An IT Project Manager is responsible for project planning, estimating and scheduling, developing and monitoring the progress throughout its development life cycle. The selection of a particular methodology is heuristic and the performance of the system developed is unpredictable. The authors suggest that a designed process performance model (PPM) can help to predict the required factors of a process to help achieve set goals for the process. This, in turn, can help to control factors that the project and the organizations need to control and ensure expected results. PPMs may enable to work out the relationship between different variables for a well-defined project and this knowledge becomes basis for prediction of performance solution, and helps in implementation of solution. This approach related to designing of PPMs, for various real life projects situations has not been attempted by industry in a big way. The authors demonstrate how to work out the PPMs, based on the given inputs of projects, by an Indian IT company. The solution works out number of bids which arrive at a given time or predict when the next bid will arrive at service centre, based on time series and queuing theory approach. This solution approach is based on different problems that will become the basis to build PPMs for similar problems. The problems discussed here are from an Information Technology Company, with real life data from the projects under development. Testing these models with more projects data thus will formalize how to build PPMs in a similar way. The authors discuss problem areas where time series and queuing theory Models can be applied and benefits of the present approach. The authors have similarly worked on different mathematical models based on industry data and build PPMs based on Bayesian, regression, fuzzy logic and other models. This paper is submitted with only two models just to prove the concept. In future building PPMs is likely to be a necessity in high maturity IT organizations.
Wireless Personal Communications – Springer Journals
Published: Sep 20, 2017
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