Generative AI might be the hottest buzzword in nearly every industry (especially in manufacturing), but it’s also one of the most misunderstood concepts. It promises to make everything better and more efficient for those who invest.
Despite all the mysticism, generative AI is remarkable and worth the hype. However, implementing it in production is rarely straightforward.
In this blog, we aim to demystify generative AI for manufacturing companies, offering a clear path to implementing generative AI use cases in your business. It isn’t a high-level overview of generative AI but a roadmap for manufacturing companies of all sizes to reference when developing a plan to harness generative AI at scale.
Why Implement Generative AI in Manufacturing?
At first glance, Generative AI may not seem a good fit for the manufacturing industry. When people think of Generative AI, they think of customer service chatbots or a Chat GPT writing an essay for them. However, Generative AI can do so much more. It can significantly benefit the manufacturing industry by enhancing product design, improving production efficiency, and enabling data-driven decision-making.
What are the Biggest Challenges to Implementing Generative AI in Manufacturing?
Implementing Generative AI can be difficult as there are some hurdles to overcome for any business to get up and running:
Data Quality
You get the same quality output as the data you use for any AI system, so having accurate and unbiased data is of the utmost importance.
Integration With Existing Systems
AI needs a lot of data to give beneficial results, probably from many different sources and systems. Integrating these in a centralized manner can be challenging.
Talent and Expertise
Having the right people to set up and fine-tune your Generative AI is imperative to a successful implementation. If you don’t have the right people for the job in-house, you may look to consulting services to augment your team (such as the AI experts at phData).
The types of manufacturers best suited to implement Gen AI right away are those that have a centralized cloud-based data warehousing solution such as Snowflake AI Data Cloud to hold all the data needed and have the computing power to run AI services, have a data-centric culture where data quality is expected, and have the talent (or access to outside talent) to implement AI successfully. Once these pieces are in place, the biggest challenges of implementing Gen AI are out of the way.
Use Cases to Get Started Utilizing Gen AI in Manufacturing
Customer Service Automation
It seems like every company is implementing some sort of chatbot for their customer service, and with good reason. Gen AI can be used to automate customer service interactions, which reduces the time to resolution while also being available to the customer 24 hours a day, 7 days a week. In manufacturing specifically, the chatbot could answer questions about product troubleshooting, ordering replacement parts, how to use products, and general product information.
Like most Gen AI use cases, the first step to achieving customer service automation is to clean and centralize all information in a data warehouse for your AI to work from.
Document Search
Everyone who’s ever read a product manual knows it can be notoriously complex, and finding the information you’re looking for is difficult. This is especially true when there are several versions of the same product, so there are multiple product manuals to sift through. This can hinder service technicians searching for information to fix a broken product or sales teams attempting to ask a specific question about a product for a customer.
A huge amount of information like this, with users searching for specific details, is a perfect use case for Gen AI. The AI can quickly sift through all versions of product manuals to deliver detailed information and even step-by-step instructions for technicians and salespeople alike so they can stop wasting time and get to work faster.
As with customer service automation, the main challenge is to have all your product manuals and documentation in a central database for the AI to process. Once accomplished, your team can ask simple questions to get the needed information more efficiently than searching for it themselves.
Implementing Advanced Generative AI Manufacturing Use Cases
Building a Data Foundation
As we’ve seen with the first two use cases mentioned above, the first step in using Generative AI is having all your data in one place. However, this can be particularly challenging in manufacturing, where data comes in from sensors on production lines, suppliers, customers, and partners. All this data comes in different forms and doesn’t easily work together in a centralized manner.
One way to combat these challenges is to work with a cloud-based data warehouse with a track record of serving manufacturers such as Snowflake.
Snowflake has key differentiators over other solutions to build a foundation of data that your AI can use:
Machine Data Ingestion
Proven MQTT integrations
Multi-language flexibility via SnowPark
IoT and Edge Performance
Near-real-time streaming with SnowPipe
Ease of scaling
Unstructured and semi-structured data support
Native data loading
Native connectors similar to machine data ingestion
Utilizing these Snowflake features can help you centralize your data, eliminate data silos, promote collaboration, and get your organization ready to properly implement Gen AI.
