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學(xué)術(shù)動(dòng)態(tài)

學(xué)術(shù)動(dòng)態(tài)

學(xué)術(shù)活動(dòng)

題目:Project Management Frontiers with Artificial Intelligence

作者: 編輯:賈峰菊 發(fā)布時(shí)間:2023-05-06

題目:Project Management Frontiers with Artificial Intelligence

主講人:Prof. Mauro Mancini

時(shí)間:5月8日(周一)上午 10:00-11:30

地點(diǎn):bwin必贏唯一官網(wǎng)315室

嘉賓介紹:Prof. Mauro Mancini,意大利米蘭理工大學(xué)管理、經(jīng)濟(jì)和工業(yè)工程系項(xiàng)目管理教授,米蘭理工大學(xué)管理研究生院副院長(zhǎng),國(guó)際碩士項(xiàng)目主任,在Industrial Plants Management, Project Management, Building Information Modelling and Megaprojects等國(guó)際期刊發(fā)表文章100余篇,出版著作10余部,在石油和天然氣、建筑、軍事、能源、數(shù)字支付和基礎(chǔ)設(shè)施建設(shè)等不同工業(yè)領(lǐng)域主持國(guó)家及國(guó)際研究、咨詢項(xiàng)目20余項(xiàng)。


講座摘要:The era of Digital transformation is proposing the usage of computer science in completely new ways and areas of application mainly the continuous interaction with human beings. This phenomenon is fostering the possibility to test those applications in a very wide range of the worldwide population giving the chance to speed up the process of new needs identification and implementation. The Artificial Intelligence is following exactly this path emerging as one of the most promising hypotheses for improving many processes that still suffer from inefficiency and inaccuracy. Nevertheless, AI usage is at the very infant stage. The speech aims at presenting the state of art of AI application and their potentialities in project management field through a wide literature review followed by a discussion and identification of new trends both in academic research and industrial applications. The result shows that in almost all knowledge areas, the application of AI techniques can help to reduce the time spent on repetitive, low-value-added tasks, allowing the project manager to focus on managing the team and creating added value for the company. Nevertheless, many challenges remain. On the one hand, the need to change the figure of the project manager in their professional and educational paths. On the other hand, the need for large amounts of data to achieve acceptable levels of accuracy of predictive models makes the use of AI techniques still partially distant from practice.


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