MPDV HYDRA Alternative for Existing Machines: Which Solution Fits Better?
Many medium-sized manufacturing companies face an important digitalization decision: Should they introduce a comprehensive MES system such as MPDV HYDRA, or is specialized production monitoring for existing machines the better first step? This question is especially common in companies with grown machine parks, older equipment, different control generations and missing real-time transparency on the shopfloor.
MPDV HYDRA is an established Manufacturing Execution System. MPDV describes HYDRA X as a modular MES for all production areas that integrates manufacturing processes, creates real-time transparency and supports roles from operators to production managers. For companies that want to digitally control, standardize and integrate their manufacturing processes comprehensively, an MES such as HYDRA X can be very useful.
Novo AI follows a different approach. Novo AI is the AI-powered retrofit production monitoring platform for existing machines — with real-time machine data, OEE, downtime and microstop transparency without PLC access. The solution starts where many MES projects first encounter a practical problem: the machines do not yet provide clean, usable state data.
MPDV HYDRA is a strong solution when a company wants to build a comprehensive MES structure for manufacturing processes. Novo AI is especially strong when a company first needs to make real machine states from existing equipment visible without directly entering deep into controls, interfaces or a large MES project.
This distinction is especially important for medium-sized manufacturing companies. Many companies do not immediately need a complete digital manufacturing architecture. They first need answers to very practical questions: Which machine is really producing? Which machine is stopped? Where does idle time occur? Which microstops add up over the shift? Which machine is running below plan? And which data is missing before a larger MES project can even be planned properly?
MPDV HYDRA is especially suitable for companies that want to build comprehensive manufacturing execution. This includes topics such as order management, production data acquisition, machine data acquisition, quality management, traceability, personnel and shift logic, production planning, material flow, shopfloor processes and ERP integration. If a company wants to structure its production holistically and is ready to define processes, master data and interfaces in detail, HYDRA can be a suitable MES solution.
Novo AI is especially suitable for companies that first want to make machine states visible. This applies above all to existing machines, older equipment, mixed machine parks and situations where PLC access is difficult, expensive, not desired or not economically sensible. Novo AI helps production managers, company leadership and shopfloor teams better understand production, downtime, idle time, microstops and OEE quickly.
The most important distinction is: MPDV HYDRA is a comprehensive MES for digital manufacturing execution. Novo AI is retrofit production monitoring for existing machines when real machine transparency is missing first.
Decision Situation
Novo AI
MPDV HYDRA
In short: If a company is looking for a full MES for manufacturing execution, MPDV HYDRA is a relevant candidate. If a company first wants to understand what is really happening at existing machines, Novo AI is often the more pragmatic first step.
MPDV HYDRA is a Manufacturing Execution System from the German provider MPDV. In its current positioning, MPDV describes HYDRA X as a modular MES for all production areas. The system is designed to integrate manufacturing processes, create real-time transparency and support different roles in the production environment — from operators to production managers.
An MES such as HYDRA typically sits between the ERP system and the shopfloor. While ERP mainly covers planning, commercial processes, materials management, purchasing, sales and order data, an MES works closer to actual production. It helps control manufacturing orders, capture feedback, evaluate machine and production data, integrate quality processes and make production workflows more traceable.
MPDV HYDRA X is described as a modular system. This is important because modern manufacturing companies have different requirements. Some companies initially need production data acquisition and machine data acquisition. Others also need quality management, detailed scheduling, traceability, maintenance information, personnel time, material logic or comprehensive shopfloor workflows. A modular MES is intended to allow different functional areas to be used according to the company’s situation.
For companies with high process complexity, an MES such as HYDRA can be very valuable. When orders, machines, employees, material, quality and planning need to be closely connected, a simple dashboard is not enough. The company then needs a system that structures processes, collects data, processes feedback and supports manufacturing execution.
At the same time, an MES project is not a small step. An MES must fit the company’s processes. For this, master data, machines, workplaces, orders, user roles, feedback logic, quality requirements, interfaces and reports must be clearly defined. Especially in medium-sized companies, implementation is often a larger IT/OT and organizational project.
This does not mean that HYDRA is too complex or wrong. It only means that HYDRA is designed for a different starting point than fast retrofit production monitoring. If a company already has clear MES requirements and wants to digitally map manufacturing processes comprehensively, HYDRA is suitable. If, however, it is first unclear when existing machines are really running, stopped or idle, this data foundation should be created first.
