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Step into a Paperless Shop Floor with Augie

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In conversations with our customers, a recurring theme emerges when discussing their transition to digital processes: the largest cost and burden often lies in the time and effort required to digitize existing paper-based materials.

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Is your manufacturing operation still clinging to outdated paper-based processes? It’s time to face the facts: paper is holding you back. The effort spent managing paper trails, outdated Standard Operating Procedures (SOPs), and inconsistent training materials isn’t just inconvenient—it’s a direct drain on your productivity, efficiency, and bottom line.

paperless shop floor with augie industrial generative ai suite

Generative AI (GenAI) offers a transformative way to digitize content, automating the conversion of paper-based materials like SOPs, work instructions, and checklists into structured, editable digital formats. It can enhance content by improving clarity, integrating visuals, translating languages, and customizing workflows. This significantly reduces manual effort and accelerates transitions to cost-effective digital systems.

Read below to learn more about the challenges of content digitization, the potential benefits of going paperless, and how Augie, a Generative AI solution from Augmentir, is empowering manufacturing companies to accelerate their digital transformation.

The Benefits of a Paperless Shop Floor

A paperless shop floor in manufacturing offers numerous benefits, including enhanced efficiency by eliminating time-consuming manual paperwork and reducing errors. It provides real-time access to digital work instructions, improving accuracy and productivity for frontline workers. Digitization supports better compliance with safety and quality standards while enabling faster updates to workflows.

Additionally, going paperless reduces environmental impact by minimizing waste, aligns with sustainability goals, and fosters a connected, data-driven environment where insights from real-time data can drive continuous improvement and innovation.

Here are the top benefits of going paperless:

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

Challenges

Going paperless in manufacturing isn’t without challenges, and ignoring them can stall your progress.

Standard Operating Procedures (SOPs), work instructions, and checklists are typically built over years, representing a significant repository of organizational knowledge. Converting these into digital formats while maintaining accuracy and accessibility can be daunting. Digitizing years’ worth of SOPs, work instructions, and checklists can feel overwhelming, but staying stuck in outdated systems costs more in the long run. Resistance from workers who are comfortable with paper-based processes can slow adoption, and if your digital tools aren’t intuitive, you risk alienating your team. Integration with existing systems is no small task, and if you’re not prioritizing data security, you’re leaving your operation vulnerable. Overcoming these hurdles is essential to stay competitive.

This is where Generative AI and Augie can provide transformational value.

Generative AI has transformative potential for digitizing content by automating the conversion of paper-based materials into structured, digital formats. It can analyze and extract information from documents like SOPs, work instructions, or checklists, quickly translating them into editable, standardized templates. GenAI also enables content enhancement, such as rewriting for clarity, integrating visuals, language translation, or adapting content for specific workflows. By accelerating the digitization process and reducing manual effort, GenAI empowers organizations to transition to digital systems more efficiently and cost-effectively.

Your Paperless Shop Floor with Augie

Augie, a suite of Industrial Generative AI tools from Augmentir, revolutionizes industrial digital transformation by combining advanced AI capabilities with practical, human-centric applications. Augie uses generative AI and the power of advanced Large Language Models (LLMs) to transform digital content creation, create adaptive workflows, provide real-time worker guidance, and analyze data to deliver actionable insights.

augie convert content paperless shop floor

 

Augie is a powerful tool for accelerating the transition from paper-based to digital operations in manufacturing and industrial settings. Quickly generate standard work procedures from Excel, Word, PDFs, images, or videos. The Augie Content Assistant takes your existing content and generates digital smart forms, checklists, and digital work instructions. Augie automatically analyzes SOPs, work instructions, and other documents to create clear, simplified training modules. It generates interactive quizzes to reinforce key concepts and adapts learning materials to individual skill levels, ensuring workers engage with relevant content.

augie gen ai content assistant - convert video to procedure

With Augie, you can move past the inefficiencies of paper-based frontline work. Augie transforms your paper-based workflows into dynamic digital tools in just days, streamlining processes, reducing errors, and equipping your workforce to excel in today’s fast-paced environment.

