A Glossary of Terms in Smart Manufacturing

This glossary helps reduce inconsistencies in terminology when we teach Smart Manufacturing techniques. This is a good starting point for your own glossary needs. Feel free to adopt these definitions and suggest enhancements.

Terms:? ?

Algorithms - In mathematics and computer science, an algorithm is a finite sequence of instructions used to solve a class of specific problems or to perform a computation. Advanced algorithms can perform automated deductions and use mathematical and logical tests to divert program logic to different routes. Algorithms are used to code rules in AI systems.

Application (App) is a software program or module that uses data as input and is written to enable a particular automated or user role driven function or operation such as visualization, record keeping, alert or workflow action. An App is not limited to being a data consumer, it can also produce new data such as a calculated value, new set point, or prediction.

Application Programming Interface (API) - A set of programmed instructions, definitions, and standards that define how one piece of software interacts with another. APIs enable greater interoperability between devices in a smart manufacturing infrastructure.

Artificial Intelligence (AI) –A computer program with algorithms that enable a machine or computer to perform tasks that normally require human intelligence, such as visual perception, deduced correlation, speech recognition, and decision-making. Artificial intelligence allows machines to perform a process with autonomy. AI can learn from experience, sometimes with human assistance, in order to improve future decisions.

Augmented Reality (AR) - A technology that superimposes a computer-generated image overlaid on a view of the real world. AR may be used to train and guide employees through work processes.

Augmented Worker - An operator, technician, or other employee empowered by information technology (IT) and operational technology (OT). The augmented worker has improved effectiveness and enhanced capabilities enabled by smart manufacturing tools.

Autonomous - Self-governing. Autonomous systems can be configured to make routine rules-based decisions independent of human interaction.

Big Data - A large collection of structured and unstructured information from devices, assets, or processes during their operation. Big Data can be analyzed to make calculations and reveal patterns, trends, and associations between process inputs and outputs.

Cloud Computing – A combination of hardware and software computing technology typically provided by a third party that allows clients to access, store, and process data remotely through an internet connection. Cloud computing relies on sharing computing resources rather than having local servers or personal devices to handle applications. Servers used in cloud computing can provide multiple clients with access to unlimited storage and processing capabilities.

Cyber-Physical Systems - A system that combines physical equipment and devices with software to monitor and control industrial processes. In a cyber-physical system, physical objects and processes are synchronized in real time with their counterpart virtual objects.

Connectivity – The capability for devices and systems to exchange messages and share data across networks. Connectivity is a key aspect of smart manufacturing.

Connector - edge technology, software and/or hardware, responsible for acquiring data from manufacturing processes/operations and publishing it to an information or interoperability platform.

Controller - A hardware device that uses software and logic-based programming to provide electrical or API control signals to machines. A controller may be a central processing unit (CPU) or a programmable logic controller (PLC).

Data - A collection of numbers, facts, and measurements about a process or product. Data can be created, communicated, and recorded by sensors and communicated via data gateways.

Data Analytics (Analytics)- The techniques and tools used for analyzing process data to make decisions and conclusions about the process based on patterns and trends in the data. Data analytics are becoming more automated and advanced with the implementation of artificial intelligence.

Data Contextualization - The process of identifying data to the context around it (like the what, when, and who) to make it more useful. Data contextualization allows users to better interpret data and use it to make decisions.

Data Exchange Standards - facilitate the sharing of structured data across different information systems. A data exchange model is the intermediate representation used as a specification for data transfer. The source applications must translate their data into the data exchange format.

Data Historian (Historian) - is a type of database that’s designed to collect and store time-series data from various sources around a process plant. Historian software is often used with SCADA systems to collect data created by operational technology devices.

Data Lake - A computer application that manages massive amounts of raw data from a variety of sources including structured and unstructured data. Data lakes can be cloud-based or located on premises.

Data Models - An abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities.

Data Visualization - The organized graphical representation of information collected from a system or process. Data visualization tools, like graphs and interactive maps, help humans understand data collected in smart manufacturing and make better informed decisions.

Digital Thread - refers to the communication framework that allows a connected data flow and integrated view of the asset’s data throughout its lifecycle across traditionally siloed functional perspectives. The digital thread concept raises the bar for delivering “the right information to the right place at the right time.

Digital Twin – a digital representation of a product, asset or process that is synchronized to the current conditions of the physical product or process via real-time collected data. Digital twins that are enhanced with AI capabilities can use real-time and historical data to simulate and predict future conditions.

