Confused or just looking to get to grips with smart industry terminology? In this glossary, we define the most important terms you need to understand and explain how they differ from each other—whether you’re a plant manager, production line operator, factory owner, or student.
Application Programming Interface (API)
Computerized Maintenance Management System (CMMS)
Condition Monitoring / Condition-Based Monitoring
Condition-Based Maintenance (CBM)
Dark Factory / Lights-out factory
Digital Product Dossier / Digital Product Passport
Enterprise Asset Management (EAM)
Enterprise Resource Planning (ERP)
Application Programming Interface (API)
Software that defines functions, protocols, and data formats to allow different software applications to communicate with each other.
Artificial Intelligence (AI)
A technology that enables computers and machines to simulate human intelligence. It aims to performs tasks by applying human learning, comprehension, problem solving, decision making, and creativity.
Machine Learning (ML) is a subset of AI that enables systems to learn from data and improve performance without being explicitly programmed.
Augmented Reality (AR)
A technology that enhances interaction with the real-world environment by overlaying extra digital information, usually via visuals or sound. This is in contrast to Virtual Reality (VR) which is completely virtual.
Cloud computing
The practice of delivering computing services, such as data storage, management, and processing via the internet. This takes places with remote servers, instead of on local on-premise servers or on a personal computer.
Computerized Maintenance Management System (CMMS)
A CMMS is a software solution designed to digitize and automate maintenance management in industry. It helps manufacturers plan, track, and optimize maintenance activities by managing work orders, scheduling preventive maintenance, monitoring equipment performance, and tracking spare parts inventory.
It is digital version of a Maintenance Management System (MMS).
Condition Monitoring / Condition-Based Monitoring
Terms used to refer to the monitoring of machinery to detect anomalies and identify breakdown risks, as part of a Proactive Maintenance strategy.
These terms are often used to refer to maintenance proactive strategies, but strictly speaking monitoring itself is passive until it is applied to maintenance; in which case we use terms like Condition-Based Maintenance.
Condition-Based Maintenance (CBM)
A type of Proactive Maintenance strategy based on the real-time condition of a machine or part. When the condition of equipment falls outside predefined parameters it can be repaired or replaced before failure occurs. Condition-Based Maintenance used to be dependent upon experienced mechanics who could tell, often by sound or sight when maintenance was needed. Today IIoT platforms like innius take CBM to the next level with digital monitoring in real-time, and automatic alerts and tasks based on predefined thresholds.
Unlike Corrective Maintenance, CBM reduces unplanned downtown and potential further damage to other parts if failure occurs. And unlike Preventative Maintenance it focuses upon parts which indicate they need replacing, rather than replacing parts simply because guidelines recommend this.
Corrective Maintenance Also known as ‘Reactive Maintenance’, this is a maintenance strategy which occurs as a response to a fault being detected, and either needs to occur immediately or can be deferred until a more suitable time. This can result in costly unplanned machinery down-time; and is usually the most inconvenient form of maintenance, when compared to Proactive maintenance strategies.
Cyber-Physical Systems (CPS)
Integrated systems that combine computation, networking, and physical processes to enable advanced automation. These systems play a key role in smart manufacturing and Industry 4.0.
A practical example could be a factory using innius to monitor production lines, track machine performance, and integrate with other digital systems for real-time insights. Here, the “cyber” part consists of software, cloud computing, and analytics, while the “physical” part includes machines, sensors, and automation systems working together.
Dark Factory / Lights-out factory
A concept whereby a Smart Factory is automated to such an extent, that human intervention is not required for normal operations. It can therefore operate on its own with the lights out. These factories use advanced robotics, AI, and IoT-driven systems to handle production, quality control, and logistics.
Data Lake
A centralized storage system that holds vast amounts of structured and unstructured data. Insights are gained using data analytics, including machine learning and/or artificial intelligence.
This approach contrasts to targeted data gathering, where data is gathered to answer specific business questions and avoid the issues of gathering data which is never used.
Data Point
A single unit of data collected from a sensor.
Data Stream
A continuous flow of data points.
In the context of innius these come from a machine or PLC. The intervals at which the data points are sent is the sensor frequency.
