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Businesses benefit from AI-infused Industry 4.0 practices
It's daunting for a business to adopt Industry 4.0 technologies at scale. However, given the added value of automation and process optimization, the benefits can outweigh risks.
The Fourth Industrial Revolution is becoming reality. Organizations are envisioning what they can do with AI, IoT, digital twins and other emerging capabilities, but implementing those technologies at scale has been a struggle.
One major difference between the initial stages of the Fourth Industrial Revolution, also called Industry 4.0, and its current state is AI. Businesses use AI to sift through massive amounts of information, provide customers with better experiences and enable more efficient operations.
"We've moved from information and electronics to digitizing the world around us. Now we're starting to see the digitization of the physical world and the optimization of the digital world," said Scott Likens, emerging technology leader of the U.S., China and Japan at international professional services company PwC.
Implementing these nascent technologies at scale may seem daunting, but benefits like process optimization and increased innovation can outweigh the risks.
Optimization opportunities come with Industry 4.0
Optimization is a key benefit because it focuses on the continuous improvement of an operation, whether it's better crop yields with AI and IoT or enabling predictive maintenance with digital twins. Manufacturing optimization requires manufacturers to understand the constraints of current processes. To do that well, they need a lot of data. Therefore, the use of computer vision and digital twins is growing. Digital twins are implemented in three ways: to build a complete product digitally in 3D, to model all assets used in manufacturing and to model processes.
"A product might go through 40 steps in the analog world, which is a tedious thing to predict. If you can model it, you can now do all kinds of scenario planning associated with externalities to challenge yourself around how that product is made and drive efficiencies," said Jason Bergstrom, principal and smart manufacturing leader at Deloitte.
In another optimization example, AI, IoT and cloud computing technologies provide greater visibility into supply chain operations through end-to-end tracking and the ability to simulate and predict supply chain issues.
The digitization of everything enables companies to become nimbler, adapting to changing circumstances faster and with greater accuracy.
Other benefits of an AI-focused Industry 4.0 strategy
When people are less stymied by repetitive tasks, they're empowered to innovate. An agile company culture sets them up to leverage digital technologies at scale.
For example, customization and personalization of goods grow every year. Yet, manufacturing has been about the mass production of a single product to get the lowest cost product and highest margin. That focus has been shifting to one that provides the most value to the customer and results in the highest margins.
"New technology and the emergence of new manufacturing theory is going to be critical as we move away from a high-volume type of commoditized outputs to more agile, high-value and personalized outputs," Bergstrom said.
Industry 4.0 can also help organizations improve regulatory compliance through the use of intelligent automation. Since laws and regulations change often and differ from one geographic region to another, it's difficult for those organizations to ensure compliance through human labor alone.
With AI and intelligent automation, organizations can improve their compliance in a scalable way to deal with the complexity. "Historically, it's been a post-mortem evaluation that required organizations to piece together information to answer questions from regulators," said Bergstrom. "The new world is going to be [one where] that information is available to all parties instantly."
Industry 4.0 challenges persist
In late 2022, Wakefield Research and MLOps platform provider Domino Data Lab surveyed more than 100 chief data officers and chief data analytics officers at companies with over $1 billion in revenue. They found that data science teams feel pressure to drive new business value with analytics, machine learning and AI applications, but are not empowered enough to deliver. Their IT departments underfund initiatives that can positively impact the bottom line. This leaves data leaders anxious to take control from IT -- or risk falling behind competitors when it comes to AI innovation.
For example, 95% of survey respondents said their company's leaders expect a revenue increase from AI and ML initiatives, but less than a fifth say their data science teams have sufficient resources to meet those expectations. All said their organizations have experienced negative consequences such as lost business opportunities because they were unable to operationalize data science initiatives.
Industry 4.0 may be more real than hype now, but gaps remain between the expected benefits and the ability to attain them. Constant technology innovation and roadblocks to a company's ability to meet its own goals exacerbate these growing pains. While there is no quick fix for every business interested in Industry 4.0 to quickly adopt and scale a wide range of these advanced technologies, this tedious process is also rewarding. Now that companies have become increasingly digital, it's time to optimize that digital presence.
