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The purpose of this paper is to use systemic thinking to explain and predict the cost of logistics outsourcing, and to devise policies to minimize the cost of risk.

A method of system dynamics is adopted to capture the dynamic interaction of logistics outsourcing systems and to analyze the impact of some factors in the system on policy decisions over a long‐term horizon.

This paper illustrates the internal mechanism of the logistics outsourcing cost of risk systems by virtue of system dynamic principles, to develop a system dynamics model, and to give a quite detailed description of how the model could work.

The results of the simulation analysis provide useful information for logistics outsourcing risk managers.

This paper contributes to the discussion on the use of system dynamics for studying logistics outsourcing cost of risk.

The concept of a company's cost of capital is used in capital budgeting as a potential basic discount rate to be applied to expected future cash flows from a proposed investment project being subjected to evaluation for acceptance or rejection. Discounted‐cash‐flow capital budgeting techniques derive from valuation theory that determines present value of expected future cash flows by discounting them down to the present at a discount rate appropriate to the degree of risk involved. Conceptually, this is true with regard to both domestic investment and foreign direct investment. However, there is recognition in the literature that capital budgeting for foreign direct investment decisions may involve complexities not present in the domestic case. These include economic, financial, and political factors, and related risks, e.g., foreign exchange risk, blocked currencies, expropriation. On the other hand, foreign direct investment is thought to provide diversification benefits, so that risks that are not domestically diversifiable are internationally diversifiable, thereby eliminating some otherwise systematic risk. Complexities such as these place a considerable burden upon the concept of cost of capital as a discount rate appropriately reflective of the degree of risk involved in a foreign direct investment project. Furthermore, cost of capital may be affected by environmental factors associated with what country the parent corporation calls “home” (Stonehill and Dullum).

Power plant projects are very complex and encounter serious cost overruns worldwide. Their cost overrun risks are not independent but interrelated in many cases, having structural relationships among each other. The purpose of this study is, therefore, to establish the complex structural relationships of risks involved.

In total, 76 published articles from the previous literature are reviewed using the content analysis method. Three risk networks in different phases of power plant projects are depicted based on literature review and case studies. The possible methods of solving these risk networks are also discussed.

The study finds critical cost overrun risks and develops risk networks for the procurement, civil and mechanical works of power plant projects. It identifies potential models to assess cost overrun risks based on the developed risk networks. The literature review also revealed some research gaps in the cost overrun risk management of power plants and similar infrastructure projects.

This study will assist project risk managers to understand the potential risks and their relationships to prevent and mitigate cost overruns for future power plant projects. It will also facilitate decision-makers developing a risk management framework and controlling projects’ cost overruns.

The study presents conceptual risk networks in different phases of power plant projects for comprehending the root causes of cost overruns. A comparative discussion of the relevant models available in the literature is presented, where their potential applications, limitations and further improvement areas are discussed to solve the developed risk networks for modeling cost overrun risks.

This paper aims to minimize the mean-risk cost of sustainable and resilient supplier selection, order allocation and production scheduling (SS,OA&PS) problem under uncertainty of disruptions. The authors use conditional value at risk (CVaR) as a risk measure in optimizing the combined objective function of the total expected value and CVaR cost. A sustainable supply chain can create significant competitive advantages for companies through social justice, human rights and environmental progress. To control disruptions, the authors applied (proactive and reactive) resilient strategies. In this study, the authors combine resilience and social responsibility issues that lead to synergy in supply chain activities.

The present paper proposes a risk-averse two-stage mixed-integer stochastic programming model for sustainable and resilient SS,OA&PS problem under supply disruptions. In this decision-making process, determining the primary supplier portfolio according to the minimum sustainable-resilient score establishes the first-stage decisions. The recourse or second-stage decisions are: determining the amount of order allocation and scheduling of parts by each supplier, determining the reactive risk management strategies, determining the amount of order allocation and scheduling by each of reaction strategies and determining the number of products and scheduling of products on the planning time horizon. Uncertain parameters of this study are the start time of disruption, remaining capacity rate of suppliers and lead times associated with each reactive strategy.

