Data intelligence drives total cost of ownership in Industry 4.0

Read the article in Manufacturing Dive

By Philip Carpenito, Industry Advisor & Former Chief Procurement Officer at L3Harris Commercial Aviation

We are in an era of extended supply chain uncertainty. Is the Strait of Hormuz open as you read this? Will it be open tomorrow?  What will the next geopolitical issue bring?

Companies need to get comfortable with uncertainty and implement real-time software to help manage it. Preparing for a new technological era under these global constraints means extending digital transformation beyond the production environment to become more knowledgeable, agile and flexible in procurement, engineering and overall supply chain. This enables companies to truly understand the Total Cost of Ownership (TCO).

Industry 4.0 is often defined by automation and connectivity on the manufacturing floor. In reality, it depends on how decisions are made across procurement, engineering, and supply chain. Bringing the level of data visibility required for Industry 4.0 procurement decisions and processes is even more critical than modernizing manufacturing operations. It provides the connected data foundation that AI needs to generate intelligence for faster, better sourcing decisions. From a procurement standpoint, the challenge is the inability to see supplier capability, cost history, and part requirements in one place when decisions have to be made. This data is also necessary when designing new products to ensure the best cost at launch and a robust long-term supply chain.

Organizations that empower more functions with component data intelligence move away from reactive crisis management and into sustained operational efficiencies backed by data-driven decisions. That said, improving upstream tools and systems is only part of the answer. The leaders running procurement have to evolve, too.

Why Procurement Can’t Afford to Be Left Behind

Sixty percent of procurement professionals report sourcing parts at higher costs than necessary due to insufficient component and supplier data for effective negotiations. In practice, procurement teams operate under time pressure, often without a complete view of supplier performance, cost history, or prior part sourcing decisions.

Similar to other upstream functions, procurement teams remain burdened by siloed systems, outdated tools, static reports and spreadsheets that don’t match future-forward Industry 4.0 changes in decision-making. Most procurement professionals often must search across multiple ERPs, PLMs and shared drives to answer basic questions about suppliers, pricing, and parts history. This is sometimes referred to as “search tax”; that time loss translates into slower quote cycles, delayed product launches and higher costs when people can’t see what’s been purchased, from whom, and for what.

Bringing procurement into the digital transformation is beyond critical for two key reasons:

• Decision blind spots. What slows procurement down is the time it takes to find, validate, and trust the information needed to make a decision. For many manufacturers, decades’ worth of information about which suppliers can handle specific parts, where they are located, why quality issues tend to surface, and what trade-offs have been made often live only in the memory of key decision-makers within the organization. Without capturing this context, organizations will repeat past mistakes. Increase cost, lead times, and supplier qualifications
• Knowledge loss. When seasoned employees retire, those insights leave with them unless they’re captured, digitized, and democratized. Digitizing data is no longer a nice-to-have but an absolute necessity, and must be accomplished before it is no longer available. Digitizing procurement intelligence preserves institutional knowledge and reduces the need to reinvent the wheel.

Just like operations teams need real-time visibility into production, procurement teams need real-time visibility into factors that drive sourcing decisions. Visibility is now a foundational requirement for running a global manufacturing business. You have to know what’s going on with suppliers, costs, and risks if you want to run your organization. In the end, it comes down to communication. Procurement decisions require a clear view of engineering requirements, supplier capability, and historical outcomes. This is what Industry 4.0 looks like in procurement.

Implementing AI for procurement enables it to operate strategically. Organizations that unify procurement intelligence can reclaim this lost time and redirect it toward supplier collaboration, cost optimization, quicker time to market for new products and overall innovation.

A More Complex Role Demands Better Data

In Industry 4.0, procurement is no longer a downstream function. It is part of how cost, risk, and supplier decisions are managed in real time.

• The chief procurement officer or CPO’s mandate has shifted drastically from placing POs and chasing cost reductions to managing a lack of manpower, tariffs, supply chain disruptions, supplier concentration, global trade shifts, logistics and risk exposure across regions. Every sourcing decision is a trade-off between cost, supply, lead time, and risk. The challenge is making that trade-off without full visibility into what has already been done before.
• Modern procurement teams function as intelligence hubs informing executives on decision-making.

This all plays out in a world where data can change every hour on the hour; where supplier conditions, costs, and availability shift constantly — often faster than teams can validate the data behind their decisions.  It has never been more important for the CPO to understand the financial health of their suppliers and the immense logistics challenges that now define global sourcing. Without better tools and connectivity, these procurement leaders are being asked to manage modern risks with yesterday’s infrastructure.

