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 results of statistical analysis applied in order to evaluate the effect of the high melting point elements to pressure die cast silumin on its tensile strength Rm, unit elongation A and HB were discussed. The base alloy was silumin with the chemical composition similar to ENAC 46000. To this silumin, high melting point elements such as Cr, Mo, V and W were added. All possible combinations of the additives were used. The content of individual high melting point additives ranged from 0.05 to 0.50%. The tests were carried out on silumin with and without above mentioned elements. The values of Rm, A and HB were determined for all the examined chemical compositions of the silumin. The conducted statistical analysis showed that each of the examined high melting point additives added to the silumin in an appropriate amount could raise the values of Rm, A and HB. To obtain the high tensile strength of Rm = 291 MPa in the tested silumin, the best content of each of the additives should be in the range of 0.05-0.10%. To obtain the highest possible elongation A of about 6.0%, the best content of the additives should be as follows: chromium in the range of 0.05-0.15%, molybdenum 0.05% or 0.15%, vanadium 0.05% and tungsten 0.15%. To obtain the silumin with hardness of 117 HB, chromium, molybdenum and vanadium content should be equal to about 0.05%, and tungsten to about 0.5%.

As experience shows the practical, reliable assessment and optimisation of total costs of logistical processes implemented in supply chains

of foundry plants is a quite complex and complicated process, because it requires to enclose all, without exception, performed actions,

including them in various reference cross-sections, systematic activities and finally transforming them in a totally homogenous collection.

Only solid analysis and assessment of assortment management in logistical supply systems in foundry plants of particular assortment

groups allows to lower the supply costs significantly. In the article the analysis and assessment of the newest implemented optimising

algorithms are presented in the process stock management of selected material groups used in a production process of a chosen foundry

plant. A practical solution to solve a problem of rotary stock cost minimisation is given as well as of costs while creating a stock with the

usage of economical volume and value of order.

The study presents the results of the application of a statistical analysis for the evaluation of the effect of high-melting additions introduced into a pressure cast Al-Si alloy on the obtained level of its proof stress Rp0.2. The base Al-Si alloy used for the tests was a typical alloy used for pressure casting grade EN AC-46000. The base alloy was enriched with high-melting additions, such as: Cr, Mo, V and W. The additions were introduced into the base Al-Si alloy in all the possible combinations. The content of the particular high-melting addition in the Al-Si alloy was within the scope of 0.05 to 0.50%. The investigations were performed on both the base alloy and alloy with the high-melting element additions. Within the implementation of the studies, the values of Rp0.2 were determined for all the considered chemical compositions of the Al-Si alloy. A database was created for the statistical analysis, containing the independent variables (chemical composition data) and dependent variables (examined Rp0.2 values). The performed statistical analysis aimed at determining whether the examined high-melting additions had a significant effect on the level of Rp0.2 of the Al-Si alloy as well as optimizing their contents in order to obtain the highest values of the Al-Si alloy's proof stress Rp0.2. The analyses showed that each considered high-melting addition introduced into the Al-Si alloy in a proper amount can cause an increase of the proof stress Rp0.2 of the alloy, and the optimal content of each examined high-melting addition in respect of the highest obtained value of Rp0.2 equals 0.05%.

The paper presents the results of the application of a statistical analysis to evaluate the effect of the chemical composition of the die casting Al-Si alloys on its basic mechanical properties. The examinations were performed on the hypoeutectic Al-Si alloy type EN AC-46000 and, created on its basis, a multi-component Al-Si alloy containing high-melting additions Cr, Mo, W and V. The additions were introduced into the base Al-Si alloy in different combinations and amounts (from 0,05% to 0,50%). The tensile strength Rm; the proof stress Rp0,2; the unit elongation A and the hardness HB of the examined Al-Si alloys were determined. The data analysis and the selection of Al-Si alloy samples without the Cr, Mo, W and V additions were presented; a database containing the independent variables (Al-Si alloy's chemical composition) and dependent variables (Rm; Rp0,2; A and HB) for all the considered variants of Al-Si alloy composition was constructed. Additionally, an analysis was made of the effect of the Al-Si alloy's component elements on the obtained mechanical properties, with a special consideration of the high-melting additions Cr, Mo, V and W. For the optimization of the content of these additions in the Al-Si alloy, the dependent variables were standardized and treated jointly. The statistical tools were mainly the multivariate backward stepwise regression and linear correlation analysis and the analysis of variance ANOVA. The statistical analysis showed that the most advantageous effect on the jointly treated mechanical properties is obtained with the amount of the Cr, Mo, V and W additions of 0,05 to 0,10%.

