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Connected Workers and Digital Learning for Manufacturing Efficiency

January 30, 2024

connected worker

Have you ever stopped to think about the profound changes people-centric technology is bringing to the factory floor?

It's not just a matter of new gadgets or software; it’s about an entire paradigm shift in the way industrial organizations approach the manufacturing process. A silent yet pivotal revolution is taking root in modern manufacturing, driven by connected worker technology and digital learning.

It's a world where machines and humans collaborate more closely than ever, where learning is no longer confined to classrooms or manuals but integrated seamlessly into daily workflows.

Our new landscape fuses tech with human approaches more than ever, and the change we’re going through is as much about the people and culture in manufacturing as it is about technology. 

How are connected manufacturing and digital learning redefining efficiency and productivity? More importantly, what does this mean for the future of manufacturing, and how do we ensure that this future is not only technologically advanced but also ethically grounded and human-centric?

This article will address these increasingly relevant questions, offering insights and answers that illuminate the path forward in this technological renaissance.

Connected manufacturing: advanced integration of IoT and real-time analytics

A new phase of digital transformation is underway in manufacturing, with the internet of things (IoT) taking center stage. Sensors and connected devices are now integral parts of manufacturing equipment, providing a continuous flow of data to factory personnel. This real-time data is vital for instant decision-making and shaping long-term business strategies. 

With the ability to closely monitor production, the health of equipment, and energy consumption, manufacturers gain the agility to swiftly address inefficiencies, boosting productivity and cutting down on costs.

The significance of real-time analytics extends beyond data harvesting. 

These analytics provide insights that help manufacturers address problems more proactively, using the wealth of data from various manufacturing processes to predict maintenance needs, optimize production processes, and boost overall equipment effectiveness. 

Such capabilities mark a shift from traditional manufacturing to a more intelligent, agile, and efficient paradigm.

Leading companies can transform their operations with IoT for real-time machine monitoring. These moves significantly enhance operational efficiency, allowing these businesses to extend production hours, manage more jobs, increase parts production, and equip workers with the information they need to do their best work. 

The key to these improvements is the real-time data provided by IoT devices.

Facing challenges in monitoring diverse machinery, leading tool manufacturers have already turned to IoT.

Embracing an Industry 4.0 platform can help these companies gain greater operational visibility. By retrofitting equipment with IoT sensors and employing a cloud-based platform for data analysis, they can achieve real-time monitoring, which leads to improved operational efficiency and accuracy in order fulfillment.

IoT integration in manufacturing

To ensure a successful IoT integration in manufacturing operations, certain key practices stand out. These practices not only smooth the transition but also maximize the potential benefits of IoT technology:

  • Clearly define integration goals: It's essential to pinpoint specific goals that IoT integration aims to achieve, whether it's improving productivity, reducing downtime, or enhancing quality control.
  • Ensure system compatibility: Verify that new IoT devices can integrate smoothly with existing machinery and software systems, avoiding potential conflicts and ensuring a unified operational framework.
  • Start with pilot projects: Implement IoT solutions on a small scale initially to test their effectiveness and adjust the approach as needed, minimizing disruption and maximizing learning opportunities.
  • Invest in employee training: Providing comprehensive training for employees is crucial to ensure they can effectively utilize the new technology and interpret the data it provides.
  • Strengthen data security measures: Given the sensitive nature of manufacturing data, robust security protocols are paramount to protect against breaches and ensure data integrity.
  • Regular performance reviews and adjustments: Continuously monitor the performance of the IoT integration, making adjustments and improvements to stay aligned with evolving operational needs.

These practices guide manufacturers in effectively incorporating IoT solutions into their operations. Moving forward, we'll reveal how connected worker technologies extend the benefits of these advancements, creating a more dynamic and innovative manufacturing environment.

Maximizing operational efficiency with connected worker technologies

According to McKinsey, today’s manufacturing landscape has been shaped by various factors, such as retiring baby boomers, regionalization, the proliferation of shop floor data, and the fallout from COVID-19. 

These changes create a workforce that is more distributed and overwhelmed by data, necessitating tools that help workers collaborate and stay connected across different functions.

There's an urgent need to bridge the current manufacturing skills gap by empowering both new and tenured workers with critical digital skills and factory know-how. 

