Submitted by admin tede (tede@pucgoias.edu.br) on 2018-04-18T12:08:35Z No. of bitstreams: 1 ISIS JULIANE ARANTES GRANJA.pdf: 1309143 bytes, checksum: 3c564f96f6c30173df19ea1c01f8fe4b (MD5) / Made available in DSpace on 2018-04-18T12:08:35Z (GMT). No. of bitstreams: 1 ISIS JULIANE ARANTES GRANJA.pdf: 1309143 bytes, checksum: 3c564f96f6c30173df19ea1c01f8fe4b (MD5) Previous issue date: 2018-03-12 / This research aimed to analyze the application of exploratory statistical methods, in particular the PCA and HCA methods, in the analysis of data related to the production management in a chemical industry of household sanitation. The chemical industries of cleaning products, also known as sanitizing industries, shows a great market growth and consequently competitiveness, thus forcing the improvement in their productive performance. The software used in this analysis was the digital (commercial and managing system) and the "STATÍSTICA", version 7.0 (system of statistical calculations). The data are related is the 3A Química e Farmacêutica Ltda, located in the city of Caturaí, state of Goiás. Twelve months of the year 2016. The following variables were established: quantities produced and effectively sold of sanitizers in plastic gallons of 20 liters, 25 liters, 50 liters, 200 liters, 240 liters, gallons of can Iron) of 200 liters, boxes with 04 gallons of 05 liters; numbers of employees involved; cost with electric energy and global gross billing. The results obtained were analyzed through graphs and statistical tables, which represent three large groups of samples and effectively pointed to July as the most productive month of the year. It has also been shown that the rainy months are effectively the months of lower performance, which indicates a new strategy to be developed for improving this period and also considering that the production of boxes with 02 gallons of 5 liters and plastic gallons with 25 liters should be implemented to make the results even better. In this way, a methodology for the implementation of new packaging was consolidated in obtaining the improvement of productivity and performance in an industry of sanitation sector in the State of Goiás. / Esta pesquisa visou analisar a aplicação de métodos estatísticos exploratórios, em especial os métodos PCA e HCA, na análise de dados relativos à gestão de produção numa indústria química de saneantes domissanitários. As indústrias químicas de produtos de limpeza, também conhecidas como indústrias de saneantes, encontram-se em um grande crescimento de mercado e, consequentemente, competitividade, obrigando, assim, a melhoria em seu desempenho produtivo. A empresa analisada foi 3A Química e Farmacêutica Ltda, localizada no município de Caturaí, estado de Goiás. Os softwares utilizados nessa análise foram o da digital (sistema comercial e gerencial), pacote "R" e o “STATÍSTICA”, versão 7.0 (sistema de cálculos estatísticos). Os dados coletados referem-se ao período de doze meses do ano de 2016. Como variáveis estabeleceram-se as seguintes: quantidades produzidas e efetivamente vendidas de saneantes em galões plásticos de 20 litros, 25 litros, 50 litros, 200 litros, 240 litros, galões de lata (ferro) de 200 litros, caixas com 04 galões de 05 litros; números de funcionários envolvidos; custo com energia elétrica e faturamento bruto global. Os resultados obtidos foram analisados por meio de gráficos e tabelas estatísticas, resultados que configuram três grandes grupos de amostras e efetivamente apontaram para o mês de julho como sendo o mês mais produtivo do ano. Também se mostrou que os meses chuvosos são, efetivamente, os meses de menor desempenho, o que indica necessidade de elaborar uma nova estratégia para que esse quadro se modifique, sabendo, inclusive, que a produção de caixas com 02 galões de 5 litros e galões plásticos com 25 litros deverão ser implementadas para que os resultados sejam ainda melhores. Dessa forma se consolidou uma metodologia de implantação de novas embalagens na obtenção da melhoria da lucratividade e desempenho em uma indústria do setor de saneantes do Estado de Goiás.

