Augmentir CEO Russ Fadel outlines why the next wave of AR implementations in the service industry must also harness Artificial Intelligence. There has been a lot of advocacy for using Augmented Reality (AR) in the field service industry due to benefits from improved field technician performance to reductions in field service operating costs. However, what […]

artificial intelligence

Augmentir CEO Russ Fadel outlines why the next wave of AR implementations in the service industry must also harness Artificial Intelligence.

There has been a lot of advocacy for using Augmented Reality (AR) in the field service industry due to benefits from improved field technician performance to reductions in field service operating costs. However, what these early success stories don’t mention is how companies have been slow to adopt this technology and have struggled to move beyond the pilot phase.

It was believed early on that wearable technology would be the core of Enterprise AR by 2018 and thus, vendors were overly focused on getting work instructions on a variety of wearables. Many also heavily invested in using AR to present information to technicians with rich content and 3D CAD overlays. Since then, it’s become clear that these investments haven’t delivered enough value to the enterprise due to a lack of adoption.

What has been overlooked is the opportunity to create sustainable value throughout the entire organization by connecting to service workers not only by delivering personalized information, but also using artificial intelligence and machine learning to augment the intelligence of the organization.

This is the beginning of a new era, an era not of Enterprise Augmented Reality, but of Augmented Operations where AR is but one of many ways to present data, support, and guide field workers. This transformation is driven by the combination of two key technology trends – Enterprise AR and Artificial Intelligence/Machine Learning.

Why is Artificial Intelligence and Machine Learning Important?

Historically, Artificial Intelligence and Machine Learning (AI/ML) has been applied against external data sets. A recent trend however, is to embed AI in software platforms, having it act on the internal data, eliminating the estimated 80% of AI/ML project efforts around labelling and cleansing external data. This is frequently being applied to solutions focused on improving business processes where the human worker is at the center.

At Augmentir, we use our AI engine to identify patterns in noisy data generated by technicians and highlight areas that can improve overall worker performance and also provide personalized procedures based on the proficiency of each worker in real-time.

The AI engine is able to continually deliver insights and recommendations based on that human worker data which is valuable intelligence that can be used to help drive continuous improvement across the entire organization – from operations to training to quality.

  • AI helps each worker perform at their peak by changing the instruction to one that optimizes for speed, while meeting quality and safety targets.
  • AI understands the patterns and outliers in the vast instruction/job execution data to identify the largest opportunities in the areas of: productivity, worker effectiveness, training materials effectiveness, and instruction effectiveness. Insights and recommendations are made on how to capture these opportunities and drive continuous improvement on a year-over-year basis.
  • With AI, companies can capture tribal knowledge through interactions between experts and frontline workers, making the expertise a scalable corporate asset over time.

With this concept of Augmented Operations (using AI/ML to deliver intelligence across the organization from your augmented workforce), we are seeing a change in how organizations are making informed decisions, empowering workers, and improving the productivity of humans in the workplace.

Augmenting the Service Workforce of the Future

Despite some early momentum, Enterprise AR alone isn’t enough to deliver sustainable value in the field service sector.

What has been ignored is a real opportunity to create sustainable value throughout the organization – not only giving workers the ability to consume information and apply knowledge, but also augmenting the intelligence of the organization relative to how it engages empowers, and continually improves its human workforce. At Augmentir, we are calling this Augmented Operations, and we believe that this will transform the service workforce of the future.

To learn more about how Augmentir’s platform leverages AR and AI to continually improve the productivity of your frontline workforce download our free white paper, “Rise of the Augmented Worker.”


There have been countless changes in technology over the past couple of decades: Machine Learning, Cloud Computing, Internet of Things, Artificial intelligence, and Augmented Reality (to name a few). But with all of these advances in technology, the 350 million workers in manufacturing are being asked to perform increasingly complex jobs using technology that has […]

digital transformation strategy

There have been countless changes in technology over the past couple of decades: Machine Learning, Cloud Computing, Internet of Things, Artificial intelligence, and Augmented Reality (to name a few). But with all of these advances in technology, the 350 million workers in manufacturing are being asked to perform increasingly complex jobs using technology that has remained relatively unchanged for 20 years, and, according to Deloitte, manufacturing is already looking at a potential skilled labor shortage of 2.4 million workers in the next decade. Whether this is because enterprise software solutions are expensive, technically complex, difficult to implement, or lack continuous improvement opportunities, these users and processes have been underserved and require a well-planned digital transformation strategy to keep them competitive.

