Manufacturing is undergoing a technology-enabled transformation as businesses seek to increase competitiveness, efficiency and responsiveness in a global business landscape. The smart factory is at the core of that transformation. It uses connected equipment, integrated applications and advanced technologies, such as robotics, machine learning and artificial intelligence (AI), to share data and achieve high levels of automation and flexibility on the factory floor and across the supply chain. A fully realized smart factory can run entire production processes autonomously, adapting to changing conditions in real time. The benefits include higher revenue, reduced cost and improved product quality.

What Is a Smart Factory?

Simply put, a smart factory integrates multiple technologies, people and big data into a single, digitally connected ecosystem to create a highly automated, self-adapting manufacturing system. It continuously collects data from the shop floor and other internal and external sources and applies analytical methods, such as machine learning, to gain insights from the data in real time. The factory then uses the insights to automatically adjust and optimize manufacturing operations, predict problems and respond to issues and trends. Smart factories also apply technology to transform other aspects of product manufacturing, from product design to supply chain management.

Key Takeaways

  • A smart factory combines interconnected machines, integrated applications, big data and advanced technologies, such as robotics and AI, to share data and achieve high levels of automation and manufacturing flexibility.
  • A fully realized smart factory can run production processes autonomously, adjusting in real time to problems and changing conditions.
  • Smart factory benefits include increased revenue, lower costs, higher product quality, greater flexibility and improved responsiveness to customer demands.

Smart Factories Explained

Smart factories are central to the digital transformation of manufacturing known as Industry 4.0, or the fourth industrial revolution. Like the industrial revolutions before it, Industry 4.0 is expected to usher in changes as dramatic as the introduction of mechanized manufacturing (the first industrial revolution), mass production and assembly lines (the second) and electronics and information technology (the third).

Smart factories replace the disparate, nonconnected equipment and applications that characterize traditional factories with a highly connected system based on technologies such as the Industrial Internet of Things (a subset of the larger IoT that focuses on the specialized requirements of industrial applications), sensors, cloud computing, big data analytics, AI and robotics. These technologies enable companies to collect large amounts of data in real time, analyze the data and quickly act on the information. An advanced smart factory implementation can run production processes without requiring manual input, automatically adjusting on the fly. However, companies can enjoy many of the benefits of smart factories through smaller, incremental improvements to existing manufacturing operations. Those potential benefits include greater productivity, less downtime, less waste, more accurate forecasting and greater responsiveness to changing requirements.

How Do Smart Factories Work?

While traditional factories commonly use technologies such as networking, automation and sensors, these systems are typically not interconnected and operate in isolation. Smart factories integrate those technologies to collect data and deliver new levels of efficiency, flexibility and autonomous operation. For example, many traditional factories have applied automation to aspects of their operations, such as production line equipment and warehouse management. But those islands of automation remain largely disconnected from each other. As a result, human input is still required at many stages in the production process, and solving problems can require manually examining data from multiple disparate systems.

In smart manufacturing, technologies are integrated to create an intelligent, self-adjusting ecosystem. For example, sensors in a smart factory gather information from networked manufacturing equipment throughout the shop floor. All that data is centralized and combined with information from other sources, such as current orders and demand forecasts. Advanced analytical techniques — including machine learning — can then be used to gain new insights from vast amounts of aggregated data, flagging potential problems before they occur. By analyzing data from image sensors, for instance, a machine-learning algorithm can automatically identify defects and initiate adjustments to production processes to compensate for a problem. Similarly, low inventory or spikes in demand can automatically trigger procurement of new raw materials.

In short, a smart factory not only curates and analyzes data, but it also actually learns from experience to forecast trends and events, and self-optimize workflows and automated processes.

Four Levels of Smart Factories

Smart factories can be categorized into four levels, representing successively higher degrees of data integration, analysis and automation. Companies can evaluate themselves against these criteria to help identify the steps they need to take to advance to the next level of smart manufacturing. The four levels are:

  1. Basic data.

    At this level, data is available, but it’s not easy to integrate or analyze. Data from each system exists in an isolated silo. Time-consuming manual work is needed to combine data from multiple systems for analysis. This is expensive and limits a company’s ability to gain insights from the data. As a result, it’s more difficult to quickly identify and respond to trends and problems.

