The essay presents an original application of using the coolhunting method to discover new trends in architecture and design. The ability to identify trends is tied in with the possibility of attaining an advantage over the competition with the use of new designs that can become hits on the market, gaining the favor of customers. The term coolhunting can be broadly defined as the pursuit of inspiration and the forecasting of the directions of development. Initially, the term was applied to fashion, but quickly spread to other spheres of activity, like music, the arts, lifestyle and finally, to architecture and design. The essay is a slightly altered and improved rendition of the author's article published in Zastosowania ergonomii. Wybrane kierunki badań ergonomicznych w roku 2014 . (ed. Charytonowicz J.), Publ. Polskie Towarzystwo Ergonomiczne PTErg, o/Wrocław, 2014, p. 289-304. The method outlined therein is the result of research conducted under the author's supervision at the Institute of Architecture and Spatial Planning of the Poznań University of Technology between the years 2012 and 2014.
The aim of the paper is to point out that the Monte Carlo simulation is an easy and flexible approach when it comes to forecasting risk of an asset portfolio. The case study presented in the paper illustrates the problem of forecasting risk arising from a portfolio of receivables denominated in different foreign currencies. Such a problem seems to be close to the real issue for enterprises offering products or services on several foreign markets. The changes in exchange rates are usually not normally distributed and, moreover, they are always interdependent. As shown in the paper, the Monte Carlo simulation allows for forecasting market risk under such circumstances.
The irregularity profiles of steel samples after vapour blasting were measured. A correlation analysis of profile parameters was then carried out. As the result, the following parameters were selected: Pq, Pt, PDq, Pp/Pt and Pku. Surface profiles after vapour blasting were modeled. The modeled surfaces were correctly matched to measured surfaces in 78% of all analyzed cases. The vapour blasting experiment was then carried out using an orthogonal selective research plan. The distance between the nozzle and sample d and the pressure of feed system p were input parameters; selected surface texture coefficients were output parameters. As the result of the experiment, regression equations connecting vapour blasting process parameters p and d with selected profile parameters were obtained. Finally, 2D profiles of steel samples were forecasted for various values of vapour blasting parameters. Proper matching accuracy of modeled to measured profiles was assured in 75% of analyzed cases.
The article is an attempt to evaluate accuracy of Marx’s predictions and to present some reasons for Marx’s ineffectiveness as a forecaster. The article discusses contemporary research on forecasting, uses the results to Marx, and analyses the dialectic aspect of laws in order to explain forecasting weaknesses of Marx. The author of Capital turns out to be – in P.E. Tetlock’s typology – a ‘hedgehog’, i.e.: a bad forecaster, who uses questionable methods to defend his predictions at all costs.
Potato white mold caused by Sclerotinia sclerotiorum is an important plant disease occurring in many potato-producing areas throughout the world. In this study, a specific diagnostic method was used to detect and quantify S. sclerotiorum ascospores, and its forecasting ability was assessed in potato fields during flowering periods of 2011 to 2014 in Bahar County, Hamedan Province. Using GenEMBL database, a primer pair, HZSCREV and HZSCFOR, was designed and optimized for the pathogen. After testing the sensitivity of primers, DNA was extracted from samples of outdoor Burkard traps from potato fields. A linear association was observed between pathogen DNA and the number of ascospores using the quantitative PCR (qPCR) technique in the presence of SYBR dye. The qPCR could successfully detect DNA amounts representing two S. sclerotiorum ascospores and was not sensitive to a variety of tested fungi such as Botrytis cinerea, Alternaria brassicae, Fusarium solani. In contrast to the amount of rainfall, a direct relationship was found between ascospore numbers and the incidence of potato white mold from 2011 to 2014.
In order to prepare a coal company for the development of future events, it is important to predict how can evolve the key environmental factors. This article presents the most important factors influencing the hard coal demand in Poland. They have been used as explanatory variables during the creation of a mathematical model of coal sales. In order to build the coal sales forecast, the authors used the ARMAX model. Its validation was performed based on such accuracy measures as: RMSE, MAPE and Theil’s index. The conducted studies have allowed the statistically significant factors out of all factors taken into account to be identified. They also enabled the creation of the forecast of coal sales volume in Poland in the coming years. To maintain the predictability of the forecast, the mining company should continually control the macro environment. The proper demand forecast allows for the flexible and dynamic adjustment of production or stock levels to market changes. It also makes it possible to adapt the product range to the customer’s requirements and expectations, which, in turn, translates into increased sales, the release of funds, reduced operating costs and increased financial liquidity of the coal company. Creating a forecast is the first step in planning a hard coal mining strategy. Knowing the future needs, we are able to plan the necessary level of production factors in advance. The right strategy, tailored to the environment, will allow the company to eliminate unnecessary costs and to optimize employment. It will also help the company to fully use machines and equipment and production capacity. Thanks to these efforts, the company will be able to reduce production costs and increase operating profit, thus survive in a turbulent environment.
