes the generated project baseline in forecasting project duration at completion, considering critical activities only and their associated risk factors. It introduces a new risk adjustment factor (RAFcr) that quantifies the impact of future uncertainties associated with critical activities in estimating project duration at completion. This unique aspect of the developed model addresses the main drawback of earned duration management (EDM) its reliance on past performance data only. It also assists project managers in estimating more accurate and realistic required time to project completion.

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• Published: 24 May 2024

Beyond probability-impact matrices in project risk management: A quantitative methodology for risk prioritisation

• F. Acebes   ORCID: orcid.org/0000-0002-4525-2610 1 ,
• J. M. González-Varona 2 ,
• A. López-Paredes 2 &
• J. Pajares 1  

Humanities and Social Sciences Communications volume  11 , Article number:  670 ( 2024 ) Cite this article

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• Business and management

The project managers who deal with risk management are often faced with the difficult task of determining the relative importance of the various sources of risk that affect the project. This prioritisation is crucial to direct management efforts to ensure higher project profitability. Risk matrices are widely recognised tools by academics and practitioners in various sectors to assess and rank risks according to their likelihood of occurrence and impact on project objectives. However, the existing literature highlights several limitations to use the risk matrix. In response to the weaknesses of its use, this paper proposes a novel approach for prioritising project risks. Monte Carlo Simulation (MCS) is used to perform a quantitative prioritisation of risks with the simulation software MCSimulRisk. Together with the definition of project activities, the simulation includes the identified risks by modelling their probability and impact on cost and duration. With this novel methodology, a quantitative assessment of the impact of each risk is provided, as measured by the effect that it would have on project duration and its total cost. This allows the differentiation of critical risks according to their impact on project duration, which may differ if cost is taken as a priority objective. This proposal is interesting for project managers because they will, on the one hand, know the absolute impact of each risk on their project duration and cost objectives and, on the other hand, be able to discriminate the impacts of each risk independently on the duration objective and the cost objective.

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Introduction.

The European Commission ( 2023 ) defines a project as a temporary organizational structure designed to produce a unique product or service according to specified constraints, such as time, cost, and quality. As projects are inherently complex, they involve risks that must be effectively managed (Naderpour et al. 2019 ). However, achieving project objectives can be challenging due to unexpected developments, which often disrupt plans and budgets during project execution and lead to significant additional costs. The Standish Group ( 2022 ) notes that managing project uncertainty is of paramount importance, which renders risk management an indispensable discipline. Its primary goal is to identify a project’s risk profile and communicate it by enabling informed decision making to mitigate the impact of risks on project objectives, including budget and schedule adherence (Creemers et al. 2014 ).

Several methodologies and standards include a specific project risk management process (Axelos, 2023 ; European Commission, 2023 ; Project Management Institute, 2017 ; International Project Management Association, 2015 ; Simon et al. 1997 ), and there are even specific standards and guidelines for it (Project Management Institute, 2019 , 2009 ; International Organization for Standardization, 2018 ). Despite the differences in naming each phase or process that forms part of the risk management process, they all integrate risk identification, risk assessment, planning a response to the risk, and implementing this response. Apart from all this, a risk monitoring and control process is included. The “Risk Assessment” process comprises, in turn, risk assessments by qualitative methods and quantitative risk assessments.

A prevalent issue in managing project risks is identifying the significance of different sources of risks to direct future risk management actions and to sustain the project’s cost-effectiveness. For many managers busy with problems all over the place, one of the most challenging tasks is to decide which issues to work on first (Ward, 1999 ) or, in other words, which risks need to be paid more attention to avoid deviations from project objectives.

Given the many sources of risk and the impossibility of comprehensively addressing them, it is natural to prioritise identified risks. This process can be challenging because determining in advance which ones are the most significant factors, and how many risks merit detailed monitoring on an individual basis, can be complicated. Any approach that facilitates this prioritisation task, especially if it is simple, will be welcomed by those willing to use it (Ward, 1999 ).

Risk matrices emerge as established familiar tools for assessing and ranking risks in many fields and industry sectors (Krisper, 2021 ; Qazi et al. 2021 ; Qazi and Simsekler, 2021 ; Monat and Doremus, 2020 ; Li et al. 2018 ). They are now so commonplace that everyone accepts and uses them without questioning them, along with their advantages and disadvantages. Risk matrices use the likelihood and potential impact of risks to inform decision making about prioritising identified risks (Proto et al. 2023 ). The methods that use the risk matrix confer higher priority to those risks in which the product of their likelihood and impact is the highest.

However, the probability-impact matrix has severe limitations (Goerlandt and Reniers, 2016 ; Duijm, 2015 ; Vatanpour et al. 2015 ; Ball and Watt, 2013 ; Levine, 2012 ; Cox, 2008 ; Cox et al. 2005 ). The main criticism levelled at this methodology is its failure to consider the complex interrelations between various risks and use precise estimates for probability and impact levels. Since then, increasingly more academics and practitioners are reluctant to resort to risk matrices (Qazi et al. 2021 ).

Motivated by the drawbacks of using risk matrices or probability-impact matrices, the following research question arises: Is it possible to find a methodology for project risk prioritisation that overcomes the limitations of the current probability-impact matrix?

To answer this question, this paper proposes a methodology based on Monte Carlo Simulation that avoids using the probability-impact matrix and allows us to prioritise project risks by evaluating them quantitatively, and by assessing the impact of risks on project duration and the cost objectives. With the help of the ‘MCSimulRisk’ simulation software (Acebes et al. 2024 ; Acebes et al. 2023 ), this paper determines the impact of each risk on project duration objectives (quantified in time units) and cost objectives (quantified in monetary units). In this way, with the impact of all the risks, it is possible to establish their prioritisation based on their absolute (and not relative) importance for project objectives. The methodology allows quantified results to be obtained for each risk by differentiating between the project duration objective and its cost objective.

With this methodology, it also confers the ‘Risk Assessment’ process cohesion and meaning. This process forms part of the general Risk Management process and is divided into two subprocesses: qualitative and quantitative risk analyses (Project Management Institute, 2017 ). Although Monte Carlo simulation is widely used in project risk assessments (Tong et al. 2018 ; Taroun, 2014 ), as far as we know, the literature still does not contain references that use the data obtained in a qualitative analysis (data related to the probability and impact of each identified risk) to perform a quantitative risk analysis integrated into the project model. Only one research line by A. Qazi (Qazi et al. 2021 ; Qazi and Dikmen, 2021 ; Qazi and Simsekler, 2021 ) appears, where the authors propose a risk indicator with which they determine the level of each identified risk that concerns the established threshold. Similarly, Krisper ( 2021 ) applies the qualitative data of risk factors to construct probability functions, but once again falls in the error of calculating the expected value of the risk for risk prioritisation. In contrast, the novelty proposed in this study incorporates into the project simulation model all the identified risks characterised by their probability and impact values, as well as the set of activities making up the project.

In summary, instead of the traditional risk prioritisation method to qualitatively estimate risk probabilities and impacts, we model probabilities and impacts (duration and cost) at the activity level as distribution functions. When comparing both methods (traditional vs. our proposal), the risk prioritisation results are entirely different and lead to a distinct ranking.

From this point, and to achieve our purpose, the article comes as follows. Literature review summarises the relevant literature related to the research. Methodology describes the suggested methodology. Case study presents the case study used to show how to apply the presented method before discussing the obtained results. Finally, Conclusions draws conclusions about the proposed methodology and identifies the research future lines that can be developed from it.

Literature review

This section presents the literature review on risk management processes and probability-impact matrices to explain where this study fits into existing research. This review allows us to establish the context where our proposal lies in integrated risk management processes. Furthermore, it is necessary to understand the reasons for seeking alternatives to the usual well-known risk matrices.

