The study improved budgeting efficiency at industrial enterprises with evidence from Russia, Italy, and the Middle East. In the era of contemporary globalization and technological advancement, a budgeting system holds paramount significance in the effective management of the financial operations and activities, enhancing the overall efficiency of the firm's cash management, mitigating the risk of finance misallocation, and improving the overall financial performance of the enterprise. However, despite its effectiveness, there is a lack of evidence supporting budgeting automation and its efficiency in managing industrial enterprises. More so, limited theoretical and practical relevance is found in the context of Russia, Italy, and the Middle East. This research intended to fill the existing research gap where a qualitative research design was opted. Primary data were collected from the budgeting heads of 3 pharmaceutical firms, each located in Russia, Italy, and Iran. In-depth interviews with 3 budgeting heads identified that the conventional incremental budgeting system needed amendment and replacement with a consolidated and contemporary yet flexible approach to bring radical improvements at the macro-environment level within the industrial enterprise. The key findings led to the development of a model to improve budgeting efficiency, comprising three components: information and analytical/accounting support for budgeting, production accounting information, and a combination of the regulated operation prices. The consolidation of these three components can yield budgeting efficiency. Doi: 10.28991/ESJ-2023-07-01-013 Full Text: PDF
Because of the proliferation of digital technologies, organizations now have access to previously unimaginable troves of data. In order to make educated choices and generate beneficial results, accurate data analysis and interpretation are essential. The use of data visualization in this context has proven its value. Recent studies found that data visualization increased business owners' drive to make a profit. To aid business owners in evaluating issues related to self-service data resources, a dynamic IoT-based enterprise management framework (IEMF-IDM) was presented. The suggested system uses fusion optimization techniques to maximize the fusion score and enhance decision-making through the use of various models and methods, such as machine learning and fuzzy approaches. Simulation studies in a number of domains, including robots, cloud settings, and multimedia data fusion, attest to the system's efficacy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.