Using artificial intelligence to improvetime estimation for project management

Sammanfattning

Time estimation is an important aspect in project management. Failure to make accurateestimates can lead to large consequences. Despite this, humans tend to make fairly inaccurateestimates when tasked to, often underestimating the time something will take substantially. Thisthesis explores using artificial intelligence and machine learning to produce time estimates forthe life science company Biotage. A predictive model can be trained using previous projects assamples, including time reporting data for employees as the output variable.A total of 12 completed projects were found that had both sufficient time reporting data andsome project information. Previous projects took on average 55.1% longer to complete thanestimated at the start of the project. Every project had one or more of the following: projectdescription, work breakdown structure and/or Gantt chart. However, the level of detail in almostall of the projects was very low, making it difficult to extract useful features. A constant-timemodel (predicting that every project takes the same amount of time), had a Root Mean SquaredError (RMSE) of 5058 hours and a Mean Absolute Percentage Error (MAPE) of 282%. Anothermodel that took into account whether the project was a software only, hardware only or both hada RMSE of 4269 hours and MAPE of 320%. Due to the scarcity of data, no furtherimprovements were made. It was determined that in order to develop a predictive model thatcan match human estimates, at least one of the following had to be true: Better level of detail inthe data, bigger sample size of previous projects, or projects being more similar so that theyshare common features more often.