Risk management methodologies and standards

It is interesting to start with the definition of ‘Risk’ because it is a term that is not universally agreed on, even by different standards and norms. Thus, for example, the International Organization for Standardization ( 2018 ) defines it as “the effect of uncertainty on objectives”, while the Project Management Institute ( 2021 ) defines it as “an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives”. This paper adopts the definition of risk proposed by Hillson ( 2014 ), who uses a particular concept: “risk is uncertainty that matters”. It matters because it affects project objectives and only the uncertainties that impact the project are considered a ‘risk’.

Other authors (Elms, 2004 ; Frank, 1999 ) identify two uncertainty categories: aleatoric, characterised by variability and the presence of a wide range of possible values; epistemic, which arises due to ambiguity or lack of complete knowledge. Hillson ( 2014 ) classifies uncertainties into four distinct types: aleatoric, due to the reliability of activities; stochastic, recognised as a risk event or a possible future event; epistemic, also due to ambiguity; ontological, that which we do not know (black swan). Except for ontological uncertainty, which cannot be modelled due to absolute ignorance of risk, the other identified uncertainties are incorporated into our project model. For this purpose, the probability and impact of each uncertainty are modelled as distribution functions to be incorporated into Monte Carlo simulation.

A risk management process involves analysing the opportunities and threats that can impact project objectives, followed by planning appropriate actions for each one. This process aims to maximise the likelihood of opportunities occurring and to minimise the likelihood of identified threats materialising.

Although it is true that different authors have proposed their particular way of understanding project risk management (Kerzner, 2022 ; Hillson and Simon, 2020 ; Chapman and Ward, 2003 ; Chapman, 1997 ), we wish to look at the principal methodologies, norms and standards in project management used by academics and practitioners to observe how they deal with risk (Axelos, 2023 ; European Commission, 2023 ; International Organization for Standardization, 2018 ; Project Management Institute, 2017 ; International Project Management Association, 2015 ) (Table 1 ).

Table 1 shows the main subprocesses making up the overall risk management process from the point of view of each different approach. All the aforementioned approaches contain a subprocess related to risk assessment. Some of these approaches develop the subprocess by dividing it into two parts: qualitative assessment and quantitative assessment. Individual project risks are ranked for further analyses or action with a qualitative assessment by evaluating the probability of their occurrence and potential impact. A quantitative assessment involves performing a numerical analysis of the joint effect of the identified individual risks and additional sources of uncertainty on the overall project objectives (Project Management Institute, 2017 ). In turn, all these approaches propose the probability-impact or risk matrix as a technique or tool for prioritising project risks.

Within this framework, a ranking of risks by a quantitative approach applies as opposed to the qualitative assessment provided by the risk matrix. To do so, we use estimates of the probability and impact associated with each identified risk. The project model includes these estimates to determine the absolute value of the impact of each risk on time and cost objectives.

Probability-impact matrix

The risk matrix, or probability-impact matrix, is a tool included in the qualitative analysis for risk management and used to analyse, visualise and prioritise risks to make decisions on the resources to be employed to combat them (Goerlandt and Reniers, 2016 ; Duijm, 2015 ). Its well-established use appears in different sectors, ranging from the construction industry (Qazi et al. 2021 ), oil and gas industries (Thomas et al. 2014 ), to the healthcare sector (Lemmens et al. 2022 ), engineering projects (Koulinas et al. 2021 ) and, of course, project management (International Organization for Standardization, 2019 ; Li et al. 2018 ).

In a table, the risk matrix represents the probability (usually on the vertical axis of the table) and impact (usually on the horizontal axis) categories (Ale et al. 2015 ). These axes are further divided into different levels so that risk matrices of 3×3 levels are found with three levels set for probability and three others to define impact, 5 × 5, or even more levels (Duijm, 2015 ; Levine, 2012 ; Cox, 2008 ). The matrix classifies risks into different risk categories, normally labelled with qualitative indicators of severity (often colours like “Red”, “Yellow” and “Green”). This classification combines each likelihood level with every impact level in the matrix (see an example of a probability-impact matrix in Fig. 1 ).

Probability – impact matrix. An example of use.

There are three different risk matrix typologies based on the categorisation of likelihood and impact: qualitative, semiquantitative, and quantitative. Qualitative risk matrices provide descriptive assessments of probability and consequence by establishing categories as “low,” “medium” or “high” (based on the matrix’s specific number of levels). In contrast, semiquantitative risk matrices represent the input categories by ascending scores, such as 1, 2, or 3 (in a 3×3 risk matrix), where higher scores indicate a stronger impact or more likelihood. Finally, in quantitative risk matrices, each category receives an assignment of numerical intervals corresponding to probability or impact estimates. For example, the “Low” probability level is associated with a probability interval [0.1 0.3] (Li et al. 2018 ).

Qualitative matrices classify risks according to their potential hazard, depending on where they fit into the matrix. The risk level is defined by the “colour” of the corresponding cell (in turn, this depends on the probability and impact level), with risks classified with “red” being the most important and the priority ones to pay attention to, but without distinguishing any risks in the different cells of the same colour. In contrast, quantitative risk matrices allow to classify risks according to their risk level (red, yellow, or green) and to prioritise each risk in the same colour by indicating which is the most important. Each cell is assigned a colour and a numerical value, and the product of the value is usually assigned to the probability level and the value assigned to the impact level (Risk = probability × impact).

Risk matrix use is frequent, partly due to its simple application and easy construction compared to alternative risk assessment methods (Levine, 2012 ). Risk matrices offer a well-defined structure for carrying out a methodical risk assessment, provide a practical justification for ranking and prioritising risks, visually and attractively inform stakeholders, among other reasons (Talbot, 2014 ; Ball and Watt, 2013 ).

However, many authors identify problems in using risk matrices (Monat and Doremus, 2020 ; Peace, 2017 ; Levine, 2012 ; Ni et al. 2010 ; Cox, 2008 ; Cox et al. 2005 ), and even the International Organization for Standardization ( 2019 ) indicates some drawbacks. The most critical problems identified in using risk matrices for strategic decision-making are that risk matrices can be inaccurate when comparing risks and they sometimes assign similar ratings to risks with significant quantitative differences. In addition, there is the risk of giving excessively high qualitative ratings to risks that are less serious from a quantitative perspective. This can lead to suboptimal decisions, especially when threats have negative correlations in frequency and severity terms. Such lack of precision can result in inefficient resource allocation because they cannot be based solely on the categories provided by risk matrices. Furthermore, the categorisation of the severity of consequences is subjective in uncertainty situations, and the assessment of probability, impact and risk ratings very much depends on subjective interpretations, which can lead to discrepancies between different users when assessing the same quantitative risks.

Given this background, several authors propose solutions to the posed problems. Goerlandt and Reniers ( 2016 ) review previous works that have attempted to respond to the problems identified with risk matrices. For example, Markowski and Mannan ( 2008 ) suggest using fuzzy sets to consider imprecision in describing ordinal linguistic scales. Subsequently, Ni et al. ( 2010 ) propose a methodology that employs probability and consequence ranks as independent score measures. Levine ( 2012 ) puts forward the use of logarithmic scales on probability and impact axes. Menge et al. (2018) recommend utilising untransformed values as scale labels due to experts’ misunderstanding of logarithmic scales. Ruan et al. ( 2015 ) suggest an approach that considers decision makers’ risk aversion by applying the utility theory.

Other authors, such as Duijm ( 2015 ), propose a continuous probability consequence diagram as an alternative to the risk matrix, and employing continuous scales instead of categories. They also propose utilising more comprehensive colour ranges in risk matrices whenever necessary to prioritise risks and to not simply accept them. In contrast, Monat and Doremus ( 2020 ) put forward a new risk prioritisation tool. Alternatively, Sutherland et al. ( 2022 ) suggest changing matrix size by accommodating cells’ size to the risk’s importance. Even Proto et al. ( 2023 ) recommend avoiding colour in risk matrices so that the provided information is unbiased due to the bias that arises when using coloured matrices.