  2. Proactive data.

    Data is continually gathered and combined in a structured database or a data lake. This makes it possible to analyze data much faster and more effectively, with less work. However, manual work is still required to interpret the data and gain insights from it.

  3. Active data.

    Machine learning and AI are used to automatically analyze data and identify patterns. The system can start to analyze trends and anomalies — such as product defects — without human input. By analyzing data over time, the system can also start to make predictions and recommendations. For example, it may suggest preventive maintenance schedules based on the observed frequency of equipment failures.

  4. Action-oriented data.

    The manufacturing system can automatically take actions based on the insights and recommendations gained through machine learning and AI. To do this, it typically has to have already gathered an extensive amount of data that enables it to understand the impact of changes it makes. For example, a system could automatically make changes to machine settings to correct defects it observes in manufactured parts.

Smart Factory Technologies

The smart factory is not about deploying one application across the entire shop floor and seeing immediate improvements in the production process. Successful smart factories use a combination of technologies related to Industry 4.0 to optimize smart manufacturing processes. Key technologies include:

  • Industrial Internet of Things (IIoT)

    The term IIoT encompasses a wide variety of connected devices and equipment, from production-line machines to air-conditioning systems to wearable devices. The IIoT plays a vital role in any smart factory because it enables the entire factory to be connected to cloud-based applications and databases. Applications can use the IIoT to monitor, manage and automate factory operations. Basically, data sent from devices reports on their status and activity, and data sent to devices controls and automates their actions.

  • Sensors

    These devices allow machines to continuously gather operational information, which is then fed to centralized systems via the IIoT. Many types of sensors are used in manufacturing. Image sensors are used in visual product inspection systems. Touch sensors in robots help them safely manipulate objects. Temperature sensors detect when machines are overheating and need to be shut down.

  • Cloud computing

    Switching from on-premises to cloud-based applications offers key advantages for business agility, scalability and access to applications. Manufacturers can more quickly adopt new applications without the need to acquire, install and maintain on-site servers and associated hardware. Cloud application performance scales automatically as business needs grow. Companies with global operations and a mobile workforce can access applications from anywhere.

  • Virtual reality (VR) and augmented reality (AR)

    These technologies are making their way into a variety of smart factory applications that assist technicians and increase their productivity and sensory awareness. VR uses specialized eyeglasses to display information that helps technicians do their jobs. For example, VR glasses can enable warehouse workers to pick items faster by displaying their exact location on shelves, or help technicians assemble aircraft parts faster by showing how they fit together. AR, meanwhile, displays digital information overlaid across reality and viewed via a smartphone.

  • Big data and analytics

    Connected machines and devices produce vast quantities of data. The ability to manage and analyze that data — typically by taking advantage of the almost limitless capacity and computing power available in the cloud — is critical to the success of the smart factory. Big data analytics supports many smart factory functions. Exploiting historical and seasonal demand data can help companies create more accurate forecasts, for example. Tracking and analyzing historical equipment reliability data enables predictive and preventive maintenance — the ability to predict when failures are likely to occur so that preventive maintenance can be performed before the factory suffers downtime.

  • Robotics

    Robots augment a skilled workforce by performing a growing range of repetitive jobs. Logistics robots pick inventory and transport it around the warehouse. Robotic arms perform product assembly tasks on the factory floor.

  • AI and machine learning

    These technologies, embedded in many smart factory applications, work behind the scenes to automatically identify useful patterns in the information that’s stored in databases and arriving in streams of sensor data, and provide real-time insights, recommendations and predictive maintenance. They support computer-vision applications, such as enabling robots to recognize the objects they manipulate and letting quality inspection systems learn to identify defects.

  • Digital twins

    A digital twin is a virtual copy of a machine, process or even a digital supply chain or business ecosystem. It’s created using data supplied from the original system, such as IIoT data streams from factory equipment. The digital twin allows companies to model the behavior of the actual system. For instance, engineers can make changes to the twin to see what would happen to the system and how it would respond in real life, without any risk to the system itself. Use of the digital twin concept is expanding. During product design, creating a digital twin product prototype lets companies quickly test the effect of design changes. Data from production-line sensors can be used to model a production process, allowing companies to simulate the effect of process changes.

