Design for Automation in MedTech: What Engineers Are Prioritizing in 2026

Why Design for Automation Matters in MedTech

As medical devices become smaller, more complex, and more personalized, the way teams design for manufacturing is changing just as quickly.

Automation is no longer something engineers can layer on at the end of development. For many MedTech programs, automation needs to be considered from the beginning, especially when teams are designing products that must scale from early builds to validated production.

In Chamfr’s webinar, Automating Medical Device Molding: From First Part to Lights-Out Production, Steve Maxson moderated a discussion with Travis Garrison, Global Automation Manager at Nissha Medical Technologies, and Vijay Kudchadkar, Director of Plastics and Sales Engineering at Nissha Medical Technologies Isometric.

A key takeaway from that conversation was that automation should be treated as a lifecycle strategy, not a one-time equipment purchase.

For R&D engineers, manufacturing engineers, and process development teams, that means early design decisions should answer practical questions:

• Can this part be picked, oriented, and handled reliably?
• Can critical-to-quality features be inspected automatically?
• Can the design support higher production volumes later?
• Can the process move from manual assembly to semi-automation or full automation?
• Can the manufacturing system produce repeatable, measurable results?

Today, these questions are becoming central to Design for Automation, or DFA, in MedTech.

What Is Design for Automation (DFA) in MedTech?

Design for Automation in MedTech is the practice of designing medical device components, assemblies, and manufacturing processes so they can be produced, inspected, and scaled using automated or semi-automated systems.

DFA is closely related to Design for Manufacturing, or DFM, but places more emphasis on automation readiness.

In medical device manufacturing, DFA may include:

• Part geometry that supports robotic handling
• Features that allow reliable pick-and-place
• Materials that behave consistently during molding or assembly
• CTQs that are visible and measurable by automated inspection systems
• Assembly steps that can be reduced, combined, or eliminated
• Process data that can be tracked over time
• Manufacturing systems that can scale as demand increases

This is especially important for molded medical components, drug delivery devices, wearable devices, catheter components, diagnostics, surgical tools, and other devices where part size, tolerances, and production volumes can create significant scale-up challenges.

For regulatory context, FDA’s Quality Management System Regulation became effective on February 2, 2026, and incorporates ISO 13485:2016 by reference. ISO 13485 applies to organizations involved in medical device design, production, installation, servicing, and related services.

1. Engineers Are Designing for Scalability from Day One

One of the biggest shifts in MedTech manufacturing is the expectation that products must scale faster than before.

Automation can increase efficiency, throughput, and capacity, but it cannot fully compensate for designs that were never built for automated production.

In the webinar, Travis Garrison explained that Nissha Medical Technologies uses a Manufacturing Technology Assessment Process, or MTAP, to help teams think about automation early. The goal is to understand where a program is today, where it needs to go, and what path will support future production requirements.

Why Scalability Needs to Start Early

Scalability depends on more than production volume. It also depends on whether the design can be handled, assembled, inspected, and measured consistently.

Engineers should evaluate:

• Number of components
• Number of assembly operations
• Part orientation requirements
• Robotic handling requirements
• Packaging and presentation of incoming materials
• Tolerance stack-ups
• Inspection access
• Supplier variability
• Process monitoring requirements

When these factors are addressed early, design transfer to manufacturing becomes smoother. When they are ignored, teams may face costly redesigns, delayed validation, and automation systems that cannot reach the intended throughput or quality targets.

DFA Questions for Scalable Medical Device Manufacturing

Before locking in a design, engineers should ask:

• Can this part be picked and oriented reliably?
• Can the part be moved without damage?
• Can the part be manipulated consistently by automation?
• Can critical features be inspected automatically?
• Does the geometry amplify or dampen variation?
• How will incoming material variability affect the process?
• Can this system be reused across future product platforms?

One of Travis’ clearest points was that DFA is where teams “earn automation later.” Design changes made early may not solve every current issue, but they can open the door to future automation and scale-up.

2. Teams Are Reducing Secondary Operations

Another major priority in MedTech automation is reducing secondary operations.

Secondary operations can include manual assembly, bonding, labeling, inspection, trimming, handling, testing, or packaging steps that happen after an initial molding or manufacturing process.

These steps can add labor, introduce variability, increase floor space requirements, and create bottlenecks as production scales.

Manufacturing Methods That Reduce Secondary Operations

Engineers are increasingly exploring manufacturing strategies that combine multiple steps into fewer operations, including:

• Insert molding
• Overmolding
• Two-shot or multi-shot molding
• In-mold assembly
• In-mold labeling
• Automated post-molding inspection
• Integrated packaging workflows

In the webinar, Vijay Kudchadkar discussed how in-mold labeling can eliminate downstream decoration or labeling steps by placing the label into the mold before molding over it. He also described two-shot molding applications where separate bonding or gluing steps can be eliminated because two materials are molded together in the same process.

