Manufacturing
Leaders in the manufacturing industry are contending with numerous challenges, including supply chain disruptions, labor shortages, and the need for sustainable practices. Additionally, they must adapt to rapidly changing market demands and maintain operational efficiency. However, by leveraging technologies such as automation, AI, and advanced analytics, these challenges can be effectively addressed and transformed into opportunities for growth and innovation.
Common business concerns of manufacturing leaders
Technology integration challenges
Manufacturing sector leaders are concerned about the high initial costs and complexity of integrating new technologies like AI and robotics. Integrating new technologies with legacy infrastructure is expensive and requires technical expertise.
Skill gap concerns
They worry about the shortage of workers with the necessary technical skills to operate and maintain advanced technologies. There is a significant gap of availability for skilled workers which raises the need for new worker training, identifying new talent or simplifying operations to meet current skill levels.
Production and cost efficiency
Leaders find it challenging to optimize production ecosystems for cost-efficiency and supply chain resilience. Improved production efficiency can lead to lower costs and higher outputs but can be resource intensive.
Data security
Ensuring the security of sensitive data in increasingly digital operations is a significant concern for manufacturing leaders. Robust data security measures protect intellectual property and maintain customer trust.
Unlock the power of AI in manufacturing
By tapping into AI’s nearly limitless potential, manufacturers can transform challenges into opportunities—driving new levels of innovation and efficiency across their operations.
1
Efficient technology adoption
AI has the power to make the adoption of new technologies smoother by offering predictive maintenance and streamlining workflow. AI-driven software enhances these processes, boosting production and cutting costs by minimizing unnecessary downtime.
2
Virtual training and upskilling
Training programs powered by AI and virtual simulations can enhance the skills of workers, making them adept at handling new technologies. By upskilling both existing and incoming employees, companies can boost productivity and reduce the expenses associated with recruiting, hiring and training new staff.
3
Process optimization
AI tools analyze data in manufacturing to identify inefficiencies and suggest improvements. They also fine-tune supply chain management and boost employee efficiency, leading to reduced expenses and increased production.
4
Real-time threat detection
Training programs powered by AI and virtual simulations can enhance the skills of workers, making them adept at handling new technologies. By upskilling both existing and incoming employees, companies can boost productivity and reduce the expenses associated with recruiting, hiring and training new staff.
Statistics
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72% of surveyed manufacturers report reduced costs and improved operational efficiency after deploying AI technology.10
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41% of surveyed manufacturers report improved process optimization and control after deploying AI technology.11
Using AI to meet the changing customer demands
A hyperscaler provides extensive cloud services to a diverse range of end customers, including enterprises, startups, and individual developers. These customers rely on the hyperscaler for scalable computing resources, storage, and advanced networking capabilities. However, as data volumes grow exponentially, the need for efficient data offloading and low-latency processing becomes critical. The corporation's end customers are experiencing increased latency and bandwidth constraints due to the massive amounts of data being generated and processed.
On a factory floor, data signals are continuously streamed from environmental IoT sensors, cameras, and other industrial equipment creating a massive data repository.
Additionally, next gen solutions such as automated guided vehicles (AGVs) and video analytic applications require network transport that supports high bandwidth and low latency requirements.
By leveraging the vast data collected from its diverse systems, the company has developed an AI model aimed at boosting warehouse efficiency. This model refines algorithms that optimize stock levels, enhance sustainability measures, and predict maintenance needs for its automated machinery fleet.
AI technology is now a part of the video monitoring systems that oversee automated sorting machines. This integration has enhanced the analytics, significantly boosting the machines' capability to identify defective products during the quality control stage.
Data on quality control, maintenance alerts, and overall efficiency of products are gathered and shared with employees on the production floor through smart devices, providing them with immediate feedback in real time.
The manufacturing company now sends categorized data sets to data centers where they are utilized to enhance the AI model's efficiency and scenario planning capabilities.
The industrial firm has effectively integrated artificial intelligence into their information technology framework, addressing distinct business challenges and establishing its dominance in the field of manufacturing. Leveraging AI solutions combined with a robust IT backbone contributes to the company's heightened productivity and enhances operational process enhancement across the board.
