Learn about performance management in manufacturing, best practices and implementation methods, and key examples and use cases.

Manufacturing performance management is the process of setting, monitoring, and optimizing key performance indicators (KPIs) related to workforce performance and production processes in manufacturing environments. It includes real-time monitoring and evaluation of employees’ work, as well as the continuous improvement of operational workflows to ensure optimal efficiency, product quality, and adherence to both safety requirements and organizational goals.

performance management in manufacturing best practices

Through data-driven insights, performance management software, and regular assessments, performance management aims to enhance employee productivity and engagement, reduce downtime, and maintain a competitive edge in the industry. Read our blog post below to learn more about performance management in manufacturing including:

5 Best Practices for Performance Management in Manufacturing

To get the best value from your performance management system here are five best practices for performance management in manufacturing:

1. Clear Goal Alignment:

Organizations must ensure that performance management processes align with overall organizational goals. They must clearly communicate objectives to employees at all levels, linking individual and team performance metrics to broader manufacturing and business objectives. This fosters a sense of purpose in frontline teams, engages workers, and helps employees understand how their efforts contribute to the company’s success.

2. Real-time Monitoring and Data Analytics:

Implement real-time monitoring of production and shop floor processes and equipment performance through the use of AI and connected worker technology. Utilize data analytics and AI-driven processing to gain insights into worker performance trends, identify bottlenecks, and facilitate data-driven decision-making. The ability to monitor operations in real-time not only enables proactive interventions to maintain efficiency, it also ensures fairness, accuracy, and transparency in performance measurement.

Pro Tip

Truly optimized performance management is only possible when the work being done is connected to worker skills and competency training. The best way to do this is with AI-powered connected worker technology that uses AI to deliver insights on workforce development and act on data collected from connected frontline workers.


3. Employee Training and Development Programs:

Prioritize ongoing training and development programs for manufacturing personnel. Equip frontline workers with the necessary skills to adapt to evolving technologies and operational requirements. Use performance management systems and other digital tools like skills matrixes to identify skill gaps, set training goals, and track progress, ensuring a skilled and adaptable workforce.

4. Regular Performance Reviews and Feedback:

Conduct regular performance reviews that provide constructive and timely feedback to employees. Use these reviews as opportunities to recognize achievements, address areas for improvement, and set new performance goals. Foster open communication between managers and employees to encourage continuous improvement.

5. Integration with Continuous Improvement Initiatives:

Integrate performance management systems with “kaizen” or continuous improvement initiatives such as Lean or Six Sigma. Use data from performance metrics to identify opportunities for process optimization, waste reduction, and efficiency improvements. This ensures that performance management is not only evaluative but actively contributes to the ongoing enhancement of manufacturing processes.

Leveraging these best practices contributes to a holistic performance management process that aligns manufacturing organizations and their frontline workforce with strategic goals, optimizes operations, and creates a culture of continuous improvement.

Key Performance Management Strategies for Manufacturing Leaders

The following are a few examples of performance management strategies that manufacturing leaders, plant managers, and shift supervisors should consider when implementing their performance management process.

Line-shift Goals

Manufacturers often use production planning and scheduling systems to manage line shifts effectively and ensure a smooth transition between different production configurations. While line shifts in manufacturing are often necessary for adapting to changing demands, introducing new products, or optimizing efficiency, they can also pose challenges, including downtime, quality control issues, employee fatigue, and planning issues. By establishing clear and measurable objectives for each line shift or individual worker that aligns with organizational goals, production leaders can ensure production goals are met.

Individual Meetings and Communication

Manufacturing leaders should implement a performance management strategy that incorporates 1-1 meetings and communication. Regularly providing constructive feedback to employees on their performance can improve performance and boost employee engagement. Offering coaching and development opportunities to enhance skills and capabilities.

Continuous Training

Continuous training in manufacturing involves enabling workers to learn new skills regularly. It’s a great way to improve employee performance and innovation, as well as engage and retain top talent. A good example of a continuous learning model is everboarding, a modern approach toward employee onboarding and training that shifts away from the traditional “one-and-done” onboarding model and recognizes learning as an ongoing process.

Performance Management Tools

Implementing performance management tools can help automate ongoing employee evaluation, as well as align employee performance with other key manufacturing KPIs, including production quality, machine uptime, and labor utilization. These tools can also be used to identify continuous improvement opportunities. This allows manufacturing leaders to adapt and refine approaches based on feedback and outcomes.

Simplifying Performance Management with Digital Tools

According to Forbes, as the future of work evolves and changes so must performance management, traditional methods may no longer be as successful in an era where the workforce is constantly changing.

Digital tools such as connected worker solutions and AI-driven analytics help simplify performance management systems by streamlining processes, improving efficiency, and providing more accurate insights. Implementing these connected worker solutions automates the collection of performance-related data from various sources including connected frontline workers, IoT devices, software systems, and more. This eliminates the need for manual data entry, reducing errors and ensuring real-time access to up-to-date information.

By digitizing the performance management process, organizations create a centralized platform for storing and managing performance-related data. This centralized knowledge base makes it easy for managers and employees to access relevant information, track progress, and collaborate on performance goals. Furthermore, AI-driven connected worker solutions allow for digital performance tracking, customized training and skills development planning, workflow optimization, and improved predictive maintenance.

performance management best practices in manufacturing

Through these digital tools and technology, manufacturing companies can simplify performance management processes, improve operational efficiency, and adapt to the demands of a rapidly evolving industry while fostering a culture of continuous improvement and development for their manufacturing workforce.

Augmentir is the world’s leading connected worker solution, combining smart connected worker and AI technologies to drive continuous improvement and enhance performance management initiatives in manufacturing.

Augmentir is trusted by manufacturing leaders as a digital transformation partner improving training and development, workforce allocation, and operational excellence through our AI-driven True Productivity™ and True Performance™ offerings, as well as digitizing and optimizing complex workflows, skills tracking, and more through our patented smart, connected worker suite. Schedule a live demo today to learn more.


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Learn how Digital Standard Work effectively transforms manufacturing production and enables operational excellence.

