The dissertation "Enhancing Automotive Manufacturing Quality and Reducing Variability Through Six Sigma Principles" provides a thorough analysis of the ways in which Six Sigma techniques can be applied to the automotive manufacturing sector to improve quality control, reduce variability, and boost operational efficiency. Utilizing a diverse of secondary data sources, such as industry reports, case studies, academic research articles, and one-on-one consultations, this study seeks to offer important insights into the implementation and efficacy of Six Sigma principles in the context of automotive manufacturing. By stressing the fundamental ideas of Six Sigma outlined by Deming and Juran and scrutinizing influential works in quality management, the literature study builds a solid theoretical basis. The study's goals and research questions centre on comprehending how Six Sigma improves quality and lowers variability in automobile production processes. This research finds important insights on how Six Sigma may improve quality control, lower process variability, and increase operational efficiency in the automobile manufacturing industry via thorough secondary data analysis. The research offers useful insights into using Six Sigma approaches, emphasizing the significance of staff involvement, data-driven decision-making, and leadership commitment in guaranteeing the success of Six Sigma projects. The thesis ends with suggestions for further research, such as investigating primary data gathering techniques, contrasting this methodology with other approaches to quality management, and using longitudinal analysis to monitor the long-term effects of Six Sigma projects. In summary, this dissertation advances our knowledge of how Six Sigma concepts may be used to promote operational excellence and continuous improvement in the automobile manufacturing sector. It also provides practitioners and stakeholders in the industry with insightful information

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2018-07-12T16:11:01Z No. of bitstreams: 1 Mauricius Munhoz de Medeiros_.pdf: 3956183 bytes, checksum: b542e87eedac0be0bf8f79f48709672f (MD5) / Made available in DSpace on 2018-07-12T16:11:01Z (GMT). No. of bitstreams: 1 Mauricius Munhoz de Medeiros_.pdf: 3956183 bytes, checksum: b542e87eedac0be0bf8f79f48709672f (MD5) Previous issue date: 2018-02-27 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Este estudo investigou o impacto das capacidades de inteligência analítica de negócios na expansão das capacidades cognitivas gerenciais, orientando a tomada de decisões (com base nos dados), de modo ágil (dinâmico), para a melhoria da gestão do desempenho organizacional. Explicou-se o fenômeno sob a perspectiva teórica das capacidades dinâmicas. Para a definição dos construtos, foram revisados, também, os elementos teóricos a respeito das capacidades de inteligência analítica de negócios e tomada de decisões. Executou-se uma pesquisa de métodos mistos, desenhada em duas etapas. A primeira, exploratória, realizada através de entrevistas com 10 gestores, permitiu o mapeamento dos relacionamentos e a identificação das variáveis, oportunizando o desenvolvimento do instrumento quantitativo. A segunda, confirmatória, realizada através de uma survey com 366 respondentes, cujos resultados foram analisados para validar o instrumento de pesquisa e mensurar o impacto por meio da modelagem de uma equação estrutural, confirmando-se 5 das 7 hipóteses definidas no modelo conceitual. O cerne da discussão está na explicação do impacto das capacidades de inteligência analítica de negócios na tomada decisões, onde os achados evidenciam impacto significativo das capacidades de inteligência analítica gerencial, governança e processamento de grandes dados, e analítica avançada de negócios. A pesquisa contribui para a teoria, por ter explicado as capacidades de inteligência analítica de negócios como capacidades dinâmicas, bem como pelo desenvolvimento e validação de um instrumento para a mensuração integrada dessas capacidades. Para o campo gerencial, o estudo aponta direcionamentos e recomendações ao indicar potencialidades e limitações para o desenvolvimento dessas capacidades. / This study investigated the impact of business analytical intelligence capabilities on the expansion of managerial cognitive capabilities, orienting decision making (based on data) in an agile (dynamic) way, to improve organizational performance management. The phenomenon was explained according to the theoretical perspective of dynamic capabilities. For the definition of the constructs, the theoretical elements regarding business analytical intelligence capabilities and decision making were also reviewed. A mixed-method research was carried out in two stages. The first, which was exploratory, was conducted through interviews with 10 managers and allowed the mapping of relationships and identification of variables, allowing the development of the quantitative instrument. The second, which was confirmatory, was performed through a survey with 366 interviewees, which results were analyzed to validate the research instrument and measure the impact through the modeling of a structural equation, confirming 5 of the 7 hypotheses defined in the conceptual model. The heart of the discussion lies in the explanation of the impact of business analytical intelligence capabilities on decision making, in which the findings evidence significant impact of managerial analytical intelligence capabilities, governance and the processing of big data, and advanced business analytics. This research contributes to the theory by explaining business analytical intelligence capabilities as dynamic capabilities, as well as by developing and validating an instrument for the integrated measurement of these capabilities. For the managerial field, this study points out directions and recommendations when indicating potentialities and limitations for the development of these capabilities.