Integrating multiple technologies can lead to operational efficiencies
Transporting systems and technologies
Transportation systems and technology are crucial for managing large data quantities, accelerating the transmission and processing speeds. They play a key role in powering data-intensive artificial intelligence applications.
AI empowered cybersecurity tools
AI empowered cybersecurity tools can protect confidential data and customer intellectual property by providing real-time monitoring and detection of threats that are foreign to the IT infrastructure. This multi-pronged approach keeps the manufacturing environment safe from cyber-attacks and maintains the customer’s trust.
Reliable edge solutions
Reliable edge solutions ensure efficient data flow from various sensors and devices on the manufacturing floor to AI datasets that sit at the edge. This allows AI applications to make timely and accurate decisions based on advanced models, enhancing data insights.
Managed and Professional Services
Managed and professional services are vital in aiding a manufacturing firm to deploy and make the most of AI, offering expertise and assistance. They can pinpoint the most beneficial applications for AI like predictive maintenance, quality assurance, and supply chain improvement, integrating it smoothly with current systems, training staff, and delivering continual support and enhancement. This all-encompassing strategy allows manufacturers to capitalize on AI, boosting output, minimizing interruptions, and improving operations.
Other ways AI can affect the manufacturing environment
Employee training and safety
Workforces can be skilled-up through AI training systems to manage sophisticated equipment and procedures. Utilization of AR/VR allows for safe replication of intricate tasks and typical mistakes, allowing employees to practice safety procedures in a controlled environment without the real world risks.
Supply Chain Optimization
AI optimizes supply chain logistics, inventory management, and procurement processes, improving efficiency, reducing costs, and enhancing overall supply chain resilience.
Enhanced Product Quality
Enhanced Product Quality: AI-powered vision systems can inspect products with far greater accuracy and speed than human inspectors, who are more prone to making errors (and overlooking them).
Enhanced Productivity
AI automates mundane tasks, allowing employees to focus on higher-level functions. It enhances process optimization by utilizing data analysis to pinpoint inefficiencies, preemptively address problems, maintain high standards of quality, and propose enhancements. Consequently, this accelerates output, reduces mistakes and material wastage, resulting in a more efficient, streamlined manufacturing workflow.
Employee training and safety
Supply Chain Optimization
Enhanced Product Quality
Enhanced Productivity
Employee training and safety
Supply Chain Optimization
Enhanced Product Quality
Enhanced Productivity
Employee training and safety
Workforces can be skilled-up through AI training systems to manage sophisticated equipment and procedures. Utilization of AR/VR allows for safe replication of intricate tasks and typical mistakes, allowing employees to practice safety procedures in a controlled environment without the real world risks.
Employee training and safety
Supply Chain Optimization
Enhanced Product Quality
Enhanced Productivity
Supply Chain Optimization
AI optimizes supply chain logistics, inventory management, and procurement processes, improving efficiency, reducing costs, and enhancing overall supply chain resilience.
Employee training and safety
Supply Chain Optimization
Enhanced Product Quality
Enhanced Productivity
Enhanced Product Quality
Enhanced Product Quality: AI-powered vision systems can inspect products with far greater accuracy and speed than human inspectors, who are more prone to making errors (and overlooking them).
Employee training and safety
Supply Chain Optimization
Enhanced Product Quality
Enhanced Productivity
Enhanced Productivity
AI automates mundane tasks, allowing employees to focus on higher-level functions. It enhances process optimization by utilizing data analysis to pinpoint inefficiencies, preemptively address problems, maintain high standards of quality, and propose enhancements. Consequently, this accelerates output, reduces mistakes and material wastage, resulting in a more efficient, streamlined manufacturing workflow.
Keep reading to discover how our capabilities can assist your company in fully leveraging the benefits of artificial intelligence.
Capabilities
As of now, you’ve seen what AI can do for your manufacturing organization by improving control and visibility into manufacturing systems and ensuring product quality across different production lines or factories. However, your AI strategy will fail without the proper infrastructure. Reliable networking is crucial to transfer all the data fed into your AI models (sensor data, production data, quality control, maintenance, energy consumption, inventory, supply chain information, etc.) from and to the cloud, data center, factories or anywhere in the perimeter. Robust cybersecurity is essential to safeguard all this valuable data and AI infrastructure from ever evolving threats. Low latency is vital for obtaining the real-time insights you are expecting from AI-powered systems to optimize your operations. Lastly, expert guidance is critical to ensure the successful infrastructure implementation and continuous support to empower your AI initiatives.
