Industry 4.0 Maturity Index: 6 Stages Explained
At Core, we believe the Industry 4.0 Maturity Index represents the north star for modern manufacturing - a practical roadmap to help organizations evolve from disconnected, reactive operations to intelligent, adaptive enterprises.
This framework doesn’t just inform how we support digital transformation for our clients - it’s embedded into the very architecture of our platform. Whether we’re integrating legacy systems, deploying real-time analytics, or implementing adaptive automation, each solution we build helps manufacturers move deliberately along this path toward full digital maturity.
The Index defines six successive stages of maturity, from basic computerization to fully adaptive, autonomous operations. Here's a brief explanation of each stage and a reference to the source:
1. Computerization
At this stage, companies equip operational resources with computers. Most modern machines and production equipment already include basic computer interfaces.
Key points:
- Machines and systems have digital controls.
- Data collection may exist, but it is mostly siloed and not integrated.
- IT is primarily used to automate repetitive tasks and replace manual processes.
Layman takeaway: You have digital tools, but they don’t yet talk to each other or provide a full view of operations.
2. Connectivity
Connectivity means linking machines, equipment, and systems through networks—wired (Ethernet) or wireless (Wi-Fi, LTE).
Key points:
- Data can flow between devices and systems.
- Integration may be one-way (read-only) or two-way (read/write).
- Systems are connected, but full coordination across the plant may not yet exist.
Layman takeaway: Your machines are “on the network,” but data is not yet used to make integrated decisions.
3. Visibility
Once connected, companies start seeing what is happening in real-time across their production processes. Sensors track temperatures, pressures, feed rates, cycle times, and other operational parameters.
Key points:
- Real-time monitoring is established.
- A digital model of production (“digital shadow” or “digital twin”) provides a live snapshot of operations.
- Visibility is about knowing the current state, not yet why it is that way.
Layman takeaway: You can watch your operations in real-time and know exactly what is happening on the shop floor.
4. Transparency
Transparency goes deeper—it’s understanding why things are happening. Data is analyzed in context to uncover correlations between variables.
Key points:
- Combine sensor data with business systems like ERP or MES.
- Use analytics to interpret process performance.
- Identify bottlenecks, inefficiencies, or patterns that affect quality.
Layman takeaway: Not only can you see what is happening, but you can explain why it is happening.
5. Predictive Capacity
At this stage, companies use historical and current data to anticipate future outcomes.
Key points:
- Predictive models estimate what will happen next in operations.
- Forecast maintenance needs, production bottlenecks, or quality issues.
- Simulate scenarios before making decisions.
Layman takeaway: You’re no longer reacting—you’re proactively planning based on predictions.
6. Adaptability
The highest stage is adaptive operations, where systems can autonomously adjust to changing conditions.
Key points:
- IT and OT systems act automatically to optimize production.
- Adjustments can include setpoints, scheduling, or resource allocation.
- Decisions are made based on plant-wide context, cost-benefit analysis, and operational rules.
Layman takeaway: The plant responds automatically to changes, without waiting for human intervention.
How We Help Manufacturers at Every Stage
Our platform provides tools and support to help manufacturers progress through these six stages:
Visibility
We deliver real-time dashboards and clear visualizations of all plant operations, so even companies with no digital overview can instantly see machine states, production metrics, and operational KPIs.
Transparency
Through contextual analytics, correlations between process variables are highlighted. Users can explore why certain readings occur and make informed decisions, with no need for advanced data science expertise.
Predictive Capacity
We employ machine learning to forecast future outcomes, from equipment failure to production throughput. This helps manufacturers anticipate issues and make smarter planning decisions before problems occur.
Adaptability
Our manufacturing operating system harmonizes multi-source data streams from across the plant, providing a single access point for business logic, rules, and workflows. Automated actions are triggered based on real-time, plant-wide context—enabling dynamic adjustments to setpoints, schedules, or resource allocations.
Summary: Whether a manufacturer is just starting with basic computerization or already exploring predictive and adaptive operations, our platform offers the visibility, insight, and automation needed to advance to the next stage, helping the entire plant operate smarter and more efficiently.
Reference: acatech - Industrie 4.0 Maturity Index