The paper presents selected issues related to the development of international coal markets. World consumption of coal dropped for the second year in a row in 2016, primarily due to the lower demand from China and the US. The share of coal in global primary energy consumption decreased to 28%. World coal production accounted to 3.66 billion toe and it was lower by 6.2% when compared to the previous year. More than 60% of this decline took place in China. The decline in global production was more than four times higher than the decrease in consumption. The sufficiency of the world resources of coal are estimated at 153 years – that is three times more than the sufficiency of oil and gas resources. After several years of decline, coal prices increased by 77% in 2016. The current spot prices are at the level of $80/ton and are close to the 2014 prices. In the European market, after the first half of the year, coal prices reached the level of around 66% higher than in the same period of the last year. The average price in the first half amounted to PLN 12.6/GJ, which is close to the 2012 prices. The share of spot trade in the total purchase amount accounted to approx. 20%. Prices in futures contracts can be estimated on the basis of the Japan-Australia contracts prices and prices in supplies to power plants located in Germany. On average, the prices in supplies to these power plants were higher by approximately 9% in the years 2010 – 2016 and prices in Australia – Japan contracts were 12% higher than CIF ARA prices in 2017. Global energy coal trade reached about 1.012 billion tons in 2016. A decline by 4.8% is expected in 2019 primarily due to the expected reduction in demand in major importing countries in Asia.
The methane hazard is one of the most dangerous phenomena in hard coal mining. In a certain range of concentrations, methane is flammable and explosive. Therefore, in order to maintain the continuity of the production process and the safety of work for the crew, various measures are taken to prevent these concentration levels from being exceeded. A significant role in this process is played by the forecasting of methane concentrations in mine headings. This very problem has been the focus of the present article. Based on discrete measurements of methane concentration in mine headings and ventilation parameters, the distribution of methane concentration levels in these headings was forecasted. This process was performed on the basis of model-based tests using the Computational Fluid Dynamics (CFD). The methodology adopted was used to develop a structural model of the region under analysis, for which boundary conditions were adopted on the basis of the measurements results in real-world conditions. The analyses conducted helped to specify the distributions of methane concentrations in the region at hand and determine the anticipated future values of these concentrations. The results obtained from model-based tests were compared with the results of the measurements in realworld conditions. The methodology using the CFD and the results of the tests offer extensive possibilities of their application for effective diagnosis and forecasting of the methane hazard in mine headings.
In this article, we review the research state of the bullwhip effect in supply chains with stochastic lead times. We analyze problems arising in a supply chain when lead times are not deterministic. Using real data from a supply chain, we confirm that lead times are stochastic and can be modeled by a sequence of independent identically distributed random variables. This underlines the need to further study supply chains with stochastic lead times and model the behavior of such chains.
The sustainable management of energy production and consumption is one of the main challenges of the 21st century. This results from the threats to the natural environment, including the negative impact of the energy sector on the climate, the limited resources of fossil fuels, as well as the unstability of renewable energy sources – despite the development of technologies for obtaining energy from the: sun, wind, water, etc. In this situation, the efficiency of energy management, both on the micro (dispersed energy) and macro (power system) scale, may be improved by innovative technological solutions enabling energy storage. Their effective implementation enables energy storage during periods of overproduction and its use in the case of energy shortages. These challenges cannot be overestimated. Modern science needs to solve various technological issues in the field of storage, organizational problems of enterprises producing electricity and heat, or issues related to the functioning of energy markets. The article presents the specificity of the operation of a combined heat and power plant with a heat accumulator in the electricity market while taking the parameters affected by uncertainty into account. It was pointed out that the analysis of the risk associated with energy prices and weather conditions is an important element of the decision-making process and management of a heat and power plant equipped with a cold water heat accumulator. The complexity of the issues and the number of variables to be analyzed at a given time are the reason for the use of advanced forecasting methods. The stochastic modeling methods are considered as interesting tools that allow forecasting the operation of an installation with a heat accumulator while taking the influence of numerous variables into account. The analysis has shown that the combined use of Monte Carlo simulations and forecasting using the geometric Brownian motion enables the quantification of the risk of the CHP plant’s operation and the impact of using the energy store on solving uncertainties. The applied methodology can be used at the design stage of systems with energy storage and enables carrying out the risk analysis in the already existing systems; this will allow their efficiency to be improved. The introduction of additional parameters of the planned investments to the analysis will allow the maximum use of energy storage systems in both industrial and dispersed power generation.
