The manufacturing industry is under a lot of pressure to digitize and adapt cutting edge-tech, but progress is slow.
A recent Gartner report found that “large operations are spending twice as much and taking twice as long to implement their digital transformation plans than they originally expected to. In addition, 53 per cent of the organizations the firm surveyed haven’t been tested by a digital challenge yet, undermining their readiness for that transformation.”
This digital transformation paralysis doesn’t have to be permanent. According to Deloitte, smart factories have the potential to add up to $3.7 trillion in value to the global economy by 2025. The approaches to building smart factories are diverse, but often center around a few different kinds of technologies, which we’ll be exploring throughout our series on manufacturing trends.
First, let’s start with some definitions. What is a smart factory?
Understanding digital transformation and smart factories
Smart factories are digitized production facilities that rely on interconnected systems to optimize processes, improve efficiency, and reduce costs. SAP broadly defines smart factories as being structured in three parts: data acquisition, data analysis, and intelligent factory automation.
Smart factories combine technologies like the Internet of Things , AI, robotics, and computer vision. These advancements enable companies to remain competitive, especially at a time when many other industries are ahead of the digital transformation curve.
We’re going to explore the key components of smart factories, including IoT, AI and machine learning, robotics, computer vision, and the industrial metaverse.
Robotics in smart factories
Robots and automation are integral to modern manufacturing, and probably the first thing people think of when they visualize a futuristic factory. In smart factories, robots and other automated machines are used to perform tasks that require precision, speed, and consistency, such as assembling products or packaging goods. These systems enhance productivity, and take on tasks that could be dangerous for human workers.
The Internet of Things in manufacturing
IoT devices like sensors, cameras and connected machinery enable real-time data collection, which can be used to monitor equipment, predict maintenance needs, and optimize production schedules. Cameras are especially useful – and cheap – ways to collect shop-floor data.
Physical IoT devices have numerous applications; they can detect when a machine is about to fail and suggest maintenance before issues cause downtime. Predictive maintenance not only reduces costs but also minimizes disruptions to the production process. Other devices like sensors can detect changes in factory conditions (like heat and humidity) and link to computer systems to send warnings and other alerts.
Artificial intelligence and machine learning
If applied correctly, AI can play a critical role in smart factories. Its computing power can be used to analyze huge amounts of data, identify patterns, and suggest useful improvements and optimizations. For example, AI systems can be applied to quality control, to detect product defects, saving time leaving more complex tasks to people on the shop floor.
Similarly, AI-powered demand forecasting helps manufacturers anticipate market trends, and adjust production accordingly, reducing waste and ensuring that supply meets demand.
Given the relative novelty of AI (and all of its associated hype), there are many opportunities for the application of artificial intelligence on the manufacturing floor. There are also many valid concerns about the technology, and any manufacturers seeking to add AI to their toolkit should make sure they’re doing their due diligence.
The industrial metaverse
The industrial metaverse is an emerging concept where physical factories are replicated in a virtual space. A “digital twin” of a factory allows manufacturers to simulate processes, test out changes, and optimize operations – all in digital space. Tests and simulations can run without disrupting actual work, and these simulations can help problem solve, troubleshoot, and identify issues that would have a deep impact on the real-world factory floor.
The role of computer vision in smart factories
Computer vision already combines some useful aspects of smart factories, by pairing the physical with the digital. Cameras installed on the shop floor can gather visual data (like machine states and manual processes) and connect it to AI-driven analytics platforms.
With the power of machine learning, computer vision-based platforms can help document and digitize manual work, providing analytics on efficiency and suggesting ideas on reducing downtime to get more out of current work.
The impact of computer vision in manufacturing is profound, offering improved accuracy in detecting defects, faster monitoring processes, and significant cost savings through reduced waste and better resource management.
The future of manufacturing is digital — don’t get left behind
Computer vision brings together the digital and the physical in an approachable, cost effective way, and doesn’t require a huge rework of processes, staff, and infrastructure. It doesn’t have to break the bank either – companies can test some of the technologies we’ve mentioned in this blog post on a single line or in a single factory. For a further exploration of how digital transformation projects can deliver quick returns, read this blog post from Deltia.ai.
If you’re assessing how to bring a more modern approach to your manufacturing operations, begin by exploring how smart factory technologies can be easily integrated into existing processes. To learn more about computer vision and its applications to manual manufacturing, check out this blog post.