 

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Augie has been instrumental in helping us quickly transform our existing paper-based SOPs and training documents into interactive digital work instructions and learning tools. We’ve reduced our digitization effort from months down to days. This has streamlined our processes, reduced errors, and accelerated the upskilling of our workforce.

Digital Transformation Lead
Fortune 100 Food & Beverage Manufacturer

 

Digitizing isn’t just an upgrade—it’s a step toward smarter, more competitive operations. Augie optimizes content for frontline workers, provides real-time feedback, and eliminates barriers like language gaps or compliance issues. Paper simply can’t keep up with the actionable insights and adaptability that Augie offers.

Drive Manufacturing Efficiency with a Paperless Shop Floor

Augie revolutionizes the connected worker journey by offering a robust suite of AI tools that grow alongside organizational needs. It begins by transforming static, paper-based processes into dynamic, interactive workflows, enhancing accessibility and efficiency for frontline workers. As operations become digitally connected, Augie uses real-time data to identify inefficiencies, optimize workflows, and drive continuous improvement.

The Augie Industrial Gen AI Suite transforms every stage of the Connected Worker Journey.

 

augie transforms your connected worker journey

With its seamless integration capabilities, Augie creates a scalable ecosystem, enabling innovation and adaptability. This comprehensive approach empowers organizations to modernize operations and build a foundation for sustained success and growth.

 

 

Ready to Learn More?

If you’re ready to modernize and future-proof your operations, Augie is here to help you take the leap into a truly paperless shop floor. If you’re serious about building a resilient, efficient operation, it’s time to stop making excuses and start taking action. Augie isn’t just a tool—it’s the future of manufacturing. Step into a paperless shop floor and see the difference for yourself.

 

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How to Digitize Layered Process Audits with Connected Worker Technology

Learn how connected worker technology can help you standardize and digitize layered process audits for improved accuracy and better quality results.

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Digitized Layered Process Audits (LPAs) are important in manufacturing for establishing and enhancing process standardization, quality management, multi-level workforce engagement, risk mitigation, and quality standards compliance. The primary function of a Layered Process Audit is to focus on observing and validating how products are made to ensure process and product quality. This effectively protects manufacturers and frontline manufacturing personnel from defects and process issues before they can do any damage.

digital layered process audits

Many crucial manufacturing processes are still carried out using outdated pen-and-paper methods; and as the industry continues to evolve, digitization is essential to remain competitive and maintain proper quality and safety standards. Layered Process Audits are no exception, with digitized layered process audits now a necessity, rather than a luxury, for organizations looking to keep pace, elevate their audit processes, and meet the demands of the evolving industry.

Learn more about how to streamline LPAs with connected worker technology, implement digital LPAs, and the benefits of AI-powered analytics for digitized LPAs and overall quality management excellence in manufacturing below.

Benefits of a Digitized Layered Process Audit

Connected worker platforms allow manufacturers to replace paper-based audit forms (like a layered process audit) with digital checklists that can be accessed and completed on mobile devices, allowing for:

  • Standardized audit formats and best practices across audit layers
  • Real-time data collection and improved audit tracking
  • Mobile access to information and knowledge including relevant documents, procedures, and historical data
  • Automated audit scheduling and notifications for consistent audit frequency across layers and reduced administrative burden
  • Real-time issue reporting and escalation for faster response and issue resolution
  • Improved data analytics and reporting to generate and apply insights as well as identify recurring issues and root causes
  • Overall system integrations with things like ERP, MES, and CMMS

These abilities offer a significant boost to manufacturers in terms of operational efficiency, risk mitigation, workforce development, and cost reduction.

