Posts

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.

A

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.

 

See Augmentir in Action
Get in Touch for a Personalized Demo

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.

 

See Augmentir in Action
Get in Touch for a Personalized Demo

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:

Onboarding

  • 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.

Training

  • 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.

A

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.

 

See Augmentir in Action
Get in Touch for a Personalized Demo

Learn how to apply everboarding in manufacturing, and how it is replacing traditional onboarding and training methods.

According to Brandon Hall Group research, investment in employee training and development programs to enhance skills and knowledge is the highest-rated initiative globally to improve the employee experience. One highly effective approach towards revolutionizing training and onboarding is a continuous learning method called everboarding.

applying everboarding in manufacturing

Everboarding is a modernized approach toward employee onboarding and training that recognizes learning as a continuous and ongoing process. Its foundational characteristic is the belief that learning doesn’t stop after the initial onboarding period. Instead, everboarding emphasizes continuous skill development and employee knowledge enhancement throughout their careers.

Applying everboarding in a manufacturing environment involves tailoring continuous learning and development approaches to the unique needs and challenges of factory floor operations. As industrial processes evolve, employees must be routinely educated on process improvements, new technologies, safety standards, and efficiency initiatives.

Read on to learn more about how to apply everboarding to the factory floor and how fostering a culture of continuous improvement and learning keeps frontline workers safe, efficient, and engaged:

Steps for Implementing Everboarding in Manufacturing Operations

Everboarding in the context of the manufacturing industry refers to a forward-looking approach that ensures employees remain well-trained, adaptable, and aligned with industry standards throughout their tenure. This is essential in dynamic and fast-paced industrial environments like manufacturing. Here are some steps and strategies to begin implementing everboarding in your operations:

  1. Operationalize Learning: Develop and maintain a systematic approach to training and workforce development and ensure that ongoing training and development are available for all shop floor workers.
  2. Develop Learning Pathways: Create clear learning pathways and career development plans for employees. These pathways should outline the skills and knowledge required for career advancement within the manufacturing shop floor.
  3. Implement Digital Learning Platforms: Leverage digital learning platforms and smart, connected solutions to provide employees with access to training materials, videos, courses, and other resources. These platforms can track progress, and employees can learn at their own pace.
  4. Integrate Learning into the Workflow: Using digital, mobile, and connected technologies, organizations can integrate training into the factory floor for moment-of-need guidance and microlearning that allows frontline workers to stay compliant and operations to continue smoothly.
  5. Provide Feedback and Improvement Loops: Create a feedback mechanism where employees can provide suggestions for improving training programs and processes. Make sure to act on the feedback to continuously enhance the training experience.
  6. Initiate Regular Skill Assessments: Implement regular assessments and evaluations to identify areas where employees need further training or improvement.

Everboarding in a manufacturing factory floor environment is critical for keeping the workforce skilled, adaptable, and able to meet changing demands and technological advancements. By fostering a culture of continuous learning and improvement, you can ensure that the factory floor remains efficient and productive.

5 Useful Everboarding Technologies

Implementing Everboarding in manufacturing requires the use of various technologies to facilitate continuous learning and skill development. Here are five (5) useful technologies that can help speed the adoption of everboarding methods on the factory floor and support frontline workers on their continuous learning paths.

  1. Learning Management Systems (LMS): LMS platforms are essential for delivering and managing training content. They allow manufacturing companies to organize courses, track employee progress, and ensure compliance with training requirements.
  2. Connected Worker Applications: Connected worker applications provide mobile solutions, real-time data, and actionable insights that enable customized and personalized training dedicated to the needs of individual workers and specific tasks.
  3. Artificial Intelligence (AI): AI-driven systems can personalize training content based on employee performance and preferences. AI’s ability to process vast amounts of data, provide personalized experiences, and offer real-time feedback makes it a powerful tool for implementing everboarding.
  4. Internet of Things (IoT): IoT sensors can be integrated into manufacturing equipment to gather data on machine performance and employee interactions. This data can inform training needs and help identify areas for improvement.
  5. Wearable Technology: Wearable devices can be used for on-the-job training and performance monitoring. They are especially useful in high-risk manufacturing environments.

These technologies leverage connectivity, digital tools, and data to create a more dynamic and adaptive learning environment for frontline employees. By integrating emerging technologies like smart, connected worker solutions into manufacturing operations, companies can create a more agile and adaptive learning environment that supports the foundations of everboarding.

