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SWOT
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Eli Lilly Engineering

To create innovative medicines that make life better for people by pioneering transformative scientific breakthroughs in healthcare.

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Align the strategy

Eli Lilly Engineering SWOT Analysis

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To create innovative medicines that make life better for people by pioneering transformative scientific breakthroughs in healthcare.

Strengths

  • PIPELINE: Strong diabetes/obesity portfolio led by Mounjaro/Zepbound
  • INNOVATION: Industry-leading R&D capabilities with 18.4% of revenue
  • EXPERTISE: Deep expertise in protein-based medicines and antibodies
  • INFRASTRUCTURE: Advanced manufacturing facilities for biologics
  • FINANCIAL: Strong balance sheet with $8.9B in free cash flow

Weaknesses

  • TECHNICAL_DEBT: Aging core systems impeding development velocity
  • CAPACITY: Limited manufacturing scale for GLP-1 high demand products
  • DIGITAL: Underdeveloped data science and ML infrastructure
  • TALENT: Skills gap in emerging computational biology disciplines
  • INTEGRATION: Siloed development environments across divisions

Opportunities

  • AI_ADOPTION: Accelerate drug discovery through AI/ML technologies
  • OBESITY: Expand GLP-1 treatments beyond diabetes to obesity market
  • ALZHEIMER'S: First-mover advantage with donanemab approval
  • AUTOMATION: Implement lab automation to increase R&D throughput
  • CLOUD: Modernize research computing through cloud transformation

Threats

  • COMPETITION: Novo Nordisk & emerging players in GLP-1 market
  • PRICING: US drug pricing reforms affecting long-term profitability
  • CYBER: Increasing sophistication of cyberattacks on pharma IP
  • TALENT_WAR: Intensifying competition for AI and biotech talent
  • COMPLEXITY: Growing regulatory requirements for digital health

Key Priorities

  • MODERNIZE: Accelerate technical infrastructure modernization
  • AI_STRATEGY: Develop comprehensive AI strategy for R&D acceleration
  • CAPACITY: Rapidly expand manufacturing capacity for GLP-1 products
  • TALENT: Acquire and develop specialized AI and computational talent
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Align the plan

Eli Lilly Engineering OKR Plan

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To create innovative medicines that make life better for people by pioneering transformative scientific breakthroughs in healthcare.

MODERNIZE

Transform our technology foundation for the future

  • CLOUD: Migrate 75% of R&D workloads to cloud platforms by Q3, reducing on-prem footprint by 40%
  • ARCHITECTURE: Implement API-first architecture for 5 critical systems, enabling 3x faster integration
  • TECHNICAL DEBT: Retire 10 legacy systems and replace with modern solutions, reducing maintenance by 25%
  • DEVOPS: Achieve 85% automated testing coverage and reduce deployment cycle time from weeks to days
AI ACCELERATION

Harness AI to revolutionize drug discovery

  • PLATFORM: Launch unified AI research platform used by 90% of research teams with 99.9% availability
  • MODELS: Train 5 novel ML models that predict protein interactions with 85%+ accuracy on test data
  • DISCOVERY: Utilize AI to identify 200+ new drug candidates, advancing 15 to pre-clinical testing
  • COMPUTE: Expand GPU infrastructure capacity by 300% to support complex molecular modeling
SCALE CAPACITY

Dramatically expand our production capabilities

  • MANUFACTURING: Increase GLP-1 production capacity by 200% through new automated production lines
  • INFRASTRUCTURE: Commission 3 new AI-optimized facilities with 40% higher output efficiency
  • AUTOMATION: Implement robotic process automation for 75% of quality control testing procedures
  • MONITORING: Deploy IoT sensors across 100% of manufacturing lines with real-time ML analytics
TALENT GROWTH