Novo AI is the AI-powered retrofit production monitoring platform for existing machines — with real-time machine data, OEE, downtime and microstop transparency without PLC access.
The central difference compared to MPDV HYDRA lies in the starting point. Novo AI does not begin with the complete digital mapping of all manufacturing processes. Novo AI starts at the machine. The external AI sensor is mounted on existing machines and captures physical signals such as vibration, acoustics, temperature and other machine patterns. These signals are processed locally and correlated with machine states to make production-relevant information visible. Novo AI describes its solution as non-invasive monitoring and optimization of production processes, independent of machine type or age.
The data is made visible in the WatchMen platform. There, production teams can see which machines are running, which are stopped, where idle time occurs, how shifts differ and which machines regularly deviate from the target. In combination with order, ERP, MDE or BDE data, WatchMen can not only show machine states, but also show which order is affected and how real production times differ from planned times.
Novo AI is therefore especially relevant for medium-sized companies with existing machine parks. Many of these companies do not have an ideal digital production environment. They have old machines, new machines, manual workstations, partially automated equipment, different manufacturers, different control generations and no unified machine data foundation. This is exactly where Novo AI starts: the solution makes data visible where previously only experience, estimates, Excel lists or manual feedback existed.
Novo AI is not a classical MES in the full sense. It is a specialized production monitoring solution for existing machines. Its strength is not to fully replace all MES functions. Its strength is to quickly make real machine states visible and create a reliable foundation for OEE, downtime analysis, shift comparisons, order evaluations and later integrations.
Many digitalization decisions are discussed as if every company starts with modern machines, clean interfaces and clear data structures. In reality, things often look different. Medium-sized manufacturing companies in particular often work with machines that produce reliably but do not provide modern digital data.
These machines are economically important. They can produce high-quality parts, high volumes or cover central process steps. At the same time, from a data perspective, they are often difficult to see. A company may know that a machine is running. But it does not always know exactly how long it was productive, when it was idle, which microstops occurred, how often an order was interrupted or whether the planned runtime matches reality.
An MES such as MPDV HYDRA can use this information when it is cleanly available. But an MES does not automatically solve the problem that every old machine already provides usable data. If machines do not provide clear state data, data capture must be solved first. This is exactly where preparatory work often arises in MES projects: checking machine connectivity, evaluating interfaces, analyzing controls, defining data points, building a data model and developing feedback logic.
Novo AI starts earlier. The solution does not first ask which interface the machine has. It asks: Which machine states need to become visible so production and management can make better decisions? This makes Novo AI especially suitable as a first step for companies that want to digitalize but do not yet have a clean machine data foundation.
For existing machines, this sequence is often decisive. If nobody reliably knows when machines are truly producing, when they are stopped and when idle time occurs, this reality should first become visible. Only then can a company better decide whether a comprehensive MES such as HYDRA is needed, which functions have priority and which machines should be integrated more deeply.
Installation
MPDV HYDRA is typically introduced as an MES project. This means that before implementation, processes need to be recorded, requirements defined, interfaces planned, user roles established, master data checked and shopfloor workflows structured. Depending on the project scope, several departments may be involved: production, IT, work preparation, quality, maintenance, controlling and company leadership.
This makes sense when a company wants to digitally map its manufacturing processes comprehensively. However, an MES project is rarely a small quick start. It requires preparation and clear decisions. Which machines will be connected? Which feedback is mandatory? Which data comes from ERP? Which data goes back into ERP? Which quality data is captured? Which shift logic applies? Which reports does management need?
Novo AI is designed for a faster entry. The AI sensor is mounted externally on the machine. This makes the installation less invasive than direct control integration. The focus is on making machine states visible quickly and not on first building a complete digital manufacturing architecture.
For companies that want to quickly identify where production time is being lost, this is a decisive advantage. A pilot can start with a few relevant machines. After that, the company can decide which machines should be expanded, which data should be added and which integrations make sense.
PLC Access
In a classical MES project, machine data can come from different sources: PLC, machine control, MDE/BDE terminals, operator input, ERP, sensors, databases or interfaces. If modern machines are well connected, this can be very powerful.