Pro Tip

Digital training tools can help implement everboarding and improve learning speed and retention. For example, workers who need visuals or real-world scenarios can access them using AI-powered software to create a comprehensive everboarding and training program that supports frontline employees throughout the entire skills and training lifecycle.

A

Improving Manufacturing Training with Everboarding

Implementing new learning technologies in any industry is met with a certain number of challenges. This remains especially true for the factory floor where training and development are traditionally separate from the work being done, and where traditional onboarding has been a one-and-done type of approach.

However, because everboarding is a process of continuous learning, organizations can improve their industrial training and onboarding, ensuring employees continually acquire new skills and knowledge to adapt to evolving technologies and processes. This not only helps in training new employees but also enables continuous learning and skill development for the entire workforce, improving productivity, safety, and quality in the process.

Implementing everboarding in factory floor operations can seem complex but it is a rewarding process that can be streamlined through solutions like Augmentir’s connected worker solution. With our AI-driven insights, our connected solution reduces onboarding time and transforms workforce training, bringing learning to the factory floor through intelligent guidance that delivers information to workers at the point of need.

Learn how manufacturers are implementing Augmentir’s AI-driven connected worker tools to capture and digitize tribal knowledge, reskill and upskill their workers, and empower their frontline teams – schedule a live demo today.

 

See Augmentir in Action
Get in Touch for a Personalized Demo

A connected worker strategy is critical to the success of your connected enterprise and digital transformation initiatives.

In today’s always-changing industrial landscape, organizations are acutely aware that adopting innovative technologies and processes is not just a “nice-to-have” but a “must” to stay competitive. According to PwC, 75% of manufacturers believe that Connected Enterprise technologies will have a major impact on their business over the next five years. By 2025, the number of connected devices in industrial settings is expected to reach 21.5 billion, making it clear that the adoption of connected technologies is a critical step for any organization that wants to succeed in the future.

connected enterprise

However, there is one aspect of a truly connected enterprise that many manufacturers overlook – their frontline workforce.

Frontline workers play a critical role in ensuring the safety, quality, and uptime of production operations, yet too often these workers are disconnected from the rest of the business. Connected frontline worker (CFW), refers to the use of technology to connect workers with the digital systems and processes in their organization, making it easier for them to collaborate, access information, and perform their jobs more efficiently. To fully realize the benefits of a connected workforce, it is essential to understand how they fit into the larger Connected Enterprise concept.

Learn more about what a connected enterprise is and the role that connected worker solutions play in the following sections:

What is a connected enterprise?

Connected Enterprise refers to the integration of digital technologies, data, and analytics across an organization’s entire operational landscape to improve efficiency, productivity, and profitability. Companies are rapidly adopting advanced technologies to improve their business operations. This concept spans several initiatives within an organization: assets and equipment, the products being manufactured, the end customer, operations, workers, and the entire supply chain.

connected enterprise - LNS Research

Source: LNS Research

Connected worker (or connected frontline worker – CFW) technology is a crucial part of this concept – as it connects the human workforce with the digital systems and processes in the organization.

How to create a connected enterprise

The first step to creating a connected enterprise is implementing smart, connected worker solutions. AI and connected frontline worker technologies are some of the leading solutions organizations are turning to on their path toward a Connected Enterprise. Augmentir has seen manufacturers achieve significant results after successfully implementing connected frontline worker solutions in conjunction with AI-driven analytics:

  • Up to a 72% reduction in training and onboarding times
  • More than 20% decrease in downtime
  • Nearly a 25% increase in productivity

Solutions that incorporate enhanced mobile capabilities and combine training and skills tracking with connected worker technology and on-the-job digital guidance can deliver significant additional value for an organization’s connected enterprise initiative. Data from actual work performance combined with AI-based analytics can inform workforce development investments, and deliver smart insights that reduce time to productivity, enable targeted reskilling and upskilling, and provide individualized guidance and support at the point of work so that you get the best each person has to offer.

connected worker as part of connected enterprise

However, companies need to be strategic and take a structured approach. It is imperative that the right solutions are identified and tested by the right divisions, personnel, and business units.

LNS Research has developed an “Industrial Transformation Reference Architecture” approach that provides a framework and simplifies implementation into four layers:

  1. Business Processes and Systems
  2. Connected Assets and Operations
  3. Data and Analytics
  4. Connected Worker

These guidelines give organizations reference points to help guide them along their path of industrial transformation and set them up for success in connecting their operations.

