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Discover key strategies to boost production efficiency in manufacturing—maximize output, cut waste, and improve operations with smart, practical solutions.

In today’s competitive industrial landscape, production efficiency in manufacturing is a critical factor that directly impacts profitability, customer satisfaction, and long-term business success. To achieve production efficiency, the actual output must match the optimal standard output, which involves minimizing waste, reducing downtime, optimizing labor, and ensuring consistent quality at every step of the manufacturing process.

production efficiency in manufacturing

Introduction to Production Efficiency

Production efficiency refers to the ability of a manufacturing process to produce the maximum output with the given resources, while minimizing waste and reducing costs. It is a key concept in economics and operational analysis, essential for businesses to remain competitive in the market. Achieving production efficiency involves optimizing processes, reducing waste, and improving productivity to achieve higher profitability and competitiveness. By focusing on improving production efficiency, manufacturers can increase their production capacity, reduce costs, and enhance product quality. This, in turn, leads to increased customer satisfaction and loyalty, as high-quality products are delivered consistently and on time.

What is Production Efficiency in Manufacturing?

Production efficiency refers to the ability of a manufacturing operation to produce goods using the least amount of resources—time, materials, and labor—without compromising on quality. An efficient production line runs smoothly, minimizes bottlenecks, and ensures equipment and workforce are fully utilized. To measure production efficiency, metrics such as Overall Equipment Effectiveness (OEE), cycle time, yield rates, and labor productivity are used.

Pro Tip

Using digital tools, AI-powered analytics, and connected worker platforms are revolutionizing how factories operate. These technologies provide visibility into operations, uncover hidden inefficiencies, and support agile decision-making.

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Why is Production Efficiency Important?

In manufacturing, even small inefficiencies can lead to significant cost overruns, missed deadlines, and quality issues. Improving production efficiency is essential for maximizing output while minimizing input—helping manufacturers stay competitive, agile, and profitable in an ever-evolving market. Manufacturing efficiency, on the other hand, focuses specifically on optimizing the effectiveness of manufacturing processes, workforce deployment, and overall productivity on the shop floor. Efficient production processes enable manufacturers to do more with less, which not only boosts the bottom line but also enhances the overall customer experience.

Here are some of the key benefits:

Lower Operational Costs

By reducing machine downtime, optimizing labor, and minimizing material waste, companies can optimize processes to significantly cut overhead costs and improve profitability.

Reduced Waste and Rework

Quality control ensures that products are made right the first time, decreasing scrap rates and minimizing costly rework caused by defects or human error.

Shorter Lead Times

Streamlined workflows and fewer production delays, coordinated through efficient production schedules, mean faster turnaround times, allowing manufacturers to fulfill orders more quickly and meet tight delivery schedules.

Better Resource Utilization

Whether it’s labor, machinery, or raw materials, efficient production ensures every resource is used to its full potential throughout the entire production cycle—maximizing value and reducing idle time.

Higher Customer Satisfaction

Consistently delivering high-quality products on time builds trust with customers and strengthens relationships, leading to repeat business and positive brand reputation. Manufacturers improve efficiency by leveraging modern technologies and real-time data analytics, which helps streamline processes, reduce downtime, and enhance productivity.

Greater Competitiveness in the Market

Manufacturers that improve efficiency can offer better prices, respond faster to market changes, and innovate more effectively—gaining a clear edge over less agile competitors.

Ultimately, production efficiency is not just about internal gains—it’s a strategic advantage that drives growth, scalability, and long-term success.

Key Strategies to Improve Production Efficiency

Here are some proven strategies to improve production efficiency:

1. Implement Lean Manufacturing Principles to Drive Continuous Improvement

Lean manufacturing methodologies focus on improving efficiency by eliminating waste (or “muda”) from every aspect of the production process. Tools such as 5S Audits, Kaizen, and value stream mapping help identify inefficiencies and areas for continuous improvement.

2. Invest in Autonomous Maintenance and TPM

Encouraging operators to handle basic maintenance tasks—such as Clean, Inspect, Lubricate (CIL)—as part of an Autonomous Maintenance and Total Productive Maintenance (TPM) strategy minimizes equipment downtime, improves machine efficiency, and ensures machines run at peak performance.

