A dynamic systems approach to risk assessment in megaprojects

Purpose- Megaprojects are large, complex, and expensive projects that often involve social, technical, economic, environmental and political (STEEP) challenges. Despite these challenges, project owners and financiers continue to invest large sums of money in megaprojects that run high risks of being over schedule and over budget. While some degree of cost, schedule and quality risks are considered during planning, the challenge of understanding how risk interactions and impacts on project performance can be modelled dynamically still remains. The consequences learnt from past experiences indicate that there was a lack of dynamic tools to manage such risks effectively in megaproject construction. In seeking to help address these problems, this research put forward an innovative dynamic systems approach called SDANP to risk assessment in megaprojects construction. Design/methodology/approach – The research has developed an innovative SDANP method which involves an integrative use of system dynamics (SD) and analytic network process (ANP) for risk assessment. The SDANP model presented in the thesis has been testified by using data and information collected through a questionnaire survey and interviews from supply-side stakeholders involved in the Edinburgh Tram Network (ETN) project at the Phase One of its construction stage. The SDANP method is a case study risk assessment driven process and can be used against STEEP challenges in megaprojects. Findings – The result of the case study project revealed that the SDANP method is an effective tool for risk assessment to support supply-side stakeholders in decision making in construction planning. The SDANP model has demonstrated its efficiency through case study, and has convinced construction practitioners in terms of its innovation and usefulness. Research limitations/implications – Although the SDANP model has been developed for generic use in risk assessment, data and information used to run the simulation were based on the ETN project, which is in Edinburgh, Scotland. The use of the SDANP model in other megaprojects requires further data and information from local areas. Practical implications – The SDANP method provides an innovative approach to a comprehensive dynamic risk assessment of STEEP issues at the construction planning stage of megaprojects for the first time. It provides an interactive quantitative way for developers to prioritise and simulate potential risks across the project supply network, to understand and predict in advance the consequences of STEEP risks on project performance at the construction stage. Originality/value - The research made an original contribution in quantitative risk assessment with regard to the need for a methodological innovation in research and for a powerful sophisticated tool in practice. The SDANP has shown its advantages over existing tools such as the program evaluation and review technique (PERT) and the risk assessment matrix (RAM)

Internet of Things and Their Coming Perspectives: A Real Options Approach

Internet of things is developing at a dizzying rate, and companies are forced to implement it in order to maintain their operational efficiency. The high flexibility inherent to these technologies makes it necessary to apply an appropriate measure, which properly assesses risks and rewards. Real options methodology is available as a tool which fits the conditions, both economic and strategic, under which investment in internet of things technologies is developed. The contribution of this paper is twofold. On the one hand, it offers an adequate tool to assess the strategic value of investment in internet of things technologies. On the other hand, it tries to raise awareness among managers of internet of things technologies because of their potential to contribute to economic and social progress. The results of the research described in this paper highlight the importance of taking action as quickly as possible if companies want to obtain the best possible performance. In order to enhance the understanding of internet of things technologies investment, this paper provides a methodology to assess the implementation of internet of things technologies by using the real options approach; in particular, the option to expand has been proposed for use in the decision-making process

Risk analysis in manufacturing footprint decisions

A key aspect in the manufacturing footprint analysis is the risk and sensitivity analysis of critical parameters. In order to contribute to efficient industrial methods and tools for making well-founded strategic decisions regarding manufacturing footprint this paper aims to describe the main risks that need to be considered while locating manufacturing activities, and what risk mitigation techniques and strategies that are proper in order to deal with these risks. It is also proposed how the risk analysis should be included in the manufacturing location decision process

A new maturity model for analysing project risk management in the global automotive industry

