Smart manufacturing: Key trends and features in 2023 [Updated]

Smart factory

Smart manufacturing is a term coined to describe in short as “a data-intensive application of information technology at the shop floor level and above to enable intelligent, efficient and responsive operations.”

There are more comprehensive definitions of smart manufacturing. They all highlight the use of Information and Communication Technology (ICT) and advanced data analytics to improve manufacturing operations at all levels of the supply network, be it the shop floor factory or Supply Chain.

Smart Manufacturing incorporates various technologies, including but not limited to CP(P)S, IoT, robotics/automation, big data analytics, and cloud computing to realize the vision of a data-driven, connected supply network.

An important aspect that differentiates smart manufacturing from other initiatives is the specific emphasis on human ingenuity within the framework. Humans are not simply replaced by Artificial Intelligence and automation on the shop floor, but their capabilities are to be enhanced by smartly designing the customized solution for the specific area.

Key features of smart manufacturing

  • Digital presence – Ability to create digital or cyber-physical models of parts or the complete manufacturing system to develop a simulation environment for advanced planning, decision support, and validation capability before any action is implemented physically.
  • Modularity – Create “economies of scale” within the manufacturing system.
  • Heterogeneity – Create “economies of scope” within the manufacturing system.
  • Scalability – Adjust (e.g., increase) production capacity through the manufacturing system reconfiguration with minimal cost and minimal time.
  • Context-awareness – Automatically and in real-time collect manufacturing system data via a network of sensors (e.g., loT), and subsequently conduct real-time processing (event or data-driven) to provide the proper information to the right people or system and at the right time.
  • Autonomy – Support (autonomous) reasoning, planning, and decision-making via hardware, software, sensors, and communication technology to increase a manufacturing system’s productivity and flexibility.
  • Adaptability – Manage unforeseen events during production as a manufacturing system. Adaptability may include flexibility and robustness.
  • Robustness – Cope with problems during production as manufacturing. Robustness can be achieved through redundancy.
  • Flexibility – Produce different products on the same manufacturing system.
    Fully automated – Fully control utilizing a computer system parts or the complete manufacturing system.
  • Asset self-awareness – Sense a phenomenon or event within itself (the asset), such as its location, condition, or availability within the manufacturing system.
  • Interoperability – Allow communication through interfaces between the components/subsystems of a manufacturing system, allowing it to work with or use parts of other components/subsystems.
  • Networkability – Allow information exchange and communication between the components/subsystems of a manufacturing system.
  • Information appropriateness – Acquire information from one or more sources within the manufacturing system components and subsystems, store it, and assure its quality, accessibility, and understandability and its provisioning to the right people or system at the right time.
  • Integrability – Bring together different component subsystems (e.g., machine tools, robots, computer systems, humans) of a manufacturing system into one integrated system and ensuring that all the subsystems function together as a coordinated whole.
  • Sustainability – Conduct all manufacturing processes and system operations with a minimum environmental footprint (e.g., resource efficiency).
  • Compositionality – Provide recombinant components within a manufacturing system that can be selected and assembled in various combinations to satisfy specific production requirements.
  • Proactivity – Anticipate (predict) using continuous situation-awareness capabilities, events (e.g., problems) in the production or manufacturing system components (e.g., machine tool), and react ahead of time (e.g., proactive maintenance).
  • Reliability – Perform the required manufacturing processes and operations as a “reliable” manufacturing system under stated conditions to achieve production objectives. Condition monitoring and defect diagnosis are enablers to improve the reliability of a manufacturing system.
  • Agility – Respond to external changes (e.g., market changes) that affect production plans.
  • Responsiveness – Provide a “quick response” to changes in production plans.
  • Accuracy – Produce with minimal or zero waste in all manufacturing processes and operations (e.g., lean manufacturing).
  • Reusability – Use existing assets as they are or modify them in the manufacturing system in some form or other to reduce the introduction of new ones.
  • Decentralized – Allow the components or subsystems of a manufacturing system to operate on local information to accomplish global production goals.
  • Distributed – Produce in dispersed manufacturing facilities that are coordinated using information and communication technology.
  • Resilience – Tolerate large perturbations during production and still achieve production goals (e.g., product quality, delivery time, production cost).