3. Leverage Digital Work Instructions and Connected Worker Tools

Modern digital approaches like digitizing standard operating procedures (SOPs) and adopting connected worker tools helps ensure consistency, reduce errors, and make it easier to train workers by providing accurate data.

improve production efficiency in manufacturing with augmentir

In a recent survey conducted by LNS Research, more than 70% of the most profitable manufacturers are currently utilizing in Future of Industrial Work (FOIW) initiatives and connected worker technology, with the vast majority of them seeing meaningful progress and delivered corporate value through these workforce initiatives. Connected Worker platforms like Augmentir enable manufacturers to create AI-powered workflows that guide frontline workers through each task efficiently and accurately.

3. Use Real-Time Data and Analytics to Track Key Performance Indicators

Data-driven decision-making is critical for efficiency. Historical data can provide insights into the maximum output achieved by a facility under full capacity, which can help in defining standard outputs accurately. Real-time monitoring of machine performance, operator productivity, and process quality helps identify issues quickly and supports predictive maintenance strategies.

4. Streamline Workforce Management

Matching the right tasks to the right workers based on skills, experience, and availability reduces errors and idle time for any manufacturing company. Smart workforce tools can track training, performance, and certification to ensure optimal labor allocation.

Critical Components of Production Efficiency

Equipment Efficiency

Equipment efficiency is a critical component of production efficiency, as it directly impacts the overall productivity and effectiveness of the manufacturing process. Equipment efficiency refers to the ability of machinery and equipment to operate at optimal levels, with minimal downtime and maintenance. To achieve equipment efficiency, manufacturers can implement regular maintenance schedules, invest in modern and efficient equipment, and provide training to operators to ensure they are using the equipment correctly. By improving equipment efficiency, manufacturers can reduce waste, minimize downtime, and increase overall production efficiency. This not only enhances the reliability of the production process but also ensures that machinery operates at peak performance, contributing to higher output and better product quality.

Capacity Utilization

Capacity utilization is a key performance indicator (KPI) that measures the extent to which a manufacturing facility is using its available capacity to produce goods. It is calculated by dividing the actual output by the maximum potential output and is expressed as a percentage. Capacity utilization is essential for production efficiency, as it helps manufacturers identify areas of inefficiency and optimize their production processes. By improving capacity utilization, manufacturers can increase their production capacity, reduce costs, and improve product quality. High capacity utilization indicates that a manufacturing facility is effectively using its resources, leading to more efficient operations and better alignment with market demand.

Inventory Management

Inventory management is a critical component of production efficiency, as it directly impacts the availability of raw materials and finished goods. Effective inventory management involves managing the flow of goods, services, and related information from raw materials to end customers. By implementing efficient inventory management systems, manufacturers can reduce waste, minimize stockouts, and improve overall production efficiency. Inventory management involves tracking inventory levels, managing supply chains, and optimizing inventory turnover to ensure that the right products are available at the right time. This not only helps in meeting customer demand promptly but also reduces the costs associated with excess inventory and stockouts, contributing to a more streamlined and efficient production process.

Workforce Management

Workforce management (WFM) is a critical component of production efficiency because it directly impacts how well human resources are aligned with operational goals. Here are the key reasons why:

  • Optimal Staffing: WFM ensures the right number of workers with the right skills are available when needed, reducing overstaffing (which increases costs) and understaffing (which leads to delays or quality issues).
  • Productivity Monitoring: Through tracking attendance, breaks, and output, WFM helps identify performance gaps and opportunities to improve workflow or training.
  • Cost Control: Efficient labor scheduling and time management reduce overtime expenses, idle time, and unplanned labor costs.
  • Compliance and Risk Management: Proper WFM systems help companies stay compliant with labor laws, union rules, and safety standards, reducing legal and financial risk.
  • Employee Engagement: Transparent scheduling, fair workload distribution, and career development through performance data can boost morale and reduce turnover, which supports consistent productivity.
  • Forecasting and Planning: WFM tools use historical data to predict future labor needs based on demand, helping operations run smoothly during peak and off-peak periods.

Connected worker platforms are a vital solution for workforce management because they digitize and streamline the way organizations engage with their frontline employees, enabling real-time communication, task coordination, and data capture. These platforms empower workers by providing instant access to schedules, training, and support tools, while giving managers visibility into performance and resource needs. By collecting operational data at the source, they support better forecasting, faster decision-making, and improved compliance with safety and regulatory standards. Ultimately, they enhance agility, reduce inefficiencies, and ensure that the workforce is aligned with evolving production demands.

