Manufacturing leaders are leaning into three fronts simultaneously: move faster on complex deals, make smarter decisions about where to grow, and protect margins that cost pressure is squeezing from every direction.  

Digital transformation was supposed to help with all three, and for many manufacturers, it has. Manufacturing digital transformation in 2026, however, looks different from the last wave. The first wave of transformation automated processes within functions, but automation across functions in the order-to-delivery lifecycle is lacking. Many of the systems manufacturers implemented to get there weren’t built to share data and important product logic across sales, engineering, and production, let alone support the speed and scalability that selling highly customized products demands. 

Here’s what the data says about where the real leverage is in 2026, based on survey data from 280 global manufacturing leaders.  

1. The trend toward mass customization grows as leaders look for speed 

Speed is the first casualty of high product complexity, and 67% of manufacturers in the U.S. and Europe are producing very to extremely complex products.  

New and existing configuration rules across thousands of growing product variants and options create a new place for something to slow down. Buyers disengage due to overwhelming product portfolios. Quotes require more engineering input and downstream adjustment.  

More manufacturing leaders are increasing configure-to-order coverage as a direct path to faster, more scalable sales. In 2026, 39% of manufacturers have 20–39% of their portfolio in configure-to-order, while another 30% cover up to 59%, meaning nearly 7 in 10 are running hybrid ETO-CTO models simultaneously. The goal is to reduce the manual, case-by-case effort that engineer-to-order demands by standardizing more of the portfolio into repeatable, configurable modules, but that model only delivers speed if the configuration logic behind it is consistent across sales, engineering, and production. 

Leaders moving fastest on complex deals have one thing in common: they’ve connected the configuration logic of their modular or configurable product options that governs what can be sold to what can be built, thereby removing the back-and-forth that kills cycle time. 

2. Legacy quoting tools aren’t solving for customization  

Although nearly half of manufacturers have adopted third-party CPQ software, with others using homegrown solutions, organizations are still struggling to deliver quotes that are correct and optimized to the customer’s needs. Most can respond quickly to RFQs (within 48 hours for most survey respondents), but legacy quoting and CPQ tools aren’t addressing the need for more intelligent customization.  

Customization is now the number one challenge for sales teams. Manufacturers now need CPQ tools that don’t just address the first problem of speed, but that also address the problem of product complexity.  

Faster quotes that require downstream correction aren’t faster at all: the time lost to engineering review, pricing adjustments, and post-signature changes erases the front-end speed gain Those who invest in guided selling capabilities, AI-driven configuration matching, and CPQ tools that can keep up with configuration rule maintenance see speed and accuracy improve. 

3. Leading IT and the C-suite teams are viewing margin at a bird’s eye  

Dwindling margins won’t be fixed by any single department. They’ll be fixed by having a digital thread that accurate translates one definition of product configuration across PLM, CPQ, ERP, MES, and other systems.  

With 62% of manufacturers experiencing moderate to severe margin erosion across quote to delivery, each part of the value chain contributes due to a lack of people, data, and processes that can work from the same source of truth.  

Margin declines at the handoffs between teams. That means optimizing one department’s process doesn’t fix the total cost. Under severe cost pressure, the instinct is to cut within functions by reducing headcount, tightening procurement, and speeding up production. But the biggest margin leak is the coordination failure between functions that no budget line captures.  

The manufacturers protecting margins most effectively have built shared visibility across the lifecycle, so the cost of a bad configuration decision at quote time is visible before it becomes a change order, a delay, or a reputational problem at delivery. 

4. Strategic leaders know which products and configurations are actually driving growth

Most manufacturers can track revenue and win rates, but less than half (45%) have visibility into which specific configurations, variants, or product options are driving profitability versus quietly adding engineering cost without closing deals.  

This data gap makes strategic growth planning harder than it needs to be. To build the most defensible growth roadmaps, successful leaders can answer questions like: which variants always require manual engineering review? Which configurations correlate with the fastest sales cycles? Which options are specified frequently but rarely convert? 

Having configuration-level data requires a working digital thread so that your organization has the full context of every quote decision. Digital transformation efforts that focus on a connected source of truth will have deeper insights into the configuration behavior that drives portfolio performance.  

5. AI will accelerate growth with engineering teams positioned for immediate gains 

AI—especially generative AI—is becoming ubiquitous. In 2026, 79% of manufacturers are investing in or exploring AI, up from 64% in 2025. The top priorities are practical: automating complex configurations, reducing quoting errors, guided selling, faster RFQ responses.   

