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Case Studies > Specialty biopharmaceutical company reaps data analysis and process efficiencies

Specialty biopharmaceutical company reaps data analysis and process efficiencies

Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Analytics & Modeling - Real Time Analytics
  • Functional Applications - Manufacturing Execution Systems (MES)
Applicable Industries
  • Life Sciences
  • Pharmaceuticals
Applicable Functions
  • Process Manufacturing
  • Quality Assurance
Use Cases
  • Predictive Maintenance
  • Process Control & Optimization
  • Regulatory Compliance Monitoring
  • Remote Asset Management
Services
  • Software Design & Engineering Services
  • System Integration
  • Training
The Challenge
Shire’s Rare Diseases Business Unit faced significant challenges in managing vast amounts of manufacturing data using a complex network of heterogeneous systems, including LIMS, Excel, and JMP. The manual collection and reporting of key findings were extremely time-consuming and prone to errors. The division needed a validated data capture and analysis tool to conduct statistical process control, monitor processes, and identify areas of improvement. The key requirement was for an off-the-shelf application with validatable output and demonstrated use in the biopharma industry.
About The Customer
Established in 1986, Shire is a specialty international biopharmaceutical company that provides treatments in neuroscience, rare diseases, gastrointestinal, and internal medicine. The company has a significant presence in the United Kingdom and the United States, employing over 5,300 people. Shire’s Rare Diseases Business Unit, part of its U.S. operations, focuses on developing and delivering treatments for rare diseases. The unit had been using a complex network of systems to manage manufacturing data, which was time-consuming and prone to errors. The division sought a more efficient and validated solution to streamline data management and improve process control.
The Solution
Shire’s Rare Diseases Business Unit selected Statistica’s advanced analytics platform to replace its existing heterogeneous systems. The solution was implemented in three environments across multiple sites: development, validation, and production. Statistica provided controlled access to validated, real-time process data, supporting manufacturing operations management across the enterprise. The implementation included a validated connection to import historical LIMS data for statistical process control. The Statistica graphical workflow management interface, Enterprise Workspaces, facilitated rapid configuration of analytics steps, allowing users to build targeted analytic flows without programming. The implementation occurred in two phases: Phase I involved developing and rolling out a Good Manufacturing Practices (GMP) system, while Phase II focused on replicating historical batch record data for a seamless retrospective view of process history.
Operational Impact
  • Statistica provided a validated single point of entry for data capture across processes and locations, eliminating the need for training on multiple systems and reducing data entry errors.
  • The solution enabled teams to close process investigations more efficiently with root cause analysis, reducing operational risk and costs.
  • Verified data and reports were visible across sites, increasing efficiency, reducing costs, and improving communication.
Quantitative Benefit
  • Shire’s Rare Diseases Business Unit saw immediate benefits with the new Statistica GMP system, as business users could publish charts, graphs, and reports to a web portal via their company intranet.
  • The implementation of Statistica reduced the time needed for system validation and implementation, thanks to the division’s excellent use case and requirements documentation.
  • The solution enabled real-time tasks that were previously labor-intensive and time-consuming, allowing one engineer to send real-time LIMS data summaries and analyses needed urgently for an ongoing project within moments of going live.

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