Acting on the abundant data manufacturers have, whether it’s win-loss data, product performance, or price sensitivity, is difficult when most users are not data scientists. 

Traditional BI tools and manual data analysis require technical consultants and IT dependencies, which places knowledge gaps firmly between your teams and faster opportunity creation. It also increases the workload for IT departments who field frequent requests to pull data. 

While BI tools and analytics APIs still hold their place, self-service analytics for manufacturing is now an essential avenue for IT teams to respond dynamically to the market. IT must invest in self-service analytics tools built for real users, and Configure, Price, Quote (CPQ) is an effective place to start, as it sits at the intersection of product and engineering, pricing, and sales. And next generation analytics tools are connecting easily accessible data across the entire manufacturing lifecycle.

Why embedded, use-case specific analytics tools accelerate insights 

Traditional BI tools require specialized skills in data modeling and maintenance. And while APIs provide flexibility, they often return raw, low-level data that non-technical users struggle to interpret.  

Modern self-service analytics embed data dashboards and visualization directly into the systems that your sales, engineering, or product teams use every day. They scale data access and also decouple data for specific use cases. For example, a self-service analytics tool may have specific use cases that analyze and drill down on product portfolio performance or order fulfillment activity.  

Why is this so important?  

Generalized data through APIs can be helpful for higher-level, technical analysis, but the benefit of use case-specific self-service analytics tools are two-fold.  

• For teams, they focus the data in a way that makes it easy to manage and retrieve based on user roles and what’s most relevant to them.  
• For IT teams, these system-embedded analytics tools (e.g., analytics embedded in CPQ) can optimize the specific data feed or source as well as the analytics tool itself, making the data analysis much richer. And with data coming directly from the system, you already have a data lake to work from without the need for consistent uploads.  

 

Instead of forcing IT to build and maintain dashboards from scratch, embedded and use-case-specific analytics bring structured, ready-to-use insights directly into the tools business teams already use. IT can still maintain control and governance while equipping non-technical business users to explore trusted data independently. 

The new generation of self-service analytics tools 

Self-service analytics refers to simplified, embedded, or low-code tools that allow non-technical users to visualize and act on data. They eliminate pesky dependencies and the need to create completely new tools.  

What makes the new generation different? 

• New generation tools are built around pre-modeled data sets designed for specific manufacturing processes like quoting, configuration, and product performance. They offer no-code customization and easy shareability amongst and across manufacturing teams and functions. 
• They combine data preparation, visualization, and export tools into a single, governed environment, so there is no need for separate BI licenses or external consultants. 
• They offer extensibility (i.e., introducing additional external data) for IT through clean APIs and customizable data models. 

Self-service analytics vs. BI tools: finding the right balance 

It’s not necessary to choose entirely between one or the other, but often, there is a pushback when upsetting the current processes for analyzing data.  

“We already have BI tools. What makes this more insightful?”  

“We need flexibility and data control.” 

“APIs give us more freedom.” 

The answer is to keep them. Self-service analytics complements traditional BI by handling everyday operational insights directly within business tools. IT still has full oversight of data sources, permissions, and scalability while reducing manual requests. And, users will receive data that’s governed and contextualized, while IT can still manipulate raw data as needed.  

With the right balance, IT can focus on data strategy and governance (not dashboard maintenance) and empower business users to make faster decisions with only the most relevant data they need. 

What manufacturing teams can achieve today with self-service data analytics 

Using self-service analytics, your teams can start acting on data today to improve profitability, growth, and ROI for the company.  

Sales & Commercial Teams: 

• Identify which configurations, channels, or regions drive the highest margins. 

• Track quoting velocity and conversion to improve forecasting accuracy. 

• Compare adoption and performance across direct and partner channels. 

 

Product & Engineering Teams: 

• Analyze which features or configurations correlate with higher win rates. 

• Spot over-engineered or underperforming product variants to streamline design. 

• Detect trends that guide smarter assortment and pricing strategies. 

 

IT & System Administrators: 

• Monitor user adoption, data quality, and engagement without building reports. 

• Maintain centralized control of data governance while reducing recurring support requests. 

• Extend data access through APIs when deeper enterprise integrations are required. 

 

These insights can happen in minutes, rather than weeks. For IT leaders and CIOs, their departments can focus on building a data-driven organization where teams understand performance metrics and align across functions. This pushes organizations toward greater data maturity, where there is a single source of truth and cross-functional data sharing that takes the organization from reporting to analysis.  