Manufacturing organizations are feeling the pressure of increased customer demands, skilled labor shortages, and intense shifts in their frontline workforces, however, they can effectively overcome these obstacles with digital standard work enabled by smart connected worker technology. Digital standard work promotes operational excellence in manufacturing through facilitated knowledge-sharing, enhanced process standardization, increased employee engagement, improved workforce agility, and overall optimization of workforce abilities.

digital standard work in manufacturing

Standardized work in manufacturing (centerlining, machine setup/maintenance, inspection checklists, workforce training, lubrication procedures, etc.) is effective for continuously improving the most efficient and safe methods for performing work to meet customer demand while minimizing waste. Digital Standard Work takes these processes one step further, enhancing them with digital technology to establish a true culture of continuous improvement where frontline workers and shop floor processes benefit from digital workflows, collaboration, AI-powered guidance, generative AI assistants, real-time access to centralized knowledge bases, and more.

By redefining standard work for the digital age, manufacturers can achieve operational excellence through increased efficiency, quality, flexibility, and innovation across their frontline workforces. Read more on Digital Standard Work and how it effectively transforms manufacturing production and enables success:

Digitizing Standard Work in Manufacturing

According to Forbes and McKinsey, through digital tools manufacturers can reduce machine downtime by 30% to 50% and quality-related costs can be reduced by 10% to 20%. Effectively digitizing manufacturing standard work through smart, AI-driven connected worker technology involves:

  • Interactive Digital Work Instructions
    Replace paper-based standard operating procedures (SOPs) with interactive digital work instructions that include multimedia elements like videos, images, and animations. These can be accessed by workers on tablets, wearables, and other mobile devices right on the shop floor.
  • Data Capture and Integration
    Leverage smart tools and sensors to automatically capture data from the manufacturing process, such as torque values, cycle times, and quality checks. This data can be integrated into the digital work instructions to provide real-time feedback and ensure adherence to standards.
  • Workflow Automation
    Automate non-value-added tasks like data entry, approvals, and documentation through connected worker platforms. This streamlines workflows, reduces errors, and frees workers to focus on value-adding activities aligned with standard work.
  • Knowledge Management
    Digitize and centralize tribal knowledge, best practices, and process documentation in a connected worker platform. This ensures standardized methods are easily accessible and updatable for consistent knowledge sharing across the workforce.

Using smart, connected worker platforms to digitize and optimize standard work in manufacturing drives improved productivity, ensures better and more consistent product quality, and fosters a safer work environment for enhanced operational success.

Pro Tip

Using a low-code no-code workflow builder simplifies the creation of complex digital workflows for frontline work processes. Furthermore, integrating remote collaboration tools facilitates real-time guidance, knowledge sharing, and the ability to update standard work procedures based on captured tribal knowledge.


Engaging Frontline Workers with Digital Standard Work

As manufacturing workforces shift due to retirement and tribal knowledge loss, effective workforce training has become critical for continued success. Interactive digital interfaces, augmented and enhanced capabilities, and wearable technologies make standard work practices like workforce training more engaging and accessible, improving workforce adoption and adherence.

Digital tools facilitate information visibility and knowledge sharing among frontline workers, enabling them to learn from each other, share best practices, and contribute to a culture of continuous improvement. By tracking and analyzing performance data from digital systems, manufacturers can identify top performers, provide personalized feedback, and recognize achievements, fostering a sense of engagement and motivation among frontline workers.

Digital Standard Work empowers frontline workers by involving them in process improvements, recognizing their contributions, and providing opportunities for learning and growth, leading to increased job satisfaction and commitment. By leveraging digital technologies and interactive interfaces, manufacturers can transform Standard Work procedures into engaging and empowering experiences for their frontline workforce, driving productivity, quality, and a culture of continuous improvement.

Most importantly it gives manufacturing frontline and factory staff the tools they need to be successful and thus create a more satisfied environment where employees come to work and feel good about what they are doing and how they are doing it.

Driving More Effective Collaboration

Digital standard work also facilitates better collaboration across your frontline teams. Effective communication starts with digital tools, and by implementing digital standard work with connected worker technology, manufacturers can connect frontline team members across shifts, departments, locations, and languages, improving visibility into workforce planning, training, skills tracking, daily management, troubleshooting, and more.

industrial collaboration with augmentir

From frontline workers to executives, a digital standard work strategy that leverages connected worker technology allows employees to collaborate seamlessly and easily access information. Connected worker solutions that include industrial collaboration tools allow workers to virtually connect to subject matter experts for remote guidance and assistance. These software tools are becoming commonplace in manufacturing and other industrial settings, where companies are faced with an increasingly distributed and remote workforce, yet still require team collaboration to help troubleshoot and resolve issues. In a nutshell, workers can get more done with greater accuracy in less time.

Interested in learning more?

If you’d like to learn more about how Augmentir and our AI-powered connected worker solution digitizes standard work, streamlines operations, improves communication, and empowers frontline workers with the tools and information they need, schedule a demo with one of our product experts.


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Learn the difference between skills development and training in manufacturing, how they are important, and how the management of them can be improved through continuous learning methodologies and emerging technology.

At first glance, training and skills development seem synonymous and are often used interchangeably, but they have different purposes and goals. However, despite these differences, both are equally important for every organization, especially in the case of the manufacturing industry. According to Training Magazine, 57% of manufacturing organizations reported training and workforce development budget increases to address the widening skills gap and the skilled labor shortage.

the difference between skills development and training in manufacturing

At the most basic level, training is the process companies use to build the skills of new employees so they’re well-equipped to perform the role that they were hired for. While skills development, on the other hand, includes ongoing education, mentoring, and professional experiences that help employees grow into future roles and opportunities.

Both are extremely valuable to overall organizational growth and success, however, it’s important to apply them at the right time and in the right way. Read more on both skills development and training in manufacturing, why they are important, and how they can be improved and enhanced through the proper application of learning technology:

What is Skills Development in Manufacturing

Skills development goes beyond training by aiming to enhance a broader set of competencies and capabilities. It focuses on building a more well-rounded and adaptable workforce encompassing not only the acquisition of specific skills, but also the improvement of problem-solving abilities, critical thinking, creativity, adaptability, and continuous learning.

Skills development in manufacturing refers to the process of enhancing the knowledge, abilities, and competencies of individuals involved in the manufacturing process. It involves providing training and education to workers, engineers, and technicians to improve their technical, operational, and problem-solving skills. By providing training and development opportunities, manufacturing organizations can ensure that their workforce possesses the necessary skills and knowledge to perform their jobs effectively and efficiently.

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Start tracking worker skills, certifications, development progress, and capacity planning with our free Excel Skills Matrix Template. Download our template to get started, and learn more about tracking and manage your employee skills digitally with Augmentir.


Many manufacturing industries face a shortage of skilled workers. Skills development programs help bridge the gap by training existing employees or new hires in the required competencies.

