Arman Process Journal (APJ)

Abstract Today's systems, approaches, and technologies leveraged for managing oil and gas supply chain operations fall short of providing operational transparency, traceability, audit, security, and trusted data provenance features. Also, a large portion of the existing systems are centralized, manual, and highly disintegrated, which make them vulnerable to manipulation and the single point of failure problem. Emerging technologies such as the Internet of Things (IoT), fog computing, cloud computing, and block chain can play a vital role in boosting the operational efficiency of the oil and gas industry. In this paper, we explore the potential opportunities and applications of Artificial Intelligence technology in managing the exploration, production, and supply chain and logistics operations in the Natural Gas industry as it can offer traceability, immutability, transparency, and audit features in a decentralized, trusted, and secure manner. This research highlights the use cases of AI in decentralized Block chain with smart contracts, the company’s trading policies, and its advantages for effectively handling market risk assessments during socio-economic crisis. Results spotlight the use of AI in decision accuracy for the developed smart contract-based Natural Gas Industry, thereby qualitatively limiting the threshold level of costs, energy and other control functions in procurement, production and distribution.

Abstract Background and objectives: Due to the digitalization of life and the fiercely competitive global market, the fourth industrial revolution was inevitable. Industry 4.0 utilizes several interconnected technologies such as artificial intelligence (AI), machine learning (ML), big data (BD) and so on to provide new solutions. The aim of this article is to provide an overview of the vital role that Business Intelligence (BI) play in the realization and adoption of Industry 4.0.

Methods: The present paper addresses this shortcoming by systematically reviewing scholarly articles published in this research domain. It integrates previous insights on the topic to provide a far-reaching theoretical framework that highlights antecedents, practices, and outcomes of BI & Industry 4.0 research.

Findings: Our framework shapes a holistic approach of the BI & I4.0 domain and illuminates different relevant elements up on which future studies in this area be developed.

Conclusion: it addresses the future expected for Industry 4.0 primarily in BI and how companies should face this revolution. This article provides knowledge contribution about the current state and positive consequences of Industry 4.0, and high development in business intelligence when implemented in the organization and the harmonization between production and intelligent digital technology.

Abstract Basically, the foundation of every scientific society is based on research and research is actually the driver of progress and sustainable development of today's information-oriented societies. Statistical researches are generally carried out in two ways: quantitative and qualitative. Knowing the types of research methods and the characteristics of each of them, as well as understanding the difference between quantitative research and qualitative research, helps the researcher to choose the right and appropriate research method. Qualitative research is a research that is based on non-numerical and experimental, physical and exploratory analyzes to understand concepts, opinions, thoughts and experiences in a completely natural and real context. Qualitative researches use different software infrastructures to analyze information. Understanding these software infrastructures leads to greater success and development of research achievements. According to the necessity of the research, in this article we examine the most important software infrastructures in qualitative analysis research.