Great products do not guarantee business success
For decades, manufacturers measured product development by one overriding question: could engineering design a better product? Technical excellence remains essential, but it is no longer enough to guarantee commercial success. Products must now move through increasingly complex supply chains, satisfy evolving regulatory requirements, reach the market quickly, and continue delivering value throughout years of operation. Every decision made after the design phase can determine whether an innovative product succeeds or struggles.
That broader view of product development has become increasingly important, according to Tom Shoemaker, Vice President of Product Marketing at Propel Software. He argues that manufacturers have spent years refining engineering processes while overlooking the wider network of functions that ultimately determine a product’s success. Procurement, manufacturing, quality, sales, marketing, service and suppliers all influence the commercial outcome, making product development a business-wide discipline rather than an engineering exercise.
“It is too expensive a problem to do it that way,” Shoemaker explains. “You are not in the business of just building the world’s greatest product. What you are trying to do is build a product that succeeds. To succeed, you must purchase the right materials, produce it in the right way, market it, sell it and then support it throughout its life. To limit that within engineering is to leave money on the table.”
His argument reflects a broader shift taking place across manufacturing. Product lifecycle management is no longer simply about controlling engineering documentation or managing design revisions. Increasingly, it is becoming the mechanism through which companies connect engineering decisions with sourcing, production, customer experience and long-term service, ensuring that information follows the product throughout its commercial life.
Connecting the business around the product
Manufacturers have invested heavily in specialist technologies for almost every business function. Engineering teams rely on CAD applications. Procurement operates through enterprise resource planning systems. Manufacturing uses execution platforms, while quality, service, finance and sales all maintain their own environments. Individually these systems perform well. Collectively, however, they often make it difficult for organizations to understand the complete picture surrounding a product.
Shoemaker believes the challenge is not that manufacturers have adopted too many technologies, but that too few organizations have successfully connected them around the product itself. “There is never going to be one system that does everything from start to finish better than the combination of a few discrete technologies,” he says. “You still have to use the right technology in the right purpose, in the right context. We think there should be a core product thread, but it has to have the right integration points with the right tool for the job.”
That distinction matters because digital transformation initiatives frequently begin with technology consolidation rather than business outcomes. Replacing multiple applications with a single platform may simplify architecture, but it does not necessarily improve decision-making. Manufacturers instead need consistent product information flowing between the systems already supporting different business functions.
The value of that approach extends beyond engineering. When procurement teams understand proposed design changes before orders are placed, sourcing risks can be addressed earlier. Manufacturing gains visibility into product revisions before production begins. Service organizations understand how changes affect equipment already operating in the field. Marketing and sales can prepare product launches using accurate information instead of waiting for engineering to complete documentation.
Girtz Industries, now part of Legrand, is an Indiana-based manufacturer of custom-engineered modular power enclosures and packaged power systems serving data centre, industrial and commercial applications, provides an example of the operational impact this connected approach can achieve. Seeking to overcome fragmented engineering, finance and sales processes, the company adopted a shared platform connecting product, customer and enterprise information. The result was a twenty-five percent reduction in engineering change order times, twenty percent fewer meetings and a monthly financial close reduced from five days to one, demonstrating that better product information benefits the entire business rather than engineering alone.
Engineering changes are business decisions
Few activities expose disconnected organizations more quickly than engineering change management. Component shortages, supplier issues, quality improvements and customer feedback all require products to evolve. The engineering modification itself may be relatively straightforward. Understanding every consequence of that change is considerably more difficult.
Shoemaker believes manufacturers should worry less about preventing change and more about understanding its impact across the product lifecycle. “It is not necessarily that change is bad,” he explains. “Especially early in the lifecycle, you want a high level of interaction and change before you start committing costs. But you also need to understand the blast radius. If this component fails and I need to swap it out, where else is it being used? Is it in products already with customers? Is it in products still under development? Do I need preventative maintenance? Those are the questions you have to answer.”
That broader perspective transforms engineering changes from isolated technical activities into business decisions. A single component revision may affect supplier commitments, inventory, production schedules, warranty exposure and customer service operations simultaneously. Without visibility across those relationships, organizations risk solving one problem while unintentionally creating several others.