For example, a regional sales director compares recent quoting data and sees that configurations including optional service packages consistently deliver higher margins and faster approvals. Within minutes, she shares this insight with other teams, prompting sales reps to highlight those packages earlier in the sales cycle. It’s a small change that boosts average deal size without adding IT workload or waiting weeks for a BI report. 

The next step: bringing analytics closer to the source

As manufacturers look to extend digital transformation beyond engineering and production, CPQ is becoming a natural starting point for data-driven insights. Embedding analytics directly into quoting and configuration processes allows every team—from sales to engineering—to act on trusted, contextual data in real time.

At Tacton, we’re focused on helping manufacturers connect the dots between product, customer, and performance data. Want to see how analytics within CPQ can unlock faster, smarter decisions across your organization?

Learn More About Tacton’s CPQ Analytics 

To non-technical teams, the concept of AI in manufacturing is often imagined as an AI assistant or machine learning algorithm that outputs recommendations or recognizes patterns in data. But a lesser recognized and foundational type of AI, symbolic AI, helps manufacturers take control of increasingly complex products in a way that generative AI, machine learning, and other emerging forms of AI cannot. 

If you’re a manufacturer with hundreds of possible product configurations, symbolic AI is a powerful tool that uses rule-based logic to guarantee that impossible combinations cannot be configured.  

By preventing impossible combinations, your sales team builds correct configurations without requiring frequent engineering input. And that yields a faster, more precision-based operation.  

What Is Symbolic AI?  

Think of symbolic AI as a digital engineer or product specialist. It uses rules, logic, and your company’s human expertise to make decisions that are transparent and explainable. They come straight from the engineering data and logic that you currently use.  

In manufacturing, it means faster configuration and quoting and fewer errors without needing a data scientist to navigate the data.  

How Does Symbolic AI Work?

Symbolic AI is an approach that has existed for many years. While it’s quite different from machine learning and agentic AI, it’s just as essential in manufacturing processes and tools.   

Symbolic AI uses “if–then” rules or constraint-based logic instead of guessing based on patterns, as generative AI would. For example, constraints may look like: “If option A, then option B is required,” or “Option C and D cannot coexist”. By encoding certain constraints based on the knowledge of your engineers and product specialists, sales can configure or quote following the same logic. No missed dependencies. No rework. 

Consider truck manufacturers, for example. If payload capacity exceeds a set limit, symbolic AI automatically validates a reinforced chassis and upgraded suspension. Selecting an off-road model from a catalog ensures raised suspension and deep-tread tires. It can even validate specifications down to the nuts and bolts, so that every component, fastener, and fitting in the configuration can actually be manufactured. 

Why It Matters for Manufacturing

Manufacturing is full of rules. What parts fit together? Which features are compatible? Which designs meet regulations? 

Symbolic AI makes these rules digital and reusable, so teams don’t have to rely on dense spreadsheets and the limited availability of their engineers. For engineers who spent years studying their craft, it also means that they have the time to innovate rather than taking on the support role for sales.  

• Sales or even self-service users can quote correctly the first time. 

• Engineers have fewer last-minute design fixes. 

• Operations deal with fewer production delays.  

Unlike machine learning, symbolic AI doesn’t need large amounts of data to work. It works with the expert knowledge you already have, so that knowledge is available in a more democratic manner across the manufacturing lifecycle.  

It’s especially valuable for complex, configurable products like trucks, industrial machines, or medical equipment, where one small mistake can cost thousands. 

How Symbolic AI Is Being Used Today in Manufacturing

Many manufacturers are using symbolic AI inside their CPQ (Configure, Price, Quote) or product configuration systems. For CPQ systems that use a constraint-based configuration engine, the ability to reason across thousands of interrelated rules means every product variation—no matter how complex—remains valid and manufacturable. Unlike simple “if–then” logic, which can quickly become unwieldy as options grow, constraint-based symbolic AI understands how each part, feature, and specification interacts with the rest. 

It may also be used in design validation, quality control, and digital twins to simulate and test product options automatically. 

This doesn’t mean, however, that investing in AI is one-or-the-other option between machine learning and traditional, symbolic AI. In fact, when combined, these two forms of AI create a more trustworthy, determinative solution.  