Key benefits of connecting your workforce to your enterprise

By leveraging AI and other smart technologies, companies are providing workers with real-time guidance and assistance, enabling them to perform their jobs more effectively. Frontline workers can access information, collaborate with colleagues, and receive real-time alerts on potential hazards, all of which help to improve their productivity and safety.

The benefits offered by AI and connected technologies are significant:

  • Improved efficiency: By automating routine tasks and providing real-time information, AI and connected worker technologies can help streamline operations and reduce errors.
  • Increased productivity: AI and connected worker technologies can help workers perform their jobs more effectively, enabling them to produce more goods in less time.
  • Better quality control: By providing real-time data on production processes and product quality, AI and connected worker technologies can help identify issues early and prevent defects.
  • Enhanced safety: Connected worker technologies can provide workers with real-time guidance and assistance, enabling them to perform their jobs more safely and avoid accidents.
  • Cost savings: By reducing downtime, improving efficiency, and enhancing product quality, connected worker technologies can help companies save money and increase profitability.
  • Improved decision-making: By providing real-time insights and data analytics, connected worker technologies can help companies make more informed decisions about their operations and identify new opportunities for growth.

According to McKinsey & Company, by 2030, the adoption of “Connected Enterprise” technologies is expected to generate $1-2 trillion in value for the global economy, including the manufacturing industry. As the transformation from paper processes to digital continues, industrial organizations are consistently finding that CFW solutions are an essential component of a larger “Connected Enterprise”. By leveraging AI and other advanced technologies to better analyze data and provide actionable insights, companies empower workers with the tools to perform their jobs more effectively, improving productivity, efficiency, and safety. Adopting AI and connected worker technologies is a key part of industrial transformation and of “Connected Enterprise” initiatives, offering industrial organizations an enhanced competitive advantage and solutions to many of the obstacles they face in today’s markets.

Implementing a connected enterprise with Augmentir

If you are interested in learning for yourself why companies are choosing Augmentir to help them connect, digitize, and optimize their frontline operations – reach out to book a demo.

 

See Augmentir in Action
Get in Touch for a Personalized Demo

 

LNS Research reviewed dozens of Connected Frontline Worker (CFW) vendors, ranking Augmentir as the leading CFW solution innovator.

Efforts to enable the frontline industrial workforce through connected worker and other digital technologies have become increasingly common over the past several years, recently, LNS Research found that over half of industrial organizations globally have undertaken Connected Frontline Workforce (CFW) initiatives. CFW has become a strategic part of Industrial Transformation (IX) initiatives as manufacturers seek to solve critical labor shortages, skills gaps, and retention issues in frontline operations.

CFW-enabling technologies hold the promise of helping companies meet their frontline workforce challenges while optimizing operational performance across safety, quality, and productivity dimensions. However, industrial business and technology leaders must navigate the uncertain waters of the relatively immature and highly fragmented CFW Applications market to capture the opportunity fully.

LNS Research Connected Worker Solution Selection Matrix

From their extensive analysis, LNS Research has created the CFW Applications Solution Selection Matrix™ (SSM) – a comprehensive guide intended to help man­ufacturers better understand, evaluate, and even select from a shortlist of Connected Frontline Worker technology vendors.

LNS Research reviewed dozens of vendors within the CFW ecosystem and categorized them based on various key criteria, including product capabilities, market potential, and company presence.  Augmentir was named by LNS Research as a leading CFW solution innovator in their SSM.

Augmentir positioned as a leading front runner and innovator

According to LNS Research, Augmentir is well-positioned for future growth, with a trajectory that gives it the potential to be among a small set of likely market leaders in the Connected Frontline Worker (CFW) Applications space. This assessment is based partly on the strength of differentiated capabilities of its AI-enabled solution suite to enable proactive, data-driven performance improvement, personalization of work execution support and training, and the integration of individual and team skills and qualifications to guide workforce development and shift-specific work assignment.

Other key factors impacting Augmentir’s potential are the strength and proven experience of the leadership and management teams, strong momentum in the market, a record of successful product innovation, ecosystem partnerships, and likely continued access to adequate funding and resources to support the expansion of go-to-market initiatives. Augmentir’s track record indicates a strong likelihood of continued growth and the potential over time to be among a select group of market leaders in the CFW Applications space.

Read the full report here.

Augmentir’s results from the field

Manufacturers are using connected frontline worker solutions to empower their employees with real-time, actionable data; driving better decision-making and improving safety, training, and more.