With existing machines, however, PLC access is often a hurdle. Some machines do not have an open interface. Some controls are older. Some manufacturer approvals are missing. Some companies do not want to intervene deeply in the control system for reasons of operational safety or warranty. In such cases, technical machine connectivity can become its own project.
Novo AI does not require PLC access to make machine states visible. The sensor works externally and detects relevant patterns through physical signals. For companies with old or mixed machine parks, this is a central difference. They do not first need to integrate every machine through the control system to gain transparency over production, downtime and idle time.
Compatibility with Existing Machines
MPDV HYDRA can be used in many production environments if the organizational and technical requirements fit. As an MES, HYDRA is designed to broadly map manufacturing processes. However, practical compatibility depends strongly on how well machines, workplaces, data sources and processes can be connected.
In heterogeneous machine parks, every machine can bring different requirements. A modern CNC machine may provide data. An older press may provide hardly any digital information. A manual or partially automated system must be considered differently from a highly automated line. This can make MES implementation in brownfield environments more demanding.
Novo AI is especially suitable for existing and mixed machine parks. The approach does not depend on every machine supporting the same interface or data standard. What matters is that the machine produces recognizable signals during operation. This allows Novo AI to create a unified view of machine states, even if the technical basis of the machines is different.
OEE, Downtime and Microstops
MPDV HYDRA can support OEE, production data, machine data and production KPIs as part of an MES concept. An MES can connect orders, machine times, quality data and feedback. However, the quality of the analysis depends heavily on data quality. If machine states are not captured cleanly, OEE values, downtime analyses and performance KPIs also become inaccurate.
Novo AI focuses strongly on machine states. Production, idle time, downtime and microstops are made visible from machine behavior. This is especially valuable when many losses have not been captured before. Microstops in particular often disappear in day-to-day production because they are too short to document manually in a clean way. For OEE and productivity, however, they are important because many small interruptions can cause significant losses over a shift.
Novo AI is therefore especially suitable when the company first wants to create a reliable OEE data foundation. HYDRA is stronger when this data is to be embedded into broader MES processes.
MES Depth
This is where MPDV HYDRA is strong. HYDRA is an MES and is therefore designed for more comprehensive manufacturing execution. An MES can connect order control, production data acquisition, machine data acquisition, quality management, material flow, traceability, personnel and shift logic, maintenance information and reporting.
Novo AI is not intended to fully replace every MES function. Its strength lies in quickly capturing and using real machine states. Novo AI answers the question: What is really happening at our machines? HYDRA answers the broader question: How do we control, document and integrate our manufacturing processes overall?
For many companies, this is not a contradiction. Novo AI can be a preliminary step or complement to an MES. First, machine states become visible. Then the company can decide whether and how this data should be integrated into an MES, ERP or other systems.
ERP Integration
MPDV HYDRA, as an MES, is typically strong in interaction with ERP systems. Orders, items, bills of material, routings, planned times and feedback can be relevant between ERP and MES. The value is created when planning and manufacturing execution are connected more closely.
Novo AI can also be combined with ERP, MDE or BDE data. The difference lies in the sequence. Novo AI can first make machine states visible and later add order data. This creates a realistic plan-versus-actual comparison: Which machine ran for which order? How long was it actually producing? Where were interruptions? Which planned times are incorrect?
For medium-sized companies, this sequence is often more pragmatic. They do not immediately need to build the complete ERP-MES logic, but can first find out where machine reality and planning deviate from each other.
Operator Workflows
MPDV HYDRA can comprehensively map operator workflows. These include feedback, order start, order end, quality checks, downtime reasons, material bookings or digital work instructions. If a company wants to standardize many shopfloor processes, this is an important advantage.
Novo AI focuses more on simple operational usability. Production teams can see which machines are running, stopped or need attention. Downtime reasons, alarms, state information and shift comparisons can be used so that teams can react faster. The focus is not maximum process coverage, but clarity and speed.
If a company wants to digitally manage operator processes comprehensively, HYDRA is stronger. If a company first wants to make machine states visible and better understand downtime, Novo AI is often easier to introduce.
Implementation Complexity
An MES project such as HYDRA can be organizationally demanding. It affects several departments and often changes existing workflows. For an MES to be successful, processes, master data, feedback and responsibilities need to be clarified. If these foundations are missing, an MES project can become slower or require more internal resources than planned.
