Power of Excel & The Case for Moving Beyond the Beloved Tool for Clinical Trial Forecasting

Let’s face it, people love Excel. It’s easy to use and there is an undeniable level of comfort with a known platform. We’ve all used it to manage our finances, inform projects, and perform calculations, among other things. Wouldn’t it be nice if we could use Excel for everything?

All kidding aside, it’s no surprise that Excel is by far the most used system to model and calculate clinical supply demand forecasts. For experienced users, it is a powerful tool and provides transparency and insight into the data. There are, however, limitations to Excel that you should consider when operating clinical trials. Before I get into that further, let me clarify that Excel is a great tool. Just maybe not the best tool for your study.

Here are the top 5 reasons you should consider another tool for your next study:

1. Building the model is a time-consuming, manual process.

Clinical supply chain experts spend significant time translating study design and supply chain variables into self-made models. By the time the work is completed, very frequently changes have already been made to the clinical trial design, resulting in major modifications to the model. Getting it right, and keeping it current, is a labor of love.

2. Manual processes are at risk of human error.

Garbage In = Garbage Out. If the users don’t input data correctly, the output may be wrong and you are basing critical supply decisions on bad data. Additionally, any mistakes in formulas or incorrect links of cells can cause an error in the output. Actuals from IRT and manufacturing systems are copied and pasted with a risk of error as well. Particularly when the timing is critical and decisions need to be made fast, the potential for human error increases.

3. Scenario modeling is complicated. 

A case can be made to use Excel for simple studies, or for early feasibility planning. However, its value is limited by its difficulty to create scenario models. Changing different parameters to do an impact analysis and then changing it back is not an easy task.   

4. Not automated, and difficult to scale.

Almost every study design is unique and therefore each study demand and supply model is unique. As a result, there is not a high level of reusability of work from study to study creating labor-intensive processes – especially with tools such as excel. Again, Excel is powerful but its weakness is the stability and maintenance of formulas and the ability to use for future studies.

5. Self-made models are linked to the expert.

It is risky to put all your eggs in one basket, that of the Excel model creator. Change is inevitable in any industry. People change roles, have leaves of absence, and leave companies. What if the expertise behind the model leaves with them? Having a tool that doesn’t rely on an SME can be very beneficial for the longevity of your trial. 

Fundamentally, we believe clinical trial forecasting tools should be quick to build, quickly adapt to changes, fully automated and scalable and offer in-depth scenario planning – as well as easy to use.

Interested in discussing this further? Contact us to start a conversation today.