Insourcing and Production ScaleUp Strategy
- 1. Engineering Process Insourcing, Tech Transfer, and Scaling-Up Production R&D Prototype Transfer to Large-Scale Automated Manufacturing by Jose Bautista MSc, PMP, LSSGB
- 2. Introduction: A Strategy for Scalable Manufacturing Success Key Topics for Exploration • Transitioning Assembly Scale-Up by Automated Manufacturing • Implement change management strategies. • Conduct gap analysis for seamless scale-up. • Deploy phased approaches to mitigate risks. • Defining Clear Requirements and Specifications • Establish early to align expectations and minimize risks. • Streamline operations by addressing critical needs upfront. • Shifting to Automated Production • Navigate technical and operational hurdles in automation. • Redesign processes to ensure robust, efficient workflows. • Strategic Equipment Selection and Compliance • Choose the right equipment for scalability. • Validate processes to ensure cost-effective regulatory compliance. 2
- 3. Problem Statement 3 Manual manufacturing process faced significant challenges that hindered scalability and quality. Current production volumes were insufficient to meet increasing turn around times, quality, and operational efficiency targets. Challenges: 1. Frequent manual assembly errors negatively impacted throughput and reliability. 2. High variability in production outputs due resource limitations (e.g., workforce, raw materials) outsourced components causing inconsistent yields, impacting product quality. 3. Inefficient workflows and unaddressed bottlenecks were limiting scalability. 4. Low yields of below 80% with extended production cycle times, which were insufficient to meet market demands. 5. Lack of defined metrics made it difficult to measure performance or track progress in efficiency improvements risking maintaining quality during scale-up. 6. Absence of a structured approach for process monitoring and improvement, which left the team unable to: 1. Meet production volume targets. 2. Ensure compliance with regulatory standards, including ISO 13485 and FDA Title 21 CFR Part 820. The challenge was to design, develop and implement a robust, scalable, and repeatable insourced automated manufacturing process to achieve higher production rates without sacrificing quality or compliance.
- 4. Initial Actions: DMAIC 4 1. Data Collection & Initial Assessment: • Gemba Walks: Documented every manufacturing step to identify inefficiencies like inconsistent reagent dispensing and manual assembly errors. • Process Mapping: Draw process flow diagrams to identify high-variability areas. • Gap/Data Analysis: Conducted a Pareto analysis on reject data, finding reagent variability and incomplete device assembly as the primary contributors to low yields. 2. Root Cause Analysis: • Employed Ishikawa diagrams to identify root causes and collaborated with cross- functional teams (R&D, Quality, Manufacturing) to address issues. • Collected equipment performance data and identified calibration drift as a critical source of variability. 3. Process Design & Improvement: • Automation: Explored semi-automated dispensing systems with integrated SPC (Statistical Process Control) to ensure consistency. • Standardization: Developed detailed SOPs and standardized operator workflows to reduce variability during manual steps. • Validation: Designed and executed DOE (Design of Experiments) • Training: Delivered targeted operator training on new equipment and workflows. 4. Monitoring & Iteration: • Introduced KPIs: yield (%), cycle time (min), and defect rate (ppm) to monitor process performance, by introducing status dashboards. • Instituted production reviews and regular meetings to identify improvement opportunities.
