Investment in AI for manufacturing is expected to grow by 57% by 2026. That’s hardly surprising — with AI’s ability to augment worker productivity, improve efficiency and drive innovation, its potential in manufacturing is vast. AI’s predictive capabilities can help manufacturing leaders anticipate market trends and make data-driven decisions, creating financial opportunities for suppliers as well as customers.
Since data forms the bedrock of generative AI, staying ahead of the most recent trends in data and AI is crucial for harnessing this technology to gain a competitive advantage.
To learn more about how the manufacturing sector will use AI in 2024, we sat down with our Snowflake industry and technology experts. For their full insights, read the new report, Manufacturing Data + AI Predictions 2024.
Here’s a quick look at their top three industry predictions for the year ahead:
1. AI will overhaul how manufacturers work
Manufacturers are always looking for ways to make their operations more efficient and cost effective. Snowflake’s Global Head of Manufacturing Tim Long says smart manufacturing — the use of advanced technologies to improve the efficiency of traditional processes — is a “huge area of interest” that industry leaders can supercharge with data and AI.
To use AI to its full potential, data should be consolidated in one place. Historically, manufacturers have struggled to consolidate data derived from information technology (IT), operational technology (OT), and second- and third-party data sources, which is commonly housed in multiple siloed environments. Manufacturers are overcoming this challenge by bringing data off the shop floor and into the cloud, where they can combine it with other operational data from the factory.
Integrating data from the equipment with other manufacturing process data has been a big trend among manufacturers, and it will continue to be a major focus in 2024. Long says, “Whether it’s oil and gas or high tech or industrial manufacturing, there is high demand to bring the data out into an environment where it can be merged with other data.”
2. Crucial decisions across the value chain will be informed by AI and data
Supply chain challenges continue to have a big impact even after some of the strain of the global pandemic has eased. Data collaboration is already helping, Long says. Data collaboration is the process of gathering and sharing data from various sources. This typically involves combining data sets from internal teams and empowering domain experts to contribute their unique perspectives to provide insights.
Data collaboration also takes the form of data-sharing partnerships or supplementing existing data with third-party data sets. This is where Long says he is seeing some manufacturers get ahead of supply chain issues.
Manufacturers are seeing the benefits of data sharing in three specific areas:
- Collaboration: There is a notable emphasis on data collaboration in the shipping and logistics sector, particularly with third-party entities to facilitate the flow of products from manufacturers to customers. By collaborating with third parties, manufacturers can get products to customers more efficiently.
- Planning: Manufacturers are using data to understand the availability and projected costs of raw materials and energy, helping them plan more effective production processes.
- Risk management: Manufacturers can monitor potential suppliers’ performance against various risk indicators so they can be proactive in addressing or mitigating supply chain disruptions.
Manufacturers should identify the areas with the biggest potential payback and where the biggest business challenges exist, then assess whether gen AI is the right solution to make the impact they need in those areas.Tim Long, Global Head of Manufacturing, Snowflake
3. A robust data foundation will distinguish leaders in manufacturing
As more and more businesses develop a comprehensive and forward-looking data strategy, AI advances have accelerated and expanded data-driven insights and analytics.
A unified data structure is essential to properly train gen AI and large language models (LLMs). Data silos are likely to result in incomplete or inaccurate outputs, or require a lot of extra work to overcome. A single data platform also allows companies to set the relevant privacy features across their ecosystem, and preserve consent around data that customers have willingly shared.
Combining integration, storage, governance and management of data into a single platform establishes a single source of truth in a quickly evolving competitive environment. It improves data quality, cuts costs, improves efficiency and leads to better-informed decisions. A holistic approach to data management will help to improve metadata, promote consistency, and raise the quality of what goes into gen AI and LLMs — and, thus, the quality of what comes out. A modern data cloud platform is critical to successfully executing on such a robust data strategy, and the presence of one will fundamentally dictate the success of AI strategies going forward.