  • 3D printing

    Also known as additive manufacturing, 3D printing lets companies quickly produce custom one-off parts and small production runs quickly on demand, without expensive production-line setup costs or the risk of building up a stock of inventory that may go unsold.

Smart vs. Traditional Factories

Significant differences exist between smart and traditional factories across many aspects of manufacturing operations. The advanced technologies and connectivity that underpin smart factories lead to improvements in areas such as downtime, manufacturing flexibility, product development, quality control processes and decision-making. Increased data availability enables faster analysis and better-informed decision-making. Here are some of the key differences:

Traditional factory Smart factory
Connectivity Isolated, disconnected systems. Systems and devices connected and continuously supplying data via the IIOT.
Data availability and use Data stuck in silos, requiring manual effort to combine and not readily available for analysis. Data centralized across all manufacturing operations and immediately available for analysis.
Downtime Inability to predict equipment problems resulting in greater unplanned downtime and cost. Predictive and preventive maintenance, resulting in reduced unplanned downtime.
Manufacturing flexibility Difficult to make changes to production processes. Highly flexible, rapidly responding to changes in demand.
Product and process development Slow, expensive, requiring multiple iterations of physical prototypes. Enabled by digital twins allowing companies to quickly test and make changes to virtual model.
Quality control Costly and time-consuming manual inspection. Fast, low-cost automated inspection, with automatic process corrections.
Analysis and decision-making Slow and labor-intensive, requiring extensive data aggregation and manual analysis. Faster, data-driven, using advanced analytic tools.
Key differences between smart and traditional factories.

Benefits of Smart Factories

Be leveraging Industry 4.0 technologies, smart factories can improve manufacturing operations management, helping to deliver a wide range of benefits at every stage of the supply chain — from product design and assembly to quality control. These benefits include:

  • Increased efficiency. Production lines can run more autonomously, with little or no need for manual adjustments. Sensor-equipped machinery and predictive maintenance capabilities enable companies to identify potential issues early and tackle them before they interrupt production. Technicians can fix machines faster using VR tools, such as smart glasses, that show them exactly what to do.

  • Better-informed, faster decisions. Data is continuously collected into centralized data stores, where it can be analyzed in real time. Advanced analytics tools and dashboards help companies track and respond to emerging trends and problems.

  • Greater agility. Flexible manufacturing tools, such as robots, can be quickly reconfigured to make different products as demand changes. Accurate forecasting based on historical, seasonal and current demand trends enables companies to make timely adjustments to production schedules.

  • Improved warehouse efficiency. Advanced warehouse management software optimizes day-to-day warehouse operations using industry-leading practices, such as mobile RF bar-code scanning, defined strategies for put-away and picking, task management, return authorization receipts and cycle count plans.

  • Continuous improvements to process and product quality. Automated visual inspection systems track product quality, providing feedback that is used to continuously optimize production processes.

  • Faster product design. Using digital twins — virtual entities that mirror the behavior of physical products or processes — companies can quickly create multiple iterations of new product designs without having to build physical prototypes at each stage. Digital twins can also help companies diagnose problems in existing products.

How to Create a Smart Factory

To create a smart factory, start by analyzing your goals and looking at where technology can deliver the most value. Examine ways to integrate data from across your operations, which will facilitate analysis and broader automation. Think about where it makes sense to invest in completely new systems and where you can achieve benefits by enhancing existing systems.

  • Assess needs and goals. Start by looking at what you want to achieve and the problems you need to solve. Every smart factory initiative should improve your operations in concrete, measurable ways. Do you need to increase manufacturing flexibility? Improve product quality? Better forecast demand?

  • Learn what’s possible. Find out what smart factory technologies can really do. Where can they help most? What kinds of improvements are feasible within your budget?

  • Get your people involved. Involve your employees in the transition. Smart factory technologies typically augment a skilled workforce, taking on repetitive tasks and allowing employees to focus on higher-level work. Employees understand how processes work and where the problems lie, so they can often provide helpful input on how to improve operations.

  • Integrate systems and data. Implementing a smart factory isn’t feasible if your data is locked in a series of disconnected silos. Look for ways to connect equipment and feed data into a centralized location in real time so that you can analyze and act on the information more quickly. Cloud-based ERP suites integrate a comprehensive set of manufacturing applications and store all data in a common database where it can be analyzed in real time.

