Project Risk Response Research Articles

Logistics project risk response decision-making for global supply chain resilience and agility: an optimised case-based reasoning

ABSTRACT Logistics projects are critical to the functioning of the global supply chains (GSC), encountering various disruptions and risks. Sound Risk Response Decisions (RRDs) on logistics projects are imperative to ensure the GSC’s resilience and agility. Besides risk correlations, Project Interdependencies (PIs) and Limited Historical Information (LHI) impede RRDs. To address these challenges, an optimised Case-Based Reasoning combining the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Stratified Ordinal Priority Approach (SOPA) is proposed. Risks are categorised as referable and non-referable based on LHI using the Jaccard similarity. The strategies for referable risks are derived from similar cases retrieved through the weights of risks and project attributes and three similarities considering PIs. SOPA is developed to find weights with the Interdependent Uncertain Events (IUEs). DEMATEL is employed to access risk centrality degree similarity considering PIs and risk correlations. Expert input is sought for non-referable risk strategies. An optimisation model incorporating secondary risk correlations is built to generate strategies for all risks. The proposed approach is validated through a numerical example. Analysis informs that (1) PIs, LHI, and IUEs are necessary for sound RRDs; (2) decision outcomes are sensitive to logistics project managers who are risk-averse or exhibit moderate consideration of secondary risks.

Project Risk Management Strategies and Performance of Socio-Economic Projects in Rwanda: A Case of Urban Economic Development Initiative Project

The purpose of this study was to assess the effect of Project risk Management Strategies on the Performance of the Urban Economic Development Initiative Project. The study was guided by the following specific objectives: To assess the effect of project risk planning strategy on the performance of the Urban Economic Development Initiative Project, to evaluate the effect of risk assessment strategy on the performance of the Urban Economic Development Initiative Project, to assess the effect of risk responses on the performance of the Urban Economic Development Initiative Project. A descriptive design was used for this research project. The study’s target population was equal to sample size of 45 due to population less than 100. Both primary and secondary research approaches were utilized in order to compile the data. For the primary data collection, the researcher adopted questionnaires, and for the secondary data collection, the researcher applied a documentary review. The gathered data was evaluated using both descriptive statistics, such as the use of frequency and percentage as well as the mean and standard deviation, and inferential statistics, such as the use of multiple linear regression analysis. Tables were employed to exhibit the results of the investigation, and a pilot study was carried out to evaluate the validity and reliability of the instrument that was utilized in the data collection process. The results showed that Pearson Coefficient Correlation indicated that .694 relationship between project risk assessment strategy on the performance of Urban Economic Development Initiative Project. The third specific objective revealed the effect of project risk responses on the performance of the Urban Economic Development Initiative Project, the results showed that 8.9% strongly agreed and 88.9% agreed that reducing project risks contributes to the performance of the Urban Economic Development Initiative Project. In addition, project risk response improves the performance of the Urban Economic Development Initiative Project at r=.861 which is a strong positive correlation. The study concluded that risk planning, risk assessment, and risk response contribute highly to the performance of the project. In recommendation, project key stakeholders should apply risk avoidance strategy to improve the performance of socioeconomic project Keywords: Project Risk Management, Performance Measurement, Socio-Economic Projects, Urban Economic Development, Risk Management Strategies, Rwanda's Development Initiatives

Influence of Risk Management on Project Implementation, a Case Study of Indoor Residual Spraying Project in Rwanda