- 5. 5 Chip prep Seal Chip Body Pressure Test Chip 2 PSI Tape Fluid Path Visual QC 3 Chips @ a time Oligo Spotting Probe/ Primer Micro Dispenser Spot P/P (12 chips) Dry Station Silica–Based Filter (4mm) Cut Filter Pressure Punch Based Insert Filter Vacuum Pen Metal Bearing (4 balls) Insert Magnet-Based Amount selector Solid Inserts Master Mix 2 Internal Control 1 Cryo Beads Store Beads Dispene Enzymes Lyo-Beads MM/Control Elution Buffer Wash Buffer Macro Dispenser Load on chip Buffer Dispensing Macro Dispenser Load on chip Manual Steps • High risk/error prone • Molding outsourcing dependency •High error rates/high variation • Low precision • No real-time quality control • High downtime • Low throughput • Slow/low scalability • High per-unit cost • Not compatible with production forecast/market need Outsourced Chip Molding and accessory components High Complexity > 100 parts/module/node interactions Prototype Medical Device Manual Assembly Outsourced Lyo Beads Multistep Manual Assembly Diagram Biochip Molding Raw material variability impacts batch consistency. Address supplier audits. Cycle time optimization is critical—target: reduce by 10%
- 6. 6 Operational Status Gap Analysis Goal: Ensure seamless scale-up, manufacturing readiness, and change adoption. 1.Define Scope: • Focus on process design, CQAs, regulatory compliance, and manufacturing capabilities. 2.Collect Data: • R&D: Process parameters, CQAs, risk assessments, SWOT analysis. • Manufacturing: Equipment, facilities, constraints. 3.Analyze Gaps: 1. Process: Scale, equipment, raw material variability. 2. Regulatory: GMP compliance, documentation readiness. 3. Knowledge: Missing process understanding or variability data. 4. Quality: Alignment of CQAs and CPPs. 5. Operational: Training, automation, material specs. 4.Prioritize Gaps: • Use FMEA for criticality and assign risk priority numbers (RPN). • Categories: Critical, Major, Minor. 5.Mitigate Gaps: • Action plan: Assign responsibilities and timelines. • Adjust processes, revise documents, and validate solutions. 6.Validate and Monitor: • Conduct pilot trials to confirm closures. • Use SPC and periodic reviews for continuous improvement.
- 7. Prototype Device Manual Assembly Tool: 6 M’s Fishbone Diagram Manual Device Assembly Manual to Automated Device Assembly Issues * Managing environmental impacts on new automated processes (e.g., dust, temperature). MEASUREMENT METHOD MAN NATURE MATERIAL MACHINE * Skill gaps and training requirements for new processes or automated systems. * Resistance to change or lack of operator engagement in automation adoption. * Selection and validation of automation equipment * Downtime or integration challenges with existing systems during technology transfer * Redefining workflows and standard operating procedures (SOPs) for automated processes. * Scaling manual processes to automation without losing efficiency or accuracy. * Compatibility of materials with automated equipment. * Variability in components that could affect automation performance. * Ensuring robust data collection systems during automation for real-time feedback. *Calibration of automated inspection tools and alignment with regulatory requirements * Facility upgrades required for automation (e.g., power, layout, environmental controls). • Equipment Downtime: Reduce by 40%. • Cost per Unit: $4.00 (Post-scale-up target: $2.25). • Pilot Yield: 85% (Target: 90%).
- 8. 8 Change Management Framework Goal: A robust, validated, and scalable process aligned with organizational goals. 1. Prepare for Change • Vision: Communicate the need for scalable production to meet demand. • Stakeholders: Identify and address team impacts proactively. • Change Team: Form cross-functional leaders to drive alignment. 2. Manage the Transition • Phased Plan: • Pilot, validate, and scale automated production. • Communication: Regular updates and feedback loops. • Training: Equip teams with skills for new processes. 3. Sustain the Change • Monitor: Track KPIs like efficiency, cost, and compliance. • Reinforce: Celebrate early wins and maintain engagement. • Continuous Improvement: Embed Lean and Kaizen principles.
