Industry 4.0 has become a crowded term—often used to mean very different things depending on who’s speaking. That isn’t just semantics. It’s why many initiatives deliver impressive dashboards, impressive pilots, and impressive capital spend… yet struggle to produce durable operational gains at scale. McKinsey has described this as “pilot purgatory,” where digital manufacturing activity is high but bottom-line impact fails to materialize broadly. (McKinsey & Company)
If we strip the buzzwords away, most definitions of Industry 4.0 reduce to a promise: use connected technologies to improve industrial performance. The conflict is how that happens—through visibility, automation, ERP-centric planning, or something else entirely.
This article assesses the three common “camps,” then argues that operational governance and control—closed-loop execution with enforced decision-making and accountability—is what manufacturers are actually buying, even when they don’t say it that way.
The Three Camps of Industry 4.0 (and what each is really optimizing for)
1) The Connectivity / Real-Time Visibility Camp
Their definition of Industry 4.0 is connect machines + processes, capture real-time status, calculate KPIs, and expose operational truth quickly.
This camp optimizes "Awareness speed", the ability to know what is happening right now (downtime, pace, WIP, scrap, utilization, OEE). I understand this camp because I've been delivering it for fifteen years. The premise for success was that with this real-time information, it would enable local responsiveness, allowing supervisors and engineers to react faster to abnormalities. Relating to this data was supposed to produce a standardized measurement, where shared numbers reduce argument over “what happened.”
What this camp gets right
- Visibility is foundational—especially when data quality is poor. Many manufacturers still report major data issues and fragmentation that directly block improvement and innovation. (Hexagon)
- Without reliable “truth signals,” every downstream system (ERP planning, quality, scheduling, maintenance) becomes a debate.
Where this camp alone commonly fails to deliver value:
- Dashboards don’t change outcomes by themselves. A screen can show the line is late, but it doesn’t translate into actionable results. It is a dynamic billboard providing visibility to the chaos on the shop floor. The result is a familiar anti-pattern: more metrics, same execution.
- At scale, this becomes exactly what McKinsey flags: siloed implementations and disconnected digital teams that don’t translate to sustained operational performance. (McKinsey & Company)
Bottom line: visibility is necessary—but not sufficient. It’s the “sense” layer, not the operating system.
2) The Automation / “Remove the Human” Camp
Their definition of Industry 4.0 is the implementation of robotics, automated material handling, lights-out production, and AI-driven process control, thus reducing labor dependence and variability.
What this camp is optimizing for is repeatability, with fewer human-induced deviations. In theory and non-scaled practice, it proposes throughput stability: predictable cycle time and yield, and labor substitution: offset shortages, wage pressure, and skill gaps.
What this camp gets right
- There are real macro drivers: labor scarcity, rising labor costs, and competitiveness pressure. (Many manufacturers are actively planning automation expansions over the next few years.) (Mmh)
- Automation can be an enormous win when applied to the right constraint and stabilized process.
Why is automation adoption is slower than the hype.
- Capital intensity + integration complexity. Automation isn’t just buying robots—it’s fixtures, guarding, process engineering, maintenance capability, programming, spare parts, and re-layout. The “last mile” of OT/IT integration is often the hardest part of scaling Industry 4.0. (McKinsey & Company)
- Fragility risk: automating a poorly governed process often accelerates waste, not value. Automation projects have uncovered an inherent problem: inflexibility. The automation cell does not adapt well to changes in product mix and SOP.
- Economics are uneven: high-mix / low-volume plants rarely get the same ROI profile as high-volume repeatable lines.
A critical truth: automation amplifies the underlying operating model. If governance is weak, automation can institutionalize bad decisions faster.
3) The ERP / “System of Record” Camp
Their definition of Industry 4.0: push accurate shop-floor truth into ERP/MRP/APS so planning becomes correct, and ERP remains the one source of truth for the enterprise.
What this camp is optimizing for is "Plan integrity": schedules reflect reality (actual cycle times, availability, yields, constraints). By adding a layer of real-time data and trending metrics, they hope to achieve "Enterprise coordination": improvements across purchasing, customer promise dates, inventory, costing, and capacity planning align. They also expect "Auditability": traceability and compliance are easier when records are unified.
