Learning-based testing is a testing paradigm that combines model-based testing with machine learning algorithms to automate the modeling of the SUT, test case generation, test case execution and verdict construction. A tool that implements LBT been developed at the CSC school at KTH called LBTest. LBTest utilizes machine learning algorithms with off-the-shelf equivalence- and model-checkers, and the modeling of user requirements by propositional linear temporal logic. In this study, it is be investigated whether LBT may be suitable for testing a micro bus architecture within an embedded telecommunication device. Furthermore ideas to further automate the testing process by designing a data model to automate user requirement generation are explored. / Inlärningsbaserad testning är en testningsparadigm som kombinerar model-baserad testning med maskininlärningsalgoritmer för att automatisera systemmodellering, testfallsgenering, exekvering av tester och utfallsbedömning. Ett verktyg som är byggt på LBT är LBTest, utvecklat på CSC skolan på KTH. LBTest nyttjar maskininlärningsalgoritmer med färdiga ekvivalent- och model-checkers, och modellerar användarkrav med linjär temporal logik. I denna studie undersöks det om det är lämpat att använda LBT för att testa en mikrobus arkitektur inom inbyggda telekommunikationsenheter. Utöver det undersöks även hur testprocessen skulle kunna ytterligare automatiseras med hjälp av en data modell för att automatisera generering av användarkrav.

The Nordic power system is in a changing phase, with more power electronic interfaced connections, and HVDC connections to other synchronous areas, which impacts the frequency quality. This is a challenge for the transmission system operator of Sweden, Svenska Kraftnät, who, with the other Nordic transmission system operators, is responsible for the physical balancing of the Nordic power system. To ensure that the grid can maintain stable operation when power imbalances or a disturbance occur, the frequency quality is important to evaluate. According to the current measurement of frequency quality, minutes outside of the standard frequency band, the frequency quality in the Nordic power system has been deteriorating. The current measurement does not capture what impact the frequency quality and needs to be redefined, with more precise measurements, for Svenska Kraftnät to be able to take necessary actions to ensure stability of the power system. Therefore, the purpose of this project is to determine which key performance indices, KPIs, can be used to develop the definition of frequency quality, and which system parameters are captured by the different KPIs. This project is executed with simulations in Matlab/Simulink to determine the impact five system parameters have on 15 different KPIs, and the results from the simulations are validated with historical data. The results indicate that all system parameters can be captured with KPIs, but after validation with historical data only two system parameters, which correlated with four KPIs, were deemed to be valid. The amount of FCR-N energy activated can be captured with the standard deviation of frequency, frequency area, and number of FCR-D activations. The kinetic energy can be captured by the standard deviation of RoCoF. The KPIs are recommended to be used to identify, and measure, the impact of new technical requirements for frequency control, and how the frequency stability is impacted by changes in the system. The conclusion is that four key performance indices are recommended to improve the definition of frequency quality, and further research is recommended to further define the concept of frequency quality. / För tillfället sker stora förändringar i det nordiska elsystemet. Fler produktionsanläggningar och industrier ansluts med kraftelektronik och fler HVDC-anslutningar byggs till andra synkronområden, vilket har stor inverkan på frekvenskvalitén. Detta leder till nya utmaningar för det svenska transmissionsnätets systemansvariga myndighet, Svenska Kraftnät, som gemensamt med sina nordiska motsvarigheter är ansvariga för den fysiska balanseringen i det nordiska synkronområdet. Det är viktigt att kontinuerligt följa upp frekvenskvalitén i transmissionsnätet så att stabil drift kan garanteras både vid mindre obalanser eller i händelse av större störningar. Det nuvarande nyckeltalet för uppföljning av frekvenskvalité, minuter utanför normalbandet, indikerar att frekvenskvalitén i det nordiska synkronområdet har blir sämre de senaste åren. Det nuvarande nyckeltalet indikerar inte vad som påverkar frekvenskvalitén och behöver omdefinieras med mer precisa nyckeltal för att Svenska Kraftnät ska kunna vidta nödvändiga åtgärder för att garantera en stabil drift av transmissionnätet. Syftet med examensarbetet är att definiera nya nyckeltal för att beskriva frekvenskvalité samt undersöka vilka systemparametrar som fångas av de olika nyckeltalen. I detta projekt har simuleringar utförts i Matlab/Simulink för att bestämma den inverkan fem olika systemparametrar har på 15 olika nyckeltal. Resultaten från simuleringar valideras med historisk data. Resultaten från simuleringen indikerar att de fem systemparametrarna kan fångas av olika kombinationer av de föreslagna nyckeltalen, men efter validering med historisk data bedöms enbart två systemparametrar fångas av totalt fyra nyckeltal. Systemparametern aktiverad FCR-N energi fångas av tre nyckeltal: frekvensens standardavvikelse, frekvensarea och antal FCR-D-aktiveringar. Systemparametern kinetisk energi fångas av ett nyckeltal: frekvensderivatans standardavvikelse. De fyra identifierade nyckeltalen kan användas för att identifiera och mäta hur frekvenskvalitén påverkas av nya tekniska krav, samt hur frekvensstabilitet påverkas av förändringar i systemet. Slutsatsen för detta projekt är att fyra nya nyckeltal rekommenderas för att utveckla definitionen av frekvenskvalité. Vidare arbete rekommenderas för att kunna utveckla begreppet vidare.

