Why Digital Investments Don't Deliver: The Planning-Execution Gap

An observation reinforced in recent industry discussions, including at Hannover Messe 2026.

Manufacturing analyst relations leader Duncan Chapple recently observed in his Hannover Messe 2026 analysis that only a handful of companies could clearly articulate the measurable outcomes they've delivered for customers.

That observation is accurate—and important.

But it points to a deeper issue. The problem is not that outcomes aren't being measured. The problem is that most manufacturing environments are not structured to execute reliably in the first place.

Industry Perspective (Duncan Chapple)

Duncan Chapple interviews Aaron Muhl, who shares his Hannover Messe 2026 observations on why so few manufacturers can clearly articulate measurable outcomes—and why that matters.

Source: Duncan Chapple, Analyst Relations Leader at Elisa Industriq, in conversation with Aaron Muhl, Co-Founder & Managing Partner at On Time Edge.  → Read Duncan's LinkedIn article, "What On Time Edge teaches industrial software providers about selling outcomes"

The Planning-Execution Gap: Why Digital Investments Stall

Over the last decade, manufacturers have made substantial investments in ERP, MES, APS, and an expanding ecosystem of digital technologies. From a systems perspective, many of these environments appear integrated: data flows, interfaces are in place, and connectivity has largely been achieved.

Yet despite this progress, a different reality continues to play out on the shop floor.

Planning systems are designed to define intent. They establish what should happen—production targets, material flows, and timelines aligned to business objectives. Execution systems, by contrast, capture what is happening "right now" — machine states, operator actions, disruptions, and variability inherent to real operations.

The challenge isn't that either side is failing in isolation.

It's that they're not working in concert.

Between planning and execution sits the production schedule. In theory, it should serve as the mechanism that translates intent into action. In practice, it's often unstable: frequently adjusted, difficult to trust, and disconnected from the day-to-day realities it's meant to govern.

This is where performance begins to erode.

Not because manufacturers lack systems, but because the critical link between planning and execution—the system responsible for making decisions executable and outcomes reliable—remains fragmented and dependent on manual translation. In many environments, ERP generates a plan, and schedulers are left to reinterpret it—often in spreadsheets—into something the plant can actually run. That process is time-consuming, difficult to maintain, and inherently unable to respond to real-time conditions on the shop floor.

Production Scheduling: The Control Layer Between Planning and Execution

In many organizations, production scheduling is still treated as a downstream activity—a step that simply translates planning outputs into a sequence of work. It is positioned as operational detail rather than as a driver of performance.

In practice, that assumption does not hold.

As plans move closer to execution, they encounter the realities of the shop floor: constraints that were abstracted at the planning level, resource conflicts, variability in cycle times, and disruptions that cannot be predicted in advance. It is at this point that the production schedule takes on a different role. It becomes the mechanism that determines whether intent can actually be carried out.

It is the control layer that must:

• reflect real-world constraints

• respond to disruptions

• coordinate across planning and execution systems

• drive what actually happens on the shop floor

When scheduling consistently performs this role, it connects planning decisions to execution outcomes in a way that is both stable and responsive. When it does not, the gap between what was planned and what is achievable widens, and the system begins to rely on manual intervention, workarounds, and constant adjustment.

In that environment, even well-integrated systems cannot deliver reliable performance. The issue is not the presence of technology, but the absence of a control layer capable of turning coordination into execution.

Why "Staying in the Room" Matters

Duncan correctly pointed out that many vendors implement manufacturing systems and move on, while others remain engaged over time. That distinction is real, but it does not go far enough to explain why performance so often fails to materialize.

Ongoing presence, by itself, is not the differentiator. Extended involvement does not inherently produce better outcomes if the underlying operating model remains unchanged. The critical question is not how long a partner stays, but what is established and reinforced during that time—specifically, whether the organization is structured to execute decisions reliably.

