Hundreds of decisions are made every day in manufacturing, from early-stage design selections to day-to-day production planning. The vast majority of these choices are made without access to a complete set of data and knowledge or a clear view of how siloed decisions impact the rest of the operation over the course of the product lifecycle.
Traditionally, engineering has focused on design performance and quality, while procurement prioritizes supplier pricing and availability. Meanwhile, operations is responsible for throughput and delivery. These groups may work closely together, but they aren’t working toward the same outcomes. Each function is measured on its own priorities and KPIs. This approach often leads to organizations causing additional headaches for their own teams.
For companies that operate this way, short-sighted planning and objectives often trump long-term organizational strategy. Quarter-by-quarter performance takes precedence over building the integrated and aligned capabilities that will prepare the organization for the years ahead.
If they want to respond effectively to new pressures like AI, electrification, and autonomy, however, manufacturers will have to find ways to make faster, more coordinated decisions. And it starts with connecting the data that supports those decisions, combined with the experiences and knowledge of team members involved in those decisions.
Create the Conditions for Connected Decisions
Connecting engineering, procurement, finance, supply chain, and operations information gives teams a common understanding of cost, supply, risk, and operational reality, so they can make decisions with the full business impact in mind. That’s not how most manufacturers operate today.
Drawings, specifications, cost data, supplier information, contracts, and planning records live in different systems and formats. There’s no shared, reliable view of data. Workers spend valuable time looking for, validating, and reconciling information before they can take any action. This leaves no time for employees to think about improving the work itself. Teams can’t see dependencies until late in the process, so they waste even more time and money resolving problems after a decision is made. A design choice could affect material availability and supplier costs. A sourcing decision might affect production schedules, performance levels, and margins. But no one knows until it’s too late.
On a scale of one to 10, the pain of disconnected data is probably a three or four for most manufacturers today. In three to five years, the pain factor is predicted to increase to seven or eight. So what do you do before that happens? Take action, or wait and see how things play out? In my experience, the companies that win are the ones that start solving the problem instead of just understanding it or seeing the impacts on performance.
It is a little like preventive medicine. If you run the checks early, you catch the problem before it blows up. If you wait until it hurts, you have a much harder story to tell, and recovery is much more complicated. The years ahead will be more demanding than today, and manufacturers need to adapt their operating culture to become more resilient, integrated, and agile.
To turn disconnected information into a shared asset that helps teams see those tradeoffs before they create costly problems, five foundational elements must be in place:
- A clear vision for where the organization needs to go with its data and how it supports the organization's strategy
- Alignment of senior leadership and support from them as work moves forward
- A cross-functional project team that can represent the functions affected
- An accountable leader to guide the work and remove obstacles
- A practical plan to build capability gradually, from basic data readiness to more complex use case
Bring the Right People into the Work
While all five of these elements are important, connected data ultimately comes down to the people who use it. Employees need to be involved in data initiatives from the beginning, and they need a clear picture of what’s ahead so they can get excited about the improvements these initiatives will bring.
To build a cross-functional project team that supports data work, bring people from engineering, procurement, supply chain, operations, aftermarket, and finance together. Some organizations call this their Center of Excellence, established to manage the maturity steps and introduce standard operating procedures (SOP) required to leverage and scale a shared data foundation.
This group can help:
- Determine which decisions need better information (and where that information lives).
- Identify which outcomes matter most across the organization and why.
- Pinpoint which processes must change to successfully support these outcomes without creating new obstacles.
- Redirect resources toward creating incremental value vs. administrative tasks.
- Strengthen organizational effectiveness by working towards common vs. competing objectives.
I have been on both sides of this. Over nearly 20 years working with this class of software, I learned that the technology was never the hard part. Back then, the biggest challenge was the operating procedures, the alignment, and the goal setting; getting the functions to agree on how they were going to target and achieve a common set of objectives before we automated anything. When we standardized the process first, people got more efficient the more they used it, and second, people experienced less tension in their day-to-day work. When we first bought the tool and hoped the process would follow, it did not. Standardize the process, then connect the data, then apply the technology — and for sure monitor for learning or improvement opportunities that will likely surface with the cross-functional teams involved being empowered.
As data initiatives roll out, the people who’ll be impacted should have a say in the design; let them pressure-test the new workflows, too. This not only creates a sense of ownership but also reduces resistance, gives teams confidence that this change will address the problems they’ve been dealing with for years, and empowers teams to support organizational objectives.
Make Institutional Knowledge Usable
Experienced engineers, planners, buyers, and category managers hold years’ worth of accumulated knowledge. Valuable details and decision histories are even buried in drawings and documents that are not always available to support future decisions.
This risk grows every year. CADDi's 2026 Study found that 79% of manufacturers cite the skilled labor shortage as their biggest external challenge, a pressure that intensifies as experienced workers retire. But, with AI available, many manufacturers assume that using AI-enabled tools is the shortcut to making this knowledge available. And it can be — but before it can produce useful guidance, AI needs access to this knowledge first, translated into clean, connected source information. Then, parallel processes must be put in place to ensure the data elements are managed with standardized, known methodologies. As experienced employees retire, their firsthand insights must be captured to feed these AI systems. Otherwise, recreating the context behind past design, sourcing and cost decisions will become almost impossible. And AI won’t be able to help.
Connected Data: An Operational Advantage
The future will be more challenging than today. Manufacturers need to find ways to start optimizing their operational culture to be more resilient, integrated, agile and effective.
Connecting disparate sources of information is the way to build that kind of culture. When every employee is working from the same starting point, this is the operational advantage that will separate the frontrunners and help them serve customers in ways that are hard to match.
And if you build the five foundational elements and bring the right people into the work, be prepared: You’ll likely see success early and be asked to build on that momentum. I saw this firsthand during a European pilot project. When the CFO learned that a digitization project's internal rate of return had reached 73% — a 31-point improvement over the business-case projection of 42% — he immediately asked what could be done to accelerate implementation. The benefits they were already receiving made the company want more, quickly.
For manufacturers that want to turn decades of disconnected paper documents into actionable intelligence, CADDi’s whitepaper will help identify the best place to begin and deliver results that impact the entire value chain.
About Greg Toornman
Greg Toornman is a recognized thought leader in global supply chain management, recently retiring as AGCO Corporation’s Global Vice President of Sales & Operations Planning/Execution and Supply Chain. Over his two‑decade tenure beginning in 2004, he led transformative, award‑winning initiatives that modernized AGCO’s global value chain by driving digital integration, supplier collaboration, and operational excellence across every region. Having lived and worked in the USA, Brazil, and Switzerland and spent large amounts of time in China, Greg brought a unique international perspective to AGCO’s operations, enabling culturally attuned leadership and accelerating innovation in planning, logistics, cost optimization, manufacturing, customer experience, and aftermarket support. His global experience reshaped AGCO’s end‑to‑end supply chain into a more resilient, data‑driven, and customer‑centric ecosystem, strengthening performance and competitiveness worldwide.
About CADDi
CADDi is a global manufacturing technology company driven by the vision to accelerate physical innovation tenfold. Headquartered in Chicago and Tokyo, the company was founded in 2017 by industry veterans Yushiro Kato and Aki Kobashi, formerly of McKinsey and Apple. CADDi currently serves manufacturing businesses in more than 20 countries, including many of the largest publicly traded companies in Japan and the United States. To learn more, visit caddi.com or follow the conversation on LinkedIn.
