Risk management in build-operate-transfer projects

Construction projects are risky. A build-operate-transfer (BOT) project is recognised as one of the most risky project schemes. This scheme has been employed rather frequently in the past few decades, in both developed and developing countries. However, because of its risky nature, there have been failures as well as successes. Risk analysis in an appropriate way is desirable in implementing BOT projects. There are various tools and techniques applicable to risk analysis. The application of these risk analysis tools and techniques (RATTs) to BOT projects depends on an understanding of the contents and contexts of BOT projects, together with a thorough understanding of RATTs. This paper studies key points in their applications through reviews of relevant literatures and discusses the application of RATTs to BOT projects. The application to BOT projects is considered from the viewpoints of the major project participants, i.e. government, lenders and project companies. Discussion is also made with regard to political risks, which are very important in BOT projects. A flow chart has been introduced to select an appropriate tool for risk management in BOT projects. This study contributes to the establishment of a framework for systematic risk management in BOT projects.

Risk based decision support system for effective implementation of projects

Conventional project management techniques are not always sufficient to ensure time, cost and quality achievement of large-scale construction projects due to complexity in planning, design and implementation processes. The main reasons for project non-achievement are changes in scope and design, changes in government policies and regulations, unforeseen inflation, underestimation and improper estimation. Projects that are exposed to such an uncertain environment can be effectively managed with the application of risk management throughout the project's life cycle. However, the effectiveness of risk management depends on the technique through which the effects of risk factors are analysed/quantified. This study proposes the Analytic Hierarchy Process (AHP), a multiple attribute decision making technique, as a tool for risk analysis because it can handle subjective as well as objective factors in a decision model that are conflicting in nature. This provides a decision support system (DSS) to project management for making the right decision at the right time for ensuring project success in line with organisation policy, project objectives and a competitive business environment. The whole methodology is explained through a case application of a cross-country petroleum pipeline project in India and its effectiveness in project management is demonstrated.

Differential patterns of activity and functional connectivity in emotion processing neural circuitry to angry and happy faces in adolescents with and without suicide attempt

Background - Neural substrates of emotion dysregulation in adolescent suicide attempters remain unexamined. Method - We used functional magnetic resonance imaging to measure neural activity to neutral, mild or intense (i.e. 0%, 50% or 100% intensity) emotion face morphs in two separate emotion-processing runs (angry and happy) in three adolescent groups: (1) history of suicide attempt and depression (ATT, n = 14); (2) history of depression alone (NAT, n = 15); and (3) healthy controls (HC, n = 15). Post-hoc analyses were conducted on interactions from 3 group × 3 condition (intensities) whole-brain analyses (p < 0.05, corrected) for each emotion run. Results - To 50% intensity angry faces, ATT showed significantly greater activity than NAT in anterior cingulate gyral–dorsolateral prefrontal cortical attentional control circuitry, primary sensory and temporal cortices; and significantly greater activity than HC in the primary sensory cortex, while NAT had significantly lower activity than HC in the anterior cingulate gyrus and ventromedial prefrontal cortex. To neutral faces during the angry emotion-processing run, ATT had significantly lower activity than NAT in the fusiform gyrus. ATT also showed significantly lower activity than HC to 100% intensity happy faces in the primary sensory cortex, and to neutral faces in the happy run in the anterior cingulate and left medial frontal gyri (all p < 0.006,corrected). Psychophysiological interaction analyses revealed significantly reduced anterior cingulate gyral–insula functional connectivity to 50% intensity angry faces in ATT v. NAT or HC. Conclusions - Elevated activity in attention control circuitry, and reduced anterior cingulate gyral–insula functional connectivity, to 50% intensity angry faces in ATT than other groups suggest that ATT may show inefficient recruitment of attentional control neural circuitry when regulating attention to mild intensity angry faces, which may represent a potential biological marker for suicide risk.

Development of a risk-oriented strategic sourcing (ROSS) framework for the construction and electronics manufacturing industries

Strategic sourcing has increased in importance in recent years, and now plays an important role in companies’ planning. The current volatility in supply markets means companies face multiple challenges involving lock-in situations, supplier bankruptcies or supply security issues. In addition, their exposure can increase due to natural disasters, as witnessed recently in the form of bird flu, volcanic ash and tsunamis. Therefore, the primary focus of this study is risk management in the context of strategic sourcing. The study presents a literature review on sourcing based on the 15 years from 1998–2012, and considers 131 academic articles. The literature describes strategic sourcing as a strategic, holistic process in managing supplier relationships, with a long-term focus on adding value to the company and realising competitive advantage. Few studies discovered the real risk impact and status of risk management in strategic sourcing, and evaluation across countries and industries was limited, with the construction sector particularly under-researched. This methodology is founded on a qualitative study of twenty cases across Ger-many and the United Kingdom from the construction sector and electronics manufacturing industries. While considering risk management in the context of strategic sourcing, the thesis takes into account six dimensions that cover trends in strategic sourcing, theoretical and practical sourcing models, risk management, supply and demand management, critical success factors and the strategic supplier evaluation. The study contributes in several ways. First, recent trends are traced and future needs identified across the research dimensions of countries, industries and companies. Second, it evaluates critical success factors in contemporary strategic sourcing. Third, it explores the application of theoretical and practical sourcing models in terms of effectiveness and sustainability. Fourth, based on the case study findings, a risk-oriented strategic sourcing framework and a model for strategic sourcing are developed. These are based on the validation of contemporary requirements and a critical evaluation of the existing situation. It contemplates the empirical findings and leads to a structured process to manage risk in strategic sourcing. The risk-oriented framework considers areas such as trends, corporate and sourcing strategy, critical success factors, strategic supplier selection criteria, risk assessment, reporting, strategy alignment and reporting. The proposed model highlights the essential dimensions in strategic sourcing and guides us to a new definition of strategic sourcing supported by this empirical study.