By bearing in mind the difficulties presented by the results offered by risk matrices, we propose a quantitative method for risk prioritisation. We use qualitative risk analysis data by maintaining the estimate of the probability of each risk occurring and its potential impact. Nevertheless, instead of entering these data into the risk matrix, our project model contains them for Monte Carlo simulation. As a result, we obtain a quantified prioritisation of each risk that differentiates the importance of each risk according to the impact on cost and duration objectives.

Methodology

Figure 2 depicts the proposed method for prioritising project risks using quantitative techniques. At the end of the process, and with the prioritised risks indicating the absolute value of the impact of each risk on the project, the organisation can efficiently allocate resources to the risks identified as the most critical ones.

Quantitative Risk Assessment Flow Chart.

The top of the diagram indicates the risk phases that belong to the overall risk management process. Below them it reflects the steps of the proposed model that would apply in each phase.

The first step corresponds to the project’s “ risk identification ”. Using the techniques or tools established by the organisation (brainstorming, Delphi techniques, interviews, or others), we obtain a list of the risks ( R ) that could impact the project objectives (Eq. 1 ), where m is the number of risks identified in the project.

Next we move on to the “ risk estimation ” phase, in which a distribution function must be assigned to the probability that each identified risk will appear. We also assign the distribution function associated with the risk’s impact. Traditionally, the qualitative risk analysis defines semantic values (low, medium, high) to assign a level of probability and risk impact. These semantic values are used to evaluate the risk in the probability-impact matrix. Numerical scales apply in some cases, which help to assign a semantic level to a given risk (Fig. 3 ).

Source: Project Management Institute ( 2017 ).

Our proposed model includes the three uncertainty types put forward by Hillson ( 2014 ), namely aleatoric, stochastic and epistemic, to identify and assess different risks. Ontological uncertainty is not considered because it goes beyond the limits of human knowledge and cannot, therefore, be modelled (Alleman et al. 2018a ).

A risk can have aleatoric uncertainty as regards the probability of its occurrence, and mainly for its impact if its value can fluctuate over a set range due to its variability. This aleatoric risk uncertainty can be modelled using a probability distribution function (PDF), exactly as we do when modelling activity uncertainty (Acebes et al. 2015 , 2014 ). As the risk management team’s (or project management team’s) knowledge of the project increases, and as more information about the risk becomes available, the choice of the PDF (normal, triangular, beta, among others) and its parameters become more accurate.

A standard definition of risk is “an uncertain event that, if it occurs, may impact project objectives” (Project Management Institute, 2017 ). A risk, if defined according to the above statement, perfectly matches the stochastic uncertainty definition proposed by Hillson ( 2014 ). Moreover, one PDF that adequately models this type of uncertainty is a Bernoulli distribution function (Vose, 2008 ). Thus for deterministic risk probability estimates (the same as for risk impact), we model this risk (probability and impact) with a Bernoulli-type PDF that allows us to introduce this type of uncertainty into our simulation model.

Finally, epistemic uncertainties remain to be modelled, such as those for which we do not have absolute information about and that arise from a lack of knowledge (Damnjanovic and Reinschmidt, 2020 ; Alleman et al. 2018b ). In this case, risks (in likelihood and impact terms) are classified into different levels, and all these levels are assigned a numerical scale (as opposed to the methodology used in a qualitative risk analysis, where levels are classified with semantic values: “high”, “medium” and “low”).

“ Epistemic uncertainty is characterised by not precisely knowing the probability of occurrence or the magnitude of a potential impact. Traditionally, this type of risk has been identified with a qualitative term: “Very Low”, “Low”, “Medium”, “High” and “Very High” before using the probability-impact matrix. Each semantic category has been previously defined numerically by identifying every numerical range with a specific semantic value (Bae et al. 2004 ). For each established range, project managers usually know the limits (upper and lower) between which the risk (probability or impact) can occur. However, they do not certainly know the value it will take, not even the most probable value within that range. Therefore, we employ a uniform probability function to model epistemic uncertainty (i.e., by assuming that the probability of risk occurrence lies within an equiprobable range of values). Probabilistic representations of uncertainty have been successfully employed with uniform distributions to characterise uncertainty when knowledge is sparse or absent (Curto et al. 2022 ; Vanhoucke, 2018 ; Helton et al. 2006 ).

The choice of the number and range of each level should be subject to a thorough analysis and consideration by the risk management team. As each project is unique, there are ranges within which this type of uncertainty can be categorised. Different ranges apply to assess likelihood and impact. Furthermore for impact, further subdivision helps to distinguish between impact on project duration and impact on project costs. For example, when modelling probability, we can set five probability levels corresponding to intervals: [0 0.05], [0.05 0.2], [0.2 0.5], and so on. With the time impact, for example, on project duration, five levels as follows may apply: [0 1], [1 4], [4 12], …. (measured in weeks, for example).

Modelling this type of uncertainty requires the risk management team’s experience, the data stored on previous projects, and constant consultation with project stakeholders. The more project knowledge available, the more accurate the proposed model is for each uncertainty, regardless of it lying in the number of intervals, their magnitude or the type of probability function (PDF) chosen to model that risk.

Some authors propose using uniform distribution functions to model this type of epistemic uncertainty because it perfectly reflects lack of knowledge about the expected outcome (Eldosouky et al. 2014 ; Vose, 2008 ). On the contrary, others apply triangular functions, which require more risk knowledge (Hulett, 2012 ). Following the work by Curto et al. ( 2022 ), we employ uniform distribution functions.

As a result of this phase, we obtain the model and the parameters that model the distribution functions of the probability ( P ) and impact ( I ) of each identified risk in the previous phase (Eq. 2 ).

Once the risks identified in the project have been defined and their probabilities and impacts modelled, we move on to “ quantitative risk prioritisation ”. We start by performing MCS on the planned project model by considering only the aleatoric uncertainty of activities. In this way, we learn the project’s total duration and cost, which is commonly done in a Monte Carlo analysis. In Monte Carlo Methods (MCS), expert judgement and numerical methods are combined to generate a probabilistic result through simulation routine (Ammar et al. 2023 ). This mathematical approach is noted for its ability to analyse uncertain scenarios from a probabilistic perspective. MCS have been recognised as outperforming other methods due to their accessibility, ease of use and simplicity. MCS also allow the analysis of opportunities, uncertainties, and threats (Al-Duais and Al-Sharpi, 2023 ). This technique can be invaluable to risk managers and helpful for estimating project durations and costs (Ali Elfarra and Kaya, 2021 ).

As inputs to the simulation process, we include defining project activities (duration, cost, precedence relationship). We also consider the risks identified in the project, which are those we wish to prioritise and to obtain a list ordered by importance (according to their impact on not only duration, but also on project cost). The ‘MCSimulRisk’ software application (Acebes, Curto, et al. 2023 ; Acebes, De Antón, et al. 2023 ) allows us to perform MCS and to obtain the main statistics that result from simulation (including percentiles) that correspond to the total project duration ( Tot_Dur ) and to its total cost ( Tot_Cost ) (Eq. 3 ).

Next, we perform a new simulation by including the first of the identified risks ( R 1 ) in the project model, for which we know its probability ( P 1 ) and its Impact ( I 1 ). After MCS, we obtain the statistics corresponding to this simulation ([ Tot_Dur 1 Tot_Cost 1 ]). We repeat the same operation with each identified risk ( R i , i  =  1, …, m ) and obtain the main statistics corresponding to each simulation (Eq. 4 ).

Once all simulations (the same number as risks) have been performed, we must choose a confidence percentile to calculate risk prioritisation (Rezaei et al. 2020 ; Sarykalin et al. 2008 ). Given that the total duration and cost results available to us, obtained by MCS, are stochastic and have variability (they are no longer constant or deterministic), we must choose a percentile (α) that conveys the risk appetite that we are willing to assume when calculating. Risk appetite is “ the amount and type of risk that an organisation is prepared to pursue, retain or take ” (International Organization for Standardization, 2018 ).