The study entitled “Influence of Risk Management on Project Implementation, a case of Indoor Residual Spraying (IRS) Project in Rwanda”. The purpose of the study was to investigate the influence of project risk management on project implementation in Rwanda with reference to Indoor Residual Spraying (IRS) Project. The study was guided by four specific objectives: to examine the influence of project risk identification on implementation of Indoor Residual Spraying (IRS) Project, to assess the influence of Project Risk Analysis on the implementation of Indoor Residual Spraying (IRS) Project, to find out the influence of project risk response planning on the implementation of Indoor Residual Spraying (IRS) Project and to ascertain the influence of Project risk monitoring and control on the implementation of Indoor Residual Spraying (IRS) Project. The research designs that were used in this study are descriptive research design and analytical research design. The study used 83 employees of Indoor Residual Spraying (IRS) Project as sample size. The study used stratified sampling techniques to select whole population to participate in this study. The study used questionnaires, interview guide and documentary review as method of data collection and finally the study used descriptive statistics and inferential statistics such as correlation analysis and multiple linear regression analysis as method of data analysis. For the first objective, the findings revealed that there existed a significant moderate positive correlation (R = 0.552**, p-value=0.002<0.01) between project risk identification and implementation of (IRS) Project and also results revealed that project risk identification have significance positive effect on implementation of (IRS) Project as indicated by β1= 0.199, p=0.000<0.05, t= 3.257. The implication is that an increase of one unit in project risk identification would lead to an increase in implementation of (IRS) Project by 0.199 units. For the second objective, the findings revealed that there existed a significant weak positive correlation (R = 0.375**,p-value=0.000<0.01) between project risk analysis and implementation of (IRS) Project and also the results revealed that project risk analysis have significance positive effect on implementation of (IRS) Project as indicated by β2=1.226, p=0.000<0.05, t=8.797. For the third objective, the findings revealed that there existed significant high positive correlation (R = 0. 797**,p-value=0.003<0.01) between project risk response and implementation of (IRS) Project and also the results revealed that project risk response planning have significance positive effect on implementation of (IRS) Project as indicated by β3= 0.396, p=0.000<0.05, t= 3.781. For the fourth objective, the findings revealed that the study established the existed of a strong positive correlation (R = 0.681**,p-value=0.001<0.01) between project risk monitoring & control and implementation of (IRS) Project and also the regression results revealed that project risk monitoring and control have significance positive effect on implementation of (IRS) Project as indicated by β4= 0.341, p=0.040<0.05, t= 2.085. As such, the project management team should explore ways for enhancing the effectiveness of the tools in risk analysis by providing more orientation of stakeholders on this.

Delay-oriented risk network model for project risk response decisions

Risk interactions change the occurrence probability of risks and the impact due to the risks. Accurate risk interaction modeling becomes the key point in making project risk response decisions to ensure successful project completion. However, previous researches mostly focus on the probability of triggering a risk by another, ignoring the time delay existing in risk interaction, which can lead to inaccurate risk criticality assessment and reduce the efficacy of risk response decisions. Departing from the risk interaction network, whose nodes and edges are the risks and risk interactions respectively, this paper constructs a risk delay network (RDN) by taking time delay as an edge attribute. And accordingly, put forward an integrated approach to support decision making on risk responses, comprising an RDN simulation model for evaluating risk criticality and an RDN structural analysis to propose effective solutions on the critical risks. The simulation model suggests that risk criticality varies depending on whether time delays are considered or not. The RDN structural analysis applies a novel network index to compute the contributions of the nodes and edges to the risk criticality from the bird’s eye view on the RDN. An example is applied to validate the integrated approach.

Risk response incorporating risk preferences in international construction projects

PurposeThe objective of this research is to develop a decision method that can generate appropriate risk response strategies for international construction project managers (PMs) and allow these strategies to reflect their different risk preferences.Design/methodology/approachThe optimal model approach is adopted. A credibility-based fuzzy chance constrained programming (CFCCP) model is developed, which simultaneously minimizes the expected losses of risk events and total costs of risk response. To solve this multi-objective model, a fuzzy interactive solution method is used. Moreover, the model performance is demonstrated by a real international industrial plant project. In addition, a sensitivity analysis of the model is conducted.FindingsThe result of the sensitivity analysis indicates that PMs with a greater risk aversion can lead to a higher mitigation ratio of expected losses of risk events and a higher total cost of risk response.Practical implicationsThis research provides contractors with an effective decision-making model to develop a project risk response plan, and it will assist contractors to minimize risk losses and enhance the project performance in the international construction market.Originality/valuePrevious studies overlook the risk preference, which is an important behavioral factor influencing decisions in risk response strategy selection. This research proposed a novel risk response strategy selection decision method that considers different attitudes toward risk among decision makers.