Although leaders in the manufacturing industry recognize the need for digital transformation, they often underestimate its scope and requirements. 

Digital transformation is a significant undertaking that encompasses improving agility, streamlining operations, and using technological capabilities to enhance worker performance. Leadership commitment is crucial, but so is understanding the full extent of this industrial transformation — including addressing potential worker resistance.

Real-world examples of successful industrial digitalization

Frito-Lay adopted wearable technology to reduce workplace hazards and promote ergonomic practices. The company used belt-mounted devices that detected improper posture, alerting workers to adjust their stance.

This initiative led to a 72% reduction in improper postures within five months across two pilot programs and a 19% decrease in Occupational Safety and Health Administration-recordable injury rates at nine manufacturing sites compared to the previous year.

Holder Construction benefitted from advanced display systems for collaborative discussions. The company's pre-construction teams, spread across six locations, were able to share and discuss information simultaneously as if they were in the same room — leveraging cutting-edge video conferencing and real-time data-sharing technologies.

This led to increased efficiency, quicker decision-making, and reduced travel costs, positively impacting the company culture by creating a more inclusive and collaborative workspace.

Implementing connected worker solutions

Implementing connected worker solutions in manufacturing requires a strategic approach considering unique organizational requirements and objectives. 

Key implementation strategies include:

  • Assessing your needs: Begin by understanding your organization's current challenges, identifying areas that need improvement, and defining the goals you aim to achieve with connected worker solutions.
  • Selecting the right technology: Choose technology that aligns with your objectives, evaluating options based on features, capabilities, compatibility, and cost.
  • Developing a deployment plan: Create a detailed plan for system implementation, covering installation, employee training, and a rollout timeline.
  • Training employees: Training is essential for successfully using connected worker solutions. Educate employees on how to use the new technology, its benefits, and its role in their daily activities.
  • Monitoring and evaluating: After deployment, continually monitor and evaluate the performance of the connected worker solutions to identify issues, gauge employee adoption, and measure the impact on operational efficiency and productivity​.

These strategies are critical for effectively integrating connected worker technologies into manufacturing operations, enhancing safety, increasing productivity, and improving operational efficiency.

The transformative impact of digital learning in manufacturing

Digital learning tools in manufacturing, such as e-learning platforms, augmented and virtual reality (VR/AR) training modules, and mobile learning applications, are redefining how knowledge and skills are imparted in this sector. These modern learning tools are instrumental in building a workforce that is both skilled and adaptable to change.

Digital learning tools offer a range of interactive and immersive learning experiences that cater to various learning styles, making education more accessible and engaging. They are crucial in upskilling employees, equipping them with the necessary competencies to handle evolving technological landscapes. 

The adaptability fostered by digital learning tools is particularly vital in an industry constantly evolving with new technologies and processes. The tools offer personalized learning experiences, allowing workers to focus on specific areas of interest or need. 

Interactive simulations and real-time feedback mechanisms help workers quickly grasp complex concepts and apply them in real-world scenarios.

The flexibility of digital learning means that training can be continuously updated to reflect the latest advancements, ensuring that the workforce remains at the forefront of industry developments.

Real-world examples of digital learning in manufacturing

General Electric launched the Brilliant Learning program to upskill its workforce in advanced manufacturing technologies and digital solutions. This program trains employees in digital tools, data analytics, lean manufacturing, and additive manufacturing techniques. The initiative has increased efficiency, innovation, and productivity in GE's manufacturing processes.

For example, Lockheed Martin has invested in digital learning for its employees as part of its digital transformation initiatives. The company’s training programs cover advanced digital technologies like 3D printing, AI, and robotics.

Bosch, a leading multinational engineering and technology company, has implemented extensive training programs for its employees in support of its digitalization journey. This includes digital skills related to automation, IoT, and data analytics. The initiative aims to make the company’s workforce adept at handling smart factory technologies.

Creating an effective digital learning environment

Creating an effective digital learning environment requires a strategic approach that aligns with the broader objectives of the manufacturing organization, identifying and addressing skill gaps, customizing training programs, leveraging technology, ensuring adaptability, and measuring effectiveness.

