حبیب الله جوانمرد

To improve the operation level of the companies tailored to industry 4.0 it is necessary to develop logistics system. In order to develop the logistics level, the first step is to measure the growth and evolution of all logistics process. For measuring the evolution and growth of the logistics system (LS), the processes and components of the logistics system and then the evolution level should be determined, at the end their level of evolution should be measured for each firms. The purpose of this paper is to determine the evolution level of the LS in the large industries of Iran. The research method is descriptive - applied and Data were gathered in two steps. First utilizing interviews with logistics experts for identifying the logistics process and components. Second step was data collection by check list in local visits and direct observation in firms. Checklist was completed in group sessions with the help of managers and experts in each firm. Using the mean of the logistics components and degree of adoptive (DOA) technique, evolution of LS was measured in industries. Results shown that firms with heavy products have little progress in the evolution of logistics compared to other firms and also the firms that have automatic production system will growth in logistic evolution better than others especially in intra-logistics and material handling. The method and result of this paper can be seen as a roadmap for managers and organizations and they will plan development and evolution of logistics processes.

Understanding tourist behavior is a requirement for marketing planning for the supply of goods and services to satisfy tourists. Nowadays, many tourists decide to travel to any place by searching through internet explorers. The present study was conducted with the aim of analyzing the behavior of tourists in Iran based on data mining (search rate on Google Trend). The research is applied and has been done with a causal descriptive method. The method of collecting data is by searching keywords on Google. . To collect the keywords needed for the research, we had to turn to the experts and authorities on the field as well as the relevant scientific articles. The process of data collection is that a series of key words in tourism were selected and accordingly, it was determined how much people used these words in different places in Iran. Correlation matrix (covariance) model has been used for data analysis. In this research, the structural information of these keywords was obtained and edited based on the keywords related to tourism extracted from Google Trend by time series and with the help of Pearson correlation matrix. Nine keywords were selected for search, including hotel, entertainment, pilgrimage, tourism, nature, places of interest, archeology, travel and travel tour. The keywords are not searched at random, but they are related and correlated and stem from a structural thinking. The results of the data analysis have shown the type and intensity of connections between the words that had a communication structure. It was also found that the words pilgrimage, recreation, and archeology have less connection with other words.

The automotive industry, as a job-creating and infrastructure industry, needs an executive model for success in the domestic and international markets. In this regard, the present study has been conducted with the aim of designing a model for implementing the fourth generation industry to achieve sustainable development goals in Iran Khodro Company. The study is an applied-developmental study in terms of purpose and cross-sectional survey research. Also, in this study, a mixed research method (qualitative-quantitative) was used. Content analysis method and MaxQDA software were used for data analysis in the qualitative section. Then, using Interpretive Structural Modeling (ISM) with MICMAC software, the initial pattern was drawn. In the quantitative section, one-sample t-test and SPSS software were used to measure the current situation. The research findings showed that the Collection and Analysis of Big Data affects the Simulation and Automatic Robots. These factors affect horizontally and vertically integration systems and thus lead to the Internet of Industrial Things, Augmented Reality and Cyber Security. Further, through the Cloud Computing system, Additive Manufacturing is affected and this Additive Manufacturing leads to Sustainable Development.