A frequently employed metric for assessing risk in finance is the Value at Risk (VaR) (Caron, 2013 ; Caron et al. 2007 ). In financial terms, it is traditional to choose a P95 percentile as risk appetite (Chen and Peng, 2018 ; Joukar and Nahmens, 2016 ; Gatti et al. 2007 ; Kuester et al. 2006 ; Giot and Laurent, 2003 ). However in project management, the P80 percentile is sometimes chosen as the most appropriate percentile to measure risk appetite (Kwon and Kang, 2019 ; Traynor and Mahmoodian, 2019 ; Lorance and Wendling, 2001 ).

Finally, after choosing the risk level we are willing to assume, we need to calculate how each risk impacts project duration ( Imp_D Ri ) and costs ( Imp_C Ri ). To do so, we subtract the original value of the total project expected duration and costs (excluding all risks) from the total duration and costs of the simulation in which we include the risk we wish to quantify (Eq. 5 ).

Finally, we present these results on two separate lists, one for the cost impact and one for the duration impact, by ranking them according to their magnitude.

In this section, we use a real-life project to illustrate how to apply the proposed method for quantitative risk prioritisation purposes. For this purpose, we choose an engineering, procurement and construction project undertaken in South America and used in the literature by Votto et al. ( 2020a , 2020b ).

Project description

The project used as an application example consists of the expansion of an industrial facility. It covers a wide spectrum of tasks, such as design and engineering work, procurement of machinery and its components, civil construction, installation of all machinery, as well as commissioning and starting up machines (Votto et al. 2020a , 2020b ).

Table 2 details the parameters that we use to define activities. The project comprises 32 activities, divided into three groups: engineering, procurement and construction (EPC). A fictitious initial activity ( Ai ) and a fictitious final activity ( Af ) are included. We employ triangular distribution functions, whose parameters are the minimum value ( Min ), the most probable value ( Mp ) and the maximum value ( Max ), to model the random duration of activities, expressed as days. We divide the cost of each activity in monetary units into a fixed cost ( FC ), independently of activity duration, and the variable cost ( VC ), which is directly proportional to project duration. As activity duration can vary, and the activity cost increases directly with its duration, the total project cost also exhibits random variations.

Under these conditions, the planned project duration is 300 days and has a planned cost of 30,000 (x1000) monetary units. Figure 4 shows the Planned Value Curve of the project.

Planned value curve of the real-life project.

The next step in the methodology (Fig. 2 ) is to identify the project risks. To do this, the experts’ panel meets, analyses all the project documentation. Based on their personal experience with other similar projects and after consulting all the involved stakeholders, it provides a list of risks (see Table 3 ).

It identifies 11 risks, of which nine have the potential to directly impact the project duration objective (R1 to R9), while six may impact the cost objective (R10 to R15). The risks that might impact project duration and cost have two assigned codes. We identify the project phase and activity on which all the identified risks may have an impact (Table 3 ).

The next step is to estimate the likelihood and impact of the identified risks (qualitative analysis). Having analysed the project and consulted the involved stakeholders, the team determines the project’s different probability and impact levels (duration and cost). The estimation of these ranges depends on the project budget, the estimated project duration, and the team’s experience in assigning the different numerical values to each range. As a result, the project team is able to construct the probability-impact matrix shown in Fig. 5 .

Estimation of the probability and impact ranges.

Each probability range for risk occurrence in this project is defined. Thus for a very low probability (VL), the assigned probability range is between 0 and 3% probability, for a low level (L), the assigned range lies between 3% and 10% probability of risk occurrence, and so on with the other established probability ranges (medium, high, very high).

The different impact ranges are also defined by differentiating between impacts in duration and cost terms. Thus a VL duration impact is between 0 and 5 days, while the same range (VL) in cost is between 0 and 100 (x1000) monetary units. Figure 5 shows the other ranges and their quantification in duration and cost terms.

The combination of each probability level and every impact level coincides in a cell of the risk matrix (Fig. 5 ) to indicate the risk level (“high”, “medium”, and “low”) according to the qualitative analysis. Each cell is assigned a numerical value by prioritising the risks at the same risk level. This work uses the matrix to compare the risk prioritisation results provided by this matrix to those provided by the proposed quantitative method.

A probability and impact value are assigned to each previously identified risk (Table 3 ). Thus, for example, for the risk called “Interruptions in the supply chain”, coded as R3 for impacting activity 13 duration, we estimate an L probability and a strong impact on duration (H). As this same risk might impact the activity 13 cost, it is also coded as R12, and its impact on cost is estimated as L (the probability is the same as in R3; Table 3 ).

Finally, to conclude the proposed methodology and to prioritise the identified risks, we use the “MCSimulRisk” software application by incorporating MCS (in this work, we employ 20,000 iterations in each simulation). Activities are modelled using triangular distribution functions to incorporate project information into the simulation application. Costs are modelled with fixed and variable costs depending on the duration of the corresponding activity. Furthermore, risks (probability and impact) are modelled by uniform distribution functions. Figure 6 depicts the project network and includes the identified risks that impact the corresponding activities.

Network diagram of the project together with the identified risks.

Results and discussion

In order to obtain the results of prioritising the identified risks, we must specify a percentile that determines our risk aversion. This is the measure by which we quantify the risk. Figure 7 graphically justifies the choice of P95 as a risk measure, as opposed to a lower percentile, which corroborates the view in the literature and appears in Methodology . In Fig. 7 , we plot the probability distribution and cumulative distribution functions corresponding to the total project planned cost, together with the cost impact of one of the risks. The impact caused by the risk on the total cost corresponds to the set of iterations whose total cost is higher than that planned (bottom right of the histogram).

Source: MCSimulRisk.

By choosing P95 as VaR, we can consider the impact of a risk on the project in the measure. In this example, for P95 we obtain a total cost value of 3.12 × 10 7 monetary units. Choosing a lower percentile, e.g. P80, means that the value we can obtain with this choice can be considerably lower (3.03 × 10 7 monetary units), and might completely ignore the impact of the risk on the total project cost. However, project managers can choose the percentile that represents their risk aversion.

Once the percentile on which to quantify the risk is chosen, the “MCSimulRisk” application provides us with the desired results for prioritising project risks (Fig. 8 ). For the chosen percentile (P95), which represents our risk appetite for this project, the planned project duration is 323.43 days. In other words, with a 95% probability the planned project will be completed before 323.43 days. Similarly, the P95 corresponding to cost is 30,339 ×1000 monetary units. The application also provides us with the project duration in the first column of Fig. 8 after incorporating all the identified risks (corresponding to a P95 risk appetite) into the planned project. Column 2 of the same figure shows the project cost after incorporating the corresponding risk into the model.

The first column corresponds to the risks identified. Columns Duration_with_Ri and Cost_with_Ri represent the simulation values, including the corresponding risk. Columns Difference_Duration_with_Ri and Difference_Cost_with_Ri represent the difference in duration and cost of each simulation concerning the value obtained for the chosen percentile. Finally, Ranking_Dur and Ranking_Cost represent the prioritisation of risks in duration and cost, respectively.

With the results in the first two columns (total project duration and cost after incorporating the corresponding risks), and by knowing the planned total project duration and cost (without considering risks) for a given percentile (P95), we calculate the values of the following columns in Fig. 8 . Thus column 3 represents the difference between the planned total project duration value (risk-free) and project duration by incorporating the corresponding risk that we wish to quantify. Column 4 prioritises the duration risks by ranking according to the duration that each risk contributes to the project. Column 5 represents the difference between the planned total project cost (risk-free) and the total project cost by incorporating the corresponding risk. Finally, Column 6 represents the ranking or prioritisation of the project risks according to their impact on cost.

To compare the results provided by this methodology in this paper we propose quantitative risk prioritisation, based on MCS. We draw up Table 4 with the results provided by the probability-impact matrix (Fig. 5 ).