  • Identifying the specific skills and knowledge gaps within the workforce is essential for developing effective training programs. This involves analyzing current competencies against future needs and technological advancements. 
  • Once these gaps are identified, custom digital learning programs can be developed, focusing on the most relevant and impactful areas of skills, knowledge, and competencies. Incorporating various learning formats, such as video tutorials, interactive modules, and hands-on simulations, ensures that the training is engaging and caters to different learning preferences.
  • Another key strategy is the integration of learning into the daily workflow. Digital learning should not be seen as a separate or isolated activity but as an integral part of the workday. This can be achieved by embedding learning modules directly into workstations or mobile devices, allowing workers to learn on the go. 
  • Continuously evaluating the efficacy of these training programs helps organizations ensure that their digital learning initiatives align with their manufacturing goals and drive tangible improvements in the workforce.

Process optimization through the convergence of connected worker technology

The convergence of connected worker technology and digital learning is a powerful combination for optimizing manufacturing processes.

Advanced methodologies focus on integrating these technologies to create a seamless workflow where real-time data from connected devices informs immediate decision-making and continuous learning.

The integration of IoT in manufacturing offers a powerful tool for identifying process inefficiencies in real time. This capability of IoT to quickly surface problems allows for faster and more effective problem-solving. As these issues are identified, manufacturers can analyze the data to understand the root causes and patterns. 

This analysis then informs the refinement of best practices and work instructions, ensuring that the manufacturing process is continuously improved. Rather than directly attributing process deficiencies to inadequate worker training, this approach emphasizes the use of IoT data to enhance the overall manufacturing process.

Furthermore, predictive analytics, a key aspect of connected worker technology, can be used to anticipate potential issues before they arise. This foresight, combined with digital learning's ability to quickly upskill workers in handling these anticipated challenges, creates a proactive problem-solving environment. 

Leveraging data analytics for process improvement and quality control is another critical aspect of this technological convergence: data collected from connected devices can be analyzed to uncover inefficiencies, bottlenecks, and quality issues within the manufacturing process. 

This analysis can lead to actionable insights, such as adjusting machine settings, altering workflows, or identifying areas where additional training is needed. Continuously monitoring and analyzing this data enables manufacturers to maintain a high level of quality control and consistently optimize their processes.

Additionally, the integration of machine learning algorithms with this data can further enhance processes. These algorithms can identify patterns and trends that might not be immediately apparent, leading to deeper insights and more innovative solutions for process improvement. 

The continuous feedback loop created by this data-driven approach ensures that manufacturing processes are constantly improving and keeping pace with the demands of a competitive and ever-changing market.

The ROI of investments and learning Initiatives

Defining ROI metrics for technological investments in manufacturing is crucial for justifying the adoption and continued use of connected worker technology and digital learning. 

Key metrics include production efficiency, downtime, product quality, and cost savings. For instance, a reduction in machine downtime due to predictive maintenance informed by IoT data can significantly increase overall equipment effectiveness (OEE), a crucial ROI metric.

Similarly, the impact of digital learning on workforce productivity and error rates is another important measure. Tracking the speed at which employees acquire new skills and the subsequent improvement in their performance quantifies the value added by digital learning initiatives. 

Additionally, metrics such as employee engagement and retention rates can reflect the less tangible but equally important benefits of a technologically empowered and continuously learning workforce. 

Navigating the integration of human skills and automation

The interplay between human skills and automation in modern manufacturing is a delicate balancing act. 

On one hand, automation brings efficiency, consistency, and the capacity to handle tasks beyond human limitations. On the other hand, human expertise offers critical thinking, creativity, and problem-solving skills that are currently unattainable by machines. 

The ideal scenario in manufacturing is not a choice between humans and machines, but a synergy where each complements the other. And as we increasingly integrate automation into manufacturing processes, it’s crucial to consider the ethical implications.

Automation should enhance the workforce, not replace it. 

This means using automated systems to take over repetitive, hazardous, or overly complex tasks, freeing up human workers to focus on areas where their skills are most needed. The goal is to create an environment where humans and machines work in harmony, each playing to their strengths. 

Enhancing human-machine collaboration in manufacturing requires thoughtful strategies that consider both the capabilities of technology and the strengths of the human workforce. This involves designing workflows where human skills and machine efficiency complement each other. 

For instance, machines can handle data processing and repetitive tasks, while human workers focus on areas requiring judgment, creativity, and nuanced decision-making.

