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.

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.

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.

The surface roughness of the end product is a very important indicator of laser cutting quality. The paper reports a comparison of surface roughness during CO2 laser cutting of tungsten alloy plate using oxygen 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 ichart of experimental data samples, where using evaluation of moving range of the two consecutive values, in order to estimate value of standard error by average moving range and Hartley's constant d2. Applying the criteria often used in the SPC methods for the assessment of "out of control" situations, it may be inferred that the observed differences in surface roughness during CO2 laser cutting could be used to advice on the more appropriate laser power and cutting speed for the laser cutting quality. Keyword: laser cutting process; statistical process control; control chart; surface roughness; tungsten alloy This Publication has to be referred as: Begic-Hajdarevic, D[erzija]; Pasic, M[ugdim]; Vucijak, B[ranko] & Cekic, A[hmet] (2016). Statistical Process Control of Surface Roughness during CO2 Laser Cutting using Oxygen as Assist Gas, Proceedings of the 26th DAAAM International Symposium, pp.0247-0255, B. Katalinic (Ed.), Published by DAAAM International, ISBN 978-3-902734-07-5, ISSN 1726-9679, Vienna, Austria DOI:10.2507/26th.daaam.proceedings.034

It is common and also predominant in the inventory control literature that demand follows normal distribution, and according to central limit theorem, demand per period will also follow normal distribution. However, in many real life situations, demand does not necessary follow normal distribution, and therefore, use of expressions used to calculate demand parameters per period are not suitable. This research suggests that available demand data are grouped into periods of desired length by overlapping. Demand data obtained by this approach provide valuable information for risk study. Suggested approach is evaluated using periodic review inventory model where all unsatisfied demand is backordered, and the same inventory control model is used to control inventories of slow and fast moving items. © 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of DAAAM International Vienna.

The aim of this paper is to analyze highway empirical traffic flow patterns including regression analysis at the highway tollgate station Josanica at the north exit of the capital of Bosnia and Herzegovina Sarajevo. The traffic flow analysis in this paper includes determination of arrival and service time pattern distributions and development of traffic flow time series regression forecasting models of the traffic flow. Data for this research are retrieved from the highway software system. Research shows that it is possible to build reliable regression forecasting models to predict number of vehicles per month, per week and per day in certain week as well as per defined time periods during a day. Time needed to pay at the toll station (service time) is measured by observation. This study presents arrival pattern distribution and service time distribution. Results obtained by this research will be used for the simulation of waiting line models and improvement of the existing system as well as for the highway service facilities demand forecasting.

The aim of this paper is to analyze highway empirical traffic flow patterns including regression analysis at the highway tollgate station Josanica at the north exit of the capital of Bosnia and Herzegovina Sarajevo. The traffic flow analysis in this paper includes determination of arrival and service time pattern distributions and development of traffic flow time series regression forecasting models of the traffic flow. Data for this research are retrieved from the highway software system. Research shows that it is possible to build reliable regression forecasting models to predict number of vehicles per month, per week and per day in certain week as well as per defined time periods during a day. Time needed to pay at the toll station (service time) is measured by observation. This study presents arrival pattern distribution and service time distribution. Results obtained by this research will be used for the simulation of waiting line models and improvement of the existing system as well as for the highway service facilities demand forecasting.