In this paper, several numerical examples along with different sensitivity analyses (on risk parameters, minimum sustainable-resilience score of suppliers and shortage costs) were presented to evaluate the applicability of the proposed model. The results showed that the two-stage risk-averse stochastic mixed-integer programming model for designing the SS,OA&PS problem by considering economic and social aspects and resilience strategies is an effective and flexible tool and leads to optimal decisions with the least cost. In addition, the managerial insights obtained from this study are extracted and stated in Section 4.6.

This work proposes a risk-averse stochastic programming approach for a new multi-product sustainable and resilient SS,OA&PS problem. The planning horizon includes three periods before the disruption, during the disruption period and the recovery period. Other contributions of this work are: selecting the main supply portfolio based on the minimum score of sustainable-resilient criteria of suppliers, allocating and scheduling suppliers orders before and after disruptions, considering the balance constraint in receiving parts and using proactive and reactive risk management strategies simultaneously. Also, the scheduling of reactive strategies in different investment modes is applied to this problem.

In recent years, cost overrun becomes a common problem in steel building construction projects. The average percentage can vary widely depending on the project type, size, complexity and location. The steel structure change ratio in Taiwan is from 1 to 18% in statistics. The contractors always put every possible effort into preventing or mitigating project cost overruns, and one of the approaches is an accurate cost overrun risk estimate. Traditional project cost overrun risk assessment models mainly focus on macro-level evaluation and may not function well for the project-specific level (micro-level). This study creates a network-like connection model between the outcome (i.e. cost overrun risk) and the associated root causes in which the project status evaluation checklists of design, manufacturing, construction and interfaces are used to evaluate the checklists' influences through the Bayesian network (BN) composed by intermediate causes.

Due to the constraint of data availability, BN nodes, relationships and conditional probabilities are defined to establish a BN-based steel building project cost overrun assessment model following the knowledge of experts. Because of the complexity of the BN, the construction of the BN structure is first to build BN's fault tree (FT) hierarchy. And then, basic BN framework is constructed by the transformation of the FT hierarchy. Furthermore, some worthwhile additional arcs among BN nodes are inserted if necessary. Furthermore, conditional probability tables (CPTs) among BN nodes are explored by experts following the concept of the ranked node. Finally, the BN-based model was validated against the final cost analysis reports of 15 steel building projects done in Taiwan and both were highly consistent. The overall BN-based model construction process consists of three steps: (1) FT construction and BN framework transformation, (2) CPT computation and (3) model validation.

This study established a network-like bridge model between the outcome (i.e. cost overrun risk) and the root causes in a network of which cost influences are evaluated through the project-specific status evaluation checklists of design, manufacturing, construction and interfaces. This study overcame several limitations of the previous cost overrun risk assessment models: (1) few past research support assessment of cost overrun based on real-time project-owned data and (2) the traditional causal models inadequately depict interdependencies among influence factors of cost overrun at the network. The main influence factors of the cost overrun risk at the steel building projects in Taiwan were also examined using sensitivity analysis. The main root causes of cost overrun in steel building projects are design management and interface integration.

The proposed model belongs to the project-specific causal assessment model using real-time project-owned status checklist data as input. Such a model was seldom surveyed in the past due to the complicated interdependence among causes in the network. For practical use, a convenient and simple regression equation was also developed to forecast the cost overrun risk of the steel building project based on the root causes as input. Based on the analysis of cost overrun risk and significant influence factors, proper tailor-made preventive strategies are established to reduce the occurrence of cost overrun at the project.

The study focuses on drug safety regulation capture, reveals the inner mechanism and evolutionary characteristics of drug safety regulation capture and provides suggestions for effective regulation by pharmacovigilance.

The article introduces prospect theory into the game strategy analysis of drug safety events, constructs a benefit perception matrix based on psychological perception and analyzes the risk selection strategies and constraints on stable outcomes for both drug companies and drug regulatory authorities. Moreover, simulation was used to analyze the choice of results of different parameters on the game strategy.

The results found that the system does not have a stable equilibrium strategy under the role of cognitive psychology. The risk transfer coefficient, penalty cost, risk loss, regulatory benefit, regulatory success probability and risk discount coefficient directly acted in the direction of system evolution toward the system stable strategy. There is a critical effect on the behavioral strategies of drug manufacturers and drug supervisors, which exceeds a certain intensity before the behavioral strategies in repeated games tend to stabilize.

