Pakkaus- ja graafisten kartonkien markkinakartoitus Suomessa. Case: Stora Enso Oyj:n Packaging Boards -tulosyksikön Imatran tehtaiden Kotimaanmyynti

Tutkielman aiheena on kartoittaa pakkaus- ja graafisten kartonkien markkinoita Suo-messa. Tutkimuksen teoriaosassa esitetään kilpailija-analyysi, asiakasanalyysi ja substituutit, joiden avulla yrityksen asemaa markkinoilla voidaan arvioida. Tutkimuksen empiirisessä osassa luotua teoriaa on sovellettu case-yrityksen markkinoiden kartoittamiseen. Tutkimusta varten on kerätty primääritietoa markkinatutkimuksen avulla, sekä hyödynnetty jo olemassa olevaa sekundääritietoa. Saatua materiaalia on arvioitu kvalitatiivisesti. Tutkimuksen tulokseksi saatiin kartoitus Suomen pakkaus- ja graafisten kartonkien markkinarakenteesta, kilpailijoista, asiakkaista ja substituuteista yleisellä tasolla. Samoin saatiin tulokseksi Stora Enso Packaging Boards -tulosyksikön Imatran tehtaiden Kotimaanmyynnin nykyisten ja potentiaalisten asiakkaiden ostokäyttäytymiseen vaikuttavia tekijöitä, sekä asiakkaiden mielipiteitä Kotimaanmyynnin tuotteista ja toiminnasta.

Challenges in Supplier-Buyer Relationships in Electronics Industry

This thesis examines the supplier-buyer relationships in the Finnish electronics industry. The aim of the study was to increase understanding on the challenges that suppliers face in their relationship with the buyer. The research was conducted using qualitative methods because they allow more perspective for the research problem than quantitative methods would have. Choosing qualitative method also affected the selection of a research technique. Analysis of secondary data from written documents was chosen to give more perspective to a broad problem. The main findings of this research are that the relationships between supplier and buyer in electronics industry are challenging because supplier must understand and face three types of challenges. The challenges are: understanding the environment, choosing and implementing correct strategy and managing relationships. For the supplier it is important to understand the environment so it can adjust own strategy to fit to the environment. The supplier should also be careful not to be too dependent on the buyer.

SPIRONOLACTONE FOR NONRESOLVING CENTRAL SEROUS CHORIORETINOPATHY: A Randomized Controlled Crossover Study.

PURPOSE: To evaluate the effect of spironolactone, a mineralocorticoid receptor antagonist, for nonresolving central serous chorioretinopathy. METHODS: This is a prospective, randomized, double-blinded, placebo-controlled crossover study. Sixteen eyes of 16 patients with central serous chorioretinopathy and persistent subretinal fluid (SRF) for at least 3 months were enrolled. Patients were randomized to receive either spironolactone 50 mg or placebo once a day for 30 days, followed by a washout period of 1 week and then crossed over to either placebo or spironolactone for another 30 days. The primary outcome measure was the changes from baseline in SRF thickness at the apex of the serous retinal detachment. Secondary outcomes included subfoveal choroidal thickness and the ETDRS best-corrected visual acuity. RESULTS: The mean duration of central serous chorioretinopathy before enrollment in study eyes was 10 ± 16.9 months. Crossover data analysis showed a statistically significant reduction in SRF in spironolactone treated eyes as compared with the same eyes under placebo (P = 0.04). Secondary analysis on the first period (Day 0-Day 30) showed a significant reduction in subfoveal choroidal thickness in treated eyes as compared with placebo (P = 0.02). No significant changes were observed in the best-corrected visual acuity. There were no complications related to treatment observed. CONCLUSION: In eyes with persistent SRF due to central serous chorioretinopathy, spironolactone significantly reduced both the SRF and the subfoveal choroidal thickness as compared with placebo.

