mtabusa - Blog #CAD

From Simulation to Business Model Innovation Using Digital Twins

Wed, 26 Nov 2025 06:29:46 +0000

Digital Twins for Competitive Advantage

How Digital Twins Are Evolving

I think of digital twins in three stages of maturity.

Stage 1: Engineering Simulation Twin

This is where most manufacturers begin.

A virtual model of a machine, cell, or process
Used to validate layout, reach, interference, basic logic, and cycle time
Driven by CAD, offline programming tools, and simulation environments

The value is direct: fewer design rework loops, fewer assembly failures, and fewer surprises during commissioning. This is where many of my own digital twin projects started, especially for automation cells and cost estimation.

Stage 2: Operational Performance Twin

Here, the twin does not retire once the machine is installed.

Connected to runtime data from controls, sensors, quality records, and production logs
Used to analyze bottlenecks, test schedule options, and plan changeovers
Helps answer questions such as:
– How can we recover capacity without new machines
– What sequence or staffing pattern best meets this mix
– How do we reduce the “delay tax” that shows up as late orders and premium freight

Most small and mid-market manufacturers can realistically reach this level within 6–12 months, if they select use cases carefully.

Stage 3: Organization-wide and Business Model Twin

At this stage, the twin is part of how the business earns revenue and shares risk.

Product as a Service, Robotics as a Service, or uptime guarantee contracts
Predictive service subscriptions based on real-time performance
Usage-based billing where the twin records utilization and performance

This level requires more organizational change, legal and commercial design, and deeper integration. For many small and mid-market manufacturers, the right approach is to understand this direction and mention it in strategy conversations, but focus execution on Stage 1 and Stage 2 use cases that deliver returns inside 6–9 months.

Why Manufacturers Must Be Use Case Driven

Most manufacturers share the same constraints: Limited bandwidth; Fragmented systems; Stretched workforce; ROI.

A use case first approach means you start where three conditions intersect:

A painful business question that matters right now
A reasonable path to data and modeling using tools and information you already have or can obtain without major infrastructure projects
A measurable outcome within 6–9 months and no later than 12

Examples that meet this bar:

Automation and cell design
Use a simulation twin to de-risk a new robot cell or packaging line. The target is fewer commissioning days, fewer change orders, and smoother client handover.
Bottleneck line operations
Create a performance twin of a bottleneck workcenter or line and test schedules, batches, and staffing patterns before touching the live system. Advanced planning tools such as Phantasma.global can complement this work.
New Product Introduction (NPI)
Build a process twin for the first 6–12 weeks of production for a new product. Model routings, cycle times, staffing, changeover rules, and WIP limits. A simple model that captures planned routing and approximate cycle times can expose unrealistic assumptions before first manufacturing cut. For a mid-market manufacturer, that can be the difference between a calm launch and a three-month scramble.

Where Manufacturers Already Pay for “As a Service”

If digital twins are eventually going to support “as a service” revenue models, it helps to start with where manufacturers are already comfortable paying recurring fees for digital capabilities. Today, it is visible in at least three areas:

Monitoring OEE and machine performance
Using computer vision for quality inspection as a service
Cloud-based ERP and MES for production, traceability, and planning

In each case, the pattern is consistent:

Clear operational pain
Limited in-house capability to build and maintain the solution
Fast, visible payback
Willingness to treat the service as operating expense, not a one-time project

Visibility as Service OEE and Machine Monitoring	Quality as a Service Computer Vision	System of Record as a Service Cloud ERP and MES
Manufacturers now pay monthly or annual subscriptions for OEE and machine monitoring tools. These systems tap into CNC controls, track uptime and downtime, and provide dashboards and daily reports. Even a small improvement in spindle time or throughput more than covers the subscription.	Manufacturers introducing AI-based visual inspection already pay for it as a subscription service, quality as a service. They upload images, label defects, and deploy models to cameras on the line. The vendor maintains the platform and the models. A subscription model allows them to start small and scale without a large upfront investment.	Cloud ERP and MES are now standard. They pay per user, per month, for order management, inventory, production tracking, and quality records. Leaders accept this because it enforces some process standardization. The value is concrete: better traceability, fewer stock-outs, fewer data entry errors, and improved planning.
Today, this is visibility as a service. Over time, the same data streams can form the backbone of an operational twin of the shop, enabling scenario planning and performance-linked contracts in specific relationships. A shout-out to my good friend Srihari at LeanWorx, who writes entertaining and relevant posts on this topic.	The image and defect data, combined with process parameters, can become part of a product and process twin for critical SKUs, supporting traceability, root cause analysis, and even warranty and insurance discussions. Quality Assurance as a Service is a natural evolution in the manufacturing ecosystem.	These systems are, in effect, system-of-record as a service. As more operational and equipment data feed into them, they become the data spine for operational digital twins of lines, plants, or even supply chains. Vendors and partners can then layer planning as a service or capacity-analysis as a service using twin-like models. (Phantasma.global)

Why XaaS and Twins Are Converging

The move toward “as a service” in manufacturing is not only about pricing. It reflects real pressure in three areas.

