{"title":"Artificial Intelligence in Business Simulation Analysis","authors":"M. Arshad","doi":"10.47672/ejt.629","DOIUrl":null,"url":null,"abstract":"Purpose: Research on business simulation and machine learning has attracted immense interest in the last few years. The aim of this study was to provide a comprehensive view of machine learning in business simulation. To review the use of artificial intelligence in business simulation analysis. A review of the literature, however, shows little systematic reviews on the application of machine learning techniques to business simulation, yet systematic reviews have gained prominence in the academic jargon. \nMethodology: Thus, this study does reviews systematically a total of 123 shortlisted articles that focus on the machine learning techniques in the business simulation process. \nFindings: There are immense algorithms of machine learning which can be used in a business simulation, although this study was able to review ten machine learning algorithms in the business simulation process. As a whole, the machine learning algorithms have been deployed to yield lead-time production in the industry. In inventory and storage, machine learning has been applied to improve efficiency in identifying inventory patterns that would have never been revealed and thus saves on costs. Future direction also discussed. \n ","PeriodicalId":55090,"journal":{"name":"Glass Technology-European Journal of Glass Science and Technology Part a","volume":"22 1","pages":""},"PeriodicalIF":0.3000,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Glass Technology-European Journal of Glass Science and Technology Part a","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.47672/ejt.629","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, CERAMICS","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: Research on business simulation and machine learning has attracted immense interest in the last few years. The aim of this study was to provide a comprehensive view of machine learning in business simulation. To review the use of artificial intelligence in business simulation analysis. A review of the literature, however, shows little systematic reviews on the application of machine learning techniques to business simulation, yet systematic reviews have gained prominence in the academic jargon.
Methodology: Thus, this study does reviews systematically a total of 123 shortlisted articles that focus on the machine learning techniques in the business simulation process.
Findings: There are immense algorithms of machine learning which can be used in a business simulation, although this study was able to review ten machine learning algorithms in the business simulation process. As a whole, the machine learning algorithms have been deployed to yield lead-time production in the industry. In inventory and storage, machine learning has been applied to improve efficiency in identifying inventory patterns that would have never been revealed and thus saves on costs. Future direction also discussed.
期刊介绍:
The Journal of the Society of Glass Technology was published between 1917 and 1959. There were four or six issues per year depending on economic circumstances of the Society and the country. Each issue contains Proceedings, Transactions, Abstracts, News and Reviews, and Advertisements, all thesesections were numbered separately. The bound volumes collected these pages into separate sections, dropping the adverts. There is a list of Council members and Officers of the Society and earlier volumes also had lists of personal and company members.
JSGT was divided into Part A Glass Technology and Part B Physics and Chemistry of Glasses in 1960.