Benefits of Manufacturing SOPs and How to Write Them

Learn about the best practices for optimal asset maintenance performance and how to track your assets to ensure that everything is in working condition.

Standard operating procedures, or SOPs, will change the way you run your manufacturing operations.

SOPs are imperative to a properly organized management structure. They are step-by-step guidelines workers must follow when carrying out tasks to standardize work and are designed to meet industry regulations.

Essentially, they provide general info about assignments, including the tools, methods, or machinery needed to complete projects. SOPs indicate what the task is, who will perform it, how it should be completed, and when it should be completed.

manufacturing sop

For example, manufacturers may write SOPs for employee training to reduce risk and injury. Leadership may also use procedures to assign goals and measure employee performance.

Read on to find out more about the benefits of manufacturing SOPs and how to write them by exploring the following topics:

Advantages of Implementing Standard Operating Procedures

According to Forbes, a comprehensive SOP keeps workers on the same page and improves efficiency and accuracy. Without documented procedures, there is no way to set proper standardized processes and workers might try to complete jobs in non-standard methods, which leads to disruptions in the production processes and causes all sorts of quality issues in a manufacturing environment. Thankfully, SOPs work to prevent that from happening.

Some of the advantages of using SOPs include:

  • Meets regulatory compliance: Product inspectors constantly ask to review SOPs when conducting audits. These serve as the point of reference for whether specific measures followed meet industry guidelines.
  • Standardizes tasks: The point of written procedures is to establish a standard way of completing tasks. They enable tasks to be performed in the same way across the company.
  • Improves accountability and tracking: SOPs define who is responsible for a work order, maintenance check or inspection. This reporting can improve accountability across departments. If a task wasn’t completed accordingly or a procedure was missed, management can take necessary steps to prevent it from happening again.
Pro Tip

Digitized SOPs can further improve tracking and traceability features, helping manufacturers comply with regulations and quality standards. With digital SOPs it becomes easier to maintain records of every step in the production process, including who performed each task and when.

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How to write a manufacturing SOP

Writing a comprehensive set of SOPs can help workers perform tasks in the safest and most efficient way possible. Although there isn’t an official way to write procedures, you can follow certain steps to make them more effective:

Step 1: Establish a goal.

It’s important to think about what you want your SOP to accomplish. Regardless if you’re starting a new process or improving an existing one, figuring out the end goal will make it easier to complete the document.

Step 2: Pick a format.

There are different formats you can use to write your document: step-by-step, hierarchical, narrative, etc. We recommend the sequential step-by-step format for its straightforwardness.

Step 3: Write the procedures.

Make sure your procedures are clear, concise, current, consistent, and complete.

Step 4: Review and update.

It’s important to review your SOP for any discrepancies and update them if necessary. Consider asking fellow leaders knowledgeable in procedure creation to read them over.

Why SOPs are Important in Manufacturing

Compliance with manufacturing SOPs is crucial for a number of reasons, including:

  • Prevents accidents and ensures worker safety
  • Promotes worker consistency
  • Improves product quality
  • Protects your business’s reputation

SOPs are a critical component of manufacturing operations because they provide a structured framework for achieving consistent quality, safety, and efficiency in the production process. They help manufacturers meet regulatory requirements, reduce errors, and ensure that employees are trained to perform tasks consistently and safely.

Digitizing Manufacturing SOPs with Connected Worker Solutions

Using connected worker technologies to create digital SOPs can significantly improve their impact on manufacturing by enhancing accessibility, effectiveness, and overall utility.

Through digitization and smart, connected worker technology manufacturers can improve SOPs with features like real-time access, remote collaboration and guidance, data-driven insights, workflow automation, enhanced training, traceability and compliance, and more. Essentially, with these advanced technologies, manufacturing organizations can augment and support their workers with optimized processes and SOPs creating an environment of continuous improvement.

Augmentir offers customized AI-powered connected worker solutions that transform how you write and create manufacturing standard operating procedures. Request a live demo today to learn more about why leading manufacturers are choosing our solutions to improve their manufacturing processes.

 

 

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Using AI to Operationalize Training for Manufacturers

AI and connected worker technology is helping frontline managers combat employee burnout and improve engagement and retention.

In today’s fast-paced manufacturing industry, staying ahead of the curve is critical to success. To remain competitive, companies must continuously reskill and upskill their workforce. One way to achieve this is to operationalize training and bring it closer to the factory floor using artificial intelligence (AI) and connected worker technology. Operationalizing training means taking a more systematic approach to training and workforce development, rather than treating it as a one-time event.

operationalizing learning

According to a report by McKinsey, companies that embrace AI-powered learning reduced training time by up to 50% and improved learning outcomes by up to 60%.