Digital Supply Chain – A network of companies that contribute to a product line with materials, components, and services, and are connected via internet data exchanges between their enterprise systems to enhance the products and services delivered to their customers. A digital supply chain connects suppliers and stakeholders throughout the entire product lifecycle.

Edge Computing – Edge computing devices store and process data on local devices at or near the data source. Edge computing can distribute processing tasks across multiple edge devices to improve processing speeds and it is usually an intermediary step before sending data to a cloud computing system. Edge devices and software can process and/or store critical data locally and make it possible to push received data to a central or cloud system or information platform.

Efficient – A process is efficient if time and resource usage is optimized including labor, material, and energy.

Enterprise Quality Management System (EQMS) – A software application that integrates and manages all processes that relate to ensuring quality compliance. An EQMS can connect quality data from multiple sources across the product value chain including inspection systems, MES, and ERP systems.

Enterprise Resource Planning (ERP) – Manufacturing ERP systems manage and improve the use of company resources, from production scheduling to inventory control and production orders. ERP systems can be made up of dozens of integrated modules such as procurement, general ledger, material requirements planning, etc. ERP is often integrated into the product lifecycle management, manufacturing execution systems, and supply chain management systems.

Event-driven - Decisions are event-driven if made when critical events occur or are predicted to happen instead of queued for next reporting period.

Human-Machine Interface (HMI) - is a user interface or dashboard that allows a user to interact with a device, controller, or machine. An HMI can be connected to a PLC that allows an operator to monitor a program and interact with a PLC. An HMI can range from a physical control panel with buttons and indicator lights to an industrial PC running dedicated HMI software with a graphical user interface.

Information - Information is when you take the raw data you have and analyze it or manipulate it by combining it with other contextual data, trending it over time, assessing patterns, and relating it to experiential knowledge to transform that data into insights you can use to make decisions.

Information Technology (IT) – IT has traditionally been associated with the office environment and includes the information systems and communication infrastructure used to run the business functions. IT resources include computers, data storage, networking devices, and processes to create, process, store, secure and exchange all forms of electronic data.

Industry 3.0 - The third industrial era of manufacturing development, which began in the late 1970s. Industry 3.0 revolutionized machine manufacturing by introducing microcomputers and developing advanced software applications for automation.

Industry 4.0 - The current industrial era of manufacturing development, starting in the early 2000s, which is characterized by devices and equipment that connect to the Industrial Internet of Things (IIoT). Industry 4.0 uses automation, digital communication, and data analytics to create a more connected manufacturing enterprise.

Industrial Control System - An automatic mechanism used to manage dynamic processes and maintain proper operating conditions by adjusting or maintaining physical control parameters. Industrial control systems allow for more precise and repeatable processes across networks of manufacturing processes and equipment.

Infrastructure - The network, data transfer and computing hardware and software used to run information systems in the enterprise and exchange digital communications with external partners through the internet.

Internet Of Things (IoT) - the connection via the internet of computing devices and sensors embedded in devices and equipment we use every day that enables them to send and receive data among each other via standard internet communication methods.

Industrial Internet of Things (IIoT) – A network of physical sensors, equipment, instruments, and other devices used in manufacturing connected via embedded computing systems or external gateways allowing them to send and receive data. The IIoT allows devices to exchange data and automate processes with minimal human intervention. This connectivity allows for data collection and exchange in support smart manufacturing techniques for improved decision making and performance improvement.

Information Model - includes data values organized with the relationships, grouping, constraints, and rules to specify data semantics and data structure, ontology, and graphical representation. Information models are sharable, stable, and organized structure of information requirements for the domain context and specific application. Information models improve data analysis, management, and security mechanisms.

Information Silo - Data collected and organized for a specific process control purpose which is closed off from the larger enterprise data landscape. Information siloes hinder collaborative and enterprise optimization efforts by preventing information to reach other departments and parts of the value chain.

Information Technology (IT) - includes the information systems and communication infrastructure used to collect and organize the information necessary to run the business functions of an organization. The IT department ensures an organization's systems, networks, data, and applications all connect, function properly, and are secure.

Interoperability - the ability of software and hardware from different machines, processes, and vendors to exchange and interpret data to realize the intended benefits of the exchange.

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Enabling interoperability often requires additional computing devices, like connectors and gateways, that can translate and reformat different types of data. Interoperable devices often communicate through an application programming interface (API) and data exchange standards such as OPC UA.

