The manufacturing industry, the backbone of our world, needs accuracy, efficiency, and innovation. And a silent revolution at the heart of this—Artificial Intelligence (AI)—promises to redefine how we build, create, and produce. AI in manufacturing promises a world where machines assist and optimize processes, foresee faults, and extract critical data from extensive reports with unparalleled accuracy.
This blog will explore the transforming AI use cases in manufacturing. But before that, let’s first understand the need for Artificial Intelligence in manufacturing.
Importance of AI in Manufacturing
Without AI, manufacturing often faces operational inefficiencies and manual quality control, leading to
- Product defects
- Reactive maintenance
- Unplanned downtime
- Rising operational costs
The competitiveness of the industry is hindered by
- Absence of data-driven insights
- Inflexibility in production
- Supply chain hurdles
The importance of AI in manufacturing is further accentuated by
- Safety risks
- Environmental impact, and
- Inability to keep up with tech-savvy competitors
But with the intervention of AI, it’s a whole different game.
Here are the top six use cases of AI manufacturing that one of the world’s largest manufacturing industries experienced when it adopted AI
Top 6 Use Cases of AI in Manufacturing
1.AI-powered information extraction: Streamlining data crunching
Challenge: Extracting and manually analyzing unstructured data from extensive drilling reports is laborious and error-prone for manufacturing companies. This manual process consumes valuable time and introduces the risk of inaccuracies that hinder effective decision-making.
The manufacturing company was spending hours
- Manually filtering through hundreds of pages of drilling reports
- Keying in data
- Worrying about missed details.
It resulted in delayed insights, potential errors, and increased operational costs for the company.
Solution: Aerobot helps extract vital data from drilling reports—PDFs, Handwritten, Scanned, and Invoices—with pinpoint accuracy. This gives access to necessary information swiftly and accurately, accelerating decision-making and optimizing operations.
By automating data extraction using Aerobot, the company
- Improved operational efficiency
- Saw faster decision-making
- Saved manufacturers time
- Increased cost savings
- Minimized errors
2.AI in document management: Streamlining contracts and agreements
Challenge: Managing many contracts, including agreements, compliance reports, and vendor quotes, can be overwhelming and prone to oversight. Manually sorting through these documents consumes valuable resources and increases the compliance risk issues.
The company was amidst the chaos of
- Navigating stacks of contracts
- Fear of overlooking critical details
- Trying to locate specific documents
- Risk of non-compliance and mismanagement
Solution: Aerobot’s AI-powered document processing capabilities swiftly categorize and organize contracts, simplifying document management. With Aerobot’s assistance, the manufacturers effortlessly managed their contracts, and the streamlined process led to
- Reduced errors
- Improved compliance
- Increased transparency
- Enhanced operational efficiency
- Easy recovery of essential documents
3.Automating process audits: From laborious to lightning-fast
Challenge: Conducting manual process audits is labour-intensive and time-consuming for manufacturing companies. The MRO maintenance audits faced issues such as lost, missing, incomplete, or disorganized maintenance records, resulting in inconsistencies.
Manually auditing processes and gathering data increased the company’s potential for missing critical discrepancies. This approach
- Consumed valuable time
- Harmed the product quality
Solution: Aerobot automates process audits by efficiently analyzing vast datasets, identifying discrepancies, and ensuring adherence to quality standards. It automates the ecosystem of Maintenance, Repair, and Operations on the shop floor to streamline the MRO maintenance audits.
The manufacturing company maintained high-quality standards with Aerobot, as it
- Saves time
- Accelerates audits
- Improves accuracy
- Has complete record-keeping
- Sticks to superior quality standards
- Alert technician when inconsistency arises
4. AI for early detection of faults and anomalies: Preventing disruptions
Challenge: Detecting faults, anomalies, and discrepancies in real-time is crucial to prevent upheavals in manufacturing processes. Manual monitoring and detection are prone to delays, increasing the risk of costly errors.
Delayed fault detection in this company led to massive disruptions and financial losses. Manual monitoring alone couldn’t keep up with real-time changes, making it challenging to maintain seamless operations.
Solution: Aerobot proactively identifies anomalies—entry errors, missing data, no sign-off, and inadequate data—in real time as follows-
- It uses transcoding with Optical Character Recognition (OCR) tags attributed to elements on forms or fields
- It continuously analyzes data and raises alerts when deviations occur
- It enables swift corrective actions and minimizes disruptions
When the company used Aerobot, its early detection capabilities ensured
- Smooth manufacturing operations
- Better tallying and cross-checks
- Improved operational efficiency
- Reduced downtime
- Cost savings
5.Automated product-part data correlation: Optimizing efficiency</ h3>
Challenge: Correlating the relationships between product parts and manufacturing processes is essential for efficiency and quality control. However, manual correlation is time-consuming and prone to errors.
This noted manufacturing company didn’t have a structured framework to understand the interlinking. It failed to manually correlate the product and part data across complex manufacturing processes. These errors in data correlation lead to inefficiencies and quality issues.
Solution: Aerobot’s knowledge graphs revolutionize data correlation effortlessly by
- Automatically characterizing damages based on the damaged product, component, and the extent of damage
- Interlinking various stages and activities of MRO and vendor-related operations
- Forming a foundation for the NLP and the cognitive engines to function on
- Connecting product and part data across processes
- Providing comprehensive view
- Streamlining operations
- Integrating components
Aerobot’s cognitive engine automatically created a knowledge graph for this large manufacturing organization, leading to
- Faster and efficient fact-finding and data-fetching from multiple sources
- Simplified assessment and classification of damages
- Enhanced production efficiency
- Optimized operations
- Better quality control
- Reduced errors
6. Centralized data platform for unified data repository: Simplifying compliance
Challenge: Optimizing the critical functions of acquiring, processing, and predictive modeling across the data pipeline while ensuring effective access management.
Managing and auditing various records—maintenance logs, compliance reports, and engineering data—was daunting and error-prone when the company did it manually. The compliance challenges consumed valuable time and resources of the company.
Solution: Aerobot’s centralized data repository automates auditing across records of Maintenance, Vendor Quotes, Compliance Reports, and Engineering Division. Using AI-driven analysis, it simplifies
- Tracking compliance
- Maintenance schedules
- Engineering data
With Aerobot’s centralized data platform, the manufacturing company could effortlessly acquire, process, and audit diverse records, leading too
- Simplified compliance
- Improved transparency
- Protected sensitive information
- Accommodating growing data volumes
- Cost-effective regulatory management
Bottom Line
Artificial Intelligence has a significant impact on manufacturing. It is reshaping manufacturing processes from streamlining data extraction and document management to automating audits and enhancing product quality.
Manufacturers who embrace AI platforms like Aerobot thrive in this data-driven, decision-making, and automation era. But one thing is clear: AI is not just a tool; it’s the driving force behind the industry’s transformation. It empowers us to reach new heights of efficiency, quality, and competitiveness. So, manufacturers must harness the power of AI for a more efficient and competitive future.
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