AI-powered solutions make learning more accessible, engaging, and effective; and by integrating training and learning solutions into the everyday operations of the company, manufacturers can create a culture of continuous learning and improvement. In fact, here at Augmentir we’ve seen manufacturing companies use this approach to reduce new-hire onboarding and training time by up to 72%.

Learning: When and Where it’s Needed

AI has the potential to revolutionize many industries, and manufacturing is no exception. Many workers in the manufacturing industry work in shift-based environments, making it difficult for them to attend traditional classroom-based training sessions.

With AI, organizations can incorporate more learning processes into the everyday workday of frontline workers – essentially operationalizing training and bridging the gap between knowing and doing. This “active learning” aligns with the Pyramid of Learning visual model that illustrates the different stages of learning and their relative effectiveness.

pyramid of learning

Active learning involves the learner actively engaging with the material, often through problem-solving, discussion, or application of the knowledge while they are on the job.

In general, active learning (or workflow learning) is considered more effective than passive learning in promoting deep understanding and retention of information. Therefore, learning leaders often strive to design learning experiences that involve higher levels of active learning, moving beyond the lower levels of the pyramid and promoting critical thinking, creativity, and problem-solving skills.

This approach can be implemented with mobile learning solutions that leverage connected worker technology and AI to provide workers with bite-sized, on-demand training modules that they can access on smartphones or tablets. These modules can be customized to each worker’s skill level, making it easier for them to learn at their own pace.

Additionally, AI-driven learning solutions offer:

  • Personalized Learning: AI-powered learning solutions can be customized to each worker’s skill level, making it easier for them to not only learn at their own pace, but also matched to their experience level. For example, novice workers may be required to watch a micro-learning video as a safety prerequisite to performing a task, whereas a more senior worker with the appropriate level of job experience and proficiency may not be required to watch the learning video.
  • Performance-based Learning: AI-powered solutions provide workers with hands-on learning experiences that are customized based on their actual job performance. These experiences can be delivered through a variety of content mediums – rich media, self-help guides, microlearning videos, and even augmented reality (AR) experiences.
  • Real-Time Feedback: AI-powered solutions can monitor worker performance in real-time, providing instant feedback to help workers improve to provide access to content to help resolve issues in the flow of work.

AI can also help with the assessment of employee performance. Traditional performance evaluations often rely on subjective assessments from managers. Conversely, AI-powered performance evaluations can provide a more objective and data-driven assessment of employee performance, while also providing a more accurate picture of an employee’s strengths and weaknesses.

Better Training, Better Work

By implementing AI-based solutions, companies can identify and operationalize training needs across the organization. Using performance data, AI can uncover gaps in knowledge or skills across the workforce, which can then be used to develop targeted training programs to “fill” these gaps.

Once implemented, AI can be used to effectively track and improve learning and training effectiveness, leveraging data on worker performance before and after training to measure impact and refine training programs to ensure that they are delivering the best outcome.

As the manufacturing industry continues to evolve, so must how they approach learning solutions. A recent Deloitte survey found that over 90% of companies believe that AI-powered learning will be important for their organization’s success in the next three years. AI has the potential to operationalize training and transform learning in the manufacturing industry by bringing it closer to the factory floor. By leveraging AI-powered personalized learning, real-time feedback, data-driven performance evaluations, and identifying training needs, industrial organizations can create a more efficient, effective workforce.

Onboarding vs. Training in Manufacturing

Learn about the best practices for optimal asset maintenance performance and how to track your assets to ensure that everything is in working condition.

Onboarding and training are essential components of integrating new employees into a manufacturing environment. Research by Brandon Hall Group found that organizations with a strong onboarding process improve new hire retention by 82% and productivity by over 70%. Additionally, research from NAM and The Manufacturing Institute has found that manufacturing organizations invest an average of 51.4 hours per employee in training and are increasing overall investment in training by an average of 60% in response to the growing skilled labor crisis.

onboarding vs training in manufacturing

Onboarding and training are two key components of a skilled workforce that, while similar, serve different purposes and cover distinct aspects of the employment process.

Both processes are crucial, as onboarding ensures that employees understand the organization’s broader context, and training ensures that they have the expertise to contribute to the manufacturing processes and meet quality and safety standards.

A successful combination of effective onboarding and comprehensive training can lead to more engaged, skilled, and productive employees in the manufacturing industry. Unfortunately, according to Gallup, only 29% of new hires say they feel fully prepared and supported to excel in their role after their onboarding experience.

Read below to learn more about the differences between onboarding and training in manufacturing, why they are both critical to manufacturing success, the benefits of improving them, and how continuous learning strategies coupled with connected worker solutions can improve both and deliver impressive results.

Breakdown of Onboarding and Training Differences

Onboarding in manufacturing is about orienting new hires to the company as a whole, while training is about equipping them with the specific skills and knowledge needed to perform their job functions effectively. Below a breakdown of the differences between onboarding and training in a manufacturing setting:

Onboarding

  • Purpose: Onboarding integrates a new employee into the organization and its culture. It aims to familiarize employees with the company, its policies and procedures, and their roles within the organization.
  • Focus: Onboarding focuses on introducing employees to the broader aspects of the company, such as its mission, values, and culture, as well as administrative and safety procedures.
  • Duration: Onboarding is typically a short-term process, often lasting a few days, but could extend to a few months in certain manufacturing environments.
  • Components: It may include activities like completing paperwork, understanding company policies, meeting the team, plant/site safety, and familiarizing a new hire with the physical workplace.

Training

  • Purpose: Training in manufacturing is a more specific and in-depth process that imparts the knowledge, skills, and competencies necessary to perform the job effectively. It is task-oriented and aimed at ensuring that employees can carry out their roles proficiently.
  • Focus: Training focuses on the technical aspects of the job, safety protocols, equipment operation, quality standards, and other job-specific skills.
  • Duration: Training is an ongoing process and may vary in duration depending on the complexity of the role and the employee’s experience level.
  • Components: Training tends to include hands-on instruction, demonstrations, practice exercises, and assessments to ensure that employees gain the necessary skills and knowledge.
Pro Tip

Both initial onboarding and ongoing training can be implemented with mobile learning solutions that leverage connected worker technology and AI to provide workers with bite-sized, on-demand training modules that they can access on smartphones or tablets. These modules can be developed with customized learning paths that are focused on the type of tasks and work employees are doing on the factory floor.

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Why are training and onboarding important to manufacturing success

Onboarding and training are crucial to manufacturing success for several reasons including safety, compliance, quality, and more. A well-trained manufacturing workforce that has a deep understanding of company policies, its mission, and overall values drives successful initiatives by producing quality products, complying with both industry-wide and company-specific standards, and meeting production goals in a manner that is both safe and efficient.

The manufacturing industry is subject to numerous regulations related to safety, environmental practices, and product quality. Proper training ensures that employees are aware of and adhere to these regulations, reducing the risk of compliance violations and a well-structured onboarding program leads to lower turnover rates and a more effective and cohesive workforce, ultimately contributing to manufacturing success.

In summary, these two tools are essential in manufacturing for setting the stage for employee success and overall organizational success. Onboarding aligns new employees with the company’s culture, policies, and expectations, enhances their safety awareness, and fosters engagement and productivity, while training plays a pivotal role in contributing to manufacturing success by equipping employees with the knowledge, skills, and competencies necessary to perform their roles effectively.

What are the benefits of improving training and onboarding in manufacturing

Improving manufacturing employee onboarding and training offers several advantages, benefiting both the company and its employees. Comprehensive onboarding makes new hires feel connected to the company’s culture and values, while ongoing training can offer growth and development opportunities, leading to increased employee engagement and job satisfaction.

Companies with a skilled, well-trained workforce are more competitive in the marketplace, as they can produce higher-quality products at a lower cost and adapt to industry changes more effectively.

Training and development opportunities are often cited as a key factor in employee satisfaction. When employees feel that their skills are being enhanced and their careers are advancing, they are more likely to be satisfied with their jobs.

How continuous learning and connected worker solutions improve training and onboarding in manufacturing

Continuous learning and connected worker solutions can significantly enhance training and onboarding in manufacturing by providing more dynamic, effective, and adaptable approaches.

By incorporating continuous learning and connected worker solutions into the these processes, manufacturing companies can create more efficient, engaging, and rewarding experiences for employees. This not only accelerates the integration of new employees but also supports ongoing skill development and knowledge retention once on the job, ultimately improving productivity and the overall success of the organization.

connected worker as part of connected enterprise

Augmentir’s AI-based connected worker solution is being leveraged by manufacturing leaders to deliver continuous learning and development tools to optimize onboarding training for a rapidly changing and diverse workforce. Our innovative, smart connected worker suite is transforming how manufacturing organizations hire, onboard, train, and deliver on-the-job guidance and support.

 

digital skills management in a paperless factory

Schedule a live demo today to learn how our smart, connected worker solutions, AI-driven insights, and digital skills management are optimizing training and onboarding programs, tracking individual and team progress, and delivering targeted training and upskilling.