The medical device manufacturing industry has a deficiency in determining how to improve supplier performance for the components and systems they purchase. Many complex medical devices require components from superb suppliers. But how does a medical device manufacturer (MDM) impartially assess supplier performance to know which suppliers to continuing with (or even boost purchase volumes) and which suppliers they should exit? This study describes which supplier-specific metrics are most important to medical device manufacturers (MDMs) so they can utilize this supplier performance scorecard backed by real-world inputs. This research will focus on five categories to measure MDM supplier performance (Quality, Price, Delivery, Customer Service, and Partnership) across twenty-three (23) metrics. Because this is a focus of MDM supplier performance, we are not focusing on analysis of device sales to the final customer (e.g., distributors or group purchasing organizations {GPO}). The study will follow a framework including research analysis of supplier performance management in other industries, methods to attain data from MDMs via survey, results and analysis of the data, conclusions, and an easily understandable MDM supplier performance scorecard. In the survey, 135 MDM professionals replied when asked to rate twenty-three (23) supplier performance metrics, across five (5) categories aggregated from nine (9) different industries. The survey yielded a myriad of results including, weighting factors of each of the metrics, and those data results were used to compile an MDM supplier performance scorecard utilizing real-world feedback. The analysis revealed the ratings of importance of the categories as: Quality (43%), Delivery (24%), Customer Service (4%), Partnership (13%), Price (8%) and associated weights for the twenty-three (23) metrics that matter most to an MDM when creating a performance scorecard for their supplier base. Three contributions that this research will add to the body of knowledge: An in-depth review of supplier performance across many different industries (i.e., non-healthcare and healthcare) for contrasting/comparing evidence. A detailed MDM survey and statistical analysis on the topic of supplier performance management. An easily understandable and useable MDM supplier performance scorecard (via MS Excel) for MDM supply chain and/or operations users and/or managers.

La tesis doctoral que nos acontece, muestra las premisas básicas acerca de la investigación que se realiza sobre la implantación de Mantenimiento Preventivo y Predictivo en industrias de Proceso, gracias al desarrollo y personalización de la metodología RCM a partir del AMFEC. La metodología RCM, sirve para determinar las actividades de mantenimiento reactivas y proactivas, con objeto de optimizar la fiabilidad de los activos industriales. Como antecedentes de la tesis doctoral se comenta que la metodología RCM, sirve para determinar las actividades de mantenimiento reactivas y proactivas, con objeto de optimizar la fiabilidad de los activos industriales. Se basa en la obra de John Moubray, donde se basa la implantación del RCM en 7 preguntas. Los métodos de cálculo de criticidades que actualmente se reflejan en los estudios actuales, denotan la oportunidad de desarrollo de métodos de cálculo donde se tengan en cuenta multitud de variables. El número de ponderación del riesgo (NPR) para cada modo de fallo, viene calculándose en función de tres parámetros gravedad, frecuencia de fallos y detectabilidad, siendo habitualmente valorados de 1 a 10, pero invariantes ante variaciones de parámetros técnicos y de operación. Los planes de mantenimiento, aunque bien definidos por las obras de expertos, se vislumbra la oportunidad de desarrollarlos en función de tipos de equipo, tipos de mantenimiento y criticidad de NPR de modos de fallos. Los estudios de indicadores de gestión, son amplios y muy bien desarrollados, aunque para la detección de repeticiones de incidencias en el Mantenimiento Correctivo, se requiere de una definición o desarrollo. El objetivo principal del presente estudio es ayudar a la implantación de un método de gestión técnica y económica de activos, basado en la actual implantación de RCM, Mantenimiento Peventivo e indicadores de gestión y definir un método para implantar Mantenimiento Preventivo y Predictivo, controlar las incidencias, costes y aplicar soluciones técnico-económicas. / Gardella González, M. (2010). Mejora de metodología RCM a partir del AMFEC e implantación de mantenimiento preventivo y predictivo en plantas de procesos [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/9686