The first set of columns in Table 4 corresponds to the implementation of the risk matrix (probability-impact matrix) for the identified risks. The second group of columns represents the prioritisation of risks according to their impact on duration (data obtained from Fig. 8 ). The third group corresponds to the risk prioritisation according to their impact on cost (data obtained from Fig. 8 ).

For the project proposed as an example, we find that risk R3 is the most important one if we wish to control the total duration because it corresponds to the risk that contributes the most duration to the project if it exists. We note that risks R10 to R15 do not impact project duration. If these risks materialise, their contribution to increase (or decrease, as the case may be) project duration is nil.

On the impact on project costs, we note that risk R15 is the most important. It is noteworthy that risk R5 is the fourth most important risk in terms of impact on the total project costs, even though it is initially identified as a risk that impacts project duration. Unlike cost risks (which do not impact the total project duration), the risks that can impact project duration also impact total costs.

We can see that the order of importance of the identified risks differs depending on our chosen method (risk matrix versus quantitative prioritisation). We independently quantify each risk’s impact on the cost and duration objectives. We know not only the order of importance of risks (R3, R5, etc.) but also the magnitude of their impact on the project (which is the absolute delay caused by a risk in duration terms or what is the absolute cost overrun generated by a risk in cost terms). It seems clear that one risk is more important than another, not only because of the estimation of its probability and impact but also because the activity on which it impacts may have a high criticality index or not (probability of belonging to the project’s critical path).

As expected, the contribution to the total duration of the identified risks that impact only cost is zero. The same is not valid for the risks identified to have an impact on duration because the latter also impacts the cost objective. We also see how the risks that initially impact a duration objective are more critical for their impact on cost than others that directly impact the project’s cost (e.g. R5).

Conclusions

The probability-impact matrix is used in project management to identify the risk to which the most attention should be paid during project execution. This paper studies how the risk matrix is adopted by a large majority of standards, norms and methodologies in project management and, at the same time, practitioners and academics recognise it as a fundamental tool in the qualitative risks analysis.

However, we also study how this risk matrix presents particular problems and offers erroneous and contradictory results. Some studies suggest alternatives to its use. Notwithstanding, it continues to be a widely employed tool in the literature by practitioners and academics. Along these lines, with this work we propose an alternative to the probability-impact matrix as a tool to know the most critical risk for a project that can prevent objectives from being fulfilled.

For this purpose, we propose a quantitative method based on MCS, which provides us with numerical results of the importance of risks and their impact on total duration and cost objectives. This proposed methodology offers significant advantages over other risk prioritisation methods and tools, especially the traditional risk matrix. The proposed case study reveals that risk prioritisation yields remarkably different results depending on the selected method, as our findings confirm.

In our case, we obtain numerical values for the impact of risks on total duration and cost objectives, and independently of one another. This result is interesting for project managers because they can focus decision-making on the priority order of risks and the dominant project objective (total duration or total cost) if they do not coincide.

From the obtained results, we find that the risks with an impact on the cost of activities do not influence the total duration result. The risks that impact project duration also impact the total cost target. This impact is more significant than that of a risk that impacts only the activity’s cost. This analysis leads us to believe that this quantitative prioritisation method has incredible potential for academics to extend their research on project risks and for practitioners to use it in the day-to-day implementation of their projects.

The proposed methodology will allow project managers to discover the most relevant project risks so they can focus their control efforts on managing those risks. Usually, implementing risk response strategies might be expensive (control efforts, insurance contracts, preventive actions, or others). Therefore, it is relevant to concentrate only on the most relevant risks. The proposed methodology allows project managers to select the most critical risks by overcoming the problems exhibited by previous methodologies like the probability-impact matrix.

In addition to the above, the risk prioritisation achieved by applying the proposed methodology is based on quantifying the impacts that risks may have on the duration and cost objectives of the project. Finally, we achieve an independent risk prioritisation in duration impact and project cost impact terms. This is important because the project manager can attach more importance to one risk or other risks depending on the priority objective that predominates in the project, the schedule or the total cost.

Undoubtedly, the reliability of the proposed method depends mainly on the accuracy of estimates, which starts by identifying risks and ends with modelling the probability and impact of each risk. The methodology we propose in this paper overcomes many of the problems of previous methodologies, but still has some limitations for future research to deal with. First of all, the results of simulations depend on the estimations of variables (probability distributions and their parameters, risk aversion parameters, etc.). Methodologies for improving estimations are beyond the scope of this research; we assume project teams are sufficient experts to make rational estimationsbased on experience and previous knowledge. Secondly, as risks are assumed to be independent, the contribution or effect of a particular risk can be estimated by including it in simulation and by computing its impact on project cost and duration. This is a reasonable assumption for most projects. In some very complex projects, however, risks can be related to one another. Further research should be done to face this situation.

As an additional research line, we plan to conduct a sensitivity study by simulating many different projects to analyse the robustness of the proposed method.

Finally, it is desirable to implement this methodology in real projects and see how it responds to the reality of a project in, for example, construction, industry, or any other sector that requires a precise and differentiated risk prioritisation.

Data availability

Data will be made available on request.

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This research has been partially funded by the Regional Government of Castile and Leon (Spain) and the European Regional Development Fund (ERDF, FEDER) with grant VA180P20.

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FA developed the conceptualisation and the methodology. JMG contributed to the literature review and interpretations of the results for the manuscript. FA and JP collected the experimental data and developed all the analyses and simulations. AL supervised the project. FA and JP wrote the original draft, while AL and JMG conducted the review and editing. All authors have read and agreed to the published version of the manuscript.

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Acebes, F., González-Varona, J.M., López-Paredes, A. et al. Beyond probability-impact matrices in project risk management: A quantitative methodology for risk prioritisation. Humanit Soc Sci Commun 11 , 670 (2024). https://doi.org/10.1057/s41599-024-03180-5

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SCRUM IN CONSTRUCTION INDUSTRY TO IMPROVE PROJECT PERFORMANCE IN DESIGN PHASE , YINGCHEN LIU

IMPROVING SOFTWARE PROJECTS WITH CLOUD COMPUTING , SUNIL MADDIPATLA

BUSINESS PERSPECTIVE E-BANKING SYSTEMS IN INDIA , DIVYA NALLURI

Applying the Agile Mechanism in the Clinical Trails Domain for Drug Development , Jitendrakumar Narola

IMPLEMENTING AGILE LEAN IN TELECOM INDUSTRY , SWAPNIL NARVEKAR

Internet of Things (IoT) and Changing Face of Project Management , Vikram Singh Prasher

Risk Management in Telemedicine Projects in Healthcare , Shalini Sakinala

Study of the impact of team morale on construction project performance , Adrian Gerard Saldanha

Key Competencies for Project Managers: An Empirical Study , Sahil Sandhu

IDENTIFICATION OF RIGHT LEADERSHIP STYLE FOR AGILE TEAMS , AASHIK SEKHARAN

ROLE OF EFFECTIVE QUALITY RISK MANAGEMENT IN DRUG SAFETY PROJECT , Bhawna Sharma

SOFT SKILLS INFLUENCE IN PROJECT MANAGERS IN THE CLOUD SPACE , KATHERINE SILVA

Managing Business Process Transformation Projects Using Contextual Hybrid Agile Methodology , John Tu

Using IOP as a mechanism for project team management , Maryam Selah Varzi

RELATIONSHIP BETWEEN LEADERSHIP STYLES AND JOB SATISFACTION IN LUXURY RETAIL PROJECT , CUIPING ZHANG

Theses/Dissertations from 2017 2017

Applying Agile Lean to Global Software Development , Piyansh Gupta

Agile in Construction Projects , Chen Jin

Role of Agile Methods in Global Software Development , Dinesh Chandra Kalluri

Innovation through Agile Project Management , Raju Kona

The Use of Effective Risk Management in Cloud Computing Projects , Usha Kiran Marichetty

