Manufacturers of configurable products have the unique challenge of selling complex solutions amid growing demand to compress delivery timelines and without losing margin. While the traditional factory is either built around mass standardization and high internal efficiency, or high customization with unpredictable delivery, the buyer-centric smart factory is where manufacturers will be able to achieve both customer satisfaction and efficiency. That means delivering on unique promises to their customers in a smarter, faster way.   

Tomorrow’s manufacturers must go from fragmented, reactive, and unsure, to unified functions, proactive data, and predictable business outcomes. The buyer-centric smart factory starts by meaningfully connecting the end-to-end process.  

The challenging disconnects in today’s manufacturing model  

Today, manufacturing happens in silos. Buyer engagement happens in ecommerce sites or in conversations with sales teams who may be unclear if a solution is truly configurable. Engineering and product teams are checking for errors and handholding for quote accuracy. Order fulfillment is adjusting and reforecasting supply chain needs.  

At each step, and with each competing priority, comes risk to profitability.  

This disconnect also shows up in what buyers experience: before purchase, they expect convenience and transparency, but once the order is placed, they demand reliability, quality, and predictable lead times. Manufacturers struggling with silos can rarely deliver both, and they often sacrifice margin or trust in the process. 

What is the buyer-centric smart factory? 

In the buyer-centric smart factory, the entire value chain works from the same source of truth.  

In an ideal setup, engineering centralizes rules for valid configurations, ensuring consistent product logic across PLM, ERP, and other systems. Updates flow automatically to sales, preventing errors and protecting margins, while fulfillment receives automated BOMs, flexible scheduling, and order change management. The result is a seamless value chain where profitability is safeguarded end to end, reducing rework and avoiding costly delays that impact margin.

Defining ‘buyer-centric’ and ‘smart factory’ 

When broken down, what makes up the buyer-centric smart factory?

Buyer-centric engagement is where manufacturers are currently differentiating for today’s digital-first customers through:  

• outcome-based configuration 

• full solutions (i.e., both products and services) 

• optimized pricing 

• visualized buying 

• automated compliance 

These experiences both fuel and are fueled by shared smart factory data, which includes data from digital twins, PLM, quote and order fulfillment, and supply forecasting.  

However, the buyer-centric smart factory adds two layers that make the connection truly work: 

• AI to codify knowledge: Capturing engineering expertise and rules so sales can configure an optimized solution accurately and independently, while ensuring compliance and feasibility in real time. 

• Analytics for optimization: Using data across sales, engineering, and fulfillment to forecast demand, protect margins, and continually improve the buyer experience, product portfolios, and operational performance. 

With a single source of truth, buyer demands flow directly into design, pricing (both products and services), and production. Customization is validated in real time—compliance checks, margin optimization—before an order hits the shop floor. And at every step, data and AI intelligence helps optimize the process.  

One source of truth leads to stronger business outcomes 

When organized effectively, a buyer-centric smart factory creates a seamless connection across the value chain. Sales can confidently deliver validated quotes and orders to fulfillment, while engineering can introduce and launch new products without delays to sales and order fulfillment. The result for the buyer is faster access to innovative products, with greater reliability and confidence in every purchase.

The benefits extend across the entire organization:

• It’s easier for sales to sell complex products by codifying engineering knowledge and using that to guide selling.  

• Codified product knowledge and AI make it easier to create the best possible solution for customers to increase win rates.  

• Engineering has more time to innovate due to detailed and technically validated quotes.  

• Order fulfillment has more reliability in forecasting and supply chain management and is able to deliver production excellence.  

• Analytics and planning tools help manufacturers adapt to shifting demand without sacrificing margins. 

The key to achieving a buyer-centric smart factory 

For many manufacturers, achieving this level of efficiency requires breaking deep-rooted barriers between sales, engineering, and fulfillment through adjustments in people, process, and technology. 

It starts with mapping workflows and data to gather the necessary information from each of your systems. With the help of generative and symbolic AI, manufacturers can then determine what can and cannot be delivered based on that data. 

But technology alone won’t solve the ‘people’ issue: resistance to change. Teams often cling to familiar processes, but cultural change is just as important as system change. Leadership and teams must reframe priorities so that sales, engineering, and fulfillment are not competing, but rather working toward the same customer outcomes.  

While it’s tempting to tackle challenges separately—pricing today, fulfillment tomorrow—priorities should be addressed together. When data, AI, and analytics are integrated on one platform, manufacturers can connect the front end with reliable fulfillment on the back end. Then, efficiency, profitability, and customer satisfaction stop being trade-offs and start reinforcing each other. 

Vision to reality with Tacton 

Moving from vision to reality requires the right platform to connect these moving parts in a data-driven way. 

Recognized as a leader in CPQ, Tacton is much more than CPQ. We provide an end-to-end lifecycle platform that connects buyer engagement with the smart factory. CPQ remains a critical link, ensuring every configuration, price, and quote is validated and optimized. Through seamless integration with ERP, PLM, CLM, and other core systems, manufacturers can unify the value chain, streamline order fulfillment, and gain the data needed to innovate with confidence.