Implementing Digital Layered Process Audits

Manufacturers can digitize LPAs and streamline their quality management processes through AI-powered connected worker technologies, improving data quality and driving faster, more effective quality improvements across the organization.

layered process audit framework

Implementing digital Layered Process Audits (LPAs) involves several steps, from selecting the right platform to engaging the team and ensuring proper integration with existing systems. Below is a step-by-step guide to implementing digital LPAs effectively:

1. Choose the right Digital LPA Platform

Research and select a digital LPA platform that meets your organization’s needs. Consider factors like:

  • Ease of use (especially for mobile devices)
  • Customizability (to fit your audit checklist and process requirements)
  • Integration with existing systems (e.g., ERP, quality management systems)
  • Reporting and analytics capabilities
  • Scalability for future needs

2. Develop and Digitize Audit Checklists

  • Standardize Audit Checklists: Create or review the audit checklists for each layer of the audit process. Ensure they are aligned with your goals, operational requirements, and industry standards (e.g., ISO, IATF).
  • Digitize the Checklists: Input these checklists into the digital platform. Ensure that they are tailored to different levels of the audit process, from shop floor employees to higher-level management.
  • Customize Alerts and Criteria: Set up criteria for success/failure and alerts for non-conformance. This can include conditional triggers where a failed audit automatically prompts corrective actions.

digitized layered process audit LPA with augmentir quality control checklist

3. Integrate with Other Systems

  • Link to Quality Management Systems (QMS): Integrate the LPA platform with your existing QMS, ERP, or other relevant systems to streamline data sharing and analysis.
  • Automate Corrective Action Processes: Ensure that non-conformance findings in the audit automatically trigger corrective action workflows, and link them to task management or follow-up procedures.

4. Monitor, Analyze, and Improve

  • Track Real-time Results: Use the platform’s dashboards and analytics features to monitor performance metrics, such as audit completion rates, non-conformance trends, and the time taken to close corrective actions.
  • Conduct Regular Reviews: Hold periodic review meetings with the audit team and management to discuss audit findings and trends. Use this information to drive continuous improvement in processes.
  • Make Adjustments: Based on the insights from the audits, adjust the audit checklists, procedures, and corrective action plans as needed.

5. Foster a Culture of Continuous Improvement

  • Encourage Engagement: Foster a culture where employees see the value in LPAs and actively participate in the process. Offer incentives or recognition for high levels of engagement or process improvements resulting from audits.
  • Regularly Update the System: Keep the digital platform and audit processes updated to reflect changes in standards, regulations, or internal processes.
  • Leverage Advanced Analytics: Over time, use advanced analytics and machine learning (if available) to predict potential non-conformance areas and further streamline corrective actions.

By following these steps, you can effectively implement a digital Layered Process Audit system that enhances visibility, accountability, and process control across your organization.

Driving Continuous Quality Improvement with Digitized LPAs

Excellence in quality management drives success in manufacturing. Digitizing and updating old processes with AI, connected worker platforms, and even simple digital layered process audit software allows manufacturing organizations to better identify and prevent defects at their source and protect against rework, customer complaints, costly product recalls, and reputational damage.

Recent innovations in AI technology and applications caused an explosion of growth all across the world and in various industries. Manufacturing is uniquely situated to adopt these technologies for massive growth. One valuable use case is the use of AI to optimize quality management, specifically to optimize audit processes like LPAs for drastically improved results and insights that simply weren’t possible previously.

AI analytics combined with connected worker technologies digitize and streamline layered process audits allowing manufacturers to capitalize on shop floor data data capture for:

  • Trend analysis across different audit layers, departments, and locations
  • Automated population of audit forms with relevant data
  • Seamless creation of digital work instructions from audit findings
  • Application of cobots, generative AI assistants, or AI copilot technologies to support auditors and workers alike.

But this does not stop there, according to a study by McKinsey & Company, companies that prioritize quality management achieve higher levels of employee engagement. Engaged employees are more likely to be committed to producing high-quality products, resulting in increased productivity and customer satisfaction. The addition of AI to capitalize on connected worker data and feedback to generate insights, support enhanced decision-making, and create better processes offers manufacturers a path forward into the future with a better-equipped and supported frontline workforce.

Interested in learning more?

If you’d like to learn more about how Augmentir streamlines and optimizes quality management processes like digital layered process audits and more, schedule a demo with one of our produce experts.