Ausgangspunkt der vorliegenden Arbeit ist die Suche der gartenbaulichen Beratung nach Visualisierungsmöglichkeiten umfangreicher gartenbaulicher Datensätze, die einerseits zu einer graphischen Zusammenfassung der in den Daten enthaltenen Informationen dienen und die andererseits auf interaktivem Weg Möglichkeiten der graphischen Analyse von Erhebungsdaten liefern. Die weitgehende Freiheit von Modellannahmen, der überwiegend deskriptive Charakter der Untersuchungen, das interaktive, schrittweise Vorgehen in der Auswertung, und die Betonung graphischer Elemente kennzeichnet die Arbeit als Beitrag zur explorativen Datenanalyse. Das ausgewählte Methodenspektrum, das ausführlich besprochen wird, schließt Verfahren der Dimensionserniedrigung (Hauptkomponentenanalyse, Korrespondenzanalyse und mehrdimensionale Skalierung) und darauf aufbauende Biplots, die Analyse gruppierter Daten (Prokrustes-Rotation und Gruppenanalysemodelle in der Hauptkomponentenanalyse), Linienverbände (Liniendiagramme der formalen Begriffsanalyse, Baumdiagramme und graphische Modelle), sowie ergänzende graphische Verfahren, wie zum Beispiel Trellis-Displays, ein. Beispielhaft werden eine betriebsbegleitende Untersuchung mit Cyclamen aus der Beratungspraxis der Landwirtschaftskammer Westfalen-Lippe und die Kennzahlen der Jahre 1992 bis 1994 der Topfpflanzenbetriebe des Arbeitskreises für Betriebswirtschaft im Gartenbau aus Hannover analysiert. Neben einer Vielzahl informativer Einzelergebnisse, zeigt die Arbeit auch auf, daß die qualitativ relativ schlechten Datengrundlagen nur selten eindeutige Schlußfolgerungen zulassen. Sie sensibilisiert also in diesem Bereich für die Problematik, die der explorativen Analyse wenig perfekter Daten innewohnt. Als besonders sinnvolle Hilfsmittel in der graphischen Analyse erweisen sich Biplots, hierarchische Liniendiagramme und Trellis-Displays. Die Segmentierung einer Vielzahl von Objekten in einzelne Gruppen wird durch Klassifikations- und Regressionsbäume vor allem unter dem Gesichtspunkt der Visualisierung gut gelöst, da den entstehenden Baumstrukturen auch die die Segmente bestimmenden Variablen visuell entnommen werden können. Diskrete graphische Modelle bieten schließlich einen guten Ansatzpunkt zur Analyse von multivariaten Beziehungszusammenhängen. Einzelne, nicht in der statistischen Standardsoftware vorhandene Prozeduren sind in eigens erstellten Programmcodes zusammengefaßt und können mit dem Programm Genstat genutzt werden. / In order to interpret large data sets in the context of consultancy and extension in horticulture, this thesis attempts to find ways to visually explore horticultural multivariate data, in order to obtain a concise description and summary of the information available in the data and moreover develop possibilities to interactively analyse survey data. The thesis is part of an exploratory data analysis which analyses data without making specific model assumptions, is predominantly descriptive, analyses data step by step in a highly interactive setting, and makes full use of all kinds of graphical displays. The methods used comprise various dimensionality reduction techniques (principal components analysis, correspondence analysis, multidimensional scaling), biplots, the multivariate analysis of grouped data (procrustes rotation and groupwise principal components), graphical models, CART, and line diagrams of formal concept analysis. In addition, further graphical methods are used, like e.g. trellis displays. Data from an on-site investigation of the production process of Cyclamen in 20 nurseries and from the microeconomics indicators of 297 growers in Germany (so called Kennzahlen) from the years 1992 to 1994 are used to demonstrate the analytical capabilities of the methods used. The data present a perfect example of unperfect data, and therefore represent the majority of the data sets that horticultural consultancy has to work with. Thus, it becomes clear, that despite the variety of results, which helps to enhance the understanding of the data at hand, not only the complexity of the processes observed, but also the low data quality make it fairly difficult to arrive at clear cut conclusions. The most helpful tools in the graphical data analysis are biplots, hierarchical line diagrams and trellis displays. Finding an empirical grouping of objects is best solved by classification and regression trees, which provide both, the data segmentation, and an intuitively appealing visualisation and explanation of the derived groups. In order to understand multivariate relationships better, discrete graphical models are well suited. The procedures to carry out a number of the methods which cannot be found in general statistics packages are provided in the form of Genstat codes.