Feasibility in Applying Agile Project Management Methodologies To Building Design and Construction Industry , Roy S. Moriel

Pharmacovigilance: The Role Of Pharmaceutical Companies To Protect Patients From Adverse Drug Reactions , Srikanth Nukala

Team Performance and Project Success , Ijeoma Okoronkwo

Key to Success of Offshore Outsourcing , Deep Patel

Implementation of Agile Methodology in Public Sector , Kajal Patel

Project Manager’s Perception of Agile Methods Success , Ankit Sachdeva

Adapting Agile in Regulated (Pharmaceutical) Environment , Prachiben K. Shah

The Implication of Agile & Traditional Method as a Practice in Pharmaceutical Industry , Vishant Shah

Project Management for E-Commerce Businesses , Jui Tamhane

Critical Risk Assessment and Management in Pharmaceutical Industry , Abida Zameer

Theses/Dissertations from 2016 2016

Improved Sprint Results with Offshore Indian Teams , Fnu Abdul Hasheem

A Different Approach to Project Management: The Use of Soft Skills , Hannah Adams

ERP Critical Success Factors: Importance of ERP Consultants in ERP Implementation , Adekunle S. Balogun

Scrum Sim - A Simulation Game to Learn the Scrum Agile Framework , Anshuman Bassi

Hybrid Project Management Approach for Software Modernization , Chintan Bhavsar

Adopting Agile Scrum , Anirudh Chaganti

Supply Chain Risks: Causes & Mitigation Strategy for the Medical Device Companies , Dipak Patil

Research Paper on Content Management Systems (CMS): Problems in the Traditional Model and Advantages of CMS in Managing Corporate Websites , Elanchezhian Ramalingam

Challenges When Using Scrum in Globally Distributed Teams , Sweta Shah

A Case Study on PPL's Journey to Agile Transition , Jayalakshmi Tenali

To Overcome Communication Challenges in Distributed/Virtual Scrum Teams , Priyamvada Walimbe

Hybrid Agile Approach: Efficiently Blending Traditional and Agile Methodologies , Rashmi Wankhede

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Managing risk and uncertainty in research projects with experiments

• September 2018
• Thesis for: MSc in Technology Management (Open)
• Advisor: Dr Rob Walker

• Paul Scherrer Institut

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Top 150 Project Management Dissertation Topics [Updated]

Project management is like the conductor of an orchestra, harmonizing various elements to achieve a masterpiece. Dissertation topics in this field are crucial as they delve into the heart of managing projects effectively. Whether you’re a budding project manager or a seasoned professional looking to deepen your understanding, choosing the right project management dissertation topic is paramount. Let’s embark on a journey to explore some intriguing project management dissertation topics that could spark your interest and contribute to this dynamic field.

How To Pick A Dissertation Topic?

Table of Contents

Picking what you’ll study for your big research project (dissertation) is a really important choice. Take your time and think about it carefully. Here are some steps to help you pick the right topic:

• Reflect on your interests: Consider topics that genuinely interest you and align with your passion and expertise. Your enthusiasm will sustain you through the research process.
• Review existing literature: Conduct a thorough review of literature in your field to identify gaps, unanswered questions, or emerging trends that could form the basis of your research.
• Consider practical relevance: Choose a topic that has practical relevance and real-world implications for your field, industry, or community. Aim to address pressing issues or challenges faced by practitioners or organizations.
• Consult with advisors and peers: Seek feedback from your academic advisors, mentors, or peers to get their perspectives on potential topics. They can give you good advice and assist you in making your ideas better.
• Narrow down your focus: Once you have a broad topic in mind, narrow it down to a specific research question or area of investigation. Make sure your topic is manageable within the scope of your dissertation and aligns with the available resources and timeline.
• Evaluate feasibility: Figure out if your topic is doable by checking if you can find enough information, if you have the right tools to study it, if it’s morally okay, and if there are any real-life limits that might get in the way. Ensure that you have access to the necessary resources and support to conduct your research effectively.
• Stay flexible: Stay ready to change or improve your topic as you learn more during your research and find out new things. Your dissertation topic might change as you go, so it’s important to be open to that and be able to adjust along the way.
• Consider your long-term goals: Think about how your dissertation topic aligns with your long-term academic or career goals. Choose a topic that will allow you to develop valuable skills, make meaningful contributions to your field, and position yourself for future opportunities.

150 Project Management Dissertation Topics: Category Wise

Traditional vs. agile methodologies.

• A comparative analysis of traditional waterfall and agile project management methodologies.
• Evaluating the effectiveness of agile methodologies in software development projects.
• Implementing agile practices in non-IT industries: challenges and opportunities.
• The role of project management maturity models in transitioning from traditional to agile methodologies.
• Agile project management in dynamic and uncertain environments: case studies from various industries.
• Integrating hybrid project management approaches: combining elements of traditional and agile methodologies.
• Assessing the impact of agile project management on team dynamics and collaboration.
• Agile project management in large-scale and complex projects: lessons learned and best practices.
• Overcoming resistance to agile adoption: strategies for organizational change management.
• The future of project management: trends and innovations in agile methodologies.

Project Management Tools and Software

• Evaluating the effectiveness of project management software in improving project outcomes.
• Adoption and implementation of project management tools: a case study approach.
• Comparing different project management software solutions: features, benefits, and limitations.
• Customization vs. out-of-the-box implementation: factors influencing the choice of project management software.
• The impact of cloud-based project management tools on remote team collaboration.
• Enhancing project management efficiency through the integration of collaboration platforms and project management software.
• Project management software usability and user experience: implications for adoption and usage.
• Assessing the security and data privacy risks associated with project management software.
• Trends in project management software development: artificial intelligence, automation, and predictive analytics.
• The role of project management software vendors in driving innovation and industry standards.

Project Risk Management

• Identifying and prioritizing project risks: a systematic approach.
• Quantitative vs. qualitative risk analysis: comparing methods and outcomes.
• Risk management strategies for high-risk industries: construction, aerospace, and defense.
• The role of project risk management in achieving project success: evidence from case studies.
• Incorporating risk management into project planning and decision-making processes.
• Stakeholder engagement in project risk management: challenges and best practices.
• Resilience and adaptability: building a risk-aware project culture.
• Emerging risks in project management: cybersecurity threats, geopolitical instability, and climate change.
• Risk management in agile projects: adapting traditional approaches to dynamic environments.
• The future of project risk management: predictive analytics, big data, and machine learning.

Project Scheduling and Planning

• Critical path analysis and its applications in project scheduling.
• Resource leveling techniques for optimizing project schedules and resource allocation.
• The role of project management offices (PMOs) in project scheduling and planning.
• Earned value management (EVM) as a performance measurement tool in project scheduling.
• Lean project management principles: minimizing waste and maximizing efficiency in project schedules.
• Agile project planning techniques: iterative planning, sprint planning, and release planning.
• Time management strategies for project managers: prioritization, delegation, and timeboxing.
• The impact of schedule compression techniques on project duration and cost.
• Project scheduling under uncertainty: probabilistic scheduling models and Monte Carlo simulation.
• Real-time scheduling and adaptive planning: harnessing technology for dynamic project environments.

Leadership and Team Management

• Transformational leadership in project management: inspiring vision and empowering teams.
• The role of emotional intelligence in project leadership and team performance.
• Cross-cultural leadership in multinational project teams: challenges and strategies.
• Building high-performing project teams: recruitment, training, and team development.
• Distributed leadership in virtual project teams: fostering collaboration and trust.
• Conflict resolution strategies for project managers: mediation, negotiation, and arbitration.
• Motivating project teams: rewards, recognition, and intrinsic motivation.
• The impact of leadership styles on project outcomes: autocratic, democratic, and laissez-faire.
• Gender diversity in project teams: implications for leadership and team dynamics.
• Team resilience and psychological safety: creating a supportive and inclusive project environment.