For R&D teams, the takeaway is simple: every manual secondary step should be questioned early.

DFA Questions for Reducing Secondary Operations

Engineers should ask:

• Can two components be combined into one molded assembly?
• Can labeling or graphics be integrated into the molding process?
• Can bonding, gluing, or manual placement steps be eliminated?
• Can inspection be built into the production cell?
• Can packaging be integrated into the automated workflow?
• Can the design reduce operator-dependent variation?

Reducing secondary operations can improve consistency, reduce production cost, and make automation more feasible as demand increases.

For teams evaluating components, tooling, or equipment, leveraging in-stock and quick-turn medical device components and equipment, fixtures, and tools can accelerate early-stage development and process planning.

3. Inspection and Data Are Becoming Core to Automation Strategy

As automation expands, inspection and data visibility are becoming more important.

Automated manufacturing is not just about moving parts faster. It is about producing consistent parts and collecting the data needed to understand whether the process is stable.

During the webinar, Travis emphasized that inspection should be more than a pass/fail result. When teams collect and trend inspection data, they can detect process drift, identify cavity-specific issues, and respond before defects become large-scale fallout.

Why CTQs Matter in Automated Medical Device Manufacturing

Critical-to-quality features, or CTQs, are the measurable features that determine whether a component or device meets its functional requirements.

For molded medical components, CTQs may include:

• Dimensional features
• Wall thickness
• Flash
• Short shots
• Through-holes
• Cavity-specific variation
• Insert placement
• Bonding or joining features
• Surface defects
• Electrical or functional test results

Travis shared examples of how inspection data can help identify trends by cavity and feed information back to suppliers or process engineers. This type of data can help teams reduce firefighting and focus on root-cause analysis.

Automated Inspection Methods in MedTech

Automated inspection systems may include:

• Vision inspection
• Dimensional measurement
• Probes
• Electrical testing
• Functional testing
• In-line process monitoring
• Cavity-level trend analysis
• Data dashboards tied to OEE

For micro molded components, automated inspection can become even more important because the features may be too small or too subtle to evaluate reliably through manual inspection alone.

DFA Questions for Inspection and Data

Engineers should ask:

• Are the CTQs visible to an automated inspection system?
• Can the part be oriented consistently for inspection?
• Can the inspection system detect dimensional drift, not just pass/fail status?
• Can the data identify cavity-specific or supplier-specific variation?
• Can the system detect failure modes before they create large scrap events?
• Can inspection data support process improvement after release to manufacturing?

This shift reflects a broader move toward data-driven manufacturing. Instead of reacting to defects, teams are using inspection data to identify issues earlier, improve processes, and maintain tighter control over quality.

4. Micro Molding Is Raising the Bar for Precision Manufacturing

Miniaturization is a major driver of automation strategy in MedTech.

Medical device manufacturers are developing smaller and more complex parts to support new functionality, less invasive procedures, and more compact device platforms.

In the webinar, Vijay Kudchadkar shared examples of extremely small and complex molded medical components, including parts measured in fractions of a milligram, features measured in microns, bioresorbable parts, PEEK components, microfluidic channels, and fragile inserts.

These examples highlight a key point: as parts get smaller, everything becomes harder.

Why Micro Molding Creates Automation Challenges

Micro molded components can create challenges around:

• Tooling precision
• Part handling
• Insert orientation
• Material degradation
• Flash control
• Feature replication
• Dimensional measurement
• Automated inspection
• Multi-cavity consistency
• Packaging and transfer between operations

A part may meet print requirements but still fail in automation if the automation equipment is designed with tighter requirements than the molded component itself. Vijay cautioned that automation tooling should not require tighter tolerances than the part specification allows.

That is a practical but important DFA lesson: the part, tool, and automation system need to be designed together.

DFA Questions for Micro Molded Medical Components

Engineers should ask:

• Can the part be handled without damage?
• Can the part be picked, placed, and oriented consistently?
• Can the automation system tolerate the approved part variation?
• Can the part be inspected at the required resolution?
• Are molded features robust enough for automated handling?
• Will the material tolerate molding, handling, and downstream assembly conditions?
• Can the tooling and automation strategy scale to multi-cavity production?

As Vijay explained in the webinar, teams should design as if the device will be successful at scale. If a device may eventually require millions or billions of parts, the component and assembly strategy should account for future automation from the beginning.