Supply Chain Performance
Supply chain management can make or break a manufacturing business. It’s also poised as one of the areas in which Gen AI can make the biggest impact.
Here are just a few of the areas where Gen AI can be used to improve the performance of your supply chain:
Accurate Demand Prediction
Many manufacturers are using Gen AI to analyze large volumes of historical sales, market trends, and external economic factors to forecast demand more precisely than ever. This can be performed regularly to combat supply and demand volatility.
Optimize Inventory Levels
More accurately predicting demand allows AI to recommend optimal inventory levels, preventing overstocking or stockouts.
Route Optimization
With transportation costs increasing, AI can continuously determine the optimal routes to reduce costs while improving delivery times.
Some of the key challenges of implementing Gen AI for supply chain management include:
Disruptions and Risks
The architecture of your AI must be able to predict, identify, and respond to disruptions and risks within your supply chain.
Integration of Departments
Many departments are involved in the supply chain, and all their data must be integrated to use AI effectively.
Reliability of Data
If the underlying data isn’t reliable, the AI won’t be reliable either. With any AI, you get out the quality you put into it.
Forecasting Abilities
Have the computing power and functionality to be able to forecast effectively
Utilizing Snowflake can help combat some of these challenges with key differentiators from other data warehouses, including:
Being built on a scalable, secure technology to enable near real-time business decisions.
Facilitating collaboration across departments, customers, suppliers, and vendors.
The ability to create event-driven architectures to allow for risk management and predictive action based on real-time visibility inputs.
Multiple options for forecasting, from native series functions to Snowpark for model development.
Marketplace options to “buy” the relevant data from external factors to get more intuitive insights from the AI.
Power Smart Manufacturing
One of the newest innovations in manufacturing is the idea of Power Smart Manufacturing or Industry 4.0. The general idea is to use Industrial Internet of Things (IIoT) devices to monitor equipment and processes to optimize everything about the production process. Power Smart Manufacturing can perform tasks such as:
Predictive Maintenance
By analyzing real-time data from machinery, manufacturers can identify potential issues early and schedule maintenance before something breaks. This approach not only extends the life of equipment but also minimizes costly interruptions in production.
Innovation
This new technology allows faster and more accurate data analysis, accelerating innovation. Research and development cycles can also be accelerated by experimenting with new materials and processes based on the feedback from AI models utilizing the data.
Quality Control
Data from sensors allow manufacturers to identify patterns and root causes of defects, leading to improved quality control.
The obvious challenge of utilizing Gen AI for IIoT technologies is having IIoT data stored in a way that is compatible with your other data. This can be difficult as IIoT needs a data warehouse to ingest data in real time and process the unstructured and semi-structured data these devices provide.
Snowflake is the ideal solution as it has the ability to:
Ingest sensor data in near real-time.
Full support for unstructured and semi-structured data.
Marketplace offerings around intelligent manufacturing.
Connected Product (IoT)
One of the benefits of manufacturing connected products is the plethora of information about the product’s performance in users’ hands that is sent back to the manufacturer. Now, the products themselves can be monitored while the consumer uses them. Generative AI can use all this information to answer questions about customer usage patterns, product performance, and customer preferences, leading to even more insights about new services or products to create next.
Your company most likely builds many IoT devices for consumers, and this amount of information can be daunting. Retrieving and storing all that data requires a data warehouse built to handle it, such as Snowflake. For IoT data, Snowflake has a few features that set it apart:
Ease of scaling for large amounts of data flowing in.
Unstructured and semi-structured data support.
Native data loading from IoT devices.
Integrated end-to-end platform to connect disparate data sources across product lifecycle.
Closing
Generative AI has arrived and has truly become a transformative force across manufacturing. However, its power is still obscured by hype and complexity. This blog has tried to cut through much noise and give practical insight into manufacturers’ roadmaps to harness this technology.
Manufacturers can run successful generative AI solutions, bringing real business value, if they understand the basics of building strong data foundations and choose the right use cases. Generative AI is not a magic bullet, but if applied strategically, it can become a powerful catalyst for innovation, efficiency, and growth.
If you’re ready to unlock the potential of generative AI in your manufacturing processes or need guidance on where to start, phData’s experts are here to help. Contact us today to learn how we can partner with you to drive innovation and achieve measurable results.