Who's fault is it ? : An analysis of the press coverage of football betting scandals in France and the United Kingdom

Sport betting is a lucrative business for bookmakers, for the lucky (or wise) punters, but also for governments and for sport. While not new or even recent, the deviances linked to sport betting, primarily match-fixing, have gained increased media exposure in the past decade. This exploratory study is a qualitative content analysis of the press coverage of sport betting-related deviances in football in two countries (UK and France), using in each case two leading national publications over a period of five years. Data analysis indicates a mounting coverage of sport betting scandals, with teams, players and criminals increasingly framed as culprits, while authorities and federations primarily assume a positive role. As for the origin of sport betting deviances, French newspapers tend to blame the system (in an abstract way); British newspapers, in contrast, focus more on individual weaknesses, notably greed. This article contributed to the growing body of literature on the importance of these deviances and on the way they are perceived by sport organizations, legislators and the public at large.

Driver costs in small firms: Empirical analysis for farms

The agricultural sector has always been characterized by a predominance of small firms. International competition and the consequent need for restraining costs are permanent challenges for farms. This paper performs an empirical investigation of cost behavior in agriculture using panel data analysis. Our results show that transactions caused by complexity influence farm costs with opposite effects for specific and indirect costs. While transactions allow economies of scale in specific costs, they significantly increase indirect costs. However, the main driver for farm costs is volume. In addition, important differences exist for small and big farms, since transactional variables significantly influence the former but not the latter. While sophisticated management tools, such ABC, could provide only limited complementary useful information but no essential allocation bases for farms, they seem inappropriate for small farms

Driver costs in small firms: Empirical analysis for farms

The agricultural sector has always been characterized by a predominance of small firms. International competition and the consequent need for restraining costs are permanent challenges for farms. This paper performs an empirical investigation of cost behavior in agriculture using panel data analysis. Our results show that transactions caused by complexity influence farm costs with opposite effects for specific and indirect costs. While transactions allow economies of scale in specific costs, they significantly increase indirect costs. However, the main driver for farm costs is volume. In addition, important differences exist for small and big farms, since transactional variables significantly influence the former but not the latter. While sophisticated management tools, such ABC, could provide only limited complementary useful information but no essential allocation bases for farms, they seem inappropriate for small farms

A Comparative Study of Desktop, Fishtank, and Cave Systems for the Exploration of Volume Rendered Confocal Data Sets

We present a participant study that compares biological data exploration tasks using volume renderings of laser confocal microscopy data across three environments that vary in level of immersion: a desktop, fishtank, and cave system. For the tasks, data, and visualization approach used in our study, we found that subjects qualitatively preferred and quantitatively performed better in the cave compared with the fishtank and desktop. Subjects performed real-world biological data analysis tasks that emphasized understanding spatial relationships including characterizing the general features in a volume, identifying colocated features, and reporting geometric relationships such as whether clusters of cells were coplanar. After analyzing data in each environment, subjects were asked to choose which environment they wanted to analyze additional data sets in - subjects uniformly selected the cave environment.

Advanced chemometric methods: Applicability on industrial data

Recent years have produced great advances in the instrumentation technology. The amount of available data has been increasing due to the simplicity, speed and accuracy of current spectroscopic instruments. Most of these data are, however, meaningless without a proper analysis. This has been one of the reasons for the overgrowing success of multivariate handling of such data. Industrial data is commonly not designed data; in other words, there is no exact experimental design, but rather the data have been collected as a routine procedure during an industrial process. This makes certain demands on the multivariate modeling, as the selection of samples and variables can have an enormous effect. Common approaches in the modeling of industrial data are PCA (principal component analysis) and PLS (projection to latent structures or partial least squares) but there are also other methods that should be considered. The more advanced methods include multi block modeling and nonlinear modeling. In this thesis it is shown that the results of data analysis vary according to the modeling approach used, thus making the selection of the modeling approach dependent on the purpose of the model. If the model is intended to provide accurate predictions, the approach should be different than in the case where the purpose of modeling is mostly to obtain information about the variables and the process. For industrial applicability it is essential that the methods are robust and sufficiently simple to apply. In this way the methods and the results can be compared and an approach selected that is suitable for the intended purpose. Differences in data analysis methods are compared with data from different fields of industry in this thesis. In the first two papers, the multi block method is considered for data originating from the oil and fertilizer industries. The results are compared to those from PLS and priority PLS. The third paper considers applicability of multivariate models to process control for a reactive crystallization process. In the fourth paper, nonlinear modeling is examined with a data set from the oil industry. The response has a nonlinear relation to the descriptor matrix, and the results are compared between linear modeling, polynomial PLS and nonlinear modeling using nonlinear score vectors.

