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Darwin in Manufacturing: The Evolution of Connected Worker Software

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.

darwin in manufacturing

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:

  1. 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.
  2. 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.
  3. 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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.

 

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Generative AI Learning Copilots: Transforming Learning as We Know It

Watch Augmentir’s presentation at Learning & HR Tech 2024 and see how Generative AI Copilots transform learning and development in manufacturing.

Generative AI in learning and development has started to shape the future of HR across the board including attracting, developing, engaging, and retaining talent.

AI has revolutionized how organizations approach:

  • Talent acquisition – for smarter recruiting
  • Talent development – for skills analysis and performance evaluations
  • Worker relations – capitalizing on its ability to personalize employee relations
  • Workforce planning – leveraging its ability to make sense of data to perform more accurate forecasting and capacity planning
  • People analytics – using AI to make sense of employee data from an engagement and skills optimization standpoint
  • Performance management – relying on it for benchmarking and progress evaluation
  • HR operations – leveraging AI’s ability to automate and support onboarding and offboarding processes
  • Learning and development – using AI in everything from content creation to delivering personalized and adaptive content

generative ai learning copilots

However, Generative AI in learning and development has yet to make a significant impact on employees where it matters the most – in the flow of work.

This is where Generative AI learning copilots and AI-powered connected worker solutions come in. Together these technologies are transforming learning for frontline workers, improving onboarding, enabling learning in the flow of work, and driving more efficient upskilling and reskilling.

Watch our full presentation from Learning and HR Tech 2024 “Generative AI Learning Copilots: Transforming Learning as We Know It”, on-demand below.

Key Highlights:

  • Generative AI in learning and development has started to shape the future of HR across the board including attracting, developing, engaging, and retaining talent.
  • Deskless workers make up 80% of all workers globally and are underserved from a learning and development perspective, with 78% feeling they don’t have the right amount of training to succeed.
  • Generative AI Learning Copilots can generate training content, translate languages, provide real-time feedback, give on-demand guidance and answers, and serve as a digital performance support tool.

Generative AI Learning Copilots for Deskless Workers

Deskless workers, often referred to as “frontline workers”, generally do not sit in front of a desk and make up about 80% of all workers globally, they are on the front lines – in factories, at retail counters, construction sites, hospitals, and more.

While frontline workers and activities have undergone dramatic changes over the past few years, they are still woefully underserved from a learning and development standpoint.

  • 78% of frontline workers feel they don’t have the right amount of training to succeed at work
  • 65% want information on-demand and “in the flow of work”
  • Only 12% of HR operations leaders are actually satisfied with their L&D processes in support of their frontline employees

The reality is that traditional onboarding and training practices have been proven to be ineffective, however, much like AI has historically been used to improve the efficiency and output of machines, we can do the same with our frontline workforce.

AI learning and development tools and GenAI assistants can help:

  • Identify areas for content improvement, and implement those improvements
  • Measure training effectiveness
  • Create personalized, job-relevant training and curriculums
  • Measure and improve workforce effectiveness

Managing Manufacturing Workforce Challenges with GenAI Learning Copilots

The workforce crisis in manufacturing is accelerating and at the forefront of the minds of operations and HR leaders.

In fact, even if every skilled worker in America were employed, there would still be 35% more unfilled job openings in the manufacturing sector than skilled workers capable of filling them. Deloitte predicts that the skilled labor crisis will cost manufacturers upwards of $1 trillion by 2030.

In 2019, the average tenure in manufacturing was 20 years, the average time in position was 7 years, and the average 90-day retention rate was 90%. As of 2023, however, the average tenure is 3 years, the average time in position is 9 months, and the average 90-day retention rate was 50%.

These are representative of drastically different manufacturing realities. The workforce of 2019 is not coming back, and neither will productivity, unless organizations make significant investments and strides in supporting frontline workers with the appropriate tools and training. Luckily, smart connected worker and generative AI technologies offer a path forward.

Generative AI helps manufacturers answer:

  • What is the skills inventory of the team that is in attendance today?
  • Who can/should perform this work?
  • Who would benefit the most from targeted training?
  • Where should they focus on for process improvement?
  • What type of training would give them the biggest return?
  • What training materials need Improvement?

Generative AI-powered copilots and digital assistants can take this further, allowing frontline manufacturing workers access to vast amounts of knowledge in the flow of work when they need it most, helping to predict and prevent skills gaps before they impact production, and to design efficient and personalized development curriculums to shorten the time it takes for workers to be effective and competent in their positions.

Interested in learning more?

If you’d like to learn more about Augmentir and see how our AI-powered connected worker platform improves onboarding, training, skills management, and other learning and development aspects across organizations, schedule a demo with one of our product experts.

 

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The Evolution of AI in Manufacturing

The evolution of AI in manufacturing has seen tremendous growth over the past few decades, now becoming more adaptive and collaborative, and being used to augment and directly support frontline workers.

The evolution of artificial intelligence and machine learning technologies in manufacturing has seen tremendous growth over the past few decades, with astounding leaps in technology and industry-wide transformations.

evolution of ai in manufacturing

Dating back to the 1960’s, manufacturers started using AI in robotics and basic automation. This early usage focused on automating manual, highly repetitive human tasks such as assembly, parts handling, and sorting, allowing for higher levels of production and efficiency.

Over time, this evolved with AI-enabled machine vision systems, which were used to automate visual inspections, allowing for better quality control and precision during production cycles. More recently, AI has been at the center of warehouse automation, as well as the Industrial Internet of Things (IIoT), where physical machines and equipment are embedded with sensors and other technology for the purpose of connecting and exchanging data, which is used in predictive analytics for machine health monitoring. Manufacturers can now glean valuable insights from data collected over time about optimizing their operations for maximum efficiency without sacrificing quality.

Despite the breath of applications that AI has in the industrial setting, there is a common thread across all of the above examples – AI has largely been used to automate highly repetitive or manual tasks, or perform functions designed to replace the human worker.

However, these examples laid the groundwork for the adoption of AI in manufacturing and for the use of AI technologies that augment and directly support frontline workers today.

Read below for more information on how the use of AI and GenAI is evolving in manufacturing, and being used to augment the human worker, transforming productivity and efficiency at a time when workforce optimization is needed most.

Using AI to Augment, not Replace the Workers in our Factories

Today, AI technologies in manufacturing have evolved to encompass a diverse range of applications. According to Deloitte, 86% of surveyed manufacturing executives believe that AI-based factory solutions will be the primary drivers of competitiveness in the next five years. Robotics and automation have become more adaptive and collaborative, working alongside and augmenting human workers to streamline production processes and increase efficiency – rather than simply trying to replace them.

As computing power and algorithmic capabilities improved, AI in manufacturing has become more advanced and widespread. The emergence of Industry 4.0, characterized by the convergence of digital technologies, further accelerated AI’s role in manufacturing. By leveraging tools like connected worker solutions to gather frontline data, manufacturing organizations can now capitalize on AI’s extraordinary computing power to analyze that data and derive actionable insights, improved processes, and more.

Much like the industry has learned to optimize equipment from the 1.7 Petabytes of connected machine data that is being collected yearly, we are now able to optimize frontline work processes and people from highly granular connected worker data, with one major caveat: In order to leverage this incredibly noisy data, a system has to be designed with an AI-first strategy, where the streaming and processing of this data is intrinsic to the platform – not added as an afterthought.

The potential for AI to help augment the human worker is there, but why now?

Because for today’s manufacturers, time is not on your side.

The workforce crisis in manufacturing is accelerating, and at the forefront of the minds of Operations and HR leaders. Job quitting is up, tenure rates are down, and manufacturers struggle daily to find the skilled staff necessary to meet production and quality goals. The threat is huge – with significant impacts to safety, quality, and productivity.

AI-based connected worker solutions allow industrial companies to digitize and optimize processes that support frontline workers from “hire to retire”. These solutions leverage data from your connected workforce to optimize training investments and proactively support workers on the job, across a range of manufacturing use cases.

 

paperless factory

Furthermore, solutions that leverage Generative AI and proprietary fit-for-purpose, pre-trained Large Language Models (LLMs) can enhance operational efficiency, problem-solving, and decision-making for today’s less experienced frontline industrial workers. Generative AI assistants can leverage enterprise-wide data, provides instant access to relevant information, closes skills gaps with personalized support, offers insights into standard work and skills inventory, and identifies opportunities for continuous improvement.

Augmentir’s AI-First Journey

At Augmentir, since the beginning, we pioneered an AI-first approach toward manufacturing and connected frontline worker support. 

augmentir's ai-first journey

Many manufacturing solutions incorporated AI technology as an add-on or afterthought as the technology gained more advanced capabilities and popularity. We, however, have been championing and building a suite of solutions using AI as a foundation. Our platform was designed from the bottom up with AI capabilities in mind, placing us as a leader in the connected frontline worker field. 

  • 2019 – Augmentir launched the world’s first AI-first connected platform for manufacturing work empowering frontline workers to perform their jobs with higher quality and increased productivity while driving continuous improvement across the organization. This marked the start of our AI-first journey, giving industrial organizations the ability to digitize human-centric work processes into fully augmented procedures, providing interactive guidance, on-demand training, and remote expert support to improve productivity and quality.
  • 2020 – Augmentir unveiled True Opportunity™, the first AI-based workforce metric designed to help improve operational outcomes and frontline worker productivity through our proprietary machine learning algorithms. These algorithms take in frontline worker data, then combine it with other Augmentir and enterprise data to uncover and rank the largest capturable opportunities and then predict the effort required to capture them.
  • 2021 – Building on user feedback and field data, Augmentir reveals True Opportunity 2.0™, with improved and enhanced capabilities surrounding workforce development, quantification of work processes, benchmarking, and proficiency. By Leveraging anonymized data from millions of job executions to significantly improve and expand the platform’s ability and automatically deliver in-app AI insights we were able to increase benefits and returns for Augmentir customers.
  • 2022 – Augmentir announces the release of True Productivity™ and True Performance™. True Productivity allows industrial organizations to stack rank their largest productivity opportunities across all work processes to focus continuous improvement teams at the highest ROI and True Performance determines the proficiency of every worker at every task or skill enabling truly personalized workforce development investments.
  • 2023 – Augmentir launches Augie™ – the GenAI-powered assistant for industrial work. By incorporating the foundational technology underpinning generative AI tools like ChatGPT, we enhanced our already robust offering of AI insights and analytics. Augie adds to this, improving operational efficiency and supporting today’s less experienced frontline workforce through faster problem-solving, proactive insights, and enhanced decision-making.
  • 2024 – As this year progresses, we have already continued to refine our AI-first solutions and apply user feedback and additional features to best support frontline industrial activities and workers everywhere.
  • 2025 and beyond – True Engagement™, looking forward we predict the evolution of AI in manufacturing activities will continue, progressing until we can accurately measure signals to detect the actual engagement of industrial workers and derive useful information and insights to further enhance both HR and manufacturing processes.

We are deeply involved in applying AI and emerging technologies to manufacturing activities to augment frontline workers, not replace them. Providing enhanced support, access to key knowledge (when and where it does the most good), and improving overall operational efficiency and productivity.

The Future of AI in Manufacturing – The Journey Forward

As we press onward into the future, we at Augmentir are determined to champion the application of AI and smart manufacturing to augment and enhance frontline workers and industrial processes. We will continue to evolve our application of AI and its use cases in manufacturing to help frontline teams and workforces, reinforcing our AI-first pedigree.

The addition of Augie to our existing AI-powered connected worker solution is an important step forward. Augie is a Generative AI assistant that uses enterprise-wide data, provides instant access to relevant information, closes skills gaps with personalized support, offers insights into standard work and skills inventory, and identifies opportunities for continuous improvement. Augie is a result of our dedication to empowering frontline workers, leveraging AI to support manufacturing operations, and giving manufacturing workers better tools to do their jobs safely and more efficiently.

With patented AI-driven insights that digitize and optimize manufacturing workflows, training and development, workforce allocation, and operational excellence, Augmentir is trusted by manufacturing leaders as a digital transformation partner delivering measurable results across operations. Schedule a live demo today to learn more.

 

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AI: Revolutionizing How Humans Work in Manufacturing

AI is playing a key role in changing the manufacturing landscape, augmenting workers and empowering them with improved, optimized processes, better data, and personalized instruction.

Deloitte recently published an article with the Wall Street Journal covering how AI is revolutionizing how humans work and its transformative impact. They emphasized that AI is not merely a resource or tool, but, that it serves almost as a co-worker, enhancing work processes and efficiency. This article discussed how the evolving form of intelligence augments human thinking and emphasized this as a catalyst for accelerated innovation.

Manufacturing is uniquely situated to benefit from AI to improve operations and empower their frontline workforces. The skilled labor gap has reached critical levels, and the market is under tremendous stress to keep up with growing consumer demand while staying compliant with quality and safety standards. Manufacturing workers are crucial to the success of operations – maintenance, quality control and assurance, and more – manufacturers rely upon their workforce to ensure production proceeds smoothly and successfully.

AI is playing a key role in changing the manufacturing landscape, augmenting workers and empowering them with improved, optimized processes, better data for informed decision-making, troubleshooting, personalized instructions and training, and improved quality assurance and control. According to the World Economic Forum, an estimated 87% of manufacturing companies have accelerated their digitalization over the past year, the IDC states 40% of digital transformations will be supported by AI, and a recent study from LNS Research found that 52% of industrial transformation (IX) leaders are deploying connected worker applications to help their frontline workforces. Not only that, AI technology is expected to create nearly 12 million more jobs in the manufacturing industry.

Integrating AI into manufacturing not only enhances productivity, but also opens the door to new possibilities for worker safety, training, and innovative new manufacturing practices. Here are some ways AI is transforming manufacturing operations:

  • AI-based Workforce Analytics: Collecting, analyzing, and using frontline worker data to assess individual and team performance, optimize upskilling and reskilling opportunities, increase engagement, reduce burnout, and boost productivity.
  • Personalized Training in the Flow of Work: With AI and connected worker solutions, manufacturers can identify and supply training at the time of need that is personalized to each individual and the task at hand.
  • Personalized Work Instructions: AI enables manufacturers to offer customized digital work instructions mapped to their skill levels and intelligently assign work based on each individual’s capabilities.
  • Digital Performance Support and Troubleshooting Guide: Generative AI assistants and bot-based AI virtual assistants offer support and guidance to manufacturing operators, enabling access to collaborative technologies and knowledge bases to ensure the correct actions and processes are taken.
  • Optimize Maintenance Programs: AI algorithms analyze data from sensors on machinery and other connected solutions to predict when equipment is likely to fail. This enables proactive maintenance, minimizing downtime and reducing maintenance costs. Additionally, with AI technologies, manufacturers can implement autonomous maintenance processes through a combination of digital work instructions and real-time collaboration tools. This allows operators to independently complete maintenance tasks at peak performance.
  • Improve Quality Control: AI-powered solutions can improve inspection accuracy and optimize quality control and assurance processes to identify defects faster. With connected worker solutions, manufacturers can effectively turn their frontline workforce into human sensors supplying quality data and enhancing assurance processes.
  • Ensure Worker Safety: AI-driven safety systems coupled with connected worker technologies monitor the work environment, supplying real-time data and identifying potential hazards to ensure a safer workplace for employees.

connected enterprise

As AI continues to advance, the manufacturing industry is poised for even greater transformation, improving both the quality of products and the working conditions for employees. AI is revolutionizing the way humans work and how the manufacturing industry approaches nearly every process across operations, augmenting work interactions, productivity, efficiency, and boosting innovation.

How well can we really know our workers?

AI-powered technology may be the missing puzzle piece for today’s workforce crisis.