Although there has been a recent trend towards a digital transformation that looks at applying new technologies to improve operational processes, the workers who actually perform these processes are not being considered. Because of this, the frontline worker is largely disconnected from the digital thread of the business, and improvement in productivity seems stagnant.

Key Challenges Manufacturers Face Today

As with any transformational change, adopting a digital transformation strategy is no easy undertaking. We currently see 4 key challenges industrial organizations face when adopting a digital transformation strategy:

1.) Tribal Knowledge and the “Skills Gap”
Senior production workers and subject matter experts have accumulated valuable experience and knowledge, which has been typically hard to capture and convert into an asset that is able to be easily shared and used by others. The younger workforce that is entering the manufacturing sector does not have the knowledge that their senior peers have, but are expected to perform the same jobs, at the same level of productivity and quality.

2.) Lack of Insight
Lack of insight into how workers are performing their jobs on a day-to-day basis is also an issue. There is no fine-grained detail regarding worker activity – how are workers performing vs. benchmarks, are they having trouble on certain steps, what are they doing well, do they have feedback on operational procedures that could help the rest of the workforce? This lack of data and insight has made it extremely difficult to improve the performance of frontline workers. As a result, there is little or no basis for making decisions for improvement across the organization.

3.) Lack of Guidance and Accurate Information
Organizations are struggling with the quality of human-centric processes, as they often suffer from inaccurate, outdated paper-based work instructions. In many cases productivity is also an issue because workers are not equipped with the right tools or instrumented with the appropriate guidance that would help them perform their jobs at peak productivity.

4.) Workers are Disconnected
And lastly, frontline workers are not integrated with their work environment. The human-centric and job-specific workflows are not digitally integrated into the overall business environment and enterprise systems (ERP, CRM) that are critical to the business. The reality of today’s frontline workforce in manufacturing is that workers are not connected to the digital fabric of the business.

Bridging the Digital-Reality Gap

The good news is that there are a number of new strategies and technologies that manufacturing organizations are implementing to solve these problems. In particular, the rise of Enterprise Augmented Reality has lead to a major shift in improving the productivity of the frontline workforce of manufacturing organizations.

Although this is a great first step, Enterprise Augmented Reality alone isn’t enough to deliver sustainable value in manufacturing. In order to see true transformational results, it is key to have a combination of the following:

  • Enterprise AR: Delivers initial improvements in productivity and quality for the frontline workforce.
  • Consumerization of Software: Enables ease-of-use and ubiquity across the manufacturing landscape.
  • Artificial Intelligence: Drives continuous improvement throughout the organization.

Only when these three elements are combined will you see continuous improvements in the productivity of your frontline workforce.

To learn how Enterprise Augmented Reality, Artificial Intelligence, and the Consumerization of Software are delivering transformational value in manufacturing download our white paper, “The Rise of the Augmented Worker.”


“There is an increasing pressure on the sector to make the most out of every field service technician.” “The impact of lost knowledge and customer relationships built over the years and decades by retiring technicians is keeping service leaders up at night.” “Many companies have not been able to capture their ‘tribal knowledge’ in a […]

field service

“There is an increasing pressure on the sector to make the most out of every field service technician.”

“The impact of lost knowledge and customer relationships built over the years and decades by retiring technicians is keeping service leaders up at night.”

“Many companies have not been able to capture their ‘tribal knowledge’ in a systematic way, risking the loss of valuable insight into service operations.”

These quotes come from “The Future of Field Service”, a February 2018 article in Field Technologies Online. Of course, they also could have been quotes from a 2008 or even earlier version of the article. Why is it that these problems that have been considered significant issues by Field Service executives are not solved and still considered problems year after year? Are they simply intractable problems that have no solution? Perhaps the answer to these questions is hidden in another quote from the article:

“The core of field service, the technician’s visit, is the aspect least addressed by field service management solutions.”

To date, everything before and after a site visit is digitized and chronicled to great extent, but much of what goes on during the visit is still very much a “black box”. Sure, there are now Remote Expert video based collaboration tools that may allow recording of a session, but what if the person on site IS the expert and doesn’t need to make that call? In addition, these solutions don’t capture what went on before or after the the call. What did the tech do that lead up to the call? Without that information we (a) put the expert at a disadvantage because they have no context to help solve the problem and (b) fail to capture the tribal knowledge of what NOT to do, or understand the common mistakes that might lead to difficulties in the field.