But the manufacturers getting the most from AI aren’t the ones who invested in it first. They’re the ones who built the data and workflow foundation that gives AI something useful to work on 

Under intense cost pressures, AI’s most immediate value isn’t automation for its own sake. It’s reducing the maintenance burden that consumes engineering capacity, catching configuration errors before they become margin problems, and surfacing the product intelligence needed to make smarter growth decisions faster.  

The three highest-priority AI use cases tell that story directly: automating complex product configurations tops the list at 56% (the single biggest lever for reducing the engineering validation bottleneck that slows every complex deal). That’s followed by real-time pricing optimization (removing the manual pricing adjustments that pull engineering into commercial decisions they shouldn’t own) and reducing quoting errors and approval delays before they reach engineering. For sales, the gains are equally direct: fewer deals that unravel downstream, faster quote cycles, and commitments that production can keep. 

While only 41% of manufacturers currently see AI-assisted CPQ model maintenance as a potential priority, it’s where engineering teams stand to gain the most by freeing capacity from upkeep and putting it back into innovation.  

How to align manufacturing digital transformation for greater speed, growth, and profitability 

The manufacturers who move faster, grow more strategically, and protect profitability under cost pressure in 2026 aren’t doing it with the most expensive tools. Their foundation is built on a single source of truth that automatically aligns each part of the lifecycle. That looks like connected configuration logic, shared data across the lifecycle, and emerging technology built on top of something solid.  

The full data behind these trends comes from 280 manufacturing leaders across 8 countries.  

Download the 2026 State of Manufacturing report. 

A digital thread is a chain of connected data from engineering design through production and the serviceable life of a product. In a traditional view, the digital thread provides end-to-end traceability starting in engineering systems like CAD and PLM and continuing through delivery and aftermarket systems to provide the most up-to-date and accurate information to all functions. However, most conversations don’t include configuration, quoting, and customer intent, which happens earlier in the commercial lifecycle.  

Creating a digital thread in manufacturing requires a full view of the product lifecycle to capture and carry customer requirements, configuration logic, and engineering data forward as a single, continuous source of truth for the rest of the lifecycle.  

What is a digital thread in manufacturing?  

A digital thread provides continuity across each manufacturing function by connecting product lifecycle data and presenting it in the right context for each team. It’s achieved by properly integrating key design, manufacturing, and service systems, so that data is made available across these systems in the appropriate and most relevant format.  

Take the example of a manufacturer of customized industrial machinery that uses configure, price, quote (CPQ) to validate thousands of configuration combinations. Once the product is configured, a connected data framework allows systems to automatically generate engineering and manufacturing BOMs and documentation, which translates that CPQ quote data specifically for these departmental needs. This ensures the entire build process traces back to the original customer configuration and their needs. 

While a digital thread sounds similar to a digital twin, there are key differences.  

Digital thread vs digital twin: what’s the difference?  

A digital twin is a virtual representation of a specific product or process across its lifecycle. At its core, it’s a simulation used to understand how a physical product will perform, helping inform decisions in engineering, manufacturing, and service.

A digital thread, by contrast, is not a simulation. It’s a connected data framework that ensures consistent, accurate product information flows across all functions and systems involved in designing, selling, building, and servicing products. The digital thread provides the foundation that enables digital twin technology by making trusted lifecycle data accessible in the right context.

Critically, a complete digital thread needs to start earlier than engineering. By capturing customer requirements, configuration decisions, and commercial intent through CPQ, manufacturers create what can be thought of as an engagement twin—a digital representation of the configured solution that was actually sold. This engagement twin becomes the foundation of the digital thread, ensuring engineering, manufacturing, and service teams work from the same validated product definition throughout the lifecycle.

The benefits of the digital thread  

By creating a way for all teams—from customer-facing sales to services—to access crucial product lifecycle information, you can create efficiencies and optimize processes more effectively.  