With this level of responsibility and exposure. The new CPO has become a strategic advisor to the executive team and is responsible for designing resilient global supply chains and networks rather than simply managing purchasing transactions. This is exactly why the technology and data environment must catch up and support these new responsibilities

Closing the Digital Disconnect in Total Cost of Ownership

If fragmented systems and workarounds prevent procurement from using AI effectively, the first step is to consolidate data into a single, unified view. For many businesses, that means building a data warehouse that connects the core systems procurement depends on, so teams can access part numbers, part specifications, pricing, supplier, and cost context in one place.  This includes enterprise resource and demand planning software, quality systems, product lifecycle management tools, bills of materials, and engineering drawings.

Cleaning up and consolidating data does more than simply break down silos; it also creates a foundation for AI-powered tools to turn that unified data into actionable data intelligence. Once data is connected, AI can be deployed to support faster, smarter decisions and total cost. Teams describe what they need, and the system surfaces the information in minutes. Industry 4.0 data is consolidated and built to support decisions at the speed required by supply chain operations.

Connected data helps procurement, engineering, quality, and manufacturing to tap into real-time supplier intelligence instead of working from snapshots in time. For instance, when a labor strike hits one region or a natural disaster threatens production in another, you can quickly see which suppliers and parts are exposed, along with possible alternatives. The question is not just who can supply the part. It is who has supplied it successfully before, what location they are in, under what conditions, and at what cost. It also supports predictive cost and supply risk insights to provide a forward-looking view of where material prices or lead times will be heading in the future.

The result of these insights is better communication and faster sourcing decisions. AI can connect historical sourcing data — such as pricing, supplier performance, delivery, and defect history — to drive data-driven outcomes. Teams know who their best or approved suppliers are, where costs are headed, and how to stay within budget before decisions become too expensive to reverse. This reduces the need to re-source known parts, revisit prior decisions, or absorb unnecessary cost from incomplete information. You have a system to share with your supplier; your engineer can share design tools and capabilities. This collaboration helps optimize sourcing and supplier collaboration.

Crucial Steps to Effectively Embrace the Fourth Industrial Revolution

Adopting a system of insight can accelerate sourcing decision velocity, improve supplier selection, collaboration, and enable early cost optimization. AI software partner selection is critical to ultimately ensure the technology adoption within procurement and the full supply chain workflow. You’re looking for a technology adviser who can provide not only implementation but also training and ongoing support. This will help manufacturers reduce sourcing cycles, achieve cost savings and improve quality.

Verify that their platform will integrate with the systems you run without a disruptive overhaul. And finally, prioritize ease of use — a system that intimidates users won’t be adopted, and unused systems produce no value. The data you need to work smarter already exists within your organization. You just need to put it to work. The lack of integration makes it difficult for teams to get a unified view of operations and can delay decision-making processes. Yet, redefining operational strategies can drive substantial cost savings.

To see what this might look like, CADDi offers a free procurement playbook that defines 11 actionable strategies and a data-driven approach to start driving Total Cost of Ownership. Investing in a data intelligence platform isn’t like cutting a PO, putting it in a drawer, and calling it a day. These systems directly affect how data is stored, and how procurement decisions are made and a new system will be as effective as your strategic approach to gain buy-in at all levels of the organization.

Finally, Industry 4.0 in procurement is not defined by systems. It is defined by how decisions are made. Cost control depends on how quickly and confidently teams can act with complete, connected information.

What is it costing you to operate without the full picture?

Author Bio

Philip Carpenito is a Strategic Advisor and Consultant at CADDi and former Chief Procurement Officer at L3Harris Technologies Commercial Aviation. He brings more than three decades of global supply chain and procurement leadership across complex manufacturing environments and industries. His experience spans procurement, operations, NPI, supply chain, subcontracts, and program management, where he has led large-scale initiatives focused on cost control, supplier strategy, supplier relationship and operational performance. He currently advises organizations on how to strengthen decision-making across engineering, procurement, and supply chain functions. He has also held leadership roles at Nature’s Bounty, Motorola, Raytheon and Bose.

About CADDi

CADDi is an international technology company that develops AI-powered data intelligence platforms for manufacturing, engineering and design. Headquartered in Tokyo and Chicago, the company was founded in 2017 by industry veterans Yushiro Kato and Aki Kobashi, formerly of McKinsey and Apple. CADDi brings AI technology expertise to help manufacturers preserve decades of engineering and design knowledge. Recognized globally for innovation, CADDi was listed in Fast Company’s Most Innovative Companies and received the SaaS Award for Best Business Intelligence and Engineering Management Software. To learn more, visit us.caddi.com/company