Project risk maturity models, which encompass change management, continuous improvement and knowledge management issues, can be used to improve risk management in projects. The purpose of this research is to develop and apply a new maturity model for the assessment and on-going management of project risk management capability in the automotive industry. The success of strategic projects is critical for innovation in the automotive industry, and project outcomes directly influence time to market and future revenues for companies operating in this sector. Projects in this industry are generally characterised as high risk. The belief that the use of carefully acquired information put into some kind of rational order can avoid poor decision making and project failure is the foundation of traditional project management and, by extension, of project risk management. Prescriptive guides and methodologies are often too mechanistic and simplistic as regards the risk management process. This research presents a theoretical framework applying the centricity concept to four major project risk management dimensions, namely risk identification, risk assessment, risk allocation and risk appetite. The centricity concept was critical in the development of several of the labels that are an integral part of the maturity model, thereby furthering the understanding of risk management. The research design is based on a multi-project case study analysis in a major German automotive company. The approach is qualitative and inductive, using 12 indepth interviews with major stakeholders in the project management function in the company to provide data for the construction of the initial maturity model. This model is then verified and refined via an online survey and three further follow-up interviews. The findings provide material for the construction of a new maturity model that can be used for the assessment of project risk management capability and as a tool for ongoing monitoring and improvement. The model is structured around the four dimensions of risk management – identification, assessment, allocation and appetite – and has four maturity stages – rudimentary, intermediate, standardised and corporate. The model is based on a detailed analysis of in-depth interview material in a specific industry sector. The model adds to existing risk management maturity models and is unique in being specific to the automotive industry. It can be used by risk and project managers, and can also be adapted to other industry sectors

Data analytics and algorithms in policing in England and Wales: Towards a new policy framework

RUSI was commissioned by the Centre for Data Ethics and Innovation (CDEI) to conduct an independent study into the use of data analytics by police forces in England and Wales, with a focus on algorithmic bias. The primary purpose of the project is to inform CDEI’s review of bias in algorithmic decision-making, which is focusing on four sectors, including policing, and working towards a draft framework for the ethical development and deployment of data analytics tools for policing. This paper focuses on advanced algorithms used by the police to derive insights, inform operational decision-making or make predictions. Biometric technology, including live facial recognition, DNA analysis and fingerprint matching, are outside the direct scope of this study, as are covert surveillance capabilities and digital forensics technology, such as mobile phone data extraction and computer forensics. However, because many of the policy issues discussed in this paper stem from general underlying data protection and human rights frameworks, these issues will also be relevant to other police technologies, and their use must be considered in parallel to the tools examined in this paper. The project involved engaging closely with senior police officers, government officials, academics, legal experts, regulatory and oversight bodies and civil society organisations. Sixty nine participants took part in the research in the form of semi-structured interviews, focus groups and roundtable discussions. The project has revealed widespread concern across the UK law enforcement community regarding the lack of official national guidance for the use of algorithms in policing, with respondents suggesting that this gap should be addressed as a matter of urgency. Any future policy framework should be principles-based and complement existing police guidance in a ‘tech-agnostic’ way. Rather than establishing prescriptive rules and standards for different data technologies, the framework should establish standardised processes to ensure that data analytics projects follow recommended routes for the empirical evaluation of algorithms within their operational context and evaluate the project against legal requirements and ethical standards. The new guidance should focus on ensuring multi-disciplinary legal, ethical and operational input from the outset of a police technology project; a standard process for model development, testing and evaluation; a clear focus on the human–machine interaction and the ultimate interventions a data driven process may inform; and ongoing tracking and mitigation of discrimination risk

Sustainable R&D portfolio assessment.

Research and development portfolio management is traditionally technologically and financially dominated, with little or no attention to the sustainable focus, which represents the triple bottom line: not only financial (and technical) issues but also human and environmental values. This is mainly due to the lack of quantified and reliable data on the human aspects of product/service development: usability, ecology, ethics, product experience, perceived quality etc. Even if these data are available, then consistent decision support tools are not ready available. Based on the findings from an industry review, we developed a DEA model that permits to support strategic R&D portfolio management. We underscore the usability of this approach with real life examples from two different industries: consumables and materials manufacturing (polymers).R&D portfolio management; Data envelopment analysis; Sustainable R&D;