Recent advances in artificial intelligence (AI) are actively changing how we live and work. Generative AI (gen AI) is already impacting manufacturing by improving efficiency and facilitating global collaboration. These changes are just the beginning. To explore the full impact AI and other developments will have in the coming years, we consulted in-house experts and compiled insights in our report, Data + AI Predictions 2024. Here, we’ll focus specifically on what’s next for the manufacturing industry.
Gen AI Will Overhaul Manufacturing by Driving Efficiency, Productivity, and Innovation
Manufacturers are continually seeking ways to enhance operational efficiency and reduce costs. According to Tim Long, Snowflake’s Global Head of Manufacturing, smart manufacturing—leveraging advanced technologies to improve traditional processes—is a significant area of interest. Centralizing data, particularly by integrating operational technology (OT) data with other manufacturing process data in the cloud, is a critical trend. This integration helps manufacturers achieve a holistic view of their operations, elevating yield and performance (Snowflake) (McKinsey & Company).
AI and Data Will Inform Critical Business Decisions Across the Value Chain
AI technologies, especially generative AI and large language models (LLMs), will play a pivotal role in extracting actionable insights from complex data. These technologies lower the barrier for employees to access and understand data, facilitating global collaboration and improving decision-making. For instance, LLMs can analyze data beyond traditional business intelligence tools, helping identify root causes of issues and simulate various scenarios to optimize processes without physical intervention (Snowflake) (Grand View Research).
A Robust Data Foundation Will Distinguish Leaders in Manufacturing
A unified data strategy is essential for effectively leveraging AI technologies. Manufacturers need to consolidate their data into a single platform that provides security, governance, and ease of access. This approach improves data quality, reduces costs, and enhances decision-making capabilities. Leading manufacturers are already investing in modern data cloud platforms to support their AI strategies and ensure they remain competitive in a rapidly evolving industry (Business Wire) (McKinsey & Company).
Centralizing Data for AI Optimization
To maximize AI’s potential, manufacturers must consolidate data from various sources, including IT, OT, and third-party data. Centralizing this data in the cloud enables comprehensive analysis and integration, providing insights that drive operational improvements. This trend is particularly prevalent in sectors like oil and gas, high tech, and industrial manufacturing (Snowflake) (McKinsey & Company).
Utilizing Gen AI and LLMs for Advanced Analytics
Gen AI and LLMs can distill complex data into actionable insights, enhancing problem-solving and operational performance. These technologies enable manufacturers to conduct advanced data queries, simulate control changes, and optimize processes in ways traditional BI tools cannot. For example, LLMs can help troubleshoot equipment maintenance efficiently, reducing downtime and improving overall productivity (Snowflake) (McKinsey & Company).
Data Collaboration Across the Supply Chain
Data collaboration is becoming increasingly vital in managing supply chains. By sharing data with third-party entities, manufacturers can streamline shipping and logistics, plan production more effectively, and mitigate supply chain risks. Advanced data sharing and collaboration tools help manufacturers gain real-time insights into supplier performance and predict potential disruptions, enhancing supply chain resilience (McKinsey & Company) (Grand View Research).
Proactive Supply Chain Management
Manufacturers are building predictive models to monitor supply chain risks and optimize logistics. These models leverage AI to provide early warnings of potential issues, enabling proactive management of supply chains. This approach not only minimizes risks but also improves customer satisfaction by ensuring timely delivery of products (McKinsey & Company) (Grand View Research).
Breaking Down Data Silos
A unified data strategy is crucial for the successful deployment of AI technologies. Breaking down data silos and integrating data sources across the organization ensures that AI models are trained on comprehensive and accurate data. This integration enhances data quality and governance, providing a single source of truth that drives better decision-making (Snowflake) (Business Wire).
Investing in Modern Data Platforms
Modern data cloud platforms are essential for managing and leveraging data effectively. These platforms provide the necessary infrastructure to support AI applications, ensuring data is accessible, secure, and easily governable. Leading manufacturers are investing in these platforms to build a flexible, future-ready foundation that accelerates time to value and enhances overall operational efficiency (Snowflake) (Business Wire).
The S-Curves of Industrial Revolution
Global industry transformation has never been instantaneous. Each “revolutionary shift” saw a lag period between the introduction of the enabling foundation and widespread adoption. Consider the steam engine. The Roman architect Vitruvius mentioned a rudimentary steam-powered device as early as 15 BC. Why did widespread adoption take more than 1,800 years? The answer lies in practicality and cost-effectiveness; steam became viable only with breakthrough engine technologies and coal supply chains, allowing the “doing curve” to steepen. The front-runners had done the learning, and by the late 18th century, steam engine adoption in industry surged from near-zero to 80 percent in just 20 years.
The Adoption S-Curve
Each industrial revolution has followed an “S-curve” pattern. The initial phase is a lengthy learning curve marked by trial and error. It then transitions to the “doing” phase, where foundational technologies are deployed across production networks. Finally, an optimization curve emerges, characterized by industry alignment around best practices, ingrained standards, and stabilized costs. The smartphone's evolution exemplifies this three-phase S-curve in action.
AI Defining the Fourth Industrial Revolution
The true power of AI for the Fourth Industrial Revolution lies in its position at the top of a “pyramid” of 4IR technologies. AI acts as the conductor, orchestrating a symphony of impacts through various 4IR technologies (Exhibit 2).
Rapid Changeover Example (Exhibit 3)
Consider a rapid changeover at a production site. This requires flexible robotics to handle different products, automated guided vehicles to move materials and parts, 3D printing to customize line fixtures, and wearable technology to keep managers and technicians informed with real-time data. AI orchestrates this complex interplay of elements, each of which is individually complex.
AI relies on vast amounts of data generated from a wide range of sources: enterprise systems, machine sensors, connectivity infrastructure, and human workers. Advanced manufacturers have built the data foundations necessary to power AI technologies and unlock their potential impact. These foundations are crucial for AI to function effectively and drive transformative change (see sidebar “Understanding AI: How it actually works”).
The manufacturing sector is poised for a significant transformation driven by AI and data management advancements. By centralizing data, leveraging gen AI and LLMs, and establishing robust data foundations, manufacturers can enhance efficiency, improve productivity, and drive innovation. These strategies will enable manufacturers to stay competitive in a rapidly evolving landscape and fully realize the potential of AI technologies.
For further insights, explore reports from Snowflake, McKinsey, and Grand View Research.