My diploma thesis is focused on study of processes during the engagement in electronics manufacturing service provider in Nová Dubnica – Slovakia. This thesis is divided to three main parts. First part is concerned with the theoretical knowledge. Second part focusing on the analysis of the current status of processes in order. In the last part, with correct research of important points of view during the managing of customer’s solution and adjustment of effective remedial measures, we will be able to improve satisfaction of our customers, optimise inventory level or even save time and costings.

Rangelands are extremely important for livestock grazing purposes in South Africa. Grazing should thus be regulated in order to conserve grass, shrubs and trees thereby ensuring sustainability of rangelands. In South Africa, the existing national grazing capacity estimate was developed in 1993 and updated in 2005 using National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite data. Largely due to changing land use practices (as well as changing data availability), there exists a clear need to create a new estimate, making use of current available data. For Limpopo, a province shown to be prone to recent land degradation, droughts and climate change, developing such an updated carrying capacity (CC) product (adjusted monthly according to monitoring data and seasonal forecasts) may help support more sustainable agricultural practices. The main objectives of the study are to update current CC products and to create deviation maps from CC for several years with relevant data. For estimation of the CC product, input data have included Satellite Pour l'Observation de la Terre (SPOT) VEGETATION Dry Matter Productivity (DMP), vegetation map of 2009 and downscaled coupled model data (ECHAM4.5–MOM3-DC2). A tree density product of 2003, observed rainfall and secondary ground truth data are also used. Study results show that Remote Sensing (RS) and Geographic Information System (GIS) technology, Earth Observation System (EOS) data and products, climate data and ground truth data are successfully used in a series of steps, processes with modelling to ultimately estimate grazing capacity. It is clear that rainfall is a primary determinant of DMP. The Coupled General Circulation Model (CGCM) shows that the December-January-February (DJF) rainfall season is important as a predictor season for the November through to April (NDJFMA) DMP growing season for the Limpopo Province. This model can discriminate high and low DMP (and GC) seasons. This study shows that the DMP product can, with certain assumptions, be used as a proxy for grass biomass. There is a strong drive towards the application of seasonal forecasts in agriculture. This project demonstrates the development of a tailored forecast, an avenue that should be explored in enhancing relevance of forecasts to agricultural production. / Dissertation (MSc)--University of Pretoria, 2016. / Geography, Geoinformatics and Meteorology / MSc / Unrestricted

In the current market, there is a large number of SMEs that have a large margin of economic losses due to lack of stocks, due to the supply process. In other words, the lost sales and the costs of the services generated by not having their products available in their warehouses is a critical scenario in the distribution companies, whose added value lies in maximizing their level of customer service. To solve this problem, we propose a system that integrates the development of the attention and the model of the inventories of the periodic review, the bases based on the framework of the work. The results, after analyzing the demand, their patterns and choosing the best method to use, are antecedents to develop the management of inventories and their policies. Likewise, knowledge management will act as an integrated support. Through the simulation carried out for a distribution of lubricants, results were obtained that indicate a reduction of 93% in losses due to stock-outs and an increase in the service level that goes from 77% to 91%. This is an integrated system of interest to be applied as a solution for SMEs that have high stock-outs and lack this type of tools..

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The extant academic literature considers enterprise risk management (ERM) to be the fundamental paradigm for managing the portfolio of risks confronting organizations. However, there is debate as to whether ERM actually enhances stakeholder value. This study investigates whether ERM is associated with increased earnings predictability and a lower risk of firm failure, two theoretical predications regarding ERM's impact on stakeholder value. My research utilizes the Security and Exchange Commission's (SEC) enhanced proxy statement disclosures as of February 28th, 2010 to measure ERM performance. Additionally, in order to quantify the operational construct, textual analysis is performed to develop a measure of ERM performance to be used in econometric analyses. The analyses presented in this paper investigate whether key predicted benefits of ERM are observable. Results support the proposition that ERM is associated with increased earnings predictability. Specifically, earnings and accruals are found to be more persistent for firms with better ERM performance. Additionally, analysts' earnings forecasts are more accurate in the presence of enhanced ERM performance. Results are inconclusive with regards to ERM's ability to influence the risk of firm failure during this study's sample period (i.e., 2007-2009). One explanation for this departure, the economic volatility during the financial crisis of 2008-2009, may make it difficult to empirically detect the relationship between ERM performance and the risk of firm failure. / Ph. D.