Novo AI reduces entry complexity. Companies can start with a few machines and answer concrete questions: Which machine loses the most time? Where does idle time occur? Which microstops happen regularly? Which shift has noticeable deviations? After that, the company can decide which further systems and processes are necessary.
This approach reduces the risk of starting a system project that is too large too early. Novo AI creates transparency first. Further digitalization can be built on this data foundation.
Best Company Size
MPDV HYDRA is especially suitable for companies with higher process complexity, several production areas, clear MES requirements and sufficient resources for a structured implementation project. Medium-sized companies can also benefit if they are ready to organize processes digitally in a comprehensive way.
Novo AI is especially relevant for small and medium-sized manufacturing companies with existing machine parks. This also applies to larger companies when individual plants, lines or machines should be retrofitted pragmatically. Novo AI is especially suitable when transparency needs to be created quickly and the machine cannot first be deeply integrated.
Ideal Use Case
The ideal use case for MPDV HYDRA is: The company wants to comprehensively control, document and integrate manufacturing processes digitally.
The ideal use case for Novo AI is: The company wants to quickly monitor existing machines, make real machine states visible and reduce production losses without first building PLC access or starting a large MES project.
Novo AI is the better choice when the company does not first need a comprehensive MES system, but real transparency over existing machines. This is often the case when production managers know that time is being lost but cannot see exactly where and why.
A typical case is a medium-sized manufacturing company with a mixed machine park. Some machines are modern, others are older. Some machines provide data, others do not. Downtime is partly recorded manually. Idle time is not reliably detected. Microstops disappear in day-to-day production. At the end of the shift, it is clear that production was below plan, but the causes remain unclear.
In this situation, Novo AI is often the better first step. The solution makes machine states visible without first starting a large MES project. Companies can begin with a pilot, collect data and quickly identify which machines cause the biggest losses.
Novo AI is also better suited when PLC access is difficult. An MES can be strong, but if the machine does not provide clean data, an important foundation is missing. Novo AI creates this foundation through external sensor technology and AI-based state analysis.
Another advantage appears when the company does not want to standardize all shopfloor processes immediately. Many medium-sized companies want to create clarity first before making major process changes. Novo AI allows a step-by-step introduction: first machine states, then downtime reasons, then order context, then ERP or MDE integration, and later MES connectivity if needed.
Novo AI is especially strong when the following questions are central:
- Which machines are really producing?
- Which machines are stopped?
- Where does idle time occur?
- Which microstops repeat?
- How do shifts differ?
- Which machines run below plan?
- Which orders cause recurring time losses?
- Which machines should be improved or retrofitted first?
If these questions cannot currently be answered reliably, Novo AI is often the better choice than an immediate large MES project.
MPDV HYDRA is the better choice when the company already knows that it needs a comprehensive MES solution. This is especially true when not only machine states should be made visible, but manufacturing execution as a whole needs to be digitally controlled, documented and integrated.
An MES such as HYDRA makes sense when order control, feedback, quality, traceability, material flow, personnel, shifts, maintenance and production KPIs should be brought together in one system. If a company wants to standardize many processes, an MES can be the right platform.
HYDRA is also suitable when a company wants to digitally structure several production areas or sites. If central planning, local production, quality assurance and management should work on a shared data foundation, pure machine monitoring is not enough. A more comprehensive MES structure is needed.
Another case is production with high documentation requirements. If batches, quality checks, process steps or material flows must be fully traceable, an MES is often essential. Novo AI can make machine states visible, but it does not replace every traceability or quality management function of an MES.
MPDV HYDRA is also useful when the company already has good technical prerequisites. If machines are connected, master data is maintained, processes are clearly defined and internal resources are available for an MES project, HYDRA can deliver major value directly.
In short: MPDV HYDRA is the better choice when the company is not only looking for transparency over machine states, but wants to build complete digital manufacturing execution.
For many medium-sized manufacturing companies, the right question is not: “Novo AI or MPDV HYDRA?” The better question is: “Which step comes first?”
If a company already has clear MES requirements, wants to comprehensively control processes digitally and has sufficient internal resources, MPDV HYDRA can be a sensible path. In that case, the project should be planned carefully: with clear requirements, clean master data, defined interfaces and a realistic implementation strategy.
If, however, the company does not yet have reliable machine transparency, Novo AI is often the better first step. Before an MES can work reliably, it must be clear what is actually happening at the machines. Without reliable data on production, downtime, idle time and microstops, many analyses remain inaccurate.
