According to an official report by the US government, in 2008, the US manufacturing industry took a significant fall and took until 2017 to recover to the levels before the drop. The COVID-19 pandemic in 2020 made things worse for the industry, with many jobs lost in manufacturing, almost as bad as the late 2000s recession.
Berk Birand, the CEO of Fero Labs, emphasized that AI could help solve the labor shortage in the manufacturing sector, which is expected to have over 2 million unfilled jobs by 2030.
By shortening the training period for mid-level operational roles from years to a few months, AI makes it easier for individuals to enter these professions. This not only helps in addressing the current labor shortage but also allows existing data scientists to focus on more complex and innovative tasks, enhancing the potential of Industry 4.0.
In this article, we have a detailed look into how AI in manufacturing will significantly boost the economy and industry growth.
What is AI in Manufacturing?
In 2022, the worldwide market value for AI in manufacturing amounted to $3.8 billion. It is projected to escalate to approximately $68.36 billion by the year 2032, expanding at a CAGR of 33.5% from 2023 to 2032.
Like every other industry that AI touches, manufacturing companies can expect massive growth opportunities with AI implementation.
We briefly touched upon “Industry 4.0” in the article’s introduction, but what is it, and how can manufacturing companies benefit from it?
Industry 4.0 is a movement characterized by increased automation and extensive data generation and transmission within manufacturing environments. In simple words, Machine Learning and Artificial Intelligence in manufacturing will allow professionals to extract valuable insights from the vast data produced by manufacturing equipment.
Leveraging these insights, AI in manufacturing industry can be used to enhance existing manufacturing processes. This will materialize into benefits such as cost reduction, enhanced safety measures, streamlined supply chains, and various other benefits.
However, predictive analysis happens to be AI’s strongest suit.
Therefore, another significant application of artificial intelligence in manufacturing is predictive maintenance. Here AI algorithms analyze continuous data streams from sensors to detect patterns and predict potential issues before they occur. This enables timely maintenance and prevents costly downtimes.
Is The Hype Behind AI In Manufacturing Justified?
McKinsey notes that the manufacturing sector has the second-highest adoption of AI. If you’re wondering what the secret behind this accelerated onboarding is, Stefan Jockusch, vice president for strategy at Siemens Digital Industries Software answers it for us.
According to him, the future factories will be “self-organizing”, capable of dynamically reshuffling and adapting to different products, powered by AI technologies from the chip level to the entire manufacturing ecosystem.
“Depending on what product I throw at this factory, it will completely reshuffle itself and work differently when I come in with a very different product. It will self-organize itself to do something different,” said Jockusch.
Let us introduce you to “digital twins.” They are virtual replicas of physical entities, which allow for precise manufacturing process design and problem resolution during fabrication. These digital twins facilitate the simulation of products and their behaviors before actual production, offering a comprehensive view and understanding of potential outcomes.
Jockusch has also emphasized the significance of informed decision-making. AI facilitated special chips designed to ensure a smooth “chip to city” transition, allowing future urban centers to be driven by data transmission, with factories and cities being self-managed, relying on continuous improvement through AI.
However, AI in manufacturing industry is not just confined to large enterprises. Small to medium-sized enterprises (SMEs) also leverage AI-driven production for innovative applications, particularly in technology-intensive industries like aerospace.
The Evolving Faces Of Manufacturing Through AI
The manufacturing sector is undergoing a significant transformation with AI implementation.
Major corporations are making billion-dollar acquisitions to move up in the industrial automation sector, such as Emerson Electric’s (EMR) $8.2 billion acquisition of National Instruments Corp (NATI). This strengthens Emerson’s foothold globally in the semiconductor, transportation, and electric vehicle markets.
Here, we explore various applications of AI in manufacturing, emphasizing the need for strategic implementation to work alongside skilled human workers for optimal results.
1. Predictive Analysis: Manufacturing’s Revolutionary Approach to Maintenance
Imagine a world where manufacturing plants have a sixth sense, foreseeing breakdowns before they occur and saving millions in the process. This isn’t science fiction, but the reality is being shaped by predictive maintenance (PdM), a cornerstone in modern manufacturing.
