Abstract In agile software processes, the issue of team size is an important one. In this work we look at how to find the optimal, or near optimal, self-organizing team size using a genetic algorithm (GA) which considers team communication efforts. Communication, authority, roles, and learning are the team’s performance characteristics. The GA has been developed according to performance characteristics. A survey was used to evaluate the communication weight factors, which were qualitatively assessed and used in the algorithm’s objective function. The GA experiments were performed in different stages: each stage results were tested and compared with the previous results. The results show that self-organizing teams of sizes ranged from five to nine members scored more. The model can be improved by adding other team characteristics, i.e. software development efforts and costs.

As-built engineering information is mission critical to safe, reliable operations. Due to inevitable modifications throughout an asset's life, the quality and availability of as-built information erodes over time and is no longer indicative of the actual as-operated facility. This paper highlights the new technology, methods and processes in as-built campaigns. Also, it explains why it is necessary to move from a document-centric to data­ centric approach in engineering information management. Abu Dhabi Marine Operating Company (ADMA-OPCO) as-built approach is illustrated utilizing laser scanning technology, consolidating the legacy engineering information combined with site survey data capture to build intelligent databases, 3D models and 2D engineering deliverables. ADMA-OPCO leverages these data silos into an ElMS (Engineering information Management System) to provide a single source of organized and interlinked as-built asset Information. ADMA-OPCO implemented procedures for quality assurance through data completeness, consistency across various sources, reporting and solving the issues impacting information integrity and reliability. Having a digital asset throughout the life cycle of an operating plant creates a sound basis for asset modifications, reduces the need of site visits for data collection and site surveys, minimizes the exposure of engineering personnel to potentially hazardous environments and provides quick and high quality input for decision making. The paper reveals the limitations of a document centric approach to management of asset information and explains how rigid, non-integrated information databases lead to data silos and the impact on the as-built information quality, resource productivity and operational safety. This paper raises awareness of the benefits of utilizing the digital asset to gain instant access to asset engineering objects and their associated references. The digital asset can be used to support effective decision making and ensure data is centralized, accurate, change-controlled and highly accessible in the context of the processes it supports.

The successful delivery of mega projects is a challenging terrain. ADMA-OPCO continues to face numerous challenges of providing technical assurance to multiple mega projects running in parallel while meeting not only short and medium term expansion goals, but also ensuring a sustainable long term future. The objective of this paper is to share ADMA-OPCO’s experience and success factors in driving Technical Assurance during projects execution. At ADMA OPCO, the Discipline Engineering Division (DED) acts as the center of technical expertise in support of all ADMA’s business activities and in particular, in the delivery of the ongoing complex project programs. The DED is part of the Projects and Engineering Business Unit, in a seamless integration with the multiple project teams. DED is a custodian of Technical Assurance,Providing engineering & subject matter experts (SME) expertise to projects,Participating in project reviews and witnessing selected functional and performance testsDeveloping and updating relevant engineering technical codes & standards and assuring their compliance in all ADMA-OPCO projectsBeing actively involved in the technical evaluation and selection external contractors,Reviewing and evaluating project changes through a Management of Change (MOC),Ensuring consistency across projects in terms of technical compliance,Being actively involved in all project phases from idea development to execution,Managing inter projects interface issues utilizing access to overall long term plansActing as an authority for novel technology selection and adoptionAssuring the continuous improvement of the technical skill set within the company. At ADMA OPCO, the current organizational setup of an integrated technical assurance provider and project teams is a model that is proving to be successful when executing multiple parallel mega projects.

In ADNOC Oil and Gas 4.0 mission, we are committed to empower people with the needed capabilities and Artificial Intelligence (AI) technologies to fuel innovation, efficiency and more importantly achieve and sustain a 100% HSE, by transforming the way of handling HSE events by moving from reactive to proactive approach. The ultimate objective is to save lives, empower the vessel Captains to immediately identify and respond to violators, improve the HSE culture of the crew, and automatically generate live data analytics and statistics with the aim of improving safety in operations. The implemented AI use cases are; deviation for not wearing Protective Safety equipment in designated areas, violation of not utilizing safety passages, alert when no watchman in muster station, alarm when man overboard incident, alarm when man fell on stairs, and live Personnel on board each weather-deck. When introduced the Artificial Intelligence cameras, our marine vessels will adopt a smarter automated response and reporting culture, which will in turn, lead to increased safety oversight of our critical offshore operations. Therefore, with the advent of the AI technology, many common business processes have been automated thus enabling personnel to increase their focus on more important tasks while technologies like the AI System can handle many of the time consuming tasks. The solution components consists of Artificial Intelligence platform, high definition cameras, local server, wide-range WiFi access point, network infrastructure and a tablet. On the tablet device, the captain have full coverage of the vessel weather decks, working areas and restricted zones with a feature to generate alerts when detecting an emergency situation. This was provided to empower the vessel Captain to acknowledge and respond to violations as well as take a proactive action to prevent incidents from happening. The Machine Learning algorithm has been trained on actual scenarios and will be continuously improved by adding more recorded event to retrain the initial model. Currently, the prediction model is performing on the vessel operation mode and recording events with high rate of accuracy. In case of automatically detecting an alerting or non-compliance event, the captain would be notified, beacon lights and sound, and log recorded in the local and central system with a photo and a short video clip of the incident. The process of identifying HSE deviations are becoming digitally transformed by deploying AI capabilities on real-time video streams. The AI-based camera system leverages Computer Vision features that enables machines to get and analyze visual information and take action. The whole process of identifying HSE violation events has been digitally transformed by deploying an artificial intelligence solution to perform real time video analytics.

Machine Performance is evaluated and indicated by several factors such as vibration, bearing temperature, pressure and flow among other important factors used in machine maintenance and repair decision. The majority of the Gas Turbine condition monitoring system depends on single factor with fixed values to judge the condition of the machine. In this paper the Authors proposed using algorithmic approach to model multiple factors, by developing a model using machine conditions monitoring data to forecast and anticipate the Machin's performance. Following HBR the new Technology Stack for connected smart devices which has three core elements: the mechanical and electrical parts are the physical components; the sensors, control systems, data storage, OS and software are the smart components; and the connection between all parts as the connectivity components. The capability of the connected smart devices can go through 4 Stages (Monitoring -> Control -> Optimization -> Autonomy). The scope of this paper will cover the optimization stage only, but also depends on the monitoring and control stages. The computer systems and artificial intelligence used to build a Machine Learning (ML) model that can predict the machine performance. The predicted results of this model can help operations to take the right decision in an advance proactive steps. The Support Victor Machine (SVM) learning algorithm has been used to training the model based on actual site data. The results of training and test data were accurate. The model which predicts the machine condition/status is promising and will provide the operators and the site engineers with a proactive support tool.