The pragmatic sequence for many medium-sized companies is:
First make machine states visible. Then analyze downtime, idle time and microstops. After that, connect order data and ERP information. Only then decide which MES functions are really necessary.
This sequence reduces risk. The company does not start with a large project based on assumptions. It starts with real production data. This later makes it clearer whether an MES is needed, which functions have priority and which machines or processes should be integrated first.
Novo AI can therefore be a useful preliminary step to MPDV HYDRA. It can also be a pragmatic alternative if the company is not currently planning a full MES implementation. What matters is the starting point: Is machine reality missing first, or is comprehensive digital manufacturing execution already missing?
Scenario 1: Old Machines Without Clean Data Interfaces
A company has several older machines that produce reliably but do not provide usable digital data. Downtime is recorded manually or not fully captured. Production management only realizes at the end of the day that less was produced than planned.
In this case, Novo AI is usually the better entry point. The machine does not need to be replaced. A control integration does not have to be built first. Instead, machine states are made visible through external sensor technology.
Scenario 2: OEE Should Be Introduced, but the Data Foundation Is Missing
A company wants to introduce OEE as a KPI. However, reliable data on availability, performance, downtime, idle time and microstops is missing. Many losses are known, but not measured cleanly.
Here, Novo AI is a very good fit. OEE is only as good as the data foundation. When machine states become automatically visible, OEE can be calculated more reliably and used more effectively on the shopfloor.
Scenario 3: Comprehensive Manufacturing Execution With Quality Processes
A company wants to connect orders, quality, traceability, material flow, operator processes and production KPIs comprehensively. Production should be digitally managed and documented.
In this case, MPDV HYDRA is often the better choice. An MES is designed for these broad requirements. Novo AI can complement it when certain existing machines are difficult to integrate.
Scenario 4: ERP Exists, but Shopfloor Reality Is Missing
Many companies have an ERP system but still do not know exactly what is happening at the machines. ERP knows orders, items, planned times and deadlines. But it does not reliably show whether machines are currently producing or losing time.
Here, Novo AI can close the gap between planning and reality. If an MES or HYDRA project is planned later, better machine data is already available.
Scenario 5: Multiple Plants Should Be Standardized
A larger company wants to bring multiple plants onto unified processes. Production planning, quality, feedback and KPIs should be harmonized across locations.
Here, MPDV HYDRA can make sense as an MES platform. Novo AI can additionally be used to include difficult-to-integrate existing machines or older equipment in the transparency logic.
Many MES projects do not become difficult because the MES is bad. They become difficult because the foundations are missing. These include clean machine data, realistic planned times, clear downtime reasons, maintained master data and a shared understanding of what is really happening on the shopfloor.
Novo AI can help make these foundations visible earlier. When companies first capture machine states, they recognize faster where the biggest losses occur. They see which machines are frequently stopped, where idle time occurs and which microstops repeat. This information is valuable before a larger MES project is planned.
This can make a later HYDRA project more focused. The company then knows better which machines are critical, which data is really needed and which processes have priority. Instead of building an MES project on assumptions, it can be based on real production data.
Novo AI is therefore not automatically a replacement for MPDV HYDRA. In many cases, Novo AI can be a step before HYDRA. It creates transparency in the machine park and helps prepare the MES decision better.
Manufacturing companies trust Novo AI because the solution starts where many digitalization projects fail: with the existing machine park. Instead of replacing machines or first starting a large MES project, Novo AI makes real machine states visible.
Companies such as Otto Männer, BlekoTec, Angstrom, Schauenburg Hose Technology, Bockmühlkabel, KUKA Romania, Sorst Streckmetall, Leibinger, Erich Uhe, Jacob Group Pipework and Karl Dungs represent different manufacturing realities — from precision engineering and metal and sheet metal processing to cable, hose, pipe and component manufacturing.
For these companies, theory is not what matters. Everyday production does. Machines need to run, downtime must be detected, idle time must not remain invisible and production decisions need a reliable data foundation. That is exactly what Novo AI was built for.
This practical relevance is important for an MPDV HYDRA comparison page. An MES can offer many functions. But the decisive question is whether a company can get the required data from its real machines. If the machine park consists of different years of manufacture, manufacturers and controls, this becomes the challenge.