The Future of Symbolic AI

Symbolic AI is becoming even more powerful when combined with machine learning, creating “hybrid” or neuro-symbolic AI. In plain English, that means that systems can both learn from data and reason with rules, which connects the intuition of machine learning or generative AI with the logic of symbolic AI.  

For manufacturers, that could mean predictive systems that not only flag an issue but also explain why it happened and how to fix it. 

How Tacton Uses Symbolic AI  

Our constraint-based configuration engine applies engineering logic to every product choice, so that your manufacturing functions can be sure that what’s sold can be built, every time.  

By combining deep product knowledge with powerful AI reasoning, Tacton’s CPQ software helps manufacturers deliver accurate quotes while digitizing the knowledge of your product talent to future-proof your operations and instill customer trust.  

Schedule a Demo  

Data maturity in industrial manufacturing is one of the greatest barriers to achieving more substantial efficiency both upstream and downstream. An immature data foundation is indicative of who will trail in AI adoption and automation or lose margin to poorly optimized processes and portfolio decisions.  

While leadership teams have plenty of data at their fingertips, their ability to act on that data is reactive, rather than proactive. And fragmented reporting means that manufacturers can only answer “what” happened but still struggle with the “why”. What manufacturers will need to accelerate maturity is the ability to retrieve, validate, and act on information in a centralized way.  

Data reporting is going digital, but not cross-functional  

Sales, engineering, and production teams understand the pain of making IT requests to retrieve specific data points, or waiting over a week to get the right report and share it with the right teams. In fact, 55 percent of sales and product reports are still generated manually, according to the Tacton 2025 State of Manufacturing report.  

Fortunately, this tide is changing as more manufacturers invest in dashboard tools, integrate systems with BI tools, or use embedded analytics within their CRM or ERP. The Manufacturing Leadership Council (MLC), in conjunction with Tacton, found that surveyed manufacturers may be more digitized in their reporting than previously thought.  

In the MLC and Tacton webinar, “State of Manufacturing: How Leaders Are Creating a Customer-Centric Smart Factory in 2025,” 31 percent of surveyed manufacturers were using limited dashboards, and 53% were using embedded analytics within systems like CRM or ERP. Yet only three percent had any advanced analytics capabilities.  

Though the shift from manual reporting to dashboards and embedded insights is promising, manufacturers are still only getting a handle on reporting. Where they fall behind is analyzing.  

What is the gap in data analysis?  

Reporting is easy with the right technology, but analysis is a strategic undertaking.  

Though teams may use their CRM’s analytics to see what it says about opportunities, for example, these metrics are not tied to the rest of their sales data. Analysis is completely siloed rather than cross-functional across sales, engineering, or supply chain. And it’s not systematic. 

This makes it nearly impossible to understand the “why” behind outcomes. Imagine in your CRM seeing that you have a 50% win rate for the same truck, at the same price point, across 10 customers. But why? Separate data on configuration will show that different customers have very different use cases—driven in snow versus desert conditions, for example. Getting data in a systematic way is the starting point to interpreting that data.   

The next layer of data maturity: reactive to proactive  

When you understand the “why” behind an outcome, you can proactively prevent or recreate that outcome. Now, you can use opportunity information to configure vehicle solutions that are optimized for customers in that snowy condition at the right price point. Or, you can tweak a product that’s not performing.  

Reports are just a reactive snapshot. A descriptive analysis. Predictive and prescriptive analysis tells you what will happen and what to do about it, based on trends that are visible and the connections made across functions. Centralized data that makes it easy for each function to work from a single source of truth allows these predictive and prescriptive insights to surface.  

Levers for achieving proactive and predictive analytics in industrial manufacturing  

Two strategies, which are not exclusive to each other, can make data maturity acceleration easier than ever.  

First, codifying end-to-end lifecycle data into a single platform or source of truth is essential. Sales data and configurations, engineering and product data, and fulfillment information embedded in a single platform, such as CPQ or a manufacturing lifecycle platform, makes cross-functional analysis possible.  

It also sets the stage for AI, the second lever. AI can turn fragmented, unstructured (and structured) data, such as emails or BOMs or quotes, into structured and actionable insight. When different types of AI are combined, it can also validate this data to ensure accuracy and trust. Using AI, you can accelerate data maturity and scattered data to create structured data with guardrails. 

Integrated data combined with generative AI capabilities takes manufacturers to that next layer. Then, when you make that data easy to access, share, and build in an embedded, intuitive system, decision making is sophisticated and proactive.  

