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Supply chain management (SCM) has a dynamic structure involving the constant flow of information, product, and funds among different participants. SCM is a complex process and most often characterized by uncertainty. Many values are stochastic and cannot be precisely determined and described by classical mathematical methods. Therefore, in solving real and complex problems individual methods of artificial intelligence are increasingly used, or their combination in the form of hybrid methods. This paper has proposed the decision support system for determining economic order quantity and order implementation based on Adaptive neuro-fuzzy inference systems - ANFIS. A combination of two concepts of artificial intelligence in the form of hybrid neuro-fuzzy method has been applied into the decision support system in order to exploit the individual advantages of both methods. This method can deal with complexity and uncertainty in SCM better than classical methods because they it stems from experts’ opinions. The proposed decision support system showed good results for determining the amount of economic order and it is presented as a successful tool for planning in SCM. Sensitivity analysis has been applied, which indicates that the decision sup- port system gives valid results. The proposed system is flexible and can be applied to various types of goods in SCM

Prethodna istraživanja su utvrdila da je mjerenje uzdužne hrapavosti kolnika izraženo kroz međunarodni indeks hrapavosti (International Roughness Index - IRI) jedan od najvažnijih pokazatelja za ukupnu ocjenu stanja cestovne mreže. U isto vrijeme IRI predstavlja ključni okidač za planiranje i primjenu različitih radova održavanja cesta kao što su sanacije ili rekonstrukcije kolnika. U radu se razmatraju postojeće metode za mjerenje hrapavosti kolnika i ocjenu stanja cestovne mreže u Republici Srbiji u ukupnoj dužini od 13 191,34 km., Earlier researches have established that the measurement of pavement longitudinal roughness expressed through International Roughness Index (IRI) is one of the most important indicators for overall evaluation of road network condition. At the same time IRI presents the key trigger for planning and applying the different road maintenance works like pavement rehabilitation or reconstruction. This paper examines the existing methods for measurement of pavement roughness and evaluation of the road network condition in the Republic of Serbia in total length of 13 191,34 km.