Vista D2.1 Supporting Data for Business and Regulatory Scenarios Report

Vista examines the effects of conflicting market forces on European performance in ATM, through the evaluation of impact metrics on four key stakeholders, and the environment. The review of regulatory and business factors is presented. Vista will model the current and future (2035, 2050) framework based on the impact of regulatory and business factors. These factors are obtained from a literature review of regulations, projects and technological and operational changes. The current value of those factors and their possible evolution are captured in this deliverable

‘Modernisation’ and the role of policy levers in the Learning and Skills Sector

This paper examines the changing use of policy levers in the English post-compulsory education and training system, often referred to as the Learning and Skills Sector. Policy steering by governments has increased significantly in recent years, bringing with it the development of new forms of arms-length regulation. In the English context these changes were expressed during the 1980s and 1990s through neo-liberal New Public Management and, since 1997, have been extended through the New Labour government’s project to further ‘modernise’ public services. We look here at the changing use of policy levers (focussing in particular on the role of targets, funding, inspection, planning and initiatives) over three historical phases, paying particular attention to developments since the formation of the Learning and Skills Council (LSC) in 2001. We conclude by considering the range of responses adopted by education professionals in this era of ‘modernisation’

View on 5G Architecture: Version 2.0

The 5G Architecture Working Group as part of the 5GPPP Initiative is looking at capturing novel trends and key technological enablers for the realization of the 5G architecture. It also targets at presenting in a harmonized way the architectural concepts developed in various projects and initiatives (not limited to 5GPPP projects only) so as to provide a consolidated view on the technical directions for the architecture design in the 5G era. The first version of the white paper was released in July 2016, which captured novel trends and key technological enablers for the realization of the 5G architecture vision along with harmonized architectural concepts from 5GPPP Phase 1 projects and initiatives. Capitalizing on the architectural vision and framework set by the first version of the white paper, this Version 2.0 of the white paper presents the latest findings and analyses with a particular focus on the concept evaluations, and accordingly it presents the consolidated overall architecture design

Enterprise Systems: Installing and Configuring ERPNext on MacOS

Enterprise Resource Planning (ERP) systems integrate business processes across organizations onto unified digital platforms through data and workflow consolidation. However, high licensing costs of proprietary ERP solutions like SAP and Oracle limit adoption for small and medium enterprises. This led to the emergence of open-source ERP alternatives like ERPNext which provide sophisticated capabilities at much lower total cost of ownership. However, ERPNext faces documentation gaps that hamper onboarding, customization, and widespread adoption. Accelerating ERPNext implementation by developing a comprehensive installation and configuration guide tailored for developers using Mac environments will be examined furthermore. The background on ERP systems explores critical functions like process automation, analytics, and cost cutting made possible by unified data and workflows, examining the evolution from legacy vendors to open-source platforms. The need for improved documentation is established as a crucial driver for ERPNext adoption within small and medium businesses to realize open-source cost benefits. Current documentation gaps cause slowed implementation. Simplified visual installation guides can ease onboarding, community involvement, and customization of ERPNext to unique workflows. Enhanced documentation can drive viral ERPNext adoption by empowering self-implementation of tailored systems. Step-by-step manuals build user confidence to control enterprise systems over relying on costly consultants. This catalyzes ERPNext\u27s viability as an affordable yet sophisticated open source ERP alternative, making integrated automation and insights accessible across organization sizes

Data mining and predictive analytics application on cellular networks to monitor and optimize quality of service and customer experience