What this camp gets right
- ERP is excellent at enterprise reconciliation—orders, inventory, finance, purchasing, and customer delivery commitments. Better “actuals” reduce systemic planning error.
What ERP-centric Industry 4.0 often misses
- ERP is not designed to run minute-by-minute execution. ISA-95 formalizes this separation: Level 4 (ERP) plans the business; Level 3 (Manufacturing Operations Management / MES) runs operations—dispatching, execution, performance, quality, maintenance, and personnel coordination. (isa.org)
- When you try to make ERP behave like an execution governor, you typically get:
- Net effect: ERP becomes more entrenched, but operational performance may not move proportionally—because the missing piece is execution enforcement, not recordkeeping.
Bottom line: ERP truth helps, but it doesn’t automatically create operational control.
Why “Data Alone” Hasn’t Delivered the Promised Value
My claim matches what many transformation postmortems say (even if they phrase it differently):
- Industry 4.0 value isn’t blocked by sensors—it’s blocked by the operating model.
- Technology pilots fail to scale when execution, ownership, change management, and governance aren’t built into the system. (McKinsey & Company)
- Data governance and security foundations matter, but they still don’t equal operational governance (decision enforcement). (KPMG Assets)
This also aligns with the worker/competence perspective in the research literature: transformation depends heavily on the workforce’s competencies and the socio-technical system, not technology alone. (Taylor & Francis Online)
So if visibility ≠ value, and automation is expensive and uneven, and ERP truth ≠ execution… what are manufacturers actually seeking?
The Governance Camp: Industry 4.0 as Operational Control
Operational governance is the missing layer that turns Industry 4.0 from information into control.
A practical definition:
Operational Governance (Control) is the system of enforced decision-making that ensures the plan is executed, exceptions are owned, actions are verified, and learning is institutionalized.
It’s not “more meetings.” It’s closed-loop execution.
The closed-loop model: Sense → Decide → Execute → Verify → Improve
Most Industry 4.0 deployments stop at Sense (connectivity + dashboards). Governance completes the loop:
- Sense (truth signals): machine + labor + quality + material status
- Decide (authority & rules): who decides, using what policy, at what threshold
- Execute (action workflow): dispatch changes, holds, escalations, approvals
- Verify (proof of execution): was the action done, did it work, what changed
- Improve (institutional learning): update standards, routings, training, constraints logic
This is why ISA-95’s Level 3 (MOM/MES) matters: it’s the operational layer where dispatch, execution, performance, quality, and resource coordination live. (isa.org)
What “Governance-First Industry 4.0” Looks Like in a Plant
Here are the concrete capabilities that make governance real (and measurable):
1) Decision enforcement (not optional dashboards)
- Every exception creates a required decision with:
2) Process holds and gates that actually stop bad outcomes
- Quality hold means the job cannot advance until disposition.
- A safety event means the asset cannot restart until verified.
- Material not kitted means dispatch cannot release.
3) Plan vs actual is not a report—it’s a governed contract
- If the plan changes, the system forces:
4) Feedback that changes the system, not just the shift
- If a routings’ standard times are wrong, governance captures evidence and routes it to engineering with priority, not “someday.”
- If a constraint is repeatedly starved, it triggers a systemic countermeasure, not another dashboard.
5) Accountability that is fair and operationally useful
This is crucial: governance is not “blame.” It is:
- clear decision rights,
- clear expectations,
- and clear learning loops.
Why this is the real “need” manufacturers search for
A manufacturer rarely says, “I want operational governance.” They say:
- “We don’t execute the schedule.”
- “We don’t know who owns the miss.”
- “We have too many fires.”
- “We keep fixing the same problem.”
- “ERP says one thing, the floor does another.”
Those are governance failures—not data failures.
And the World Economic Forum’s Lighthouse framing repeatedly emphasizes that success requires the right capabilities and governance to scale—not just deploying technologies. (World Economic Forum)
Industry 4.0 is not a race to instrument everything or automate everything. It’s a race to control outcomes:
- Visibility without enforcement becomes “metric theater.”
- Automation without governance becomes expensive fragility.
- ERP truth without execution control becomes audited fiction.
The winning posture is governance-first:
The plant that wins is the plant that can consistently remove ambiguity in execution—by enforcing decisions, verifying actions, and institutionalizing learning.