 

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Why You Should Be Thankful For AI in Manufacturing This Year

Being thankful for AI might not seem like one of the usual items to include on your “What I’m Thankful For” list, but, AI truly has laid the foundation for not only the Augmentir platform, but for transforming the manufacturing workforce in positive ways

Every year as Thanksgiving approaches in the United States, we take time to reflect on what we are thankful for in our personal lives, such as family, friends, and health to name a few. As we started thinking about what we’re thankful for from a work perspective here at Augmentir, many things came to mind: our wonderful clients, an awesome team, our incredible founders, but one item high on our list is something that has allowed us to stand out in the Connected Worker platform space and make our product what it is today – Artificial Intelligence. Specifically AI in manufacturing. 

Being thankful for AI might not seem like one of the usual items to include on your “What I’m Thankful For” list, but, AI in manufacturing truly has laid the foundation for not only the Augmentir platform, but for transforming the workforce in positive ways as you’ll see below.

Improved Safety in the Workplace

One of the most common use cases for adopting AI has been in workplace screening and safety primarily as a result of the pandemic. Manufacturers found use in AI to monitor interactions of employees that needed to be in person on the shop floor during the pandemic so that they could conduct contact tracing and facility sanitization if necessary. Seeing the value of AI in workplace safety, manufacturers have continued to implement AI strategies for long-term solutions to identify safety events before they happen or to speed up post-incident root cause analysis for accidents like trips and falls. Industrial companies that implement AI-powered connected worker solutions as part of their digital transformation strategy have seen up to an 80% decrease in reportable injuries.

Connecting the Frontline Worker

According to Cisco, there are over 3 billion workers across the globe, and nearly two-thirds of these workers are frontline or field workers, whose day-to-day duties require that they physically show up to their jobs. Over the years, the manufacturing industry has done a really good job of connecting machines in the fabric of the business and giving operators the necessary data to help run those machines better. Our frontline workers are the least connected set of workers in the company. Frontline workers should be fully integrated into the fabric of the business from a collaboration standpoint so that they have access to the data that they need, when they need it. AI-powered connected worker tools provide not only a path to connect workers, but also intelligently deliver the right level of performance support so they can perform at their best.

Making Sense of Valuable Data

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 productivity, quality, and workforce development, setting the stage to address the needs of a constantly changing workforce. AI algorithms in manufacturing are ideal for analyzing large amounts of data collected from a connected workforce. AI can detect patterns, find outliers, cleanse data and find correlations and patterns that can be used to identify opportunities for improvement and create a data-driven environment that supports continuous learning and performance support. Using AI insights derived from Augmentir’s Connected Worker Platform, Colgate-Palmolive was able to save 10-30 minutes saved per shift and as much as 120 minutes reduced between Maintenance Notification and Maintenance Order Closure (Maintenance Execution Time).

Continuous Learning & Development

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

 

At Augmentir we believe that the purpose of a Connected Worker platform isn’t simply to deliver digital work instructions and remote support to a frontline worker, but rather to continually optimize the performance of the connected worker ecosystem. AI is uniquely able to address the fundamental macrotrends of skills variability and the loss of tribal knowledge in the workforce. With an ecosystem of content authors, frontline workers, subject matter experts, operations managers, continuous improvement engineers, and quality specialists, there are dozens of opportunities to improve performance – and that’s something to be thankful for.

 

To learn more about how AI is being used to digitize and modernize manufacturing operations, check out our latest eBook – Build a Modern, Connected Workforce with AI.

How Digital Transformation Shapes the Future of Food Manufacturing

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

Last week, Augmentir participated as a sponsor in the 2021 American Food Manufacturing Summit. This 3-day virtual event was designed to bring food and beverage manufacturers together to discuss current trends, strategic insights, and best practices in an ever-evolving environment. The event focused on addressing today’s top challenges and future of food processing and manufacturing, specifically around embracing digital transformation and technology for manufacturing excellence. Attendees were able to connect with top industry influencers and learn about different strategies to improve automation, operational excellence, quality, and safety in the food manufacturing industry through open roundtables and 1:1 meetings.

Augmentir’s Enablement Director, Shannon Bennett, hosted an open roundtable discussion on the role digital transformation plays in food and beverage manufacturing, and how technologies like artificial intelligence (AI) and connected worker platforms are helping companies kick-start their digital transformation efforts. During the discussion, Shannon opened the floor to the attendees to discuss the day-to-day challenges they face at their manufacturing organizations and the tools they’re looking into to solve those challenges. 

Solving Manufacturing’s Biggest Challenges with AI and Connected Worker Technology

The roundtable consisted of executives and manufacturing leaders from some of the world’s largest food and beverage companies to smaller family-owned and operated specialty food and beverage manufacturers. Throughout the roundtable, we heard the same challenges and frustrations related to standardization, moving from paper to digital processes, data collection, lack of traceability, and an overall need for digital transformation.