How Tacton brings it all together 

Tacton helps manufacturers move from disjointed reporting to actionable insights by unlocking CPQ data in a single, intuitive platform. 

With Tacton, you gain visibility into how products are configured, priced, and sold, helping you identify performance drivers, growth avenues, and bottlenecks. 

In addition, AI capabilities ensure your teams are ready to move faster with your data. 

Schedule a Demo  

Watch the Full MLC + Tacton Webinar

Pump and fluid handling manufacturers are accelerating competitiveness through digital transformation. Advanced technologies, e-commerce-inspired buying experiences, and growing demand for energy efficiency are helping brands differentiate themselves, but ongoing trends reveal a common thread: connectivity. Pump manufacturers are linking data with predictability, sustainability with performance, and customers with real-time insight. These forces are catalyzing innovation and transforming go-to-market efficiency across the industry, and the way manufacturers will win is by bringing that same, efficient connectivity to their manufacturing lifecycle.

1. Adoption of digital twins in the pump & fluid handling market

The adoption of digital twins is changing how pumps and fluid systems are designed, sold, and serviced. A twin is a virtual replica of a pump or system that goes beyond CAD by integrating performance curves, materials, operating conditions, and live sensor feedback to mirror real-world behavior.

Adoption is accelerating as manufacturers link IoT data and physics-based simulation to improve reliability and predict maintenance. A recent collaboration between SPX FLOW and Siemens resulted in a digital twin of a mixing tank that uses CFD simulations, real-time industrial data, and flexible control modules. The collaboration demonstrates how virtual testing and optimization de-risk operations and speed improvement cycles for fluid processes that include pumps and valves.  

For commercial teams, digital twins are becoming powerful selling tools. When integrated into configuration and quoting workflows, they allow every quote to double as a performance simulation that validates duty points and helps sales teams demonstrate measurable value. This capability also powers new business models ,such as performance guarantees and pay-per-use contracts, which are increasingly common in utilities and process industries. 

A generational shift is amplifying this trend. The next wave of engineers and buyers—most of them digital natives—expect interactive product selection, 3D visualization, and instant feedback before engaging sales. Manufacturers that embed digital twin logic into their configuration tools and digital experiences stand out as more transparent and aligned with how modern buyers evaluate solutions.

2. Efficiency and sustainability as business drivers

As energy costs rise and environmental regulations tighten, pump and valve manufacturers are prioritizing efficiency as both an engineering and business imperative. According to the Hydraulic Institute, pump systems may account for up to 40% of industrial energy usage. Even a one- or two-percent gain in design efficiency can deliver enormous environmental and economic impact at scale.  

Advances in fluid dynamics and additive manufacturing, for example, are enabling progress by reducing leakage or power demand while extending product lifespan. Buyers now expect verified sustainability documentation as part of every specification, and metrics like carbon data or lifecycle energy are no longer nice-to-haves in the quoting process.

3. Reliability, uptime, and the total cost of ownership mindset

Reliability has become a currency of trust, and it’s the highest pain point for buyers of industrial products, according to Accenture. Buyers are looking beyond purchase price to the true economics of uptime and lifecycle performance. Smart pumps equipped with self-diagnostics, remote monitoring, and predictive analytics are minimizing unplanned downtime and optimizing service schedules. 

Manufacturers offering connected service models, where digital records and real-time insights support every installed asset, are using this to change their business model from product-focused to solution and outcome-focused. This evolution has made total cost of ownership (TCO) the metric that matters most in procurement, guiding decisions toward transparency and long-term value. 

In effect, the most competitive suppliers are providing continuous assurance that every pump in operation performs as expected, with data and service woven into the customer experience. Services and predictive maintenance are also becoming part of the buying process, as manufacturers shift to outcome-based solutions, rather than products alone. But they’re also ensuring that customers get more transparency and reliability in lead times and delivery, as well, which requires a connected experience from sales to production.  

4. Digital and e-commerce-driven procurement

Manufacturers and distributors are seeing double-digit growth in online sales channels as buyers increasingly adopt self-service quoting tools and digital comparison platforms. According to a recent study, e-commerce now represents approximately 13.4 % of total revenue in industrial distribution.  

Platforms designed for spare parts and mid-tier assemblies are increasingly integrating with enterprise procurement systems, making ordering faster and more transparent. At the same time, digital commerce is reshaping how buyers expect to engage with manufacturers. For example, Xylem, a global pump and water technology company, now offers an online catalog and purchasing portal where customers can easily browse, configure, and order pumps or consumables directly. This growing trends reflects how industrial buyers are seeking the same immediacy and convenience they experience in consumer e-commerce.