The multiple pheromone ant clustering algorithm and its application to real world domains

The Multiple Pheromone Ant Clustering Algorithm (MPACA) models the collective behaviour of ants to find clusters in data and to assign objects to the most appropriate class. It is an ant colony optimisation approach that uses pheromones to mark paths linking objects that are similar and potentially members of the same cluster or class. Its novelty is in the way it uses separate pheromones for each descriptive attribute of the object rather than a single pheromone representing the whole object. Ants that encounter other ants frequently enough can combine the attribute values they are detecting, which enables the MPACA to learn influential variable interactions. This paper applies the model to real-world data from two domains. One is logistics, focusing on resource allocation rather than the more traditional vehicle-routing problem. The other is mental-health risk assessment. The task for the MPACA in each domain was to predict class membership where the classes for the logistics domain were the levels of demand on haulage company resources and the mental-health classes were levels of suicide risk. Results on these noisy real-world data were promising, demonstrating the ability of the MPACA to find patterns in the data with accuracy comparable to more traditional linear regression models. © 2013 Polish Information Processing Society.

Linking risk management to strategic controls:a case study of Tesco plc

Definitions and perceptions of the role and styles of risk management, and performance management/strategic control systems have evolved over time, but it can be argued that risk management is primarily concerned with ensuring the achievement of strategic objectives. This paper shows the extent of overlap between a broad-based view of risk management, namely Enterprise Risk Management (ERM), and the balanced scorecard, which is a widely used strategic control system. A case study of one of the UK's largest retailers, Tesco plc, is used to show how ERM can be introduced as part of an existing strategic control system. The case demonstrates that, despite some differences in lines of communications, the strategic controls and risk controls can be used to achieve a common objective. Adoption of such an integrated approach, however, has implications for the profile of risk and the overall risk culture within an organisation.

Developing a probabilistic graphical structure from a model of mental-health clinical risk expertise

This paper explores the process of developing a principled approach for translating a model of mental-health risk expertise into a probabilistic graphical structure. The Galatean Risk Screening Tool [1] is a psychological model for mental health risk assessment based on fuzzy sets. This paper details how the knowledge encapsulated in the psychological model was used to develop the structure of the probability graph by exploiting the semantics of the clinical expertise. These semantics are formalised by a detailed specification for an XML structure used to represent the expertise. The component parts were then mapped to equivalent probabilistic graphical structures such as Bayesian Belief Nets and Markov Random Fields to produce a composite chain graph that provides a probabilistic classification of risk expertise to complement the expert clinical judgements. © Springer-Verlag 2010.

Defining an OWL ontology that could be used to integrate mental-health risk information within a decision support system

In the field of mental health risk assessment, there is no standardisation between the data used in different systems. As a first step towards the possible interchange of data between assessment tools, an ontology has been constructed for a particular one, GRiST (Galatean Risk Screening Tool). We briefly introduce GRiST and its data structures, then describe the ontology and the benefits that have already been realised from the construction process. For example, the ontology has been used to check the consistency of the various trees used in the model. We then consider potential uses in integration of data from other sources. © 2009 IEEE.

Psychological cue use and implications for a clinical decision support system

Effective clinical decision making depends upon identifying possible outcomes for a patient, selecting relevant cues, and processing the cues to arrive at accurate judgements of each outcome's probability of occurrence. These activities can be considered as classification tasks. This paper describes a new model of psychological classification that explains how people use cues to determine class or outcome likelihoods. It proposes that clinicians respond to conditional probabilities of outcomes given cues and that these probabilities compete with each other for influence on classification. The model explains why people appear to respond to base rates inappropriately, thereby overestimating the occurrence of rare categories, and a clinical example is provided for predicting suicide risk. The model makes an effective representation for expert clinical judgements and its psychological validity enables it to generate explanations in a form that is comprehensible to clinicians. It is a strong candidate for incorporation within a decision support system for mental-health risk assessment, where it can link with statistical and pattern recognition tools applied to a database of patients. The symbiotic combination of empirical evidence and clinical expertise can provide an important web-based resource for risk assessment, including multi-disciplinary education and training. © 2002 Informa UK Ltd All rights reserved.

Handling varying amounts of missing data when classifying mental-health risk levels

One of the main challenges of classifying clinical data is determining how to handle missing features. Most research favours imputing of missing values or neglecting records that include missing data, both of which can degrade accuracy when missing values exceed a certain level. In this research we propose a methodology to handle data sets with a large percentage of missing values and with high variability in which particular data are missing. Feature selection is effected by picking variables sequentially in order of maximum correlation with the dependent variable and minimum correlation with variables already selected. Classification models are generated individually for each test case based on its particular feature set and the matching data values available in the training population. The method was applied to real patients' anonymous mental-health data where the task was to predict the suicide risk judgement clinicians would give for each patient's data, with eleven possible outcome classes: zero to ten, representing no risk to maximum risk. The results compare favourably with alternative methods and have the advantage of ensuring explanations of risk are based only on the data given, not imputed data. This is important for clinical decision support systems using human expertise for modelling and explaining predictions.

Emerging of human factor in risk analysis of home care service

Risk management in healthcare represents a group of various complex actions, implemented to improve the quality of healthcare services and guarantee the patients safety. Risks cannot be eliminated, but it can be controlled with different risk assessment methods derived from industrial applications and among these the Failure Mode Effect and Criticality Analysis (FMECA) is a largely used methodology. The main purpose of this work is the analysis of failure modes of the Home Care (HC) service provided by local healthcare unit of Naples (ASL NA1) to focus attention on human and non human factors according to the organization framework selected by WHO. © Springer International Publishing Switzerland 2014.