From my early days working on Internet based Remote Service, first with Questra and then with ThingWorx, I have seen many companies that have tried to address the Tribal Knowledge issue in many ways. Knowledge Management systems, social networking tools, video chat sessions, etc. have all been moderately successful at best, and usually at very high cost. The reason for this is that they largely relied on “after the fact” documentation. Asking the tech to remember everything that happened while on-site (while they are rushing off to the next job) is often a lesson in futility.

So, what is the answer? How do we break open that black-box? To quote from the article once again:

“It seems so paradoxical that so few field service management solutions focus on these aspects of field service”

Some folks have seen IIoT as a solution, letting the equipment itself collect and send data. While this is certainly helpful, it doesn’t reveal the true story of what the tech is experiencing onsite. Others have said that the aforementioned video collaboration tools are the answer, but again, there is the critical before and after the call information that is missing. And mixing Social Networking and people heading towards retirement is almost never a good idea(!).

So what is the answer? Are we destined to forever be wandering around the darkened room of the customer site visit with a blindfold on? At Augmentir we think perhaps not. But much more on that later…

Recently, I co-founded Augmentir with three key executives from my prior start-up, ThingWorx, and have been exploring Enterprise AR. ThingWorx was the company that created the Industrial IoT application platform category and was arguably the most successful start-up in the IIoT space, getting acquired by PTC in early 2014. The acquisition of ThingWorx was the […]

enterprise ar

Recently, I co-founded Augmentir with three key executives from my prior start-up, ThingWorx, and have been exploring Enterprise AR. ThingWorx was the company that created the Industrial IoT application platform category and was arguably the most successful start-up in the IIoT space, getting acquired by PTC in early 2014. The acquisition of ThingWorx was the first, non-distressed acquisition in the space and directly led to the wave of acquisitions that have reshaped the IIoT landscape.

Prior to founding Augmentir, I did a fairly deep dive into the marketecture of Enterprise AR and found that it reminded me of the early IIoT space — fragmented with lots of companies occupying niches:

  • Custom solution builders around Smart Glasses
  • Vertical Solution Builders
  • Smart Glass vendors
  • Technology providers
  • And more

Gartner’s recent release of the IIoT “Magic Quadrant” was both reaffirming and disappointing. It was personally reaffirming to see that ThingWorx, under the leadership of Jim Heppelmann, CEO of PTC, occupies the “Most Magic” position in the IIoT space. However, it was disappointing to see that after ten years, the IIoT space has not yet “crossed the chasm” as evidenced by the fact that no vendor is in THE Magic Quadrant.

Why would IIoT, a space that has so many compelling ROI stories, still be stuck between early adopter and mainstream? I have a point of view on this — while IIoT solutions can be extremely valuable and transformative, they have to be easy to own in order to achieve mainstream adoption. IIoT is anything but “easy to own”: this starts with the high friction, traditional enterprise sales process, long pilots, expensive, “Value-based” pricing, long, risky implementation cycles, and vendor lock-in/high switching costs. This makes it hard for even large enterprise businesses to fully adopt, and given these dynamics, it’s easy to understand how Small and Mid-sized businesses have been essentially locked out of the IIoT opportunity.

Unfortunately, in Enterprise AR I see the same dynamics unfolding. High friction sales and POCs, combined with long implementation cycles and high prices, will keep Enterprise AR mired in the early adopter phase. Already, sales of Smart Glasses for the enterprise are hugely disappointing, signaling that the market is unfolding much more slowly than analysts have projected. This will disappoint many investors and dash the hopes of many startups who believe in market projections, not realizing that they are a key part of the problem.

Certainly, we need a new direction if Enterprise AR has any hope of ever crossing the chasm and achieving mainstream adoption.

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

Industrial work comes with an immense amount of stress. Without providing the worker with the right level of support, this stress can lead to increased errors, poor work performance, and eventually, employee burnout. Recently, Gallup reported that 76% of employees experience some form of workplace burnout. This not only affects performance and productivity but much more, including engagement and employee retention.

employee burnout

To offset employee burnout, managers should aim to:

  • Reduce employee stress
  • Remove roadblocks ensuring their workers have the proper tools to complete their tasks
  • Ensure workers are a good match skill-wise for the work they are doing
  • Give workers a say in how the work is completed
  • Empower workers to believe that the work they are doing is valued and important

road to flow

In a 2022 Gallup poll, 79% of employees responded as not being engaged at work, this same poll found that most employees don’t find their work meaningful and do not feel hopeful about their careers.

