A biotech company engaged Clarkston to conduct a pack and label regional strategy assessment for their largest therapy across markets to evaluate the feasibility of a regional-based model to optimize cost and lead times. Read a synopsis of the project below or download the full case study.

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An international biotech company experienced substantial growth over the last couple of years and forecasts additional growth and expansion with their upcoming pipeline therapies. The rapid growth encouraged the company to prioritize launch timing rather than certain operational efficiencies and rapidly increased the level of supply chain complexity. 

To accomplish this, Clarkston created a working team of SMEs from the core categories they were going to analyze, such as trade, logistics, planning, tax, etc. From there, Clarkston followed a hypothesis-based approach where the team used preliminary insights to develop initial hypotheses. Following that, the team collected and analyzed data to test hypotheses on 40 markets across 50 categories focused on distribution costs and lead times, pack site pricing and capabilities, and current distribution flows. 

From the interviews, calibration, data collection, and analysis, the Clarkston team developed a recommended pack site strategy for 40 SKUs, balancing redundancy, regionalization, and cost neutrality. The team also identified longer-term pack site strategy recommendations that would further support the client’s ability to scale and cut costs. 

Clarkston delivered a recommendation roadmap to leadership as well as an assessment approach so that the methodology could be used for future markets and product launches. If the client implements Clarkston’s recommendations, they should expect to see reduced supplier lead times, lower freight costs per unit, and easier future scalability. 

Download the Pack and Label Regional Strategy Assessment case study, and learn more about our Strategy Services by contacting us below. 

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Human-created: This content was ideated, drafted, edited, and approved by a human. AI use, if any, was limited to incidental support.