Interoperability is required at multiple levels of the data landscape including automation controls, asset management, operational production systems, and supply chain management systems.? Interoperability is a key characteristic of Smart Manufacturing systems and requires all three: syntactical, semantic, and contextual data interoperability.

??????? Syntactical interoperability - The structure or format of data exchange, where there is uniform movement of data from one system to another such that the purpose and meaning of the data is preserved and unaltered. Syntactical interoperability defines the syntax of the data – organization of the bits and bytes – and certain structural descriptions of intermediate processing such as processing for storage, describing what data is provided, data descriptions, and pipelining. It ensures that data exchanges between systems can be interpreted at the individual data field level.

??????? Semantic interoperability - The ability for two or more information systems or elements to exchange information and to enable the use of the information that has been exchanged, processed, interpreted, or otherwise used, independent of the syntax by which it was exchanged. Semantic interoperability is about a shared, common interpretation of data. This degree of interoperability supports the exchange and other operations on data among authorized parties via potentially dependent and independent systems, if required. The semantics include metadata about the data such as the relationship of timing to instances of data.

??????? Contextual interoperability – The business rules about the validation and authorization of data. As with any interaction between systems, the data exchanged will be driven by how the data are used. The content and format of data exchanges is driven by the intended purpose of the exchange—specifically, where, when, how, and why the receiving system will use the exchanged data.

ISA-95 - is an international standard for the integration of enterprise and control systems. ISA-95 consists of models and terminology. ISA-95 defines a framework for organizing information factory automation and manufacturing operations.

IT-OT Convergence - is the end state sought by manufacturing organizations whereas instead of separation between IT and OT as different technical areas of authority and responsibility, there is an integrated approach to process optimization and information flow between production automation and enterprise information systems.

Gateway - a device and/or software to move data between networks or system layers. For example, moving local network data to a cloud data store.

GraphQL?- An open-source data query and manipulation language for APIs and a query runtime engine. GraphQL enables declarative data fetching where a client can specify exactly what data it needs from an API. As an alternative to REST, GraphQL lets developers construct requests that pull data from multiple data sources in a single API call.

GraphQL Schema?? - defines the structure and types of data that can be queried or mutated in a GraphQL service. It specifies the capabilities of the API and defines how clients can request the data. It is often seen as a contract between the data server and client.?

Machine Learning (ML) - the use of computer systems that learn and adapt to make decisions without following explicit programmed decision rules. ML uses algorithms and statistical models to analyze and draw inferences from patterns in data. ML models may use supervised, unsupervised, or reinforcement learning methods. ML systems can analyze data to build predictive models and make decisions autonomously.

Manufacturing Execution Systems (MES) - a software application that monitors, tracks, and controls the performance of the processing of materials and production of finished products. MES applications collect performance data from multiple machines, monitor quality and manage the execution of automated and manual production tasks.

Manufacturing Operations Management (MOM) – is a term often used as an alternate to MES. It is sometimes used to refer to the functional components of manufacturing operations management (as in ISA-95 standard) in contrast to using the term as a category of software applications.

Namespace – is a scheme used to organize data and objects to prevent name collisions. It is an organized set of labels used to identify and refer to objects in code of various kinds. A namespace ensures that all of a given set of objects have unique names so that they can be easily identified.

Open Source - is computer software that is released under a license in which the copyright holder grants users the right to use, study, change, and distribute the software and its source code to anyone and for any purpose. However, specific open-source licenses must be reviewed because they may add some use, sale and distribution restrictions on the specific open-source software.

Operational Technology (OT) – a system comprised of hardware and software that controls industrial operations. The term OT has traditionally been associated with industrial environments and includes the hardware and software systems that control and execute processes on the shop floor including data acquisition, supervisory control systems (SCADA), programmable logic controllers (PLC), and computerized numerical control (CNC) machining systems.

Pattern - A pattern can be defined as anything that follows a repeating trend and is exhibited regularly in the data. The recognition of patterns can be done physically, mathematically or with algorithms. Pattern recognition in machine learning indicates the use of computer algorithms for identifying the regularities in the given data. Pattern recognition is widely used in the new age technical domains like computer vision, speech recognition, face recognition, and predictive maintenance.

Predictive Maintenance - A maintenance approach that involves collecting data related to machine operation to establish normal operating parameters, anticipate machine failures, and schedule service based on observed performance instead of based on a predefined preventive maintenance schedule. Predictive maintenance reduces maintenance downtime while preventing issues and unplanned downtime.