5. Automation Is Moving from Manual Assembly to Lights-Out Production

Many MedTech programs do not begin with full automation.

Early volumes may justify manual or semi-automated assembly first. But if the product is expected to scale, the manufacturing strategy should leave a path toward higher automation.

In the webinar, Travis described a case study where the team developed a progression from manual assembly to semi-automation, full automation, and eventually a lights-out concept. The team used a plug-and-play architecture so workcells could be expanded as the process matured.

This staged approach is important because it allows teams to validate technology, collect process data, and understand failure modes before moving to lights-out production.

Why Teams Should Not Jump to Lights-Out Too Early

Lights-out manufacturing requires more than an automated machine.

Before a process can run with minimal operator intervention, teams need to understand:

• Common error modes
• Auto-recovery requirements
• Scrap thresholds
• Material presentation
• Equipment uptime
• OEE performance
• Process maturity
• Inspection strategy
• Maintenance and intervention points

Travis cautioned that teams can jump into lights-out manufacturing too soon if they do not understand the true nature of machine errors and failure modes. Without that understanding, the machine may sit idle instead of producing unattended.

DFA Questions for Lights-Out Readiness

Engineers should ask:

• Is the process repeatable enough for unattended production?
• Are the top failure modes understood?
• Can the system auto-recover from common errors?
• Are inspection and rejection mechanisms integrated?
• Is the equipment producing at the expected OEE?
• Does the process have enough data to support continuous improvement?
• Can the system maintain quality without constant operator intervention?

For many MedTech teams, the best approach is not to automate everything immediately. It is to design a path from manual to semi-automated to fully automated production.

6. OEE Is Becoming a Design and Business Planning Tool

Overall Equipment Effectiveness, or OEE, is often used after release to manufacturing to measure how a machine is performing.

In the webinar, Travis explained that OEE can also be useful much earlier. By modeling run rate, scrap, throughput, and expected performance during concept and quoting, teams can align engineering, finance, operations, and executive stakeholders around what the automation system is expected to achieve.

Why OEE Matters for MedTech Automation

OEE can help teams understand:

• Whether automation is meeting the expected output
• Whether scrap rates are within acceptable thresholds
• Whether the process is stable enough to scale
• Whether the capital investment is producing the expected return
• Whether the system is ready for further automation
• Where continuous improvement efforts should focus

This is especially important when teams are making capital investment decisions.

Automation systems can require significant upfront investment. OEE helps teams connect the engineering plan to business outcomes, including throughput, cost, capacity, and return on investment.

DFA Questions Related to OEE

Before investing in automation, teams should ask:

• What run rate is required?
• What scrap threshold is acceptable?
• What OEE target is realistic for this stage?
• What failure modes will affect uptime?
• What manual interventions are still required?
• What data will prove whether the automation plan is working?
• How will OEE be tracked after release to manufacturing?

When teams define these expectations early, they can reduce friction during design transfer, stabilization, and production scale-up.

Want a Deeper Technical Discussion on Medical Device Molding Automation?

This article summarizes several key trends shaping Design for Automation in MedTech, but the full webinar includes a deeper technical discussion from Travis Garrison and Vijay Kudchadkar of Nissha Medical Technologies.

In the full Chamfr webinar, we discuss:

• Automation as a lifecycle strategy
• MTAP, DFA, and OEE frameworks
• Defining user requirements for scalable automation
• Moving from manual assembly to lights-out production
• CTQs, inspection, and process monitoring
• Micro molding challenges
• Insert molding, in-mold labeling, and two-shot molding
• Post-molding automation strategies
• How to think about automation readiness from the first part

Final Thoughts: Designing for Automation from the Start

As MedTech continues to evolve, design and manufacturing are becoming more tightly connected.

Automation is no longer just a downstream consideration. It is shaping how products are designed from the start.

For engineers, staying ahead means understanding not just the automation tools available, but also the design decisions that make automation possible.

The most successful teams will be the ones that ask the right questions early:

• Can this design scale efficiently?
• Can it be handled and oriented automatically?
• Can it be assembled and inspected consistently?
• Can it support measurable, repeatable production?
• Can it move from manual assembly to higher automation without a major redesign?

By treating automation as a lifecycle strategy, MedTech teams can reduce scale-up risk, improve manufacturing consistency, and build more resilient production systems.

To accelerate early-stage product development, engineers can source medical device components from multiple qualified suppliers on one PO, plus equipment, fixtures, and tools, raw materials, R&D lab kits, and more on Chamfr.

Join us in the discussions helping advance medical device product development with Chamfr MPP events, webinars, podcasts, and technical blog articles.

FAQ: Design for Automation in MedTech