It wouldn’t be fair to attribute all of manufacturing’s current labor shortage woes to the pandemic–there are a lot of factors contributing to this frustrating situation, and many of them were looming long before we ever heard of COVID-19. Did it make things worse? Probably. And the forecast doesn’t look very sunny if you believe what analysts have to say about it. However, despite the current crisis, there is hope yet for manufacturing, specifically in the form of AI and Connected Worker Technology.

Sure, the face of the workforce has changed dramatically. The pool of potential laborers has shrunk. Businesses are being forced to hire people traditionally considered under-qualified. And that leads to a whole host of other complications, including a drop in operational efficiency, a rise in safety issues, and more. The pessimists out there would only see the threat to the global market these challenges pose–the manufacturing industry makes between 11 and 12 percent of the US economy after all.

Good thing we’re optimists at heart! Behind every challenge is an opportunity, as far as we’re concerned. And when it comes to this challenging labor market in particular, we see a huge opportunity for businesses to work with what they’ve got, and still reach operational goals. We have the potential to assess how every worker performs on the job, regardless of the experience and skill set they bring on day one, and use that information to improve individual and enterprise-wide performance. Puts a new light on the labor shortage, doesn’t it?

You can’t fix what you can’t see.

We know using data is important to directing and improving operations–that’s business best practices 101. But insights drawn are only as good as the data itself. And even though there can’t be many businesses out there who haven’t yet jumped on the digital transformation bandwagon, we suspect a lot still rely on outdated data collecting and reporting mechanisms. Those digital spreadsheets had their moment, but we’ve got better options now. Maybe you opted for a Bluetooth software program or distributing a digital survey for your workers. But even with those innovations, what do these data indicators really tell you? Is this reliable and usable information? We didn’t think so either.

Imagine what you could do with real-time data, rather than a summary of operational KPIs at the end of set periods? Even better–imagine capturing the performance metrics of each individual worker rather than their self-generated assessments and observations and having the potential to use that knowledge to improve their skill set and operational proficiency. That’s when data becomes intelligence. And that intelligence has the potential to become so valuable to your enterprise that you’ll wonder how you ever operated without it.

Not convinced you could benefit from data at that level of individual performance? Let us draw an analogy we think you’ll appreciate.

Think of each worker as a newly licensed driver; what happens after passing the road test?

Remember the day you got your driver’s license? We spent hours, if not days and weeks practicing behind the wheel, eagerly waiting to be evaluated by a driving instructor. And let’s be honest, plenty of us winged it, too. Either way, once you show them you can do a three-point-turn and know to stop at the flashing pedestrian crossing sign, everyone walks away with the proof of their proficiency–a driver’s license. 

Then what happened? Nothing. Maybe a celebratory high-five and then eventually years of driving. In one, five, or ten years, what do we know about each person’s capabilities? Unless they’ve wracked up a stack of tickets for traffic violations, we don’t know anything. For all we know, they haven’t sat behind the steering wheel since passing. There is no mechanism to re-assess whether drivers are highly skilled or at-risk of creating an accident in operations.

Now what if we looked at our frontline workers through that lens? You know when they were hired that they could perform X, Y and Z. Some could do even more. But what about after that? What if you could assign an AI-based driver instructor to follow each new driver around for ongoing assessment and intervention in the moment of need?

Put smart connected worker technology in the passenger seat

Adopting connected worker technology powered by artificial intelligence (AI) increases the reliability and credibility of data by analyzing employee performance in ‘real-time.’ That individualized data can be used to connect workers with a company’s digital library of training tools and resources, having an immediate impact on operational proficiency and cultivating a healthy learning environment for workers.

Connected worker technology that leverages AI offers self-guided learning processes when opportunities are identified, reduces human error and improves safety, provides updates on pressing issues and equipment failures and access to a variety of applications. Who wouldn’t want to work for an organization like this? One that offers a high probability of job satisfaction and encourages personal skill development? A culture like that can help the operation on many levels, from reducing operational costs to attracting new employees. 

