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How to reduce downtime and increase productivity in manufacturing

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

Downtime in manufacturing refers to a period of time when a production line or machine is not in operation due to maintenance, repairs, or other issues. This can result in a loss of productivity, increased costs, and missed production targets, and it is estimated that in the US alone it costs manufacturers around $1 trillion dollars per year. Not surprisingly, the biggest factors that contribute to unplanned downtime are human error and improper maintenance.

To minimize downtime, manufacturers are turning to digital technology to transform their frontline operations and provide a foundation for a holistic preventive maintenance strategy.

reduce downtime in manufacturing

Reducing downtime with a Connected Frontline Operations Platform

Manufacturers often implement Total Productive Maintenance (TPM) as part of a more comprehensive preventive maintenance approach. TPM is a strategy commonly used in manufacturing and production operations to improve the effectiveness and reliability of equipment, which in turn can increase productivity and reduce downtime.

Total productive maintenance strives to reduce workplace losses by placing the responsibility of basic maintenance upkeep on the primary equipment user: the machine operator. This preventive practice consists of “8 pillars” to help improve equipment reliability and elevate worker productivity:

Autonomous Maintenance as pillar of TPM

At the front of this framework is Autonomous Maintenance. Autonomous maintenance is a technique used in TPM that involves giving operators and other frontline employees the responsibility and authority to take care of their own equipment and work areas (e.g cleaning, safety checks, etc.). This can improve employee engagement by giving them a sense of ownership over their work and equipment, as well as a greater understanding of how their actions can impact productivity and quality. Additionally, involving employees in the maintenance process can lead to improved communication and teamwork, which can further enhance engagement.

This is where frontline operations platforms come into play.

Connected frontline operations platforms are digital software tools that can help standardize and improve the way operators perform maintenance tasks. They are used to improve communication, training, collaboration, guidance, and support for the operators.

Reduce machine downtime with Augmentir’s Connected Worker Solution

See how Augmentir can help you implement an effective autonomous maintenance program and optimize your frontline operations.

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Your connected workforce – a key to reducing downtime

Factory workers can have a significant impact on downtime in manufacturing. Whether it’s due to mistakes made by operators, non-optimal scheduling, or lack of communication, your workforce is at the center of your frontline operations. Factory workers can impact downtime in a variety of ways:

  1. Improper operation or maintenance: If factory workers are not properly trained on how to operate and maintain equipment, they may inadvertently cause downtime by making mistakes or not following proper procedures. Modern connected worker tools, like Augmentir’s connected worker solution, are increasingly being used to streamline training and digitize skills tracking to help ensure that the right people with the right skills are on the job.
  2. Safety incidents: Workers who do not follow proper safety procedures can cause accidents that lead to downtime while equipment is repaired or replaced. By digitizing safety procedures, manufacturers can ensure that workers perform the proper steps, and follow the proper protocols before performing a maintenance routine.
  3. Human error: Workers may make mistakes that lead to downtime, such as not properly setting up equipment or not noticing when a machine needs maintenance. Properly training employees on how to maintain and operate equipment can help to minimize downtime due to human error
  4. Quality issues: Workers may produce products that do not meet quality standards, which can lead to downtime while the products are reworked or scrapped.
  5. Lack of proper communication: Having clear and effective communication channels can help to quickly identify and address any issues that arise. Frontline communication tools like Augmentir can help improve communication and digitally record issues to better understand root causes. By identifying the underlying causes of downtime, manufacturers can take steps to prevent similar issues from occurring in the future.
  6. High turnover rate: High turnover rate can lead to a lack of experienced workers and can cause downtime while new employees are trained. While it’s difficult to completely prevent a high turnover rate, you can take measures to both expedite training for new hires, as well as create a more engaged, more empowered workforce. For example, Augmentir’s connected worker solution helps to accelerate new hire training and onboarding, and provides a skills management framework that helps to ensure that workers are excelling at their jobs.

It is important to note that factory workers are a crucial part of the manufacturing process and their role is vital for the success of the business. However, by providing workers with proper training, safety procedures, and communication channels, downtime in manufacturing due to human error can be reduced. Additionally, involving workers in the decision-making process and continuous improvement initiatives, can help to increase their ownership and responsibility towards the equipment, processes, and the whole factory’s performance.

Interested in learning more?

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

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Autonomous vs. Preventive Maintenance

Learn about autonomous and preventive maintenance, and how they can maximize machine efficiency and worker productivity on the shop floor.

