Offshore oil and gas exploitation is principally conducted using dry or wet tree systems, otherwise called the subsea Xmas tree system. Due to the shift to deeper waters, subsea production system (SPS) has come to be a preferred technology with attendant economic benefits. At the centre of the SPS is the subsea control module (SCM), responsible for the proper functioning and monitoring of the entire system. With increasing search for hydrocarbons in deep and ultra-deepwaters, the SCM system faces important environmental, safety and reliability challenges and little research has been done in this area. Analysis of the SCM reliability then becomes very fundamental due to the huge cost associated with failure. Several tools are available for this analysis, but the FMECA stands out due to its ability to not only provide failure data, but also showcase the system’s failure modes and mechanisms associated with the subsystems and components being evaluated. However, the technique has been heavily challenged in various literatures for several reasons. To close this gap, a novel multi-criteria approach is developed for the analysis and ranking of the SCM failures modes. This research specifically focusses on subsea tree-mounted electro-hydraulic (E-H) SCM responsible for the underwater control of oil and gas production. A risk identification of the subsea control module is conducted using industry experts. This is followed by a comprehensive component based FMECA analysis of the SCM conducted with the conventional RPN technique, which reveals the most critical failure modes for the SCM. A novel framework is developed using multi-criteria fuzzy TOPSIS methodology and applied to the most critical failure modes obtained from the FMECA evaluation using unconventional parameters. Finally, a validation of these results is performed using a stochastic input evaluation and SCM failure data obtained from the offshore industry standard reliability database, OREDA.

Offshore oil and gas exploitation is principally conducted using dry or wet tree systems, otherwise called the subsea Xmas tree system. Due to the shift to deeper waters, subsea production system (SPS) has come to be a preferred technology with attendant economic benefits. At the centre of the SPS is the subsea control module (SCM), responsible for the proper functioning and monitoring of the entire system. With increasing search for hydrocarbons in deep and ultra-deepwaters, the SCM system faces important environmental, safety and reliability challenges and little research has been done in this area. Analysis of the SCM reliability then becomes very fundamental due to the huge cost associated with failure. Several tools are available for this analysis, but the FMECA stands out due to its ability to not only provide failure data, but also showcase the system’s failure modes and mechanisms associated with the subsystems and components being evaluated. However, the technique has been heavily challenged in various literatures for several reasons. To close this gap, a novel multi-criteria approach is developed for the analysis and ranking of the SCM failures modes. This research specifically focusses on subsea tree-mounted electro-hydraulic (E-H) SCM responsible for the underwater control of oil and gas production. A risk identification of the subsea control module is conducted using industry experts. This is followed by a comprehensive component based FMECA analysis of the SCM conducted with the conventional RPN technique, which reveals the most critical failure modes for the SCM. A novel framework is developed using multi-criteria fuzzy TOPSIS methodology and applied to the most critical failure modes obtained from the FMECA evaluation using unconventional parameters. Finally, a validation of these results is performed using a stochastic input evaluation and SCM failure data obtained from the offshore industry standard reliability database, OREDA.

In the Swedish railway network there are about 12000 units of track switches and crossings, which at 13000 Km, make up about 5.5 percent of the total track length. However, the maintenance cost for S&C is more than 13 percent of the total maintenance cost which is high in comparison with their proportion. The aim of the project is to conduct research into classification of the different modes of failure in S&C components and to perform a statistical analysis to converge the data in order to determine the most important failures that occur in turnouts.

<p>The internal and external challenges construction companies face such as variability, low productivity, inefficient processes, waste, uncertainties, risks, fragmentation, adversarial contractual relationships, competition, and those resulting from internal and external challenges such as cost overruns and delays negatively affect company performance and profitability. Though research publications abound, these challenges persist, which indicates that the following gaps exist. Lean construction, process improvement, and performance improvement research have been conducted wherein improvement principles, and best practices are used to ameliorate performance issues, but several knowledge gaps exist. Few companies use these improvement principles and best practices. For those companies applying improvements, there is no established link between these improvements and performance/profitability to guide companies. Further, even when companies use improvement principles and best practices, they apply only one or two, whereas an integrated application of these improvement principles and best practices would be more effective. The other gap the author identified is the lack of strategic tools that construction companies can use to improve and manage their profitability. This thesis tried to fill the knowledge gap, at least partially, by developing a two-part excellence model for profitability improvement of construction companies. The excellence model lays out strategies that would enable companies to overcome the challenges and improve their profitability. The excellence model also gives an iterative and recursive continuous improvement model and flowchart to improve the profitability of construction companies. The researcher used high impact principles, guidelines, and concepts from the literature on organizational effectiveness, critical success factors, strategic company profitability growth enablers, process improvement, and process maturity models, performance improvement, and organizational excellence guidelines to develop the two-part excellence model.</p> <p>The author also translated the two-part excellence model into the diagnostic tool and Decision Support System (DSS) by use of process diagrams, fishbone diagrams, root cause analysis, and use of improvement principles, countermeasures and best practices at the most granular (lowest intervention) levels to do away with root causes of poor performance. The author developed the diagnostic tool and Decision Support System (DSS) in Access 2016 to serve as a strategic tool to improve and manage the profitability of construction companies. The researcher used improvement principles, and best practices from scientific and practitioner literature to develop company and project process flow diagrams, and fishbone (cause and effect) diagrams for company, department, employee, interactions and project performance for the profitability improvement, which are the engines of the diagnostic tool and DSS. The diagnostic tool and DSS use continuous improvement cycles iteratively and recursively to improve the profitability of construction companies from the current net profit of 2-3 percent to a higher value.</p>