Overall, skills development in manufacturing is crucial for maintaining competitiveness in a rapidly changing industry. It ensures that the workforce remains adaptable, skilled, and capable of meeting the evolving demands of modern manufacturing processes.

Pro Tip

Implementing skills management software programs allow manufacturing organizations to digitize and effectively track worker skills, development progress, and intelligently assign work based on skills competencies, improving work allocation and workforce utilization.


What is Training in Manufacturing

Training in manufacturing primarily focuses on imparting specific knowledge, skills, or information to individuals. It often involves structured and organized programs designed to teach employees how to perform specific tasks or operate machinery and equipment. Training is often of shorter duration and may be task-specific or role-specific. It is designed to quickly bring employees up to a certain proficiency level in their current job.

The specific type of training required in manufacturing depends on the roles and responsibilities of the individuals involved, the company’s processes, and the industry in which they operate. Training in manufacturing is essential for several reasons:

  • Safety: Manufacturing processes often involve machinery, equipment, and materials that can be hazardous. Proper training ensures that employees understand and follow safety protocols, reducing the risk of accidents and injuries.
  • Quality Control: Quality in manufacturing is a critical, essential factor. Training programs teach employees how to maintain consistent product quality through accurate measurements, inspections, and adherence to quality standards.
  • Operational Efficiency: Training helps employees become more efficient in their tasks, reducing downtime, minimizing waste, and optimizing manufacturing processes.
  • Technology: Manufacturing is becoming increasingly technology-driven. Training equips employees with the skills to operate and maintain advanced machinery and systems.
  • Productivity: Engaged workers tend to be more productive, contributing to increased output and profitability for the manufacturing company.
  • Compliance: Manufacturing is subject to various regulations and industry standards. Training ensures that employees understand and comply with these requirements, avoiding legal and regulatory issues.

Effective training programs are designed to align with the organization’s goals and objectives, ensuring that the workforce is well-prepared and capable of contributing to the success of the manufacturing operations.

In summary, training in manufacturing is a subset of skills development, with a narrower and more specific focus on teaching job-related skills and knowledge. Skills development, on the other hand, is a more comprehensive and ongoing process that aims to develop a well-rounded and adaptable workforce capable of meeting the evolving challenges of the manufacturing industry. Both training and skills development are important for the success of a manufacturing organization, and they often complement each other in the development of a skilled and competent workforce.

How Can Technology Improve Manufacturing Skills Development and Training

Technology can significantly enhance manufacturing skills development and training by making the process more efficient, effective, and accessible. Incorporating these technological advancements into manufacturing skills development and training can lead to a more skilled and adaptable workforce, increased safety, reduced training costs, and improved overall manufacturing efficiency.

For example, technology enables experts to remotely assist and guide trainees through complex tasks. Workers can wear smart glasses or use mobile devices to share live video streams and receive real-time instructions. AI-driven connected worker solutions can assist in creating personalized learning paths for trainees based on the work they do, their progress, and their learning style.

Smart connected worker platforms, Learning Management Systems (LMS), and mobile apps can provide access to a wide range of training materials, including video tutorials, interactive modules, and assessments. These platforms allow workers to learn at their own pace and on their schedule, reducing the need for expensive and time-consuming in-person training.

Augmentir is the world’s leading, smart, connected worker solution using the foundational AI technologies underpinning ChatGPT to enhance manufacturing training, onboarding, and skills development. Leading manufacturing organizations are using our smart, connected worker suit and AI-driven insights to foster environments of continuous learning, and make skills development and training processes more personalized, accessible, and effective.

Schedule a live demo to learn why manufacturing leaders are choosing us to improve the training lifecycle with digital skills management tools, real-time insights, and more.


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Gartner identifies Augmented Connected Workforce initiatives as a top manufacturing technology trend for 2024.

According to Gartner, an Augmented Connected Workforce is the intentional management, deployment, and customization of technology services and applications to support the workforce’s experience, well-being and ability to develop their own skills. It is a revolutionary approach that leverages smart connected worker platforms, artificial intelligence (AI), Internet of Things (IoT) technologies, and other innovative solutions to augment and support frontline workers and create a seamlessly connected and dynamic work environment.

gartner augmented connected workforce

Gartner predicts that through 2027, 50% of Fortune 500 manufacturers will create new positions through innovative engagement models enabled by Augmented Connected Worker strategies.

In manufacturing, specifically, the driving factor behind the rapid increase in Augmented Connected Workforce adoption is the need to accelerate and scale talent. There is a significant gap in the skills of the workforce today and consumer demands are rapidly increasing. Even the World Economic Forum recognizes the benefits an Augmented Connected Workforce brings to the workplace, stating that it:

  • enables workers to acquire new skills and knowledge
  • creates a more accessible and inclusive working environment
  • improves worker well-being and safety
  • increases the efficiency and effectiveness of industrial operations
  • supports human connection and collaboration
  • and more…

Given these benefits it is clear that enabling an Augmented Connected Workforce will be key for manufacturing success going forward.

Augmentir Recognized in 5 Gartner Hype Cycles for its Connected Workforce Solution

Augmentir empowers organizations to embrace an Augmented-Connected Workforce by providing a comprehensive platform that combines connected worker and AI technologies. Through Augmentir, companies can seamlessly connect frontline workers with digital tools and knowledge bases, enabling them to access real-time guidance, instructions, and support directly within their workflows. This integrated approach augments frontline workers enhancing their capabilities, productivity, and overall business processes. By leveraging Augmentir’s platform, organizations can enhance productivity, quality, and safety while fostering a culture of continuous learning and innovation within their workforce.

Gartner recently highlighted Augmentir as a key software vendor providing functionalities and features that allow manufacturers to implement an Augmented Connected Workforce and empower frontline workers with AI-driven insights and real-time data for more productive, efficient, and safe frontline activities.

Augmentir was recognized in five separate Gartner Hype Cycle reports covering generative AI and emerging technologies in manufacturing.

augmentir recognized in gartner hype cycles


These five reports include:

  • Hype Cycle for Generative AI
  • Hype Cycle for Emerging Technologies
  • Hype Cycle for User Experience
  • Hype Cycle for Frontline Worker Technologies
  • Hype Cycle for Workforce Transformation

These hype cycle reports and innovation profiles are provided by Gartner to help organizations decide which new innovations and technology to adopt, as well as what value they can provide to their manufacturing operations.