One of the Sustainable Development Goals set in 2015 by the United Nations aims to ensure access to affordable, reliable, sustainable, and modern energy for all, increasing the global share of renewable energy to 32-35% by 2030. Moving towards this goal, the University of Trento funded the interdepartmental strategic project ERiCSol (Energie Rinnovabili e Combustibili Solari), in order to promote the research on renewable energy storage and solar fuels. The research activity presented in this thesis lies in the framework of this project, focusing on the development of new advanced simulation approaches to improve the estimation of the wind resource availability and the related power production for Italian wind farms in complex terrain. The wind farms, operated by the company AGSM S.p.A., are located in two different geographical contexts: Rivoli Veronese and Affi are at the inlet of the Adige Valley, while Casoni di Romagna and Carpinaccio Firenzuola, are on the crest of the Apennines close to the borders between the provinces of Bologna e Firenze. The analysis of data from year-long field measurements highlighted the different peculiarities of these areas. The wind farms at the mouth of the Adige Valley are influenced by a daily periodic thermally-driven circulation, characterised by a nocturnal intense down-valley wind alternating with a diurnal weaker up-valley wind, while the Apennines wind farms are primarily affected by synoptic-scale winds. Simulations, with the mesoscale Weather Research and Forecasting (WRF) model, are performed and compared with field measurements in both cases, to highlight strengths and weaknesses. The results show that the model is able to capture with good accuracy wind speed and direction in the Apennines wind farms, while larger errors arise for Rivoli Veronese and Affi wind farms, where the intensity of the nocturnal down-valley wind is generally underestimated. Considering the former case, modelled and observed yearly wind speed density distributions are compared, in order to evaluate the impact of model errors in the estimation of the wind resource at these sites. Since reliable simulations of the wind resource are also essential to ensure the security in power transmission and to prevent penalties to energy operators, an analysis of the power production is also performed, to evaluate how errors in the estimate of the resource translate into errors in the estimate of the production. Considering the wind farms at the mouth of the Adige Valley, the research work mainly focuses on the evaluation of the impact of data assimilation by means of observational nudging on model results, in order to optimize the setup for operational forecasts. Different configurations are tested and compared, varying the temporal window for the assimilation of local data.

Yes / Expert knowledge elicitation lies at the core of judgmental forecasting—a domain that relies fully on the power of such knowledge and its integration into forecasting. Using experts in a demand forecasting framework, this work aims to compare the accuracy improvements and forecasting performances of three judgmental integration methods. To do this, a field study was conducted with 31 experts from four companies. The methods compared were the judgmental adjustment, the 50–50 combination, and the divide-and-conquer. Forecaster expertise, the credibility of system forecasts and the need to rectify system forecasts were also assessed, and mechanisms for performing this assessment were considered. When (a) a forecaster’s relative expertise was high, (b) the relative credibility of the system forecasts was low, and (c) the system forecasts had a strong need of correction, judgmental adjustment improved the accuracy relative to both the other integration methods and the system forecasts. Experts with higher levels of expertise showed higher adjustment frequencies. Our results suggest that judgmental adjustment promises to be valuable in the long term if adequate conditions of forecaster expertise and the credibility of system forecasts are met.