Budget allocation decisions for project risk response

Purpose The purpose of this paper is to propose a method integrating fault tree analysis and optimization model to allocate response budget from the preventive and protective perspectives. Design/methodology/approach The proposed method consists of two main steps. The first step is to analyze and calculate the probability and the loss of the risk. The second step is to build an optimization model for allocating response budget. Findings First, there exists an optimal response budget. Second, risk protection is preferred to risk prevention when the total budget is limited. Third, the protective budget should be first invested for the consequence event with greatest expected loss. Fourth, the preventive budget should be first allocated to the risk cause with highest occurrence probability that belongs to the OR set in the fault tree. Practical implications Managerially, our results indicate that project managers (PMs) should make a tradeoff between the budget invested for risk response and reduced expected loss of the risk. Then, in the case of inadequate response budget, PMs should pay more attention to risk protection and cope with the event that can cause severe loss. In addition, under this circumstance, PMs had to better allocate the risk preventive budget in proper order. Originality/value Project risk response is a critical issue in project risk management as PMs can take actions actively to cope with project risks in this phase. Effective risk response, in general, requires financial support in practice, and reasonable allocation of the total budget among risk response strategies can produce better response effects.

Establishing Core Factors of Risk Management Influencing Performance Outcome of Small and Medium Firm's Construction Projects in Gauteng

The quest for delivering successful construction projects has urged South African small and medium enterprises (SMEs) to adopt risk management in their projects. However, it has been evinced that SMEs projects in South Africa especially in the Gauteng province have encountered poor performances. Thus, this article determines core risk management factors influencing project outcome of SMEs. A deductive approach was embraced using a questionnaire. The data were collected from 181 conveniently sampled respondents in Gauteng, graded from Grade 1 to 6 of the CIDB (Construction Industry Development Board) grading system. The Statistical Package for the Social Science (SPSS) version 23 was used to analyse the data by computing exploratory factor analysis and multiple regression analysis. It was revealed that SMEs performance outcome is influenced by eight risk management factors. The influential factors are organisational environment, defining project objectives, resource requirements, risk measurement, risk identification, risk assessment, risk response and action planning and monitoring, review and continuous improvement. The risk management factors established in this article are reliable and valid in projects undertaken by SMEs in the South African construction industry and the findings can serve as a guideline for contractors to achieve success in this context. The study may be repeated in other countries globally, however, it cannot be generalised due to the restrictions pertaining to the geographical area.

Research on project risk response strategy selection based on grey K-shell algorithm

PurposeThere are a large number of interdependent risk factors in complex project. Risk response strategy without considering risk correlation cannot achieve good risk response. Therefore, the purpose of this paper is to propose a risk response strategy selection model considering risk correlation based on the grey K-shell algorithm.Design/methodology/approachThis paper mainly focuses on the measurement of two aspects of risk factors. One is the ability of the risk factors to influence other risk factors, another is the degree affected by other risk factors. Both of the above are measured by the grey K-shell algorithm improved in this paper, and the weights of these are used for the constructing of risk response strategy selection model.FindingsThe risk response strategy considering risk relevance is more effective than the risk response strategy without considering risk relevance. Also, results indicate that as the risk response budget increases the risk response effect also increases, and the increasing trend is weakens. The relative gap between the effect of response strategies considering risk relevance and the strategy without considering risk relevance increases first and, then, decreases with the increase of budget.Originality/valueThe results of this paper demonstrate that the risk response strategy considering risk relevance is more effective than not considering risk relevance. The approach presented in this paper can guide project managers’ risk decisions and may also help to find the best risk response budget.

Integrating case-based analysis and fuzzy optimization for selecting project risk response actions

This article proposes a method based on case-based analysis and fuzzy optimization to provide decision support in project risk response. The main steps of the method are: (1) the formulation of alternative risk response actions (RRAs) based on case-based analysis, and (2) the determination of the optimal set of RRAs using a fuzzy optimization model. Based on the method, project managers (PMs) can find out alternative RRAs and further determine an optimal set of RRAs. Some managerial suggestion and implication are drawn from the results of the article. First, to perform better risk response in future, it is suggested that organizations should always capture a long-term perspective, with an awareness of keeping documents of all handled historical projects. Second, since each RRA obtained from alternative historical cases needs to be adapted in accordance with the current situations, adaptation costs should also be considered when allocating budget for selecting RRAs.