For smaller business it is important to have good and concrete data to make decisions because there are no big margins to test and fail or go on intuition. The purpose of this study is to create an understanding of how data can be used as a decision-making tool in small business. This paper studies the data two smaller companies collect and how they use the collected data. The study has a qualitative method with interviews which have been used for collecting result. The informants were selected based on their previous knowledge and experience.The collection of data in smaller business is both from internal and external sources that complement each other. It is important to have an understanding and knowledge of the visualizations otherwise it can be misleading. Visualizations are used to find patterns, trends and other affecting factors in the data. Segmentation is used by smaller business to understand their target market, customers and which customers they should direct their attention to. Data-driven decision making uses different sources of information, visualization and segmentation. With data-driven decision making it is important to maintain the overall perspective of the business, while all parts of the business are involved in the process. / För mindre företag är det viktigt att ha bra och konkret data för att fatta beslut eftersom det inte finns så stora marginaler att testa sig fram eller gå på intuition. Syftet med studien är att skapa förståelse för hur data kan användas som ett beslutsfattande verktyg i mindre företag. Studien undersöker vilken data mindre företag samlar in samt hur de använder den data som samlas in. Studien är en kvalitativ där intervjuer använts för datainsamling. Dessa gjordes på två mindre företag där informanterna valdes ut baserat på deras tidigare kunskaper och erfarenheter.Insamlingen av mindre företagens data sker både från interna och externa källor som kompletterar varandra. Det viktigt att ha förståelse och kunskap om visualiseringarna för att de inte ska bli missvisande. Visualiseringar används för att lättare hitta mönster, trender och andra påverkande faktorer i data. Segmentering används hos mindre företag för att förstå företagets målgrupp, kunder och vilka kunder de ska rikta sin uppmärksamhet till. För datadriven beslutsfattning används olika informationskällor, visualiseringar och segmentering. Med datadriven beslutsfattning är det viktigt att behålla helhetsperspektiv över företaget samtidigt som alla delar av företaget är involverade i processen.

<div>During 2017 and 2018, two of the costliest years on record regarding natural disasters, the U.S. experienced 30 events with total losses of $400 billion. These exuberant costs arise primarily from the lack of adequate planning spanning the breadth from pre-event preparedness to post-event response. It is imperative to start thinking about ways to make our built environment more resilient. However, empirically-calibrated and structure-specific vulnerability models, a critical input required to formulate decision-making problems, are not currently available. Here, the research objective is to improve the resilience of the built environment through an automated vision-based system that generates actionable information in the form of probabilistic pre-event prediction and post-event assessment of damage. The central hypothesis is that pre-event, e.g., street view images, along with the post-event image database, contain sufficient information to construct pre-event probabilistic vulnerability models for assets in the built environment. The rationale for this research stems from the fact that probabilistic damage prediction is the most critical input for formulating the decision-making problems under uncertainty targeting the mitigation, preparedness, response, and recovery efforts. The following tasks are completed towards the goal.</div><div>First, planning for one of the bottleneck processes of the post-event recovery is formulated as a decision making problem considering the consequences imposed on the community (module 1). Second, a technique is developed to automate the process of extracting multiple street-view images of a given built asset, thereby creating a dataset that illustrates its pre-event state (module 2). Third, a system is developed that automatically characterizes the pre-event state of the built asset and quantifies the probability that it is damaged by fusing information from deep neural network (DNN) classifiers acting on pre-event and post-event images (module 3). To complete the work, a methodology is developed to enable associating each asset of the built environment with a structural probabilistic vulnerability model by correlating the pre-event structure characterization to the post-event damage state (module 4). The method is demonstrated and validated using field data collected from recent hurricanes within the US.</div><div>The vision of this research is to enable the automatic extraction of information about exposure and risk to enable smarter and more resilient communities around the world.</div>

Nowadays, becoming data-driven is the vision of almost all organizations. However, achieving this vision is not as easy as it may look like and there are many factors that affect, enable, support and sustain the data-driven ecosystem in an organization. Among these factors, this study focuses on data democracy which can be defined as the intra-organizational open data that aims to empower the employees getting faster and easier access to data in order to benefit from the business insight they need without the interfere of external help.  In the existing literature, while the importance of becoming data-driven has been widely discussed, when it comes to data democracy within organizations, there is a noticeable gap. As a result, this master’s thesis aims to justify the importance and role of the data democracy in becoming a data-driven organization, focusing on the case of a Swedish bank. Additionally, it intends to provide extra investigation on the role of data analytics tools in achieving data democracy.  The results of the study show that there is a strong connection between the benefits of the empowering different actors of the organization with the needed data knowledge, and the speeding up of the data-driven transformation journey. Based on the study, shared data and the availability of data to a larger number of stakeholders inside an organization result into a better understanding of different aspects of the problems, simplify the data-driven decision making and make the organization more data-driven. In the process of becoming data-driven, the organizations should provide the analytics tools not only to the data specialists but even to the non-data technical people. And by offering the needed support, training and collaboration possibilities between the two groups of employees (data specialists and non-data specialists), it should be attempted to enable the second group to extract the insight from the data, independently from the help of the data scientists.  An organization can succeed in the path of becoming data-driven when they invest on the reusable capabilities of its employees, by discovering the data science skills across various departments and turning their domain experts into citizen data scientists of the organization.