Looking beneath Dalí's paint: non-destructive canvas analysis

A new analytical method was developed to non-destructively determine pH and degree of polymerisation (DP) of cellulose in fibres in 19th 20th century painting canvases, and to identify the fibre type: cotton, linen, hemp, ramie or jute. The method is based on NIR spectroscopy and multivariate data analysis, while for calibration and validation a reference collection of 199 historical canvas samples was used. The reference collection was analysed destructively using microscopy and chemical analytical methods. Partial least squares regression was used to build quantitative methods to determine pH and DP, and linear discriminant analysis was used to determine the fibre type. To interpret the obtained chemical information, an expert assessment panel developed a categorisation system to discriminate between canvases that may not be fit to withstand excessive mechanical stress, e.g. transportation. The limiting DP for this category was found to be 600. With the new method and categorisation system, canvases of 12 Dalí paintings from the Fundació Gala-Salvador Dalí (Figueres, Spain) were non-destructively analysed for pH, DP and fibre type, and their fitness determined, which informs conservation recommendations. The study demonstrates that collection-wide canvas condition surveys can be performed efficiently and non-destructively, which could significantly improve collection management.

Analysis of patterns in mining time series using Qlucore

Raw measurement data does not always immediately convey useful information, but applying mathematical statistical analysis tools into measurement data can improve the situation. Data analysis can offer benefits like acquiring meaningful insight from the dataset, basing critical decisions on the findings, and ruling out human bias through proper statistical treatment. In this thesis we analyze data from an industrial mineral processing plant with the aim of studying the possibility of forecasting the quality of the final product, given by one variable, with a model based on the other variables. For the study mathematical tools like Qlucore Omics Explorer (QOE) and Sparse Bayesian regression (SB) are used. Later on, linear regression is used to build a model based on a subset of variables that seem to have most significant weights in the SB model. The results obtained from QOE show that the variable representing the desired final product does not correlate with other variables. For SB and linear regression, the results show that both SB and linear regression models built on 1-day averaged data seriously underestimate the variance of true data, whereas the two models built on 1-month averaged data are reliable and able to explain a larger proportion of variability in the available data, making them suitable for prediction purposes. However, it is concluded that no single model can fit well the whole available dataset and therefore, it is proposed for future work to make piecewise non linear regression models if the same available dataset is used, or the plant to provide another dataset that should be collected in a more systematic fashion than the present data for further analysis.

Gender Might Be Factor for Student Admission in Two Brazilian Medical Schools

This is a secondary data-based study conducted to investigate whether gender is related to acceptance. Two Brazilian Medical Schools, Universities A and B, were studied. Their entrance exams (EE) were analysed and the number of candidates who took the EE was compared to the number of students admitted to the MS according to gender, in the period between 1995 and 2009. The same data from MS in the United States in 2011 was also evaluated. There was an increase in the percentage of female applicants but it did not correspond to the percentage of admitted students of the same gender. There was a trend of selecting men. At A, 39.3% of the applicants and 47% of the admitted students were men (OR = 1.37; CI95% = 1.24 – 1.51). In B, men represented 39.3% of the applicants and 65.4% of the admitted students (OR = 2.93; CI 95% = 2.76 – 3.11). This was not seen in US MS. The analysis of the EE suggests that the greater selection of men could be a product of EE format.