Applying social network analysis to genetic algorithm in optimizing project risk response decisions

Risk interaction changes the probability of occurring a risk and also the impact of the risk, which calls for new approaches for making risk response decision (RRD). In this work, a simulation-based network model of abstracting the risk interactions is built for evaluating the RRDs. Meanwhile, genetic algorithm (GA) is tailored and improved for optimizing the RRDs, whose crossover operator is designed and enhanced by the social network analysis (SNA). Specifically, the application of SNA is two-fold: transforming the network for changing risk and risk interaction into the same network element; designing a new index to quantify the element’s significance in the network view. Double-sorting map crossover is proposed for tailoring GA, and accordingly, multi-sorting map crossover is designed by integrating the quantified significance. An application example of the proposed approach is provided to illustrate its process and utility. Furthermore, contrastive analysis is conducted based on three different size cases, and the result demonstrates that the improvement in the GA is effective.

Improved simulated annealing based risk interaction network model for project risk response decisions

Risk interaction changes the probability and impact of a given risk, which may result in a less effective risk response decision (RD). This study presents an approach for supporting the project manager in making RDs, comprising a simulation model of risk interaction network (RIN) for evaluating the RDs and an improved simulated annealing (SA) algorithm for optimizing the RDs. The simulation model considers different risk levels and the corresponding risk interaction cases, which is closer to the reality. In addition to tailoring the SA algorithm to optimize RDs, it is improved through enhancing its neighborhood search with the aid of social network analysis. Specifically, two new network indices are designed for calculating the quantitative significance of RIN elements, i.e. the nodes that denote risks and edges that reflect the risk interactions. The element with a higher significance is more likely to be dealt with when generating a new RD in the neighborhood search. An application is provided to illustrate the utility of the proposed approach; a contrastive analysis of the improved SA and standard SA is also conducted to validate the effectiveness and efficiency of the former.

A multiobjective optimization method considering process risk correlation for project risk response planning

Project risk response planning is an important component of project risk management to help ensure project success. However, thus far, the relevant research has ignored the correlations between risk factors during different project development subprocesses. This study proposes a novel risk response method by opening the black box of the project process and considering the risk correlations among different subprocesses to help practitioners create a practical risk response plan. In our method, we first identify process risk factors from two-dimensional criteria called risk categories and project development subprocesses. Then, we consider the risk correlations between different subprocesses to assess the true effect of each process risk factor. Finally, we build a multiobjective risk response model under resource constraints to minimize the total expected losses, the total expected schedule delay, and the total expected quality reduction. Based on the model, we obtain a set of Pareto-optimal risk response plans. Project managers can select a satisfactory risk response plan based on their experience and objective project requirements. Finally, we apply the proposed method to a real software development project. We find that the risk response effects are better if managers control the process risks earlier. Moreover, the number of initial risk factors in each subprocess is an important reference in risk response resource allocation.

Measuring the Effect of Project Risks Based on Shapley Value for Project Risk Response

Project risk response is an important phase in project risk management to ensure project success. In practice, some of risk factors may occur simultaneously and the negative effects of these risk factors are usually no less than the sum of negative effects when they occur separately, which accords with the super-additivity characteristic in cooperative game when the negative effect is seen as the characteristic function. In this paper, we propose a method based on Shapley value to measure the actual effect of risk factors. A numeric example is provided to illustrate the proposed method. The result can be helpful to not only find the optimal/ideal risk response plan, but also provide decision making support for allocating the risk response resources to risk response actions.

Fuzzy multi-objective model for project risk response selection considering synergism between risk responses

Fuzzy multi-objective model for project risk response selection considering synergism between risk responses

Fuzzy multi-objective model for project risk response selection considering synergism between risk responses

Most projects significantly fail to achieve the corresponding pre-determined objectives due to uncertainty. There is wide agreement that the risk response strategy selection is an important issue in project risk management (PRM). This paper proposes a fuzzy multi-objective method for solving the risk response strategy selection problem. A linear integer programming has been used to solve a problem in order to choose the appropriate risk. An optimisation model is developed, which integrates several critical elements that are the project cost, project schedule, project quality and another objectives. The relationship between responses during implementation has been considered in this study. The time and quality saved by implementation of each of the responses in the activities have been considered in an uncertain and fuzzy manner and Zimmermann method has been used to solve the problem. Finally, a case study related to petroleum projects has been presented and the corresponding results have been analysed.