Improving Production Efficiency with Augmentir

Modern manufacturing is increasingly driven by digital transformation. Tools like Industrial IoT (IIoT), AI-powered analytics, and connected worker platforms are revolutionizing how factories operate. These technologies provide visibility into operations, uncover hidden inefficiencies, and support agile decision-making.

Connected Worker Technology is transforming the way manufacturers approach production efficiency by bridging the gap between frontline workers and digital operations. These platforms equip workers with real-time access to information, interactive digital work instructions, and collaboration tools—right at the point of work. By digitizing tasks, capturing live performance data, and enabling guided workflows, connected worker solutions ensure that every job is done accurately, efficiently, and consistently.

augmentir connected worker platform

With features like AI-driven insights, skills tracking, and remote expert support, Connected Worker platforms help manufacturers identify bottlenecks, reduce errors, and optimize workforce deployment. Tools such as Augmentir go a step further by personalizing guidance based on an individual’s skill level, automatically suggesting improvements, and helping identify opportunities for continuous training and upskilling. Ultimately, Connected Worker Technology empowers teams to work smarter, adapt faster, and drive sustainable gains in production efficiency.

Augmentir serves as a digital frontline operating system for your lean strategy, and helps improve production efficiency. With Augmentir, you can digitize, manage, and optimize all aspects of your frontline operation:

  • Daily Direction Setting (DDS)
  • Daily Management System (DMS)
  • Centerline Management
  • Clean, Inspect, Lubricate processes
  • Defect Management
  • Breakdown Elimination
  • Changeover Management
  • Shift Handover
  • 5S and Layered Process Audits
  • Quality Management on the Shop Floor
  • Safety

augmentir connected worker platform – digital frontline operating system for iws

 

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Learn how connected worker technology helps eliminate breakdowns in manufacturing, boosting uptime, efficiency, and operational resilience.

Breakdowns are one of the most significant disruptors in manufacturing operations. Whether caused by mechanical failure, human error, or insufficient maintenance, equipment breakdowns lead to unplanned downtime, lost productivity, and increased operational costs. For manufacturers striving for world-class performance, Breakdown Elimination (BDE) is a foundational pillar of reliability-centered maintenance and operational excellence.

breakdown elimination in manufacturing

In this article, we explore what Breakdown Elimination entails, how Connected Worker technology transforms the approach to managing breakdowns, and how innovative platforms like Augmentir empower frontline teams to drive sustainable improvements.

What is Breakdown Elimination?

Breakdown Elimination is a proactive approach focused on identifying, analyzing, and permanently eliminating the root causes of equipment failures. It is a cornerstone of Total Productive Maintenance (TPM) and Lean Manufacturing, targeting improved Overall Equipment Effectiveness (OEE) through systematic problem-solving and process improvement.

Breakdown elimination directly tackles unplanned stops—one of the Six Big Losses in manufacturing—by reducing equipment failures and boosting uptime. Japanese entrepreneur Seiichi Nakajima developed both TPM and the six big losses as a framework for reducing waste and bringing more value to the customer. Eliminating breakdowns improves availability and helps address other losses tied to performance and quality, making it a key driver of overall efficiency.

Unlike reactive maintenance, where the focus is on fixing machines after failure, BDE emphasizes:

  • Root cause analysis (RCA) to understand underlying issues
  • Frontline involvement in identifying and solving problems
  • Continuous improvement cycles to prevent recurrence
  • Standardized work to sustain gains

The goal is not only to restore functionality but also to implement corrective and preventive actions that stop the problem from reoccurring. Successful BDE programs often involve cross-functional collaboration between operators, maintenance teams, engineers, and management.

Pro Tip

Using digital tools and connected worker technology can help to support Breakdown Elimination at every stage—from detection to resolution and long-term prevention.

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The Impact of Breakdown Elimination

Breakdown Elimination drives significant value across manufacturing operations, including:

  • Reduced downtime: Identifying and resolving systemic causes of failure increases equipment availability
  • Increased productivity: With more reliable assets, output levels rise without added costs.
  • Lower maintenance costs: Preventing breakdowns reduces emergency repairs, spare part usage, and overtime.
  • Improved safety: Eliminating frequent equipment failures reduces the risk of accidents and injuries.
  • Better workforce engagement: Empowering frontline workers to solve problems promotes ownership and morale.