Autonomous and preventive maintenance are two manufacturing strategies for maintaining machinery on the shop floor. The main difference between the two is that autonomous maintenance (AM) places greater responsibility for equipment upkeep on operators, while preventive maintenance (PM) is carried out by maintenance workers. Both autonomous and preventative maintenance strategies benefit from smart, connected worker technologies, although in different ways.

autonomous vs preventive maintenance

AM, for example, focuses on training machine operators to be the point of reference for cleaning, inspecting, and making minor repairs on the spot. This approach aims to empower operators to take the initiative in monitoring their equipment and identifying issues early on. By introducing smart, connected worker technology, like Augmentir’s suite of connected worker tools and closed-loop autonomous maintenance solution, manufacturing leaders can give operators more control over inspections and help intelligently guide and support operators, resulting in minimized machine downtime.

PM, on the other hand, consists of scheduling regular maintenance activities like part replacement, lubrication, and calibration. Workers tasked with PM ensure equipment remains in tip-top condition, which helps to prevent future breakdowns. The goals of this strategy are to avoid machine downtime and reduce the need for unplanned repairs. Smart, connected worker solutions improve the quality, transparency, and efficiency of both autonomous and preventive maintenance and repair procedures by standardizing and optimizing maintenance procedures.

You can learn more about autonomous and preventive maintenance by exploring the following sections:

What’s autonomous maintenance and its advantages?

Autonomous maintenance involves machine operators tackling basic equipment upkeep tasks to ensure that everything runs smoothly on the production floor.

When implemented, AM can yield a number of benefits:

  • Reduced equipment downtime: Conducting routine upkeep activities can prevent breakdowns and limit the need for unplanned maintenance.
  • Greater machine reliability: Operators who are trained to maintain their own equipment are more likely to pinpoint problems before they lead to machine failure.
  • Prolonged lifespan of machinery: Equipment that is maintained will last longer and require fewer repairs or replacements.
  • More operator involvement: Operators who take an active role in preserving their machinery feel empowered.
  • Increased safety: It’s easier to troubleshoot potential hazards before they turn into accidents when operators frequently inspect and maintain their equipment.
  • Cost-effectiveness: Reducing unplanned maintenance can save manufacturers significant money over time.

When coupled with smart, connected worker technology and AI-driven analytics, AM’s benefits are further enhanced. Digitizing autonomous maintenance processes increases standard work adherence, clears defects faster, and improves auditability. Connected worker technology enables operators to share knowledge and gives them access to the resources they need right when they need them.

autonomous maintenance

 

What’s preventive maintenance and its benefits?

Preventive maintenance focuses on performing routine equipment upkeep tasks at scheduled intervals. The goal is to avert equipment failure and limit unplanned downtime or repairs.

The benefits of having dedicated workers perform preventive maintenance are:

  • Enhanced machine reliability: Regular maintenance increases the odds of identifying and fixing problems before they turn into mechanical failures.
  • Decreased downtime: Conducting routine upkeep at scheduled times can decrease unplanned maintenance and increase production efficiency.
  • Greater compliance: PM can help manufacturers better comply with regulatory requirements to prevent unnecessary penalties for non-compliance.
  • Better planning protocols: Recruiting specialized maintenance personnel with extensive training on machine upkeep and repair can lead to better planning and allocation of resources.
  • Increased safety: Training workers on basic maintenance techniques ensures that deficiencies are addressed in a timely manner to avoid any injury.

PM’s impact is improved when used alongside smart, connected worker solutions that allow for digital work instructions and remote collaboration to effectively and efficiently guide technicians. Additionally, by digitizing and automating maintenance notifications, organizations can improve communications, speed up maintenance procedures, and minimize machine downtime.

How to implement AM

Applying autonomous maintenance to everyday maintenance tasks can mitigate potential machine disasters. Organizations can take this even further by creating “smart” autonomous maintenance processes and implementing advanced connected worker solutions with AI-driven insights. This gives operators improved control over maintenence process and expert guidence through a searchable asset hierarchy, maintenance history, and troubleshooting database.

The seven steps of effective AM implementation:

  • Boost operator expertise: It’s important to train operators on the machines themselves and how to perform maintenance tasks. This type of training can be made more effective through AI-based insights that integrate skills management into the flow of work and identify workforce development opportunities for upskilling and reskilling.
  • Conduct initial cleaning, inspection, and repairs: Operators should execute regular maintenance activities to avoid unplanned downtime. Furthermore, with connected worker solutions, operators can use mobile devices to digitally track and manage issues and activities as well as automate maintenance notifications further reducing overall downtime and avoiding unplanned downtime.
  • Eliminate causes of contamination: Routine cleaning and inspection minimize sources of contamination such as improper calibration and defective equipment. This alone can help prevent unexpected machine breakdowns. By building smart workflows into the autonomous maintenance process, manufacturers can schedule and assign standard work procedures (such as routine cleaning and calibration) digitally that have built-in work reporting for better visualization and auditing.
  • Define standards for cleaning, lubricating, and inspecting: Nailing down how to clean, lubricate, tighten and inspect, and how often to perform these upkeep duties, can help keep equipment in pristine condition. