You may have noticed that our website and brand look a little different. Augmentir has a new look, but under the hood, its the same powerful AI that is helping to transform the industrial workforce of the future.
You may have noticed that our website and brand look a little different. Well, that’s because behind the scenes for the past few months, we’ve been under construction (no pun intended).
Augmentir was founded in 2018 with the vision to use AI to empower the industrial frontline workforce to perform at their best. This was a continuation of our rich history – our founding team has been at the forefront of three of the most important of these software technology revolutions in manufacturing over the past three decades – Wonderware Software in 1987, Lighthammer in 1997, and ThingWorx in 2008.
A lot has changed since then. The world we live in today is not the same as it was 4 years ago. And in the last two years, the COVID-19 pandemic has altered the stability of the workforce and drastically magnified some of the industry’s top workforce challenges, which stem from the unprecedented levels of dynamism in the areas of skills diversity, reduced tenure, and increased churn from the “Great Resignation”. Unlike the stable and predictable workforce of the recent past, today companies have to live in the new normal where workers are hard to find, hard to engage, and hard to keep.
These top challenges of today have only reinforced the need for an AI-powered, data-driven approach to empowering frontline workers.
This data-driven era we’re entering into is one of continuous learning and development with tools like remote collaboration and digitized work processes truly integrating frontline workers into the fabric of the business from a collaboration standpoint whereas they may have been overlooked before.
Augmentir’s AI-powered connected worker platform provides the tools to not only survive in this new normal but to thrive.
You can’t build a truly modern, connected workforce without AI
The term “connected worker” has become a recent buzzword in the manufacturing world, and is now considered a tool that the new generation of workers expect to work with. But true connected work means using AI to allow frontline workers to have access to internal and external resources that are appropriate for when and how they need them.
Augmentir isn’t your typical connected worker platform. Our platform was built from the ground up on an AI foundation. AI algorithms are ideal for analyzing large amounts of data collected from a connected workforce. AI can detect patterns, find outliers, cleanse data and find correlations and patterns that can be used to identify opportunities for improvement and create a data-driven environment that supports continuous learning and performance support. Our connected worker platform utilizes AI to help train, guide, and support today’s frontline workers in a dynamic workforce by combining digital work instructions, remote collaboration, continuous development and advanced on-the-job training capabilities.
This approach aligns perfectly with the dynamic, changing nature of today’s workforce, and is ideally suited to achieve and sustain effective on-the-job performance.
As the world’s only AI-powered connected worker platform, we decided it was time to refresh our brand identity to accentuate our strongest feature and the thing that makes Augmentir unique – AI. We’re still the same AI-first connected worker platform that you know – just with a new look.
https://www.augmentir.com/wp-content/uploads/2021/10/augmentir-2021.png6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2021-10-26 19:26:282024-08-16 16:45:16Augmentir: New Look, Same Powerful AI and Connected Worker Solution
Augmentir, a data-driven, AI-powered Connected Worker solution ensures that your frontline workers can go the extra mile every day by providing training, guidance, and support through the combination of digital work instructions, remote collaboration, and advanced on-the-job training.
In 2009, theExtra Mile America Foundation, a foundation promoting action and empowering positive change, first celebrated the “Extra Mile Day”. Fast forward to 2014 and 527 US cities declared November 1st as the official “Extra Mile Day”. This day is designated to living life to your full potential, having a positive influence, and always striving to make the world a better place. At Augmentir, we believe in positive change, living to your full potential, and going the extra mile every day. In 2018, our founders decided to continue their journey of creating some of the most important software technology revolutions in manufacturing (Wonderware (1987), Lighthammer (1997), and ThingWorx (2008) by focusing on the most important asset, the frontline workers.
We recognize that today’s industrial workforce is different, and the old way of supporting workers just doesn’t work anymore. The static “one size fits all” approach used in the past, no longer applies to this generation of workers.
Augmentir, is a data-driven, AI-powered Connected Worker solution that ensures that frontline workers can go the extra mile every day by providing training, guidance, and support through the combination of digital work instructions, remote collaboration, and advanced on-the-job training.