 

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How to Combat the Manufacturing Skills Gap with an Augmented Connected Workforce (ACWF)

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

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

augmented connected workforce acwf manufacturing

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

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

Implementing an ACWF in Manufacturing

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

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

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

Supporting Learning in the Flow of Work

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

pyramid of learning

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

ACWF tools further enhance frontline activities through:

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

augmented connected workforce in manufacturing

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

Future-proofing Manufacturing Operations with an ACWF

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

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

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

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

 

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Production Efficiency in Manufacturing: Strategies for Maximizing Output and Reducing Waste

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

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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

 

Contact us today for a live demo.

 

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How AI is Revolutionizing Manufacturing Training

Explore how Augmentir uses AI to personalize manufacturing training, boost performance, and deliver real-time support and content creation.

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In today’s fast-paced manufacturing environment, staying competitive means more than just upgrading equipment—it requires investing in people. But traditional workforce training methods haven’t kept up. Static instruction manuals, one-size-fits-all onboarding programs, and outdated SOPs often fall short of preparing workers for the dynamic challenges of the modern factory floor.

ai in manufacturing training

Enter artificial intelligence (AI). From analyzing worker performance to delivering personalized training and real-time support, AI is transforming how manufacturers develop and empower their frontline workforce. Solutions like Augmentir are at the forefront of this shift, using advanced analytics, machine learning, and generative AI to create more effective, agile, and personalized training ecosystems.

Here’s how AI is reshaping manufacturing training across four critical areas.

1. Smarter Upskilling and Reskilling Through Performance Analytics

One of AI’s most valuable contributions to manufacturing training is its ability to turn raw performance data into actionable insight. Every worker interaction—how long a task takes, whether they need help, how often they make mistakes—tells a story. Traditionally, these insights were anecdotal at best. With AI, they’re measurable and immediately useful.

Platforms like Augmentir use AI to analyze real-time worker performance data and automatically surface trends and gaps. Suppose a maintenance technician consistently struggles with certain procedures. The system flags this, allowing supervisors to deliver targeted retraining or create a learning path that addresses the weak areas. Likewise, workers who consistently perform well might be fast-tracked for cross-training or more advanced roles.

the difference between skills development and training in manufacturing

This approach enables continuous learning and ongoing upskilling and reskilling, not just during onboarding or annual reviews, but every day. By matching training efforts with actual needs—based on data, not guesswork—companies can build more agile, responsive workforces that are always learning and improving.

2. Personalized Work Instructions for Every Skill Level

Manufacturing is not a one-size-fits-all environment—so why should training be?

AI can help tailor work instructions and learning experiences to the individual. With Augmentir, for example, AI dynamically adjusts work instructions and guidance based on a worker’s experience, proficiency, and even recent performance. This personalization helps new employees ramp up faster and allows seasoned workers to bypass unnecessary detail and focus on what matters most.

For a novice, instructions might include step-by-step visual aids, safety warnings, and prompts for supervisor sign-off. A veteran might receive a streamlined checklist with optional references. The experience becomes smoother and more relevant for each person, improving accuracy and reducing the time it takes to perform tasks.

using ai to improve manufacturing trainingThis kind of adaptive guidance is especially valuable in high-mix, low-volume environments or where production processes change frequently. Workers stay productive while learning in the flow of work—a win for both efficiency and engagement.

3. On-the-Job Support with Generative AI Factory Assistants

Even the best training can’t prepare workers for every situation they’ll encounter. That’s where generative AI assistants—often called copilots—come in.

Imagine a frontline operator faced with an unfamiliar error code on a CNC machine. Instead of stopping work, digging through documentation, or calling a supervisor, they can ask an AI assistant integrated into their work app or wearable device. The assistant quickly provides context-aware help: maybe it’s a diagnostic procedure, a video walkthrough, or a simple checklist.

This is not science fiction—it’s happening now. With tools like Augmentir’s Augie, workers get real-time guidance, support, and training while they work, tailored to the exact task and situation. These industrial generative AI assistants learn and improve with each interaction, so the more they’re used, the better they get at helping.

augie generative ai assistant for manufacturing standard work

This not only boosts productivity but also reduces downtime, prevents errors, and improves worker confidence. AI copilots act like a mentor in your pocket—one that’s always available, always up to date, and always ready to help.

