How Generative AI is Driving Efficiency in Manufacturing

Manufacturing involves massive amounts of complex data across supply chains, equipment sensors, product specs, and daily operations. Generative AI is proving to be a game-changer for extracting value from this data to optimize production.

For manufacturers, this technology allows optimizing maintenance, supply chains, quality control, and planning by detecting patterns in operations data.

While generative AI won’t be making final decisions, it gives manufacturers a powerful advantage by automating the analysis of huge data volumes. This allows staff to focus on higher-value work like process improvements and innovation.

In this post, we’ll explore some main instances leading manufacturers are using AI to increase efficiency and boost business returns.

Streamline supply chain management

Managing global supply chains with multiple tiers of suppliers is highly complex, often leading to excess inventory and costs. Generative AI can analyze supplier, logistics, and demand data to detect inefficiencies and delays.

For instance, an automotive manufacturer could input purchasing records, production plans, and supplier performance data into AI models to identify better distribution patterns to avoid regional part shortages. This allows optimizing where inventory is stored and transported.

MORE: Keep up with supply chain management to minimize disruption

Streamlined supply chained due diligence

Additionally, AI allows you to get ahead on your supply chain due diligence and risk management. By analyzing large datasets across the supply chain, AI can identify potential issues and risks much faster than humans manually reviewing documents and data.

For example,  risk management teams can use an AI system can quickly scan supplier financials, news reports, SEC filings, and other unstructured data to flag potential financial instability, lawsuits, sanctions, or other compliance issues. This allows companies to proactively monitor and mitigate third-party risks.

This real-time risk monitoring across thousands of suppliers globally can be difficult for humans to replicate manually because of the sheer scale and complexity of modern supply chains. AI can keep up with the speed of developments while synthesizing large data sets and proposing objective analysis.  Thus, automating risk management due diligence with AI can result in massive time and cost savings while also improving supply chain resilience.

MORE: Third-party risk checklist for compliance officers

Automate manufacturing document processing

Manufacturing involves massive document volumes like equipment manuals, test specifications, maintenance records, and more. AI tools can rapidly read and extract key details from these documents at scale for streamlined manufacturing market intelligence.

For example, an electronics manufacturer can have AI scan device manuals to immediately retrieve applicable safety procedures. This accelerates document search and analysis to get specific information to workers faster.

MORE: How third-party data helps manufacturers fuel innovation and growth

Monitor product and process quality

By continually reviewing product test data and production metrics, generative AI can rapidly detect anomalies that impact quality. For instance, AI can correlate higher product failure rates with a particular component from a specific vendor. For example, an AI system can continuously analyze:

• Incoming component inspection data: Dimensions, performance metrics, photographic/video imagery, etc. flagged for each component received from suppliers. AI can detect outliers and trends to identify potential quality issues with specific component batches or suppliers.
• In-process inspection data: AI can similarly analyze metrics captured through in-process checks at each manufacturing step to flag anomalies. This data may include temperatures, pressures, test results, micrometer readings, operator comments, etc. Deviations from optimal parameters can be correlated back to specific process steps, equipment, and operators.
• Finished product test data: Results from tests done on products coming off the production line help AI models define normal vs abnormal performance. Tests may include durability, lifespan, electronics functionality, mechanical tolerances, and more. Failures can be traced back to specific batches, production runs, and dates.
• Customer returns and warranty claims: By connecting customer product issues and complaints to manufacturing dates, batches, and suppliers, AI can identify systemic quality problems. Natural language processing can analyze unstructured data like customer emails and call transcripts.
• External data: Reviews, social media, and discussions can alert manufacturers to potential quality perceptions. AI can also link news of component defects and recalls to internal supply chain sources.

By correlating these disparate data sources, generative AI models can identify root causes of quality issues and enable rapid corrective actions. This can prevent larger-scale defects and improves customer satisfaction.

MORE: How to capitalize on generative AI to enhance decision-making

Generate data-driven production plans

The best manufacturing plans balance production targets, equipment maintenance, energy costs, labor constraints, and other factors. Generative AI can analyze all these parameters to generate optimized production schedules. For example, AI could be used in

• Analyzing equipment maintenance logs: AI can review downtime, repairs, part replacements, etc. to forecast future maintenance needs.
• Factor in energy costs: Energy rates that vary by time-of-day, day-of-week, and seasonality are inputted so AI can optimize when to schedule high throughput versus lower production periods.
• Labor constraints: Worker schedules, shift patterns, vacations, and absences are used by AI to ensure adequate staffing for each production step while avoiding overtime labor costs.
• Order deadlines: Customer order due dates, rush orders, and shipping cutoffs can be prioritized in the optimized schedule.
• Inventory levels: Current work-in-progress and finished goods inventory levels guide the production pace and align with output.
• Machine learning: As more data is generated, the AI model continually learns and improves its ability to balance the many constraints and output realistic production schedules.
• Simulation modeling: AI can run through numerous what-if simulations to stress test optimized schedules before finalizing. This ensures robustness against disruptions.

By holistically addressing all these factors, generative AI can provide dynamic production scheduling that maximizes productivity within real-world constraints. Manufacturers can continuously adapt as new orders, equipment issues, and other changes occur.

MORE: Turning data into decisions

The Future of Manufacturing with AI

While it requires quality control and human oversight, generative AI unlocks huge efficiency gains in manufacturing by detecting hidden patterns in operations data. As the technology advances, manufacturers will continue finding new applications for leveraging AI across their value chain.

With responsible implementation, AI has the possibility of revolutionizing the productivity and sustainability of manufacturing.

For more exploration of how Generative AI is changing manufacturing, download the LexisNexis^® Future of Work Report 2024: How Generative AI is Shaping the Future of Work.