5. The move toward modular systems

A growing trend in the fluid handling industry is the shift toward modular, configure-to-order systems. Manufacturers are increasingly offering modular pumps, configurable manifolds, and quick-connect assemblies that allow plants to reconfigure or scale operations with minimal disruption. 

This modular approach supports agility in sectors such as pharmaceuticals, food processing, and semiconductors—industries where uptime and flexibility directly influence profitability. It shortens engineering cycles, simplifies maintenance, and reduces total lifecycle cost while improving responsiveness to fluctuating demand. 

When combined with digital twins and connected service data, modular systems can be used to deliver more adaptive industrial performance. 

Maximize efficiency to stay ahead of the market: who is Tacton?  

The pump and fluid handling industry is evolving toward connected, data-driven, and sustainable operations—and Tacton is helping manufacturers lead that change. Purpose-built for complex manufacturing, Tacton’s CPQ and lifecycle management solutions connect sales configuration, engineering, and fulfillment data to create a single source of truth. 

Leading pump OEMs and distributors trust Tacton to streamline every step of the customer journey. Discover how Tacton helps pump manufacturers turn complex products into connected customer experiences.  

Schedule a Demo  

Nearly half of manufacturers are still exploring AI uses cases, with a minority successfully integrating AI into daily operations, according to a Tacton survey of global brands. Ask any manufacturing leader how their organization is integrating AI into their business, and you’ll hear similar sentiments about trust in AI.  

“It makes manual tasks so much faster, but we’re still trying to understand how to harness it correctly.”  

“The majority of our organization is curious about AI, but some are still hesitant. They don’t think that it should replace knowledgeable employees.”  

“AI is still a black box. We want to make sure we invest in a tool that’s reliable and doesn’t hallucinate results when our customers expect accuracy.”  

These are valid concerns in an industry where compliance, complexity, and accuracy factor into every decision. And, these reflections highlight a trend of manufacturers intrigued by AI’s potential but cautious about its reliability, according to a recent Tacton and Manufacturing Leadership Council (MLC) masterclass, “The State of Manufacturing: How Leaders Are Creating a Customer-Centric Smart Factory in 2025”. 

Building trust in AI as a workplace partner, especially in the order-to-delivery process, requires pairing human understanding and education with deterministic intelligence. 

You don’t lack data, you lack structured data 

There’s a common misconception that to successfully use AI, you must have better data—and more of it. “We’ll do AI later, after our data is perfect.”  

While clean data is vital (garbage in equals garbage out), Nils Olsson, Chief Strategy Officer at Tacton, challenged the thinking that data must be perfect.  

“The typical manufacturing company doesn’t lack data. It lacks structure,” stated Olsson. 

Data often lives in scattered silos, such as emails, spreadsheets, or the heads of veteran engineers and sales representatives. It may be inconsistent and lack guardrails for AI tools, like generative AI. But “deterministic” AI can bring structure and guardrails to that data when generative AI and symbolic AI work together.  

Generative AI vs. symbolic AI: demystifying the black box through multiple AI types 

How does this combination work?  

Generative AI in manufacturing learns patterns from large datasets. It synthesizes your unstructured data from emails, CAD notes, service logs, and more, and uses those patterns to surface insights, like optimal configurations based on a customer RFP, for example.  

However, generative AI alone can be prone to hallucinations, and without understanding the logic or reasoning behind it, AI can cause skepticism about its unpredictability.  

Symbolic AI, however, encodes expert knowledge, product logic, and engineering constraints into structured rules. As a type of AI used in technology for decades before AI’s recent propulsion, it helps validate and constrain what generative AI produces.  

As a result—and with the final validation of human expertise—manufacturers get explainable, trustworthy AI outputs and deterministic reasoning that comes from real-life engineering logic. This application is ideal for configuration, pricing, and quoting complex products quickly and accurately.  

Both types of AI alone have limits, but together, they help solve the trust problem and build confidence in AI-assisted user workflows. 

Accelerating AI maturity in manufacturing: strategies for leadership 

Despite its promise, manufacturers need not build proprietary large language models early.   

“Some companies are saying, ‘AI is so important, we’re going to build our own large language model.’ That’s extremely expensive. Why not start with the tools that are already available?” said Olsson.  