Enabling an Augmented Connected Workforce in Manufacturing

Manufacturing is uniquely situated as an industry to benefit from an Augmented Connected Workforce leveraging AI-powered connected worker solutions for process improvements, quality, management, enhanced training, and more.

As manufacturing workers become more interconnected, organizations gain access to a valuable source of data related to manufacturing activities, execution, and team dynamics. By utilizing emerging AI tools in conjunction with smart connected worker solutions, companies can derive insights that pinpoint areas with significant potential for improvement.

At Augmentir, we believe that a connected worker platform’s purpose goes beyond just delivering instructions and remote support; it should continually optimize the entire connected worker ecosystem. AI plays a crucial role in addressing overarching trends like skills variability and the loss of tribal knowledge within the workforce. It serves as the cornerstone for implementing data-driven improvements in operational performance and continuous enhancement.

For example, after Augmentir is deployed for a period of time, our AI engine will start identifying patterns in the data that will allow manufacturers to focus efforts on the areas that have the biggest customer satisfaction, productivity, and workforce development opportunities. This gives organizations the ability to answer questions like:

  • What areas should they invest in to improve operational performance?
  • Where are their biggest areas of opportunity to improve productivity or quality management?
  • Where do they have skills gaps and what kind of training do their frontline workers need?

Augmentir’s AI continuously updates its insights to enable companies to focus on their largest areas of opportunity, enabling you to deliver year-over-year improvements in key operational metrics.

Interested in learning more?

If you’d like to learn more about Augmentir and see how our AI-powered connected worker platform improves safety, quality, and productivity across your workforce, schedule a demo with one of our product experts.


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Learn how manufacturers combat the manufacturing skilled labor shortage and close skills gaps with an Augmented Connected Workforce (ACWF).

Generative AI in manufacturing refers to the application of generative models and artificial intelligence techniques to optimize and enhance various aspects of the manufacturing process. This involves using AI algorithms to generate new product designs, optimize production workflows, predict maintenance needs, and improve production efficiency within frontline operations.

generative ai in manufacturing

According to McKinsey, nearly 75% of generative AI’s major value lies in use cases across four areas: manufacturing, customer operations, marketing and sales, and supply chain management. Manufacturers are uniquely situated to benefit from generative AI and it is already a transformative force for some. A recent Deloitte study found that 79% of organizations expect generative AI to transform their operations within three years, and 56% of them are already using generative AI solutions to improve efficiency and productivity.

Manufacturing is rapidly evolving and by integrating cutting-edge technologies like Generative AI, manufacturers can better support, augment, and enhance their frontline workforces with improved decision-making, collaboration, and data insights.

Join us below as we dive into generative AI in manufacturing exploring how it works, the benefits and risks, and some of the top use cases that generative AI, specifically generative ai digital assistants, can provide for manufacturing operations:

What is Generative AI in Manufacturing

Generative AI refers to artificial intelligence systems designed to create new content, such as text, images, or music, by learning patterns from existing data. In manufacturing, this involves the use of Large Language Models (LLMs) and Natural Language Processing (NLP) to analyze vast amounts of data, simulate different scenarios, and generate innovative solutions that can impact a wide range of manufacturing processes.

Large Language Models

Large Language Models (LLMs) are a type of generative artificial intelligence model that have been trained on a large volume – sometimes referred to as a corpus – of text data. They are capable of understanding and generating human-like text and have been used in a wide range of applications, including natural language processing, machine translation, and text generation.

In manufacturing, generative AI solutions should leverage proprietary fit-for-purpose, pre-trained LLMs, coupled with robust security and permissions.  Industrial LLMs use operational data, training and workforce management data, connected worker and engineering data, as well as information from enterprise systems.

Natural Language Processing

Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans using natural language. It involves the development of algorithms and models that enable computers to understand, interpret, and respond to human language in a way that is both meaningful and useful.

For generative AI, NLP is a key technology that enables the assistants to understand and generate human-like text, providing seamless conversational user experiences and valuable assistance to frontline workers, engineers, and managers in manufacturing and industrial settings.

NLPs allow the AI to process and interpret natural language inputs, enabling it to engage in human-like interactions, understand user queries, and provide relevant and accurate responses. This is essential for common manufacturing tasks such as real-time assistance, documentation review, predictive maintenance, and quality control.

generative ai in manufacturing with LLMs and NLP

By combining large language models and natural language processing, generative AI can produce coherent and contextually relevant text for tasks like writing, summarization, translation, and conversation, mimicking human language proficiency.

Benefits of Leveraging Generative AI in the Manufacturing Industry

Generative AI and solutions that leverage them offer several benefits for manufacturing operations, including:

  • Operational/Production Optimization and Forecasting: GenAI technology offers a significant boost to manufacturing processes by monitoring and analyzing in real-time, spotting problems quickly, and providing predictive insights and personalized assistance to boost efficiency for manufacturing workers. Additionally, AI assistants empower manufacturers to explore multiple control strategies within their process, identifying potential bottlenecks and failure points.
  • Proactive Problem-Solving: Generative AI-powered tools provide real-time monitoring and risk analysis of manufacturing operations, enabling the quick identification and resolution of issues to optimize production and efficiency. They can spot events as they happen, providing valuable insights and recommendations to help operators and engineers rapidly identify and resolve problems before they escalate.
  • Reduce Unplanned Downtime: Generative AI solutions can analyze vast datasets to predict equipment maintenance needs before issues arise, allowing manufacturers to schedule maintenance proactively, minimizing unplanned disruptions. This not only improves downtime but also contributes to the overall operational resilience of mission-critical equipment.
  • Personalized Support and On-the-job Guidance: Generative AI tools can be tailored to diverse roles within the manufacturing plant, offering personalized assistance to operators, engineers, and managers. It can provide role-based, personalized assistance, and proactive insights to understand past events, current statuses, and potential future happenings, enabling workers to perform their tasks more effectively and make better, more informed decisions.

These benefits demonstrate the significant impact of generative AI on frontline manufacturing activities, improving overall operational efficiency, adjusting processes where needed, and driving operational excellence.

Pro Tip

Generative AI assistants can take these benefits one step further by incorporating skills and training data to measure training effectiveness, identify skills gaps, and suggest solutions to prevent any skilled labor issues. This guarantees that frontline workers have the essential skills to perform tasks safely and efficiently, while also establishing personalized career development paths for manufacturing employees that continuously enhance their knowledge and abilities.