VAST Data, a provider of AI infrastructure and software-defined data storage platforms, encountered exactly this challenge as the company expanded rapidly. Its previous PLM environment limited flexibility and slowed process improvements, making engineering changes increasingly difficult to implement efficiently. Following its move to Propel, engineering change implementation fell from fifty days to fewer than thirty while improving collaboration with contract manufacturers and providing greater visibility across bills of material. Factory teams also gained real-time access to new product introduction updates, reducing daily administrative effort while helping suppliers respond more quickly to design revisions.
For manufacturers operating increasingly global supply chains, that responsiveness is becoming a competitive advantage rather than simply an operational improvement.
Information should evolve with the product
The concept of the digital thread has become a familiar topic across manufacturing conferences, yet Shoemaker prefers a simpler description. “I actually do not like the term digital thread,” he says. “It had better be digital. I prefer product thread because it speaks to what you are trying to do. It is all things around the product.”
That distinction highlights an important difference. The objective is not simply to digitize engineering records. It is to ensure that product knowledge continues growing as products move from design into production, sales, operation and service.
Many organizations already possess most of the information they need. Engineering specifications exist within PLM systems. Commercial content resides elsewhere. Customer experience data often sits inside CRM platforms, while service histories remain isolated within aftermarket applications. Individually these systems provide value. Together they should create a continuously evolving understanding of the product.
That also changes how manufacturers think about product information. Engineering data alone rarely provides everything sales teams or customers require. Technical specifications must be translated into meaningful commercial content without losing the underlying engineering accuracy.
AMS Technologies, a European provider of high-tech components, systems and custom-engineered solutions, confronted precisely this issue as it expanded beyond component distribution into bespoke product development. Internal collaboration had improved through connected product lifecycle management, but providing consistent product information externally remained slow and labor-intensive.
Managing thousands of product records manually across multiple systems proved unsustainable. By connecting product lifecycle management with product information management, AMS Technologies reduced new product setup to under one minute while maintaining a single source of truth for technical specifications, certifications and customer-facing content across its digital channels.
Rather than treating engineering and commercial information as separate disciplines, the company established a continuous flow of product knowledge supporting both internal collaboration and customer engagement.
AI’s greatest opportunity may be understanding change
Artificial intelligence is beginning to influence every stage of manufacturing, but Shoemaker believes some of its most valuable applications will be found in helping organizations understand complexity rather than replacing engineering expertise. “I think change impact analysis is an absolutely huge opportunity,” he says. “AI allows you to understand what products are affected, what inventory is affected, what is already installed with customers and what products are still under development.”
Another promising application involves compliance. Manufacturers operating in highly regulated industries often manage extensive documentation describing sourcing requirements, quality procedures and engineering policies. Instead of expecting engineers to remember every requirement manually, AI could interpret those policies and help ensure development activities remain aligned with company standards throughout the product lifecycle.
The common theme is context. Rather than generating new designs independently, AI can help manufacturers understand relationships that already exist but are difficult for people to analyze across thousands of components, suppliers and installed products.
Shoemaker expects this contextual intelligence to become increasingly important as connected products begin feeding operational information back into product development. “I think the future is continuous intelligence,” he says. “Products will be able to tell manufacturers they are only running at forty percent efficiency or that a part is likely to fail. That feedback comes back into the organization, giving the right people the right context at the right time, with AI wrapped throughout the whole process.”
Savant Systems, a developer of smart home automation and energy management technologies, is already demonstrating elements of that vision. By bringing customer, service and product information together on a shared Salesforce and Propel platform, the company created a unified data foundation supporting faster product launches, AI initiatives and improved customer service. The business reduced order-entry labor by fifty percent, shortened ecommerce time-to-market by seventy-four percent and now resolves around ninety percent of customer support requests through self-service tools, illustrating how connected product information continues delivering value long after products leave the factory.
Ultimately, the companies gaining the greatest advantage from modern product lifecycle management are not simply designing better products. They are creating better-informed businesses, where engineering decisions remain connected to every function responsible for turning technical innovation into lasting commercial success.
