From supply chains to ecosystems: The role of flow and modularity in manufacturing

In the first two blogs in this series, “The new manufacturing mandate,” and “Preparing the enterprise for AI-led transformation,” I explored how manufacturers are building greater resilience amid continuous uncertainty and why scaling AI requires a more adaptive enterprise foundation.

The next challenge extends beyond the enterprise. Competitive advantage will depend on how effectively manufacturers connect with suppliers, logistics providers, customers and technology partners to make better decisions and respond as conditions continuously change.

External signals matter, too. Changes in demand, weather events, trade barriers, tariffs and geopolitical developments can quickly affect manufacturing operations. Organizations that also bring those signals into decision-making will be better positioned to respond.

For manufacturing leaders, I believe two capabilities will increasingly shape that advantage: flow and modularity. Flow is the ability to move information, materials and decisions across the network with fewer barriers. Modularity is the ability to evolve technologies, capabilities or relationships without redesigning the entire network.

Together, they provide a way to think about the next stage of manufacturing transformation, creating the conditions for shared visibility, trusted collaboration and increasingly shared capability.

Supply chains are becoming value networks

Manufacturers have spent decades optimizing supply chains for efficiency, cost and just-in-time operations. Recent disruptions have exposed the limits of optimizing for efficiency alone.

Our 2026 Voice of Our Clients findings underline the pressure. Sixty-eight percent of manufacturing executives say shifts in global trade and economic order are having a high impact on their organizations.

Manufacturers are responding by increasing end-to-end visibility, diversifying sourcing and regionalizing supply networks. They are also using AI to improve forecasting and support faster decisions as conditions change.

Looking toward 2030, supply networks are likely to become more predictive and increasingly autonomous, using AI, digital twins and real-time collaboration to anticipate disruption and respond more effectively.

The result is a shift from linear supply chains toward adaptive value networks. Resilience will depend on how well the wider network can respond, not only on the strength of an individual enterprise.

Digital shape and trust determine ecosystem readiness

Information must move across organizational boundaries for these networks to work. At the same time, manufacturers need confidence in what they share and how others can use it.

Our research highlights the challenge. Fifty-four percent of manufacturers report having a holistic data strategy, yet only 48% say they can effectively locate their data assets. Eighty-four percent prioritize cyber risk assessment when selecting IT providers, while only 36% consider their IT/OT data convergence mature.

These figures point to a broader issue. Ecosystem participation requires what I call the right “digital shape”: the ability to connect through modular platforms, manage data securely, strengthen IT/OT convergence, and collaborate without losing control or sovereignty.

Manufacturers cannot participate effectively if fragmented systems or inconsistent data keeps them isolated. Nor can they build trusted relationships if they cannot assess the security of their partners and providers.

The leadership challenge is therefore not simply how to connect more systems. It is how to connect selectively and securely while maintaining the governance and control each participant requires.

AI and smarter data ecosystems

Data spaces offer one approach. They create governed environments in which organizations can exchange agreed data while retaining control over its use.

Applied effectively, they can support traceability, planning, sustainability reporting and faster responses to disruption, forming part of a new operating model for modern manufacturing.

While trusted data creates visibility across manufacturing networks and ecosystems, AI helps organizations act on that information.

Manufacturers can use AI to identify disruption earlier, anticipate variability and coordinate responses across planning, procurement, production and logistics.

Our research shows that 66% of manufacturers are already applying AI in core business and operational processes, and 73% are exploring agentic AI.

The next wave of AI value won't come from smarter analysis inside functional silos. It will come from orchestrated response across the network: connecting planning with procurement, production with logistics and visibility with faster decisions.

And the horizon extends beyond AI. Emerging technologies such as quantum computing could eventually expand what is possible in optimization, simulation, materials science and supply network design.

But it's not fully clear when, or how, these technologies will mature enough to act on. That uncertainty can create hesitation. Waiting too long can slow progress, but committing too early to a rigid architecture can limit future choices.

Manufacturers therefore need the kind of foundation that lets them introduce capabilities progressively as the technology and business cases mature. Modularity gives them a way to adapt without betting the entire architecture on trying to predict what comes next.

The next step requires shared risk, shared reward and trusted guidance

The next phase of ecosystem transformation will increasingly depend on shared risk and shared reward. Manufacturers will need partners that can help modernize foundations, shape AI strategies, strengthen security, support organizational change and scale execution without simply shifting the risk back onto the enterprise.

Talent pressures reinforce this shift. Forty-four percent of manufacturers report significant difficulty recruiting IT talent. Forty-one percent say talent shortages are having a high business impact, while 71% expect to rely substantially or fully on providers for IT services over the next three years.

This is more than a case for outsourcing. It is a strategic shift toward partner-enabled capability, where organizations can access specialist expertise while retaining the knowledge and accountability that differentiates the business.

In practice, this means reengineering how governance, architecture, security, adoption and value creation come together as manufacturers become more interdependent with their partners.

Next-generation manufacturing leaders will master flow and modularity

The manufacturers that lead won’t be those with the best factories, the lowest costs or the most AI pilots. They will be the ones who can participate confidently in trusted ecosystems and adapt as those ecosystems evolve.

This raises a different question for today's leaders: are we optimizing individual parts of the business, or creating the conditions for value to move across the network?

The answer will depend on how well manufacturers have built the foundations described above. Can information move when decisions depend on it? Can new capabilities be introduced without rebuilding the architecture? Can partners collaborate without compromising control?

These are practical tests of flow and modularity, and they point to a broader shift: leadership is no longer only about optimizing what happens inside the enterprise, but about creating the conditions for value to move beyond it.

If you are exploring how these changes are reshaping your manufacturing network, I would welcome the opportunity to compare perspectives.