Industrial Internet of Things

The integral part of any smart manufacturing system is the IIoT (Industrial Internet of Things) that leads to the proliferation of connected smart machines, devices, and sensors that result in an explosion of data. This integration throughout different data and functional layers provides insights to improve safety, quality, cost, and schedule, allowing the engineers to have an efficient product lifecycle and value chain management with a fully connected supply chain and customer management.

IIoT is one critical enabling technology for smart manufacturing since it represents a form of a global information network composed of large numbers of interconnected “Things,” such as sensors, actuators, controllers, robots, human operators, machines, equipment, products, and material handling equipment to name but a few. Thus, it provides an unprecedented opportunity to link manufacturing “Things,” services, and applications to achieve effective digital integration of the entire manufacturing enterprise. This integration can be extended from enterprise resource planning (ERP) to supply chain management (SCM) to manufacturing execution systems (MES) to process control systems (PCS). IIoT solutions also cover the following themes in smart manufacturing: equipment monitoring, maintenance, quality control, energy management, productivity, logistics, and safety.


Cloud-based manufacturing execution systems (MES) and quality management systems (QMS) are changing the manufacturing landscape to manage company-wide operations across thousands of factories and tailor to individual machines and roles. Transitioning to cloud-based systems is about agility, speed, skill, and gaining a 360-degree perspective on factory operations in real-time. Here are some of the solutions enabled by the cloud for manufacturers.

  • Customer relationship management (CRM): CRM is how a sales team manages its market and customer data and the engagement of the sales process.
  • Product Lifecycle Management (PLM): PLM solutions allow manufacturers to create and manage product structures and product family, and successfully implement change control processes for their products. It provides easy access to suppliers and customers for collaboration in engineering processes.
  • Advanced planning systems: Advanced planning systems effectively plan and schedule parts and materials in the supply chain.
  • Enterprise Resource Planning (ERP): ERP is the heart of most manufacturers’ transaction management for financials, order entry, purchasing, work order management, and scheduling.
  • Manufacturing Execution Systems (MES): Cloud-based MES solutions make it easier to roll up metrics across a network of distributed manufacturing plants.

Additive Manufacturing

Additive manufacturing (AM) or three-dimensional (3D) printing, one of the top emerging technologies for smart manufacturing, has been a game-changer for manufacturers, allowing them to move an idea through execution magnitudes faster than previously possible. It’s an umbrella term for various techniques for constructing three-dimensional structures, which are frequently custom-made. Today’s primary purpose is to prototype products more quickly and at a lower cost than in previous years. In addition, it has pioneered a new design, manufacturing, and distribution method for end-users.

This technology has given designers a lot of freedom when designing complex components, highly customizable products, and waste minimization. Due to numerous advantages such as time and material savings, rapid prototyping, high efficiency, and decentralised production methods, AM plays a key role in smart manufacturing. Among its many benefits, AM has a significant environmental impact in terms of improving production system sustainability when compared to traditional manufacturing methods. The benefits of AM can be summarized into:

  • time and material saving
  • high resource efficiency
  • rapid prototyping
  • high efficiency, and decentralized production methods.
  • reconfigured value chain

Collaborative robots (cobots)

Cobots (collaborative robots) is another technology that will gain traction on factory floors as companies struggle to increase production while maintaining social distance. Robots are embedded with sensors in smart factories to allow for human-robot collaboration in a safe environment. When compared to traditional industrial robots, collaborative robots (cobots) have several advantages.

These robots are human-safe and can free up space that traditional robots require, such as a guarding fence. The safety mechanisms can be a mix of technologies, such as proximity sensors to slow the robot down when humans approach; force limitations to reduce risks to humans or the environment; and human intent and maneuvering. Different levels of human-robot collaboration can be implemented in addition to these safety mechanisms. Humans, for example, perform tasks that require the most skill, whereas robots perform repetitive, heavy, and boring tasks.