Effective collaboration across demography through digital dash boards and machine learning
In a large sized project with globally distributed environment, it is challenging to have an information radiator which is common across all the demographic locations. Different time zones make it difficult to collaborate and cater to a customer who demands frequent releases and transparency. This paper demonstrates a unique solution driven by a novel mix of digitization and machine learning to solve this classical problem.
Value stream mapping helped us to identify valueless paths in task assignment and hence the team developed a tool backed with machine learning which saved huge time, helping us to deliver our concepts faster to market. We took help of digital dash board, and came up with custom unconventional dash boards, which helped our customer to understand and collaborate better with the development team located in different time zones.
This paper proposes further extensions to machine learning in project management. Improvements can be brought about in areas task estimation and assignments, velocity or turnaround time prediction.
This paper is targeted to agile practitioners who are interested in improving workflows and building intelligent process systems using new modern technologies in globally distributed environment. This modern approach reduces waste and brings in collaboration irrespective of the demography.
Mon 28 May Times are displayed in time zone: (GMT+02:00) Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
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Miguel Botto TobarEindhoven University of Technology, Weslley Silva TorresEindhoven University of Technology, The Netherlands, Angela LozanoHealthConnect, Mark van den BrandEindhoven University of Technology, The Netherlands, Bogdan VasilescuCarnegie Mellon University, Alexander SerebrenikEindhoven University of TechnologyDOI Pre-print
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Mateusz KapicaBanedanmark, Denmark (Copenhagen)
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