The spatial and temporal complexity of tropical cyclones (TCs) raises a number of scientific questions regarding their genesis, movement, intensification, and variability. In this dissertation, the principal goal is to determine the current state of predictability for each of these processes. To quantify the current extent of tropical cyclone predictability, we assess probabilistic forecasts from the most advanced global numerical weather prediction system to date, the ECMWF Variable Resolution Ensemble Prediction System (VarEPS). Using a new false alarm clustering technique to maximize the utility of the VarEPS, the ensemble system is shown to provide well-calibrated probabilistic forecasts for TC genesis through a lead-time of one week, and pregenesis track forecasts with similar skill compared to the VarEPS's postgenesis track forecasts. To quantify the predictability of TCs on intraseasonal time scales, forecasts from the ECMWF Monthly Forecast System (ECMFS) are examined for the North Atlantic Ocean. From this assessment, dynamically based forecasts from the ECMFS provide forecast skill exceeding climatology out to weeks three and four for portions of the southern Gulf of Mexico, western Caribbean and the Main Development Region. Forecast skill in these regions is traced to the model's ability to capture correctly the variability in deep-layer vertical wind shear, the relative frequency of easterly waves moving through these regions, and the intraseasonal modulation of the Madden-Julian Oscillation. On interannual time scales, the predictability of TCs is examined by considering their relationship with tropical Atlantic easterly waves. First, a set of easterly wave climatologies for the CFS-R, ERA-Interim, ERA-40, and NCEP/NCAR Reanalysis are developed using a new easterly wave-tracking algorithm. From the reanalysis-derived climatologies, a moderately positive and statistically significant relationship is seen with tropical Atlantic TCs. In relation to large-scale climate modes, the Atlantic Multidecadal Oscillation (AMO) and Atlantic Meridional Mode (AMM) exhibit the strongest positive covariability with Atlantic easterly wave frequency. Besides changes in the number of easterly waves, the intensification efficiency of easterly waves has also been evaluated. These findings offer a plausible physical explanation for the recent increase in the number of NATL TCs, as it has been concomitant with an increasing trend in both the number of tropical Atlantic easterly waves and intensification efficiency. The last component of this dissertation examines how the historical variability in U.S. landfalling TCs has impacted the annual TC tornado record. To reconcile the inhomogeneous, historical tornado record, two statistical tornado models, developed from a set of a priori predictors for TC tornado formation, are used to reconstruct the TC tornado climatology. While the synthetic TC tornado record reflects decadal scale variations in association with the AMO, a comparison of the current warm phase of the AMO with the previous warm phase period shows that the median number of tornadoes per Gulf TC landfall has significantly increased. This change likely reflects the increase in median TC size (by 35%) of Gulf landfalling TCs along with an increased frequency of large TCs at landfall.

The purpose of this study is to provide the case company Nudient with a recommendation of what demand forecasting methods and strategies they should use. To be able to make a tailored recommendation, a literature study is conducted to explore what demand forecasting methods are commonly used on applications similar to the case being studied. The forecasting methods and the strategy regarding when and how to use them are thereafter explored in a main literature review. Empirical data is gathered from the case company in the form of interviews and demand data. The empirical data is then used to evaluate which of the methods found in the literature review are a good fit for Nudient, thereafter the demand forecasting strategy is laid out. The final recommendation is divided into two categories, forecasting the demand for new products and forecasting the demand for mature products. For new products, the recommendation is for Nudient to make use of associative modeling, expert consensus, the Delphi method, and market research. For mature products, the recommendation is for Nudient to make use of the moving average method, double exponential smoothing, regression analysis, associative modeling, expert consensus, and the Delphi method. / Syftet med detta examensarbete är att ge fallstudieföretaget Nudient en rekommendation angående vilka metoder och strategier för efterfrågeprognoser de bör använda. För att kunna ge en skräddarsydd rekommendation genomförs en litteraturstudie med syfte att undersöka vilka efter frågeprognoser som vanligtvis används i applikationer som liknar det fall som studeras. Prognosmetoderna och strategin för när och hur metoderna ska användas utforskas därefter i en huvudlitteraturöversikt. Empirisk data samlas in från fallstudieföretaget i form av intervjuer och efterfrågedata. Den empiriska datan används sedan för att utvärdera vilka av metoderna som identifierades i litteraturöversikten som är passande för Nudient, därefter tas strategin fram för efterfrågeprognoser. Den slutliga rekommendationen är uppdelad i två kategorier, efterfrågeprognoser på nya produkter och efterfrågeprognoser på mogna produkter. För nya produkter är rekommendationen att Nudient bör använda associativ modellering, expertkonsensus, Delphi-metoden och marknadsundersökningar. För mogna produkter är rekommendationen att Nudient bör använda sig av glidande medelvärde, dubbel exponentiell utjämning, regressionsanalys, associativ modellering, expertkonsensus och Delphi-metoden.