- Networking
- Cybersecurity
- Edge Fabric
- Managed & Professional Services
Networking
Problem to solve
From smart supply chains to intelligent capacity planning, AI is revolutionizing modern manufacturing. However, AI systems face challenges in handling vast data volumes and ensuring seamless, high-speed data transmission. Without robust networking infrastructure, these systems hinder their effectiveness due to struggles with latency and bandwidth issues.
This lack of proper infrastructure can lead to significant delays in data processing, causing disruptions in production schedules and inefficiencies in operations. End customers might experience longer lead times, reduced product quality and inconsistent supply chain performance. Ultimately, the inability to maintain a reliable and efficient AI-driven system could result in lost revenue and diminished customer trust, putting manufacturing leaders at a competitive disadvantage.
Why network infrastructure is critical for AI
A robust networking infrastructure is the backbone that enables manufacturing institutions to fully leverage AI’s transformative potential. Without it, the seamless flow of data from the cloud, data center or factories necessary for real-time decision-making and predictive analytics would be severely hampered. This could lead to bottlenecks in production, delayed responses to market demands and an inability to swiftly adapt to changes in the supply chain.
Without a reliable network, the predictive maintenance system may experience delays in data transmission from equipment sensors to the central monitoring system. This can lead to slower detection of potential issues, increasing the risk of equipment failure and unplanned downtime.
Manufacturing leaders must recognize that the speed and reliability of their networks directly impact the efficiency of AI-driven operations. Poor networking infrastructure can result in significant downtime, increased maintenance costs and a loss of competitive edge. Furthermore, the absence of a scalable network can impede innovations in product quality, staff training and the use of AR/VR to replicate complex tasks safely, hindering the integration of advanced AI applications that could revolutionize manufacturing processes.
Ultimately, investing in a state-of-the-art networking infrastructure is not just about supporting current operations but also about future-proofing the organization. It ensures that manufacturing institutions can continuously evolve, meet customer expectations and maintain a leading position in the industry.
How network solutions enable AI
Networking infrastructure is the lifeline that enables AI systems to function seamlessly within manufacturing environments. High-bandwidth cloud connectivity is essential for AI systems to operate at their full potential, ensuring that data flows smoothly and swiftly across the organization. This connectivity supports everything from real-time analytics to predictive maintenance, allowing manufacturing leaders to make informed decisions quickly and accurately. Without it, the benefits of AI, such as enhanced product innovation and optimized logistics, would be significantly diminished.
Moreover, a strong networking infrastructure ensures that AI applications can scale effortlessly to meet growing demands in supply chain operations, enhanced product quality, monitoring facility performances, maintaining operational efficiency and performance. This scalability is crucial for staying competitive and agile in a rapidly evolving manufacturing market. Investing in advanced networking solutions is not just about supporting current operations but also about paving the way for future advancements and sustained growth in the manufacturing sector.
Conclusion
AI systems are the backbone of modern manufacturing, from smart supply chains to intelligent capacity planning. Networking solutions are not just beneficial but essential for manufacturing organizations leveraging AI systems. They ensure that these organizations can fully harness the power of AI to improve efficiency, accuracy and decision-making in their operations. Reliable, dedicated and scalable networking infrastructure is critical to reducing latency and ensuring ample bandwidth for AI systems to function effectively.
Cybersecurity
Problem to solve
AI systems in manufacturing handle vast amounts of sensitive data from providers and customers, including intellectual property, making them targets for unauthorized access, model manipulations and cyberthreats. Additionally, the increasing sophistication of AI has paved the way for more elaborate cyberthreats. Highly robust, cutting-edge cybersecurity standards are critical to protect data integrity, confidentiality and operational availability in a more complex security landscape.
Without proper cybersecurity infrastructure, AI systems in manufacturing organizations become prime targets for cyberattacks, leading to potential data breaches, intellectual property theft and operational disruptions that could impact production or customer experience. End customers could face compromised product quality, delays in delivery and a loss of trust in the manufacturer’s ability to safeguard their data, whether in transit or at rest. This not only jeopardizes the company’s reputation but also results in significant financial losses and legal ramifications, ultimately putting manufacturing leaders at a severe competitive disadvantage.