Build the most capable technical team in pharma

  • RECRUITMENT: Hire 75 AI/ML specialists and computational biologists with 90% offer acceptance rate
  • TRAINING: Launch AI Academy with 1000+ engineers completing advanced ML certification program
  • RETENTION: Improve technical talent retention to 92% through enhanced career development paths
  • COLLABORATION: Establish 5 strategic university partnerships creating 20 research fellowships
METRICS
  • PIPELINE PROGRESSION: 15+ molecules in Phase 3 trials by end of 2024
  • AI DISCOVERY RATE: 30% of new candidates identified through AI/ML by Q4 2025
  • DEVELOPMENT VELOCITY: Reduce time-to-market for priority molecules by 25%
VALUES
  • Integrity: Doing the right thing in all circumstances
  • Excellence: Pursuing high-quality science and operational performance
  • Respect for People: Valuing diversity and treating everyone with dignity
  • Innovation: Creating breakthrough solutions through creativity and risk-taking
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Align the learnings

Eli Lilly Engineering Retrospective

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To create innovative medicines that make life better for people by pioneering transformative scientific breakthroughs in healthcare.

What Went Well

  • REVENUE: Record Q4 revenue of $9.35B, up 28% YoY driven by Mounjaro
  • PIPELINE: FDA approval of donanemab for early Alzheimer's disease
  • EFFICIENCY: Manufacturing capacity expansions completed ahead of plan
  • INNOVATION: 4 additional molecules advanced to late-stage development
  • DIGITAL: Successful deployment of new clinical trial data platform

Not So Well

  • SUPPLY: GLP-1 product shortages limiting full commercial potential
  • SYSTEMS: Technical debt has slowed development of digital solutions
  • SECURITY: Two significant cybersecurity incidents requiring remediation
  • INTEGRATION: Post-acquisition systems integration challenges persist
  • AGILITY: Development lifecycle remains slower than industry benchmarks

Learnings

  • FORECASTING: More robust demand modeling needed for new drug classes
  • ARCHITECTURE: Modular tech architecture accelerates delivery timelines
  • COLLABORATION: Cross-functional digital teams produce better outcomes
  • TECHNICAL: Legacy systems are major bottlenecks for digital innovation
  • METHODOLOGY: Hybrid agile approach works best for regulated processes

Action Items

  • CAPACITY: Implement rapid manufacturing scaling plan for GLP-1 products
  • MODERNIZE: Accelerate legacy system replacement with cloud solutions
  • SECURITY: Enhance cybersecurity posture with advanced threat protection
  • AUTOMATION: Deploy lab automation to increase experiment throughput
  • TALENT: Recruit 50 specialists in AI/ML and computational biology
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Drive AI transformation

Eli Lilly Engineering AI Strategy SWOT Analysis

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To create innovative medicines that make life better for people by pioneering transformative scientific breakthroughs in healthcare.

Strengths

  • INVESTMENT: $500M commitment to AI/ML research initiatives
  • PARTNERSHIPS: Strategic collaborations with leading AI companies
  • DATA: Extensive proprietary clinical and molecular datasets
  • ADOPTION: Successful early AI applications in target identification
  • LEADERSHIP: Executive commitment to AI transformation

Weaknesses

  • INFRASTRUCTURE: Legacy IT systems limiting AI model deployment
  • SKILLS: Limited internal AI expertise across development teams
  • INTEGRATION: Poor integration between AI tools and workflows
  • GOVERNANCE: Underdeveloped AI ethics and governance frameworks
  • FRAGMENTATION: Decentralized AI initiatives lacking coordination

Opportunities

  • DISCOVERY: Reduce drug discovery timelines by 50% using AI
  • TRIALS: Optimize clinical trial design and patient recruitment
  • PREDICTIVE: Develop predictive models for drug efficacy/safety
  • PERSONALIZATION: Create AI-driven personalized medicine approaches
  • AUTOMATION: Automate routine lab processes via robotics and AI

Threats

  • COMPETITION: Tech companies entering pharmaceutical space
  • REGULATION: Evolving FDA requirements for AI in drug development
  • TALENT: Fierce competition for limited AI/ML talent pool
  • SECURITY: Data privacy concerns limiting AI model training
  • VALIDATION: Challenges validating AI-generated drug candidates

Key Priorities

  • PLATFORM: Build unified AI/ML research platform for all divisions
  • TALENT: Launch AI Academy to upskill existing technical workforce
  • PARTNERSHIPS: Expand strategic AI collaborations with tech leaders
  • GOVERNANCE: Establish robust AI ethics and compliance framework