The benefits of the digital thread include: 

• Engineering time savings and faster quote-to-production: With validated, up-to-date product logic available to sales teams through CPQ, engineering spends far less time reviewing configurations. This frees engineers to focus on higher-value design and reduces quote-to-production cycle time.
• Margin protection: Misalignment between what was quoted, what engineering interprets, and what manufacturing ultimately builds can hurt your profitability. A connected digital thread provides consistent, accurate data across every stage, catching discrepancies earlier in the manufacturing lifecycle. 
• Error elimination: Traceability and connected data provides all teams with the most up-to-date information, so everyone works from the same data source even as changes are made. 
• True traceability: By linking downstream performance, service or maintenance data, and lifecycle information back to the original sales configuration and requirements, the digital thread enables variant-specific insights and equips sales and aftermarket teams to identify upgrade and service opportunities.
• Faster change order management: A connected digital thread synchronizes updates to product logic, engineering data, BOM structures, and manufacturing instructions across every system and team. When changes are made in engineering—such as redesigns, compliance updates, or new options—those updates flow to CPQ, ERP, and MES automatically. This reduces the risk of selling outdated configurations.
• Scalability across partners: Your entire distribution network works from the same accurate, governed product logic for better consistency across regions and channels.  

Steps for establishing a digital thread across commercial, engineering, and production processes

In most digital threads, manufacturers begin by defining a product within CAD, PLM, and CAE systems to create engineering BOMs, drawings, and models. The thread is then built through integrations and structured product data. Via the PLM, product data is pushed to systems like the ERP and MES for costing, work instructions, and routing. From there, production can build the product and provide data to services teams through MES and quality systems.  

Commercial decisions aren’t traditionally part of the digital thread, creating information siloes that lead to incompatible product configurations in the sales process. So, how can you build a true digital thread that encompasses the full lifecycle?  

• Build a centralized product configuration model. Define options, rules, constraints, BOM logic, and dependencies into a single, authoritative model.
• Map the configuration model to lifecycle systems. Connect commercial tools like CPQ, PLM, ERP, and MES so they interpret the same configuration definitions without duplicate rule sets or manual translation.
• Orchestrate data flow across systems. Establish structured integrations so configuration data flows automatically into engineering and manufacturing systems and returns lifecycle data back upstream when needed.
• Connect configuration decisions to engineering data.Connect commercial configuration decisions directly to engineering systems, so selected options and constraints automatically drive CAD models, variant-specific EBOMs, and engineering workflows. This ensures engineering works from the same validated product definition that was sold, without manual reinterpretation.
• Maintain synchronized BOM structures. Ensure eBOMs, mBOMs, and commercial BOMs remain coordinated as constraints or engineering definitions change.
• Automate lifecycle transitions. Enable transitions from one part of the lifecycle to the next to follow defined rules and workflows instead of manual handoffs.
• Implement change management. Propagate engineering changes, new rules, and product updates through all systems, so outdated configurations are never sold or built. 
• Establish a standard configuration service for all channels. Expose validated configuration logic to all sales channels so every buyer interaction uses the same governed model.
• Capture lifecycle data for traceability and feedback loops. Tie as-built, quality, and service data back to the original configuration and lifecycle definitions to improve future designs and customer engagement.
• Govern variants across their full lifecycle. Manage how configurations are introduced, updated, retired, or replaced. 

Real-world examples: strengthening the digital thread with configuration

Manufacturers like Piab vacuum automation and lifting solutions and Vantage Elevator Solutions show how establishing a digital thread between commercial and design functions creates immediate, measurable results.

By establishing configuration as a governed source of product truth, Piab unified pricing, visualization, and ERP synchronization with engineering data. This enabled more than 40,000 self-service configurations per month and direct linkage of 58,000 configured items to product and engineering structures. The result was faster, more consistent quoting and a reliable handoff into downstream manufacturing systems.

Vantage Elevator Solutions achieved similar gains by connecting constraint-based configuration with ERP-integrated quoting. This eliminated unworkable designs, reduced turnaround times, and ensured that what was sold aligned with what manufacturing could build, removing costly manual interpretation between sales and engineering.

In both cases, extending the digital thread to configuration improved accuracy, scalability, and traceability at the front end of the lifecycle. Just as importantly, it creates a stronger foundation for future lifecycle connections, making it possible to feed fulfillment and service insights back into the thread over time and further strengthen decisions across the product lifecycle.

Connect data across your manufacturing lifecycle 

The digital thread needs to start earlier to truly eliminate quoting, engineering, and production issues. Manufacturers that connect CPQ, lifecycle management, and core operations get faster, more accurate and profitable outcomes. 

Tacton, the most complete end-to-end lifecycle platform for manufacturers, helps your teams create the continuity, accuracy, and configurability needed to deliver the right product every time. Explore how a smarter approach to configuration and data flow can strengthen your entire value chain. 

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