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Configure, Price, Quote (CPQ) software is part of a larger ecosystem that helps your business connect your front of house sales with engineering and delivery functions. Rather than a one-off project, your CPQ implementation is a core part of digital transformation that impacts sales, engineering, operations, and ultimately, your customers.

Many implementation challenges come from how well organizations prepare their tech stack, teams, and processes. And the process is not one-size fits all, but there are best practices to position your business for success. These eight steps will help you build the right foundation, so you can go beyond go-live and deliver lasting value across your entire business.

1. Define and align on vision

The first question of implementation isn’t technical. It’s strategic.

“Why are you implementing CPQ?”

Define the business outcomes you want to achieve: faster time-to-market, improved customer satisfaction, a better e-commerce experience.

Because CPQ is a cornerstone of the quote-to-order process, it’s also a critical driver of digital transformation. That’s why it requires CEO sponsorship and a regular place on your leadership agenda to ensure the implementation continues to align with overall strategy.

2. Translate vision into tangible success metrics

A bold vision is inspiring, but to guide a project it must be measurable. Break it down into clear success criteria: efficiency improvements, reduced time-to-delivery, margin protection, or customer satisfaction gains. What are you limited by with your current solution, and what long-term needs do you need to address? Use that to help guide what your team should improve and measure.

Use frameworks your teams already know, such as KPIs, OKRs, or something similar so that you don’t have to reinvent the way that your teams track success.

3. Map target processes

Copying the past only leads to the same outcomes that your current solution provides, which are limiting. Simply digitizing legacy processes means you miss the opportunity to rethink how you work. If the system isn’t intuitive, you risk underwhelming adoption and poor ROI.

Instead, map your target processes. Where are today’s bottlenecks? Which steps are unnecessarily manual? What would a seamless end-to-end sales journey look like? A new CPQ solution is your chance to design for the future.

Start by mapping the user journey. Define which systems should lead at each step, clarify handover points, and establish a master data strategy so product, pricing, and customer information flows consistently. At the same time, challenge the status quo:

• Do all existing systems still need to be in place?
• Can you eliminate redundant process steps?
• Where can you reduce manual handovers?

4. Prepare systems and data

Where do you start to create a master data strategy? Clean, structured data is the backbone of any CPQ implementation. Focus on these three areas first:

1. Organization and governance: Document how your company sells today, emphasizing approvals, workflows, and governance across all business units and regions. Consolidate it into one clear picture instead of scattered notes or tribal knowledge.
2. Product data structure: Identify gaps or “white spots” where documentation is weak. Transition product data into a consistent format, clean it up, and use the opportunity to simplify your portfolio and reduce complexity.
3. Pricing data and strategy: Review regional variations and outdated models. Clarify how pricing should work in the future, and align stakeholders on a common strategy before building it into CPQ.

Getting these three areas right not only speeds up implementation but also ensures your CPQ system reflects how your business actually operates.

5. Choose a project approach that fits

There’s no universal “best” methodology. What matters is what works for your organization. Agile methods can be useful, but not everything in CPQ can be done in small, flexible steps. For example, product modeling and pricing usually need clear, fixed targets.

Two rules of thumb:

• Don’t over-plan upfront. Different vendors solve problems in different ways, so leave room for flexibility.
• Avoid the “big bang.” Pilot with a product line, market, or region to learn before scaling.

6. Build and train the right team

Your people are the driving force behind your CPQ implementation. Define a focused core team with clear responsibilities and make CPQ a business priority rather than a side project.

Training should begin early. Teams need to understand both the system and the project methodology. Just as importantly, involve long-term maintainers from the start. CPQ lives well beyond go-live; those who will manage it later need to be part of the build today.

7. Set reasonable budgets

If you can’t explain why money is being spent, it will be hard to defend when priorities shift.

Start by building a business case that flows directly from your vision. Then, group budgets by themes or project phases rather than individual requirements. This makes it easier to manage spend and connect budget to overall value without getting lost in the line-item details.

Finally, make budgets transparent. Share not just the numbers but the reasoning behind them. When stakeholders see how budgets connect to value, it’s easier to set priorities and keep the project moving forward.

8. Make change management a critical part of leadership’s agenda

Perhaps the most underestimated factor in readiness is change management. CPQ reshapes how sales, engineering, and operations collaborate, as well as how customers experience your company.

Engage end users early, especially sales. Align the program with your company culture and pace of change. Give executive sponsors a visible role in leading the transformation and use your roadmap as a communication tool to show progress and reinforce the bigger picture.

Set the stage for success with Tacton CPQ

A CPQ project’s success is decided long before go-live. With a clear vision and a strong focus on structured change, you create the foundation for lasting value.

At Tacton, we know CPQ is part of a larger transformation in how you sell, engage buyers, and connect the front office with operations. Our Buyer Engagement Platform is built to guide manufacturers through this journey, combining industry expertise with proven implementation practices for a smoother, more fruitful rollout.

Ready to start your CPQ journey the right way?

