mtabusa - Blog #Design Engineeringmtabusa - Blog #Design Engineeringhttps://www.mtabusa.com/blogs/tag/design-engineeringWed, 06 May 2026 02:28:43 -0700http://zoho.com/sites/<![CDATA[Structuring Engineering Data for Smart Manufacturing]]>https://www.mtabusa.com/blogs/post/structuring-engineering-data-for-smart-manufacturingWe explore how CAD and other engineering data can be enriched to boost reuse, speed quoting, and support cross-functional use of design data]]>

How Engineering Data Drives Manufacturer Efficiency

Smart manufacturing often conjures images of robotic arms, IoT sensors, and AI-powered dashboards. But one of the most under-leveraged assets in this transformation journey is engineering data, especially the rich yet fragmented information embedded in CAD models, drawings, and BOMs.


Manufacturers today sit on terabytes of design files, yet struggle to convert them into organizational-ready assets. These files are often locked in siloed folders, poorly tagged, and disconnected from downstream systems like ERP, MES, CPQ, and maintenance platforms. As a result, companies face repeated engineering effort, misalignment across departments, and missed opportunities to accelerate delivery and reduce cost.


This engineering problem of silos, fragmented data and tagging is receiving a lot of attention, with startups and CAD players looking to solve with ML/AI. Time to start taking advantage of these tools.

This blog builds on our prior post on design reuse and moves one layer deeper, exploring how to structure and connect engineering data so it becomes an operational and financial advantage.

The Hidden Cost of Unstructured Engineering Data


Engineering teams generate immense value and assets: product drawings, component models, configuration variants, and manufacturing instructions. I found that the basic SOPs for design data management frequently became diluted. 

  • Standard naming conventions

  • Searchable metadata (material, performance, usage context)

  • Clear lineage of changes or reuse across projects

  • Links to production and ERP systems


If your CAD libraries are hard to search, or if each engineer creates their own naming logic, you are paying a tax across the product lifecycle. Here are common symptoms of poor data structure:

  • Long quoting cycles because prior designs are hard to retrieve

  • Rework caused by minor variations of already-solved problems

  • Inventory bloat due to unnecessary part variants

  • Slow onboarding of engineers who cannot easily navigate legacy designs

  • Maintenance delays from unclear service procedures or component lineage


These are not isolated engineering problems. They erode operational responsiveness, inflate working capital, and delay revenue.


Making Design Assets Discoverable and Usable


The first step in unlocking value from engineering data is to treat it as a product, not just project output. This means:

  • Tagging and Metadata: Standardize feature-level tags (function, fit, interface, material, tolerances) so components are searchable.

  • Version Control and Lineage: Track the evolution of parts and subassemblies. Know which project used what design, and what changes were made.

  • Searchable Repositories: Move beyond folder trees. Use visual search, attribute filters, or try AI-based similarity search to locate parts.

  • Bill of Information (BOI): Go beyond the BOM. Include manufacturability, quality, and service attributes in the design record.


When done well, this transforms a CAD file from static geometry into a connected, reusable, and monetizable asset. Your CAD asset now serves multiple stakeholders, both internal and external, and becomes the foundational data that can be used in multiple tools used in your organization. Here are a few ways in which engineering data enables other functions practically:


 CapabilityEngineering Data Role
 Digital TwinFeed geometry, tolerance and fit checks into virtual simulations
Predictive MaintenanceLink component history to service manuals and sensor alerts
Production schedulingEnable cycle time estimation via process linked parts
Configure-Price-QuoteIdentify reusable configurations, validate constraints

Making engineering data usable across the organization requires changes in standards, ownership, and mindset.

  • Design Library Stewardship: Assign owners for tagging, quality, and version control.

  • Process Integration: Link PLM or PDM systems with ERP, MES, and service platforms.

  • Training and Onboarding: Educate engineers to design with reuse and metadata in mind.

  • KPIs: Track reuse rate, part lineage clarity, and time-to-quote to demonstrate impact.

Engineers can take advantage of new tools/ feature sets inside CAD that are making this administrative task easier. We pay attention to $$ for financial accuracy. Accuracy and ease of design data usability has direct and indirect cost impact on the organization (rework costs, warranty costs, lost opportunity cost, onboarding & time to competency, etc.). 


Call to Action: Start with your next project. 

A full overhaul is not required to see impact. Start with one product family or platform:

  1. Audit existing CAD files for redundancy and undocumented variations.

  2. Define tagging standards for components and assemblies.

  3. Link designs to quoting, production, or service use cases.

  4. Demonstrate business impact via quoting time reduction, reuse rate, or inventory streamlining.

Once the value is visible, it becomes easier to scale.