Sustainable performance requires:

• clear decision ownership

• alignment across planning, scheduling, and execution

• a schedule that is operationally authoritative—not constantly overridden

These conditions are not achieved through implementation alone, nor are they guaranteed by continued support. They require deliberate design of how decisions are made, how they propagate through systems, and how they are upheld on the shop floor.

For that reason, the issue is not best understood as a deployment problem. It is an execution accountability problem—one that determines whether systems and processes ultimately translate into consistent, measurable outcomes.

Measurement Only Works If the System Can Execute

Measuring outcomes at the outset of a digital transformation—and continuing to measure them over time—is necessary. It provides visibility, establishes benchmarks, and creates a mechanism for accountability. However, measurement alone does not produce better results.

Too often, organizations assume that if outcomes are defined and tracked, performance will follow. In reality, measurement only reveals what the system is capable of delivering. If the underlying environment is unstable or misaligned, measurement will simply make that instability more visible.

Performance comes from:

• systems that are interoperable

• data that is reliable and actionable

• decisions that propagate consistently across the environment

These elements form the foundation that allows performance to emerge and sustain. Without them, even the most rigorous measurement framework becomes descriptive rather than transformative—it documents issues but does not resolve them.

When this foundation is in place, measurement takes on its intended role. It no longer exposes inconsistency; it reflects a system that is functioning as designed, where decisions translate into predictable, repeatable outcomes.

Depth Matters—But It Must Be Structured Correctly

Another insight from the article is the emphasis on depth over breadth. That direction is sound, but it is frequently interpreted too narrowly.

Depth is not about limiting scope or doing fewer things. It is about ensuring that the expertise applied is appropriate to the problem being solved. In manufacturing, that distinction matters. Operating environments are not interchangeable—discrete and process industries function in fundamentally different ways, with different constraints, decision cycles, and definitions of success. Even within those categories, industry-specific realities shape what can and cannot be executed effectively.

When those differences are not accounted for, solutions may appear viable in theory but fail under real operating conditions. The result is inconsistency—plans that cannot be executed as intended, systems that require constant adjustment, and outcomes that vary from site to site.

Effective execution requires:

• domain-specific expertise (process vs. discrete)

• industry context

• teams structured to match the operating reality

These elements ensure that decisions are grounded in how operations actually function, not how they are assumed to function. Without them, capability remains abstract, and performance remains uneven.

The Pattern Behind the Few Who Deliver

Duncan's core observation remains valid: very few organizations consistently demonstrate measurable outcomes. The gap is not explained by a lack of technology. Most manufacturers have already made investments in a digital strategy and achieved at least some degree of connectivity.

What differentiates those who realize outcomes from those who do not is a set of choices—how the organization structures its operations, aligns its systems, and enforces accountability in execution.

It is a set of choices:

• prioritizing execution over implementation

• aligning systems and data to support real-world decisions

• treating scheduling as a control layer, not a byproduct

• establishing accountability across planning and execution

• applying domain expertise with precision

None of these principles are new. They are well understood across the industry. The difference is that they are rarely applied consistently and in a disciplined way. Where they are, measurable outcomes follow. Where they are not, performance remains variable regardless of the technology in place.

What This Means for Manufacturers

When evaluating the current environment—or planning the next wave of investment—it is common to focus on technology selection. The conversation quickly turns to which system to implement next, what capabilities are missing, or how to extend the existing stack.

That line of thinking, while understandable, often leads organizations away from the real issue.

The more consequential questions are not about adding systems, but about understanding how effectively the current environment translates decisions into action. Specifically, where breakdowns occur, whether plans can be executed, and whether the operating model supports the plant's pace and variability.

The more important questions are:

• Where does our schedule break between planning and execution?

• Is our schedule executable under real-world conditions?

• Do our systems and data support decisions at the pace of the plant?

• Is there clear ownership of how decisions translate into action?

These questions shift the focus from capability to execution. They examine whether the organization is structured to deliver outcomes with the systems it already has, rather than assuming that additional technology will resolve underlying issues.

