Inventory systems that use continuous review policy are under risk during lead time, when stock-out can occur. Therefore, system must have enough on-hand units to prevent such situations. Generally, in inventory control literature it can be found that lead time demand follows normal distribution and all other conclusions are derived from this assumption. However, in real life this does not have to be true, so it is of crucial importance to get better estimates of stochastic demand parameters over lead time. The objective of this research is to estimate the optimal (s, Q) continuous review inventory policy parameters that reduce risk of stock-out during lead time and to enhance robustness of such estimated parameters. This is done using approach we propose for demand modeling. Performances and adequacy of the proposed approach for lead time demand modeling, with various demand patterns, and its application in (s, Q) continuous inventory models are obtained by simulation and show very good results.

In this paper two parameter Same Slope Seasonality forecasting model was developed. Analytical expressions for determining optimal parameter values of the developed two parameter Same Slope Seasonality forecasting model were developed too. Furthermore, performance of Two Parameter Same Slope Seasonality model was compared with performance of Holt-Winters exponential model as well as with performance of Same Slope Seasonality model. All tests were performed on M2-Competition time series. To determine the parameters of Holt-Winters exponential model, nonlinear mathematical programming was used. Performed tests proved that Two Parameter Same Slope Seasonality model is more successful than Holt-Winters exponential model and Same Slope Seasonality model. Expressions developed for determination parameters of two parameter Same Slope Seasonality forecasting model give parameter values identical to the values obtained by using Solver in MS Excel, set to solve nonlinear programming model, in a way that the problem is set up to search the parameter values which minimizes the mean squared error of the model.

Original scientific paper The paper presents analysis of the influence of cutting parameters on surface roughness during CO2 laser cutting process of tungsten alloy by using nitrogen as assist gas, based on control charts made by statistical process control (SPC) approach. Dependent variable is surface roughness, while independent variables are laser power and cutting speed. The control chart used within this paper is a variation of the moving means chart of experimental data samples, that calculates mean and range values using the three consecutive individual values. Applying the criteria often used in the SPC methods for the assessment of "out of control" situations, it may be inferred that increasing the cutting speed leads to worsening of control status for the process with lower laser power used.

Laser cutting is a popular manufacturing process utilized to cut various types of materials. There are many non-linear interaction factors responsible for the performance of the laser cutting process. Identification of dominant factors that significantly affect cut quality is important. In this paper, the effect of the process parameters is analysed on the laser cut quality of an uncommon alloy, the tungsten alloy sheet with thickness of 1 mm. The experiments are conducted at three levels of the selected process parameters such as assist gas type (oxygen, air and nitrogen) and assist gas pressure. Taguchi design of experiments is used to obtain the optimum conditions through the study of signal to noise ratio for three quality characteristics such as kerf width, heat affected zone and surface roughness. Results of this study indicate that the optimal assist gas pressure is entirely different for kerf width, heat affected zone and surface roughness. The nitrogen as assist gas is suggested as the optimal assist gas for all of three quality characteristics.

In this paper analytical expression for determining the optimal parameter value of the Same Slope Seasonality model was developed. Then performance of the Same Slope Seasonality model was compared to the performance of Holt-Winters exponential model, performing tests on M2-Competition time series. To determine the parameters of Holt-Winters exponential model, nonlinear mathematical programming was used. Performed tests proved that Same Slope Seasonality model is more successful than Holt-Winters exponential model. Tests revealed that Same Slope Seasonality model gives unreliable forecasts when time series has specific charac teristics, giving us valuable information for model improvement.