Data modernization and digital manufacturing: optimizing a global biopharmaceutical organization’s supply chain
The situation
A multinational pharmaceutical company with operations around the world lacked supply chain visibility, which cast a shadow over operational efficiency. This not only led to inefficiencies but also exacerbated manufacturing problems, frequently necessitating time-consuming, hands-on interventions. Organizational and technological complexity that built up through reorganizations, M&A, and market requirements added an additional layer of difficulty.
Siloed source systems scattered across the enterprise manufacturing IT infrastructure also significantly diminished the speed at which critical insights could be gleaned.
To foster data-driven initiatives across manufacturing sites, data assets are now meticulously cataloged, governed, and managed.
The solution
To help face these challenges, Deloitte developed a robust data modernization strategy. A use case collection and prioritization framework emerged, designed to target key business priorities effectively.
To support the transformation, we helped enable near real-time processing, harmonization, and ingestion from source systems into the manufacturing data lake became the central focus. To deliver this, Deloitte deployed multiple long-term data pods across the globe. Enhanced scalability and flexibility were also driven by a cloud-based modular architecture.
To foster data-driven initiatives across manufacturing sites, data assets are now meticulously catalogued, governed, and managed. The Deloitte team also implemented a use case-centered solution enablement approach, which facilitates data use throughout the manufacturing process.
The impact
The outcomes included enhancing supply chain visibility across teams, locations, and systems. Also achieved was timely value realization through end-to-end visibility and integrated data models across systems. Other impacts include:
• 20+ manufacturing sites are enabled with modernized data
• 150+ data entities across 5 source systems integrated into the contextualized data layer, including enterprise resource planning (ERP), laboratory information management systems (LIMS), and quality management systems.
• Enabled 400+ users across manufacturing, supply chain, quality, and data science teams globally to utilize the data.
Having saved time and effort, there was an overall 75% reduction in time required to transform the data into insights at manufacturing sites. Also, there was an 85% reduction in the manual effort required to extract and consolidate raw data before it could be consumed by the analytics applications.
reduction in time needed to transform data into valuable insights.
reduction in required manual effort to extract and consolidate raw data.
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Todd Konersmann