The overarching roundtable discussion was around digital transformation. Food and beverage manufacturers are accelerating the pace of digitization to address their top challenges – the labor crisis, increasing skills gap, and increased pressure for improved production efficiency, changes in consumer demands, and increased regulatory compliance related to food safety.

Moving from Paper to Digital

During our roundtable discussion, most of the manufacturing leaders were in the discovery phase of their modernizing process, where they were beginning to look into digital solutions to solve their challenges around manual processes and efforts to reduce paper. Some of the discussion around paper included issues with quality on the shop floor and wanting to go paperless, easier access to training for employees, lack of traceability (for example, maintenance schedules need more visibility of completion, where issues arise, and more transparency all around), and digitizing information from a quality standpoint.

Digital work instructions reduce the need for paper and deliver information to frontline workers when and where they need it. This provides frontline workers with a standardized way of performing technical work.

Lack of Data-Driven Insights into the Work Being Done

Another key challenge was the lack of insight into how workers were performing their jobs – whether it be in quality, equipment operation, or maintenance. One participant discussed labor challenges in their organization and that when they collect data it often gets lost and when they come back to it, they don’t know or remember why they’ve collected it in the first place.

Connecting workers with digital tools is merely a first step in the process of truly understanding and getting clarity on the work being done. Connected Worker data is inherently noisy, generating misleading signals that traditional business intelligence (BI) tools aren’t designed to handle. This leads to murky or contradictory conclusions that prevent organizations from taking anything but a “one size fits all” approach to work process and workforce investments. Or, even worse, false conclusions are generated about the state of work process and workforce opportunities, leading to targeted investments into the wrong areas.

The discussion shifted to AI as a solution not only bringing clarity to the work being done, but also more generally democratization of the workplace, and giving employees the tools to use data effectively to improve manufacturing operations. AI is designed for purpose to recognize patterns in the noisy data sets generated by a factory workforce, letting your continuous improvement and operations teams focus on what’s really going on.

Training

Employee onboarding and training was also a hot topic of discussion. Many participants spoke about manual processes and how traditional training methods are proving to be ineffective.  Traditionally, there was a clear separation between training and work execution. However, many participants shared that they are starting to re-think how they are training and onboarding their workers, and shifting more towards delivering training at the moment of need. The roundtable participants discussed at length approaches and strategies for re-thinking how training is delivered for today’s workforce.

Build a Modern, Connected Workforce with AI

To address these challenges, the roundtable participants overwhelmingly agreed that digital transformation initiatives for food manufacturing should start by focusing on streamlining data collection and digitizing valuable data. Using an AI-powered connected worker platform to accelerate this effort not only furthers a company’s digital transformation efforts, but also provides a whole new set of data that can provide really interesting insights and optimization opportunities. AI doesn’t remove the human worker from the equation, but rather, takes the human worker and embeds them into the digital operation.

 

To learn more about how AI is being used to digitize and modernize manufacturing operations, check out our latest eBook – Build a Modern, Connected Workforce with AI.

 

AI Manufacturing 2021 – Event Recap

After more than a year of virtual conferences, we were finally able to participate in person at the AI Manufacturing conference in Dallas early this November and discuss how AI is shaping the future of the manufacturing workforce.

After more than a year of virtual conferences, we were finally able to participate in person at the AI Manufacturing conference in Dallas early this November. This year’s event was hybrid, face-to-face on November 3 and 4th, and virtually on November 5th. While it was refreshing to be able to network face to face with leaders in the Manufacturing industry, it was great to have the opportunity to also network virtually on November 5th. If you aren’t familiar with the AI Manufacturing conference, this conference is the leading Artificial Intelligence event for Manufacturing Industries. This year’s event focused on:

  • The use of AI to Improve Quality, Reduce Defects, and Increase Profits
  • Developing a Digital Twin to Optimize Plant Operations
  • Using Building Blocks to Modernize Manufacturing Activities and Facilitate Growth
  • Designing  Products Enabled by Additive and Hybrid Manufacturing Techniques
  • Exploring the Use of AI in Industrial Attacks and Defense

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

Dave Landreth, Augmentir’s Head of Customer Strategy had the opportunity to present on “Using AI to Unlock to the True Potential of Today’s Connected Workforce”. In this session, he discussed the variability of the workforce with generations, how they need to be trained differently, and how AI can assist in worker proficiency. Dave also discussed Bob Mosher’s 5 moments of need and how AI can be applied at the time of learning. 

The Misunderstood Fear of AI

Our founders saw that the humanistic approach was missing with traditional connected worker platforms and realized that AI was the key to saving the manufacturing world and unlocking worker potential. However, companies are reluctant to adopt AI in fear that automation will take over and eventually replace human workers in manufacturing. Others fear that AI would be used negatively to track workers, in a “big brother” type of way.  