Leading manufacturers that deployed Augmentir’s AI-driven, smart, connected worker solution have seen impressive results, such as:

  • 75% reduction in new hire training/onboarding time
  • 27% reduction in machine downtime using Autonomous Maintenance
  • 32% improvement in worker productivity

In addition to the above results, our customers have seen quality, safety, and productivity increases across all operations, as well as increases in employee retention and reductions in operating costs associated with employee churn.

 

If you are interested in learning why LNS Research ranked Augmentir as the leading connected worker solution in the market, reach out to us and request a live demo.

 

See Augmentir in Action
Get in Touch for a Personalized Demo

 

Learn how to improve quality control and assurance in the food industry with digital solutions from Augmentir.

Following quality control (QC) and quality assurance procedures in the food industry is imperative to ensure product quality and consumer satisfaction. Today’s consumers demand safe, reliable goods that meet all quality inspection protocols. The last thing you want is for a product to get recalled because of potential health concerns.

According to Food Manufacturing, quality control is one of the most important aspects of the food and beverage industry. Manufacturers who perform routine inspections of products during each stage of the production process significantly increase their chances of delivering items that are free of health hazards and liabilities. But beyond avoiding these concerns, standardizing and digitizing quality procedures benefits the entire operation.

Ultimately, preventing and catching quality issues can boost product quality, reduce waste, raise profits, increase brand reputation, and avoid media or food safety disasters. Learn more about QC and assurance in the food industry and how to improve it as we discuss:

quality control food industry

Types of quality control measures to take

There are certain QC measures you can take to ensure that all goods meet quality standards, from regular machine inspections to worker training. They fall into two general categories: preventative and reactive.

Preventative (proactive) quality control: Minimizing the number of deficiencies begins with implementing preventative QC solutions. When workers can catch mistakes before they even happen, they prevent product defects. Preventative QC measures should be practiced on a routine basis and can range from inspecting machines and equipment to offering employee training opportunities. By providing workers with real-time information and guidance through mobile, connected worker solutions, manufacturers enable them to make better decisions about product quality, reducing the risk of errors and identifying potential quality issues before products are shipped to customers, reducing the risk of product recalls, and preserving consumer trust.

Reactive quality control: Catching every defect on the production floor is nearly impossible, even if the most fool-proof strategies are taken. That’s why creating a plan of action ahead of a crisis can help solve quality issues as they happen.

What to put in your plan will depend on the potential problems. For example, you can include specific instructions on what to do if machinery breaks down or stops unexpectedly. It’s vital to collect any data at this stage. Analyzing this data can help you improve preventative quality control in the future to make sure the same problems don’t happen again.

Pro Tip

By utilizing AI and modern, digital technologies, companies can connect, engage, and empower frontline workers to drive quality improvements, resolve quality issues faster, and share timely insights with teams across the value chain.

A

Keep in mind that practicing quality control in the food industry should be part of every manufacturing process, from product ideation and development to production and delivery. Problems can develop at any time, so it’s crucial to follow protocols at every stage of production to prevent even the slightest of mistakes.

All workers should also uphold QC and assurance protocols in their everyday tasks to ensure continuous product improvement.

Better organization of equipment can also help workers understand how the action of one affects the other to solve any potential problems. This is another benefit of integrating your asset hierarchy with a connected worker solution. In a nutshell, strong hierarchies are a solid foundation for proper maintenance management and reliability.

How to improve QC and assurance procedures in food production

Effective quality control and assurance procedures prevent defective food products from making their way into grocery stores and homes. That’s why manufacturers should document the quality of their goods at every stage of the operational process. Strategies like first time quality (FTQ), or first time right, plans coupled with smart, connected solutions help decrease product deficiencies and increase customer satisfaction.

Manufacturing firms in the food industry must follow specific requirements set by the Food and Drug Administration (FDA), Good Manufacturing Practices (GMP) system, and the Hazard Analysis and Critical Control Points (HACCP). The guidelines set by these regulatory bodies can give businesses a better idea of how their processes should look and what data they need to collect and report.

Data should be collected for real-time production processes. These vary by product but may range from product chilling and thermal processing to testing raw materials for metal toxins and other chemical deposits.

The following steps provide a roadmap for how to improve quality control in the food industry.

Step 1: Source the correct ingredients

A successful assembly line run begins with finding and using the correct ingredients. Some things to think about when deciding which ingredients to choose: where the raw material was sourced, when, and its condition.