Digital Product Dossier (DPD) / Digital Product Passport (DPP)
A digital record that contains comprehensive information about a product’s lifecycle. It enhances traceability, compliance, and sustainability in manufacturing.
Digital Transformation
Digital Transformation refers to adoption of digital technologies across all aspects of a business’s operations. In the context of Industry 4.0, Digital Transformation is where manufacturing companies work towards becoming Smart Factories as they adopt cyber-physical systems, the Industrial Internet of Things (IIoT), big data analytics, cloud computing, and artificial intelligence.
Digital Transformation goes beyond just implementing new technology, as it requires changes to business models and organizational culture to fully benefit from data-driven operations.
Digital Twin
A virtual model of a physical machine, part, process, or system that enables real-time monitoring and predictive analysis. Digital twins enable users to model how physical assets are currently performing and may perform in different scenarios. This help predict and prevent downtime and aids decision-making.
Edge Computing
A decentralized computing model where data processing occurs close to the source—such as on industrial devices, gateways, or local servers—rather than relying solely on cloud computing. This reduces latency, optimizes bandwidth usage, and enables real-time decision-making in industrial and IoT applications.
In practice this usually works together with cloud computing, in a hybrid-model.
Enterprise Asset Management (EAM)
A comprehensive management strategy of an organization’s physical assets throughout their lifecycle, from acquisition and operation to maintenance and disposal. EAM systems integrate tracking, maintenance, performance monitoring, and compliance management to optimize equipment reliability, reduce costs, and improve operational efficiency.
While both CMMS and EAM manage maintenance tasks, Computerized Maintenance Management Systems (CMMS) is specifically focused on maintenance scheduling, work orders, and spare parts tracking. EAM extends beyond maintenance to include asset lifecycle management, financial tracking, and compliance management.
Enterprise Resource Planning (ERP)
ERP software is a system that integrates and manages core business processes within an organization, such as production, supply chain, finance, and human resources. By centralizing data and workflows, ERP software improves efficiency, decision-making, and overall operational visibility. The innius platform can complement ERP systems by providing real-time production insights.
Gateway
An IoT Gateway is a bridge between different networks and communciation protocols. In Industrial IoT applications this is usually a hardware device, containing software to enable device-to-device or device-to-cloud communication. Gateways do everything a modem does but also handle protocol translation and secure data routing between different systems. In an industrial setting, this means enabling compatibility between machines, sensors, and cloud platforms that use different communication protocols (e.g., OPC UA, MQTT, Modbus, etc.). Connecting to the innius platform is usually via gateways such as Ewons and Ixons.
Industrial Internet of Things (IIoT)
IIoT refers to the use of connected sensors, industrial devices, and cloud-based software to collect, process, and analyze real-time data in industrial environments. By integrating machinery, vehicles, and control systems into a network, IIoT enables process optimization.
IIoT platforms, like innius, work alongside Supervisory Control and Data Acquisition (SCADA) systems, which monitor and control machine-level processes, and Manufacturing Execution Systems (MES), which manage production workflows. IIoT enables manufacturers to gain a deeper understanding of how their production lines are actually performing, and facilitates smarter decision-making as they transition to smart factories.
Industry 4.0 (4IR)
Also referred to as the 4th industrial revolution, the term ‘Industrie 4.0’ originates from the German federal government’s industrial strategy for the digital transformation of its manufacturing sector. The anglicized version of the term has now become associated with the wider implementation of cyber-physical systems and the Industrial Internet of Things (IIoT) within industry across the globe.
The term Industry 4.0 refers to the way this concept is viewed as the 4th industrial revolution; the 1st of which was the mechanization which started in the 19th century, the 2nd was the implementation of systems and standards associated with mass production in the first half of the 20th century and the 3rd revolution was in the latter half of the 20th century and characterized by the implementation of computerisation and automation.
To some extent this is now being superseded, at least in industry literature, by Industry 5.0.
Industry 5.0 (5IR)
An evolution of Industry 4.0 that emphasizes human-machine collaboration, customization, sustainability, and resilience. It focuses on using advanced technologies while shifting priorities from economic value to environmental and social responsibility.
IO-Link
A standardized, point-to-point industrial communication protocol (IEC 61131-9) that connects sensors and actuators to industrial networks. It enhances machine monitoring and control by enabling bidirectional real-time data exchange, remote configuration, and real-time diagnostics.
