Case Study: How a pharma & biotech company transformed warehouse capacity planning for strategic growth

Gaining transparency for better decisions

As the pharma & biotech company’s global network continued to grow, so did the complexity of managing warehouse space across diverse sites, product flows and limited capacity. Site managers and planners needed a clearer picture: How is space being used today? Which materials are driving saturation in key zones or temperature classes? What capacity will be needed tomorrow?

In order to stay ahead, the company partnered with Miebach to transform their warehouse capacity planning with a digital model that improves decision-making at both the site and enterprise level.

A global, data-driven planning tool

To improve warehouse capacity planning across its global network, the client co-developed with Miebach a structured, data-driven planning model that combines detailed site-level insights with a harmonized global view on a company-wide platform.

Acting as an end-to-end partner, Miebach supported the project from initial data structuring and validation through tool architecture design, scenario modeling development, and on-site training sessions. This ensured a functional solution, that is also firmly embedded in the organization and scalable in the long term.

Key capabilities of the global capacity tool include:   Dynamic baseline calculations Site users can instantly assess current utilization and days-on-hand across all capacity areas, replacing manual number crunching. Scenario-based forecasting The tool shows future space needs under different forecast scenarios, helping anticipate bottlenecks before they materialize. Interactive drill-down and filtering Inventory can be analyzed by product, temperature, or storage type, to quickly identify root causes of space issues. Non-moving stock alerts The tool identifies stagnant pallets in order to remove them from the flow to free up capacity. Harmonized enterprise view the company’s supply chain leadership team gains a single source of truth to compare sites globally, identify pain points and align strategic investments.

The tool effectively bridges the gap between operational data and strategic planning, delivering measurable results for the client:

• Faster decision-making
  What once required manual analysis over days or weeks can now be assessed in near real time.
  
  
• Freed storage space
  Non-moving pallets and quantities are identified and flagged in order to make decisions to recover valuable capacity.
  
  
• Smarter investments
  Long-range forecasts support data-driven decisions on automation, outsourcing, or facility expansion, avoiding costly over- or under-investment.
  
  
• Global strategy: A single source of truth allows leadership to compare sites, spot future pinch points, and harmonize logistics strategies worldwide.

Leveraging the model in demand and production planning

With the digital warehouse capacity model successfully in place, the pharma & biotech company is now exploring further integration of the tool with demand and production planning systems, creating end-to-end synchronization across its supply chain.

The article was also published in CHEManager International: Link