Choosing a data collection and analytics tool is no easy task. There are numerous tools available and no tool fits all. The choice of the tool depends on various factors and especially what type of organization and product the tool will be used for. Startups generally have less resources than established companies and the set of tools they can select therefore is limited. Therefore, the study has focused on finding and determining which tool is best suited for a startup. The study has chosen five criterias to evaluate a selected number of tools; setup, features, usability, privacy and cost. The criterias have been selected based on interviews, research and theoretical background. The study’s practical work has been performed at a startup in order to get real-world insight in how the tools operate and function. The tools evaluated are; Mixpanel, Amazon Pinpoint, and Google Sheets. The tools were selected based on answers from questionnaires, interviews and research. The result of the study shows that out of the three tools evaluated, Mixpanel is regarded as the most optimal tool for startups. Mixpanel is cost-effective, easy to set up, offers multiple features and is overall a user-friendly tool. It especially allows the company to have a transparent and strong privacy aspect. Allowing full control of the data collected and analyzed. However, what the study also concluded is that no specific tool is always best for everyone and the organizations should always understand their needs in order to pick the most suitable tool. / Att välja ett datainsamlings- och analysverktyg är ingen lätt uppgift. Det finns många verktyg men inget verktyg passar alla. Val av verktyget beror på olika faktorer särskilt vilken typ av organisation och produkt, verktyget ska användas till. Nystartade företag har i regel mindre resurser än etablerade företag vilket gör att verktyg de kan välja är begränsad. Därför har studien fokuserat på att hitta och avgöra vilket verktyg är bäst lämpat för en startup. Studien har valt fem kriterier för att utvärdera ett utvalt antal verktyg; installation, funktioner, användbarhet, integritet och kostnad. Kriterierna har valts ut utifrån intervjuer, forskning och teoretisk bakgrund. Studiens praktiska arbete har utförts vid en startup för att få verklig insikt i hur verktygen fungerar. De verktyg som utvärderas är; Mixpanel, Amazon Pinpoint, och Google Kalkylark. Verktygen valdes ut utifrån svar från enkäter, intervjuer och forskning. Resultatet av studien visar att av de tre utvärderade verktygen så anses Mixpanel vara det mest optimala verktyget för startups. Mixpanel är kostnadseffektivt, lätt att installera, erbjuder många funktioner och är generellt användarvänligt verktyg. Det tillåter företaget att ha transparent och stark integritet. Utöver det, tillåter Mixpanel full kontroll över data som samlas i och analyseras. Däremot, slutsatsen som kan också dras av studien är att inget specifikt verktyg alltid är bäst för alla och organisationerna bör alltid första sina behov för att välja det mest lämpliga verktyget.

Due to increasing data complexity, researchers are struggling to visually explore data using traditional methods. Research has shown that a variety of analytics-related tasks can be enhanced using new emerging technologies (e.g., virtual reality [VR] and augmented reality [AR]). This paper provides a detailed design approach for developing an immersive virtual engine system for conducting exploratory and descriptive data analytics. This system is called the Immersive Virtual Exploratory Engine (IVEE), is a VR system that allows users to experience full intractability with its graphical elements and examines various datasets within the same immersive environment. Basic plots—such as histograms, line plots, and other exploratory analytics—can be created in both 2D and 3D to visualize datasets. Furthermore, the proposed module provides a data sub-setting system, entitled “Lasso,” as well as a visual element merge system that allows users to subset and merge data using natural interactions only. The system also allows for the simultaneous visualization of multiple representations, thus supporting decisions that require numerous plots. Furthermore, the system supports remote collaboration, allowing users from different locations to come together in a virtual space and work collaboratively as they would in a real-life setting.