As your workers become more connected, companies have access to a new rich source of activity, execution, and tribal data, and with proper AI tools they can gain real-time insights into areas where the largest improvement opportunities exist. Artificial Intelligence lays a data-driven foundation for continuous improvement in the areas of performance support, training, and workforce development, setting the stage to address the needs of today’s constantly changing workforce.
When it comes to our frontline workers, let’s enable them to go the extra mile, not just on November 1st, but every day, by empowering them with the only AI-powered Connected Worker solution.
https://www.augmentir.com/wp-content/uploads/2021/11/go-the-extra-mile.jpg6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2021-11-01 19:58:262024-08-16 16:43:52Empowering Your Frontline Workers to go the Extra Mile with Augmentir
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 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.
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.
https://www.augmentir.com/wp-content/uploads/2024/07/gartner-augmented-connected-workforce.webp6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2024-02-05 02:40:032024-07-02 02:43:15Gartner Highlights Augmented Connected Workforce as a Top Trend for 2024
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
https://www.augmentir.com/wp-content/uploads/2024/06/generative-ai-in-manufacturing.webp6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2024-06-17 18:43:542024-06-17 18:44:37Generative AI in Manufacturing: Benefits, Risks, and Top Use Cases
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.
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.
https://www.augmentir.com/wp-content/uploads/2024/04/packaging-industry-connected-workforce.webp6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2024-04-07 18:34:312024-06-13 16:58:55How AI and the Augmented Connected Workforce is Revolutionizing the Packaging Industry
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.
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:
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.
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.
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.
https://www.augmentir.com/wp-content/uploads/2024/06/augmented-connected-workforce-acwf-manufacturing.webp6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2024-06-03 16:14:362024-06-05 14:42:18How to Combat the Manufacturing Skills Gap with an Augmented Connected Workforce (ACWF)
The evolution of connected worker software, how industrial transformation leaders are meeting modern challenges with a generation of tools.
Beginning in mid-2022 and now increasing in 2023, there is a significant trend of companies moving away from earlier investments in connected worker software tools to Augmentir’s Connected Worker Platform.
Early adopters and pioneers of V1.0 connected worker tools and technology deserve respect for leading the charge into Industry 4.0 and the concept of a connected workforce. However, we also admire those leaders who realized there are more transformations and improvements to make – such as value in the data from your connected workers and incorporating AI-driven solutions to make sense of that data. These innovative leaders dared to adapt, continue innovating, and replace the connected worker software systems that were not solving enough of the challenges faced by the modern workplace.
By combining AI-powered software and smart connected worker solutions, manufacturers are able to get next-level results and improve frontline worker productivity, engagement, and safety.
Following in the Footsteps of Industrial Transformation Leaders
According to LNS Research (a leading analyst firm in defining the connected worker space), the business case for connected worker software continues to grow, and solutions that incorporate emerging technologies like AI are leading the way. In fact, LNS states that Industrial Transformation Leaders (IX Leaders) are two times more likely to use AI-enabled advanced analytics capabilities. These leading manufacturers are supporting their frontline operations with AI-based technology for training and skills development, real-time worker performance support, and providing dynamic and personalized content.
Here at Augmentir, we have seen quite a few companies that fall into the category of the courageous, understanding that they needed to continue adapting for their business to thrive.
We have been honored to be recently chosen by several global leaders as their connected worker V2.0 solution, including:
one of the largest paint manufacturers in the world
one of the largest agricultural companies in the world
one of the largest food manufacturers in the world
one of the largest manufacturers of batteries in the world
All of these world leaders recognized that their current connected worker software solutions had become insufficient and that they needed a smarter, more complete solution to help them overcome their frontline workforce challenges and current business obstacles.
Here are three key takeaways you can use from these companies that went back to select a new connected worker solution:
Don’t be afraid to make a change that will have a positive impact on your business, even if you are the one who made the initial decision.
If you have experience choosing early connected worker tools, build on that experience. You are ideally situated to identify gaps in processes and improvement needs; and know best which tools to use to address the overall operational needs of the business.
Use your prior experiences to build processes for re-evaluating connected worker solutions from the perspective of already experiencing one fully deployed.