- 9. 9 Phase 1: Feasibility and Requirement Definition Phase 2: Design for Excellence (DfX/DfM/DfA) Phase 3: Pilot Production and Process Validation Phase 4: Scaling Up Towards Full Automation Phase 5: Regulatory and Quality Assurance Phase 6: Full-Scale Production Understand Standardize, Optimize Optimize, Digitize Digitize, Automate Integrate Integrate Multiple Goals: Phased Strategy Approach Lean Process Evolution Framework
- 10. Phase 1: Feasibility and Requirement Definition 10 Stakeholder Engagement for Process Evaluation and Planning • Assemble a cross-functional team from R&D, engineering, manufacturing, quality assurance, and regulatory departments, procurement, supply change. • Identify customer needs, regulatory requirements, and target production volumes. 1. Define Requirements and Specifications • Document product requirements (size, functionality, precision, materials). • Identify critical quality attributes (CQAs) such as sensitivity, specificity, durability, and shelf life for product being produced and for process to assemble it. 2. Assess Technology Readiness • Evaluate the prototype for manufacturability, robustness, and scalability. • Conduct small-scale pilot studies to test material behavior and assembly processes. Sample requirements and specifications
- 11. Initial Process Evaluation and Planning Key Considerations for Transitioning from Manual to Semi-Automated to Fully Automated Assembly Baseline Assessment: Document the current manual assembly workflow, including time, labor, and process bottlenecks. Critical Steps: Identify the steps most prone to errors or variability (e.g., reagent dispensing, alignment of components). Output Goals: Define target production volumes, throughput, and quality requirements for each phase of scale-up. Space and Utilities: Ensure facilities can accommodate additional equipment and automation infrastructure. 11 Sample CMAs, CPPs, CQAs
- 12. Defining Requirements, Specifications and Critical to Quality Factors Define Requirements: Start by clearly outlining what is being created, its purpose, and why it is needed, using user inputs. This ensures the product meets the right needs. Design Specifications: Collaborate with SMEs to develop a detailed plan for how the product will be made, considering materials, tolerances, and integration of processes. Critical Material Attributes (CMAs): Define material properties and set criteria for acceptance to ensure suitability for manufacturing and product construction. Critical Process Parameters (CPPs): Identify and control key process factors (e.g., flow rates, temperature, and cycle times) that impact the final product. Critical Quality Attributes (CQAs): Ensure precision, accuracy, and reproducibility across processes, instruments, and software to meet quality standards. Safety & Regulatory Requirements: Verify that the product complies with industry standards, safety considerations, and regulations such as ISO 13485, FDA standards, and CE-IVD compliance. 12
- 13. System Impact Assessment (SIA) to Component Criticality Assessment (CCA) Critical Process Parameters (CPPs) Critical to Quality (CTQs) Critical Quality Attributes (CQAs) Critical Manufacturing Attributes (CMAs) Trace Matrix: Requirements (What/Why) -> Specifications (How) Tools Methods Instruments Operators Process Output / Output Material / Product Desing Input Material Design Process IPO Model User Needs - VoC -> Requirements Product Specifications 13
- 14. Design of Experiments (DoE) – Response Surface Methods (RSM) Parameter System Characterization and Optimal Parameter Finding interactions parameters Pareto Plot Response Surface Plot 4Co 30Co 12Co 9Co 18Co 24Co 15Co 10Co 6.5Co 30nt 0nt 15nt 7nt 22nt 0.750M 0M 0.150M 0.050M 0.350M nt Temperature NaCl 14 Sample DoE/RSM analysis
- 15. Full Factorial DOE – Main Effects to Identify Factors with Largest Effect (pandas, experiment_df | scipy.stats, linregress) Effect on Sensitivity: If lower factor levels are associated with higher sensitivity values (higher on the y-axis), it suggests that decreasing the level of that factor tends to increase sensitivity. Conversely, if higher factor levels are associated with higher sensitivity values, it suggests that increasing the level of that factor tends to increase sensitivity. In this case, all conditions have same effect. Magnitude of Effect: The distance between different factor levels along the x-axis represents the magnitude of the effect of that factor on sensitivity. A larger distance between factor levels indicates a larger difference in sensitivity between those levels. 15 Sample Full Factorial DoE Analysis
- 16. Design Failure Mode and Effects Analysis (DFMEA) To identify, assess, and mitigate potential failure modes, and also determine the degree of criticality. By evaluating risks based on severity, occurrence, and detection, ensure a robust and reliable design. DFMEA minimizes redesign costs, enhances product performance, and improves user safety by addressing risks early in development. 16 Sample DFMEA Analysis