When supporting workers and battling workplace burnout, there is no “one size fits all” answer, and many organizations are realizing that taking the same approach for “desk workers” does not account for the many and uniquely different needs demanded by frontline or “deskless” workers. Managers must keep in mind these needs when combating and detecting burnout and boosting employee engagement.

Artificial Intelligence (AI) and machine learning-based technology combined with a worker-centric approach can help tremendously in this respect, accounting for the human element in industrial operations while still taking advantage of innovations.

Using AI to Enhance Worker Experience and Reduce Burnout

By utilizing the capabilities of connected worker platforms and AI, companies can take a proactive approach to reducing stress and preventing employee burnout.

The meteoric rise of AI tools like ChatGPT and natural language processing has created a surge in interest in all things AI and while it’s not a cure-all, AI has the potential to be extremely effective in helping workers get access to the information and support they need while on the job, as well as predicting, detecting, and reducing workplace 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. Organizations can use AI to engage employees by:

  • Creating communication touchpoints and streamlining communication
  • Pairing workers and tasks based on skill level
  • Suggesting training and certification opportunities for upskilling workers
  • Create feedback paths so employees have a say in how tasks are completed

To complement AI and software platforms, organizations can implement other tools such as wearable devices, mental health applications, and more to aid in engagement efforts. Finding the right balance and combination is key for knowledge exchange and conversation – making employees more engaged within the team.

The Human Element

It is important to take advantage of new technologies and implement them where needed, but technology by itself is not the answer. Finding a balance between technology integration and a worker-driven approach is key and it is paramount that the true needs of the workforce are not forgotten. Although AI and machine learning-based technology can help tremendously with detecting and reducing employee burnout, it has its limits and can only do so much. Technology cannot replace how workers feel and how they interact with management on a day-to-day basis. And at the end of the day, AI can only augment employees and should be used to empower them, never to replace them.

Connected frontline operations platforms are helping manufacturers reduce downtime and provide a foundation for a holistic preventive maintenance strategy.

First time quality plans, or first time right plans, are a manufacturing approach that ensures all processes on the production floor are performed properly the first time, every time. A FTQ plan is a document that outlines which standards, practices, and resources are needed to execute those procedures to create high-quality products.

A quality plan is a must to guarantee zero-defect goods and prevent the need for any rework or scrapping of parts. If a manufacturer’s goods do not meet internal, industry, or consumer standards, then it’s more than likely that they won’t sell.

first time quality

Every product development project should have a quality plan in place. Read on to learn how to create a first time quality plan document:

What to include in a first time quality plan

A FTQ plan, when executed correctly, can help you reach 100% FTQ, which means zero defective products. As a result, it boosts consumer trust in your product and your company’s credibility. It’s a good way to cover all bases to ensure nothing is left out, from product goals and objectives to testing requirements and distribution.

A FTQ plan may contain the following:

  • Goals and objectives, including item specifications, cycle time, materials, cost, etc.
  • A list of procedures
  • Worker expectations and responsibilities
  • What industry standards should be applied
  • A method for measuring quality
  • Testing requirements
  • Updates to procedures

3 steps for creating a FTQ plan

Developing a quality proposal is a great starting point for making sure that products are made right the first time around.

The steps below explain how to successfully create first time quality plans and strategies that give your manufacturing operations a competitive edge.

Step 1: Conduct an initial audit

Creating a FTQ document begins with an initial audit of the suppliers a manufacturer will use. An audit is the perfect way to gauge whether a supplier matches your product expectations and meets your quality standards.

An audit may check:

  • Materials
  • Equipment/machinery
  • Procedures
  • How well staff is following processes

Step 2: Determine if suppliers meet product specifications

Next, it’s vital to check whether a contractor can produce items that meet your standards and specifications. It’s also important to establish a partnership and a solid communication process with your supplier so that nothing falls through the cracks.

Step 3: Implement quality inspections

Lastly, a well-formulated plan includes quality inspections of each production run and product before distribution. This is usually completed by quality inspectors on the shop floor.

Inspection criteria typically includes:

  • Order and shipment sizes
  • Packaging and appearance
  • Product performance

Once the inspection is done, a report should be created and submitted to the quality manager before any products are shipped. This report also serves to measure quality metrics and places for improvement.