Despite its benefits, BDE can be challenging to implement without the right tools. Traditional paper-based systems often slow down data collection, obscure visibility into recurring issues, and hinder real-time collaboration.

Connected Worker Technology and Breakdown Elimination

Enter Connected Worker technology—digital platforms that empower frontline workers with real-time access to information, guidance, and collaboration tools. Connected Worker solutions play a transformative role in enabling Breakdown Elimination by addressing several critical needs in the process:

1. Real-time Data Collection

Connected Worker platforms allow operators and technicians to digitally log breakdown events as they occur. This immediate input ensures that data is accurate, timestamped, and enriched with contextual details (such as photos, sensor data, or video clips), which are crucial for effective root cause analysis.

2. Guided Workflows and Standardization

Digital work instructions and SOPs help standardize responses to breakdowns. When an operator encounters a recurring issue, they can follow an optimized troubleshooting guide, reducing variability and guesswork.

3. Enhanced Communication and Collaboration

Connected Worker tools support real-time communication across departments and shifts. Maintenance teams can be instantly alerted, engineers can review breakdown trends remotely, and best practices can be shared across sites.

4. Analytics and Continuous Improvement

With integrated analytics, Connected Worker platforms enable manufacturers to identify patterns in breakdown data. Heatmaps, Pareto charts, and KPI dashboards highlight systemic issues and help prioritize high-impact improvements.

5. Frontline Empowerment

Operators are no longer passive reporters of problems; they become active participants in problem-solving. Through digital forms, escalation tools, and feedback loops, workers contribute to eliminating the causes of breakdowns permanently.

How Augmentir Supports Breakdown Elimination

Augmentir, a leading Connected Worker platform powered by artificial intelligence (AI), provides a comprehensive suite of tools designed to support Breakdown Elimination at every stage—from detection to resolution and long-term prevention.

Augmentir serves as a digital frontline operating system for your TPM strategy. With Augmentir, you can digitize, manage, and optimize all aspects of your frontline operation:

  • Daily Direction Setting (DDS)
  • Daily Management System (DMS)
  • Centerline Management
  • Clean, Inspect, Lubricate processes
  • Defect Management
  • Breakdown Elimination
  • Changeover Management
  • Shift Handover
  • 5S and Layered Process Audits
  • Quality Management on the Shop Floor
  • Safety

augmentir connected worker platform – digital frontline operating system for iws

Here’s how Augmentir helps manufacturers eliminate breakdowns:

1. AI-Driven Work Instruction and Guidance

Augmentir’s digital workflows guide workers through inspection, troubleshooting, and maintenance procedures with step-by-step clarity. By digitizing standard operating procedures and enabling smart branching logic, Augmentir ensures the right action is taken at the right time—every time.

When equipment fails, operators can quickly access contextual work instructions based on the specific failure mode, reducing diagnosis time and improving repair accuracy.

Furthermore, with tools like Augmentir’s Augie – a generative AI assistant for frontline operations, operators can get access to real-time troubleshooting resources and digital guidance.

frontline copilot generative ai for troubleshooting

2. Smart Data Capture

Augmentir enables seamless data capture at the point of work. Operators log downtime events, causes, and corrective actions via mobile devices, tablets, or smart glasses. This data feeds directly into analytics dashboards without manual entry or delays.

Photo and video capture further enriches the data set, providing visual evidence that aids in root cause analysis and training.

3. Continuous Learning with AI Insights

The AI engine in Augmentir analyzes performance data from workers, machines, and processes to identify skill gaps, process inefficiencies, and frequent failure patterns. These insights help prioritize BDE efforts and guide targeted interventions.
For example, if a particular asset experiences frequent minor stops due to operator error, Augmentir can recommend personalized training or suggest procedural adjustments.

4. Cross-Functional Collaboration

Breakdown Elimination often requires input from multiple departments. Augmentir fosters collaboration by enabling real-time communication and task delegation within a single platform. Issues can be escalated, tracked, and resolved collaboratively, reducing mean time to repair (MTTR).

industrial collaboration using augmentir to support breakdown elimination in manufacturing

5. Knowledge Retention and Transfer

Breakdown Elimination requires that lessons learned are captured and shared. Augmentir creates a living knowledge base where best practices, successful fixes, and RCA findings can be stored and retrieved on demand. New hires benefit from instant access to tribal knowledge, improving ramp-up time and reducing repeated failures.