In this article, the authors constructed the perceived benefit matrix through the prospect value function to analyze the behavioral evolution game strategies of drug companies and FDA in the regulatory process, and to evaluate the evolution law of each factor.

Risk analysis plays a vital role in controlling and managing cost overruns in complex construction projects, particularly where uncertainty is high. This study attempts to address an important issue of cost overrun that encountered by metropolitan rapid transit projects in relation to the significance of risk involved under high uncertainty.

In order to solve cost overrun problems in metropolitan transit projects and facilitate the decision-makers for effective future budgeting, a cost-risk contingency framework has been designed using fuzzy logic, analytical hierarchy process and Monte Carlo simulation.

Initially, a hierarchical breakdown structure of important complexity-driven risk factors has been conceptualized herein using relative importance index. Later, a proposed cost-risk contingency framework has investigated the expected total construction cost in order to consider the additional budgeted cost required to mitigate the risk consequences for particular project activity. The results of cost-risk analysis imply that poor design issues, an increase in material prices and delays in relocating facilities show higher dependency and increase the risk of cost overrun in metropolitan transit projects.

The findings and implication for project managers could possibly be achieved by assuming the proposed cost-risk contingency framework under high uncertainty of cost found in this research. Furthermore, this procedure may be used by experts from other engineering domains by replacing and considering the complex relationship between complexity-risk factors.

This study contributes to the body of knowledge by providing a practical contingency model to identify and evaluate the additional risk cost required to compute total construction cost for getting stability in future budgeting.

In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty.

This paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system.

The findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty.

This study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis.

These results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice.

This study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.

Target cost contracts are commonly used to share the monetary outcome of work or a project. However, discussion is ongoing, as to what constitutes optimal sharing. The purpose of this paper is to examine optimal sharing and derives a result for defined risk assumptions on the owner (risk neutral) and contractor (risk-averse ranging to risk neutral).

The derivation is based on solving a constrained maximization problem using ideas from principal-agent theory. Practitioners were engaged in a designed exercise in order to validate the approach and propositions. The influence of the contractor's level of risk aversion, the cost uncertainty and the contractor's effort effectiveness are examined.

The paper shows that, at the optimum, the sharing ratio between contractor and owner needs to reduce and the fixed fee needs to increase when the contractor becomes more risk-averse, the level of the cost uncertainty increases, or the effectiveness of the contractor effort decreases.

The paper's findings provide practitioners with a useful benchmark for outcome sharing in target contracts.

Existing work on outcome sharing in target contracts is limited to being qualitative and anecdotal in nature. This paper extends existing knowledge by providing a quantitative treatment of optimal sharing.

This paper aims to propose a process optimization approach showing how organizations are able to achieve sustainable and efficient process optimization, based on integrated process-risk analysis using several criteria to a better decision-making.

Several approaches are used (functional/dysfunctional) to analyze how processes work and how to deal with risks forming multi-criteria decision-making. In addition, a risk factor is integrated into the structured analysis and design techniques (SADT) method forming a novel graphical view SADT-RISK; it identifies process’s failures using the traditional failure modes, effects and criticality analysis (FMECA) and economic consideration “failure mode and effect, criticality analysis-cost FMECA-C” making a multi-criterion matrix for better decision-making. Subsequently, some recommendations are proposed to overcome the failure.

This paper illustrates a methodology with a case study in a company, which has a leading brand in the market in Algeria. The authors are integrating a varied portfolio of approaches linking with each other to analyze, improve and optimize the processes in terms of reliability and safety to deal with risks; reduce the complexity of the systems; increase the performance; and achieve a safer process. However, the proposed method can be readily used in practice.

The paper provides a new approach based on integrated management using new elements as an innovative contribution, forming a novel graphical view SADT-RISK; it identifies process’s failures using the traditional FMECA and economic consideration “a new multi-criterion matrix for better decision-making and using the SWOT analysis – Strengths, Weaknesses, Opportunities, Threats – as a balance to decide about the process improvement”. The authors conclude that this methodology is oriented and applicable to different types of companies such as financial, health and industrial as illustrated by this case study.