- 17. Platform Inputs Map_ Process Engineering Pipeline Workflow 17 Sample Platform Inputs Map including components, and software/cloud platforms
- 18. Design of Manufacturing Space Spaghetti Diagram Material flow, station flow, assembly environments, operator movements. 18 Sample Work Floor Spaghetti Diagram Including Material and Operator Flow
- 19. Design of Manufacturing Space Utility Distribution Systems Clean-Humidity-Controlled Rooms, Water, Electric, Gas-Air 19 Sample Floorplan including Utilities Locations Localized for Full Operations
- 20. Phase 2: Design for Excellence (DfX/DfM/DfA) 20 1. Process Design • Optimize the design for manufacturability, reducing complexity (assembly simplification) while maintaining performance. • Incorporate modularity for future upgrades and variations. • Design parts for compatibility with automated tools like pick-and-place systems and dispensing robots. • Eliminate tight tolerances unless absolutely necessary. • Simplify the assembly process by minimizing the number of parts, using self – locating features, and avoiding symmetry where it could cause mis-assembly. 2. Material and Component Selection • Choose materials and components that are scalable, cost-effective, and compliant with regulatory standards. • Identify potential suppliers for consistent quality and volume availability. • Standardize components for easier sourcing and interchangeability. 3. Scalability Ensure the design is scalable from prototypes to mass production with minimal retooling or redesign. 4. Error Proofing (Poka-Yoke) Incorporate design elements that prevent assembly errors (e.g., asymmetric features or snap fits).
- 21. Application of Design for Excellence (DfX) Methods (DfM/DfA/DfT) Five Principles: 1. The Process 2. The Design 3. The Material 4. The Environment 5. Compliance/Testing Jose Bautista_March 2022 Purpose: • Enable informed decision-making in design optimization. • Enhance product quality, efficiency, and reliability. • Drive continuous improvement in design processes. • Analyze the impact of design factors on product effectiveness. • Identify key areas for design optimization and resource allocation. • Enhance product manufacturability, assembly, and testing processes. 21 Design for Excellence (DfX) Methods: Apply specialized methodologies to optimize various aspects of the product, such as Design for Manufacturing (DfM), Design for Reliability (DfR), and Design for Sustainability (DfS). These methods enhance product performance, durability, and cost-effectiveness. Sample DfX Analysis
- 22. Phase 3: Pilot Production and Process Validation 22 Pilot Production Setup • Establish a small-scale production line mimicking large-scale automation. • Use semi-automated systems to test the scalability of processes such as microfluidic assembly, coating, and testing. 1. Validate the Process • Monitoring critical process parameters (CPPs) and control limits. • Conduct process capability studies (Cp, Cpk) to ensure reliability and reproducibility.
- 23. • Pilot scale and scale-up batches • Risk assessment • Protocols and reports • Validate process • Define CQA’s and CPPs to monitor • Premises, utilities, equipment • Commercial-scale batches • In-line, online, or at-line monitoring • Define number of batches • Periodic review of trends • Sampling and testing • In-line, online, or at-line monitoring Process Design Process Qualification Continued Process Verification Risk Management Change Control Product Life-cycle Process Validation Development Scheme 23 GOAL: To ensure consistency, quality, and scalability of the component while optimizing costs and meeting requirements.
- 24. Transition to Semi-Automated Assembly Key Considerations Automation Targets: Focus on automating the most time-consuming or error- prone steps first, such as: • Reagent dispensing (e.g., components placement or liquid handling). • Component alignment and placement. • Thermal bonding or sealing processes. Equipment Selection: • Select semi-automated machines that can integrate with existing manual workflows, e.g., robotic arms for assembly, precision dispensing systems, or optical alignment tools. • Ensure equipment is modular and scalable for future upgrades. • Predictive Maintenance Software: Reduce equipment downtime by 15%. • Advanced Analytics: AI-driven insights for process optimization. • Process Control Systems: Real-time monitoring of critical quality parameters. Impact: Enhanced process consistency, reduced variability 24
- 25. Transition to Semi-Automated Assembly (continuation) Key Considerations Training: Train operators to use semi-automated equipment efficiently and troubleshoot minor issues. Quality Control Integration: • Incorporate inline quality checks for critical parameters like component alignment, bead placement, and microfluidic channel integrity. • Use semi-automated systems for defect detection, such as vision-based systems. Output Scaling: • Semi-automated processes should achieve 2x–5x the output of manual assembly with improved consistency.