What now? There is only one connected worker solution that can provide this level of intelligence on your workforce–contact us to learn more about how Augmentir can benefit your business and ask for a demo!

This Is Not Your Grandfather’s Workforce

Today’s dynamic and changing manufacturing workforce needs continuous learning and performance support to effectively sustain and deliver effective on-the-job performance.

Every day we hear about the growing manufacturing “Skills Gap” associated with the industrial frontline workforce. The story is that 30% of workers are retiring in the near future and they are taking their 30+ years of tribal knowledge with them, creating the need to quickly upskill their more junior replacements. In an attempt to solve the knowledge gap issues, an entire generation of companies set out to build “Connected Worker” software applications, however, they all relied on the existing training, guidance, and support processes – the only true difference with this approach has been the creation of technology that takes your paper procedures and puts them on glass.

Along with tribal knowledge leaving, today’s workforce is also more dynamic and diverse than previous generations. The 30-year dedicated employees are no longer the norm. The average manufacturing worker tenure is down 17% in the last 5 years and the transient nature of the industrial worker is quickly accelerating. An outgrowth of the COVID pandemic brings forth the Great Resignation, where workers are quitting in record numbers, and worker engagement is down almost 20% in the last 2 years. 

This new manufacturing workforce is changing in real-time – who shows up, what their skills are, and what jobs they need to do is a constantly moving target. The traditional “one size fits all” approach to training, guidance, and performance support is fundamentally incapable of enabling today’s workers to function at their individual peak of safety, quality and productivity. 

Digitizing work instructions is a great start to helping close the manufacturing skills gap, but alone, it won’t help completely solve the problem. We must go a step further to overcome the lack of a skilled and qualified manufacturing workforce. 

Enter the 2nd generation of Connected Worker software, one based on a data-driven, AI-supported approach that helps train, guide, and support today’s dynamic workforces by combining digital work instructions, remote collaboration, and advanced on-the-job training capabilities. 

These 2nd generation connected worker solutions are designed to capture highly granular data streaming from connected frontline workers. These platforms are built from the ground up on an artificial intelligence (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 creates a data-driven environment that supports continuous learning and performance support.

This approach aligns perfectly with the dynamic, changing nature of today’s workforce, and is ideally suited to support their 5 Moments of Need, a framework for gaining and sustaining effective on-the-job performance.

For example, Augmentir’s AI-powered connected worker platform leverages anonymized data from millions of job executions to significantly improve and expand its ability to automatically deliver in-app AI insights in the areas of productivity, safety, and workforce development. These insights are central to Augmentir’s True Proficiency™ scoring, which helps to objectively baseline each of your team members for their level of proficiency at every task so organizations can optimize productivity and throughput, intelligently schedule based on proficiency and skill-levels, and personalize the level of guidance and support to meet the needs of each member of the workforce.

This provides significant benefits to Augmentir customers, who leverage Augmentir’s AI in conjunction with the platform’s digital workflow and remote collaboration capabilities, allowing them to deliver continuous improvement initiatives centered on workforce development. These customers are able to utilize the insights generated from Augmentir’s AI to deliver objective performance reviews, automatically identify where productivity is lagging (or has the potential to lag), increase worker engagement, and deliver highly personalized job instructions based on worker proficiency.

Traditionally, there was a clear separation between training and work execution, requiring onboarding training to encompass everything a worker could possibly do, extending training time and leading to inefficiencies. Today, with the ability to deliver training at the moment of need, onboarding can focus on everything a worker will probably do, identifying and closing skills gap in real-time and significantly reducing manufacturing onboarding times. In one particular case, Bio-Chem Fluidics was able to reduce onboarding time for new employees by up to 80%, while simultaneously achieving a 21% improvement in job productivity across their manufacturing operation.

As workers become more connected, companies have access to a new rich source of activity, execution, and tribal data, and with proper AI tools can gain 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.

Trends from October’s Top Virtual Manufacturing Events

These virtual events were a great way to connect with manufacturing professionals and discuss some of the industry’s top challenges and topics – workforce transformation, learning and development, lean manufacturing, and autonomous maintenance.