Training programs should be developed to help workers adapt to and collaborate with automated systems, focusing on skills that enable them to work effectively alongside advanced technologies.

Manufacturers must also foster a culture of continuous learning and adaptability, and this cultural shift encourages workers to view automation as a tool that enhances their work — not as a threat to their jobs. Prioritizing communication and collaboration between humans and machines creates an innovative environment where efficiency and creativity thrive.

Real-world examples of successful integration of automation with skilled labor

Scenarios in modern manufacturing often highlight the successful integration of automation with skilled labor.

One such example is Nissan, a car manufacturing plant that implemented robotic assembly lines for routine tasks while retaining skilled workers for quality control, design improvements, and oversight. 

Pfizer has used IBM’s supercomputing and AI since 2020 to develop new drugs like PAXLOVID, an oral COVID-19 treatment approved in 2022. They claim this reduced computational time by 80-90%, stating that the technology helped the team design the drug in four months. The Big Pharma has also inked a deal with CytoReason, which has created an AI model of the immune system.

Implementing predictive maintenance strategies

Predictive maintenance is, without a doubt, revolutionizing the manufacturing industry. It's like equipping your production line with a crystal ball that shows you equipment issues before they become problematic.

This method uses advanced data analysis and machine learning to forecast equipment malfunctions, ensuring that your production line avoids unexpected hiccups. Not only does this strategy keep your operations running without interruption, but it also helps significantly reduce maintenance costs. 

The combination of the IoT and data analytics plays a central role in predictive maintenance. Think of IoT devices as vigilant sentinels, constantly monitoring your machinery and collecting a wealth of data — from temperature fluctuations to vibration patterns. 

This constant stream of data, when processed and analyzed, uncovers hidden patterns and potential issues before they escalate, transforming raw data into predictive insights and enabling timely maintenance actions.

Developing an effective predictive maintenance program involves several key steps. First, it's crucial to select the appropriate sensors and IoT devices that can capture the specific data needed from your machinery. These devices then need to be integrated with an analytics platform capable of interpreting this data and making accurate predictions. 

Equally important is training your staff to understand and act upon these predictive insights. Regularly updating and refining the predictive models based on actual equipment performance ensures the maintenance program remains effective and keeps pace with technological advancements.

Such a program enhances the overall efficiency of the manufacturing process.

Innovations in supply chain management

Connected technologies have revolutionized how supply chains are monitored and managed, making processes more transparent and efficient. Integrating IoT devices and advanced analytics enables businesses to track products in real time, anticipate delays, and respond to changing market demands swiftly. 

This real-time data improves decision-making while also enhancing the overall agility of the supply chain. It's a significant shift from traditional methods, offering a more dynamic and responsive approach to managing supply chain complexities.

Integrating digital solutions into supply chains — from advanced tracking systems to AI-driven analytics — allows for detailed monitoring of every stage of the supply chain. Having a clearer view of inventory levels, shipment statuses, and production schedules means businesses can optimize their operations, reduce waste, and respond more effectively to disruptions. 

Real-world example of technology use in supply chain

Walmart implemented blockchain technology in its supply chain management to track the origin of food products.

The innovation increased transparency and improved food safety, enhancing customer trust and satisfaction. The company significantly reduced the costs associated with food recalls, saving millions of dollars and demonstrating the financial benefits of digital transformation in supply chains.

Driving sustainable manufacturing practices

The integration of connected worker technology and digital learning is a key element in driving sustainable manufacturing practices. 

These technologies are helping manufacturers improve efficiency and reduce their environmental footprint. Advanced technologies like IoT and AI enable better resource management, energy efficiency, and waste reduction — and this shift towards sustainability is not just about complying with environmental regulations.

It requires industrial leaders to rethink the entire manufacturing process to be more eco-friendly, from sourcing materials to the final product.

The push towards sustainability in manufacturing is driven in large part by technology.

One example is digital tools, which can monitor and optimize energy usage, leading to lower carbon emissions. Similarly, connected devices can help in better tracking and management of resources, ensuring minimal waste. Data analytics help identify areas of waste in manufacturing processes, ultimately contributing to a more sustainable and responsible manufacturing ecosystem.