Process Control System (PCS) – (a.k.a. industrial control systems (ICS)) - The combination of data from sensors and machines, and programmed logic in computer systems for real-time monitoring and regulation of manufacturing processes to improve performance and reduce errors. Process controls are a key part of production automation and smart manufacturing.

Process Orchestration – refers to the practice of managing tasks in end-to-end processes to minimize waits times and optimize the use of resources. In Smart Manufacturing, process orchestration is managed through a workflow enabled systems-of systems approach with a constant pulse reading on the plant and tight collaboration with support functions to resolve issues proactively.

Programmable Logic Controller (PLC) – A control device with an embedded computer processor that uses logic programmed software to provide electrical or digital control to machines and processes. A PLC can replace many physical relays and hard-wired connections in a process. PLCs are widely used in industrial automation.

Product Lifecycle Management (PLM) - A system that maintains data on every aspect of the product from design to prototype to retirement. PLM has traditionally been focused on engineering design functions, but some PLM solutions follow a product through its entire lifecycle including design, sales, production, and service.

Production Process Optimization (Optimization) - the practice of constantly monitoring/measuring the effectiveness of production processes and striving to make continuous process improvements. Production process optimization in smart manufacturing is driven by the collection and analysis of data.

Production Management System - A software application or system that allows manufacturing organizations to coordinate all aspects of production from obtaining raw materials and components to managing production personnel and production output. Production management systems include tools to organize, collect, and analyze production data.

Productivity - is a measure of how well a business manages its resources and uses them to produce profits. It is a measure of performance that compares the amount of goods and services produced (output) with the amount of input resources used to produce those goods and services.

Protocols - standards and rules used by network devices to interact with each other. Essentially, protocols are the language that networked devices use to communicate. It was not uncommon to see different manufacturing devices designed to communicate using different protocols. IIoT and SM initiatives are promoting convergence to fewer standards to lower the cost of integrating systems.

Real-Time – transmitted at instantaneous interval of time that computers require to acquire, process, and transmit data. Real-time is virtually the same as actual time because computers process data nearly immediately. The term is often used to mean near real-time for delivery of data immediately after collection instead of accumulated and transmitted periodically with some delay.

Sensor - a device, often embedded within another device or equipment, that detects a physical stimulus and turns it into a signal that can be measured and recorded.

Smart Factory - A factory that implements Smart Manufacturing techniques to integrate automation, data, and analysis to run the entire production process. A smart factory is ready to exchange data with other nodes in digital supply chains and to respond to changing market demands with agility.

Smart Manufacturing (SM) – is the information-driven, event-driven, efficient, and collaborative orchestration of business, physical and digital processes within plants, factories and across the entire value chain.

Smart Manufacturing transforms the business by establishing the movement of information – raw and contextualized data – between real-time operations and the people and systems in the value chain to enable information based, smart decisions to improve productivity, quality, reliability, speed, sustainability, agility, and innovation.

In Smart Manufacturing, resources and processes are integrated, monitored, and continuously evaluated with the sensing, information, process modeling, predictive analytics, and workflow needed to automate routine actions, and prescribe action for non-routine situations.

In Smart Manufacturing, organizations, people, and technology work in synergy via processes and technology-based solutions that are secure, scalable, flat & real-time, open & interoperable, proactive & semi-autonomous, orchestrated & resilient, and sustainable. (First Principles of Smart Manufacturing)?

Smart Manufacturing Application (SM App) – Modular software applications that performs one or more manufacturing operations management functions and can be easily assembled, configured, and integrated through open methods. SM Apps perform functions that you might otherwise find in ERP, MES, or PCS software. SM Apps are connected to other systems and applications through interoperable smart manufacturing methods, APIs, workflow, and/or platforms.

A Smart Manufacturing App is designed to integrate with other SM Apps and platforms following open interoperability principles and standards. An SM App must include one or more of the following functionalities:

·?????? Data Capture, Transformation, Contextualization, and /or Organization

·?????? Operational Insights including predicting outcomes and triggering actions

·?????? Optimizing Flow and Control

·?????? Augmenting the Workforce

·?????? Connecting the Value Chain

Good SM Apps use SM Profiles as data contracts with data producers and SM platforms.?SM Apps include SM Bots that do not have a user interface but automate background tasks that monitor data streams for certain patterns and trigger actions when needed through other apps and systems. SM Apps and SM Bots are usually integrated via workflow or BPMN technologies.