- 26. Transition to Semi-Automated Assembly Manual >48 manually inserted components Semi-Auto Jet Spotter Port Press Tape Dispenser Welding Dispenser Leak Check Heat Sealing 26 Sample manual and semi-auto set ups
- 27. Production Pipeline Value Stream Map sklearn.decomposition dimensionality reduction and matrix factorization 27 Sample VSM analysis
- 28. Phase 4: Scaling Up to Full Automation 28 1. Equipment Selection and Procurement • Choose equipment compatible with the product’s unique requirements, e.g., micro-precision assembly, pick-and-place systems, and optical inspection tools. • Opt for flexible systems that can adapt to changes in product design or volume demands. 2. Automation Integration • Implement end-to-end automation for material handling, assembly, and quality control (e.g., robotic arms, conveyors, and automated testing systems, QA vision systems). • Use Manufacturing Execution Systems (MES) for real-time process monitoring and data collection.
- 29. Transition to Fully Automated Assembly Key Considerations End-to-End Automation Design: • Integrate all assembly steps, including: • Reagent handling (e.g., dispensing, liquid reagent filling). • Component alignment and assembly. • Microfluidic sealing or bonding (thermal or adhesive). • Final product inspection and packaging. Advanced Equipment: • Select equipment designed for high-throughput and precision, such as: • Fully automated robotic arms for assembly. • High-speed dispensing systems for components or liquid reagents. • Automated sealing/bonding systems with in-line sensors for defect detection. • Vision systems or AI-based quality control for micro-crack, alignment, and contamination detection. 29
- 30. Transition to Fully Automated Assembly (continuation) Key Considerations Process Optimization: • Optimize workflows to minimize cycle times and reduce waste. • Implement a Manufacturing Execution System (MES) for real-time monitoring and analytics. Workforce Transition: • Transition workforce roles from manual operators to technicians overseeing automated systems and performing maintenance. Redundancy and Backup: • Incorporate redundancy in key equipment to ensure minimal downtime during high-volume production. 30
- 31. Financial Analysis and ROI Equipment Selection for Automation - High-precision liquid handling systems for reagent dispensing. - Robotic arms and pick-and-place systems for assembly. - Vision-guided quality control systems. - End-to-end platforms integrating assembly, QC, and packaging. • Initial Investment: Equipment costs, facility upgrades, and training. • Operational Costs: Labor savings, energy consumption, maintenance. • Savings: Reduced defects, increased throughput, labor cost reductions. • ROI: Payback period 18 months; ROI over 5 years exceeds 300%. Projected Returns: • Revenue increase: $4M/year. • Break-Even Analysis: Achieved after producing 50,000 units. 31
- 32. Design of a Fully-Automated Assembly Line | Jidoka Implementation * Design addressed specifications and requirements, DfX, process, device/instrument, control software, materials, documentation, in-line automated vision system QC stations. 32 Sample Fully Automated Line Diagram
- 33. Phase 5: Regulatory and Quality Assurance 33 Regulatory Compliance • Ensure the manufacturing process adheres to ISO standards (e.g., ISO 13485 for medical devices/ AS9100 QMS, IPC/WHMA-A-620 Class III, NASA-STD-8739.4, ECSS-Q-ST-30-11C). • Prepare documentation for regulatory submissions (FDA, CE marking). Quality Control Implementation • Develop automated quality control systems to monitor CQAs during production. • Perform final product testing using established protocols (e.g., functional assays). For Semi-Automated and Fully Automated Phases: Process Validation: • Conduct Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ) for all new equipment. • Validate automation systems to meet regulatory requirements (e.g., ISO 13485, FDA standards). Critical Quality Attributes (CQAs): • Continuously monitor attributes like bead placement accuracy, microfluidic alignment, and product integrity during assembly. Data Integrity: • Implement automated data logging to ensure traceability and regulatory compliance.