 

 

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Boost your Clean Inspect Lubricate (CIL) processes with modern digital tools to enhance efficiency, ensure compliance, and reduce equipment downtime.

In manufacturing, it’s often the simplest routines that have the biggest impact. Take CIL—Clean, Inspect, Lubricate, a key part of Autonomous Maintenance. CIL is one of the first maintenance activities operators learn, and one of the most frequently performed. But here’s the thing: when done right, CIL can become a powerful driver of equipment reliability, lean efficiency, and frontline ownership.

clean inspect lubricate cil with digital tools from augmentir

Unfortunately, in too many factories, CIL is stuck in the past—paper checklists, inconsistent execution, and little visibility into whether it’s even being done. It becomes a checkbox exercise instead of a value-generating process.

At Augmentir, we’re changing that.

Taking CIL Beyond Machine Upkeep

CIL isn’t just about machine upkeep—it’s about unlocking the full potential of your frontline teams. While it’s often viewed as a basic task to keep machines running, we believe CIL plays a far more strategic role in manufacturing. It’s one of the few consistent, hands-on opportunities operators have to engage with their equipment and contribute to its health, performance, and longevity. When done right, CIL boosts equipment reliability, minimizes unplanned downtime, and forms the foundation for Total Productive Maintenance (TPM) and Lean Manufacturing—core principles of the Toyota Production System.

However, the traditional approach to CIL—paper checklists, manual logs, and inconsistent training—limits its potential. Without real-time visibility or standardization, it’s easy for these tasks to become rushed or overlooked, turning CIL into a reactive or even forgotten process. That’s why we approach CIL as a digitally connected, people-centric process. When CIL is embedded into daily workflows, supported with intuitive guidance, and tied into data-driven insights, it becomes far more than routine—it becomes a driver of reliability, workforce engagement, and continuous improvement. In short, we see CIL as a launchpad for smarter operations, and a powerful opportunity to elevate the role of the frontline worker.

clean inspect lubricate autonomous maintenance cil checklist

It’s about building a smarter, more proactive, more connected manufacturing environment—one that prevents problems instead of reacting to them.

Modernizing CIL for Today’s Workforce with Augmentir

At Augmentir, we believe CIL isn’t just a task—it’s a pivotal moment. A moment when operators pause to care for the equipment that powers your operation. Though simple, this act has the potential to connect people, processes, and machines in a way that drives long-term reliability and performance.

Yet in many manufacturing environments, CIL remains an undervalued routine. But we know better. These everyday actions—cleaning a part, inspecting for wear, applying lubrication—are where operational excellence begins. They’re the frontline’s first defense against equipment failure, quality issues, and lost productivity. More importantly, they’re one of the few daily touchpoints where workers can directly influence performance.

Here’s how Augmentir is modernizing CIL to make every moment count:

1. Digital CIL Workflows that Guide and Empower

Frontline workers can access step-by-step instructions on any mobile device or tablet. With clear visuals, safety prompts, and machine-specific guidance, every task is executed correctly—every time. No more guesswork. No more paper binders.

2. Smart Insights that Drive Action

Our AI-powered platform captures and analyzes every CIL activity, helping you spot inefficiencies, missed steps, and recurring issues. Routine maintenance becomes a powerful engine for continuous improvement.

3. Real-Time Visibility and Accountability

Supervisors and maintenance teams gain instant visibility across all lines and shifts. See which equipment has been serviced, who completed the work, and what issues were flagged—without chasing checklists or relying on memory.

4. Embedded Knowledge and Skill Development

CIL is also an opportunity to upskill. Augmentir embeds tribal knowledge into workflows, enabling operators to learn on the job and contribute valuable feedback that strengthens the entire system.

When powered by real-time data and AI, CIL transforms from a basic task into a strategic asset. It boosts decision-making, reveals hidden issues, supports autonomous maintenance, and empowers your workforce. The result? More uptime. More consistency. More engaged frontline teams—and less waste.

At Augmentir, we’re committed to turning these everyday moments into measurable impact. With the right tools, even routine tasks can fuel innovation and resilience.