Refer previous blog on Design Reusability:  https://www.mtabusa.com/blogs/post/why-design-reuse-belongs-in-smart-manufacturing-playbook

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]]>Wed, 12 Nov 2025 06:29:18 +0000<![CDATA[Why Design Reuse Belongs in Smart Manufacturing Playbook]]>https://www.mtabusa.com/blogs/post/why-design-reuse-belongs-in-smart-manufacturing-playbookExplore how poor design reuse slows down delivery and increases cost and why engineering asset reuse is a smart manufacturing imperative for scaling operations and boosting engineering productivity.]]>

How Reuse Drives Utilization in OEM Engineering

Smart manufacturing is not just about connected sensors, AI algorithms, or real-time dashboards. It is about improving how work gets done across the entire value chain, especially that which impacts customers. One often overlooked but critical pillar of smart manufacturing is engineering productivity. Specifically, I want to talk about how effectively a company reuses its own design assets to dramatically influence its ability to scale, automate, and respond to customer needs. Design reuse is a strategic lever for reducing lead times, improving quality, and accelerating quoting and production cycles. It makes a stronger case for smarter Product Lifecycle Management and financial metrics that matter to the C-Suite (Inventory Turns, Product Development Cost, Warranty Costs).

I am going to lean on my experience as a machine tool builder (CNCs, Robots, AGVs, Factory Automation).

With a new CNC Lathe development, the team decided that they were going to start from scratch. Everything worked well until we came to the tailstock. The way tailstock was designed and mounted, made it challenging to move and maintain position for between center jobs. A prior design had eliminated these issues and the team ended up adapting it with slight modification. Impact: delayed time to market by 12 weeks; lost first customer, whose requirements prompted the design.

Reviews showed that the design team was rebuilding similar configurations repeatedly, unable to find or trust prior design work. The engineering libraries were poorly organized, lacked documentation, and were not searchable. New team members, in particular, were unknowingly reinventing the wheel.

Further impact of this problem is that we were adding challenges to our manufacturing, warranty and service capabilities, by introducing new variables, which have been previously solved. 
 ImpactSymptom Cost Implications Benefits from Design Reuse
 Time Long quoting and engineering cyclesLost deals, delayed revenue Faster time to quote improves win rate, especially in capital sales
 Quality Rework due to new design variants Warranty claims, customer dissatisfaction Reduces rework, support costs
 Inventory SKU proliferationInventory bloat, higher carrying costs Lower BOM costs,  simplified sourcing
 Talent Repetitive work, difficult onboardingTime to competencyFrees up capacity for NPD

Critical Intervention: Embedding Reuse Principles into Design Practice  

We started the discussion on design reuse. The design team and its leader understood the context of the problem through joint reviews and discussion. Actions taken:

  • Modularize all new designs

  • Tag them with consistent feature and naming conventions

  • Document configurations so variants could be easily understood

To ensure consistency and enable scale, we aligned tagging conventions with part feature, function, and interface fit. This made it easier to navigate the design library when seeking a component with similar constraints.

My observation: Engineers value creativity, rigor in design and dislike documentation and this change initially met with some resistance. Leadership positioned designs as long-lived assets, and their reuse directly showcased how impactful the engineers' work is in driving both throughput, quality and customer experience. We defined ‘reuse’ as sub-assembly reused with less than 10% design change. This let design leaders track module lineage across projects and identify which designs were repeatedly customized. Within six months, over 20% of common design tasks were being fulfilled using existing asset configurations. Key metrics tracked: 

  • % increase in part library reuse

  • % of SKU reduction for inventory management


Digital Enablers for Reuse  

Design reuse requires more than policy. It requires digital tools and CAD & ERP data hygiene. What it looked like for us:

  • Engineering Asset Libraries with searchable metadata, images, and part lineage

  • Simple product digital twins, models that simulate product configurations, helped us pre-check fitment, compatibility and interface of reused modules.

These digital enablers also provided early visibility into feasibility, and surfaced interference or manufacturability challenges that had already been resolved elsewhere in the system.

Now, tagging Assistants that auto-categorize designs based on shape, performance, or project history are available, but we did not get to use them.

What does Governance look like?  

It requires and collaboration across departments:

  • Role Clarity: Who curates the design library? Who approves new modules?

  • Documentation Standards: What constitutes a “reuse-ready” design?

  • Reuse Metrics: How often is a design reused? By which teams? In what context?

  • Design Reviews: Periodic audits to identify reuse candidates and archive legacy variants

Incentives also matter. Tracking reuse can help showcase engineering effectiveness and free up cycles for new product innovation. In our case, reuse champions emerged organically. 