First time quality with Augmentir

Augmentir is a connected worker solution that allows industrial companies to digitize and optimize all frontline processes that are part of their quality management strategy. Augmentir’s complete suite of connected worker functionality is built on top of Augmentir’s patented Smart AI foundation, which helps identify patterns and areas for continuous improvement.

manufacturing kpi first time right

 

Smart Skills Management software is helping manufacturers bridge the gap between training, skills, and work to build a more resilient and agile workforce.

Where are you on your journey with adopting new and emerging technologies? Many manufacturers are jumping on the bandwagon for some of the latest tools that provide digital guidance to workers. Maybe you decided to implement digital work instructions to help workers safely and efficiently perform tasks. Or maybe you’ve bought skills management software to help you catalog and organize the skills and capabilities of different workers. But are either of these enough on their own to achieve all your production goals? Possibly, but unlikely.

Digital work instructions on their own deliver standard work guidelines but fail to consider the unique skills of each worker. Standalone skills management programs may offer a highlight reel of the skills and certifications of your workers but neglect to capture performance in real-time to provide accurate skills evaluations. Nor do they offer personalized training content needed to ensure workers perform their absolute best. Can we agree then these two features should go hand-in-hand?

One cannot exist without the other: Blending skills into the flow of work

In the past, standalone skills management systems were sufficient because:

  • Turnover was infrequent so line supervisors knew everyone on their team and their current skills and endorsements, making it easy for the supervisor to assign work safely and optimally
  • Investments in training, reskilling, and upskilling were performed either in a one size fits all approach or through a purely subjective or anecdotal approach

Today, however, a different situation exists.

Line supervisors are dealing with team members that they don’t know well, new ones starting every day, and experienced ones leaving.  This creates safety issues and makes optimally assigning work difficult as not only are the workers variable, but their skill levels and certifications are a constantly moving target.

An integrated, closed-loop skills management system is the solution for this era of high workforce turnover and absenteeism.

 

skills and work

 

Skills management solutions that combine skills tracking capabilities with connected worker technology and on-the-job digital guidance can deliver significant additional value. Data from actual work performance can inform workforce development initiatives allowing you to target your training, reskilling, and upskilling efforts where they have the largest impact.

It can generate an abundance of valuable data to provide tailored training support and skills endorsements and identify workforce opportunities. What else is possible? Imagine reducing training costs, optimizing job scheduling, increasing safety, and improving productivity. And now consider what will happen when you add smart technology to this all-in-one package.

 

intelligently assign jobs

The power of smart digitization! Skills management and digital work instructions together boost productivity.

According to Deloitte, organizations are shifting to a skills-based approach to meet the demand for agility, agency, and equity. Connected worker solutions that combine skills management with digital work instructions, collaboration, and knowledge management are uniquely suited to optimize today’s variable workforce. AI-generated insights are pulled from patterns identified across all work activity in real-time. These insights identify where new and experienced workers may benefit from either reskilling or upskilling.

This combination of smart digital technology can also leverage your training resources, such as instructional videos, written instructions, or access to remote experts, to deliver personalized guidance for the worker to perform their best. These tools intelligently work together to help you assign workers to procedures based on required skill levels. No second guessing! Augmentir is the only smart connected worker solution to intertwine these management tools with AI making it a powerhouse for optimizing your operations and meeting production targets.

 

 

Learn how Smart Skills Management software is helping manufacturers bridge the gap between training, skills, and work to build a more resilient and agile workforce.

Download our latest eBook – The Future of Work: Connecting Skills Management with Standard Work.

 

Learn the five steps to upskill and reskill manufacturing employees. Find out the benefits such as improving productivity and retention.

While the terms ‘upskill’ and ‘reskill’ in reference to manufacturing workers are often used interchangeably, they are not the same.

Upskilling refers to cultivating a worker’s skillset to help them excel in their current role. Meanwhile, reskilling involves teaching an employee new skills in order to transition to a new role.

For example, a programmer can be reskilled to become a systems analyst. Or workers can be upskilled to manage technology as more jobs become automated.

Half of all workers will need to be upskilled by 2025 as more jobs become digitized, according to the World Economic Forum. Workers will take on more critical thinking and problem-solving roles, leaving technical tasks to artificial intelligence and machine learning. Furthermore, the growing skilled labor gap in manufacturing has created a workforce shortage, and upskilling and training are becoming necessary to ensure production capacity is met.

Explore the following topics below to learn more about upskilling and reskilling in manufacturing, including a step by step guides to reskilling and upskilling manufacturing workers.

What does upskill mean?