Absorptive capacity of Multinational Corporation’s parent company

The objective of this paper is to contribute to the literature concerning absorptive capacity by revealing the factors affecting the absorptive capacity of MNC parent company toward subsidiary and most particularly the effects of intra-organizational antecedents. The theoretical framework is build around previous findings on knowledge sharing and absorptive capacity. The empirical part of the study is a qualitative research which includes in-depth interviews and analysis of secondary data based on a single case company. The results showed that organizational structure, internal communication, informal networks, formal networks, internationalization, human resources management, shared language, meetings, trust, participation in decision-making, level of awareness, IT system, level of adaptation to market specifications and job rotation influence parent company’s absorptive capacity. Moreover, related problems to these antecedents have been identified. Additionally, recommendations to solve these problems are formulated. In the end, directions for future research on this topic are given.

CLASSIFICATION OF FOREST ROADS AND DETERMINATION OF ROUTE USING GEOGRAPHIC INFORMATION SYSTEM

ABSTRACT Geographic Information System (GIS) is an indispensable software tool in forest planning. In forestry transportation, GIS can manage the data on the road network and solve some problems in transportation, such as route planning. Therefore, the aim of this study was to determine the pattern of the road network and define transport routes using GIS technology. The present research was conducted in a forestry company in the state of Minas Gerais, Brazil. The criteria used to classify the pattern of forest roads were horizontal and vertical geometry, and pavement type. In order to determine transport routes, a data Analysis Model Network was created in ArcGIS using an Extension Network Analyst, allowing finding a route shorter in distance and faster. The results showed a predominance of horizontal geometry classes average (3) and bad (4), indicating presence of winding roads. In the case of vertical geometry criterion, the class of highly mountainous relief (4) possessed the greatest extent of roads. Regarding the type of pavement, the occurrence of secondary coating was higher (75%), followed by primary coating (20%) and asphalt pavement (5%). The best route was the one that allowed the transport vehicle travel in a higher specific speed as a function of road pattern found in the study.

Epävarmuuden vaikutus strategisiin alliansseihin vuosina 2000-2010

Tässä pro gradu –tutkielmassa perehdyttiin globaalin telekommunikaatiosektorin allianssitoimintaan vuosina 2000-2010. Tutkimuksen tavoitteena oli tarkastella kvantitatiivisin menetelmin yrityskohtaisen ja makrotaloudellisen epävarmuuden vaikutusta solmittujen allianssien rakenteeseen, muotoon ja osapuolten maantieteelliseen sijaintiin. Lisäksi oli tarkoitus tutkia, kuinka allianssien vuosittainen määrä ja niihin osallistuvien yritysten määrä muuttuu epävarmuuden vaihtelujen myötä. Tutkielman empiirisen rungon muodosti sekundaarinen data SDC Platinum ja Thomson Datastream –tietokannoista. Lopulliseen aineistoon sisältyi 50 maailman suurinta telekommunikaatioyritystä useasta eri maasta. Tilastollinen analyysi suoritettiin logistisen ja paneelidataregression avulla. Tutkielman viidestä hypoteesista vain kaksi vahvistuivat osittain. Kyseiset hypoteesit olettivat epävarmuuden kasvun negatiivista vaikutusta vertikaalisten ja kotimaisten allianssien suosioon yrityksen silmissä. Muut regressiomallit tuottivat ristiriitaisia ja tilastollisesti ei-merkitseviä tuloksia.

An Overview of the Cleantech Sector - Evaluating Environmental Indicators

The purpose of the thesis is to give an overview of the cleantech sector and to give an answer how cleantech companies can evaluate the environmental sustainability of their business by utilizing various indicators and measures. The thesis is a literature study and it is based on a secondary data. Thesis presents the definitions to cleantech as well as its history and the main industries. Cleantech market overview in Finland and worldwide is also introduced. Furthermore, various indicators are presented in order to evaluate the environmental sustainability of companies' business. In the end, indicators used in cleantech sector are evaluated. As a result, the thesis presents the following methodologies that can be used in evaluating the environmental sustainability in the cleantech sector: Sustainability assessment framework, Environmental value analysis, COMPLIMENT - Environmental performance index for industries and Environmental assessment for cleaner production. More tools are still needed to evaluate environmental sustainability in the cleantech sector.