Laser powder bed fusion is one of the most effective ways to achieve metal additive manufacturing. However, this method still suffers from deformation, delamination, dimensional error, and porosities. One of the most significant issues is poor printing accuracy, especially for small features such as turbine blade tips. The main reason for the shape inaccuracy is the heat accumulation caused by using constant laser power regardless of the shape variations. Due to the highly complex and dynamic nature of the laser powder bed fusion, improving the printing quality is challenging. Research gaps exist from many perspectives. For example, the lack of understanding of multi-physical melt pool dynamics fundamentally impedes the research progress. The scarcity of a customizable laser powder bed platform further restricts the possibility of testing the improvement strategies. Additionally, most state-of-the-art quality inspection techniques suitable for laser powder bed fusion are costly in economic and time aspects. Lastly, the rapid and chaotic printing process is hard to monitor and control. This dissertation proposes a complete research scheme including a fundamental physics study, process signature and quality correlation, smart additive manufacturing platform development, high-performance sensor development, and a robust real-time closed-loop control system design to address all these issues. The entire research flow of this dissertation is as follows: 1. This work applies and integrates three advanced sensing technologies: synchrotron X-ray imaging, high-speed IR camera, and high-spatial-resolution IR camera to characterize the melt pool dynamics, keyhole, porosity formation, vapor plume, and thermal evolution in Ti-64 and 410 stainless steel. The study discovers a strong correlation between the thermal and X-ray data, enabling the feasibility of using relatively cheap IR cameras to predict features that can only be captured using costly synchrotron X-ray imaging. Such correlation is essential for thermal-based melt pool control. 2. A highly customizable smart laser powder bed fusion platform is designed and constructed. This platform integrates numerous sensors, including but not limited to co-axial cameras, IR cameras, oxygen sensors, photodiodes, etc. The platform allows in-process parameter adjusting, which opens the boundary to test various control theories based on multi-sensing and data correlations. 3. To fulfill the quality assessment need of laser powder bed fusion, this dissertation proposes a novel structured light 3D scanner with extraordinary high spatial resolution. The spatial resolution and accuracy are improved by establishing hardware selection criteria, integrating the proper hardware, designing a scale-appropriate calibration target, and developing noise reduction procedures during calibration. Compared to the commercial scanner, the proposed scanner improves the spatial resolution from 48 µm to 5 µm and the accuracy from 108.5 µm to 0.5 µm. 4. The final goal of quality improvement is achieved through designing and implementing a real-time closed-loop system into the smart laser powder bed fusion platform. The system regulates the laser power based on the monitoring result from a novel thermal sensor. The desired printing temperature is found by correlating the laser power, the dimensional accuracy, and the thermal signatures from a set of thin-wall structure printing trails. An innovative high-speed data acquisition and communication software can operate the whole system with a graphic user interface. The result shows the laser power can be successfully controlled with 2 kHz, and a significant improvement in small feature printing accuracy has been observed. / Doctor of Philosophy / Laser powder bed fusion is one of the most effective ways to achieve metal additive manufacturing. However, this method still suffers from defects such as deformation, delamination, dimensional error, and porosities. Due to the highly complex and dynamic nature of the laser powder bed fusion, improving the printing quality is challenging. Research gaps exist from many perspectives, such as the lack of understanding of melt pool dynamics; the scarcity of a customizable laser powder bed platform; the need for suitable sensors; and the missing of a control system that can effectively regulate the rapid and chaotic printing process. This dissertation proposes a complete research scheme to address all these issues. The fundamental study characterizes the melt pool dynamics and discovers a strong correlation between the melt pool thermal and geometrical data, enabling thermal-based melt pool control. Following that, a highly customizable smart laser powder bed fusion platform is designed and constructed. The platform allows in-process parameter changes, opening the boundary to test various control theories. A novel structured light 3D scanner with an ultra-high spatial resolution was proposed to fulfill the quality assessment need. The final goal of quality improvement is achieved through designing and implementing a real-time closed-loop system into the smart laser powder bed fusion platform. The system regulates the laser power based on real-time thermal monitoring. The result shows the laser power can be successfully controlled with 2 kHz, and a significant improvement in printing accuracy is achieved.

Abstract: Title: The emergence of Big Data and Auditors' Perception (A comparative study on India and Bangladesh) Aim: The aim of the study is to explore and compare the perception of auditors in India and Bangladesh towards the implementation of big data analytics in audit. Method: In this study a qualitative method has been applied using semi-structured interviews. The study is an exploratory research and has been analysed thematically. Results and conclusions: Employing the Technology Acceptance Model (TAM) as a conceptual framework, this study conducted a comparative analysis of auditors' perceptions, emphasizing the components of perceived usefulness, perceived ease of use, intention to adopt, and their interactions. The results of the study show that the intention to adopt big data analytics tools emerges as a shared aspiration among auditors from both India and Bangladesh.

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Объектом исследования являются cстатистические методы контроля производственных процессов. Предмет исследования – разработка ПО для статистического анализа производственных данных. В данном исследовании производится разработка программного обеспечения для контроля производственных процессов путём визуализации показателей датчиков оборудования методом контрольных карт Шухарта с использованием средства Jupyter Lab. / The object of this research are statistical production control methods. The subject of research is the development of an application for the statistical analysis of production process data. This research consists of developing software for production process control using visualization of machinery sensor data using Shewhart control charts with Jupyter Lab.