State-space ARIMA for supply-chain forecasting

ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.     

Investigation into the Influence of Sodium Lignosulfonate,Anionic Surfactants,and Their Mixtures on the Copper Cementation Rate by Zinc

Russian Journal of Non-Ferrous Metals - This article is devoted to the influence of sodium lignosulfonate (SL), anionic surfactants (sodium dodecylsulfate (SDS), sodium dodecylbenzene sulfonate...     

The Possibility of Dense Materials Based on Obtaining an AlN–SiC Solid Solution by Self-Propagating High-Temperature Synthesis Gasostating

Russian Journal of Non-Ferrous Metals - The synthesis and sintering of the (AlN)x(SiC)1 – x solid solution have been studied under conditions of self-propagating high-temperature synthesis...     

3D Investigation of 8-nm Tapered n-FinFET Model

The miniaturization has become a key word for advanced integrated circuits over the last few years. It is within this context that the fin field effect tra