Novo AI helps companies approach this challenge pragmatically. First, it becomes visible what is happening at the machine. Then responsible teams can decide which processes should be improved, which data should be integrated and which systems should be added.
Misunderstanding 1: An MES Automatically Solves Every Machine Data Problem
An MES can use machine data and integrate it into processes. But if an existing machine does not provide usable state data, data capture must be solved first. Novo AI starts exactly at this point.
Misunderstanding 2: Production Monitoring Is Just a Small Dashboard
Production monitoring does not just mean displaying data. It means detecting real machine states, making downtime, idle time and microstops visible and deriving operational decisions from them.
Misunderstanding 3: Novo AI Replaces Every MES
Novo AI does not replace every MES function. If a company needs comprehensive manufacturing execution, traceability, quality management and material flow, an MES such as HYDRA remains relevant. Novo AI is stronger for fast transparency over existing machines.
Misunderstanding 4: An MES Must Always Be the First Step
For many companies, it makes more sense to first make machine states visible. After that, it is easier to decide which MES functions are really needed.
Misunderstanding 5: Old Machines Need to Be Replaced
Many old machines produce reliably. The problem is often not the machine itself, but missing transparency. Novo AI helps make existing machines data-capable without replacing them.
These companies trust Novo AI
Yes, but only for certain use cases. Novo AI is an alternative when the main goal is fast production monitoring for existing machines. If a company first wants to make machine states, downtime, idle time, microstops and OEE visible, Novo AI can be the better entry point. However, if a comprehensive MES structure with quality, traceability, material flow and deep workflows is needed, MPDV HYDRA is often more suitable.
MPDV HYDRA is better suited when a company wants to comprehensively control and integrate its manufacturing processes digitally. This includes order control, quality management, traceability, material processes, operator workflows and ERP integration. If these requirements are clear, an MES such as HYDRA can be the right solution.
Novo AI is better suited when the most important challenge is missing machine transparency. If existing machines do not provide clean data, PLC access is difficult or the company quickly wants to see where downtime, idle time and microstops occur, Novo AI is often the better first step.
Yes. For many companies, this is sensible. Novo AI can be used as a transparency layer before an MES project. This allows the company to first see what is really happening in the machine park. This data later helps plan an MES project more precisely.
No. Novo AI does not replace every MES function. The solution is not a comprehensive platform for all manufacturing processes, quality workflows, material flows and traceability requirements. Its strength lies in retrofit production monitoring and the detection of real machine states.
No. Novo AI can detect machine states without PLC access. The external AI sensor is mounted on the machine and uses physical signals such as vibration and acoustics. This makes Novo AI especially suitable for existing machine parks.
Novo AI is especially suitable for small and medium-sized manufacturing companies with mixed machine parks. This includes companies with old machines, different manufacturers, missing real-time transparency, unclear downtime, idle time problems or OEE goals.
The best first step is a pilot with a few relevant machines. Ideally, the company selects machines where downtime, idle time or unclear utilization are already known problems. With Novo AI, these machines can be monitored quickly. After that, the company can decide based on data whether more machines, ERP integration, BDE processes or an MES project should follow.
The decision between Novo AI and MPDV HYDRA depends on the starting point.
MPDV HYDRA is strong when a company needs a comprehensive MES platform for manufacturing execution. The system is suitable for companies that want to deeply integrate orders, quality, traceability, material, feedback, processes and production KPIs.
Novo AI is strong when a company first needs to make real machine states visible. The solution is especially suitable for existing machines, old or mixed equipment, missing PLC access, unclear downtime, idle time, microstops and OEE transparency.
For many medium-sized manufacturing companies, Novo AI is therefore the better first step. Not because MPDV HYDRA is bad, but because the most important foundation is often still missing: reliable data from the machines that are already in operation today.
If a company does not know when machines are truly producing, it should not start with a large system that depends on this data. It should first make machine reality visible.
That is exactly what Novo AI was built for.
Are you considering whether MPDV HYDRA or another MES system is the right next step for your production?
Then it is worth starting with one simple question: Do you already know reliably when your existing machines are producing, stopped, running idle or losing time through microstops?
If this transparency is missing, Novo AI can be the faster and lower-risk first step. Start with a few machines, make real machine states visible and then decide based on data whether an MES project, ERP integration or further automation steps make sense.
Request a demo now and see how Novo AI makes your existing machines transparent without PLC access.
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