Why cybersecurity infrastructure is critical for AI
Despite all the benefits that come with them, vulnerabilities multiply when organizations implement AI-powered tools. Cyberattacks or disruptions could considerably impact the organization since these systems are now involved in critical processes in factories like smart supply chains, logistics, quality management and product innovation. Safeguarding customer and organizational data—whether at rest or in transit—and AI-driven processes from unauthorized access, model manipulations and cyberthreats is critical for manufacturers’ AI strategies.
The integration of AI-powered tools in manufacturing brings immense benefits, but it also introduces new vulnerabilities. Cybersecurity infrastructure becomes indispensable in this context, as any cyberattack or disruption can have far-reaching consequences. When AI systems are embedded in critical processes in factories and production lines such as smart supply chains, logistics, quality management and product innovation, the stakes are incredibly high.
Without robust cybersecurity measures, the predictive maintenance system could be vulnerable to unauthorized access and data manipulation, causing incorrect maintenance predictions and potentially damaging equipment. Additionally, sensitive operational data could be exposed, leading to financial losses and damage to the company's reputation.
Without robust cybersecurity measures, manufacturers risk exposing sensor data, production data, quality control, maintenance, energy consumption, inventory, supply chain information, and sensitive customer and organizational data to unauthorized access and cyberthreats. This not only jeopardizes the integrity of AI-driven processes but also undermines trust and reliability. Effective cybersecurity safeguards against model manipulations and ensures that data, whether at rest or in transit, remains secure.
For manufacturing leaders, investing in comprehensive cybersecurity infrastructure is not just a protective measure but a strategic necessity. It ensures that AI systems can operate without interruption, maintaining the efficiency and innovation that give manufacturers a competitive edge. By prioritizing cybersecurity, manufacturers can fully harness the power of AI, driving growth and resilience in an increasingly digital landscape.
How cybersecurity enables AI for hyperscalers
Cybersecurity infrastructure is the foundation that enables AI to thrive within manufacturing operations. Without it, the advanced AI technologies designed to enhance supplier relations, smart maintenance processes, intelligent facilities and capacity planning would be vulnerable to sophisticated threats. These threats could disrupt operations, compromise the integrity of sensor and production data, quality control, maintenance, energy consumption, inventory and supply chain information. Such disruptions could adversely affect AI models and result in significant financial and reputational damage.AI-powered cybersecurity solutions are not just about defense; they are about ensuring the seamless and safe operation of AI systems to assist with predictive maintenance and enhanced product quality across different production lines or factories. By protecting against unauthorized data access, model manipulations and cyberthreats like denial-of-service attacks or malware, these solutions maintain the integrity, confidentiality and availability of critical data. This protection is crucial for the smooth functioning of AI systems, which in turn drives operational efficiency and innovation.
For manufacturing leaders, investing in robust cybersecurity infrastructure is a strategic imperative. It builds trust and confidence among providers, customers and stakeholders, ensuring that AI-driven advancements can be fully realized without the looming threat of cyber disruptions. This commitment to cybersecurity not only safeguards current operations but also paves the way for future growth and resilience in an increasingly interconnected world.
Conclusion
By ensuring the safe and effective operation of advanced AI technologies, robust cybersecurity measures are critical for manufacturing organizations. They protect sensitive data against complex threats, minimize downtime and enhance customer experience, helping build trust and confidence among manufacturing leaders and stakeholders.
Edge Fabric
Problem to solve
As AI initiatives take off, the influx of data requires an infrastructure strategy for secure connectivity, storage, access and transmission of that data for real-time processing and decision making. Traditional networks and infrastructure simply can’t keep up with the demand.
AI systems in the manufacturing industry require minimal latency and real-time, local processing for tasks such as supply chain resilience, predictive maintenance and demand forecasting. Without these capabilities, AI systems struggle with delays and inefficiencies, hindering their ability to deliver optimal performance and customer satisfaction as data would bottleneck as it attempted to move across a disperse network to a data center and back again.
