Top 5 AI Predictions for Manufacturing in 2022 (And How to Prepare Today)

How will Big Data and AI impact manufacturing in 2022? What would be a futuristic step taken in time? Find out what industry experts have to say…

The year that has gone by—2021 was an eye-opener at many levels. Both process industries and discrete manufacturers have woken up to the crucial benefits of AI implementation. It is now a proven fact that leveraging Big Data and AI can facilitate the detection of patterns, accurate interpretations, and real-time communication of actionable insights to catalyze efficient workflows, faster decision-making, and optimized production.  

In 2022, Big Data and AI apps will undoubtedly impact how manufacturers do business, but what impressive strides will the powerful technologies take in the upcoming New Year? 

Top 5 AI Predictions for Manufacturing from experts: 

  1. Monetization of manufacturing data will be at the crux of agile manufacturing. 

Organizations that maximize their investments in data now will be the early birds to catch the business benefits and rule the roost ahead of their competitors. Stronger data teams and AI-enabled integrated data analytics will streamline processes for optimal efficiency in operations.   

Aside from that, data-drivennetworking between organizations will become the norm as AI apps will enable safe interoperability and data exchange. Ethical acquisition and exploitation of data, not only within but also across organizations, will establish data alliances like never before.  

The monetization of manufacturing data is the process that provides a measurable economic advantage by using (aggregated and transformed) manufacturing data to exploit data-driven knowledge spillover effects. AI apps can help shed inhibitions around data privacy. Strategies for monetizing manufacturing data can be manifold, offering an added economic value of more than $100 billion is predicted. [1] 

  1. A more holistic approach by factories toward AI adoption will prove to be a veritable game-changer. 

Factories dipping their toes and testing the waters will take the plunge. Instead of working in phases with incorporating AI applications in their processes, the push will be toward a system-wide change. Realized intelligent factories using integrated data from all the assets—operational and human—in the manufacturing network will become prevalent features. The gap between AI leaders and the companies following in step will get mitigated with accelerated digitalization. In fact, we can expect organizations to work with a blueprint, clearly prioritizing AI initiatives for business value and definitive manufacturing goals. 

  1. Green AI adoption for sustainability initiatives will be the mark of a responsible manufacturer. 

With the help of AI, companies will make a conscious effort of switching to environmentally sustainable manufacturing. The shift from a manufacturer-centric approach to eco-centric decision making will turn the industry on its head, allowing more responsible players to get into the front row.  

AI applications can offer tremendous new possibilities to help manufacturers adopt green manufacturing methods and reduce their carbon footprint. AI can optimize and ensure responsible energy consumption in manufacturing facilities and reduce wastage of natural resources. Integrating data analytics and AI in the manufacturing processes can facilitate the designing of products for sustainability by taking into account the environmental impact at every step of the product lifecycle.  

  1. Reinforcement learning will bring a radical transformation to the manufacturing scenario. 

Reinforcement learning (RL), an unsupervised machine learning algorithm, will deliver a transformative advancement in 2022.  

AI applications will get more intelligent with the simulation-based dynamic programming method. RL algorithms converge toward optimal process control strategies with data-driven solutions to problems in the production domains, enabling an autonomous manufacturing system. The key will be having suitable data sets to train algorithms, which requires a culture of data sharing within companies. Then machine learning can step out of the lab, where the training is slow and expensive, and get into production environments, where things get real. 

As it trains on the shop floor, instead of working with pre-programmed training modules, the complex real-time evaluation helps the AI applications to determine which actions are suitable in the long term. Reinforcement learning can thus bring a turning point in the world of manufacturing.  

Read about RL in detail HERE

  1. The Internet of Behavior (IoB) will enhance customer-centricity 

AI applications can help organizations collect and analyze behavioral data of employees engaged on the shop floor. The collection and usage of that data to influence future behavior is called the Internet of Behavior (IoB). As organizations improve the quality of data captured, they can also combine data from various sources and leverage IoB to improve the process workflow or influence interaction with customers, understanding their needs, overcoming challenges, and deriving customer-specific solutions. 

As per Gartner, IoB will tightly link customer experience and employee experience to transform the business outcome. It can help differentiate a business from competitors, creating a sustainable advantage. This trend enables organizations to capitalize on COVID-19 disruptors, including remote work, mobile, virtual, and distributed customers. [2] 

That is how we see things shaping up!  

As the world transitions from the pandemic to a state of normality, 36 % of manufacturers say they are currently engaged in AI projects, and 23 % more are planning to use AI in the coming months to unlock $13 trillion in value that industry experts anticipate from industrial sectors.  Manufacturers learned a lot during the pandemic and are now looking for the best way to become more productive, safer, and more agile by leveraging the petabytes of data insights harvested from connected factories. [3] 

We hope this article gives you a great insight into where the industry is heading in 2022. 

References: 

[1] Trauth, D. (2020, July 27). Monetization of manufacturing data. Senseering. https://medium.com/senseering/monetization-of-manufacturing-data-7e55d4c213ed 

[2] Panetta, K. (2020, October 19). Gartner Top Strategic Technology Trends for 2021. Www.gartner.com. https://www.gartner.com/smarterwithgartner/gartner-top-strategic-technology-trends-for-2021 

[3] IOT World Today. (2021, November 9). AI led Digital Transformation of Manufacturing: Time is NOW. IoT World Today. https://www.iotworldtoday.com/webinar/ai-led-digital-transformation-of-manufacturing-time-is-now/ 

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