A Case of Effective Project Portfolio Management

Transcription

1 RKC Consult pg. 1 van 5 Research and Knowledge Computing RKC Consult Hazenberg ZA Alphen Nederland A Case of Effective Project Portfolio Management This is an excerpt of a paper presented at the CE 2014 Conference held at the Jiaotong University, Beijing in September The Challenge A portfolio of 36 projects had to be monitored individually and as a portfolio over a period of 10 months. The portfolio manager requested an individual progress report and a portfolio report on the 15th and on the last day of every month. Analysis The individual projects were rather small in volume: they were Master theses of Master student in industrial engineering. The college had decided to confront the students with modern project scheduling and monitoring by asking them to run their master thesis work as a project. We concluded that these steps had to be implemented: Introduction to project scheduling Introduction to project monitoring Introduction on the scheduling tool Introduction on the monitoring tool Set up of the 36 schedules, audit, feedback and final version Startup of the monitoring Automated project reporting Automated portfolio reporting.

2 RKC Consult pg. 2 van 5 Research and Knowledge Computing Anatomy of the Solution It was from the beginning clear that we needed a simple though powerful scheduling software and a monitoring tool that allowed for fully automatic reporting. Sharing capabilities were also mandatory as every schedule and report had to be available to the students, the thesis promotors and the professors. This is the solution anatomy As scheduler we chose Smartsheet it is a shared system it has a very short learning curve it is project centric it has a powerful API alowing full integration with external tools. As monitoring tool we took our proprietary DPC engine (Dynamic Project control) it is fully integrated with Smartsheet it allows for a fully automated reporting it delivers portfolio reporting Relation of this project to data science and big data. The data have many sources, that are geographically dispersed. The data are generated by many participants (about 50 students). The data are shared with many people. The reporting is scheduled at defined moments and has to be generated automatically. Not the data volume, the schedules are small, but the multiplicity of sources and participants, make this project to a big (biggish) data problem. In any case, if this problem is solved with the proposed solution, then similar larger sets of larger projects can also be handled. It boils down to a case of linear volume growth. The Experience All of the predefined steps have been implemented in a smooth way. It must be said, that this type of project had already been implemented two times before, in slightly different configurations. The first versions of the schedules displayed the common errors, also found with even experience planners. The most common are:

3 RKC Consult pg. 3 van 5 Research and Knowledge Computing Structural errors: project tree not clean Optimistic scheduling resulting is strongly front loaded schedules These errors were easily corrected, so that the final schedules, obtained 15 days after project start, were sound. On average the students had no difficulties in assessing their (physical) progress on the projects tasks. Only few clarification questions were fired in order the understand the method for setting % complete values. The interpretation of the progress reports, shown as a set of S curves mapped on the project s WBS, was easily grasped. During common project review sessions where all projects were evaluated against their progress very professional sounding comments were made by many students. Even realistic and well thought through recovery plans were formulated when needed. It was a pleasant surprise to see how well the students on average managed to develop and display a professional attitude towards project scheduling and control. These were the major findings: 17% of the students produced excellent scheduling and tracking 51% produced good scheduling and tracking 32% had problems In other words, about 68% of the students produced a good to excellent project schedule and track record. That level of performance is not often observed in the pro world. We also notices a pattern, one that was expected in fact: Those students with a good tracking result also produced a good end work: high quality, well polished text work. Those with a poor tracking result produced on average a lesser quality end Also were we, the promotors and professors, able to predict well ahead of time, who was likely to produce high quality work and who was not.

4 RKC Consult pg. 4 van 5 Research and Knowledge Computing Ilustrations Fig 1: the final S curve of the winning team 2014 of the Golden S curve. fig 2: typical view of a progress report interface.

5 RKC Consult pg. 5 van 5 Research and Knowledge Computing Fig 3: a view taken from the portfolio report were the group tracking performance is visualised. Availability RKC Consult is now in the position to provide the full spectrum PMO assistance based on the techniques and tools used in the afore described project. For information and inquiries please contact [email protected]