Without proper edge fabric infrastructure, these delays and inefficiencies become even more pronounced, leading to significant disruptions in production processes. This can result in increased downtime, higher maintenance costs and an inability to respond swiftly to market demands. End customers might experience longer wait times for products, inconsistent quality and a lack of reliability in supply chain operations. Ultimately, the absence of robust edge fabric infrastructure can erode customer trust and satisfaction, causing manufacturing leaders to fall behind competitors who have invested in the necessary technology to support their AI-driven initiatives.
Why edge fabric solutions are critical for AI
Edge fabric infrastructure supports the seamless integration and operation of AI technologies in manufacturing organizations. By minimizing latency and enabling real-time processing, edge fabric solutions ensure that AI systems can analyze the data recorded by applications and sensors in real-time, maintaining inventory optimized in all the facilities, and provide real-time analytics for accurate decision-making processes. This reliability fosters trust and confidence among providers, customers, manufacturing leaders and other stakeholders, as they can depend on consistent and high-performance outcomes.
Without an edge fabric, each factory and production center may struggle with efficient data processing locally. This can result in delays in analyzing equipment data and predicting maintenance needs, as data must be sent to centralized servers for processing. The lack of real-time processing can increase latency, reduce system performance and lead to bottlenecks in the maintenance process, impacting overall operational efficiency.
Investing in edge fabric infrastructure is not just about immediate performance gains; it’s about building a resilient foundation that supports ongoing product innovation, high-personalization and sustained growth. It ensures that manufacturing leaders can confidently leverage AI to drive accurate decision-making with real time analytics and more efficiency with predictive maintenance features, ultimately leading to a more competitive and future-ready organization.
How edge fabric enables AI
For organizations seeking a flexible digital infrastructure with low latency, speed and security, a network connectivity fabric can be customized to leverage existing lit fiber network, expand new routes to connect to the datacenter, and consumed or reconfigured on demand as data demands increase.
Edge fabric infrastructure is pivotal in enabling AI systems to operate at their full potential within manufacturing institutions. By strategically allocating computing resources close to data sources, Edge Fabric solutions ensure that data remains local, significantly reducing latency and enhancing processing speeds. This proximity is crucial for real-time analysis and decision-making, which are essential in areas such as logistics, quality management, supplier relationship management, product innovation, connected maintenance and smart facilities.
The ability to process data swiftly and efficiently allows manufacturing leaders to respond to changes and challenges with agility and precision. Edge fabric solutions streamline cloud connectivity, ensuring that AI applications can seamlessly integrate and function without delays. This real-time capability is not just a technical advantage but a strategic one, enabling manufacturers to maintain a competitive edge by optimizing operations and driving innovation.
Investing in edge fabric infrastructure means empowering AI systems to deliver immediate insights and actions, which are critical for maintaining operational excellence by predicting machinery maintenance and meeting high expectations for quality as items are being produced. It ensures that manufacturing institutions can harness the full power of AI, leading to improved efficiency, enhanced product quality and sustained growth.
Conclusion
Edge fabric solutions are not just beneficial but necessary for manufacturing organizations to fully harness the power of AI. They ensure efficient operation and accuracy in the decision-making processes that depend on real-time information.
Managed & Professional Services
Problem to solve
Identifying and maintaining the proper infrastructure for AI systems in the manufacturing industry can be challenging. Without expert guidance about the AI-required infrastructure, AI systems may not work at their best or be integrated into operations like smart supply chain management, logistics, and quality management, leading to inefficiencies, increased costs and suboptimal performance.
Without managed and professional services, AI systems could malfunction, causing longer wait times for products, inconsistent quality and potentially higher prices due to inefficiencies in the manufacturing process. The inability to leverage AI effectively could also mean missed opportunities for innovation and competitiveness in the market. Ultimately, the absence of a robust AI infrastructure could erode customer trust and satisfaction, impacting the organization’s reputation and bottom line.Why managed and professional services are critical for AI.
For manufacturers embarking on the AI transformation journey, managed and professional services are indispensable. Expert guidance helps ensure that AI systems are not only correctly deployed but also seamlessly integrated into existing operations. This tailored approach allows manufacturers to fully capitalize on the benefits of AI, such as enhanced quality management, innovative product development and efficient smart warehouse management.