Ready to turn your CIL program into a competitive advantage?

 

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

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

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

packaging industry connected workforce

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

So welcome to you, Chris.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Chris Kuntz: Great question.

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

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

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

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

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

 

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

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

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

LNS Research Connected Worker Solution Selection Matrix

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

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

Augmentir positioned as a leading front runner and innovator

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

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

Read the full report here.

Augmentir’s results from the field

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

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

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

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

 

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

 

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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 industrial transformation partner delivering measurable results across operations. Schedule a live demo today to learn more.

 

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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.

Say hello to the newest addition to the Augmentir platform, Augie – the GenAI powered digital assistant for manufacturing.

Say hello to the future of work in manufacturing with the latest addition to Augmentir’s suite of connected worker tools, Augie™.

augie generative ai assistant for manufacturing

Augie is a digital assistant for frontline operations that utilizes Generative AI and proprietary fit-for-purpose, pre-trained Large Language Models (LLMs) to enhance operational efficiency, problem-solving, and decision-making for today’s less experienced frontline industrial workers. It leverages 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.

Continue reading below to learn more about how Augie works and how it can benefit your frontline workforce and manufacturing operations:

How our GenAI Powered Assistant Works

Generative AI-powered smart manufacturing assistants are designed to provide secure, role-based, personalized assistance to frontline workers, engineers, and managers in various industries, including manufacturing.

They work by leveraging artificial intelligence and integrations across different software systems, providing guidance and assistance in various tasks to enhance productivity and performance. This includes providing data insights, recommendations on actions to improve performance, and the ability to create analyses and dashboards with a natural language-based assistant.

A majority of smart manufacturing assistants only draw their information from manufacturing execution systems (MES), without tying in other important systems necessary for frontline manufacturing success.

Augie, however, is different. It leverages enterprise-wide data tying in information from a wide range of platforms including operational data, training and workforce management data, connected worker and engineering data, as well as information from enterprise systems.

gen ai industrial manufacturing

How Augie Benefits Your Frontline Workforce

Augie is unique among other smart manufacturing assistants. It leverages proprietary fit-for-purpose, pre-trained LLMs and generative AI, coupled with robust security and permissions, to help factory managers, operators, and engineers improve efficiency, resolve issues faster, and prevent downtime.

With information readily available via Augie, frontline workers can make decisions faster, reduce downtime, and improve troubleshooting with instant access to summarized facts relevant to a job or task. Additionally, Augie is multi-modal, meaning it can return actionable information in the form of work procedures, training videos, recorded collaborations, engineering documents and SOPs, as well as tribal knowledge.

Through Augie, manufacturers can instantly:

  • Close skills and experience gaps with personalized support
  • Gain insights into Leader Standard Work
  • Gain new insights into skills inventories
  • Convert Tribal Knowledge into Digital Corporate Assets
  • Identify opportunities for continuous improvement
  • Forecast potential operational issues

augie gen ai industrial assistant troubleshooting

With Augie by your side, you can streamline manufacturing operations, optimize performance, empower your frontline workforces, and stay ahead in today’s rapidly evolving and competitive landscape.

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6 Ways Manufacturers Can Use GenAI Today

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Improve Operational Efficiency with Augie and Augmentir

The digitization of frontline processes has become a must-have to keep up with the velocity of change – but not just digitization … smart digitization. Recently, Deloitte found that 86% of manufacturing executives believe smart factory solutions will be the primary drivers of competitiveness in the next five years. Leveraging smart, AI-driven connected worker solutions that allow industrial organizations to best support their frontline workforces and optimize processes to make them safer and more efficient is critical to overall enterprise success.

At Augmentir, we have been met with continued success in our efforts to transform manufacturing operations. Our patented Smart AI foundation helps manufacturing organizations close the loop between training and work execution, delivering the data and in-line insights necessary to continuously improve operational excellence day-over-day, year-over-year. Augmentir is the world’s leading connected worker solution, combining smart connected worker and GenAI technologies to drive continuous improvement and enhance performance management initiatives in manufacturing.

The addition of Augie to our platform is a game-changer for factory floor and other frontline workers, allowing for quick reference troubleshooting and useful, contextualized information to be delivered at the moment of need. Furthermore, with Augie, less experienced workers are provided with additional support and individualized guidance based on the job or task needs.

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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