Uptime analytics

60 páginas : ilustraciones, mapas, gráficos con anexosLas empresas intensivas en activos pueden mejorar sus resultados, aumentando sus ingresos mediante la optimización en la administración de equipos. Para lograrlo, estas compañías deben evolucionar de un proceso de decisión basado únicamente en experiencia a la toma de decisiones basadas en la tecnología digital, adoptando nuevas y mejores metodologías. Uptime Analytics proporciona una solución que se vende directamente a empresas que hacen un uso intensivo de activos. Su solución incorpora tecnologías tales como modelos predictivos y metodologías de gestión de mantenimiento dinámico para mejorar la eficiencia de mantenimiento y los tiempos productivos de los activos. Uptime Analytics es una compañía colombiana que nace en el 2017 con el propósito de cambiar la forma en la que las compañías toman sus decisiones de mantenimiento. Compuesta por un equipo conformado por ingenieros de mantenimiento, analistas de negocio y científicos de datos enfocados brindando una solución innovadora en el mercado de consultorías para compañías intensivas en activos.Asset-intensive companies can improve their results, increasing their revenues by optimizing equipment management. To achieve this, these companies must evolve from a decision process based solely on experience to making decisions based on digital ·technology, adopting new and better methodologies. Uptime Analytics provides a solution that is sold directly to companies that make intensive use of assets. Its solution incorporates technologies such as predictive models and dynamic maintenance management methodologies to improve maintenance efficiency and productive times of assets. Uptime Analytics is a Colombian company born in 2017 with the purpose of changing the way in which companies make their maintenance decisions. Composed of a team consisting of maintenance engineers, business analysts and focused data scientists providing an innovative solution in the consulting market for asset-intensive companies.Especialista en Gerencia de MercadeoEspecializació

A Quality Model for Actionable Analytics in Rapid Software Development

Background: Accessing relevant data on the product, process, and usage perspectives of software as well as integrating and analyzing such data is crucial for getting reliable and timely actionable insights aimed at continuously managing software quality in Rapid Software Development (RSD). In this context, several software analytics tools have been developed in recent years. However, there is a lack of explainable software analytics that software practitioners trust. Aims: We aimed at creating a quality model (called Q-Rapids quality model) for actionable analytics in RSD, implementing it, and evaluating its understandability and relevance. Method: We performed workshops at four companies in order to determine relevant metrics as well as product and process factors. We also elicited how these metrics and factors are used and interpreted by practitioners when making decisions in RSD. We specified the Q-Rapids quality model by comparing and integrating the results of the four workshops. Then we implemented the Q-Rapids tool to support the usage of the Q-Rapids quality model as well as the gathering, integration, and analysis of the required data. Afterwards we installed the Q-Rapids tool in the four companies and performed semi-structured interviews with eight product owners to evaluate the understandability and relevance of the Q-Rapids quality model. Results: The participants of the evaluation perceived the metrics as well as the product and process factors of the Q-Rapids quality model as understandable. Also, they considered the Q-Rapids quality model relevant for identifying product and process deficiencies (e.g., blocking code situations). Conclusions: By means of heterogeneous data sources, the Q-Rapids quality model enables detecting problems that take more time to find manually and adds transparency among the perspectives of system, process, and usage.Comment: This is an Author's Accepted Manuscript of a paper to be published by IEEE in the 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018. The final authenticated version will be available onlin

Smart Asset Management for Electric Utilities: Big Data and Future

This paper discusses about future challenges in terms of big data and new technologies. Utilities have been collecting data in large amounts but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it. Condition monitoring of assets collects large amounts of data during daily operations. The question arises "How to extract information from large chunk of data?" The concept of "rich data and poor information" is being challenged by big data analytics with advent of machine learning techniques. Along with technological advancements like Internet of Things (IoT), big data analytics will play an important role for electric utilities. In this paper, challenges are answered by pathways and guidelines to make the current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on Engineering Asset Management (WCEAM) 201

On Video Game Balancing: Joining Player- and Data-Driven Analytics

Balancing is, especially among players, a highly debated topic of video games. Whether a game is sufficiently balanced greatly influences its reception, player satisfaction, churn rates and success. Yet, conceptions about the definition of balance diverge across industry, academia and players, and different understandings of designing balance can lead to worse player experiences than actual imbalances. This work accumulates concepts of balancing video games from industry and academia and introduces a player-driven approach to optimize player experience and satisfaction. Using survey data from 680 participants and empirically recorded data of over 4 million in-game fights of Guild Wars 2, we aggregate player opinions and requirements, contrast them to the status quo and approach a democratized quantitative technique to approximate closer configurations of balance. We contribute a strategy of refining balancing notions, a methodology of tailoring balance to the actual player base and point to an exemplary artifact that realizes this process.Comment: 25 pages, 5 figure

What are the key multidimensional success criteria required for reducing LCOE through digital transformation in offshore wind farms?