4. Rapid Content Creation with Generative AI Tools Like Augie

A major pain point in manufacturing training has always been content creation. Writing SOPs, training manuals, and onboarding documents is time-consuming, and keeping them current is a constant challenge—especially when processes, tools, or equipment change.

That’s where generative AI tools like Augmentir’s Augie come in.

Augie helps training teams and subject matter experts create up-to-date, accurate, and engaging content in a fraction of the time it used to take. You can input a few notes, a video walkthrough, or an old manual, and Augie will generate structured work instructions, training modules, or even interactive checklists. This democratizes content creation—now anyone from a line lead to a maintenance engineer can contribute training content without needing to be a technical writer.

augie industrial copilot generative ai assistant for training and quiz creation

More importantly, because Augie is part of the same ecosystem, the training content it generates can be immediately pushed into the hands of workers—embedded in digital workflows, accessible via AI assistants, or served up dynamically based on user behavior.

This means your training stays fresh, relevant, and aligned with the reality on the ground. No more outdated manuals. No more lag between process changes and training updates.

The Big Picture: AI as a Training Multiplier

What ties all these innovations together is a shift from static, one-time training to ongoing, personalized support—enabled by AI.

  • AI makes training smarter by identifying who needs help and where.
  • It makes training faster by delivering content that matches each worker’s needs.
  • It makes training more effective by embedding it directly into the flow of work.
  • And it makes training more scalable by automating content creation and support delivery.

In short, AI becomes a force multiplier for training. It empowers workers to get better faster, managers to lead more effectively, and companies to stay agile in a constantly changing world.

Looking Ahead

The manufacturing skills gap isn’t going away anytime soon. In fact, it’s projected that millions of manufacturing jobs could go unfilled over the next decade due to a shortage of trained workers. Traditional training methods simply can’t scale to meet this challenge.

But AI can.

By weaving intelligence into every layer of the training experience—from data analytics to real-time support—platforms like Augmentir offer a new blueprint for workforce development. It’s faster, smarter, more engaging, and ultimately, more human.

Because at the end of the day, it’s not about replacing people with AI—it’s about helping people thrive alongside it.

 

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Breakdown Elimination in Manufacturing: Enhancing Uptime with Connected Worker Technology

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

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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.

 

 

Contact us today for a live demo.

 

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How Skills Tracking Improves Work Allocation and Workforce Utilization

Learn how skills tracking enhances work allocation and workforce utilization to improve productivity in manufacturing.

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Employee skills tracking is an excellent way to stay ahead of the curve in today’s ever-changing manufacturing landscape. Leaders can use this talent management strategy to close worker competency gaps, increase effective training, and hire qualified prospects.

Putting an emphasis on employee skills can also help manufacturers prioritize work allocation and workforce utilization. But what exactly do these two terms mean and how do they relate to tracking skills in manufacturing?

Work allocation is the process of assigning resources and roles to meet the objectives of a given task or production facility. Workforce utilization, meanwhile, refers to how a company or organization effectively utilizes its workforce to meet its operational goals and objectives.

skills tracking and workforce utilization in manufacturing

To keep up with competition, manufacturers should not only try to recruit the best possible hires, but also allocate work in an effective way to retain staff, satisfy customers, and boost profits.

Ultimately, keeping track of skills is a beneficial way to organize a company’s resources to attain sustainable business goals. Implementing a connected worker solution and digitizing skills management processes through smart manufacturing technologies is an effective way for organizations to instantly visualize the skills gaps in teams as well as track workforce skills and quickly assess both team and individual readiness.

Learn more about digital skills tracking and how it improves work allocation and workforce utilization below:

Skills tracking defined

Skills tracking helps ensure that all workers have the necessary expertise to complete tasks to their fullest potential. Basically, it closes the gap between the competencies employees already have and ones they need to further develop.