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An enterprise Configure, Price, Quote (CPQ) software implementation can take months to years and millions of dollars depending on the level of your product complexity. With CPQ ROI being a key measurement of implementation success, it’s vital to expedite that time-to-value as much as possible. AI in CPQ, but especially AI-assisted CPQ implementation in the form of AI product modeling assistants, is changing the game by increasing adaptability and reducing time, costs, and energy for both initial implementation and future product launches.  

Learn how businesses can accelerate time-to-value and cut CPQ implementation in half with the help of AI. 

AI-assisted CPQ implementation time has long-term benefits 

Time spent modeling is time not selling. When implementation is slow, teams spend weeks or months setting up product rules, features, and combinations, often manually. This delays the time when sales teams can actually use CPQ to quote and close deals.  

But beyond time-to-value, automating product modeling with the help of AI has hidden benefits for long-term go-to-market agility and customer experience.  

• Easier collaboration: Lower learning curves let more teams (even non-experts) shape and refine product models.

• Faster product launches: Quoting processes are ready to adapt when your product lineup changes.

• Continuous optimization: Faster iterations make it easier to refine configuration logic and adapt to market needs.

• Stronger customer focus: With less effort tied up in implementation, more focus shifts to sales strategy and customer experience.

How AI helps implementation teams streamline modeling

Traditionally, one of the most time-intensive parts of CPQ implementation has been building product models—defining rules, features, constraints, and dependencies that govern how products can be configured and quoted.

By applying AI and machine learning internally, CPQ vendors can:

• Interpret existing product documentation (from spreadsheets, PLM exports, or written instructions) and structure it into a usable model faster, reducing the need for manual rework.
• Generate functional base models that serve as a starting point for solution consultants, cutting weeks from setup without sacrificing accuracy.
• Automate repetitive tasks like building combination tables or cascading attributes across configurations, helping experts focus on refinement rather than data entry.
• Lower complexity for collaboration by providing dynamic, visual ways for cross-functional teams to review and validate models earlier in the process.
• The result isn’t that customers “plug in” their data directly—it’s that vendors can deliver implementations faster, with fewer bottlenecks, thanks to these behind-the-scenes AI efficiencies.
• Push the model directly into CPQ: Once the model is complete (or even partially complete), teams can export it directly into the CPQ. They can also choose to continue refining within the AI environment or switch seamlessly to the CPQ platform to test and deploy in real quoting scenarios.

How much time can AI save in CPQ implementation? 

Early applications of AI in product modeling suggest that it can significantly reduce implementation timelines. For example, what used to take a month to model in a traditional process may be shortened to about a week when supported by AI-enabled efficiencies. 

For more complex product models, we estimate that AI has the potential to cut implementation time for solution consultants as much as 50% for more complex models, depending on the quality of existing product data and the complexity of configuration rules. 

These figures aren’t exact benchmarks but rather directional estimates based on current testing and early use cases. The key takeaway is that AI can meaningfully reduce the manual effort involved, enabling faster time-to-value and easier iteration during implementation. 

Increasing CPQ ROI with AI-assisted product modeling 

Speed to value directly impacts your bottom line. When implementation is delayed, so is your ability to quote accurately, respond to customer needs, and generate revenue. Every month spent on internal configuration work is a month lost on customer engagement and growth. 

With AI-assisted CPQ implementation, you eliminate weeks or months of manual setup and drastically reduce reliance on scarce technical resources. This lets you: 

• Accelerate quoting cycles and close deals faster 

• Reduce costly errors by starting with a data-driven, validated product model 

• Enable faster feedback loops, improving pricing, bundling, and product strategy over time 

• Free up your team to focus on revenue-driving activities instead of backend maintenance 

Crucially, faster implementation helps shift the perception of CPQ from a major IT project to a strategic tool that you can use to go to market faster and more strategically. 

Go to market faster with Tacton 

At Tacton, we’re focused on reducing the complexity of CPQ implementation so you can go live faster, adapt quicker, and engage buyers more effectively. 

If you’re exploring CPQ, talk to us about how we’re making implementations more seamless and predictable, so your teams can focus on selling. 

When manufacturers roll out CPQ, the initial focus often centers on launch. Configure the system, train the team, and track early usage. But what happens after go-live matters just as much as the launch itself. Adoption levels shift over time, sales teams develop workarounds, and product portfolios grow more complex.  

CPQ introduces new ways of working, which means it requires active leadership, reinforcement, and measurement. The organizations that maximize their investment embed analytics into their change management strategy and create a cycle of continuous enablement that keeps adoption high and ROI growing. 

Moving from metrics to organizational change 

Adoption metrics are a useful starting point. Quote completion rates, error frequency, time-to-quote, and feature usage reveal how the system is performing. However, measurement alone doesn’t drive improvement. For CPQ to remain effective, those insights must be tied to change management activities: coaching, training, process adjustments, and leadership communication. 

How to achieve continuous enablement  

What is continuous enablement?  

It’s using a structured feedback loop to ensure that CPQ continues to evolve alongside the business. This continuous enablement operates as a practical extension of change management principles: 

• Keep a pulse on behavior. Beyond login counts, look at how different roles and regions are really using CPQ. Are they relying on it for every deal or still defaulting to spreadsheets for certain products? 