Start with small, practical, and safe applications first. Try applications that already exist. Even if your data is still messy, deterministic or rule-based AI systems can make it usable faster by enforcing logic, traceability, and consistency. That combination of symbolic and generative AI helps manufacturers move forward before achieving full data maturity, and every output can be verified against engineering, pricing, or product logic. 

Addressing internal skepticism 

Early success, however, depends as much on people as on technology. At JLS Automation, CEO Craig Souser described how his team addressed internal skepticism through education: 

“We have people that are interested but don’t really know where to start. And then we have the dissenters, who don’t believe AI technology is ever going to replace a good programmer.” 

To build confidence, JLS began with introductory sessions to get people comfortable with the base technology, starting small and demonstrating productivity gains in repetitive workflows.  

Building incremental success 

At Protolabs, Luca Mazzei shares a pragmatic approach to building AI maturity in manufacturing operations through incremental, explainable applications. 

“Even though we’re almost digital by design, we’re going through our own digital transformation. We’re expanding our purview from prototyping to short-run production,” Mazzei said. 

His team uses machine learning for pricing accuracy and efficiency while maintaining human oversight. Generative AI is used in customer service to help agents retrieve relevant, verified information from company systems. And, Protolabs is exploring how AI can read engineering drawings and digitize legacy parts data. 

These applications demonstrate that trustworthy AI starts small, with transparent logic and visible value. 

Build trust in manufacturing AI with Tacton  

Manufacturers that combine symbolic and generative AI are already shortening their path to AI maturity. 

Tacton embodies this hybrid approach within our configure, price, quote (CPQ) software and end-to-end lifecycle platform by offering deterministic, constraint-based configuration and pricing capabilities that seamlessly integrate symbolic AI with emerging generative AI technology. 

For manufacturers ready to move from experimentation to execution, Tacton provides the foundation for trustworthy and scalable AI transformation. 

Learn More About Tacton’s AI Solutions
Watch the Full Tacton and MLC Webinar 

As a CFO in manufacturing, you know the pressure when profits that look solid on paper shrink by the time the product ships. Some manufacturers lose 75–80% of their profit between order and delivery, not because of market swings, but because of breakdowns in execution. Sales, engineering, and fulfillment still operate on separate systems and truths, leaving gaps that digital transformation has yet to close.  

Some manufacturers focus on production maturity, including strengthening systems and manufacturing efficiency, while others chase growth through new digital customer experiences. Finance leaders are often forced to prioritize one function at the expense of the other. To truly protect manufacturing profit margins, however, CFOs must connect the entire manufacturing lifecycle through one digital thread. 

How CFOs must rethink digital transformation  

CFOs are no longer just financial stewards. They’re becoming the architects of connected enterprises. As manufacturers evolve, finance leaders are uniquely positioned to align commercial ambition with operational execution. Ultimately, if you want to grow a company and deliver profits, you need to ensure that what you sell will match up with what you deliver. 

As Nils Olsson, Chief Strategy Officer at Tacton, explained during a recent Manufacturing Leadership Council panel: 

“It’s not just about some lofty gains in terms of sales… it’s about how you make sure that you don’t lose half of the margins that you signed on for by the end of delivery of the product.” 

Great opportunity lies downstream. Digital transformation should not be a collection of isolated projects but a margin-protection strategy that connects quoting, engineering, and fulfillment in one flow of operational truth, which we like to refer to as the buyer-centric smart factory. When those functions share a single version of truth, what’s sold can be built profitably and delivered predictably. 

Start by asking: 

• Where do digital investments today stop short of connecting sales and operations? 

• Can finance see the profitability of every order as it moves through production? 

• Do teams have shared definitions of cost, margin, and delivery feasibility? 

Where margin loss happens most 

“Eighty percent of the cost in any manufacturing company is typically coming from your production apparatus,” states Olsson.  

Margin erosion often begins after the signature. A deal closed at a 25% projected margin can drop to 15% after design changes or delays and sink further to 3% once warranty claims, penalties, and overhead are factored in. 

To stop this erosion, CFOs need visibility across the entire value chain. Systems should trace every quote to its actual fulfillment cost and highlight margin variance in real time. This visibility allows leaders to ask: 

• Are quoted margins holding through production? 

• Which configurations or product lines consistently drive rework or delay costs? 

• Where are delivery issues or change orders triggering penalties? 