In one example, a global manufacturer invested in an early connected work tool and had been using the tech for nearly 4 years. However, once they decided they needed a new solution, they then went back to evaluate the market for the right tool. They made a list of selection criteria they knew they wanted from this new solution, from that they looked at approximately fifteen (15) connected worker vendors, and from there they narrowed down to the three (3) they ended up testing. They even included having a couple of integrations in their POC as they knew that an integration into their ERP, Quality Management, and Asset Management systems was something they needed, and they had poor experiences previously with vendors overcommitting.
Pro Tip
We suggest anyone evaluating a technology use this same approach – include integrations as part of your Proof-of-Concept to ensure that you are not getting hypothetical answers to hypothetical questions, and that the solution meets your true business needs.
What our customers tell us
Here is what customers are telling us they are looking for in a V2.0 connected worker solution, and the reasons they changed to Augmentir’s Connected Worker Platform:
Ease of Use: Augmentir prioritizes a user-friendly experience. Its intuitive interface and workflow builder makes it easy for employees to adopt and use the tool effectively. This can result in faster onboarding and increased overall productivity.
Augmented, Personalized Work Instructions: Augmentir provides a workflow and content creation environment that allows you to digitize standardized work instructions, and adjust content and in-line training to suit the needs of individual workers. This optimizes performance and speeds up onboarding time for new employees.
Upskilling and Reskilling: Augmentir’s ability to deliver formal skills and learning in the flow of work means a worker can stay current in their needs, continue to grow in their role, and build a structured career path within their company. This approach appears to be driving increased retention and job satisfaction.
Workforce Optimization: Augmentir’s ability to assess in real time who is available to work on any given day and then balance the skill level best suited for a task with the available workforce offers optimal productivity based upon what you have to work with on any given day.
Digitizing Complex Workflows: Most solutions on the market allow you to digitize simple workflows. With Augmentir, manufacturers can build complex workflows that satisfy use cases that are unique to their business, and extend those workflows to support greater integration into their business processes.
Industrial Collaboration: Augmentir enables remote collaboration among workers and experts. This functionality is particularly useful when experts are not physically present at the job site. Remote experts can guide workers through AR annotations and audio/video communication, fostering knowledge sharing and faster problem resolution.
Continuous Improvement: Augmentir focuses on driving continuous improvement within organizations. It leverages AI to analyze data from worker interactions and identifies areas for improvement. This data-driven approach allows companies to optimize processes, increase productivity, and reduce costs over time.
Integration and Scalability: Augmentir offers integration capabilities with existing enterprise systems, such as enterprise resource planning (ERP) or manufacturing execution systems (MES). This ensures seamless data exchange and workflow integration. Additionally, Augmentir is designed to scale with the organization’s needs, accommodating both small teams and large enterprises.
Analytics and Insights: Augmentir provides robust analytics and reporting features driven by AI-powered solutions and focuses on AI as a core component of Connected Worker V2.0. This allows managers and supervisors to gain valuable insights into worker performance, task completion times, and areas that may require additional training or support. Data-driven analytics can aid in identifying bottlenecks, optimizing processes, and making informed business decisions.
Customization and Flexibility: Augmentir allows organizations to customize their work instructions and workflows to fit their specific needs. This flexibility enables the tool to adapt to different industries, processes, and work environments.
If you are interested in learning for yourself why companies are choosing to change to Augmentir over their current connected worker solution – reach out to book a demo.
https://www.augmentir.com/wp-content/uploads/2023/06/darwin-in-manufacturing.jpg6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2023-06-07 14:44:572024-06-04 11:48:07Darwin in Manufacturing: The Evolution of Connected Worker Software
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 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:
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.
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
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:
Accelerate employee onboarding: By digitizing onboarding and moving training into the flow of work, manufacturers can reduce new hire onboarding time by 82%.
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.
Boost data accuracy: People are prone to making mistakes, but digital data capture and validation can help offset human error and improve accuracy.
Improved workforce management: Digital skills tracking and AI-based workforce analytics can help optimize production operations and maximize worker output.
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.
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.
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.
Transform how your company runs its frontline operations. Request a live demo today!
https://www.augmentir.com/wp-content/uploads/2023/09/paperless-manufacturing-augmentir.webp6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2023-09-20 14:12:392024-06-04 11:42:55Paperless Manufacturing: Your Guide to Transitioning to a Paperless Factory