Pharmaceutical Manufacturing · Lean Six Sigma · cGMP
Optimizing Manufacturing Efficiency, Quality & Compliance
An interactive case study using DMAIC, OEE, SMED, FMEA, CAPA, validation, and change control to improve a regulated liquid pharmaceutical manufacturing process.
Scrap8% → 3%
Yield92% → 97%
OEE61% → 80%
Optimization DashboardLIVE CASE STUDY
0%OEE Target
0%Yield Target
Executive Overview
Transform operational losses into a controlled future-state process
The project combines Lean Six Sigma with pharmaceutical quality systems to reduce scrap, improve yield, increase OEE, and maintain compliant operations.
Reduce Scrap
8% → 3%
Decrease material loss, rework, and avoidable cost.
Increase Yield
92% → 97%
Improve batch consistency and right-first-time performance.
Improve OEE
61% → 80%
Increase availability, performance, and quality effectiveness.
Interactive SIPOC
Select a process element
Choose a stage to explore the system.
Current State
Downtime and packaging delays dominate operational losses
Loss Analysis
Monthly Operational Losses
Packaging delays and equipment downtime account for more than 65% of the listed losses.
OEE Breakdown
Current OEE is 61% against an 80% target
Availability, performance, and quality losses indicate substantial improvement opportunity.
Value Stream Map
Current vs Future State
Analyze
Use structured problem solving to focus on the critical few
Pareto Analysis
Variation and Loss Drivers
Click a bar for the recommended response.
Select a contributor.
Interactive Fishbone
Explore root-cause categories
Production Delays
Increased Scrap
Increased Scrap
Choose a category on the fishbone diagram.
5 Whys
Equipment Failure Investigation
Select a why.
FMEA
Risk Prioritization
Improve
Build the future state around reliability, speed, and standard work
SMED
Separate internal and external setup activities to reduce changeovers.
Preventive Maintenance
Integrate maintenance into planning and monitor downtime weekly.
Standard Work
Improve SOP adherence, operator training, and repeatable execution.
Automation
Reduce manual variation in high-impact process steps.
Control Chart
Batch Yield Stability
Process Capability
Cp/Cpk Interpretation
BeforeHigh variation
AfterStable & predictable
Improved capability supports output within specification limits.
Future-State Metrics
Planned Performance
46 hrsLead Time
10 hrsWaiting
7 hrsDowntime
5 hrsChangeovers
80%OEE
39.1%PCE
Compliance
Protect patient safety through quality systems and validation
Regulatory Compliance
Maintain safety, quality, efficacy, documentation, and data integrity.
Structured Change Management
Assess impact, document approvals, train personnel, and communicate across functions.
Corrective and Preventive Action
Investigate nonconformances, address root causes, and prevent recurrence.
IQ / OQ / PQ
Verify installation, challenge operating ranges, and demonstrate repeatable performance.
IQInstallation QualificationInstalled and documented correctly
→OQOperational QualificationOperating ranges challenged
→PQPerformance QualificationRepeatable production demonstrated
Impact
Convert process performance into operational and financial value
Projected Benefits
Quality, efficiency, and cost improvement reinforce one another
Lower scrap, improved yield, and higher OEE support significant savings, predictable production, stronger service, and better use of capacity.
Review Full PresentationAssess & Train
Initial assessment, SOP training, and change-control preparation.
Implement
Launch SMED, preventive maintenance, and selected automation.
Review & Sustain
Evaluate metrics and adjust strategies using collected data.
GitHub Pages Ready
A polished interactive case study with no external dependencies
Open in VS Code, test with Live Server, customize, and publish from the repository root.