Risks of Generative AI in Manufacturing

Generative AI in manufacturing presents several risks, including data security, intellectual property concerns, and potential bias in AI models. The reliance on vast amounts of data raises the risk of data breaches and cyberattacks, potentially exposing sensitive information. Intellectual property issues may arise if AI-generated designs or processes inadvertently infringe on existing patents or proprietary technologies. Additionally, biases in training data can lead to suboptimal or unfair outcomes, affecting the quality and equity of AI-driven decisions. There is also the risk of over-reliance on AI, which may reduce human oversight and lead to errors if the AI models make incorrect predictions or generate flawed designs. Ensuring proper validation, transparency, and human intervention is crucial to mitigating these risks.

Top Use Cases for Generative AI Manufacturing Assistants

Generative AI assistants and frontline copilots are AI-powered tools designed to provide valuable assistance and insights in industrial settings, particularly in manufacturing. These assistants are a type of generative AI that are used in manufacturing operations to enhance human-machine collaboration, streamline workflows, and offer proactive insights to optimize performance and productivity for frontline workers.

What makes frontline AI assistants unique among other generative AI copilots is the enhanced human-like interaction beyond standard data analytics and analysis to understand the context around a process or issue; including what happened and why, as well as anticipate future events.

Generative AI assistants work via specialized large language models (LLMs) and generative AI, providing contextual intelligence for superior operations, productivity, and uptime in industrial settings. Additionally, they typically involve natural language processing for understanding human language, pattern recognition to identify trends or behaviors, and decision-making algorithms to offer real-time assistance. This, combined with machine learning techniques, allows them to understand user inputs, provide informed suggestions, and automate tasks.

  1. Troubleshooting:Troubleshooting is such a critical use case in manufacturing. With today’s skilled labor shortage, frontline workers are often times in situations where they don’t have the decades of tribal knowledge required to quickly troubleshoot and resolve issues on the shop floor. AI assistants can help these workers make decisions faster and reduce production downtime by providing instant access to summarized facts relevant to a job or tasks, this could come from procedures, troubleshooting guides, captured tribal knowledge, or OEM manuals.
  2. Personalized Training & Support: With GenAI assistants, manufacturers can instantly close skills and experience gaps with information personalized, context-aware to the individual worker. This could include: on the job training materials, one point lessons (OPLs), or peer/user generated content such as comments and conversations.
  3. Leader Standard Work: With Generative AI assistants, operations leaders can assess and understand the effectiveness of standard work within their manufacturing environment, and identify where there are areas of risk or opportunities for improvement.
  4. Converting Tribal Knowledge: One of the more pressing priorities that many manufacturers face is the task of capturing and converting tribal knowledge into digital corporate assets that can be shared across the organization. With connected worker technology that utilizes Generative AI, manufacturing companies can now summarize the exchange of tribal knowledge via collaboration and convert these to scalable, curated digital assets that can be shared instantly across your organization.
  5. Continuous Improvement: AI and GenAI assistants can help us identify areas for content improvement, and make those improvements, measure training effectiveness, and measure and improve workforce effectiveness.
  6. Operational Analysis: Generative AI assistants can also provide value when it comes to operational improvements. GenAI assistants can use employee attendance data to help shift managers or line leaders determine where the risks are, and potentially offset any resource issues before they become truly problematic. An organization’s skills matrix, presence data, and production schedules all can feed into a fit-for-purpose, pre-trained LLM – giving you information that manufacturing leaders need to keep their operations running.

Future-proofing Manufacturing Operations with Augie™

Generative AI and other AI-powered solutions are leveling up manufacturing operations, analyzing data to predict equipment maintenance needs before issues arise, allowing for proactive maintenance scheduling, and minimizing unplanned disruptions. With these tools manufacturers can empower frontline workers with improved collaboration and provide real-time assistance with contextual information, ensuring relevant and timely support during critical decision-making processes.

Overall, generative AI is transforming a wide array of manufacturing and industrial activities, connecting workers in ways that were previously thought impossible, and making frontline tasks and processes safer and more efficient for workers everywhere.

Augie, Augmentir’s new generative AI assistant for frontline work pulls in skill capabilities, workforce development information, and training data in addition to MES and ERP data. It offers contextual, proactive insights and automated workflows to optimize production and prevent bottlenecks, contributing to manufacturing efficiency, uptime, quality, and decision-making.

augie gen ai industrial assistant close skills gaps

Additionally, Augie ties together operational data, training and workforce management data, engineering data, and knowledge/information from various disparate enterprise systems to empower frontline workers, streamline workflows, and increase manufacturing performance.

Augmentir is trusted by manufacturing leaders as a digital transformation partner delivering measurable results across operations. Schedule a live demo today to learn more.


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Join Chris Kuntz for an interview Packaging Insights on how AI and connected worker technology can help the packaging industry overcome the skilled labor crisis.

The packaging industry has been hit by the low availability of skilled workers, but for Chris Kuntz, VP of Strategic Operations at Augmentir, AI systems offer the solution. In this interview with Joshua Poole from Packaging Insights, Chris explores how AI and the Augmented Connected Workforce could revolutionize the packaging industry and how Augmentir’s AI-powered connected worker solution supports optimal efficiencies in manufacturing. He also discusses the importance of effective regulatory frameworks for AI.

This transcript has been edited for clarity and length. View the original video interview on the Packaging Insights website here.

packaging industry connected workforce


Joshua Poole: Hello, everyone. My name is Joshua Poole, and I am the editorial team leader at CNS Media, the publisher of Packaging Insights. I am very pleased to be joined today by Chris Kuntz, who is the Vice President of Strategy at Augmentir, and who is here to talk about the benefits of AI in relation to the packaging industry.

So welcome to you, Chris.

Chris Kuntz: Thank you very much, and thanks for having me, Joshua.

Joshua Poole: So, Chris, AI systems are expected to really transform the wider society but in relation to the packaging industry, to what extent could they revolutionize operations there?

Chris Kuntz: The reality is, to a huge extent. The impact centers around the manufacturing workforce – the people that are part of manufacturing. Historically, the application of AI, artificial intelligence, and machine learning, in manufacturing anyway, has focused on automating repetitive lower-level processes, that replace humans in the factory. Today, what we need to think about, and what we focus on here at Augmentir, is how we can use AI to augment the human workforce. And so, AI, again, used throughout the industry, its served great application from a predictive maintenance, machine failure standpoint, energy efficiency – things like resource utilization and even supply chain visibility and quality control.

