The document outlines a comprehensive strategy for transitioning from manual to automated manufacturing in order to enhance scalability and quality of production processes. Key elements include conducting thorough gap analyses, implementing change management strategies, and focusing on automation technologies to address identified challenges such as high error rates and variability. The overall goal is to establish a robust and repeatable production process that ensures compliance and meets market demands while minimizing costs.

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
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