Step 2: Include an approved supplier list

Make sure that each ingredient has an approved supplier list. A good rule of thumb is to include three vendors per ingredient and record the ingredient with each supplier’s name, address, and code number on the list. The more information you include, the better. Having an approved vendor list ensures that all parties are properly vetted by the manufacturing firm and meet its requirements for quality and distribution.

Step 3: Document product and recipe creation

Documenting how each food item is made and its recipe helps set the quality standards for finished goods. This documentation can also be useful when improving product development in the future. Your document should include the types of ingredients used, their codes, batch yield, percentage formula, and more.

Step 4: Catalog production procedures

It’s also critical to log all the details of a production process, including how materials should be delivered, the appropriate conditions for storing food, what order each ingredient should be added to the batch, what tools are needed, and who is in charge of each task.

Note that this step is different from documenting product and recipe development because it includes the actual instructions for carrying out each procedure. For example, a worker may be asked to preheat the oven to a certain temperature as part of ensuring the food is ready for customer distribution.

Step 4: Record real-time processes

Machine operators should record in real-time every detail of how goods are created during actual production. This can include factors like product size, weight, expiration date, equipment conditions, and more.

Step 5: Digitize assurance and inspection processes

AI and smart, connected worker systems help digitize and link inspections and other quality control procedures. This creates an additional layer of defense, protecting customers and preventing quality issues before they can impact production.

How Augmentir helps with quality control and assurance

Augmentir offers a smarter way to improve quality control in the food industry by effectively standardizing and optimizing quality assurance and inspection procedures for all frontline workers. With our smart, connected solutions coupled with AI-powered software, food manufacturers have improved quality control and assurance by:

  • Tracking and analyzing data to identify trends and opportunities for improvements
  • Reducing human error in inspections by standardizing and improving training procedures and processes
  • Transforming connected workers into human sensors who can proactively address quality and safety events that surface during manufacturing operations

standardize and digitize quality assurance procedures

 

Our AI-powered connected worker solutions, provide digital work instructions to help employees better perform inspection checks and reduce the number of production errors and rework.

These customized solutions also include:

  • Digital standard operating procedures (SOPs) for how to complete assembly line tasks. These step-by-step instructions can greatly improve workflow efficiency, increase regulatory compliance, and reduce mistakes on the shop floor.
  • Digital workflows that convert your paper-based processes to digital work instructions and personalize them to the needs of each worker.
  • Enhanced product traceability to decrease equipment setup time, reduce process inconsistencies, and better meet customer expectations. Our digital instructions help you to easily track materials from the supply chain, inventory, and across every production process.

If you are interested in learning why companies are choosing Augmentir to help improve their quality control and assurance processes, check out our quality use cases – or reach out to schedule a live demo.

 

See Augmentir in Action
Get in Touch for a Personalized Demo

 

Learn how to digitize your operations and build a paperless factory in this paperless manufacturing guide from Augmentir.

Manually managing and tracking production in manufacturing has become a thing of the past. That’s because manufacturers are adopting a new digital approach: paperless manufacturing.

Paperless manufacturing uses software to manage shop floor execution, digitize work instructions, execute workflows, automate record-keeping and scheduling, and communicate with shop floor employees. More recently, this approach also digitizes skills tracking and performance assessments for shop floor workers to help optimize workforce onboarding, training, and ongoing management. This technology is made up of cloud-based software, mobile and wearable technology, artificial intelligence, machine learning algorithms, and advanced analytics.

paperless manufacturing and digital factory

Paperless manufacturing software uses interactive screens, dashboards, data collection, sensors, and reporting filters to show real-time insights into your factory operations. If you want to learn more about paperless manufacturing processes, explore this guide to learn about the following:

What is a paperless factory?

A paperless factory uses AI-powered software to manage production, keep track of records, and optimize jobs being executed on the shop floor. Paperless manufacturing is intended to replace written record-keeping as well as paper-based work instructions, checklists, and SOPs, and keep track of records digitally.

For example, in most manufacturing operations, everything from quality inspections to operator rounds and planned and autonomous maintenance is done on a regular basis to make sure factory equipment is operating properly and quality and safety standards are met. In most manufacturing plants, these activities are done manually with paper-based instructions, checklists, or forms.