• Spot resistance early. Underused features, repeated errors, or shadow processes are signals that something isn’t working. Addressing them quickly prevents small frustrations from becoming cultural barriers. 

• Enable with intent. Use adoption insights to focus training, simplify steps, or provide job aids that meet users where they are. Targeted support builds confidence faster than generic enablement. 

• Close the loop. Re-measure after interventions. Did the extra coaching reduce errors? Did a workflow adjustment improve quote completion? Feeding results back into the system makes enablement evidence-based instead of reactive. 

This cycle takes adoption from a one-time measurement into a continuous enablement practice that reduces resistance and strengthens user confidence.  

CPQ change management strategy in practice 

Change management becomes most effective when it’s grounded in real adoption data. Analytics provides the visibility leaders need to shape ongoing support and improvement across several dimensions. 

• Training – Usage data highlights where to invest in tailored enablement. New hires may need foundational sessions, while experienced users benefit from advanced coaching. Regional or role-specific training can address unique adoption gaps. 

• Communications – Adoption insights fuel storytelling. Sharing success metrics and reinforcing best practices keeps momentum high and helps users see the tangible value of CPQ. 

• Governance – Embedding analytics reviews into quarterly business updates or steering committee meetings ensures adoption stays on the leadership agenda and aligned with broader business goals. 

• Resistance management – Analytics reveal signs of resistance, such as users bypassing workflows or reverting to spreadsheets. Spotting these early allows managers to intervene with coaching, sponsorship, or process adjustments before disengagement spreads. 

• Usability and process design – Patterns like frequent quote abandonment or repeated errors often point to usability issues, not user reluctance. Feeding these insights into CPQ improvements reduces friction and builds user confidence. 

The key is that analytics makes change management evidence-based instead of reactive. Rather than guessing where adoption is failing, leaders can use clear signals to guide training, communication, and process improvements that keep CPQ adoption strong. 

Why continuous enablement matters 

When CPQ usage is reinforced through ongoing change management, the impact extends beyond system performance. Organizations see: 

• Faster quoting cycles as users grow confident with workflows.

• Fewer errors and less rework thanks to reinforced training and process clarity.

• Higher margins as compliance improves and manual workarounds decline.

• Improved morale as sales teams experience CPQ as a sales partner, not a barrier.

Continuous enablement ensures CPQ remains aligned with business strategy long after the initial implementation. 

How Tacton supports lasting efficiency and enablement 

At Tacton, we believe CPQ adoption should be managed like any other business transformation. That’s why our approach combines proven implementation practices, ongoing services, and analytics capabilities to ensure lasting success. 

If you want to strengthen your change management strategy and increase adoption with a data-driven approach, Tacton can help. Learn more about Tacton CPQ or schedule time with us.  

Quoting has long been treated as a back-office process to streamline for speed and accuracy. But when quoting and configuration live in a digital platform, they create a foundation for sales intelligence that reveals how customers buy, how markets shift, and where there’s growth potential. 

According to the Tacton 2025 State of Manufacturing Report, based on insights from global manufacturers, this opportunity is largely underutilized. Forty-three percent of manufacturers still rely on manual configure, price, quote (CPQ) processes, mostly spreadsheets. At the same time, nearly half of spreadsheet users say they are “satisfied” with their processes, suggesting that many organizations underestimate the strategic value of digitizing sales data, especially within CPQ.  

Data maturity in manufacturing sales: Sales intelligence vs. sales reporting

A majority of manufacturers today are still in manual reporting mode, with 55% reporting manually and only 37% using analytics embedded in their CRM, CPQ, or specific system. They track traditional CPQ metrics, such as quote turnaround times, error rates, discount frequency, that measure efficiency but stop short of strategy. 

Sales intelligence represents the next stage of data maturity. It leverages the data generated by every configuration and quote as a living dataset where sales teams work to: 

• Uncover customer preferences and price sensitivity across regions and segments. 

• Reveal demand shifts that indicate where markets are growing or contracting. 

• Show which deals and configurations deliver sustainable margins versus which erode profitability. 

The key difference isn’t in producing more dashboards. It’s in simplifying data reporting and intelligence, so sales and quoting data are embedded in systems.  

Quoting data becomes a shared intelligence base when it’s captured consistently in the same source of truth. Sales, finance, and product teams no longer rely on anecdotal knowledge or siloed reports.  

The risks of poor data maturity and disconnected sales intelligence

The risks of not using CPQ as a sales intelligence tool compound over time. 

First, knowledge gets locked away. With 30% of manufacturers expecting significant retirement in their sales and engineering workforce within the next five years, failing to digitize quoting and configuration data risks losing critical context about how products are sold. A centralized, digitized system, such as a CPQ platform, ensures that expertise isn’t confined to a few individuals. It becomes accessible to every relevant stakeholder. 

