A Framework for Evaluating Size and Structure of Pharmaceutical Contract Operations

In a world of frequent biopharma mergers and divestitures, the optimal size and structure for a pharma company’s contract operations department is a moving target. Whether you’re setting up a new department or adjusting an existing one, meeting these shifting expectations is a challenge. There is typically no single way to determine your company’s unique best fit – one that balances management expectations with budget constraints and resource capacity.

When a line manager is tasked with restructuring their contract operations department, they typically do so with no supporting framework to help create a business case that compares the available options and benchmarks against peers. The process remains more art than science.

When approached with the question of organization sizing, executives typically deal with the following business questions:

  1. What size should my contract operations team be? (contracting, rebates, chargebacks, government pricing, Medicaid, IT support, etc.)
  2. Is my current team under- or overstaffed?
  3. How does the size, structure, and skillset of my team compare with industry peers?
  4. What are the key gaps compared to peers and internal requirements?
  5. If there is a need for additional resources, how do I build a stronger business case to support incremental funding requests?

Typical approaches to sizing

The following table summarizes the various approaches we have seen used throughout the industry, along with pros, cons, and overall usability.





Overall usability


Estimate is based on prior experience

  • Simple
  • Captures non-quantifiable experience
  • Hard to justify
  • Hard to evaluate effectiveness
  • Valuable if used with other data points
  • Captures management expectation


“We have as many people as we can afford”

  • Simple but quantifiable
  • Always ensures alignment with budget
  • Reactive instead of proactive
  • Counterproductive – not viewed as an investment
  • Captures “reality”
  • Captures finance expectation


Estimate the daily maximum workload of a team member, then divide estimated total workload

  • Simple to calculate and explain
  • Ensures alignment with user expectation
  • Hard to estimate objectively
  • Easily used to justify prior beliefs instead of questioning
  • Captures “capability”
  • Captures user expectation

Competitive benchmarking

Informally ask a few peers and base final decision on specific company or average of companies

  • Thought-provoking
  • Great educational value
  • Generates insight

Essential to have:

  • Sufficient sample
  • Good data quality
  • Intimate knowledge of peers
  • Captures “competition”
  • Ineffective benchmarks can result in bad decisions

Regression modeling

Create statistical models to understand drivers of team size and structure

  • Compare vs. peers based on multiple variables
  • Estimate changes as business evolves
  • Hard to build and interpret
  • Evaluate model results against judgment
  • Scientific approach
  • Accounts for similarities and differences vs. peers


HighPoint perspective

We believe approaching the sizing question from multiple perspectives is important to not only balance budget constraints, resource utilization, and compliance risk, but also to prevent resource burnout. To enable that, we recommend a three-step approach:


Step 1: Use multiple approaches to evaluate current structure and gather data points for go-forward size and structure

Internal review

  • Interview key stakeholders to understand operational constraints and drivers
  • Review current size and structure
  • Overlay internal and external experience
  • Propose size and structure

Competitive benchmarking

  • Identify peer group
  • Gather data through published data source and anonymized surveys
  • Understand similarities and differences
  • Propose size and structure

Workload build-up

  • Use data from internal review and to build workload model
  • Reconcile against external experience
  • Propose size and structure

Regression modeling

  • Gather data for statistical analysis
  • Identify key variables
  • Run and interpret regression models
  • Plot company against peers and expected results


Step 2: Reconcile recommendations from different approaches

It is important to compare the recommendations from different approaches and determine the cause of variation. Typically, this should also be supplemented by assigning a reliability and importance weight to help reach the best go-forward option.


Step 3: Finalize and build your supporting case

Using all the data, the final step in the process is creating a summarized business case that outlines the recommendations for management consideration. If done correctly, these final results help executives, line managers, and analysts achieve an aligned perspective that balances the management expectations; financial expectations and affordability; resource skillset and capacity constraints; and competitiveness with respect to industry peers.


To learn more about evaluating your biopharma contract operations department for optimal sizing and structure, please contact Neelabh Saxena.