Upskilling involves evaluating an employee’s existing skills and helping them to advance in their current role. It helps facilitate continuous learning by providing training opportunities to develop employee skills.

It can involve refining either soft skills or technical skills to fill workplace gaps. For instance, emotional intelligence is a soft skill that can be honed in leadership roles. Similarly, technical skills are needed in many manufacturing positions. Working with technology is a must as companies automate more and more of their operations.

An HR representative with data analytics experience, for example, can hone their skills to take on more specialized tasks. This can consist of taking a class to gain more knowledge or attending a virtual conference to learn about industry-specific technology.

Upskilling staff can help your business stay on top of industry trends and pivot in an ever-changing digital landscape.

What is reskilling?

Reskilling involves learning new skills to move on to a new role within a company. It’s also a cost-effective alternative to hiring new employees.

For example, an electrician may have excellent planning and job estimation skills. The organization could choose to reskill that worker to an estimation position instead of hiring someone from the outside.

Or an employer could reskill a production assembler to work as a maintenance technician. The new role may require taking a series of training courses and completing certain safety classes or certifications.

Reskilling and upskilling are efficient ways to retain a manufacturing workforce. Both provide opportunities to help workers grow and advance skills. Learn how to upskill and reskill staff with the following steps.

How to upskill manufacturing workers

It’s important to have a clear plan to upskill manufacturing workers:

Step 1: Assess current skills.

It’s crucial to map employees’ current skills. This data will serve as the baseline for measuring employee progress.

A great way to outline worker skills is through a skills matrix, which digitizes and helps accurately track skills across your organization. This can help identify any skills gaps that exist in current departments.

skills matrix

Step 2: Access skills needed for the future.

After assessing current employee skills, it’s time to identify any skills needed for the future. Keep in mind that these must align with any changes expected to occur in the manufacturing industry or in your long-term business plan.

Step 3: Create upskilling goals.

Set employee-specific goals. For example, you may want each worker to take training courses to hone job-specific skills.

Step 4: Match workers with new learning opportunities.

Workers can develop skills through new learning opportunities. It’s important to offer training and development opportunities that help workers grow and foster their skills.

Step 5: Monitor progress.

By this stage, you should have mapped employee skills and outlined which ones are needed. It’s important to monitor any progress made. Organizations that digitally track employee skills can map “what the worker has been trained on” to actual job performance (“how the worker is doing”) to create a true representation of proficiency gaps and upskilling opportunities.

skills job proficiency mapping

How to reskill manufacturing workers

If you’re looking to reskill manufacturing workers, consider the following steps below:

Step 1: Identify what skills need cultivating.

Pinpoint which skills are the most valuable and create training programs to train workers on those skills. Think about which new roles need to be created.

Step 2: Integrate upskilling.

It’s vital to start training employees and offering resources to advance skills. For example, training your workers on how to operate digital tools or a specific piece of equipment can help them take advantage of promotion opportunities down the line.

skills job proficiency mapping

Step 3: Customize learning plan.

Develop a plan of core learning opportunities for any skillsets that are needed now or in the future. For example, you can customize learning plans to specific roles.

Step 4: Test and adjust.

Developing a perfect reskilling plan on the first try is no small feat. Be willing to acknowledge any mistakes and fix them.

Step 5: Invest in budget.

Allocating enough financial resources for reskilling employees is vital to company growth. Modify your budget to make reskilling a priority.

Benefits of upskilling and reskilling manufacturing employees

Workplace roles are changing and expanding in the age of automation. This change can bring about skill gaps that need to be filled for a business to stay ahead of the curve.

Upskilling and reskilling manufacturing employees has a number of long-term benefits for employers, such as:

  • Boosts retention. Investing in your employees’ skills development fosters better relationships. Workers who feel valued are less likely to leave. Improving retention can save businesses money on hiring and training new workers.
  • Improves morale. Businesses that offer training and development opportunities help their workers grow and move forward in the company. This can help employees feel like they’re working toward something and not staying stagnant within the company.
  • Improves quality and productivity. Beyond retention and morale improvement, upskilling and reskilling can have production benefits. A more skilled and trained workforce can result in improved quality, productivity, and efficiency throughout your organization.

Looking to improve upskilling and reskilling within your organization?

Augmentir’s suite of smart connected worker tools helps manufacturing organizations create a more skilled and productive workforce. Find out how our software can make it easier to reskill and upskill manufacturing workers in your organization. If you’d like a demo, let us know and we’ll be in touch.