Project Governance and Stakeholder Management

• Project governance frameworks: roles, responsibilities, and decision-making structures.
• Stakeholder identification and analysis: mapping stakeholder interests, influence, and expectations.
• Effective communication strategies for project stakeholders: stakeholder engagement plans and communication channels.
• Managing stakeholder conflicts and competing interests in projects.
• Make sure companies do good things for the community and talk to the people affected by their projects.
• Look at how the big bosses of a project make decisions and handle the people involved.
• Accountability and transparency in project governance: reporting mechanisms and performance metrics.
• Regulatory compliance in project management: legal requirements and industry standards.
• Balancing stakeholder interests in project decision-making: ethical considerations and social responsibility.
• Continuous improvement in project governance: lessons learned and best practices.

Project Finance and Cost Management

• Project budgeting and cost estimation techniques: top-down vs. bottom-up approaches.
• Cost-benefit analysis and return on investment (ROI) in project decision-making.
• Earned value management (EVM) as a cost control tool in project management.
• Managing project financial risks: budget overruns, resource constraints, and market fluctuations.
• Project procurement and contract management: negotiating contracts, vendor selection, and performance monitoring.
• Life cycle costing in project evaluation: considering long-term costs and benefits.
• Value engineering and value management: optimizing project value while minimizing costs.
• Financial modeling and scenario analysis in project finance: assessing project feasibility and viability.
• Funding sources for project financing: equity, debt, grants, and public-private partnerships.
• Project finance in emerging markets: challenges and opportunities for investment.

Project Quality Management

• Total quality management (TQM) principles in project management: continuous improvement and customer focus.
• Quality planning and assurance processes: setting quality objectives and quality standards.
• Quality control techniques in project management: inspection, testing, and quality audits.
• Six Sigma methodology and its applications in project quality management.
• Lean principles in project management: eliminating waste and optimizing processes.
• Measuring project quality performance: key performance indicators (KPIs) and quality metrics.
• Building a culture of quality excellence in project teams: training, empowerment, and recognition.
• Supplier quality management in project procurement: ensuring supplier compliance and performance.
• Benchmarking and best practices in project quality management.
• Continuous improvement in project quality: feedback loops, lessons learned, and process optimization.

Project Stakeholder Engagement and Communication

• Stakeholder engagement strategies in project management: stakeholder analysis, mapping, and engagement plans.
• Effective communication techniques for project managers: verbal, written, and nonverbal communication.
• Managing virtual project teams: communication tools, technologies, and best practices.
• Conflict resolution strategies for project stakeholders: negotiation, mediation, and collaboration.
• Stakeholder communication in crisis situations: managing stakeholder expectations and maintaining trust.
• Building trust and credibility with project stakeholders: transparency, integrity, and responsiveness.
• Cultural sensitivity and communication in multicultural project teams.
• The role of project managers as communication facilitators and mediators.
• Communication challenges in cross-functional project teams: aligning diverse perspectives and priorities.
• Measuring stakeholder satisfaction and feedback: surveys, interviews, and feedback mechanisms.

Project Human Resource Management

• Human resource planning in project management: resource allocation, skills assessment, and capacity planning.
• Talent management strategies for project teams: recruitment, training, and career development.
• Team-building techniques for project managers: icebreakers, team-building exercises, and bonding activities.
• Performance management in project teams: setting objectives, providing feedback, and evaluating performance.
• Conflict resolution strategies for project managers: negotiation, mediation, and conflict coaching.
• Diversity and inclusion in project teams: fostering a culture of equity, diversity, and inclusion.
• Leadership development in project management: training, coaching, and mentorship programs.
• Managing virtual project teams: communication, collaboration, and team cohesion.
• Building resilience and well-being in project teams: managing stress, burnout, and work-life balance.

Project Procurement and Contract Management

• Procurement planning and strategy development: make-or-buy decisions, sourcing options, and procurement methods.
• Contract types and structures in project procurement: fixed-price, cost-reimbursable, and time-and-material contracts.
• Supplier selection criteria and evaluation methods: vendor qualifications, bid evaluation, and supplier performance metrics.
• Negotiation techniques for project managers: win-win negotiation, BATNA analysis, and concessions management.
• Managing contracts and contractor relationships: contract administration, performance monitoring, and dispute resolution.
• Outsourcing and offshoring in project procurement: risks, benefits, and best practices.
• Legal and regulatory considerations in project procurement: compliance with procurement laws, standards, and regulations.
• Contractual risk management: mitigating contract risks through indemnification clauses, insurance, and contingency planning.
• Ethical considerations in project procurement: fairness, transparency, and integrity in procurement processes.
• Continuous improvement in procurement and contract management: lessons learned, process optimization, and supplier feedback.

Project Sustainability and Social Responsibility

• Integrating sustainability principles into project management: environmental stewardship, social equity, and economic viability.
• Sustainable project planning and design: minimizing environmental impacts, maximizing resource efficiency, and promoting resilience.
• Social impact assessment in project management: stakeholder engagement, community consultation, and social license to operate.
• Sustainable procurement practices: ethical sourcing, fair trade, and supply chain transparency.
• Green project management: reducing carbon emissions, conserving natural resources, and promoting renewable energy.
• Corporate social responsibility (CSR) in project management: philanthropy, community development, and stakeholder engagement.
• Sustainable infrastructure development: green buildings, sustainable transportation, and eco-friendly urban planning.
• Environmental risk management in projects: assessing and mitigating environmental impacts and regulatory compliance.
• Sustainable project financing: green bonds, impact investing, and sustainable finance mechanisms.
• Sustainability reporting and disclosure: communicating project sustainability performance to stakeholders.

Project Innovation and Technology Management

• Innovation management in project-based organizations: fostering a culture of creativity, experimentation, and learning.
• Technology adoption and diffusion in project management: factors influencing technology acceptance and implementation.
• Managing innovation projects: from ideation to commercialization, stage-gate processes, and innovation ecosystems.
• Open innovation and collaborative project management: partnerships, co-creation, and knowledge sharing.
• Digital transformation in project management: leveraging emerging technologies for project delivery and collaboration.
• Artificial intelligence and machine learning in project management: predictive analytics, automation, and decision support systems.
• Blockchain technology in project management: decentralized project governance, smart contracts, and supply chain transparency.
• Virtual reality and augmented reality in project management: immersive training, visualization, and virtual collaboration.
• Internet of Things (IoT) applications in project management: real-time monitoring, predictive maintenance, and asset tracking.
• Data-driven project management: leveraging big data, analytics, and business intelligence for project insights and decision-making.

Project Governance and Compliance

• Regulatory compliance in project management: legal requirements, industry standards, and certification programs.
• Ethics and integrity in project governance: code of conduct, conflict of interest policies, and whistleblowing mechanisms.
• Corporate governance and project management: alignment with organizational objectives, risk management, and performance oversight.
• Internal controls and assurance mechanisms in project governance: auditing, monitoring, and accountability.
• Project portfolio governance: prioritization, resource allocation, and strategic alignment.
• Regulatory reporting and disclosure requirements: compliance with regulatory agencies, stakeholders, and investors.
• Project audits and reviews: evaluating project performance, compliance, and lessons learned.
• Governance of public-private partnerships (PPPs): contractual arrangements, risk allocation, and stakeholder engagement.
• Continuous improvement in project governance: feedback loops, lessons learned, and process optimization.

Project Resilience and Change Management

• Building project resilience: risk management, contingency planning, and adaptive strategies.
• Change management in project management: managing resistance, communication, and stakeholder engagement.
• Organizational resilience and project management: lessons from crisis management, business continuity planning, and disaster recovery.
• Agile project management and organizational agility: responsiveness to change, iterative planning, and adaptive leadership.
• Innovation and creativity in project management: fostering a culture of experimentation, learning, and adaptation.
• Anticipatory project management: scenario planning, risk assessment, and proactive decision-making.
• Crisis leadership and project management: decision-making under pressure, communication, and stakeholder management .
• Change readiness assessment in project management: organizational culture, capacity building, and change champions.
• Learning from failure: post-mortem analysis, root cause analysis, and continuous improvement.
• Resilience in project teams: psychological safety, emotional intelligence, and well-being.

