Software Estimation and Controlled Risk

Title: Software Estimation and Controlled Risk

I’ve written before on the predictive nature of estimation software. Using cost estimating software products as project management software can help show you aspects of your project which you haven’t accounted for, particularly things like risk.

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However, software estimation products can do more than highlight areas which you haven’t seen, they can also peer around the corner and show that which you haven’t done yet. Through the software’s rather developed, and mathematically sound, understanding of your project, that understanding can be leveraged towards making theoretical changes.

Think about it this way, software estimation project management software allows you, the project manager to play the “What if…” game. What if I doubled my workforce? What if I reduced everyone’s workload by 10%? What if deadlines were all extended by 10%? What if I assigned every part of the project to just one person?

The questions can seem trivial, even comical, however they point to a very interesting application for such software – they ability to control, limit, and even potentially mitigate risk. The questions I just posed all center around one significant area – the sharing of a workload to make a project easier on employees.

How does making project members work easier help to control risk? Well, for one thing, for projects which are heavily dependent on multiple deadlines all being satisfied in a particular order, specifically where one group is waiting on the work of another, each deadline represents a potential point of failure. By making deadlines easier to achieve, the risk of failure is thus avoided.

Of course, deadlines represent just one area of risk. I like to use them as an example because hard deadlines seem to exist in every area from newspapers to software companies to automobile manufactures. The flexibility of project management software which employees these estimation techniques means that project managers can juggle all sorts of variables in a project finding the optimal mix. In the end, such juggling helps to control the risk of uncertainty.