As we’ve seen with our customers, this couldn’t be farther from the truth. When AI is leveraged ethically with the workforce in mind, it can be used to help improve and ultimately grow the talent of your workers. Assessing workers on their performance has been done for years through subjective performance reviews. Using AI allows the assessments to be based on data and can provide a path forward for worker improvement and continued growth. 

Understanding Today’s Struggles Within Manufacturing

Manufacturing workforce challenges

The struggles that manufacturers face today aren’t the same struggles that were present 40 years ago. One of the number one issues in manufacturing is hiring. Today, most manufacturers believe that hiring is a risk, with a limited pool of candidates. They are struggling with employees who don’t have the needed skill set and are questioning how they can train them and evaluate their performance. 

Manufacturing companies also struggle with retaining employees. We are all aware of the workforce retention issues right now. Employees are feeling like they aren’t heard and that they can’t contribute to the company, which causes them to look for a new career. There is also the struggle of thoughtful upskilling, meaning that formal training programs only recognize one type of worker. The average manufacturing plant sees 4 generations of workers, ranging from those fresh out of high school to the ones that have worked on a plant floor for 40+ years. Different generations learn differently and require different levels of support. There isn’t a one size fits all approach for teaching different generations. 

Another challenge with the workforce that isn’t as obvious, is with mergers and acquisitions. An acquisition means that companies now consist of two workforces doing things differently and needing to understand what part of procedures from the newly acquired company is worth incorporating into the existing procedures.

Leveraging AI to Help Build and Grow a Top Performing Workforce

Build and grow a top performing manufacturing workforce

AI is uniquely suited to solve these challenges, and we recognized that early on at Augmentir. We started looking at how AI could help build and grow a top performing workforce. One way AI can help is the ability to hire for potential by increasing the hiring of candidates to those not as skilled. AI allows companies to understand a worker’s skillset and provides the ability for personalized workflows to guide them in the context of work while they are doing their job, whether it’s a new worker or one with dozens of years of experience. AI can also help with the “Right person – Right Job – Right Time” approach – always ensuring that the correct person is performing the task at the most efficient time. 

The use of AI allows all workers to contribute by allowing inline feedback to optimize work procedures. In addition, AI can be used to ensure personalized career job competency allows workers to be hired even if they do not have the optimal set of skills and experience. Measuring a worker’s proficiency when they are completing the work allows the worker to focus on each specific step and guides them at the time of need, instead of during classroom training. AI provides workers with predictive and stable data to help them grow in their roles. Having a data-driven way to measure success and provide advancement opportunities helps establish career paths as well as opportunities to grow. 

With an AI-based onboarding approach, organizations are able to hire a wider range of individuals with varying skill sets. If we can teach someone in the context of doing their work, onboarding time is reduced due to being able to train them in the field. We also see an increase in productivity and are constantly evolving their learnings. When workers feel included and confident about their careers, they are also more likely to want to stay and grow with the company. The ability to train workers in the field while doing their jobs with AI personalization allows you to clearly and quickly assess how a worker is doing, where you focus the help to them, and driving those 1:1 work procedures is a game-changer.

AI in Manufacturing will solve many of the challenges that we are seeing. 

Learning & Development and the 5 Moments of Need

The Five Moments of Need methodology was created by Bob Mosher, a thought leader in learning and development with over 30 years of experience. He realized that after 20 years, classroom teaching was the wrong approach since it rarely teaches you things that you do in your job on the shop floor. Classroom learning allows an individual to gain a certain level of confidence, but quickly falls off when it’s time to apply it within context to a given workflow.  

According to Bob’s methodology, the 5 moments when our workforce needs knowledge and information consists of: 

  • When people are learning how to do something for the first time (New).
  • When people are expanding the breadth and depth of what they have learned (More).
  • When they need to act upon what they have learned, which includes planning what they will do, remembering what they may have forgotten, or adapting their performance to a unique situation (Apply).
  • When problems arise, or things break or don’t work the way they were intended (Solve).
  • When people need to learn a new way of doing something, which requires them to change skills that are deeply ingrained in their performance practices (Change).

The approach that Bob and his team adopted in the last 10 years is to think more about performance support. The variability of the workforce, both skilled and young, proves that there’s not a one size fits all approach. This is where AI comes in: being able to deliver personalized work procedures for every worker, allowing for continuous learning and growth. Based on proficiency, there may be a more guided set of work instructions, a session with a remote expert, or a supervisor sign-off required in order to complete the job on quality and on schedule. AI can also be used to continuously measure and assess how the workers are doing. This is where organizations can start seeing growth within their workforce.