In conclusion, selecting the right project management dissertation topics is essential for exploring new frontiers, addressing pressing challenges, and making meaningful contributions to the field. By choosing a topic that aligns with your interests, expertise, and aspirations, you can embark on a rewarding journey of discovery and innovation in project management.

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Risk Management Dissertation Ideas

Published by Owen Ingram at January 2nd, 2023 , Revised On August 18, 2023

Identifying and assessing risks in various life situations is the focus of risk management dissertation topics. The key focus of risk management research topics is on risk prevention and risk mitigation. This field is growing in popularity among students every day because of the need for businesses and organisations to prevent and manage risks as part of their damage control strategies.

The decision of what to write about for your dissertation can be difficult. But there is no need to panic yet because you’ve come to the right place if you’re looking for risk management dissertation topics .

For Your Consideration, Here Are Some Excellent Risk Management Dissertation Ideas.

• Investigating the relationship between risk management and organizational performance.
• A review of the literature on the effects of decision support on risk management strategies in business contexts.
• How do insurance companies approach risk management in their organizations? Is it fair, or do some changes need to be made to improve it?
• Earthquake risk management should concentrate on potential barriers and opportunities.
• A descriptive analysis of the relationship between earthquake risk management and earthquake insurance.
• How social and environmental factors relate to risk management, either directly or indirectly.
• A review of empirical evidence on long-term risk management.
• Geotechnical risk management: a comparison of developed and developing countries.
• Investigating the guidelines and principles related to the risk management domain.
• The impact of the relationship between key individuals and business concepts, as well as the degree to which risk management tools are related.
• Investigating the connection between consumer safety and risk management.
• A quantitative study focuses on the factors for optimizing risk management in services.
• A detailed review of empirical evidence for a futuristic analysis of the risk management domain.
• Which of the following factors is a business’s most important risk management?
• Smart grid security risk management is a new area to research.
• Investigating the risk management strategies used in organizations in the UK.
• A correlational study of risk management and population health.
• Investigating the relationship between supply chain risk management and performance measurement.
• International comparison of traditional versus modern risk management strategies.
• A review of the literature on an international disaster risk management system.
• A descriptive analysis of risk management strategies in the pharmaceutical development industry.
• A correlational analysis of the relationship between risk perception and risk management.
• Focus on potential challenges and interventions in enterprise risk management.
• Risk management and big data in engineering and science projects.
• A review of empirical evidence on community-based disaster risk management.
• Portfolio risk management should emphasize the significance of six sigma quality principles.
• Using financial tools and operational methods to integrate supply chain risk management.
• Discovering risk management’s practical applications in Third World countries. Risk Management in a Supply Chain: How Have Current Trends in Global Supply Chain Management Influenced the Evolution of Risk-Management Strategies?
• Critical Success Factors for Financial Services Organizations Implementing an Operational Management System.

Nothing is more critical to a business than managing risks, whether large or small and bringing positive results to their customers. There is no doubt that the course will be interesting, and you will be able to find topics to write about using research methods such as diversity. Get expert assistance with your dissertation topics by placing an order for our dissertation topic and outline service today. You can take inspiration from the above-mentioned risk management dissertation ideas as well.

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To find risk management dissertation topics:

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There is a rite of passage in selecting a dissertation topic in the academic world. A well-chosen subject may also lead to research questions that will keep one’s intellectual curiosity alive for years to come.

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EU AI Act: first regulation on artificial intelligence

The use of artificial intelligence in the EU will be regulated by the AI Act, the world’s first comprehensive AI law. Find out how it will protect you.

As part of its digital strategy , the EU wants to regulate artificial intelligence (AI) to ensure better conditions for the development and use of this innovative technology. AI can create many benefits , such as better healthcare; safer and cleaner transport; more efficient manufacturing; and cheaper and more sustainable energy.

In April 2021, the European Commission proposed the first EU regulatory framework for AI. It says that AI systems that can be used in different applications are analysed and classified according to the risk they pose to users. The different risk levels will mean more or less regulation.

Learn more about what artificial intelligence is and how it is used

What Parliament wants in AI legislation

Parliament's priority is to make sure that AI systems used in the EU are safe, transparent, traceable, non-discriminatory and environmentally friendly. AI systems should be overseen by people, rather than by automation, to prevent harmful outcomes.

Parliament also wants to establish a technology-neutral, uniform definition for AI that could be applied to future AI systems.

Learn more about Parliament’s work on AI and its vision for AI’s future

AI Act: different rules for different risk levels

The new rules establish obligations for providers and users depending on the level of risk from artificial intelligence. While many AI systems pose minimal risk, they need to be assessed.

Unacceptable risk

Unacceptable risk AI systems are systems considered a threat to people and will be banned. They include:

• Cognitive behavioural manipulation of people or specific vulnerable groups: for example voice-activated toys that encourage dangerous behaviour in children
• Social scoring: classifying people based on behaviour, socio-economic status or personal characteristics
• Biometric identification and categorisation of people
• Real-time and remote biometric identification systems, such as facial recognition

Some exceptions may be allowed for law enforcement purposes. “Real-time” remote biometric identification systems will be allowed in a limited number of serious cases, while “post” remote biometric identification systems, where identification occurs after a significant delay, will be allowed to prosecute serious crimes and only after court approval.

AI systems that negatively affect safety or fundamental rights will be considered high risk and will be divided into two categories:

1) AI systems that are used in products falling under the EU’s product safety legislation . This includes toys, aviation, cars, medical devices and lifts.

2) AI systems falling into specific areas that will have to be registered in an EU database:

• Management and operation of critical infrastructure
• Education and vocational training
• Employment, worker management and access to self-employment
• Access to and enjoyment of essential private services and public services and benefits
• Law enforcement
• Migration, asylum and border control management
• Assistance in legal interpretation and application of the law.

All high-risk AI systems will be assessed before being put on the market and also throughout their lifecycle. People will have the right to file complaints about AI systems to designated national authorities.

Transparency requirements

Generative AI, like ChatGPT, will not be classified as high-risk, but will have to comply with transparency requirements and EU copyright law:

• Disclosing that the content was generated by AI
• Designing the model to prevent it from generating illegal content
• Publishing summaries of copyrighted data used for training

High-impact general-purpose AI models that might pose systemic risk, such as the more advanced AI model GPT-4, would have to undergo thorough evaluations and any serious incidents would have to be reported to the European Commission.

Content that is either generated or modified with the help of AI - images, audio or video files (for example deepfakes) - need to be clearly labelled as AI generated so that users are aware when they come across such content.

Supporting innovation

The law aims to offer start-ups and small and medium-sized enterprises opportunities to develop and train AI models before their release to the general public.

That is why it requires that national authorities provide companies with a testing environment that simulates conditions close to the real world.

The Parliament adopted the Artificial Intelligence Act in March 2024 . It will be fully applicable 24 months after entry into force, but some parts will be applicable sooner:

• The ban of AI systems posing unacceptable risks will apply six months after the entry into force
• Codes of practice will apply nine months after entry into force
• Rules on general-purpose AI systems that need to comply with transparency requirements will apply 12 months after the entry into force

High-risk systems will have more time to comply with the requirements as the obligations concerning them will become applicable 36 months after the entry into force.

More on the EU’s digital measures

• Cryptocurrency dangers and the benefits of EU legislation
• Fighting cybercrime: new EU cybersecurity laws explained
• Boosting data sharing in the EU: what are the benefits?
• EU Digital Markets Act and Digital Services Act
• Five ways the European Parliament wants to protect online gamers
• Artificial Intelligence Act

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