Developing cost of poor quality calculations with emphasis on business impact

The main aim of this research was to develop cost of poor quality calculation model which will better reflect business impacts of lost productivity caused by IT incidents for the case company. This objective was pursued by reviewing literature and conducting a study in a Finnish multinational manufacturing company. Broad analysis of the scientific literature allowed to identify main theories and models of Cost of Poor Quality and provided better base for development of measurements of business impacts of lost productivity. Empirical data was gathered with semi-structured interviews and internet based survey. In total, twelve interviews with experts and 39 survey results from business stakeholders were gathered. Main results of empirical study helped to develop the measurement model of cost of poor quality and it was tied to incident priority matrix. Nevertheless, the model was created based on available data. Main conclusions of the thesis were that cost of poor quality measurements could be even further improved if additional data points could be used. New model takes into consideration different cost regions and utilizes on this notion.

Corporate green image communication through public disclosures and social media

Today, companies need to mind the environment in all their actions. Policies, regulations and growing pressure from environmentally conscious public are driving corporations to invest increasingly in their green images. Communication plays a key role in forming and maintaining that image. This thesis explores how six selected companies communicate about their environmental efforts and activities, and its linkage to their green images, in annual and sustainability reports and in Facebook. The companies come from the U.S. and Europe and operate in three different industries: ICT, oil and gas, and aerospace & defense. Qualitative and quantitative content analyses are conducted to examine 36 reports and 121 Facebook messages, collected from the period of 2010-2014, and from 2005 for comparison. The results show that although the quality and quantity of environmental disclosure is increasing, there is still room for improvement. Overall, disclosure in the ICT sector is on the highest level. The European companies disclose more and on average have stronger green images than the American ones. Emissions and ways to reduce them is by far the most covered topic in both continents and in all three industry sectors. The messages in Facebook are closer to advertising, and overall the platform is utilized surprisingly little.

Spectral retinal image processing and analysis for ophthalmology

Diabetic retinopathy, age-related macular degeneration and glaucoma are the leading causes of blindness worldwide. Automatic methods for diagnosis exist, but their performance is limited by the quality of the data. Spectral retinal images provide a significantly better representation of the colour information than common grayscale or red-green-blue retinal imaging, having the potential to improve the performance of automatic diagnosis methods. This work studies the image processing techniques required for composing spectral retinal images with accurate reflection spectra, including wavelength channel image registration, spectral and spatial calibration, illumination correction, and the estimation of depth information from image disparities. The composition of a spectral retinal image database of patients with diabetic retinopathy is described. The database includes gold standards for a number of pathologies and retinal structures, marked by two expert ophthalmologists. The diagnostic applications of the reflectance spectra are studied using supervised classifiers for lesion detection. In addition, inversion of a model of light transport is used to estimate histological parameters from the reflectance spectra. Experimental results suggest that the methods for composing, calibrating and postprocessing spectral images presented in this work can be used to improve the quality of the spectral data. The experiments on the direct and indirect use of the data show the diagnostic potential of spectral retinal data over standard retinal images. The use of spectral data could improve automatic and semi-automated diagnostics for the screening of retinal diseases, for the quantitative detection of retinal changes for follow-up, clinically relevant end-points for clinical studies and development of new therapeutic modalities.