Looking Ahead

We had a blast at this year’s AI Manufacturing conference and are already looking forward to another successful event next year! If you’re interested in learning more about why AI is an essential tool in digital transformation, from reducing costs and downtime to improving over quality and productivity, we’d highly suggest considering attending next year. In the meantime, if you’re looking for information surrounding AI, digital transformation, and building a connected workforce, check out our eBook: “Building a Modern, Connected Workforce with AI”.

The Great Resignation in Manufacturing

Recently, Augmentir completed a rigorous qualification audit as part of a Tier 1 Pharmaceutical Manufacturing company’s Good Manufacturing Practice (GMP), and we are pleased to announce that our product successfully passed the audit.

A recent article published by The Washington Post shows some shocking numbers on the amount of Americans leaving their jobs over the past year. It’s no surprise that hotel and restaurant workers are resigning in high numbers due to the pandemic, but what is surprising is the fact that the manufacturing industry has been hit the hardest with “a nearly 60 percent jump” compared to pre-pandemic numbers. This “Great Resignation in Manufacturing” is the most of any industry, including hospitality, retail, and restaurants, which have seen about a 30% jump in resignations.

However, if you dig deeper, this trend isn’t new. This recent increase in job quitting in manufacturing has simply magnified a problem that had already been brewing for years, even prior to the start of the pandemic. In fact, in the four years prior to the pandemic (2015-2019), the average tenure rate in manufacture had decreased by 20% (US Bureau of Labor Statistics).

This accelerating workforce crisis is placing increased pressure on manufacturers and creating significant operational problems. The sector that was already stressed with a tight labor market, rapidly retiring baby-boomer generation, and the growing skills gap is now facing an increasingly unpredictable and diverse workforce. The variability in the workforce is making it difficult, if not impossible to meet safety and quality standards, or productivity goals. 

Manufacturing leaders’ new normal consists of shorter tenures, an unpredictable workforce, and the struggle to fill an unprecedented number of jobs. These leaders in the manufacturing sector are facing this reality and looking for ways to adjust to their new normal of building a flexible, safe and appealing workforce. As a result, managers are being forced to rethink traditional onboarding and training processes.  In fact, the entire “Hire to Retire” process needs to be re-imagined. It’s not the same workforce that our grandfather’s experienced, and it’s time for a change.

The Augmented, Flexible Workforce of the Future

The reality is that this problem is not going away. The Great Resignation in manufacturing has created a permanent shift, and manufacturers must begin to think about adapting their hiring, onboarding, and training processes to support the future workforce in manufacturing – an Augmented, Flexible Workforce.

What does this mean?

  • It means adopting new software tools to support a more efficient “hire to retire” process to enable companies to operate in a more flexible and resilient manner.
  • It means starting to understand your workforce at an individual level and using data to intelligently closes skills gaps at the moment of need and enables autonomous work.
  • And it means taking advantage of data.  More specifically, real-time workforce intelligence that can provide insights into training, guidance, and support needs.

Investing in AI-powered connected worker technology is one way to boost this operational resiliency. Many manufacturing companies are using digital Connected Worker technology and AI to transform how they hire, onboard, train, and deliver on-the-job guidance and support. AI-based connected worker software provides a data-driven 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. 

As workers become more connected, manufacturers 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. Today’s workers embrace change and expect technology, support and modern tools to help them do their jobs.

 

To learn more about how AI is being used to digitize and modernize manufacturing operations, contact us for a personalized demo.

Performance Management in Manufacturing: Best Practices and Examples

Learn about the best practices for optimal asset maintenance performance and how to track your assets to ensure that everything is in working condition.

Manufacturing performance management is the process of setting, monitoring, and optimizing key performance indicators (KPIs) related to production processes and workforce performance in manufacturing environments. It includes real-time monitoring and evaluation of employees’ work, as well as the continuous improvement of operational workflows to ensure optimal efficiency, product quality, and adherence to both safety requirements and organizational goals.

performance management in manufacturing best practices

Through data-driven insights, performance management software, and regular assessments, performance management aims to enhance employee productivity, reduce downtime, and maintain a competitive edge in the industry. Read our blog post below to learn more about performance management in manufacturing including:

5 Best Practices for Performance Management in Manufacturing

To get the best value from your performance management system here are five best practices for performance management in manufacturing:

1. Clear Goal Alignment:

Organizations must ensure that performance management processes align with overall organizational goals. They must clearly communicate objectives to employees at all levels, linking individual and team performance metrics to broader manufacturing and business objectives. This fosters a sense of purpose in frontline teams, engages workers, and helps employees understand how their efforts contribute to the company’s success.

2. Real-time Monitoring and Data Analytics:

Implement real-time monitoring of production and shop floor processes and equipment performance through the use of AI and connected worker technology. Utilize data analytics and AI-driven processing to gain insights into worker performance trends, identify bottlenecks, and facilitate data-driven decision-making. The ability to monitor operations in real-time not only enables proactive interventions to maintain efficiency, it also ensures fairness, accuracy, and transparency in performance measurement.

