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Manufacturing & CPG Data & Analytics

Digital twin maturity and the reality of operational change

06/03/2026

by Kathryn Harrington and Gavin Watts

Most organizations investing in digital twins expect them to accelerate operational change. Increasingly, digital twins are emerging as practical change agents in complex operations, helping organizations see how work actually happens and adapt with greater confidence as conditions and priorities shift. However, for operators accountable for performance, reliability, and cost, digital twins sit on a thin line between “we’ll get to this” and “we may already be behind.”

For manufacturing and CPG leaders, the challenge is no longer whether digital twins can generate insight, but whether the organization can translate that insight into coordinated decisions and operational action quickly enough to create measurable value.

Digital twins do three things particularly well:

• Represent operations as interconnected systems rather than isolated processes
• Allow decisions to be rehearsed before commitments are made
• Provide feedback that helps organizations learn continuously, not just once

The business case for digital twins ultimately is less about pure visualization or prediction and more about enabling confident decision-making. That confidence depends less on the existence of a digital twin, but on its maturity and the organization’s ability to turn insight into coordinated operational change.

Digital twin maturity: From static models to living systems

Not all digital twins are created equal, and many organizations believe they are further along than they truly are. A useful way to think about maturity is through the decisions a twin enables.

• Descriptive twins answer the question “what is happening?” They often resemble dashboards and provide valuable visibility, but explanations or predictions still rely on the human assessment of the twin’s output.
• Diagnostic twins begin to explain “why it happened,” utilizing a solid understanding of the cause-and-effect relationships within the system.
• Predictive twins extend insights to “what will happen if…?” enabling scenario testing that is sometimes mistaken for simple forecasting but can be used to do retrospectives on past events as well.
• Prescriptive twins recommend actions—”what should we do?”—and remain rare and difficult to implement well, although AI models provide a way forward for managing even human considerations in systems.
• Autonomous twins move even further, executing decisions directly within defined boundaries. Building on classical control systems, these remain bleeding-edge and highly constrained.

For any stage, the risk isn’t necessarily the opportunity cost of not increasing maturity, but instead misalignment between expectations and capability. For example, when organizations treat a descriptive or diagnostic model as if it were predictive, or a predictive one as prescriptive, this introduces false confidence into decision making processes.

Understanding where a digital twin truly sits on this spectrum is less about your organization’s technical sophistication and more about objectivity.

Digital twins as change accelerators

The most profound effect of properly implemented digital twin technology is the ability to accelerate change. By reducing uncertainty, making trade-offs visible, and helping visualize difficult-to-synthesize data, they shorten decision cycles and lower the friction required to act.

In high-complexity environments, such as aerospace, large-scale infrastructure, or networked logistics, digital twins are often used to simulate cascading effects before physical changes are made.

These applications highlight a critical pattern: the faster organizations can see and trust the implications of a decision, the more frequently they are willing to adjust course.

This is where digital twins become change accelerators. They make experimentation safer, iteration faster, and lower the risk of reversal. However, acceleration cuts both ways. When insight outpaces governance, organizations risk increasing the pace of change without improving its quality or chances of success.

The operational reality: Why better insight doesn’t automatically mean better change

Despite improved visibility, operational changes remain as monumental as ever. Simply knowing a given change is desirable for an organization doesn’t change the complexity of making that change happen effectively. Digital twins surface complexity, not eliminate it.

In practice, digital twins often increase the speed and breadth of insight faster than they increase an organization’s capacity to absorb and govern change.

Many organizations discover that greater insights reveal hidden interdependencies that were previously ignored or managed informally. Depending on maturity, optimizing one part of a system may introduce unexpected costs or constraints in another. Feedback on the change may arrive too late, too narrowly, or in a form that decision-makers are unprepared to interpret. In some cases, having too many separate “right answers” from a model can slow decisions rather than speed them.

Equally importantly, although some bleeding-edge models are beginning to approximate this capability, many twins do not resolve human and organizational dynamics. Decision rights, incentives, and accountability still govern how change actually occurs. Without a clear understanding of who can act, under what conditions, and with what consequences, even the most accurate simulations risk remaining academic exercises.

In this way, digital twins increase the visibility of an organization’s change opportunities, but not necessarily their capacity to manage the change

Matching change strategy to digital twin maturity

Organizations leading the charge on effective twins recognize that different levels of digital twin maturity require different change strategies.

Digital twin maturity	Primary change risks	Change strategy focus
Descriptive	• False precision • Overconfidence • Top‑down mandates based on incomplete understanding	• Build shared understanding of the system • Invest in data trust • Emphasize transparency and education • Pace change conservatively • Validate improvements with frontline experience
Diagnostic	• Local optimization • Misreading correlations as levers	• Align stakeholders on system boundaries • Reinforce causal literacy • Test changes incrementally • Avoid over‑attributing outcomes to single factors
Predictive	• Analysis paralysis • Ambiguity on decision rights • Excessive option generation	• Clarify decision rights • Define which scenarios deserve action • Strengthen governance and escalation paths • Align planning cadence with operational reality
Prescriptive	• Responsibility gaps • Blind trust in recommendations	• Explicitly assign accountability • Define human override rules • Document decision logic • Invest in exception handling and auditability
Autonomous	• Loss of situational awareness • Weak exception escalation • Erosion of ownership	• Tight governance • Clear boundary conditions • Robust monitoring • Deliberate human‑in‑the‑loop design focused on supervision, learning, and ethics

Across all maturity levels, a unifying principle is that digital twins are most effective when paired with disciplined change enablement and management. The twin reveals and helps select possibilities, but organizations still need the mechanisms to choose and act.

As digital twins mature, the limiting factor shifts from insight quality to governance quality. The more authority a twin has, the more intentional an organization must be about how change is paced, owned, and corrected.

What decisions should be rehearsed before they’re made?

For operators, the right starting question is not “What can we model?” but “Which decisions are the most dangerous to get wrong?” These often include decisions where:

• Small changes produce cascading, system‑wide effects
• Feedback arrives slowly or ambiguously
• Human judgment currently compensates for limited visibility
• Reversing course is costly, disruptive, or reputationally damaging

Examples might include capacity reallocation across a constrained network, changes under variable environmental conditions, capital investments with long-term downstream consequences, and policy decisions that influence frontline behavior at scale.

In manufacturing and CPG specifically, this often includes new product introduction planning, where launch timing and inventory positioning decisions affect the broader supply chain; manufacturing network consolidation, where facility and line decisions shape long-term capacity and operational flexibility; and demand-driven replenishment model changes, where shifts in replenishment logic can create ripple effects across retail partners and internal operations before issues or imbalances become visible.

Digital twins shift these decisions from assumption‑based to testable. They allow leaders to ask “what if?” before reality asks it for them. But, that promise only holds when organizations are clear about who is allowed to act on simulated insight, under what conditions, and with what accountability.

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As twin maturity increases, so does the temptation to experiment more often, intervene more frequently, and compress decision cycles. The risk is not moving too slowly, but moving confidently in the wrong direction, or faster than the organization can coordinate.

The organizations getting the most from digital twins are not necessarily the ones with the most sophisticated models. They are the ones who have answered three questions with clarity:

Which decisions in our operation are too consequential to make without rehearsal?

Which require governance before acceleration?

Where should human judgement remain in the loop, even when automation becomes possible?

Answering those questions consistently is what separates organizations that merely have digital twins from those that use them to drive confident, coordinated change. This is where insight becomes action, and where disciplined change strategy determines whether that action succeeds. Connect with us to evaluate how your digital twin strategy can better enable action and deliver results.

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