Second, errors are normalized and signal weak data maturity. Even with rapid quoting cycles, errors persist. In fact, 58% of manufacturers that have streamlined their quoting still report frequent quote quality issues. If a company can’t trust its quotes, it can’t trust the underlying data. Analytics built on bad or inconsistent data will mislead decision-making, and quoting speed without intelligence creates noise and takes away the opportunity to learn from sales and product performance.  

Finally, strategic blind spots persist when data is layered in too many systems. Without consistent reporting embedded in the platform where sales works, such as CPQ, manufacturers miss signals about customer behavior and competitive dynamics. Deals are treated as isolated events rather than as data points that, when connected, could shape smarter account strategies or reveal emerging trends. 

Three key requirements for building a sales intelligence foundation 

To evolve from transactional quoting to strategic sales intelligence, manufacturers must first simplify their data landscape.  

• Consistency over complexity: The key to maturity is capturing data reliably in one place, not adding more tools. Integrating and embedding data into a central platform ensures inputs are standardized and usable. 

• Cross-functional accessibility: Intelligence grows when shared. Sales, finance, and product leaders need aligned visibility into how quoting patterns affect margins, growth, and competitiveness. 

• Learning loops: Every quote is a learning opportunity. Over time, trends in pricing, product mix, and deal outcomes can guide strategy—if those patterns are captured and fed back into the system. 

Develop a data-driven sales engine with Tacton

The State of Manufacturing Report shows that while quoting remains a weak link, a major opportunity lies in advancing data maturity. By treating CPQ as a sales intelligence system rather than a transactional tool, manufacturers can unify data, simplify layers of technology and data, and gain the insights needed to compete strategically.  

Learn how to build a data-driven sales engine with Tacton, the leading CPQ buyer engagement platform for manufacturers. See how CPQ can become a central layer for your smart factory to help you improve quoting, product profitability, lead times, and so much more.  

Learn More About Tacton CPQ Data  

Product portfolio optimization helps manufacturers improve margins and reduce complexity by using CPQ sales data to evaluate real product performance. Deal insights reveal which products to keep, which to fix, and which to drop, giving manufacturers a clear path to streamline their portfolio and uncover opportunities for innovation. 

For many B2B manufacturers, adding more products feels like progress. More SKUs mean more chances to meet customer needs, or so it seems. In reality, sprawling portfolios often create hidden costs: slower quoting, increased engineering workload, greater operational complexity, and shrinking margins. 

The challenge is knowing which ones actually drive value. By analyzing quoting patterns and sales behavior captured in CPQ, manufacturers can make smarter portfolio decisions, focusing resources where they matter most and even spotting gaps that spark innovation. 

The disconnect between sales volume and product profitability 

Not every product pulls its weight. Just because something is quoted often doesn’t mean it’s profitable. Many high-volume configurations require steep discounts, multiple revisions, or manual engineer intervention. These costs erode deal value and stall sales velocity. 

Some of the most quoted items in a portfolio can actually deliver slim or even negative margins. These products are often highly complex, requiring manual configuration and engineering review that eats into efficiency. Others rely on frequent discounting just to close a deal, which undercuts profitability. And many suffer from low quote-to-close ratios, where the time and effort spent preparing quotes simply doesn’t pay off.  

Warning signs include: 

• High quoting activity but poor close rates 

• Frequent discounting to win deals 

• Multiple quote revisions and manual overrides 

• Heavy reliance on engineering support 

To evaluate true product performance, manufacturers should look beyond quoting volume. 

Measure what matters with CPQ sales data 

CPQ systems capture how products behave in real-world sales scenarios.  

Instead of relying on spreadsheets or assumptions, manufacturers can measure: 

• Quote frequency vs. conversion: Does quoting effort translate into closed deals? 

• Revision and rework rates: Are products overly complex or misaligned with customer needs? 

• Discount dependency: Which products only move when margins are sacrificed? 

• Engineering time required: How much hidden cost does each product carry into the quoting cycle? 

Together, these data points show you which products are worth your team’s time and which are just noise. 

Apply the keep–fix–drop (and explore) framework 

Armed with CPQ insights, manufacturers can apply a practical framework to optimize their portfolios: 

Keep 

• High-margin products with strong quote-to-close ratios 

• Minimal rework, limited discounting 

• Steady demand from core customers

Fix 

• Products with strong revenue potential but margin pressure 

• High quoting activity but low conversion rates 

• Frequent requests for customizations (a signal for redesign or modularization) 

Drop 

• “Zombie SKUs” that are rarely quoted 

• Margin-negative products that consume engineering time 

• Legacy items that no longer fit market needs 

Explore 

• Repeated customer modification requests point to white-space opportunities 

• Products with consistent discount pressure may need new packaging or service bundles 

• Gaps in quoting patterns can signal unmet market needs worth exploring 

Italian medtech manufacturer Conf Industries used CPQ data to rationalize its product catalog, removing 50 to 60 underperforming SKUs that no longer met demand so it could focus on higher-value offerings. 

Unlock innovation through portfolio insights  

Product portfolio optimization is as much about growth and innovation as it is cutting costs. 

When manufacturers shed underperforming products, they free up engineering capacity, reduce operational drag, and focus on products that truly drive profitability. But the real opportunity lies in innovation. 