LoRa / LoRaWAN
LoRa (Long Range) is a wireless modulation technique designed for low-power, long-range data transmission between industrial sensors and devices. It operates on unlicensed radio frequencies and enables reliable connectivity for remote sensors and devices, even in challenging factory environments.
LoRaWAN (Long Range Wide Area Network) is the network protocol that operates on LoRa, defining how devices communicate with gateways and cloud-based applications. It enables scalable, secure, and bidirectional data exchange, making it ideal for remote monitoring, predictive maintenance, and industrial IoT solutions. The development and standardization of LoRaWAN are managed by the non-profit LoRa Alliance.
Machine as a Service (MaaS)
A business model whereby machine users pay for machine usage rather than for ownership. Usually the machine builder provides this service, an example of business servitization. The machine builder takes responsibility for machine uptime and maintenance, and is therefore incentivized to provide high levels of reliability. Remote machine monitoring is therefore critical to be able to measure usage, conduct proactive maintenance and remote diagnostics.
Machine Learning (ML)
A subset of AI that enables systems to learn from data and improve performance without explicit programming. In manufacturing, ML is used for predictive maintenance, quality control, and process optimization. Unlike AI, it is focused on specific tasks, without trying to mimic human intelligence.
Machine to Machine (M2M)
Direct communication between machines or devices without human intervention. It enables automated data exchange and control, typically in closed, point-to-point systems. Unlike IIoT, which integrates cloud computing and analytics, M2M focuses on localized automation.
Make-to-Order (MTO)
A production strategy where products are manufactured only after receiving a customer order. This reduces storage costs, and can allow greater customization options. This may lead to longer lead times, and so requires a highly reliable supply chain, production line and delivery service.
In order to reduce unplanned downtime to a minimum, Proactive maintenance strategies are required alongside real-time monitoring and production line analysis.
Make-to-Stock (MTS)
A production strategy where products are manufactured in advance and stored until sold. This ensures quick order fulfilment but requires accurate demand forecasting. Customization is limited because the products are already made. It also requires expensive warehouse facilities for storage, especially if this is cold storage. The warehouse can act as a buffer to cope with changes in demand and unreliable supply chains, production facilities and delivery services.
Manufacturing Execution System (MES)
Is a software system that monitors, tracks, and controls production orders through the production line process in real-time.
Whilst it can be used as a standalone system, it is usually integrated to bridge the gap between ERP (Enterprise Resource Planning) systems, which handle business functions like inventory management, financial planning, and orders; and SCADA systems, which monitor and control machine-level processes.
While ERP generates production orders based on business needs, MES translates these into detailed workflows, scheduling tasks, allocating resources, and ensuring compliance with quality standards. MES can also integrate with IIoT platforms, like innius, to gather real-time feedback as to the actual performance of production equipment, to adapt planning as required.
Message Queuing Telemetry Transport (MQTT)
MQTT is a lightweight publish-subscribe messaging protocol designed for IoT and industrial automation. It enables efficient, real-time communication between devices and cloud-based platforms. Due to its low bandwidth usage and reliability, MQTT is widely used in IIoT environments, and the innius platform includes an MQTT Connector. It supports remote monitoring, Proactive maintenance, and Machine-to-Machine communication.
Modem
A modem (modulator-demodulator) is a hardware device that enables internet or network connectivity by converting digital data from factory systems into signals suitable for transmission over communication lines (e.g., cellular (4G/5G) , fiber, or DSL) and vice versa.
In industrial applications, modems have been largely superseded by gateways.
Overall Equipment Effectiveness (OEE)
An industry standard Key Performance Indicator (KPI) used to measure the output compared to the potential capacity of manufacturing operations. OEE can be defined as the actual amount of specification compliant product manufactured, compared to the amount which could theoretically have been manufactured, for a stated period and presented as a percentage. It can be applied to individual machines or processes or complete production lines.
OPC UA (Open Platform Communications Unified Architecture)
OPC UA is an industrial communication protocol designed for secure, standardized, and scalable data exchange between machines, systems, and software applications. It operates on a client-server or publish-subscribe model, enabling interoperability between devices such as PLCs, SCADA, and MES. It is maintained by the non-profit OPC Foundation.