And those applications of AI in manufacturing will continue to provide value. But the reality is people are still needed in paper mills, on the factory floor in the areas of safety, quality, and maintenance. There are jobs that just require that humans are there. And that’s not going away any time soon. But what we are faced with, and what many manufacturers are faced with, is these workforce challenges of the aging workforce, the retiring workforce going away. They’re walking out the door with a vast amount of knowledge that is essential to operate factories and plants. Pre-pandemic we had an emerging workforce coming in that maybe didn’t have the necessary skills, but today post-pandemic era, there’s a massive job shortage. There are no workers coming in, and so manufacturers are forced to look at a pool of less-skilled workers to perform tasks that they may not be initially qualified for.

So, it is not just that the skilled labor is going out, it’s just that we don’t have any skills coming in. And so, every manufacturer is faced with a massive labor shortage and as a result a massive shortage of skills required to operate successfully any given day on the shop floor. And that’s really where we think the value is going to come from an AI standpoint, and it’s kind of transformative when you look at historically the application of AI in manufacturing.

Joshua Poole: So, you mentioned the industry is really struggling to overcome the lack of a qualified workforce. How can AI overcome this problem across the industry?

Chris Kuntz: One of the great things about artificial intelligence, and our history as a company, and one of our previous companies was focused on collecting data from connected machines and then using that data and analyzing that data with AI to understand how to make those machines operate better and improve those machines.

From a human standpoint, humans have been relatively disconnected on the shop floor. They’re using paper-based checklists and SOPs and work procedures, the same sort of technology they were using 20, 30 years ago. So, they’re relatively disconnected, and we know little about how they’re operating and how they’re performing and where they need help and where they need assistance.

If we can connect those workers – and I am talking connecting with phones, tablets, wearable devices – if we can connect those workers we have a digital portal into how they’re performing, and through AI we can analyze how they’re performing and then offer them real-time guidance almost like an AI assistant that’s sitting there helping them out if they are struggling, helping them out if they need help, guidance, or support, or if there is a potential safety or security issue that they might be running into.

The same way that AI has historically been used to act on machine data to improve machine efficiency and performance, we can use the same approach for the humans in the factory.

Joshua Poole: Mm-hmm, and can you provide any examples of the ways in which your platform, Augmentir, has benefited companies looking to embrace AI to improve their operations?

Chris Kuntz: Yes, there are a few different ways. More recently we just launched our Generative AI assistant called Augie™. And what that does is that allows workers or operations managers, using natural language, to solve problems faster, assist in troubleshooting, and provide guidance when needed.

One of the first use cases is troubleshooting. This happens every day in a plant, in a paper mill, it happens every day – there’s a problem with a machine, we need to get it back up and running. Otherwise, there’s a downtime issue, which leads to production/revenue loss. And it’s not a standard procedure to fix the machine. And so there’s troubleshooting that needs to happen. This process is very collaborative. But also from a worker standpoint, they typically have to go to 5, 6, 10 different systems to try to find information or talk to different people.

And what a Generative AI assistant can do is be that digital front end to all that wealth of information and return information on, “Hey here’s the solution to this problem. It’s been solved before, it’s in this published guide, here you go.” Or, “You may want refer at this work procedure. This is something, a troubleshooting guide that could help you solve the problem.” Or, “Here’s a subject matter expert that exists” and you can remotely connect to this person who has expertise in this particular type of equipment.

And so being able to give real-time access to that individual at the time of need is critical. And I think the other big area, at least early on here, is around training.

So, if you think about the skilled labor, workforce shortage, the tenure rates in manufacturing, people are quitting faster. They’re not sticking around for 15 years, they’re sticking around for three years, maybe, possibly, at max. And so, training and learning and development, HR leaders have to think about how to change onboarding practices because it’s not practical anymore to onboard someone for six months if they’re only gonna be around for nine months.

And so the goal, with many of the organizations that we speak with, the goal is to reimagine and rethink training and move it away from the before they’re productive in the classroom to move it onto the floor. Move it into the flow of work, they call it. And so what we can do with AI there is, we don’t understand that worker or their skill level or their competency levels. And if that’s digitally tracked, we can use AI to augment those work instructions and work procedures to say, “Hey, you’re a novice. This is your first month on the job. You’re required to watch this safety video before you do this routine.” And if you’re an expert worker, maybe you wouldn’t be required to do that. Or if you were trained, but your performance is lagging vs. the benchmark, we can come – the instructions can come and be dynamically adjusted to say, “Hey, here’s some additional guidance to help you through this procedure and through this routine.”

So, it gives visibility and insight into areas. I mean, if you had three people on the shop floor, you’d probably know exactly what they were doing. But once you get some larger organizations and they have dozens of people or hundreds of people, it becomes much much harder to understand where the opportunities for improvement are. And AI has the ability to do that, certainly in the training area.

Joshua Poole: Hmm, that’s very interesting. And of course, AI is largely unregulated worldwide, which can create problems like AI washing and irresponsible use. But what do you see as the biggest concern with the proliferation of AI systems within the packaging industry?

Chris Kuntz: So, certainly there’s a lot of concerns with respect to that, and for Augmentir, our approach is we leverage a – certainly from a Generative AI standpoint, we leverage a proprietary, fit-for-purpose, pre-trained large language model that sits behind our Generative AI solution. And when you combine that with robust security and permissions that can help factory managers, operators, and ever engineers or frontline workers only have access to the information that they need, and still provide the benefits of problem-solving faster and improved collaboration.

One of the other things though that I think is really important is this concept of “verified content” – so we’ve all used ChatGPT, right? And early on, I think they had this disclaimer, ChatGPT is 90% correct, so it could return false data. That’s not just not acceptable in an industrial settting. You can’t say, “Here’s a routine to do a centerlining on a piece of equipment” and have someone stick their hand in a place and get it chopped off. You can’t be 90%, you have to be 100%.

So, we have a concept of our Generative AI system, the ability to return verified and unverified data, and then the organization can decide what they want to do with that. So, if it’s a frontline worker, maybe, if it is unverified data, it’s labeled, and you need a supervisor that has to come over if you are going to perform that routine. And then the ability to sort of take the information that comes back and categorize it in terms of verified data, unverified data, and then be able to control how you’re using that. So, it’s not the wild wild west, it’s a very controlled environment. The scope of, if you think about our, in our world, if we’re serving a manufacturing company – and Augmentir is being used for digital manufacturing in paper and packaging companies like Graphic Packaging and WestRock, and so the information that, in our scope of their world is corporate documentation, engineering documentation, operational data, work order data, people data – could be their skills matrix and training history and things like that, but it’s all contained within their enterprise. We’re not looking outside of that, it’s really a constrained data set. And that’s what feeds our large language model.