El Terrorismo alimentario ha sido definido por la Organización Mundial de la saludos como "un acto o intento deliberado de contaminación de alimentos para consumo humano con agentes químicos, físicos o microbiológicos para el propósito de causar daño o muerte a poblaciones civiles o para interrumpir la estabilidad social, política o económica"(WHO,2008) La biodiversidad, según la Association of food and drug Officials de los EEUU, abarca los medios para prevenir y eliminar cualquier acción intencional de adulteración de alimentos destinada a provocar consecuencias negativas graves para la salud o la muerte de personas o animales, ocasionar daños a las economías de los paises como consecuencia de retricciones comerciales internacionales derivadas de la aparición de enfermedades y la falta de confianza en los controles sanitarios locales... / Navarrete Reynoso, R. (2013). MODELO DE BIOSEGURIDAD EN LA CADENA DE SUMINISTROS DE PRODUCTOS ALIMENTICIOS, TENIENDO EN CUENTA LA GESTIÓN DE LA CADENA DE SUMINISTROS Y LA VISIÓN DE PROCESOS DE NEGOCIO. APLICACIÓN A LA INDUSTRIA ALIMENTICIA, DE LA ZONA DEL BAJÍO (MÉXICO) [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19159

Tesis por compendio / [ES] La digitalización de los procesos de RRHH en soluciones basadas en la nube progresa continuamente. Esta tesis examina tales transformaciones, deriva un modelo de proceso concreto e identifica los factores críticos de éxito. La metodología utilizada para la investigación es de carácter cualitativo. Como base y medida preparatoria para abordar las cuestiones de investigación, se llevó a cabo un amplio estudio bibliográfico en el ámbito de los recursos humanos, con especial atención a las publicaciones sobre la gestión electrónica de los recursos humanos (en adelante, "e-HRM"). Basándose en este conocimiento y en la combinación de una amplia experiencia práctica con proyectos de transformación de RRHH, se publicó un estudio que presenta el desarrollo histórico de e-HRM y que ha derivado en un modelo de procesos optimizado que tiene en cuenta los requisitos técnicos de RRHH así como las limitaciones de la nueva tecnología de la nube. Posteriormente, se entrevistó a varios expertos en RRHH que ya habían adquirido experiencia de primera mano con los procesos de RRHH en un entorno de nube para averiguar qué factores de éxito eran relevantes para dicha transformación de RRHH. Las principales conclusiones de esta tesis son la derivación de un modelo de procedimiento de proyecto de mejores prácticas para la transformación de los procesos de RRHH en una solución basada en la nube y la identificación de obstáculos potenciales en la implementación de dichos proyectos. Además, se elaboran los motivos de dicha transformación, los factores que impulsan el proceso dentro de una organización, el grado actual de digitalización de los recursos humanos, los parámetros operativos y estratégicos necesarios y, en última instancia, el impacto en los métodos de trabajo. Como resultado, se realiza una evaluación del uso de las métricas de HR y se derivan nuevas ratios potenciales. / [CA] La digitalització dels processos de RRHH en solucions basades en el núvol progressa contínuament. Aquesta tesi examina tals transformacions, deriva un model de procés concret i identifica els factors crítics d'èxit. La metodologia utilitzada per a la investigació és de caràcter qualitatiu. Com a base i mesura preparatòria per a abordar les qüestions d'investigació, es va dur a terme un ampli estudi bibliogràfic en l'àmbit dels recursos humans, amb especial atenció a les publicacions sobre la gestió electrònica dels recursos humans (en endavant, "e-HRM "). Basant-se en aquest coneixement i en la combinació d'una àmplia experiència pràctica amb projectes de transformació de RRHH, es va publicar un estudi que presenta el desenvolupament històric d'e-HRM i que ha derivat en un model de processos optimitzat que té en compte els requisits tècnics de RRHH així com les limitacions de la nova tecnologia del núvol. Posteriorment, es va entrevistar a diversos experts en RRHH que ja havien adquirit experiència de primera mà amb els processos de RRHH en un entorn de núvol per esbrinar quins factors d'èxit eren rellevants per a aquesta transformació de RRHH. Les principals conclusions d'aquesta tesi són la derivació d'un model de procediment de projecte de millors pràctiques per a la transformació dels processos de RRHH en una solució basada en el núvol i la identificació d'obstacles potencials en la implementació d'aquests projectes. A més, s'elaboren els motius de la transformació, els factors que impulsen el procés dins d'una organització, el grau actual de digitalització dels recursos humans, els paràmetres operatius i estratègics necessaris i, en última instància, l'impacte en els mètodes de treball . Com a resultat, es realitza una avaluació de l'ús de les mètriques de HR i es deriven nous ràtios potencials. / [EN] The digitisation of HR processes into cloud-based solutions is progressing continuously. This thesis examines such transformations, derives a concrete process model and identifies the critical success factors. The methodology used for the investigation is of a qualitative nature. As a basis and preparatory measure to address the research questions, an extensive literature study in the HR field was carried out, with a special focus on publications on electronic human resources management (hereinafter e-HRM). Based on this knowledge and the combination of extensive practical experience with HR transformation projects, a study was published which presents the historical development of e-HRM and derived an optimised process model taking into account the technical HR requirements as well as the limitations of the new cloud technology. Subsequently, several HR experts who had already gained first-hand experience with HR processes in a cloud environment were interviewed to find out which success factors were relevant for such an HR transformation. Main findings of this thesis are the derivation of a best-practice project procedure model for the transformation of HR processes into a cloud-based solution and the identification of potential obstacles in the implementation of such projects. In addition, the motives for such a transformation, the drivers within an organisation, the current degree of HR digitisation, the necessary operational and strategic parameters and ultimately the impact on working methods are worked out. As a further result, an assessment of the use of HR metrics is given and potential new key figures are derived. / Ziebell, R. (2019). Digital transformation of HR - History, implementation approach and success factors - Cumulative PhD Thesis [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/117608 / Compendio

