Forecasting in Pharma
Forecasts are pretty valuable in any industry. However, in the pharmaceutical industry, accurate forecasting can be particularly challenging to achieve. In previous years, studies have shown exactly how difficult pharma forecasting can be as the large majority of 1,700 forecasts over nine years failed to show and prove.
So what went wrong so many times, and how can it be prevented from happening again? It turned out that predicting how one drug would sell based on the results of another was not the best solution. Following this technique, like other industries often do, did not work. While the errors in forecasts differed for each analyst, here are some common pitfalls companies faced, and should be avoided, when forecasting in the pharmaceutical industry: adding too many segments, disorganization, and using a technique that simply does not work.
They are adding too many segments.
An essential step in forecasting is sifting through data and separating what could be valuable, relevant, and helpful and knowing what to archive for later use. In some cases, adding too much data is just as confusing as not having enough. Both scenarios could yield unwanted results.
Disorganization. Being intentional is critical.
Every step needs to have a purpose, and every person involved should have a clearly defined role. Having management solutions in place to minimize confusion is not only ideal but is the difference between success and failure. Outsourcing tasks that are challenging may be the best practice. It can save time and money in the long term. Proper management and timely reporting are vital steps when it comes to keeping organized.
They use techniques that simply do not work.
Imagine running a 12-month clinical trial using the wrong equipment the entire time. Imagine using the proper equipment but improperly processing the data resulting in unreliable and invalid results. Lastly, imagine doing everything correctly and not having the appropriate system to decipher the results. In each of these scenarios, the company would likely have wasted countless hours and possibly millions, if not billions, of dollars.
So what are the solutions to these challenges?
Due to massive amounts of data, the sensitivity of the data, and the security measures necessary to safely manage the data along with various other factors, it’s a bit more complicated than offering “the answer” on a platter. However, there are solutions to assist in safe, secure, and efficient data management, minimizing or eliminating easily avoidable errors. Using reputable data management solutions to monitor critical data such as clinical trials is not only wise, but it has proven to lead to successful results.