That significantly helps the application of this, there are people that are exploring using more open AI and GPT models to do this. But then you run into the problems that you said, where there’s a lot of information that both you are feeding into the AI, which could be a security risk, and then the information that you are getting back that could be a security risk.

Joshua Poole: Okay, and as a final question. What advice would you give to politicians working to establish these regulatory frameworks for AI systems?

Chris Kuntz: Great question.

You know, our point of view is we think, you know President Biden had the AI regulation executive order here in the United States back in October, we think it’s much needed on several fronts. Certainly, every company now is saying that they’re an AI company and trying to sprinkle in AI to everything they do. And some of that can be a little problematic.

But at least in the U.S. here in Biden’s AI regulation executive order, there was a lot of talk about job disruptions and putting focus on the labor and union concerns related to AI policies. I think that reinforces our use of AI as a way to augment workers. We’re not looking to replace workers and it’s addressing a huge problem. I think the Department of Labor, they’re issuing guidance to employers around AI that you can’t use it to track workers and you can’t use it to, you know there’s labor rights that exist in the world. And I think that gets back to having these AI co-pilots or Generative AI assistants that can help workers perform their jobs safely and correctly, maximizing the potential. It’s really where on-the-job learning comes into play. It’s things that were happening off the factory floor before. Now it’s squarely suited to help address some of the big manufacturing labor workforce problems that exist today. So, there’s a lot of language in that executive order around making sure that AI is used, not just responsibly, but used for purposes that are going to further the industry. And again, that’s squarely where we sit in terms of workforce development and using it to address the labor shortages from a training and support standpoint.

But, overall, I think, absolutely we embrace the regulatory – Generative AI regulation – and control aspects of this because it could become problematic if you are not doing that, for sure.

Joshua Poole: Mm-Hmm that’s very interesting. Chris, thanks for your time today.

Chris Kuntz: Yes, thank you very much. Thanks for having me.


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Learn how manufacturers combat the manufacturing skilled labor shortage and close skills gaps with an Augmented Connected Workforce (ACWF).

An Augmented Connected Workforce (ACWF) offers manufacturing and other industrial organizations a powerful solution to combat the ever-worsening skilled labor shortage and skills gap. According to a report by Deloitte and the Manufacturing Institute, an estimated 2.1 million manufacturing jobs could go unfilled by 2030 and the cost of those missing jobs could potentially total $1 trillion in 2030 alone.

augmented connected workforce acwf manufacturing

By integrating advanced technologies like artificial intelligence (AI), connected worker platforms, and other emerging solutions manufacturers can enhance the capabilities of their existing workforce and bridge skill gaps. Connected worker tools offer real-time monitoring of your frontline workforce, ensuring seamless operations. Moreover, connectivity enables remote collaboration, allowing experts to assist frontline workers from anywhere in the world. This interconnected ecosystem empowers workers with the tools they need to succeed and attracts new talent by showcasing a commitment to innovation and technology-driven growth.

Through an ACWF, manufacturers can effectively combat the manufacturing skilled labor shortage and close the skills gap while driving productivity, innovation, and remaining competitive. Read more about ACWF in manufacturing below:

Implementing an ACWF in Manufacturing

A critical element of transitioning from a traditional workforce to an Augmented Connected Workforce (ACWF) is implementing and adopting new technologies and processes. Here are a few steps that can help with the adoption of ACWF technologies and smooth transitions in industrial settings:

  • Step 1: Assess Current Processes – Organizations must understand existing workflows and identify areas where AI, connected worker platforms, and other ACWF technology can replace paper-based and manual processes to enhance efficiency and productivity.
  • Step 2: Invest in Technology – Procure  AI-driven analytics platforms, mobile technology, and wearable technology to enable real-time data collection and remote collaboration.
  • Step 3: Training and Onboarding – Provide comprehensive training programs to familiarize workers with new technologies and workflows. Emphasize the importance of safety protocols and data privacy.
  • Step 4: Pilot Programs – Start with small-scale pilot programs to test the effectiveness of the implemented technologies in real-world manufacturing environments. Target high-value use cases that can benefit from a transition from paper to digital.
  • Step 5: Continuous Improvement – Gather feedback from workers and supervisors during pilot programs and adapt implementation initiatives based on their input. Continuously optimize processes and technologies for maximum effectiveness.

By following these steps, manufacturers can smooth the transition from a traditional manufacturing workforce to an ACWF, empowering their frontline workers with improved capabilities, skills, and overall operational excellence.

Supporting Learning in the Flow of Work

Augmented Connected Workforce (ACWF) technologies allow for increased frontline support and for new processes around learning and training to strategically upskill and reskill, reduce time to competency for new workers, and to combat the skilled labor shortage in manufacturing and more. Connected worker tools, such as wearable devices and IoT sensors, enable real-time monitoring of worker performance and environmental conditions, ensuring safety and efficiency on the factory floor.

pyramid of learning

An ACWF also allows for improved workflow learning capabilities giving frontline workers access to expert guidance, remote assistance and collaboration, microlearning, and other learning in the flow of work options regardless of the worker’s location.

ACWF tools further enhance frontline activities through:

  • Digital work instructions and guidance: Smart, connected worker platforms provide digital work instructions, procedures, and visual guidance easily accessible to workers on mobile devices.
  • Digital mentors and training: Some ACWFs incorporate “digital mentors” – GenAI-powered industrial assistants that can provide step-by-step guidance to workers, especially new hires.
  • Knowledge capture and sharing: Connected frontline worker applications capture data and insights from frontline workers, which can then be analyzed by AI software and used to improve processes, update work instructions, and share knowledge across the organization
  • Performance monitoring and feedback: ACWF solutions provide visibility into worker performance, allowing managers to identify areas where additional training or support is needed.

augmented connected workforce in manufacturing

In summary, ACWF initiatives empower frontline workers with the digital tools, knowledge, and support they need to learn and improve their skills directly within their daily workflows, rather than relying solely on formal training programs. This helps close skills gaps and drive continuous improvement.

Future-proofing Manufacturing Operations with an ACWF

Adopting an Augmented Connected Workforce (ACWF) approach centered around augmenting frontline workers with mobile technology, immersive training, collaborative decision-making, and continuous improvement, allows manufacturers to future-proof their operations and gain a sustainable competitive advantage. This concept empowers employees with powerful tools that augment and enhance their capabilities, productivity, and overall business processes by accessing critical information and fostering collaboration

AI-powered software can analyze vast amounts of data to optimize production processes and predict workforce development needs. At the same time, connected frontline worker solutions enable the integration of mobile and wearable technologies and provide real-time data insights, aiding in optimizing factory operations and adapting to evolving industry trends.