Smart Manufacturing Platform (SM Platform) – a set of integrated software tools and applications that help manufacturers to collect, distribute, and analyze data automatically to make informed decisions and facilitate continuous orchestration and optimization of business processes in response to current conditions. SM platforms leverage information modeling, IIoT, and AI technologies, and both edge and cloud computing. SM Platforms make it possible to integrate existing and future plant data with analytics, modular apps, and information systems across the manufacturing enterprise and supply chain.

SM Profile - A Reusable, Structured, Standards based, collaboratively developed Information Model that allows information to be generated and utilized in a vendor agnostic manner. The representation of data in structured information models that provide the ability to move “data-in-context” from source to consumption. A machine-readable representation of the Information Model that uses OPC UA Part 5 as the modelling language.

Supervisory Control and Data Acquisition (SCADA) – A control system architecture used to monitor and control industrial processes. SCADA can make control decisions locally or remotely for one or more facilities.

Supply Chain - A supply chain consists of complex network of companies and suppliers that produce and distribute a product. A supply chain consists of a company, its suppliers, its distributors, and its customer service providers. These companies create a network of “links” in the supply chain that move the product along from the suppliers of raw materials to those organizations that deal directly with users. Supply chains have a physical flow that involves the transformation, movement, and storage of goods and materials. As important is the information flow that not only coordinates and tracks the day-to-day physical flow but also coordinates the customer service, financial and partnering activities in the supply chain. All these activities are highly integrated via business-to-business (B2B) data exchanges in a digital supply chain.

Supply Chain Management (SCM) – is the active management of supply chain activities to maximize customer value and achieve a sustainable competitive advantage. SCM includes coordinating demand planning, sourcing, production, inventory, transportation logistics, and warranty service. ?SCM software provides functionality to plan, implement, and control the operations of a supply chain to maximize efficiency.

Supply Chain Resiliency - is the ability of a supply chain to both resist disruptions and recover operational capability after disruptions occur. A resilient node in the supply chain adapts to schedule and product changes with minimal intervention, easy reconfiguration, and optimized process and material flows. Smart manufacturing helps manufacturers become quick to react to changes in demand, resilient to disruption and capable of maintaining business continuity through adaptability, modularity, and minimal redundancy. Smart Manufacturing allows collaborative decision-making and orchestration to get the right product to the right place at the right time.

Sustainable manufacturing – is achieved when manufacturing operations are maintained with minimum negative impact on the environment. Sustainable manufacturing involves the use of green manufacturing processes that minimize environment pollution and smart manufacturing practices that optimize the use of resources. SM drives sustainable manufacturing of products through processes and systems that optimize use of resources and minimize negative environmental impacts. SM optimizes the use of energy as a direct ingredient, instead of treating it as overhead, and contributes to a circular product lifecycle by facilitating information for reuse, remanufacturing, and recycling scenarios.

Transparency – means establishing near real-time shared visibility of information across internal departments, divisions, and external partners to facilitate orchestration of supply chain process and quick resolution of issues. End-to-end transparency includes information from top-level KPIs, such as overall service level, to very granular process data, such as the progress of goods in each plant. ?Transparency among suppliers can ensure a good foundation for making decisions around planning, execution, and exception management.

Unified Namespace An event driven communication architecture where all nodes can communicate with each other and share important data via a broker. It allows collection of data from various industrial IoT (IIoT) systems, data contextualization, and transformation into a format that other systems can understand. It is a comprehensive business-encompassing space that uses a hierarchical model to represent current data.

Warehouse Management System (WMS) – A computer software program that manages an organization’s inventory and monitors supply chain fulfillment operations. WMS software coordinates incoming products, movement, storage, tracking, and delivery of products.

Workflow – is a series of activities that are necessary to complete a task. Workflows include the sequence of industrial, administrative, or other processes through which a product or work task passes from initiation to completion. Workflow software can orchestrate discrete tasks needed to capture data, contextualize it, analyze it, put it into actionable form, and trigger multiple actions through integrated enterprise systems.

The use of workflow in a systems-of-systems architecture for process orchestration manages the handover of data and tasks between collaborating functional roles and their systems in the manufacturing enterprise.

Value chain – aims to give a higher competitive advantage by going beyond the tactical processes in supply chain management and integrating companies in closer partnership for research, innovation, and after-sales services that maintain products, extend their lifetime, and enhance the consumer experience.