Honing Reforms from Clinical Development

Feb 01, 2011
By Pharmaceutical Executive Editors

Today, despite a history of deep pockets and ample resources, biopharmaceutical and medical device companies face limited financial capital. The current global economy is challenging these companies to operate under modest revenue growth, intense time-to-market pressures, and strong growth in worldwide clinical research spending and activity. In response, companies are placing more demand on their capacity planning and forecasting processes and capabilities and setting lower tolerances for variance between planned and actual performance.

Current industry practices present their own set of challenges. A recent study commissioned by ClearTrial and conducted by the Tufts Center for the Study of Drug Development (Tufts CSDD) indicates that senior managers from clinical development, operations, and outsourcing functions at biopharmaceutical companies believe their capacity planning and forecasting techniques are neither sophisticated nor data driven.

Many organizations report that they are currently relying on institutional knowledge, experience, and "gut feeling" in their planning decisions. Nearly all companies stated that there is wide variability and inconsistency in capacity planning and forecasting practices across various departments and areas within clinical development. Surveyed companies were asked about their capacity planning systems and methodologies. The answers are revealing:

"We do not have a good handle on it now. [Vendors] may be taking us for a ride—we don't know." – Mid-Sized Pharma

"Our models are like putting a finger in the wind. It's not apples to apples, and certainly not 100 percent predictable." – Large Pharma

"We have no costing model and no real forecasting. We send out a request for proposal to CROs and they tell us what they think the costs may be." – Large Pharma

"Honestly, we wing it." – Mid-Sized Pharma

Allowable Budget Variance: Then vs. Now
These responses are not too surprising when the most common capacity planning and forecasting methods used in the industry tend to be intuitive and highly unsophisticated. Some of the more "robust" methods at this time include the use of spreadsheets, CRO bids, and the reliance on past experience and intuition.

Hands down, spreadsheets are reportedly the most common planning and forecasting "system" in use today. Unfortunately for those companies using spreadsheets, there are several major shortcomings with this approach:

1) Difficulty Coordinating Decentralized Global Resources: It's extremely difficult for people working in a decentralized manner, across the globe, to collaborate using a desktop file-based spreadsheet;

2) Difficulty Mining Disparate Spreadsheets for Performance and Capacity Data: It is problematic to mine operational performance history that is scattered across individuals and desktop computers. As a result, spreadsheets are more of a static approach as opposed to a dynamic, routinely updated, and coordinated approach; and

3) Spreadsheets Lack Agility and Consistency: Spreadsheets will give different answers depending on the data inputted, the assumptions made, and who is setting them up and operating them. Today's CFOs and staff involved with planning and forecasting need a consistent and agile approach in order to make informed decisions rapidly.

Facing the growing need to improve their competence in capacity planning and forecasting, some companies are moving to more sophisticated approaches such as commercial databases of prior trials, or third-party planning software based on study tasks. Clearly these new approaches require compatibility with existing systems, processes, and company culture. In addition, they must be intuitive, quick to deploy, and easy to maintain.

As life sciences companies begin to embrace more sophisticated planning and forecasting solutions, a key success factor appears to be giving staff enough time to learn, adopt, and integrate the solution(s). The organization needs to allow sufficient time, and to set clear and measurable goals that define the success of the new solution(s).

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