This study involves the implementation of an economic order quantity (EOQ) model which is an inventory control method in a ceramic factory. Two different methods were applied for the calculation of EOQs. The first method is to determine EOQ values using a response surface method-based approach (RSM). The second method uses conventional EOQ calculations. To produce a ceramic product, 281 different and additive materials may be used. First, Pareto (ABC) analysis was performed to determine which of the materials have higher priority. Because of this analysis, the value of 21 items among 281 different materials and additives were compared to the ratio of the total product. The ratio was found to be 70.4% so calculations were made for 21 items. Usage value for every single item for the years 2011, 2012, 2013 and 2014, respectively, were obtained from the company records. Eight different demand forecasting methods were applied to find the amount of the demand in EOQ. As a result of forecasting, the EOQ of the items were calculated by establishing a model. Also, EOQ and RSM calculations for the items were made and both calculation results were compared to each other. Considering the obtained results, it is understood that RSM can be used in EOQ calculations rather than the conventional EOQ model. Also, there are big differences between the EOQ values which were implemented by the company and the values calculated. Because of this work, the RSM-based EOQ approach can be used to decide on the EOQ calculations as a way of improving the system performance.
Conducting reliable and credible evaluation and statistical interpretation of empirical results related to the operation of production systems in foundries is for most managers complicated and labour-intensive. Additionally, in many cases, statistical evaluation is either ignored and considered a necessary evil, or is completely useless because of improper selection of methods and subsequent misinterpretation of the results. In this article, after discussing the key elements necessary for the proper selection of statistical methods, a wide spectrum of these methods has been presented, including regression analysis, uni- and multivariate correlation, one-way analysis of variance for factorial designs, and selected forecasting methods. Each statistical method has been illustrated with numerous examples related to the foundry practice.
Forecasting and analysis SWOT are helping tools in the business activity, because under conditions of dynamic changes in both closer and more distant surroundings, reliable, forward-looking information and trends analysis are playing a decisive role. At present, the ability to use available data in forecasting and other analyzes according with changes in business environment are the key managerial skills required, since both forecasting and SWOT analysis are a integral part of the management process, and the appropriate level of forecasting knowledge is increasingly appreciated. Examples of practical use of some forecasting methods in optimization of the procurement, production and distribution processes in foundries are given. The possibilities of using conventional quantitative forecasting methods based on econometric and adaptive models applying the creep trend and harmonic weights are presented. The econometric models were additionally supplemented with the presentation of error estimation methodology, quality assessment and statistical verification of the forecast. The possibility of using qualitative forecasts based on SWOT analysis was also mentioned.
The aim of the study was to evaluate the possibility of applying different methods of data mining to model the inflow of sewage into the municipal sewage treatment plant. Prediction models were elaborated using methods of support vector machines (SVM), random forests (RF), k-nearest neighbour (k-NN) and of Kernel regression (K). Data consisted of the time series of daily rainfalls, water level measurements in the clarified sewage recipient and the wastewater inflow into the Rzeszow city plant. Results indicate that the best models with one input delayed by 1 day were obtained using the k-NN method while the worst with the K method. For the models with two input variables and one explanatory one the smallest errors were obtained if model inputs were sewage inflow and rainfall data delayed by 1 day and the best fit is provided using RF method while the worst with the K method. In the case of models with three inputs and two explanatory variables, the best results were reported for the SVM and the worst for the K method. In the most of the modelling runs the smallest prediction errors are obtained using the SVM method and the biggest ones with the K method. In the case of the simplest model with one input delayed by 1 day the best results are provided using k-NN method and by the models with two inputs in two modelling runs the RF method appeared as the best.