Sustainability in manufacturing

Aligning technological advancements with sustainability goals in manufacturing can include:

  • Implementing energy-efficient technologies: Adopt energy-efficient machinery and IoT devices to monitor and reduce energy consumption.
  • Using waste reduction technologies: Invest in technologies that help in reducing waste, such as advanced recycling methods and materials tracking systems.
  • Incorporating renewable energy sources: Integrate renewable energy sources like solar or wind power into manufacturing processes.
  • Optimizing supply chain sustainability: Use digital tools to create a more sustainable supply chain, focusing on reducing transportation emissions and sourcing eco-friendly materials.
  • Promoting digital learning for sustainability: Encourage continuous learning and training on sustainability practices through digital platforms, ensuring that the workforce is aware and involved in sustainable initiatives.
  • Leveraging data analytics for continuous improvement: Use data analytics to continually assess and improve sustainable manufacturing practices, making data-driven decisions for environmental impact reduction.
  • Invest in sustainable product design: Employ digital tools like CAD and simulation software for designing more sustainable products, focusing on recyclability and minimal environmental impact.

These best practices can help manufacturers make significant strides towards a more sustainable future, using technology not just for economic benefits but also for the well-being of the planet.

Adapting to connected manufacturing technologies

The future of manufacturing technology is shaping up to be an exciting era, filled with transformative possibilities. Key innovations like AI, robotics, and 3D printing are at the forefront of this evolution. 

AI and machine learning are expected to further enhance efficiency and decision-making, while robotics will continue to revolutionize assembly lines and manual tasks. 3D printing, on the other hand, is set to redefine product design and customization. Understanding and anticipating these trends is crucial if you want to stay competitive and innovative.

Adapting to these emerging technologies requires careful, strategic planning. Manufacturers need to invest in research and development to understand how these technologies can best be applied in their specific context. 

This might involve partnering with tech firms, participating in industry consortiums, or investing in pilot projects — but also upskilling and reskilling the workforce to handle these new technologies effectively.

A culture of continuous learning and innovation will be the key to successfully integrating these advancements into existing manufacturing processes.

When developing your strategy, here are some ideas worth considering:

  • Encourage experimentation and innovation
  • Invest in employee training and development
  • Leverage collaborative platforms
  • Implement agile methodologies
  • Focus on customer-centric innovation
  • Establish cross-functional teams
  • Embrace digital transformation by integrating advanced digital tools.

These strategies can create a dynamic and adaptive environment that is well-prepared for future success.

Preparing for the future

Connected worker technology and digital learning in manufacturing are bringing us to the cusp of a new industrial revolution. This journey, marked by technological advancements and innovative practices, is reshaping the fabric of manufacturing for a smarter, more sustainable future.

The key takeaway here is the importance of adaptability and forward-thinking.

The ability of manufacturers to embrace new technologies — such as connected worker platforms that enhance collaboration and efficiency — and integrate them seamlessly into their operations, then continuously learn and evolve, will be increasingly important. This adaptability is so much more than a competitive advantage. It’s a prerequisite for success in an interconnected digital world.

The future of manufacturing, driven by these technologies, holds immense potential. It promises smarter factories, more resilient supply chains, and a workforce that's more skilled and adaptable than ever. Manufacturing leaders should focus on harnessing these technologies not just for short-term gains but for long-term sustainability and growth.

The journey of digital transformation in manufacturing is an ongoing one, filled with challenges, opportunities, and endless possibilities. The manufacturing sector can look forward to a future that's not only more efficient and productive but also more responsive to the needs of our planet and its people.

Learn more about the importance of predictive maintenance in manufacturing, its benefits, use cases, and history. 

Edited by Aisha West

Connected Worker Platforms
Break free from silos

Plan, schedule, and monitor complex manufacturing and supply chain operations across multiple locations and teams.

Connected Worker Platforms
Break free from silos

Plan, schedule, and monitor complex manufacturing and supply chain operations across multiple locations and teams.

Connected Workers and Digital Learning for Manufacturing Efficiency Discover how connected worker technologies and digital learning solutions are revolutionizing manufacturing efficiency in our article.
Eric Whitley Eric Whitley, with 30+ years in manufacturing, has led Total Productive Maintenance at Autoliv ASP and taught at Ohio State University. Now Director of Smart Manufacturing at L2L, he guides digital transformation. Eric enjoys fishing and lives in Northern Utah with his wife.

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