People
Leaders and planners do not have the time or skills to build and maintain OEE systems, vision models, or integrated ERPs. Their best people are tied up with daily production issues. They are willing to pay for services that remove complexity and provide ready-to-use insight. Digital twins will follow the same logic when they are positioned as a way to answer recurring questions, not as a technology project.

Process
Mix and demand change frequently, quality expectations continue to rise, and customers expect better traceability and responsiveness. Subscription services give manufacturers flexibility to start small, adjust scope, and change direction more easily than with a one-time capital project. Twin-driven services can fit into this by offering faster learning loops around capacity, quality, and launch decisions.

Technology
OEE tools, vision systems, and cloud ERP/MES are already collecting continuous data about machines, parts, processes, and events. That data is cleaned, structured, and surfaced as dashboards and reports that people actually use. Over time, these same data streams can feed more explicit twin models without asking manufacturers to start from scratch. The real convergence is that existing subscriptions are quietly building the data spine that digital twins need.

What Leadership Needs to Do Now

A manufacturing leader reading this should walk away with clear next moves for the next 6-12 months.

Choose two or three business questions that digital twins must help answer, grounded in current pain:
– De-risk a specific automation or NPI project
– Stabilize a known bottleneck area
– Improve visibility and trust with a key CM
Assign a business owner and a technical owner for one priority use case.
Define outcome metrics that matter to the business and are measurable with existing tools, for example:
– Capacity released: additional productive hours per week on the bottleneck
– Inventory turns or days of inventory (or WIP days) for the line in twin
– Overtime or rework hours reduced on that scope
Time-box the effort inside a 6–9 month window. This is not a side experiment.
Decide your position on XaaS:
– Where you will pay for service and performance instead of owning everything
– Where, if you are an OEM or integrator, you might eventually offer performance-based offerings yourself

The detailed work of making processes, data, and people “twin-ready” is its own topic, which I will address separately. For now, the essential step is this:

Stop treating digital twin as something only large enterprises can afford.
Start treating it as a disciplined way to de-risk decisions, protect scarce capital, and build options for how you create and capture value in the future.

If you would like to start this journey of building a digital twin, please write to us.

Structuring Engineering Data for Smart Manufacturing

Wed, 12 Nov 2025 06:29:18 +0000

How Engineering Data Drives Manufacturer Efficiency

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:

Capability	Engineering Data Role
Digital Twin	Feed geometry, tolerance and fit checks into virtual simulations
Predictive Maintenance	Link component history to service manuals and sensor alerts
Production scheduling	Enable cycle time estimation via process linked parts
Configure-Price-Quote	Identify 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:

Audit existing CAD files for redundancy and undocumented variations.
Define tagging standards for components and assemblies.
Link designs to quoting, production, or service use cases.
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

Why Design Reuse Belongs in Smart Manufacturing Playbook

Tue, 28 Oct 2025 21:08:27 +0000

How Reuse Drives Utilization in OEM Engineering

Impact	Symptom	Cost Implications	Benefits from Design Reuse
Time	Long quoting and engineering cycles	Lost 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 proliferation	Inventory bloat, higher carrying costs	Lower BOM costs, simplified sourcing
Talent	Repetitive work, difficult onboarding	Time to competency	Frees 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.

Blueprint to Building Blocks: Accelerating Engineering Design Through Reuse via Workflows and AI

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

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.

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.

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:

Prepare the Assembly: Organize and constrain your .SLDASM file with configurations.
Use DimXpert and Model Items: Auto-apply manufacturing dimensions and tolerances.
Feature Recognition with FeatureWorks: Recover editable features from legacy parts.
Enhance with 3DEXPERIENCE AI: Use AI to suggest part reuse and missing metadata.
Publish with MBD: Export annotated 3D PDFs for collaboration.
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.