Generating project risk response strategies based on CBR: A case study

Risk response is an important work in project risk management (PRM). To generate project risk response strategies, retrieving and reusing information and knowledge of the similar historical cases is important, while research concerning this issue is still relatively scarce. Taking the risk response of the subway project in S city, China as a case problem, this paper proposes a pragmatic method for generating project risk response strategies based on the case-based reasoning (CBR). The procedure of the method include the five parts: first, representing the target case and the historical cases; second, retrieving the available historical cases by judging whether the risks involved in each historical case cover or are the same as those in the target case; third, retrieving the similar historical cases by measuring the similarity between each available historical case and the target case; fourth, revising the inapplicable risk response strategies involved in the similar historical cases by analyzing the response relation between each strategy and each risk of the current project; and generating the desirable risk response strategies by evaluating each candidate risk response strategy set. To illustrate the use of the proposed method, an empirical analysis of generating the risk response strategies for the subway station project is given. The proposed method can support project managers to make the better decision in PRM.

An Integrated Framework for Risk Response Planning Under Resource Constraints in Large Engineering Projects

Engineering project managers often face a challenge to allocate tight resources for managing interdependent risks. In this paper, a quantitative framework of analysis for supporting decision making in project risk response planning is developed and studied. The design structure matrix representation is used to capture risk interactions and build a risk propagation model for predicting the global mitigation effects of risk response actions. For exemplification, a genetic algorithm is used as a tool for choosing response actions and allocating budget reserves. An application to a real transportation construction project is also presented. Comparison with a sequential forward selection greedy algorithm shows the superiority of the genetic algorithm search for optimal solutions, and its flexibility for balancing mitigation effects and required budget.

An optimization method for selecting project risk response strategies

There is wide agreement that the risk response strategy selection is an important issue in project risk management (PRM). Some academic researchers have paid attention to this issue. This paper proposes a novel method for solving the risk response strategy selection problem in PRM. In the method, an optimization model is developed, which integrates three critical elements that are the project cost, project schedule and project quality. By solving the model, the optimal solution could be obtained so that the most desirable risk response strategies to cope with the risk events can be determined. If the optimal solution is not found or project managers are not satisfied with the solution, another pathway can be used to support the managers to get the desirable strategies. The pathway is based on an iterative process which involves making trade-offs between the project budget, time and quality according to objective requirements and managers' judgments. The iterative process comes to an end if the objectives predefined by the managers are reached. A simple example project is also provided to illustrate the practicality and usefulness of the proposed method.

Comparative Review on the Tools and Techniques for Assessment and Selection of the Project Risk Response Actions (RRA)

Project risk management is the process of risk identification, analysis, and response. Risk response includes assessment and selection of the efficient Risk Response Actions (RRA) in order to reduce/enhance the likelihood of the occurrence of threats/opportunities and/or the magnitude of threats/opportunities impact. There is wide agreement that development of RRA plans is an important phase of project risk management, while there are few widely accepted processes, models or tools to support the proper selection of RRAs. The present paper reviews and compares the efforts, which have been made to assess and select RRAs and address the mentioned deficiency found in the published literature. The investigations provide an insight into the need to provide the risk analysts with effective tools and techniques for assessing and selecting RRAs in the area of project risk management.

An Integrated Methodology for Assessment and Selection of the Project Risk Response Actions

In a systematic process of project risk management, after risk assessment is implemented, the risk analysts encounter the phase of assessment and selection of the project risk response actions (RA). As indicated by many researchers, there are less systematic and well-developed solutions in the area of risk response assessment and selection. The present article introduces a methodology including a modeling approach with the objective of selecting a set of RA that minimizes the undesirable deviation from achieving the project scope. The developed objective function comprises the three key success criteria of a project, namely, time, quality, and cost. Our model integrates overall project management into the project risk response planning (P2RP). Furthermore, the proposed model stresses on an equivalent importance for both "risk" and "response." We believe that applying the proposed model helps the project risk analyst in most effective and efficient manner dealing with his or her complicated RA selection problems. The application of the proposed model was implemented in projects in the construction industry in which it showed tremendous time, cost, and quality improvements.