October was an exciting month in the virtual manufacturing world! Augmentir had the pleasure of participating in several virtual events including the American Manufacturing Summit, Gartner Supply Chain Symposium/Xpo, and the Enterprise Wearable Technology Summit (EWTS). Each of these virtual events were a wonderful way to connect with manufacturing professionals and discuss some of the industry’s top challenges and topics – workforce transformation, learning and development, lean manufacturing, and autonomous maintenance. 

EWTS

The Enterprise Wearable Technology Summit (EWTS) is the longest-running and most comprehensive event dedicated to the business and industrial applications of wearables, including AR/VR/MR glasses and headsets, body-worn sensors, and exoskeletons. This year’s event took place in four bite-sized conference days (every Wednesday from October 6-27, 2021), with community, networking and additional content drops throughout the rest of the month. This unique format allowed for great networking as well as some very valuable sessions. In one of the polls, 32% said that Remote onboarding and training was the top use case for immersive/wearable technology at their company.  

American Manufacturing Summit

The American Manufacturing Summit is a leadership focused meeting designed to bring global manufacturing, operations, engineering, quality and supply chain leaders together to discuss current trends, strategic insights, and best practices in an ever-evolving manufacturing environment. Dave Landreth, Augmentir’s Head of Customer Strategy, had the opportunity to lead a fire-side chat in discussing how Artificial Intelligence and Connected Worker technologies are key pillars of the Industrial Workforce Transformation. We also enjoyed the 1:1 meetings that took place as part of the American Manufacturing Summit. 

Gartner Supply Chain Symposium/Xpo

The Gartner supply chain conference offers attendees a one-stop-shop to access research-backed sessions, get expert advice and problem-solve with colleagues. The main focus of this event is to address the strategic needs of CSCOs and supply chain executives and showcase new technologies that adapt to the ever-changing environment in which they’re operating.

Sessions from the event dealt with purpose-driven supply chains and learnings from the pandemic for the healthcare supply chain, risk assessment and global trade, top trends for smart manufacturing, the future of quality management and supply chain planning, and resolving the dichotomy of logistics outsourcing. 

Key announcements from the virtual manufacturing events:

Continuous improvement, connected worker technology, AI, and data-driven technology were among the top trends from these events. Manufacturing organizations are looking for Connected worker software, like Augmentir, to integrate frontline workers and improve productivity, training, and quality. In addition, as we continue to work remotely and see more supply chain disruptions, AI-based, data-driven technology will be essential to building flexible factories that address these challenges and allow for continuous improvement.

Using AI to Unlock the True Potential of Today’s Connected Workforce

Augmentir CEO Russ Fadel had the opportunity to be interviewed recently by Ann Wyatt, Industry 4.0 and IIoT Enthusiast, for the OnRamp Manufacturing Conference.

Earlier this month, Russ Fadel had the opportunity to be interviewed by Ann Wyatt, Industry 4.0 and IIoT Enthusiast, for the OnRamp Manufacturing Conference. OnRamp Manufacturing is the leading conference for manufacturing innovation that brings together the manufacturing industry’s leading corporations, investors, and startups. The conference highlighted innovations disrupting the manufacturing industry, the leaders making such innovations possible, and how new technologies and business models will reinvent the industry. In this exciting interview, Ann and Russ discussed some of the top challenges that today’s manufacturers face, and how technology such as AI and connected worker solutions that recognize the variability in today’s workforce are empowering workers by giving them tools and resources that will set them up for success. 

The following is a recap of some of the highlights of the discussion.

The great resignation is upon us now

The consistent story of the manufacturing workforce is that there is an aging workforce and 30-40% of that workforce will leave within the next 5 years, taking valuable, hard to capture tribal knowledge with them. Many manufacturers were under the misconception that the remitting workforce would pass down their knowledge to the next generation as they did before. However, this was a big misconception. Even prior to Covid, the dynamics of the workforce themself have changed. In the last 5 years, the tenure of manufacturing workers is down to 17% and that decrease escalated even more as a result of the pandemic.