  • Start small. It often makes sense to start with a pilot initiative or a small deployment for a specific product or process where smart factory technologies can deliver clear benefits. This approach is less likely to disrupt factory operations and provides an opportunity to see what works and what doesn’t.

  • Invest in new technology where necessary. You may not need brand new technology throughout the factory. Look for ways to augment your existing production systems, enable access from mobile devices or increase the integration between your applications and production equipment. But getting the full value from your smart factory will inevitably involve investment in new technology. You may need increased wireless and wired connectivity, sensors and new systems to collect and analyze data and automate tasks.

  • Take advantage of AI and machine learning. These technologies can elevate the smart factory to a higher level of intelligence and automation. AI and machine learning are no longer considered exotic technologies; they are incorporated into many products for companies of all sizes. They can quickly analyze, interpret and even act autonomously on vast amounts of data that would be impossible to analyze manually.

NetSuite Controls, Coordinates and Manages Factory Operations in One Place — Now That’s Smart

NetSuite underpins the smart factory by helping companies manage every facet of their operations with a single cloud-based ERP suite. NetSuite for Manufacturing replaces the disconnected applications that many manufacturers use today with an integrated set of applications encompassing order management, warehouse management, inventory management, supply and demand planning, supply chain, shop floor control, quality management, customer relationship management (CRM), marketing and more. By providing greater visibility and enhanced operational control, NetSuite helps manufacturers reduce operating cost and increase revenue.

With multicurrency, multilanguage capabilities, NetSuite for Manufacturing provides manufacturers with a unified view of global manufacturing and supply chain operations. Companies can control outsourced as well as in-house production. A flexible, intuitive, real-time scheduling engine supports complex finite and infinite capacity scheduling. Mobile capabilities include integrated bar-code scanning as well as wireless tablet functionality for real-time updates from the shop floor.

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Smart factories play a central role in the digital transformation of manufacturing. Advances in manufacturing technology — from cloud computing to AI and machine learning — enable dramatic operational improvements in areas such as manufacturing flexibility, product and process development, quality control and decision-making. The business benefits include increased productivity, improved efficiency and the ability to respond more rapidly to changing market demands. In turn, those advantages help growing companies succeed in an intensely competitive global environment.

Smart Factory FAQs

What are the key principles of a smart factory?

A smart factory is built on several fundamental principles, including:

  • Data acquisition across the factory is essential to enable coordinated operations, real-time analysis and automation.
  • Equipment and devices must be connected to share that data.
  • Centralized data and analysis are vital for monitoring and managing operations.
  • Advanced technologies, such as robotics and visual inspection systems, can speed, automate and improve many repetitive tasks, increasing productivity and efficiency.

What is meant by a smart factory using IIoT?

In a smart factory, machines, devices and systems are all connected via the Industrial Internet of Things (IIoT) using wired connection or wireless standards, such as Wi-Fi or cellular networks. Via this connectivity, the machines and devices can continuously provide updated status information. The same connection can also be used to control the factory floor and provide instructions to workers.

What companies use smart factories?

Many companies worldwide are in the process of implementing smart factories or have already done so. Prominent examples are Tesla, Siemens, Adidas and Johnson & Johnson. But smart factories are not restricted to big companies; many smaller and less well-known companies already enjoy the benefits of smart factory technologies, such as increased efficiency and flexibility.

How do you make a smart factory?

Start by analyzing your goals and thinking about where the technology provides the biggest payback. Learn what smart factories can really do for your company. It makes sense to involve your employees in determining how to implement the technology. Look at how to better integrate data from across your operations, which will facilitate analysis and broader automation. In some cases, you may be able to enhance your existing systems; in other situations, investing in completely new technology will justify the additional expense.

What are the components of a smart factory?

The components of a smart factory include connectivity; data acquisition and centralization; an integrated set of manufacturing applications and analysis tools; and advanced technologies, such as AI, machine learning, robotics and automated visual inspection systems.

Why do we need smart factories?

The goal of a smart factory is to increase manufacturing companies’ competitiveness, agility and efficiency in a global business environment. Companies can improve asset utilization, productivity and product quality by implementing highly automated production systems. These advanced technologies provide much greater operational flexibility with the ability to quickly change course and respond to changes in demand.