By analyzing where customers frequently request changes, where quoting stalls, or where discounts are the only way to close deals, manufacturers gain direct feedback for product strategy. CPQ data reveals where to design something better. 

This shift turns portfolio optimization from a defensive exercise into an engine for smarter, faster innovation. 

A smarter way to manage your product portfolio 

By leveraging CPQ quoting data, manufacturers can base their decisions on how products actually perform at the point of sale in real configurations, with real customers, and under real pricing conditions. This data-driven approach provides something traditional spreadsheets and sales volume reports may miss: objective visibility into product line profitability.  

Instead of assuming which SKUs are successful, you can see exactly which products close deals at full price, which ones require heavy discounting, and which ones stall the quoting process altogether. And when product, sales, and finance teams all work from the same real-world performance data, CPQ software creates a common language, driving alignment across product development, commercial strategy, and margin goals. 

Forward-thinking manufacturers are already using this lens to refine their portfolios, improve profitability, and focus resources where they matter most. If you’re not one of them yet, now is the time to discover how Tacton can help you refine your manufacturing product strategy. 

Few roles are as influential as the specification engineer in the construction and building materials industry. These engineers decide which products get specified, approved, and ultimately purchased. When a component becomes the basis of design in a specification engineer’s documentation, it often becomes the default choice for procurement.

That’s why winning the spec engineer is one of the most important opportunities for construction equipment manufacturers today. From HVAC systems to lift platforms, pumps, and industrial fans, specification decisions directly shape bills of materials (BOMs), vendor approvals, and final purchasing outcomes.

And increasingly, these decisions are happening online without ever speaking to a representative.

What specification engineers expect in a digital experience 

Spec engineers now expect self-service digital tools that give them instant access to technical product data, the ability to validate or configure at a high level, and CAD models. They aren’t waiting on quotes or clicking through static PDFs. They’re working fast, often on job sites or in the field, and they expect manufacturers to keep up. 

If your digital experience slows them down, they’ll move on to a competitor. But if you offer a seamless, engineering-grade experience through self-service configure, price, quote (CPQ) technology that puts speed, autonomy, and accuracy first, you’ll increase your chances of becoming the basis of design. 

However, it’s important to note that not all spec engineers want to perform full product configurations themselves. Many simply want to validate a model number, compare performance specs, or download compliant CAD/BIM files. Offering multiple entry points, whether deep configuration, quick model lookup, or “good-better-best” comparison enables users with different needs.

Top priorities of specification engineers in the digital journey 

Every minute spec engineers spend navigating a clunky configurator or waiting for access to product specs is time lost on a job where precision and deadlines matter. That’s why the top priority in the digital buying journey is speed and ease of use for spec engineers. They want to find the right product, validate its performance, and move forward without delays, distractions, or detours. 

Access is everything. Engineers expect: 

• Immediate entry to product selection and validation tools.
• Self-service product configurators that make it easy to validate options, build quick configurations, or select from pre-defined pathways.

Once they’ve selected a product, spec engineers look for: 

• CAD automation with the ability to download technical models instantly.
• Ungated access to those files, where possible, to avoid friction in their workflow.

They also expect tools that deliver: 

• Real-time compatibility checks.
• Instant feedback on selected options.
• Access to technical documentation needed for compliance, integration, and design validation.

Ultimately, specification engineers don’t need a premium product or the lowest price. They need a digital experience that helps them work faster, more accurately, and with full autonomy. The easier you make that process, the more likely your product is to be specified. 

Where most digital tools miss the mark 

Many digital tools still fail to meet specification engineers’ expectations. These tools often introduce friction into what should be a fast, intuitive workflow. The most common shortcomings include:

1. Barriers to independent access

Spec engineers expect to evaluate and configure products on their own, without hurdles. But many tools still: 

• Require engineers to request access or wait for approvals.

• Force conversations with sales reps before showing technical data. 

• Gate basic functionality behind forms or logins.

These delays can cause specifiers to abandon the process entirely.

2. Missing or delayed technical outputs

Even after gaining access, engineers often find that: 

• Real-time CAD output isn’t available. 

• Parametric detail is insufficient for validating designs. 

• File downloads are delayed or buried within complex workflows.

Confidence erodes without instant, accurate technical outputs.

3. Lack of pre-built configuration pathways

Not every engineer wants to start from scratch. Yet many configurators: 

• Don’t offer pre-defined templates or system presets.

• Lack guided filters to narrow options quickly. 

• Fail to support “good-better-best” or use-case-based selection flows. 

These gaps slow down experienced engineers who just want to validate a model or spec quickly.

4. Poor usability in real-world environments

Configuration tools are often built for desktop office use, but specifiers are frequently: 

• Working on-site or in the field. 

• Using mobile devices or tablets. 

• Operating in low-bandwidth conditions or multilingual teams. 

If tools aren’t responsive, mobile-ready, and intuitive, they won’t be used where it matters most. 

These platforms are designed for internal sales enablement, not for the external engineers driving product selection. And when the experience is frustrating, engineers will simply specify someone else’s product instead. 