Without these specialized services, manufacturers might struggle with the complexities of AI implementation, leading to potential disruptions in production and inefficiencies in quality. Managed services provide continuous support and optimization, helping to keep the infrastructure robust and capable of handling the demands of advanced AI applications. This proactive management helps prevent downtime, reduce operational risks and ensure that AI systems perform at their best. With these services, manufacturers could benefit from everything AI has to offer, such as predictive maintenance and enhanced product quality across different production lines or factories.
How managed and professional services enable AI
A team of experts can provide tailored assessments to guide manufacturing organizations during deployment and help ensure the infrastructure aligns with their needs. Once the infrastructure is up and running, skilled professionals can manage the network, edge fabric and cybersecurity tools—helping ensure AI systems are running efficiently, maximizing performance and uptime and allowing internal resources to focus on core business projects and AI-powered processes such as smart facilities and intelligent capacity planning. This strategic support not only enhances the operational efficiency of AI systems but also drives innovation and growth within the manufacturing organization. With managed and professional services, manufacturing leaders can be confident that their AI infrastructure is optimized and resilient, enabling them to fully harness the transformative power of AI in their operations.
Conclusion
By leveraging M&P services, hyperscalers can ensure that their AI systems are operating at their best, empowering their customers to be more creative and productive. This allows internal resources to focus on innovation projects, ultimately enhancing the value provided to the global customer base. M&P services enable hyperscalers to concentrate on their core competencies and innovation in AI, while outsourcing the management and operational aspects to experts in the field. This approach accelerates the development and deployment of AI solutions without being bogged down by the intricacies of infrastructure management and support.
Keep reading to find out how Lumen can serve your manufacturing organization in its AI transformational journey. By leveraging robust networking, edge fabric and cybersecurity solutions, we can empower your AI systems to enhance production quality, optimize supply chains and improve predictive maintenance, driving innovation and operational excellence in your business.
Our expertise at a glance
As a trailblazer in global networking for manufacturers, Lumen is your strategic ally in the AI era. We can deliver AI-ready infrastructure with broad coverage and unique fiber network routes, providing capacity and flexibility to meet your dynamic manufacturing demands. Our forward-thinking solutions span network, cybersecurity and Edge Fabric suites, supported by a team of experts to optimize your network performance and security.
Reliable network for manufacturers
- Expertise: Leverage Lumen knowledge and experience in AI and load balancing to optimize your manufacturing environment.
- Seamless integration: Enable smooth integration of AI tools with existing manufacturing infrastructure.
- Scalability: Get scalable solutions that grow with your manufacturing needs, helping your network handle increasing demands.
Robust cybersecurity measures for manufacturers
- Specialization: Leverage our expert knowledge in AI and cybersecurity tailored specifically for manufacturing environments.
- Seamless integration: Enable smooth integration of AI-driven cybersecurity tools with existing manufacturing infrastructure.
- Enhanced support: Take advantage of dedicated support and resources to address unique security challenges faced by manufacturers.
Edge fabric for manufacturers
- Minimal latency and real-time processing: Enable efficient data transfer and processing to meet the high demands of manufacturing environments and customer expectations.
- Scalability and flexibility: Support the growth of AI and data-intensive applications with scalable solutions tailored for manufacturers.
- Enhanced performance and cost savings: Optimize data flow and minimize operational expenses in large-scale deployments.
- Compliance and security: Enhance data sovereignty and provide robust security measures to meet stringent manufacturing operational requirements.
AI-ready infrastructure solutions
Networking Solutions
Unleash your business potential with networking solutions that enable inteligent automation, real-time performance data and dynamic bandwidth scalability.
Security Solutions
Secure your digital environments and maximize productivity with award-winning cyber defense built for today's digital security landscape.
Edge Private Cloud
Fully managed, pre-built infrastructure for high-performance private computing connected to our global fiber network.
Managed & Professional Services
Proven technology expertise and results you can trust.
Why Lumen
By partnering with Lumen, you can ensure that your AI initiatives are built on a solid foundation, enabling you to unlock new growth opportunities and stay competitive in a rapidly evolving digital landscape.
9National Association of Manufacturers, Working Smarter: How Manufacturers Are Using Artificial Intelligence, May 2024.
10National Association of Manufacturers, Working Smarter: How Manufacturers Are Using Artificial Intelligence, May 2024.
10National Association of Manufacturers, Working Smarter: How Manufacturers Are Using Artificial Intelligence, May 2024.