The stability of the workforce has decreased in the past 8 years. Old work processes were designed during a more stable time and unfortunately aren’t applicable for this generation of workers. Today’s workers are in the factory less frequently, don’t stay as long, and due to Covid, may be out for short periods of time, resulting in the need for a more dynamic workforce. To deal with this rapidly changing workforce, manufacturers will need a more data-driven approach powered by AI to empower their workforce.

A highly effective, cross functioning workforce

Over the years, the manufacturing industry has done a really good job of connecting machines into the fabric of the business and giving operators the necessary data to help run those machines better. Our frontline workers, the last piece of connectivity, are the least connected set of workers in the company. Frontline workers should be fully integrated into the fabric of the business from a collaboration standpoint so that they can access the data they need as well. Secondly, when they are working, it needs to be understood what workers are doing well and what they are struggling with, so we can match people with the tasks that they already excel at.

Top trends and key challenges in today’s workforce

At the highest level, everyone is talking about the disruption of the mobile supply chain. The role of the manufacturer is to put supply into the supply chain and to safely build products at acceptable quality and productivity levels, matching today’s workforce with today’s task load. 

The new dynamics of the workforce (lots of turnover, shorter tenure, people leaving abruptly) are at odds with what manufacturers are trying to do, which is to be a stable source of supply to the global supply network. Technology today, specifically AI, lets us understand at a data-driven level and in real time how workers can perform at their individual best, based on their training experience and raw ability for a specific task.

How Hybrid Work is impacting the manufacturing workforce

With Covid came an immediate need for remote presence, but the real issue is the idea that a subject matter expert needed to be on site for support. This way of working is now a thing of the past. When we think about having frontline workers fully connected to the organization, at any moment in time, they should have direct access to the tools and resources that would help them do their job better. Connected work in the future means using AI to allow frontline workers to have access to internal and external resources that are appropriate for them at their fingertips.

Another interesting statistic resulting from Covid is that employee engagement is down almost 20% from pre-Covid times. Manufacturers are always concerned about employee engagement, particularly with certain jobs that might be repetitive. AI is extremely helpful in measuring signals of engagement but also provides tools to the organization to increase the level of engagement of frontline workers. One thing that causes a reduction in engagement is when a worker feels like they can’t perform a job so they become frustrated. 

Using AI to give frontline workers the tools and information they need when they need it is one way to help increase engagement. The other way is to let them feel connected to the actual importance of their work.

Hiring, Training & Reskilling

The new workforce dynamics and the nature of hybrid work are also now forcing manufacturers to re-think employee onboarding and training.

The historic methods of onboarding and training taught workers everything they could “possibly” do which resulted in overtraining. The data-driven era we’re entering into is one of continuous learning and development powered by AI. Training shifts from the things frontline workers are possibly going to do to what they are probably going to do. This results in reduced training times, continuous learning and development, and the ability to upskill at any point as needed. Learning is always available, training content is available to the worker on the shop floor at the time of need. Reducing the initial onboarding training and allowing training to occur at the moment of need, coupled with AI for scoring, provides insights into the most effective training modules as well as what needs to improve based on demonstrated execution.

Transforming Today’s Workforce with AI & Connected Worker Tools

One challenge with connected worker data is that it’s inherently noisy. In many cases, up to 37% of the signals that come back are not representative of what is actually happening. Fortunately, AI excels at recognizing patterns in noisy data, so we can use that to focus companies on the work processes that have the most opportunity, allowing organizations to understand their actual proficiency at any procedure or job. This helps them understand current workforce skills, what areas need to be connected or improved, and where they should invest if they want to get the largest return, with AI being the driving technology that unlocks those signals in noisy data.

AI is largely embedded in most aspects of our lives and it will play an equally large role in helping the connected workforce progress and become part of the 21st-century solution and the next generation of how people work. Adopting these methodologies early on will make the overall digital transformation process a lot easier for manufacturers.