How leading manufacturers are winning over spec engineers 

Leading manufacturers are winning over specification engineers by building a better digital experience that prioritizes autonomy, speed, and seamless access. Instead of forcing engineers to go through account managers or wait for manual responses, they provide self-directed workflows that allow for real-time product selection, validation, and documentation. 

Here’s how top-performing manufacturers are setting themselves apart:

1. Immediate access

Spec engineers don’t want to be funneled through a sales process just to get started. The best manufacturers offer: 

• Self-registration portals tailored for engineers, with no pricing visibility required.
• Instant access to configurators without triggering sales engagement.
• Exploration without friction, empowering engineers to validate performance and download files independently.

2. Ungated, actionable technical outputs

Once a product is configured, engineers want to keep moving—not wait for email approvals. Leading tools: 

• Provide instant CAD and BOM downloads, without forms or delays.
• Deliver outputs in usable formats, ready for design workflows.
• Enable specifiers to incorporate files directly into project documentation and submittals.

3. Connected, collaborative systems

Digital tools don’t exist in isolation. They need to fit within engineers’ broader workflows. That’s why successful manufacturers ensure their CPQ or selection tool has: 

• Full integration with ERP and CAD systems.
• Version control and data accuracy, reducing manual intervention.
• Tools that support collaboration across teams and allow engineers to share configurations with project stakeholders, distributors, or internal reviewers.

4. Built for field use and global access

Spec engineers often work on job sites or in the field. The best experiences are: 

• Mobile-ready and responsive across devices.
• Designed to perform in working environments.
• Multilingual, supporting engineers working with teams around the globe.

By delivering on these fronts, manufacturers make it easier for specification engineers to work independently and efficiently and for sales teams to build trust, win specs, and ultimately drive sales. 

Business impact: why engineer-friendly tools drive more conversions 

Creating a digital experience that meets the needs of specification engineers is a business advantage. When engineers can move quickly from product selection to documentation without roadblocks, the sales process accelerates. In industries where timing can determine whether a product is written into the spec or left out entirely, being first to provide a product schedule often means being first to win the deal. 

These streamlined experiences also reduce the burden on internal teams. When specifiers can self-serve, there’s less need for pre-sales engineering support, freeing up resources for more complex or high-touch opportunities. Distributors and partners benefit as well, because they can respond to specifiers’ needs with accurate information more quickly, often without needing to involve the manufacturer directly. 

Over time, tools that make engineers’ lives easier lead to stronger relationships with the engineering firms and project consultants who drive specification decisions. In a digital-first buying environment, manufacturers known for providing the best product configurator for specification engineers stand for making engineers’ work easier. 

Is your product configurator fit for spec engineers? 

Ask yourself these questions: 

• Can engineers explore your products without triggering a back-and-forth sales conversation?
  If your tool requires a conversation with sales just to explore options, it’s not truly self-service. 

• Can they go from selection to CAD file in just a few clicks?
  Speed matters. Engineers don’t want to wait for emails or navigate complex portals to get the files they need. 

• Is your configurator mobile-friendly and accessible without friction?
  Specifiers often work in the field or on-site. Your tool needs to work anywhere, on any device, without special access or software. 

• Does it integrate with quoting, BOM generation, and CAD platforms and support collaboration?
  Seamless integration reduces errors, saves time, and gives specifiers confidence in the data they’re using. 

• Do engineers actually come back to use it again?
  Repeat usage signals that your experience is adding value. If they’re not returning, there’s likely friction somewhere. 

Make it easy with Tacton 

Specification engineers aren’t chasing fancy features. They just want to get the job done quickly, independently, and accurately. If you give them a smooth, self-service product configuration experience, you’ll win their trust and their specs. 

Want to see how your product experience stacks up? Schedule a demo today and evaluate your current experience through the eyes of a specifier with Tacton. 

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When configuring products with thousands of parts, variants, and customer-specific requirements, constraint-based configuration offers the flexibility, scalability, and resilience that rule-based configuration engines simply can’t match. 

Unlike rule-based logic, which dictates a fixed path of if-then rules, constraint-based systems define what must be true for a configuration to work, no matter where the user starts. That means configure, price, quote (CPQ) systems with constraint-based engines are ideal for manufacturers who need to rapidly launch new products, support diverse markets and applications, and adapt to changing requirements without rewriting thousands of configuration rules. 

What is constraint-based configuration?  

Traditional rule-based configuration relies on rigid “if-then” statements to define how products can be built—for example, “If component A is selected, then component B must also be selected.” While this works for simple product lines, it breaks down quickly in complex scenarios. Imagine configuring a fluid system with thousands of bolts, washers, and nuts. For every valid or invalid combination, a separate rule must be written. This leads to massive rule sets that are time-consuming to build, difficult to maintain, and fragile in the face of change. 

Constraint-based configuration takes a different approach. Instead of prescribing every allowable combination, it defines the conditions that must be true for a product configuration to be valid—regardless of the order in which selections are made. It focuses on relationships and dependencies, not fixed sequences. Users can begin with any input (like material, size, or environment), and the system automatically filters and adjusts the rest in real time to ensure compatibility. 