Tests concerning EN AC 48000 (AlSi12CuNiMg) alloy phase transition covered (ATD) thermal analysis and (DSC) differential scanning

calorimetry specifying characteristic temperatures and enthalpy of transformations. ATD thermal analysis shows that during cooling there

exist: pre-eutectic crystallization effect of Al9Fe2Si phase, double eutectic and crystallization α(Al)+β(Si) and multi-component eutectic

crystallization. During heating, DSC curve showed endothermic effect connected with melting of the eutectic α(Al)+β(Si) and phases:

Al2Cu, Al3Ni, Mg2Si and Al9Fe2Si being its components. The enthalpy of this transformation constitutes approx. +392 J g-1

. During

freezing of the alloy, DSC curve showed two exothermal reactions. One is most likely connected with crystallization of Al9Fe2Si phase

and the second one comes from freezing of the eutectic α(Al)+β(Si). The enthalpy of this transformation constitutes approx. –340 J g-1

.

Calorimetric test was accompanied by structural test (SEM) conducted with the use of optical microscope Reichert and scanning

microscope Hitachi S-4200. There occurred solution's dendrites α(Al), eutectic silicon crystal (β) and two types of eutectic solution: double

eutectic α(Al)+β(Si) and multi-component eutectic α+AlSiCuNiMg+β.

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.

A comparative analysis involving the evaluation of the effectiveness of investment projects can be based on various rules indicating

selection of the most favorable decisions. The dynamic methods for assessment of investment projects discussed in this article, which

consider the possibility of modifying the predetermined investment options, are quite complex and difficult to implement. They are used

both in the construction phase of the new company, as well as in its subsequent modernization. The assessments should be characterized

by a high coefficient of the economic efficiency. The, observed in practice, high dynamic variability of both the external and internal

conditions under which the company operates is the reason why in the process of calculating the economic efficiency of investment

projects, there is a significant number of random parameters affected by high uncertainty and risk. Investments in the metallurgical

industry are characterized by a relatively long cycle of implementation and operation. These are capital-intensive projects and often

mistakenly taken investment decisions end in failure of the investment project and, consequently, in the collapse of the company. In

addition, the applied methods of risk assessment of investment projects, especially the dynamic ones, should be fully understood by

managerial staff and constitute an easy to use, yet accurate tool for improving the efficiency of the company.

The paper presents the results of studies on the development of correlation of solidification parameters and chemical composition of nickel

superalloy IN-713C, which is used i.a. on aircraft engine turbine blades. Previous test results indicate significant differences in

solidification parameters of the alloy, especially the temperatures Tliq and Tsol for each batch of ingots supplied by the manufacturer.

Knowledge of such a relationship has important practical significance, because of the ability to asses and correct the temperatures

of casting and heat treatment of casts on the basis of chemical composition. Using the statistical analysis it was found that the temperature

of the solidification beginning Tliq is mostly influenced by the addition of carbon (similar to iron alloys). The additions of Al and Nb have

smaller but still significant impact. Other alloying components do not have significant effect on Tliq. The temperature Teut is mostly

affected by Ni, Ti and Nb. The temperature Tsol is not in any direct correlation with the chemical composition, which is consistent with

previous research. The temperature Tsol depends primarily on the presence of non-metallic inclusions present in feed materials and

introduced during the melting and casting processes.

It has been found that the area where one can look for significant reserves in the procurement logistics is a rational management

of the stock of raw materials. Currently, the main purpose of projects which increase the efficiency of inventory management is to

rationalise all the activities in this area, taking into account and minimising at the same time the total inventory costs. The paper presents

a method for optimising the inventory level of raw materials under a foundry plant conditions using two different control models. The first

model is based on the estimate of an optimal level of the minimum emergency stock of raw materials, giving information about the need

for an order to be placed immediately and about the optimal size of consignments ordered after the minimum emergency level has

occurred. The second model is based on the estimate of a maximum inventory level of raw materials and an optimal order cycle.

Optimisation of the presented models has been based on the previously done selection and use of rational methods for forecasting the time

series of the delivery of a chosen auxiliary material (ceramic filters) to a casting plant, including forecasting a mean size of the delivered

batch of products and its standard deviation