Operators and shop floor workers in paperless factories use software to execute work procedures and see production tasks in ordered sequences, which enables them to implement tasks accordingly. Workers are able to view operating procedures, or digital work instructions, using mobile devices (wearables, tablets, etc.) in real-time.

benefits of digital work instructions

Furthermore, paperless manufacturing incorporates the digitization of shop floor training, skills tracking, certifications, and assessments.  This digital approach uses skills management software helps optimize HR-based processes that were previously managed via paper or spreadsheets, and includes the ability to:

  • Create, track, and manage employee skills
  • Instantly visualize the skills gaps in your team
  • Schedule or assign jobs based on worker skill level and proficiency
  • Close skill gaps with continuous learning
  • Make data-driven drive operational decisions

digital skills management in a paperless factory

What are the benefits of going paperless in manufacturing?

There are a number of reasons for factories to go paperless, from cost-effectiveness to increased productivity and sustainability. A paperless system can revolutionize production processes, workforce management, and business operations.

Here are the top benefits of going paperless:

  1. Accelerate employee onboarding: By digitizing onboarding and moving training into the flow of work, manufacturers can reduce new hire onboarding time by 82%.
  2. Increase productivity: Digitizing manufacturing operations means no more manual, paper-based data collection or record-keeping. Workers have more time to run their equipment, execute shop floor tasks, and find solutions to problems.
  3. Boost data accuracy: People are prone to making mistakes, but digital data capture and validation can help offset human error and improve accuracy.
  4. Improved workforce management: Digital skills tracking and AI-based workforce analytics can help optimize production operations and maximize worker output.
  5. Manage real-time operations: Human-machine interface systems eliminate the need for paper, files, and job tickets. This means that workers can analyze inventory and other data in real-time.
  6. Save money: Although going paperless means that the cost of paper is eliminated, the savings extend beyond that. With greater productivity, operations in real-time, and improved production optimization, costs can be reduced in many areas.

How do you go paperless in manufacturing?

Going paperless starts with digitizing activities across the factory floor to increase productivity, and extending that value through a digital connection between the shop floor and enterprise manufacturing systems. We lay out below the four basic steps for how to go paperless in manufacturing:

Step 1: Digitize and connect your frontline operations.

Paperless manufacturing starts with the use of modern, digital tools that can connect, digitize, and optimize what your employees know and how they are doing on the job. Solutions that incorporate enhanced mobile capabilities and combine training and skills tracking with connected worker technology and on-the-job digital guidance can deliver significant additional value. A key requirement to start is to identify high-value use cases that can benefit from digitization, such as quality control or inspection procedures, lockout tagout procedures, safety reporting, or autonomous maintenance procedures.

Step 2: Augment your workers with AI and Connected Worker technology.

AI-based connected worker solutions can help both digitize work instructions and deliver that guidance in a way that is personalized to the individual worker and their performance. AI Bots that leverage generative AI and GPT-like AI models can assist workers with language translation, feedback, on-demand answers, access to knowledge through natural language, and provide a comprehensive digital performance support tool.

As workers become more connected, companies have access to a rich source of job activity, execution, and tribal data, and with proper AI tools can gain insights into areas where the largest improvement opportunities exist.

Pro Tip

Frontline operations software like Augmentir’s Connected Worker Solution helps you digitize and optimize the operations of your facility. Digitally manage safety, quality, operations, and maintenance procedures, skill requirements, training, and KPIs all through a visual interface. Connected worker solutions help digitally integrate your shop floor operations.

A

Step 3: Set up IoT sensors for machine health monitoring.

The industrial Internet of Things (IoT) uses sensors to boost manufacturing processes. IoT sensors are connected through the web using wireless or 4G/5G networks to transmit data right from the shop floor. The use of machine health monitoring tools along with connected worker technology can provide a comprehensive shop floor solution.

Step 4: Connect your frontline to your enterprise.

Digitally connected frontline operations solutions not only enable industrial companies to digitize work instructions, checklists, and SOPs, but also allow them to create digital workflows and integrations that fully incorporate the frontline workers into the digital thread of their business.

The digital thread represents a connected data flow across a manufacturing enterprise – including people, systems, and machines. By incorporating the activities and data from these previously disconnected workers, business processes are accelerated, and this new source of data provides newfound opportunities for innovation and improvement.

 

Augmentir provides a unique Connected Worker solution that uses AI to help manufacturing companies intelligently onboard, train, guide, and support frontline workers so each worker can contribute at their individual best, helping achieve production goals in today’s era of workforce disruption.

Our solution is a SaaS-based suite of software tools that helps customers digitize and optimize all frontline processes including Autonomous and Preventive Maintenance, Quality, Safety, and Assembly.

paperless factory

 

Transform how your company runs its frontline operations. Request a live demo today!

 

See Augmentir in Action
Get in Touch for a Personalized Demo