For example, instead of writing dozens of rules to manage every nut, bolt, and washer combination, constraint-based logic might define a single rule: The bolt diameter must match the washer diameter, and both must be suitable for corrosive environments if specified. That single constraint applies broadly to reduce redundancy and simplify maintenance. This approach also allows manufacturers to define product relationships once and reuse them across product lines, customer segments, and global markets.  

Constraint-based vs. rules-based configuration: a simple breakdown 

Here’s how the two approaches stack up when it comes to supporting complex manufacturing workflows: 

Capability	Rule-based configuration	Constraint-based configuration
Maintenance effort	High (hard-coded, many-to-many)	Low (logic reused across SKUs)
New product onboarding	Slow and error-prone	Fast and scalable
Flexibility in user input	Rigid (forced paths)	Flexible (any input order)
Global/multi-market support	Difficult	Built-in with constraint layering
Coverage of product portfolio	Limited	Full (with fewer constraints)

Why complex manufacturers choose constraint-based configuration  

Constraint-based configuration changes the pace, scale, and confidence with which manufacturers operate. At Tacton, we’ve seen manufacturers across industries reduce quoting complexity when choosing constraint-based CPQ software.  

As Marlande Wesselhoft, CIO at Vantage, put it: “We ultimately selected [a CPQ with] the constraints-based methodology, considering how complex our products are and the variety of products that we have. [The team] felt very confident that the constraint-based methodology would accelerate our timeline getting our products into the platform.” That confidence in scalability and speed is echoed across industries, from elevator systems to turbines and poultry housing. 

At Siemens Energy, where every gas turbine is tailored to the customer’s power plant and site conditions, quoting used to take eight weeks and required deep engineering involvement. With more intuitive and accurate configuration, quoting takes five minutes. The product logic was maintained through thousands of business rules, making the system complex and costly to manage. After transitioning to a constraint-based approach, Siemens replaced those thousands of rules with just a few hundred constraints—dramatically simplifying system maintenance while improving accuracy and speed.  

Husky, a global leader in injection molding systems, faced similar challenges in quoting for their hot runner business. Configurations required navigating 60–70 variables per product, using spreadsheets and look-up charts that often resulted in errors, rework, or missed opportunities. With a constraint-based engine, Husky reduced solution time by 75% and eliminated incorrect configurations altogether. Their quoting process now delivers 100% error-free quotes and has become a strategic differentiator. 

Across these different industries, the impact is the same: constraint-based logic empowers manufacturers to move faster, quote smarter, and support a broader product portfolio without creating a maintenance slowdown. 

The benefits constraint-based CPQ 

Constraint-based CPQ transforms how manufacturers bring products to market. You get:  

• Broader product portfolio coverage. Traditional systems, especially homegrown CPQ, often can’t scale beyond a subset of offerings. Constraint logic allows you to model and support up to 100% of your product range without exponential increases in rules. 

• Faster onboarding and less tribal knowledge. Because the logic lives in the system—not in a senior engineer’s head—new hires can contribute faster, and sales reps don’t have to rely on specialists for every quote. 

• Built-in adaptability for local markets. Apply specific constraints by geography, application, or regulation without duplicating configuration logic. 

• Resilience during change. Adding a new product variant or retiring an obsolete component only requires a logic update—not a cascade of rewrites across thousands of rules. 

• An intuitive experience for sellers and partners. Users don’t need to know the full product structure or follow a rigid process. Whether they start with the customer’s environment, preferred feature, or performance need, the system intelligently guides them to a valid solution. This makes configuration accessible for channel partners, distributors, and less technical teams. 

• Future-ready architecture. Constraint-based systems are more adaptable to AI, guided selling, and optimization, making them ideal foundations for long-term digital transformation. 

This approach also supports configure-to-order (CTO) and engineer-to-order (ETO) workflows, which demand advanced compatibility logic and customization. 

Many leading manufacturers choose a constraint-based CPQ like Tacton for this reason: it lets them stay agile, scale globally, and serve customers without compromise. 

Evaluating your current configuration approach 

If you’re unsure whether your current system is holding you back, ask yourself these questions: 

• Are we writing hundreds (or thousands) of rules per product line? 

• Does every product update require developer intervention? 

• Do users complain that configuration is too rigid or confusing? Is the configuration experience flexible, or does it force users into a rigid sequence? 

• Are we able to cover our full product portfolio, or are we limited to a subset due to system complexity? 

• How often do we need developer or IT intervention to update logic or fix configuration errors? 

• Is our system slowing down quoting, product launches, or onboarding? 

If the answer to any of these is yes, it’s time to rethink your configuration strategy. 

Future-proof your configuration strategy 

For manufacturers managing high product complexity, global reach, or customization requirements, constraint-based configuration is the best logic for manufacturing built to scale. 

Tacton’s constraint-based CPQ software gives you a faster, more flexible, and more future-ready way to sell and deliver complex products. Download our full guide to constraint-based configuration to discover how it can future-proof your product configuration. Or, reach out to see it in action.  

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