This research study focuses on the application models of Data Mining and Machine Learning covering cellular network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms have been applied on real cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: RStudio for Machine Learning and process visualization, Apache Spark, SparkSQL for data and big data processing and clicData for service Visualization. Two use cases have been studied during this research. In the first study, the process of Data and predictive Analytics are fully applied in the field of Telecommunications to efficiently address users’ experience, in the goal of increasing customer loyalty and decreasing churn or customer attrition. Using real cellular network transactions, prediction analytics are used to predict customers who are likely to churn, which can result in revenue loss. Prediction algorithms and models including Classification Tree, Random Forest, Neural Networks and Gradient boosting have been used with an exploratory Data Analysis, determining relationship between predicting variables. The data is segmented in to two, a training set to train the model and a testing set to test the model. The evaluation of the best performing model is based on the prediction accuracy, sensitivity, specificity and the Confusion Matrix on the test set. The second use case analyses Service Quality Management using modern data mining techniques and the advantages of in-memory big data processing with Apache Spark and SparkSQL to save cost on tool investment; thus, a low-cost Service Quality Management model is proposed and analyzed. With increase in Smart phone adoption, access to mobile internet services, applications such as streaming, interactive chats require a certain service level to ensure customer satisfaction. As a result, an SQM framework is developed with Service Quality Index (SQI) and Key Performance Index (KPI). The research concludes with recommendations and future studies around modern technology applications in Telecommunications including Internet of Things (IoT), Cloud and recommender systems.Cellular networks have evolved and are still evolving, from traditional GSM (Global System for Mobile Communication) Circuit switched which only supported voice services and extremely low data rate, to LTE all Packet networks accommodating high speed data used for various service applications such as video streaming, video conferencing, heavy torrent download; and for say in a near future the roll-out of the Fifth generation (5G) cellular networks, intended to support complex technologies such as IoT (Internet of Things), High Definition video streaming and projected to cater massive amount of data. With high demand on network services and easy access to mobile phones, billions of transactions are performed by subscribers. The transactions appear in the form of SMSs, Handovers, voice calls, web browsing activities, video and audio streaming, heavy downloads and uploads. Nevertheless, the stormy growth in data traffic and the high requirements of new services introduce bigger challenges to Mobile Network Operators (NMOs) in analysing the big data traffic flowing in the network. Therefore, Quality of Service (QoS) and Quality of Experience (QoE) turn in to a challenge. Inefficiency in mining, analysing data and applying predictive intelligence on network traffic can produce high rate of unhappy customers or subscribers, loss on revenue and negative services’ perspective. Researchers and Service Providers are investing in Data mining, Machine Learning and AI (Artificial Intelligence) methods to manage services and experience. This research study focuses on the application models of Data Mining and Machine Learning covering network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms will be applied on cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: R-Studio for Machine Learning, Apache Spark, SparkSQL for data processing and clicData for Visualization.Electrical and Mining EngineeringM. Tech (Electrical Engineering

Extending the Decision-Making Capabilities in Remanufacturing Service Contracts by Using Symbiotic Simulation

Remanufacturing is a critical enabler of a resource efficient manufacturing industry that has long been associated with high value products. Over time, the commercial relationship between customers and service providers has been made through the fulfilment of rights and obligations under remanufacturing service contracts. Nonetheless, financial analysis to evaluate the contract terms and conditions are becoming increasingly difficult to conduct due to complex decision problems inherent in remanufacturing systems. In order to achieve better and safer decision-making to shape the business strategies, remanufacturers often employ computer-based simulation tools to assess contractual obligations and customers’ needs. This paper discusses the roles of a symbiotic simulation system (SSS) in supporting decision-making in remanufacturing systems. An industrial case study of power transformer remanufacturing illustrates how SSS can support contract remanufacturers in managing service contracts planning and execution. By linking the simulation model to the physical system, it has been demonstrated that the capabilities of the remanufacturers to make critical decisions throughout the entire service contract period can be extended

A taxonomy for key performance indicators management

In recent years, research on Key Performance Indicators (KPIs) management has grown exponentially, giving rise to a multitude of heterogeneous approaches addressing any aspect concerning it. In this paper, we plot the landscape of published works related with KPIs management, organizing and synthesizing them by means of a unified taxonomy that encompasses the aspects considered by other proposals, and it captures the overall characteristics of KPIs. Since most of the literature centers on the definition of KPIs, we mainly focus on such an aspect of KPIs management. Our work is intended to provide remarkable benefits such as enhancing the understanding of KPIs management, or helping users decide about the most suitable solution for their requirements