Simple steps to assess design reuse

Call to Action: Reuse as a Competitive Advantage  

Steps to Assess Design Reuse:

  • Audit existing design libraries and identify duplicate work

  • Quantify time and cost savings potential

  • Recommend tagging standards

  • Evaluate readiness for CAD intelligence, CPQ and digital twin tool

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]]>Tue, 28 Oct 2025 21:08:27 +0000<![CDATA[Blueprint to Building Blocks: Accelerating Engineering Design Through Reuse via Workflows and AI]]>https://www.mtabusa.com/blogs/post/blueprint-to-building-blocks-accelerating-engineering-design-through-reuse-via-workflows-and-aiDesign reuse is not just about saving time. It is a strategic lever for improving engineering productivity, ensuring design consistency, and enabling scalable digital transformation. Think of this as a circular economy principle applied to product design.]]>

Practical Tips on How to Reframe Design Process and Thinking in Manufacturing

My goal in this article is to identify a few practical ways design engineers can incorporate reuse and automation in their design process. I come at it from a place of curiosity and how I worked with my team. Design is a critical pillar of digital transformation—not only as a contributor, but as a function that must itself evolve. Transforming the design process through automation and AI is essential to enabling agility and integration across the entire manufacturing value chain

Manufacturers, OEMs and others, are under pressure to deliver customized products faster, at lower cost, and with greater consistency. Yet in many engineering departments, tribal knowledge, limited design reuse, and underutilized tools are slowing down innovation. We frequently see repeated effort, SKU proliferation, and inconsistent product quality.

Design reuse is not just about saving time. It is a strategic lever for improving engineering productivity, ensuring design consistency, and enabling scalable digital transformation. Think of this as a circular economy principle applied to product design- extend design asset life, responsible (re)sourcing, repurposing!

The Hidden Cost of “Starting from Scratch" 

Many design departments struggle with:

  • Poor searchability of past design files, drawings, or BOMs.

  • Lack of modular design templates for commonly used sub-assemblies

  • Missing integration between mechanical, electrical, and control engineering

  • Inconsistent annotations or design history that limit reuse and collaboration

I recall how challenging onboarding a new engineer was in our teams due to the above issues and how they showed up in the manufacturing and assembly process, resulting in rework, lost time and cost. 
The image shows a factory operations with multiple stakeholder interaction and overlayed with cloud connectivity, data acquisition and analytics

Engineering Productivity Through Reuse: 3 Critical Enablers  

  1. Modular Design and Standardization

    • Create libraries of commonly used components, assemblies, and sub-systems. Many of the CAD engineering packages are including templates to ease the creation of standard parts. You can create templates for internal use as well.

Introduce configuration logic to adapt modules quickly without redrawing. A CNC bearing housing always includes a bearing, a housing and a lubrication feature. Workflow logic can guide new designers or automate 

  1. Cross-Domain Collaboration

    • Integrate MCAD and ECAD platforms for electro-mechanical builds.

    • Use simulation to validate design fitment and assembly logic before build. Fitment was one of the biggest challenges and issues cropped up during prototype fabrication. Fitment features/ analysis are underutilized.

  1. Searchable, Annotated Digital Twins: 

    • Your drawings are your entry-level digital twins

    • Transform static 2D drawings into annotated 3D models with tribal insights, design constraints, and change history embedded

    • Use PDM/PLM systems (e.g., Siemens Teamcenter, PTC Windchill, Dassault ENOVIA) to version, track, and audit reuse metrics


🔍 Industrial Foundation Models: The New Assistant on the Design Desk  

The rise of Industrial Foundation Models (IFMs)—large-scale AI models trained on multi-modal engineering and manufacturing data—is changing how design teams work. These models offer contextual intelligence, helping engineers reuse designs more confidently, test alternatives faster, and reduce errors earlier.

Real-World Application: SolidWorks Workflow for Annotating a Robot Arm Base  

Here is one possible workflow to turn an existing mechanical assembly (e.g., a robot arm base with motors and cycloidal gears) into a smart, annotated asset in SolidWorks:

  1. Prepare the Assembly: Organize and constrain your .SLDASM file with configurations.

  2. Use DimXpert and Model Items: Auto-apply manufacturing dimensions and tolerances.

  3. Feature Recognition with FeatureWorks: Recover editable features from legacy parts.

  4. Enhance with 3DEXPERIENCE AI: Use AI to suggest part reuse and missing metadata.

  5. Publish with MBD: Export annotated 3D PDFs for collaboration.

  6. Archive in SolidWorks PDM: Tag, search, and reuse designs across teams.

Sources: SolidWorks Help, FeatureWorks Guide, MBD White Paper, Dassault 3DEXPERIENCE AI Docs.

Tangible results:  

Albeit not fully automated, at MTAB Engineers, we changed our design process. We incorporated modular thinking of design and clear annotation in our design work. It helped us significantly reuse design assets and create platforms for robotics, mobile robots and CNC machines:

  • Speeding up our time to market by 25-30%;

  • Understanding impact of substitute components and extent of changes;

  • Creating virtual simulations for customer specific applications;

  • Most importantly, keeping internal and external documentation up to date.

Closing:  

In a world of increasing complexity and convergence of IT and OT, design reuse isn’t a shortcut—it is a competitive advantage. When OEMs and parts manufacturers structure their product design around reusability, engineering becomes a productivity engine rather than a bottleneck.

If you would like a white paper on this topic, drop me a note. 

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]]>Tue, 03 Jun 2025 05:20:37 +0000