In his current role at a leading U.S. industrial products manufacturer, veteran Mike Macsisak is pioneering a PdM program that is revolutionizing the industry. From early detection of steam pipeline issues to optimizing lubrication processes, his strategies are setting new standards in efficiency and reliability.
According to Next Move Strategy Consulting, the global PdM market is set to soar from 4.5 billion U.S. dollars in 2020 to a staggering 64.3 billion by 2030. This growth signifies the transformative impact of PdM, powered by AI systems that tailor maintenance schedules to individual equipment needs, preventing unexpected breakdowns and enhancing overall efficiency.
2. Supply Chain Management: Increased Efficiency Achieved Through AI
The integration of AI within supply chains and manufacturing industries is poised to accelerate notably between 2022 to 2028, growing at a robust CAGR of 20.5%, reaching $20 billion. AI is significantly enhancing this sector. By streamlining processes like invoice handling and optimizing warehouse space, AI is promising substantial returns on investment.
A prime example of AI and manufacturing transformation is seen with our client, a global leader in spend management analytics. Previously grappling with delays in financial reporting and suboptimal cost allocations, they turned to AI for a solution.
Using technologies like AWS, PyTorch, and TensorFlow, Kanerika crafted an AI/ML-powered pricing discovery engine using over 23 parameters for precise shipment pricing predictions.
This innovation facilitated real-time cost visibility at the time of shipment booking and markedly improved efficiency, evidenced by a 17% reduction in invoice processing time. Moreover, we observed a 19% boost in overall efficiency, achieving a remarkable 93% accuracy in pricing.
3. Inventory Management: A Strategic Move in the E-commerce Era
As the e-commerce wave reshapes consumer buying habits, AI in manufacturing is becoming a cornerstone in enhancing inventory management.
A significant 48% of executives in supply chain and transportation are considering revisiting their warehouse locations to stay abreast of this shift. AI is at the helm of this transformation. It is optimizing storage space and streamlining operations, which is vital for businesses vying for a competitive edge in the online marketplace.
AI’s role extends to automating various facets of warehouse management. This includes efficiently processing invoices and product labels and optimizing shelving space through intelligent algorithms.
In fact, in 2020, about 45.1% of companies had already invested in warehouse automation, with an additional 40.1% adopting AI solutions, indicating a promising trend towards heightened productivity and cost-efficiency in inventory management.
Challenges in Implementing AI in the Manufacturing Industry
While we celebrate the surge in AI adoption in manufacturing, it is a journey with a few hurdles. For instance. the sector faces a significant shortage of AI talent, with many young professionals finding the field monotonous.
Moreover, the existing technology infrastructure often lacks interoperability, hindering seamless integration between various systems. The quality of data, a critical component for the success of AI initiatives, remains a concern, often outdated or erroneous.
Today, 80% of organizations undertaking AI and ML projects face stagnation or cancellation. A staggering 96% of businesses encounter data quality issues.
Furthermore, the industry grapples with fostering trust and transparency regarding AI technologies. Additionally, many find the complex algorithms behind AI elusive. Despite these challenges, the focus remains on leveraging AI automation, smart manufacturing, and ML in manufacturing to streamline processes. So, how can Kanerika help you overcome these challenges of AI in manufacturing?
Starting your Journey with Kanerika: Your Partner in AI in Manufacturing Innovation
At Kanerika, we specialize in creating AI and ML solutions that make manufacturing processes smoother and more efficient.
We recently worked with a top perishable food production company in the USA to improve their production planning and scheduling. Our AI-driven production helped them reduce overproduction and waste, boosting their production efficiency by 13.6%.
In another case, we helped a leading perishable food producer revamp their supply chain management. We used advanced data models and the TensorFlow AI engine to increase stakeholder cooperation, improve efficiency, and reduce lead times. This led to better customer service and faster deliveries and gave the company a competitive advantage in the market.
Partnering with Kanerika helps manufacturing companies overcome AI integration challenges and prepares them for an AI-driven future. Are you ready for a sustainable and productive future for your company?
Book a free AI consultation with us today!
