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Analysis of product development on large-scale production with multi-criteria approach
Tomaž Kostanjevec, Jože Balič, Matej Rajh, 2009, original scientific article

Abstract: The multi-criteria (MC) method is applied to the development of a large-scale product in the case of mid-price range washing machines using MC approach. The concept represents the method for developing a new product in the production process. The development of a large-scale product on the basis of MC analysis provides more accurate forecasting of the most important parameters in the centrefold of a multidimensional (MD) character in the way of customer demands, and important production and development restrictions areset in this MD environment with aid of MC analysis. The production parameters are identified and through this, there is a possibility of reducing production costs. A forecasting method of production demands as possible result is powerful tool for achieving strategically and concurrent advantage.
Keywords: product development, multi-criteria analysis, forecasting, washing machines
Published: 31.05.2012; Views: 1312; Downloads: 28
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Intra-minute cloud passing forecasting based on a low cost iot sensor - a solution for smoothing the output power of PV power plants
Primož Sukič, Gorazd Štumberger, 2017, original scientific article

Abstract: Clouds moving at a high speed in front of the Sun can cause step changes in the output power of photovoltaic (PV) power plants, which can lead to voltage fluctuations and stability problems in the connected electricity networks. These effects can be reduced effectively by proper short-term cloud passing forecasting and suitable PV power plant output power control. This paper proposes a low-cost Internet of Things (IoT)-based solution for intra-minute cloud passing forecasting. The hardware consists of a Raspberry PI Model B 3 with a WiFi connection and an OmniVision OV5647 sensor with a mounted wide-angle lens, a circular polarizing (CPL) filter and a natural density (ND) filter. The completely new algorithm for cloud passing forecasting uses the green and blue colors in the photo to determine the position of the Sun, to recognize the clouds, and to predict their movement. The image processing is performed in several stages, considering selectively only a small part of the photo relevant to the movement of the clouds in the vicinity of the Sun in the next minute. The proposed algorithm is compact, fast and suitable for implementation on low cost processors with low computation power. The speed of the cloud parts closest to the Sun is used to predict when the clouds will cover the Sun. WiFi communication is used to transmit this data to the PV power plant control system in order to decrease the output power slowly and smoothly.
Keywords: photovoltaic power plant, cloud passing forecasting, algorithm, sensor, Raspberry Pi, camera, wide-angle lens, optical filters, internet of things
Published: 20.07.2017; Views: 994; Downloads: 220
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Obvladovanje tveganj pri »peer to peer« posojilih
Andrej Blagotinšek, 2017, master's thesis

Abstract: Nove digitalne tehnologije botrujejo procesu preoblikovanja obstoječih vrednostih verig finančnih produktov oz. storitev. »P2P« posojila so nov in inovativen način tako investiranja presežkov finančnih sredstev kot tudi prejemanja finančnega kapitala. Število tovrstnih posojil konstantno raste, vendar posojilodajalci niso profesionalni investitorji. Posojilodajalci prevzemajo veliko tveganje, saj so »P2P« posojila izdana brez zavarovanja. V ta namen »P2P« platforme izdajajo historične podatke o posojilojemalcih. V delu se osredotočamo na identifikacijo tveganj, ki so prisotna pri tovrstnem investiranju in na napovedovanje možnosti neplačil posojil. Empirična študija analizira podatke pridobljene iz platforme Bondora (N=1823) od leta 2009 do 2015. Opravili smo statistično analizo spremenljivk. Razvili smo Logit model za napovedovanje neplačil. Kakovost modela smo preverjali z ROC krivuljo, optimizacijo modela pa na osnovi uravnoteženja klasifikacijske natančnosti, kjer smo dololčili optimalno presečno vrednost. Rezultati so pokazali, da kreditni model za napovedovanje neplačil zmanjšuje verjetnost finančne izgube pri »P2P« investiranju.
Keywords: kreditno tveganje, verjetnost neplačila, »P2P« posojila, LOGIT model, obvladovanje tveganj, C25 Discrete Regression and Qualitative Choice Models, G21 Banks, G17, Financial Forecasting and Simulation
Published: 27.10.2017; Views: 1032; Downloads: 220
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Forecasting the primary demand for a beer brand using time series analysis
Danjel Bratina, Armand Faganel, 2008, original scientific article

Abstract: Market research often uses data (i.e. marketing mix variables) that is equally spaced over time. Time series theory is perfectly suited to study this phenomena's dependency on time. It is used for forecasting and causality analysis, but their greatest strength is in studying the impact of a discrete event in time, which makes it a powerful tool for marketers. This article introduces the basic concepts behind time series theory and illustrates its current application in marketing research. We use time series analysis to forecast the demand for beer on the Slovenian market using scanner data from two major retail stores. Before our analysis, only broader time spans have been used to perform time series analysis (weekly, monthly, quarterly or yearly data). In our study we analyse daily data, which is supposed to carry a lot of ‘noise’. We show that - even with noise carrying data - a better model can be computed using time series forecasting, explaining much more variance compared to regular regression. Our analysis also confirms the effect of short term sales promotions on beer demand, which is in conformity with other studies in this field.
Keywords: market research, time series forecasting, beer demand
Published: 30.11.2017; Views: 303; Downloads: 187
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Container throughput forecasting using dynamic factor analysis and ARIMAX model
Marko Intihar, Tomaž Kramberger, Dejan Dragan, 2017, original scientific article

Abstract: The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.
Keywords: container throughput forecasting, ARIMAX model, dynamic factor analysis, exogenous macroeconomic indicators, time series analysis
Published: 12.12.2017; Views: 610; Downloads: 211
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Radon anomalies in soil gas caused by seismic activity
Boris Zmazek, Mladen Živčić, Ljupčo Todorovski, Sašo Džeroski, Janja Vaupotič, Ivan Kobal, 2004, original scientific article

Abstract: At the Orlica fault in the Krško basin, combined barasol detectors were buried in six boreholes, two along the fault itself and four on either side of it, to measure and record radon activity, temperature and pressure in soil gas every 60 minutes for four years. Data collected have been analysed in a manner aimed at distinguishing radon anomalies resulting from environmental parameters (air and soil temperature, barometric pressure, rainfall) from those caused solely by seismic events. The following approaches have been used to identify anomalies: (i) ± 2σ deviation of radon concentration from the seasonal average, (ii) correlation between time gradients of radon concentration and barometric pressure, and (iii) prediction with regression trees within a machine learning program. In this paper results obtained with regression trees are presented. A model has been built in which the program was taught to predict radon concentration from the data collected during the seismically inactive periods when radon is presumably influenced only by environmental parameters. A correlation coefficient of 0.83 between measured and predicted values was obtained. Then, the whole data time series was included and a significantly lowered correlation was observed during the seismically active periods. This reduced correlation is thus an indicator of seismic effect.
Keywords: radon in soil gas, environmental parameters, earthquakes, correlation, regression trees, forecasting
Published: 15.05.2018; Views: 461; Downloads: 42
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