Unlike MQTT, which is optimized for lightweight, cloud-based IIoT applications, OPC UA provides a structured, context-rich approach to industrial automation, supporting built-in security, data modeling, and real-time control.
Operational Technology (OT)
Operational Technology refers to hardware and software used to monitor and control physical processes in industries. Unlike IT, which focuses on data management, OT is responsible for real-time automation and machine operation.
IIoT means that OT and IT are increasingly converging to improve manufacturing efficiency.
Planned Maintenance
Also referred to as Time or Usage Interval-Based Preventative Maintenance. Is a maintenance strategy which aims to prevent failures before they happen by inspections, cleaning, upkeep, and periodic part changes at predefined intervals. It is the most basic form of Proactive Maintenance, and the most widely used. However it does not take into consideration the actual condition of the part or machine, as Condition-Based Maintenance and Predictive Maintenance do.
Predictive Maintenance (PdM)
Is a Proactive maintenance strategy that uses real-time sensor data, artificial intelligence, and machine learning algorithms to detect patterns and predict machine and part failures before they happen. By extending the P-F interval beyond that of Condition-Based Maintenance (CBM), PdM allows maintenance to be planned and conducted just in time, reducing unnecessary servicing, minimizing downtime, and improving equipment reliability.
Eventually the implementation of PdM shifts responsibility for timely maintenance from users to the software system. A challenge faced by PdM is gathering sufficient data to train algorithms without incurring prohibitive costs.
Preventative Maintenance
Proactive Maintenance
Term which encompasses maintenance strategies to extend machine lifetimes and reduce downtime by actively taking steps to prevent breakdowns before they occur. This contrasts with Reactive maintenance which deals with breakdowns only once they have occurred. Proactive maintenance consists of 3 types: time & usage based preventative maintenance, Condition-Based Maintenance (CBM) and Predictive Maintenance (PdM).
Programmable Logic Controller (PLC)
A PLC is an industrial computer designed to automate manufacturing processes. It receives input signals from sensors, processes data, and controls machinery in real-time. PLCs are highly reliable, scalable, and used in various industries for automation. They are crucial for the digital transformation of factories, and it is usually via an Ewon or Ixon gateway; connected to a PLC, that forwards data to the innius platform.
Reactive Maintenance
Router
A router is a networking hardware device that directs data traffic between a local factory network and external systems. Unlike a modem, which only provides basic internet connectivity, a router manages multiple device connections, assigns IP addresses, and enhances security through features like firewalls and VPNs. While a gateway includes routing functions, it also translates industrial communication protocols (e.g., OPC UA, MQTT, Modbus) to enable seamless data exchange between machines, sensors, and enterprise systems. In industrial environments, routers are often used alongside gateways to structure network traffic and maintain cybersecurity.
Servitization
Servitization is a business strategy where manufacturers shift from solely selling products to also offering services, often alongside their products. A common example is machine builders adopting a Machine-as-a-Service model. This approach strengthens customer relationships, creates recurring revenue streams, and monetizes in-house expertise to achieve higher margins.
For customers, access to expert services locally can be more appealing than purchasing a cheaper machine from abroad. Servitization also aligns with Industry 4.0 by leveraging data and connectivity to deliver value-added services.
Smart Factory
A Smart Factory is a highly digitalized and automated manufacturing facility that leverages Industry 4.0 technologies, such as robots, IIoT, and AI. These technologies enhance reliability, adaptability, and resource efficiency.
Smart Factories require minimal human intervention. In some cases, they evolve into fully autonomous facilities, known as Dark Factories, where no human intervention is needed for normal operations.
Supervisory Control and Data Acquisition (SCADA)
SCADA is a system of software and hardware used to monitor, control, and automate industrial processes by collecting and visualizing real-time data. It integrates sensors, programmable logic controllers (PLCs), and human-machine interfaces (HMIs) to oversee operations in manufacturing and other industrial environments.
SCADA systems enable remote supervision, alarm management, and process optimization, ensuring efficient and safe operation of equipment. While traditionally focused on machine-level control, SCADA can also integrate with Manufacturing Execution Systems (MES) for production management and Industrial Internet of Things (IIoT) platforms for enhanced data-driven insights.