For an Augmented Connected Workforce, integrating AI and connected worker technologies serves as a vital strategy for manufacturers navigating the skilled labor crisis. Augmentir encourages organizations to embrace ACWF transformations and expedites adoption through a comprehensive connected worker platform leveraging the combined benefits of connected worker and AI technologies.

With Augmentir, frontline workers can access critical information, real-time data and insights, and expert advice and guidance all in the flow of work preventing lost time and improving both efficiency and productivity. Schedule a live demo to learn more about how an Augmented Connected Workforce future-proofs manufacturing operations and enhances frontline activities.


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Learn the differences between onboarding and training in manufacturing, their benefits, and how to improve them with continuous learning.

Onboarding and training are essential components of integrating new employees into a manufacturing environment. Research by Brandon Hall Group found that organizations with a strong onboarding process improve new hire retention by 82% and productivity by over 70%. Additionally, research from NAM and The Manufacturing Institute has found that manufacturing organizations invest an average of 51.4 hours per employee in training and are increasing overall investment in training by an average of 60% in response to the growing skilled labor crisis.

onboarding vs training in manufacturing

Onboarding and training are two key components of a skilled workforce that, while similar, serve different purposes and cover distinct aspects of the employment process.

Both processes are crucial, as onboarding ensures that employees understand the organization’s broader context, and training ensures that they have the expertise to contribute to the manufacturing processes and meet quality and safety standards.

A successful combination of effective onboarding and comprehensive training can lead to more engaged, skilled, and productive employees in the manufacturing industry. Unfortunately, according to Gallup, only 29% of new hires say they feel fully prepared and supported to excel in their role after their onboarding experience.

Read below to learn more about the differences between onboarding and training in manufacturing, why they are both critical to manufacturing success, the benefits of improving them, and how continuous learning strategies coupled with connected worker solutions can improve both and deliver impressive results.

Breakdown of Onboarding and Training Differences

Onboarding in manufacturing is about orienting new hires to the company as a whole, while training is about equipping them with the specific skills and knowledge needed to perform their job functions effectively. Below a breakdown of the differences between onboarding and training in a manufacturing setting:


  • Purpose: Onboarding integrates a new employee into the organization and its culture. It aims to familiarize employees with the company, its policies and procedures, and their roles within the organization.
  • Focus: Onboarding focuses on introducing employees to the broader aspects of the company, such as its mission, values, and culture, as well as administrative and safety procedures.
  • Duration: Onboarding is typically a short-term process, often lasting a few days, but could extend to a few months in certain manufacturing environments.
  • Components: It may include activities like completing paperwork, understanding company policies, meeting the team, plant/site safety, and familiarizing a new hire with the physical workplace.


  • Purpose: Training in manufacturing is a more specific and in-depth process that imparts the knowledge, skills, and competencies necessary to perform the job effectively. It is task-oriented and aimed at ensuring that employees can carry out their roles proficiently.
  • Focus: Training focuses on the technical aspects of the job, safety protocols, equipment operation, quality standards, and other job-specific skills.
  • Duration: Training is an ongoing process and may vary in duration depending on the complexity of the role and the employee’s experience level.
  • Components: Training tends to include hands-on instruction, demonstrations, practice exercises, and assessments to ensure that employees gain the necessary skills and knowledge.
Pro Tip

Both initial onboarding and ongoing training can be implemented with mobile learning solutions that leverage connected worker technology and AI to provide workers with bite-sized, on-demand training modules that they can access on smartphones or tablets. These modules can be developed with customized learning paths that are focused on the type of tasks and work employees are doing on the factory floor.


Why are training and onboarding important to manufacturing success

Onboarding and training are crucial to manufacturing success for several reasons including safety, compliance, quality, and more. A well-trained manufacturing workforce that has a deep understanding of company policies, its mission, and overall values drives successful initiatives by producing quality products, complying with both industry-wide and company-specific standards, and meeting production goals in a manner that is both safe and efficient.

The manufacturing industry is subject to numerous regulations related to safety, environmental practices, and product quality. Proper training ensures that employees are aware of and adhere to these regulations, reducing the risk of compliance violations and a well-structured onboarding program leads to lower turnover rates and a more effective and cohesive workforce, ultimately contributing to manufacturing success.

In summary, these two tools are essential in manufacturing for setting the stage for employee success and overall organizational success. Onboarding aligns new employees with the company’s culture, policies, and expectations, enhances their safety awareness, and fosters engagement and productivity, while training plays a pivotal role in contributing to manufacturing success by equipping employees with the knowledge, skills, and competencies necessary to perform their roles effectively.

What are the benefits of improving training and onboarding in manufacturing

Improving manufacturing employee onboarding and training offers several advantages, benefiting both the company and its employees. Comprehensive onboarding makes new hires feel connected to the company’s culture and values, while ongoing training can offer growth and development opportunities, leading to increased employee engagement and job satisfaction.

Companies with a skilled, well-trained workforce are more competitive in the marketplace, as they can produce higher-quality products at a lower cost and adapt to industry changes more effectively.

Training and development opportunities are often cited as a key factor in employee satisfaction. When employees feel that their skills are being enhanced and their careers are advancing, they are more likely to be satisfied with their jobs.

How continuous learning and connected worker solutions improve training and onboarding in manufacturing

Continuous learning and connected worker solutions can significantly enhance training and onboarding in manufacturing by providing more dynamic, effective, and adaptable approaches.

By incorporating continuous learning and connected worker solutions into the these processes, manufacturing companies can create more efficient, engaging, and rewarding experiences for employees. This not only accelerates the integration of new employees but also supports ongoing skill development and knowledge retention once on the job, ultimately improving productivity and the overall success of the organization.

connected worker as part of connected enterprise

Augmentir’s AI-based connected worker solution is being leveraged by manufacturing leaders to deliver continuous learning and development tools to optimize onboarding training for a rapidly changing and diverse workforce. Our innovative, smart connected worker suite is transforming how manufacturing organizations hire, onboard, train, and deliver on-the-job guidance and support.


digital skills management in a paperless factory

Schedule a live demo today to learn how our smart, connected worker solutions, AI-driven insights, and digital skills management are optimizing training and onboarding programs, tracking individual and team progress, and delivering targeted training and upskilling.


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