Pro Tip

Performance management software in manufacturing is crucial for optimizing production efficiency, and should integrate with other manufacturing systems, such as Learning Management Systems (LMS), Enterprise Resource Planning (ERP), and Manufacturing Execution Systems (MES), to provide a holistic view of the entire manufacturing operation.

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3. Employee Training and Development Programs:

Prioritize ongoing training and development programs for manufacturing personnel. Equip frontline workers with the necessary skills to adapt to evolving technologies and operational requirements. Use performance management systems and other digital tools like skills matrixes to identify skill gaps, set training goals, and track progress, ensuring a skilled and adaptable workforce.

4. Regular Performance Reviews and Feedback:

Conduct regular performance reviews that provide constructive and timely feedback to employees. Use these reviews as opportunities to recognize achievements, address areas for improvement, and set new performance goals. Foster open communication between managers and employees to encourage continuous improvement.

5. Integration with Continuous Improvement Initiatives:

Integrate performance management systems with “kaizen” or continuous improvement initiatives such as Lean or Six Sigma. Use data from performance metrics to identify opportunities for process optimization, waste reduction, and efficiency improvements. This ensures that performance management is not only evaluative but actively contributes to the ongoing enhancement of manufacturing processes.

Leveraging these best practices contributes to a holistic performance management process that aligns manufacturing organizations and their frontline workforce with strategic goals, optimizes operations, and creates a culture of continuous improvement.

Key Performance Management Strategies for Manufacturing Leaders

The following are a few examples of performance management strategies that manufacturing leaders, plant managers, and shift supervisors should consider when implementing their performance management process.

Line-shift Goals

Manufacturers often use production planning and scheduling systems to manage line shifts effectively and ensure a smooth transition between different production configurations. While line shifts in manufacturing are often necessary for adapting to changing demands, introducing new products, or optimizing efficiency, they can also pose challenges, including downtime, quality control issues, employee fatigue, and planning issues. By establishing clear and measurable objectives for each line shift or individual worker that aligns with organizational goals, production leaders can ensure production goals are met.

Individual Meetings and Communication

Manufacturing leaders should implement a performance management strategy that incorporates 1-1 meetings and communication. Regularly providing constructive feedback to employees on their performance can improve performance and boost employee engagement. Offering coaching and development opportunities to enhance skills and capabilities.

Continuous Training

Continuous training in manufacturing involves enabling workers to learn new skills regularly. It’s a great way to improve employee performance and innovation, as well as engage and retain top talent. A good example of a continuous learning model is everboarding, a modern approach toward employee onboarding and training that shifts away from the traditional “one-and-done” onboarding model and recognizes learning as an ongoing process.

Performance Management Tools

Implementing performance management tools can help automate ongoing employee evaluation, as well as align employee performance with other key manufacturing KPIs, including production quality, machine uptime, and labor utilization. These tools can also be used to identify continuous improvement opportunities. This allows manufacturing leaders to adapt and refine approaches based on feedback and outcomes.

Simplifying Performance Management with Digital Tools

According to Forbes, as the future of work evolves and changes so must performance management, traditional methods may no longer be as successful in an era where the workforce is constantly changing.

Digital tools such as connected worker solutions and AI-driven analytics help simplify performance management systems by streamlining processes, improving efficiency, and providing more accurate insights. Implementing these connected worker solutions automates the collection of performance-related data from various sources including connected frontline workers, IoT devices, software systems, and more. This eliminates the need for manual data entry, reducing errors and ensuring real-time access to up-to-date information.

By digitizing the performance management process, organizations create a centralized platform for storing and managing performance-related data. This centralized knowledge base makes it easy for managers and employees to access relevant information, track progress, and collaborate on performance goals. Furthermore, AI-driven connected worker solutions allow for digital performance tracking, customized training and skills development planning, workflow optimization, and improved predictive maintenance.

digital skills management in a paperless factory

Through these digital tools and technology, manufacturing companies can simplify performance management processes, improve operational efficiency, and adapt to the demands of a rapidly evolving industry while fostering a culture of continuous improvement and development for their manufacturing workforce.

Augmentir is the world’s leading connected worker solution, combining smart connected worker and AI technologies to drive continuous improvement and enhance performance management initiatives in manufacturing.

Augmentir is trusted by manufacturing leaders as a digital transformation partner improving training and development, workforce allocation, and operational excellence through our AI-driven True Productivity™ and True Performance™ offerings, as well as digitizing and optimizing complex workflows, skills tracking, and more through our patented smart, connected worker suite. Schedule a live demo today to learn more.

 

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