Explore MES, Industry 4.0, AI, OT cybersecurity and energy efficiency, and see how they support connected manufacturing.
MES and manufacturing management systems
MES, or manufacturing execution system, is the software that fills the gap between the planning layer and the shop floor. The enterprise resource planning system above answers “what will be produced”, while field automation determines “how it will be produced”. MES answers “what was actually produced, how long did it take, where did it stop and which material was used”. MOM, or manufacturing operations management, extends this scope to include quality, maintenance and inventory processes.
Collecting data from manufacturing operations is the foundation of MES applications. Manually entered data is late, incomplete and optimistic; data obtained directly from the machine reflects reality. MES projects therefore usually begin with shop-floor connectivity: machine signals are read, stoppages are classified automatically and operators are asked only for information that the machine cannot determine.
Traceability is one of the most tangible outputs of MES. When the system records which raw-material batch was used for a product, on which machine, during which operator shift and with which parameters, a customer complaint can be limited to the relevant batch rather than becoming a recall of all inventory. In automotive, food, pharmaceutical and defence supply chains, this capability is often a prerequisite for becoming a supplier.
The most common mistake when implementing a manufacturing management system is reproducing existing paper forms on screen without reviewing the process. Execution, quality, planning and laboratory processes are addressed separately by Opcenter Execution, Opcenter Quality, Advanced Planning and Scheduling, and Research, Development and Laboratory solutions. Digitalisation is an opportunity to reconsider the process itself; merely digitising a form reproduces the same inefficiency on a screen.
Industry 4.0, the Internet of Things and the smart factory
Industry 4.0 describes an approach in which manufacturing assets are connected, data is collected in real time and decisions are made based on that data. The Internet of Things, or IoT, is its technical backbone: sensors, controllers and machines produce data over a network. The Industrial Internet of Things, or IIoT, adapts this concept to manufacturing environments.
A smart factory is more than a connected facility; it is a facility that turns collected data into decisions. A machine transmitting data does not by itself create a smart factory. The term becomes meaningful when exceeding a threshold opens a maintenance work order, the production plan is updated according to actual capacity, and a quality deviation is detected before it stops the line.
Digital transformation projects must follow a realistic sequence. Measurability comes first: if reliable machine data cannot be obtained, every layer built above it rests on assumptions. Visibility comes next; information must be presented in a form that decision-makers can understand. Autonomy is the final stage, where the system makes defined decisions without human approval. Projects that skip this sequence produce impressive dashboards but do not change behaviour on the shop floor.
A digital twin is the virtual counterpart of a physical asset or process. Simulating a new production line before commissioning allows the control software to be tested at a desk instead of in the field, significantly reducing commissioning time and field risk.
Artificial intelligence and industrial machine vision
Artificial intelligence takes tangible form in manufacturing primarily in three areas: visual inspection, predictive maintenance and process optimisation. They share a common characteristic: writing explicit rules is impractical, but providing examples is possible.
Industrial machine vision captures product images using cameras and lighting to perform measurement, reading or defect detection. Conventional image processing remains the most reliable method for clearly defined geometric checks, including dimensional verification, presence inspection and code reading. Deep-learning approaches are more effective for defects that are difficult to describe, such as scratches, stains and texture irregularities.
In machine-vision projects, lighting rather than the algorithm is often the decisive factor. Simple thresholding may be sufficient when a scene is illuminated at the correct angle and wavelength, while even the most advanced model can become unstable under poor lighting.
Predictive maintenance aims to detect the signals equipment produces before failure. It uses vibration, current signatures, temperature and acoustic data. Unlike calendar-based preventive maintenance, work is performed when it is needed, reducing both unnecessary maintenance costs and unexpected downtime. A meaningful model, however, requires data to be collected over a sufficient period and real failure examples to be labelled. Predictive maintenance is therefore not a one-off installation but a programme requiring continuity.
OT cybersecurity and operational technology protection
Operational technology, or OT, encompasses all systems that manage physical processes. Its fundamental difference from information technology is the order of priorities: confidentiality comes first in information systems, while continuity and human safety take precedence in manufacturing systems. Arbitrarily stopping a production line for a security update is not possible in most facilities.
Many field controllers use protocols designed without the assumption that they would be connected to the internet; most lack authentication and encryption. The first step in OT security is therefore an asset inventory: what is not known to exist on the network cannot be protected. See our cybersecurity services for complementary measures on the corporate network. Network segmentation follows. Separating the manufacturing network from the corporate network and the internet in layers prevents malware introduced through one client from stopping the production line.
Remote access is one of the most critical areas of OT security. Access requested by machine manufacturers for maintenance becomes a permanent open door when it is not controlled. Opening access on demand, recording sessions and revoking privileges when work is complete are essential for both security and accountability.
Process is as decisive as technology in industrial cybersecurity. Taking backups regularly and actually testing restoration provides more operational continuity than many security products.
Sustainability and energy efficiency
In manufacturing facilities, sustainability is no longer only a corporate objective but a commercial requirement. Participation in supply chains increasingly requires reporting energy consumption and carbon footprint per unit of product. Reliable reporting depends on measuring consumption at machine and production-line level.
Automation contributes to energy performance in two ways. The first is direct savings through speed control with drives, prevention of idling, monitoring compressed-air leaks and heat recovery. The second is visibility. When the energy used to manufacture each product on each line is known, production planning can also take energy cost into account.
Reducing waste and scrap is another measurable component of sustainability. Early warning reduces the size of a defective batch, while removing defective products within the process through machine vision prevents them from consuming additional resources at downstream stations.
