• IoT/IIoT: When devices and machines are equipped to send and receive digital data, they comprise an IoT network. Data sent from the device reports on its status and activity, and data sent to the device controls and automates its actions and workflows. An Industrial IoT (IIoT) network is at the core of smart manufacturing as it not only comprises the connected assets, but the smart systems and automated processes with which they are integrated.
• AI/Machine Learning: The most comprehensive data in the world is meaningless until you can leverage it and use it to tell a story. AI brings manufacturing data to life with advanced analytics and the inherent ability to manage and amalgamate broad and disparate data sets. Manufacturers armed with all that data can then use machine learning algorithms to get their systems to tell them what they need to know – about what’s going on right now, and what is predicted to happen in the future.
• Big Data: If AI and machine learning put the “smart” in smart manufacturing, then Big Data is the fuel. Big Data is not so-called simply because it’s voluminous. It’s defined by its variety and complexity. By feeding an AI system with enormous sets of complex and disparate manufacturing data, you give it the scope it needs to draw increasingly accurate conclusions and learn more quickly over time.
• Autonomous robots: As already discussed, robotics is nothing new in manufacturing. It is not the ability to externally automate assets that is the game-changer – it’s the ability for those cloud-connected assets to use smart technologies to automate themselves. Smart factories depend upon autonomous automation for the agility and speed that they need.
• Additive Manufacturing/Hybrid Manufacturing: Better known as 3D printing, additive manufacturing boosts resilience and agility. For example, a Boeing 747 jet is made up of over six million parts – all which require replacement on different schedules. Instead of trying to warehouse all those parts, smart metal or plastic 3D printers can access the maintenance logs and produce the parts as needed, allowing the company to hold a “virtual inventory”.
• Cloud Computing: Cloud connectivity and computing give manufacturers on-demand availability of system resources such as IIoT data, analytics, and process automations, all across wireless channels like Wi-Fi or 5G. Large clouds may be centrally managed yet distributed over regional or global locations.
• 5G Connectivity: With 5G, businesses take the advantages and benefits of internet cloud connectivity and ramp them up with less latency, much faster speeds, and almost limitless capacity to scale.
• Edge Computing: Today’s smart factories are all about pivoting fast and responding quickly in real time. It takes time to send data gathered in one place, to systems housed in another physical location – and for smart factories, that downtime represents loss. Edge computing helps to bring the brains (AI and data analytics) to the shop floor and eliminate lags in the IoT network.  
• Simulation/Digital Twin: A digital twin or simulation is created to be an identical virtual copy of a machine or process that exists in the real world. It allows manufacturing teams to test new ways of doing things, and to push virtual prototypes to their absolute limits, without the cost and risk of damaging anything in real life.
• Design for Manufacturing: This is not so much a technology itself as it is a cross-functional practice that exists because of technology. Design for manufacturing principles allow R&D professionals to learn from data – from across the factory floor and customer base. These insights then help them design win/win products that meet customer demands for quality and personalization, and create designs are also easier, leaner, and faster to manufacture and customize.

Source: SAP Insights