- 34. Equipment Validation Scheme System Description • Specifications • Functional/Performance Requirements FAT/SAT Instrument/Manufacturer Related • Instrument Components P&ID, electrical • Instrument Performance, CV, repeatability Validation Activities Installation Qualification (IQ) • Meet manufacturer’s specifications • Manuals, maintenance plans Operational Qualification (OQ) • Test accuracy, precision and repeatability • Confirm instrument resolution Performance Qualification (PQ) Production scale testing (define batch numbers, procure material, align w/R&D) • Meet production throughput demand • Repeatability of production batches, risk-based sampling sizes, binomial distribution or AQL table • Accuracy and precision under production conditions Acceptance Criteria • IQ Acceptance Criteria • OQ Acceptance Criteria • PQ Acceptance Criteria Change Control: ECOs/DCOs • Change Request • Impact Assessment • Revalidation Requirement • Engineering/Docs Protocols & SOPs • Personnel Training • SOPs/Protocols Equipment Onboarding • Defining calibration points/frequency • Maintenance schedule Documentation • IQ Documentation • OQ Documentation • PQ Documentation • Master Validation Plan (MVP) • Design History File (DHF) • Device Master Record (DMR) Sample Size Determined by “Binomial Power Analysis” or AQL table 34
- 35. IOQ- PQ • For OQ, data was collected from the sensors to test if the process met the specified operation requirements. Checking for the mean values of these parameters to fall within the specified tolerances • IQ checked and validated the installation of sensors and then proceed by OQ. • PQ was checked based on the specific process requirements. 35 Sample IOQ measurements
- 36. Validation Testing Example: Device Heat Block Component • Verify the accuracy and consistency of temperature. • Assess the device's performance under typical manufacturing conditions. • Identify any discrepancies between measured temperatures and the known values of the heating block. • Determine if the device met the required standards for accuracy and reliability in the manufacturing environment. One-Sample T-Test Hypothesis testing 36 Sample Validation Testing Analysis
- 37. Gage Repeatability and Reproducibility (GRR) Testing • Calculating the Gauge R&R percentage, or proportion of total variability due to differences between operators and the measurement device. • Analysis of Variance (ANOVA) looking for statistically significant differences in measurements between operators. • Tukey's Honestly Significant Difference (HSD) test to identify specific pairwise differences between operators. Gauge R&R Testing.ipynb 37 Sample GR&R Analysis
- 38. Phase 6: Full-Scale Production 38 Full-Scale Production Launch • Transition from pilot to full-scale production, gradually ramping up volumes. • Implement continuous improvement processes to optimize yield and minimize waste. 1. Post-Market Surveillance • Monitor product performance in the field to identify issues or improvements. • Update processes or designs as needed based on real-world data. Yield and Defect Rates: Monitor production yield and defect rates to measure improvement compared to manual processes. Process Adjustments: Use feedback from automated systems to fine-tune workflows and address inefficiencies. Maintenance and Calibration: Establish a preventive maintenance and calibration schedule for automated systems. Key Considerations
- 39. Statistical Process Control (SPC) 39 Sample SPC Trace
- 40. Comparing Production Line Performance T-test of Two Production Lines 1. Production Output: The quantity of products manufactured within a specific timeframe. 2. Quality Control Metrics: Metrics related to the quality of manufactured products, such as defect rates or adherence to specifications. 3. Efficiency: The ratio of output to input, measuring how effectively resources are utilized in the manufacturing process. 4. Downtime: The duration of time during which production is halted due to issues or maintenance. 5. Cost-effectiveness: The balance between production costs and output, aiming to maximize profitability. 6. Lead Time: The time taken from order placement to product delivery, indicating the speed of production and delivery processes. Performance Metrics: Performance (units produced per hour) 40 Sample t-test plots