With six major truck companies holding similar market shares in Europe, advancements and improvements are crucial to avoid falling behind. However, knowing what aspects to improve is paramount, as focusing on the wrong areas can lead to wasted resources and time. At the operational level, collecting accurate data and utilizing it effectively is essential for learning and progress. One effective method is to gather data on process losses and measure effectiveness accordingly. Overall equipment effectiveness (OEE) and overall process effectiveness (OPE), originating from total productive maintenance (TPM), are commonly used metrics for this purpose.   To ensure comprehensive assessment, three main effectiveness measurements are necessary. OEE evaluates machine effectiveness, total effective equipment performance (TEEP) considers planned maintenance, and OPE encompasses all losses, internal and external.   In this thesis, all three effectiveness measurements are applied to Scania's pallet disassembly process. Due to its high variability, traditional TPM methods cannot be directly applied. Therefore, adaptations were made. Time became the common factor for measurement, eliminating the need for cycle times and unit outcomes. Additionally, the aspect of quality was excluded, given the nature of the disassembly process and its minimal quality loss.   The implemented method involves two solutions: one currently in use and one proposed for the future. The current approach involves data entry by an operator into an Excel sheet at the end of each shift. This data is then transformed into OEE, TEEP, and OPE pie charts, allowing for weekly analysis of shift, day, and week effectiveness. As for future recommendations, replacing the programmable logic controller (PLC) would enable real-time effectiveness monitoring and process simulations.

KPI(Key Performance Index)是一個落後指標，需要一套更及時的領先指標，以動態和系統化的角度，及時展現每一位員工的執行績效和顧客滿意度指標，以數位化看板的概念報導員工對公司的貢獻度和顧客對服務的接受度，因為預先管控和報導，可以確保KPI 的達成，在服務品質的改善和顧客滿意度提升，有很大的助益。. 本研究希望發展出一套具體的線上及時管控指標，讓系統整合公司可以提升顧客信賴和持續服務品質改善，而將重點發展方向轉移到提供更好的內容、產品、服務、系統的品質，以及合理的價格。 因此採用平衡計分卡的四大構面為面向，ITIL服務生命週期之IT服務管理為主軸，參考PZB三位教授之服務品質概念性模式和SERVQUAL量表衡量構面及項目，配合系統整合公司的運作，歸納出一個服務品質改善架構流程圖，並以其為基礎發展一套服務品質改善的及時管控指標；運用商業智慧、數位儀表版等工具設計以支援主管決策管控和員工績效的報導系統。

The aim of this work is to develop a universal procedure introducing the concept of IT Governance using COBIT methodology to a small business environment. This thesis understands COBIT as a tool with which is possible to create a new business strategy for a firm and which will provide more competitive force for the firm in the competitive fight. The main contribution of this thesis is a theoretical research, which resulted in the proposal as how should a small company (which close-up characteristic is described in more detail in the work) proceed in a case that it decides to efficiently manage, manage and control the business IS / IT. This theoretical approach is then tested as a case study on a real small enterprise.

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 Engineering / M. Tech (Electrical Engineering)