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Merck Engineering

To build and scale transformative technology systems that enable breakthrough innovation to save and improve lives around the world

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To build and scale transformative technology systems that enable breakthrough innovation to save and improve lives around the world

Strengths

  • INFRASTRUCTURE: Robust cloud-based computational platforms
  • TALENT: Strong bioinformatics and computational biology expertise
  • PARTNERSHIPS: Strategic tech collaborations with leading institutions
  • DATA: Vast proprietary clinical and research data repositories
  • SECURITY: Advanced cybersecurity protecting intellectual property

Weaknesses

  • LEGACY: Outdated systems impeding research velocity
  • INTEGRATION: Siloed data systems across research divisions
  • TALENT: Shortage of specialized AI/ML pharmaceutical talent
  • AGILITY: Slow technology adoption compared to competitors
  • ANALYTICS: Limited real-time data analytics capabilities

Opportunities

  • AUTOMATION: Scale high-throughput lab automation technologies
  • COMPUTATION: Quantum computing for molecular modeling
  • PARTNERSHIPS: Expand tech collaborations with startups
  • PLATFORMS: Cloud-based collaborative research platforms
  • ANALYTICS: Advanced analytics to identify promising compounds

Threats

  • COMPETITION: Tech giants entering pharmaceutical space
  • SECURITY: Increasing sophisticated cyber threats to IP
  • REGULATION: Evolving regulatory landscape for digital tools
  • TALENT: Intense competition for specialized tech talent
  • INNOVATION: Disruptive technologies upending research models

Key Priorities

  • PLATFORM: Develop unified research data platform
  • AUTOMATION: Scale automated high-throughput research systems
  • TALENT: Acquire specialized AI/computational biology talent
  • SECURITY: Strengthen cybersecurity for proprietary research data
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To build and scale transformative technology systems that enable breakthrough innovation to save and improve lives around the world

UNIFY DATA

Create seamless research data ecosystem across divisions

  • PLATFORM: Launch unified research data platform with 99.9% uptime across 5 divisions by Q3
  • MIGRATION: Complete migration of 85% of research data to new platform with zero data loss
  • ADOPTION: Achieve 75% researcher adoption rate with 80%+ satisfaction score
  • INTEGRATION: Connect 12 critical research applications to unified data ecosystem
AUTOMATE DISCOVERY

Accelerate research velocity through automation

  • DEPLOYMENT: Implement high-throughput automated screening in 3 therapeutic areas
  • EFFICIENCY: Reduce compound screening time by 40% while maintaining quality standards
  • SCALE: Process 200,000+ compounds monthly through automated systems by Q3
  • ANALYTICS: Deploy real-time analytics dashboards for all automated research systems
GROW TALENT

Build world-class tech and computational biology team

  • HIRING: Recruit 25 specialized AI/ML and computational biology experts by EOY
  • TRAINING: 90% of research tech staff complete advanced AI certification program
  • RETENTION: Improve specialized tech talent retention to 92% through targeted programs
  • COLLABORATION: Launch 3 university partnerships to develop computational talent pipeline
SECURE INNOVATION

Protect intellectual assets while enabling collaboration

  • INFRASTRUCTURE: Implement zero-trust security architecture across research platforms
  • COMPLIANCE: Achieve 100% compliance with updated regulatory requirements for all systems
  • TRAINING: 95% of research staff complete advanced security awareness training
  • RESILIENCE: Reduce mean time to detect security incidents by 50% through advanced tools
METRICS
  • Technology enablement of 3 breakthrough drug discoveries by end of 2025
  • Research velocity: 35% reduction in time from target to candidate selection
  • Digital platform uptime: 99.95% availability of critical research systems
VALUES
  • Patients First
  • Respect for People
  • Ethics & Integrity
  • Innovation & Scientific Excellence
  • Digital & Technical Agility
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Align the learnings

Merck Engineering Retrospective

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To build and scale transformative technology systems that enable breakthrough innovation to save and improve lives around the world

What Went Well

  • ONCOLOGY: Keytruda sales exceeded forecasts by 12% driving revenue growth
  • INFRASTRUCTURE: Cloud migration initiative completed 3 months ahead of plan
  • VACCINES: Strong performance in vaccines portfolio with 18% year-over-year
  • EFFICIENCY: Tech-enabled R&D productivity improvements reduced costs by 8%
  • PARTNERSHIPS: Five new strategic technology collaborations established

Not So Well

  • INTEGRATION: Post-acquisition technology integration delays impacted timelines
  • LEGACY: Technical debt in legacy systems caused research pipeline delays
  • TALENT: Higher than expected turnover in specialized technology roles
  • PROJECTS: Three digital transformation initiatives exceeded budget by 15%
  • ANALYTICS: Data analytics capabilities not meeting research team requirements

Learnings

  • TECHNOLOGY: Earlier involvement of tech teams in research planning critical
  • GOVERNANCE: Need for stronger tech governance across research divisions
  • ROADMAP: Technology roadmaps must align closer with research priorities
  • AGILITY: Increased agility needed in technology deployment for research
  • PARTNERSHIPS: Tech collaboration models need standardization and oversight

Action Items

  • PLATFORM: Accelerate unified research data platform implementation by Q3
  • TALENT: Launch specialized technology talent acquisition program in Q2
  • GOVERNANCE: Implement cross-functional technology governance council
  • AUTOMATION: Scale automated research systems across three key divisions
  • INTEGRATION: Develop comprehensive post-acquisition tech integration plan
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To build and scale transformative technology systems that enable breakthrough innovation to save and improve lives around the world

Strengths

  • COMPUTATION: Advanced computational modeling capabilities
  • ALGORITHMS: Proprietary AI algorithms for drug discovery
  • DATA: Extensive clinical trial datasets for AI training
  • EXPERTISE: Cross-functional AI research teams established
  • INFRASTRUCTURE: Scalable AI infrastructure investments

Weaknesses

  • INTEGRATION: Limited AI integration across research workflow
  • TALENT: Insufficient AI/ML specialists in key therapeutic areas
  • VALIDATION: Lack of robust AI validation frameworks
  • GOVERNANCE: Immature AI governance and ethics policies
  • LEGACY: Legacy systems creating AI implementation barriers

Opportunities

  • DISCOVERY: AI-driven target identification acceleration
  • PREDICTION: Enhanced molecular property prediction models
  • TRIALS: AI optimization of clinical trial design and recruitment
  • PARTNERSHIPS: Strategic AI research alliances
  • MANUFACTURING: AI optimization of production processes

Threats

  • COMPETITION: AI-native biotech startups gaining momentum
  • REGULATION: Uncertain regulatory environment for AI in pharma
  • PRIVACY: Data privacy concerns limiting AI applications
  • BIAS: AI bias risks in clinical applications
  • COMPUTE: Escalating costs of advanced computing resources

Key Priorities

  • PLATFORM: Build unified AI drug discovery platform
  • TALENT: Strategic acquisition of specialized AI talent
  • VALIDATION: Develop robust AI validation frameworks
  • PARTNERSHIPS: Expand strategic AI research alliances