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In today's business world, it is becoming increasingly important to have a strong enterprise information management (EIM) platform. This platform brings together data, insights, and operational strategies, acting as a vital center that enhances collaboration, improves efficiency, and supports decision-making. Our study focuses on addressing the challenges posed by the growing amount of data and aims to strengthen organizational infrastructure and processes while optimizing the use of information assets. First, we explained the importance of enterprise digitization and e-commerce and how they are interconnected. Then, we explored the evolving requirements of an EIM platform in the e-commerce space and examined the key technologies that support cloud storage. We proposed a multi-layered design for our system that handles user interface, essential business operations, and data administration at the application, business, and data levels, respectively. To make this a reality, we provide specialized functional modules, such as purchasing and sales administration, expense reimbursement, and user log-in. Furthermore, our study includes a thorough system test environment, which requires careful setup and inspection to ensure the EIM system's performance, functionality, and commitment to specifications. Our proposed system's performance is remarkable, with an average error rate of only 0.5%, significantly outperforming existing systems with error rates ranging from 0.8 to 1.2%. Even under heavy loads, the system maintains an average response time of 220 ms, showcasing its reliable responsiveness compared to other systems that can take up to 300 ms under similar conditions. [ABSTRACT FROM AUTHOR]

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Purpose: The article broaches the important topic of the relationships between governance operationalizations and productivity at the start-up level. It proposes a new approach to reconnect the contingency factors to the optimization of productivity. This helps us to identify the changing characteristics that influence the determinants of decisions, actions and management of the technological projects of the mainly innovative enterprises. Design/methodology/approach: The study uses techniques that effectively solve unobserved endogeneity and heterogeneity problems in enterprises: an empirical–structural design. With this method, this study enables rich empirical conceptualization and helps with extending theory. However, there is a need to further the research by taking into account the system analysis and the complexity of the research object: one of the options might be to explore a possible follow-up of the research through drawing on ethnostatistics and qualimetrics. Findings: The analysis reveals that the phenomenon of technological project productivity in operational governance context is thus manifested by the coexistence of the applied governance configuration variables, the contingency factors operationalization, the optimizing productivity mechanisms and this with the secular innovation and stagnation and stagnation. Ceteris paribus, the governance operationalizations have an important role in the productivity of technological projects of the innovative enterprises. Originality/value: This research is the first to mobilize as major determinants of the operationalization of governance, the oversight of the capital, the dividend strategy and the system control, the managerial follow-up, the detection of opportunistic behaviours and the application of governing incentives (among others) as governance configuration variables in order to highlight their interactions with productivity in the innovative firm technological projects. For this reason alone, the paper will be referenced by other authors in the future. [ABSTRACT FROM AUTHOR]

Recently developed advanced metering infrastructure in an intelligent physical system (IPS) sorts large quantities of data available in design, whereas the potential electricity system is implemented for profits and aid in the client's transition from inactive to an active role. This paper investigates the usage of Deep Reinforcement Learning (DRL), in Intelligent Physical System for Strategic Planning in Enterprise Information (IPSSPEI) has been proposed to achieve the online schedule optimization for building energy management of Enterprise Data in an intelligent grid context. Here, two methods such as, profound R-learning and deep policy gradient, have been proposed to compute and examine the learning procedure which performs several actions simultaneously to overcome the scheduling problems. Hence, this high-dimensional database contains information about the generation and utilization of energy by photovoltaic power cars and smart buildings, with advanced metering infrastructure. Moreover, the electrical energy forecasting approaches could be used to give a real-time input to the consumers, facilitating the better application of electricity in an intelligent physical system to overcome the scheduling problems. [ABSTRACT FROM AUTHOR]

Enterprise businesses are more than ever challenged by competitors that frequently refine and tailor their offers to clients. In this context, enterprise information systems (EIS) are especially important because: (1) they remain one of the last levers to increase the performance and competitiveness of the enterprise, (2) we operate in a business world where the product itself has reached a limit of performance and quality due to uniform capacity of industrial tools in a globalized economy and (3) the EIS can increase the product value thanks to additional digital services (built on data associated to the product) in order to meet and fit better client's needs. However, the use of EISs reaches a limit in collaborative environments because enterprises management methods diverge and EISs are mainly inflexible resource packages that are not built with an interoperability objective. Consequently, we need to make EISs interoperable in order to achieve the needed gains competitiveness and performance. This paper contribution can be summarized as follows: (1) it relates existing work and it examines barriers that, at the moment, are preventing further improvements due to current methodological and technological limits, and (2) it proposes a conceptual framework and five challenges that model based approaches must overcome to achieve interoperability between EIS in the near and long term. [ABSTRACT FROM AUTHOR]