They are not, at their core, technology questions. They are execution questions—and they determine whether digital investments ultimately deliver measurable performance or continue to stall.

Reliable performance requires designing for execution, not adding more technology

The industry does not lack tools, data, or ambition. What is often missing is the ability to consistently translate those assets into reliable performance. That capability does not reside in a single system or application. It emerges from how planning, scheduling, execution, data, and decisions are aligned—and whether that alignment holds under real-world, day-to-day operating conditions.

Organizations looking to close this gap should focus on a small number of structural priorities:

1. Design the operating model so that scheduling functions as an execution control layer, not a downstream artifact.

2. Align systems and data to support real-time, plant-level decision-making, rather than simply enabling visibility.

3. Establish clear ownership for how decisions move from planning through execution, so that accountability does not dissipate between systems.

4. Apply domain and industry-specific expertise to ensure that solutions reflect the realities of the environment, not abstract best practices.

Until these elements are deliberately addressed, outcomes will continue to vary—regardless of how advanced or connected the underlying technology landscape appears.

Frequently Asked Questions

What is the planning-execution gap in manufacturing?

The planning-execution gap is the disconnect between systems that define intent — production targets, material flows, and timelines — and systems that capture what is actually happening on the shop floor. Performance erodes when these two layers operate in isolation rather than in concert. The production schedule sitting between them is the control point where the gap most often appears, because in most environments it is unstable, manually maintained, and disconnected from real-time conditions.

Why is production scheduling considered a control layer rather than a downstream task?

In most organizations, scheduling is treated as the act of sequencing work after planning is complete. In practice, the schedule is where planning intent meets shop-floor reality — constraints, resource conflicts, cycle-time variability, and disruptions. When scheduling reflects real-world conditions, responds to disruptions, and coordinates across systems, it becomes the mechanism that determines whether plans can actually be executed. That makes it a control layer, not an operational byproduct.

Why do most manufacturers struggle to demonstrate measurable outcomes from digital transformation?

The gap is rarely a technology gap. Most manufacturers have already invested in ERP, MES, APS, and connectivity. The shortfall is structural: planning systems and execution systems are connected, but the schedule between them is unstable, manually maintained, and disconnected from real conditions. Without a stable control layer, measurement reveals inconsistency rather than driving performance.

What is the difference between connectivity and execution in a smart factory?

Connectivity means data flows between systems and interfaces are in place. Execution means decisions actually propagate to the shop floor and are carried out reliably under real conditions. A factory can be fully connected and still fail to execute, because connectivity alone does not establish decision ownership, schedule stability, or accountability.

Why doesn't measurement alone improve manufacturing performance?

Measurement is necessary but not sufficient. It only reveals what the current environment is capable of delivering. If systems are misaligned or the schedule cannot be executed under real conditions, measurement will document the inconsistency rather than resolve it. Sustained performance comes from interoperable systems, reliable data, and decisions that propagate consistently — measurement reflects that foundation rather than creating it.

What does depth over breadth mean for manufacturing software implementation?

Depth is often misread as a narrower scope of work. The more accurate interpretation is that expertise must match the operating reality of the environment. Discrete and process industries function differently, with different constraints, decision cycles, and definitions of success. Solutions that ignore those differences may appear viable in theory but fail under real operating conditions.

What questions should manufacturers ask before investing in more manufacturing technology?

Rather than asking what system to implement next, manufacturers should evaluate how reliably the current environment translates decisions into action. Specifically: Where does the schedule break between planning and execution? Is the schedule executable under real-world conditions? Do systems and data support decisions at the pace of the plant? Is there clear ownership of how decisions move into action? These are execution questions, not technology questions.

What separates the manufacturers who consistently deliver measurable outcomes?

Not the technology stack. It is a set of deliberate choices: prioritizing execution over implementation, aligning systems and data to real decisions, treating scheduling as a control layer, establishing decision accountability across planning and execution, and applying domain-specific expertise. These principles are well understood across the industry but rarely applied consistently — which is why measurable outcomes remain rare.