Every manufacturing firm has a unique set of job requirements and expectations. Tracking worker skills on a regular basis helps a company identify training needs and build workers’ knowledge so that they can meet expected targets. Skills management and tracking software help manufacturers identify and track employee expertise. You can map skills from a centralized library to individual workers, analyze the performance of your teams, and fill any skill gaps that exist.

skills tracking software

In a nutshell, measuring employee proficiencies can boost retention, decrease the amount of time spent on tasks, and improve overall productivity.

Benefits of tracking skills to improve work allocation

Through digitization and effective skills tracking, manufacturing firms can best allocate work to team members based on expertise, credentials, and actual ability. For example, an operator who has more than 10 years of experience using computer-controlled equipment may be a better fit to handle complex machinery than an entry-level worker who lacks that training.

Additionally, with a centralized digital repository managers have a better idea of each employee’s current skills level and potential areas of improvement. Then they can close any skill gaps through training opportunities. In return, workers who receive the necessary training are more likely to thrive in their roles and be productive.

In summary, measuring worker skills can help improve work allocation by:

  • Hiring or assigning current employees to the correct jobs and tasks
  • Facilitating worker development through mentorship and training
  • Retaining high-quality employees

How tracking skills boosts workforce utilization

Workforce utilization refers to how much of an employee’s time is devoted to billable work. Tracking skills can improve this, in turn boosting productivity and profits.

When you measure how efficiently employees are doing their jobs and how well a business manages its resources, you can assure that tasks are done well and see continuous increase in revenue. Think about how many hours of each staff member’s workweek need to be billable to remain profitable and whether they are on track. With a digitized tracking system, manufacturers are able to automate and streamline this process reducing errors, improving productivity, and ensuring success.

Pro Tip

Through the use of smart, connected worker solutions and AI-based workforce insights organizations can deliver continuous, on-the-job learning based on skill tracking and real job performance, promoting reskilling and upskilling efforts enterprise wide.

To summarize, tracking skills can help enhance workforce utilization by:

  • Setting profitable rates for services based on worker output and time billed
  • Compensating employees fairly
  • Gauging whether staff is being overworked or underutilized

By digitizing these tracking processes and implementing AI-driven support, organizations can also visualize, track and offset employee burnout. By taking highly granular connected worker data and using AI to filter out the unnecessary portions, industrial operations are able to not only improve tasks and productivity but better support and empower frontline workers.

Ways to track workforce skills

Tracking employee skills is a great way to improve worker performance and productivity by matching the right person with the right assignment.

One way to track an employee’s skills is through a skills matrix, which is a grid that maps staff credentials and qualifications. A skills matrix helps managers strategize and oversee current and wanted skills for a team, position, department, and more. Similarly, a job cover matrix is used to map employees to tasks, roles, or jobs, ensuring adequate coverage and identifying skill gaps. A skills matrix (as well as a job cover matrix) is usually managed using a spreadsheet, but there are alternatives to skill matrices. For example, cloud-based skills management software can help identify and track employee competence and correlate it with actual job performance. The software can also help managers filter employee databases by skills to assemble teams or assign work based on specific qualifications.

skills matrix

Leadership can also track competencies through a skills taxonomy. Taxonomies help classify and organize skills into groups to better understand which skills employees have and which they should learn. Essentially, these structured lists help management identify and track skills to better allocate resources and worker training opportunities.

Lastly, a skills-tracking application can include AI-based software to identify and measure worker expertise and actual job performance. This is an excellent method for intelligently assigning work through skills mapping, optimizing training programs, and more. With AI-based insights and connected worker technology, organizations can bridge the gap between the training room and the shop floor, integrating training into the flow of work and creating an environment of continuous learning.

Skills management with Augmentir

Augmentir offers top-notch solutions to easily track and manage your frontline’s skillset. Our connected worker solution provides customized dashboards to streamline processes to improve workforce management, skills management, and deliver in-line training and support at the point of work, closing skills gaps at the moment of need.

If you are interested in learning how Augmentir can help improve your skills management, skills tracking, and workforce development – request a live demo.

 

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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.

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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 and tacit 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.