The deeper finance can get into the “why” behind these questions, the better positioned you’ll be to forecast accurately and prevent future losses. 

Engineering: the missing link in digital transformation programs 

“The secret sauce is engineering. How do you digitize and codify that knowledge?” says Olsson.  

By capturing and codifying engineering logic (i.e., the rules, constraints, and validations that define what can be built and at what cost), companies guarantee that every quote reflects operational reality. This alignment prevents costly rework and allows your finance team to forecast more accurately, since feasibility and cost structures are built into every deal from the start. 

Partner with engineering and IT leaders to identify how product rules, constraints, and cost structures can be digitized within configuration and quoting tools. Then, engineering knowledge becomes a shared, reliable foundation for decision making and collaboration between sales, product, and fulfillment. Support pricing and configuration tools that automatically validate lead times, material costs, and manufacturability so margin assumptions hold through delivery.  

Putting transformation into practice: additional steps for protecting manufacturing profit margins 

Protecting margins in manufacturing today requires both system integration and strategic focus. CFOs can take several practical steps: 

• Build a single source of profitability truth. Integrate data from quoting, fulfillment, and financial systems so teams work from the same cost, price, and delivery assumptions.

• Operationalize margin governance. Treat margin protection as a managed process. Create shared KPIs that tie directly to margin performance, such as quote-to-delivery variance, rework rate, etc. 

• Use analytics to shift from reporting to prevention. Move from static quarterly reports to real-time analytics that surface cost variance, lead-time risk, and pricing pressure before they hit the P&L. Embedding analytics into the system will allow for more accurate forecasting and planning.  

• Start focused, then scale. Begin with one high-leak area (i.e., a product line or region with recurring margin loss). Map out how quoting accuracy, production changes, and delivery costs affect actual profitability. Once improvements are proven, replicate the model across the enterprise. 

As Luca Mozzi, Head of Product and Pricing at Protolabs, noted, many manufacturers are pursuing digital transformation “because of uncertainty, not in spite of it.” Agile, connected systems allow companies to adapt pricing, production, and delivery to protect profit margins even in volatile conditions. 

Tacton’s end-to-end lifecycle platform for manufacturers 

Create your foundation for margin improvement in manufacturing with a system purpose-built for highly configurable solutions.  

Tacton’s platform brings together configure, price, quote (CPQ) capabilities with lifecycle-wide configuration management, order fulfillment, and installed base insight, all powered by real-time analytics. Together, these capabilities create a continuous digital thread that links what’s sold, what’s engineered, what’s delivered, and how it performs in the field. 

Our end-to-end platform includes: 

• Configure, Price, Quote (CPQ) capabilities ensure every sale is feasible and margin-validated. 

• Configuration lifecycle management maintains product accuracy and cost alignment as designs evolve. 

• Connected order fulfillment: Validated configurations flow automatically into production to reduce rework, delays, or penalties. 

• Lifecycle visibility: Installed-base data and performance insights help manufacturers deliver profitable service, upgrades, and compliance. 

• Embedded analytics: Finance and operations share one source of truth, so margin protection is proactive. 

With this foundation, manufacturers can truly sell it right, build it right, and deliver it right every time. 

Explore how connected data and lifecycle intelligence can protect your margins and transform profitability. 

Learn More 

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Manufacturers of configurable products have the unique challenge of selling complex solutions amid growing demand to compress delivery timelines 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 through a single source of truth.  

The challenging disconnects in today’s manufacturing model  

Today, manufacturing suffers from a disconnect. Buyer engagement happens in e-commerce 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 re-forecasting 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 CPQ. 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. 

Map workflows

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. 

Create a seamless flow between Bill of Materials (BOMs)

In a buyer-centric smart factory, one of the most important workflows to automate is the transition from engineering design to manufacturing execution, specifically from the engineering bill of materials (eBOM) to the manufacturing bill of materials (mBOM). When the full 150% eBOM (representing all possible product variants) is digitized, each customer configuration can automatically generate a precise 100% eBOM that reflects the product exactly as sold. From there, a seamless connection between the quote and order fulfillment processes transforms that configuration into a production-ready mBOM, complete with the correct parts, routings, and work instructions for each factory’s supply chain and production setup. Achieving this level of automation requires a single source of truth that connects sales, engineering, and manufacturing.

Prepare cross-functional teams

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 manufacturing lifecycle platform that connects buyer engagement with the smart factory through a single source of truth.

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.