The enactment of the NCLB Act of 2001 and its legislative mandates for accountability testing throughout the nation brought to the forefront the issue of data-driven decision making. This emphasis on improving education has been spurred due to the alleged failure of the public school system. As a result, the role of administrators has evolved to incorporate data-driven decision-making practices to help make educational choices. While the underlying assumption of implementing data-driven decision making is that it will lead to improvements in education, this has yet to be empirically proven. The purpose of the study was to analyze the relationships among school characteristics, principals' level of experience, principals' data-driven decision making practices, and student achievement. This census study addressed principals of k-5 public elementary schools. In this quantitative study, a web-based survey was used to measure principals' data-driven ion-making practices. The student achievement data examined were the California Standards Test results for English language arts and mathematics for the 2009–2010 and 2010–2011 school years. Through a series of multiple regression analyses, the study examined the relationships among school characteristics, principals' level of experience, principals' data-driven decision making practices, and student achievement. Specifically. this study explored the amount of variance in student achievement scores in language arts and mathematics that could be explained by school characteristics, principals' level of experience, and data-driven decision-making practices. The results showed principals are incorporating data-driven decision-making practices in k-5 public elementary schools in California. In addition, the results showed that principals believe the quality of their decision making has improved due to implementing data-driven decision making. Principals indicated they were incorporating practices identified in the four constructs used in the present study: (a) establishing a data-driven culture, (b) data-driven decision making by teachers to improve student achievement, (c) supporting systems for DDDM, and (d) collaboration among teachers using data-driven decision making. A strong negative correlation was found between the number of students on free and reduced lunch and student achievement.

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The use of technology in assisting educators to use student data in well-devised ways to enhance the instruction received by students is gaining headway and the support of federal dollars across the nation. Since research has not provided insight as to whether or not reading coaches are using data technology tools with teachers, this mixed methods study sought to examine what behavioral intentions reading coaches have in using data technology tools with teachers, what variables may influence their behavioral intentions, and what trends may emerge in their views about using technology data tools with teachers. A mixed methods approach was deployed via a survey embedded in an email, and data from 61 Florida reading coaches from elementary, middle, and high schools in a large urban school district were examined using an adaptation of the Technology Acceptance Model (TAM). The results showed that collectively all reading coaches have a high level of behavioral intentions towards using a data technology tool with teachers. The study also showed that elementary, middle, and high school reading coaches vary in their degree of behavioral intentions in using a data technology tool based on different variables. Trends in data showed that reading coaches think data technology tools are helpful, but that trainings are needed and that technology tools should be user-friendly. Discussion is provided regarding the implications of the study results for all stakeholders.

Digitalisering sker idag både i privat som offentlig sektor där datadriven beslutsfattning är en av trenderna. En teknologi som vuxit fram i samband med digitaliseringen och som hjälper verksamheter utvecklas är Self Service Business Intelligence (SSBI). Offentliga verksamheters digitala utveckling går långsammare än för privata bolag. Studien syftar till att undersöka vilka utmaningar offentliga verksamheter ställs inför i sin användning av SSBI samt att presentera hanteringsförslag på dessa utmaningar. För att besvara studiens frågeställning och uppfylla dess syfte har en kvalitativ forskningsansats använts. Semistrukturerade intervjuer har genomförts där respondenterna har varit personer som arbetar på offentliga verksamheter alternativt mot offentliga verksamheter. Studien resulterade i fyra utmaningar som är vanligt förekommande inom offentlig verksamhet och som inte lyfts i tidigare litteratur. Dessa fyra är; diversifierade verksamheter, ledningen, lagar och säkerhet samt begränsad självständighet. För varje utmaning har förslag diskuterats för hur utmaningarna effektivt kan hanteras. Studiens slutsats kan vara hjälpsam för offentliga verksamheter i deras fortsatta utveckling mot att bli datadrivna i sin beslutsfattning. Med hjälp av datadriven beslutsfattning möjliggörs för offentliga verksamheter att arbeta mer hållbart och bli mer resurseffektiva. / Digitization is today taking place in both private and public sectors, wheredata driven decision making is one of the trends. Self Service BusinessIntelligence (SSBI) is a technology that has emerged in conjunction with thedigital development and is helping businesses to develop. However, thedigital development in public organizations tends to be slower than forprivate companies. Therefore, this study aims to examine the challengesfaced by public organizations in their use of SSBI and also to presentproposals for addressing these challenges.To answer the research question and fulfill the study's purpose, a qualitativeresearch approach has been used with an abductive thinking. Semistructured interviews have been conducted with respondents who work in orwith public organizations. The study resulted in four challenges that arecommon in public organizations and that have not been addressed inprevious literature. These four challenges are diversified organizations, themanagement, laws and security, limited self-reliance. For each challenge,proposals have been discussed for how the challenges can be effectivelyaddressed. This study conclusion can be helpful for public organizations inthe continued development towards becoming data driven decision making.With the help of data driven decision making, public organizations can workmore sustainably and become more resource efficient.

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