Wikipedia defines Predictive Analytics, as “Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events.” While not all of the techniques stated above are required, a lot of data mining and statistical analysis need to be performed on historical data before one can predict the future trends and outcomes. The accuracy of the prediction depends on the variables and assumptions considered and will be the key to making accurate predictions of risks and opportunities.
Case in point, the volume of cases that come in for a product safety case processing organization varies depending on many factors. The variance could be due to factors like Seasonality of Adverse Events, A news item discussing potential side effects of a product, A blog post by a physician or some influential group or organization so on and so forth. With the ever increasing cost pressures on life sciences organizations, it is very difficult to plan for peak volumes while there will be additional unused capacity during the troughs. This is the kind of situation where any organization can use some predictability so they can plan the capacity within a reasonable deviation thus normalizing the peaks and troughs.
Imagine an business intelligence solution that can mine the historical case volumes and the corresponding capacity in conjunction with the process efficiency and be able to predict the future capacity requirements. Add the ability to evaluate some ‘What if” scenarios where one can change variable like case volumes and be able to predict the capacity requirements. While this may sound like a “Holy Grail”, it is possible with some of the sophisticated tools available. A screenshot of one such solution below:
Do you have a need for such solution in your organization? Have you built a predictive analytics solution for other purposes? Please share your feedback and inputs.