- 41. Normalizing Production Line Performance (Three Production Lines) One-Way ANOVA Test _ Hypothesis Testing between Three Production Lines One Line (PL Three) Shows Different Performance After Corrective Action, the Three Show Similar Performance (units/ hour) (units/ hour) (errors) (errors) 100 105 110 115 120 41 Sample Multi-Line Performance Analysis
- 42. Automated Line Stage/Station Synchronization Using an ODE Model ODE (Ordinary Differential Equation) mathematical model to describe the dynamic behavior of systems that change over time. “All models are wrong, but some are useful” - George Box 42 Sample ODE Model Line Synchronization Analysis
- 43. Device Component Failure – Reliability Testing Overall distribution of reliability values for each device component, aiding in the assessment of component performance variability, robustness, replacement, or future maintenance programs. Nevada or caterpillar plot meta-analysis to visualize the effect sizes and confidence intervals of multiple studies or datasets of effect sizes (e.g., mean differences, correlation coefficients, odds ratios) and their corresponding standard errors or confidence intervals from multiple studies or datasets Reliability Block Diagram 43 Sample Reliability Analysis
- 44. Overall Equipment Effectiveness (OEE) for Assessing Production Line Performance Yearly OEE Metrics Trending OEE Metrics of an Assembly Line 44 Sample OEE Analysis
- 45. Strategy Implementation Results: • Production yield increased from 45% to 95% post-automation. • Defect rates reduced by 70%. • Production cycle time decreased by 40%. • Compliance with ISO 13485 and FDA standards achieved. Lessons Learned: • It’s a must to have clearly stablished requirements, specifications and CQA’s • Early stakeholder engagement is critical for smooth transitions. • Semi-automation is a practical and effective transitional strategy toward full automation. • Addressing variability in critical parameters upfront saves time. • Regular monitoring and iteration ensure sustained improvement. Future Directions: • Expand automation to include new product lines. • Implement AI-driven quality control for predictive maintenance. • Continue scaling up facilities to meet growing market demands. • Leverage lessons learned to optimize future scale-ups. 45 Results-Oriented Summary
- 46. • Efficient Transition from R&D to Manufacturing: - Implement Change Management aiming at a seamless handover that includes clear requirements and documentation. - Conduct Gap Analysis to identify areas at high risk - Early involvement of cross-functional teams to address scalability challenges. • Cost-Effective and Scalable Automation: - Invest in modular, flexible systems to adapt to production demands. - Implement automation that minimizes variability and reduces operational costs. • Compliance with Regulatory Standards: - Ensure full compliance with ISO, FDA, or other regulatory frameworks. - Document and validate processes to withstand audits and inspections. • A Strong ROI and Sustainable Operational Efficiency: - Maximize investment returns through optimized workflows and resource usage. - Integrate energy-efficient and eco-friendly practices for long-term savings. • Cross-Functional Collaboration: - Early engagement of manufacturing and supply chain teams. - Communication across R&D, automation, and regulatory teams. 46 Key Considerations for Successful Insourcing and Scale-Up Strategy
- 47. 47 Robust Process Validation Plan: - Implement DQ, IQ, OQ, PQ protocols for process stability and quality control. - Monitor Critical Quality Attributes (CQAs). • Technology Readiness: - Digital systems: MES, ERP, and AI for monitoring and predictive maintenance. - Scalable infrastructure supporting high-volume production. • Workforce Development: - Operator training on new systems and troubleshooting. - Change management strategies for smooth adoption of automation. • Customer-Centric Approach: - Support customization and rapid feedback incorporation. • Sustainability Practices (Green Practices): - Prioritize energy efficiency and waste reduction. Key Considerations for Successful Insourcing and Implementing a Scale-Up Strategy
- 48. Key Phases of Production Scale-Up 48 Comparison of Production Methods: From Manual to Fully Automated Assembly
- 49. www.linkedin.com/in/josebautista/ ᴘʀᴏᴄᴇꜱꜱ / ᴏᴘᴇʀᴀᴛɪᴏɴꜱ ᴇɴɢɪɴᴇᴇʀ https://josebautistamsc.mystrikingly.com ᴊᴏsᴇ ʙᴀᴜᴛɪsᴛᴀ, ᴍsᴄ, ᴘᴍᴘ, ʟssɢʙ