Formålet med denne studien er å undersøke de flerdimensjonale suksesskriteriene som er avgjørende for å redusere energikostnaden også kjent som Levlized cost of Energy (LCOE) gjennom digital transformasjon innenfor offshore vind prosjekter. For å besvare problemstilling vil studien sette søkelys på fire underspørsmål som omhandler: (1) For å sikre operational excellence og tilpasning til FNs bærekraftsmål gjennom digital transformasjon: Hvilke suksessfaktorer må være på plass? (2) Er data tilgjengelig for bruk til den digital transformasjon? (3) Hvordan kan man muliggjør optimal Grid Integration av vindparken? (4) Kan man utnytte digitale verktøy for å redusere LCOE i en havvindpark? Studien fremhever den uunnværlige rollen av teknologi i form av digitale verktøy og data, som spiller som katalysatorer for å styrke operasjonell effektivitet og maksimere verdiskaping i offshore vindenergisektoren. Studien er gjennomført som kvalitativ Case-studier analyse i form av ti individuelle dybdeintervjuer med deltakere fra ulike selskaper i verdikjeden til offshore vind industri. Studien undersøker den betydelige påvirkningen FNs bærekraftsmål har på utviklingen av offshore vindprosjekter, samt den vitale rollen operational excellence har for å lykkes. Den vurderer om offshore vind industrien er klar for Industri 5.0, dens evne til å redusere LCOE, og dens innflytelse på sektorens fremtid. Funnene understreker betydningen av tilgjengelig data, optimalisert effektivitet, og bruk av sanntidsdata for å forbedre sikkerhet, bærekraft og effektiv energiproduksjon i vindparker. Videre dykker studien ned i implementeringen av digital transformasjon, og viser til hvordan digitale verktøy og automatisering, sammen med menneskelig inngripen, driver informert beslutningstaking. Funnene legger vekt på nødvendigheten av datasamarbeid, kunnskapsdeling, og kompetent personell for å fremme industriell vekst, samtidig som det opprettholdes en balanse mellom kompleksitet og kompetanse, og utforsker avansert digital tvilling-teknologi og hvordan det kan påvirke i redusering av LCOE. Studien tilbyr verdifull innsikt for interessenter og hjelper til med å håndtere utfordringer og muligheter i digital transformasjon av offshore vindparker. Den fremhever offshore vinindustriens avgjørende rolle i utviklingen av renere, effektive energisystemer, og støtter en bærekraftig og fremgangsrik fremtid.This purpose of this study is to thoroughly examine the multidimensional success criteria crucial in reducing the levelized cost of energy (LCOE) through digital transformation within the context of offshore wind farm projects. To help answer the research question, this study will focus on four preliminary research questions: (1) To ensure Operational Excellence and Alignment with UN SDGs through Digital Transformation: What success factors need to be in place? (2) Is Data available to be used to enable Digital Transformation? (3) How do you enable optimal Grid Integration of the wind park? (4) Can you leverage digital tools to reduce LCOE in an offshore wind farm? The research spotlights the indispensable role of technology in form of digital tools and data, as catalysts for bolstering operational efficiency and maximizing value creation in the offshore wind energy sector. The study has been carried out as a qualitative case study analysis in the form of ten individual in-depth interviews with participants from various companies in the value chain of the offshore wind industry. The study investigates the substantial impact of United Nations (UN) sustainability goals on offshore wind project development and the vital role of operational excellence. It evaluates the industry's preparedness for Industry 5.0, its capacity to reduce LCOE, and its influence on the sector's future. The research and findings underscore the significance of accessible data, optimized efficiency, and real-time data utilization to enhance safety, sustainability, and energy production in wind farms. Additionally, the research delves into Industry 5.0's implementation, demonstrating how digital tools and automation, combined with human input, drive informed decision-making. The findings emphasize the necessity for data collaboration, knowledge sharing, and skilled personnel to foster industry growth while maintaining a balance between complexity and competence and explores advanced digital twin technology and how it can influence in reducing LCOE. The study offers valuable insights for stakeholders and aids in addressing challenges and opportunities in offshore wind farm digital transformation. It accentuates the offshore wind industry's pivotal role in advancing cleaner, efficient energy systems, promoting a sustainable and prosperous future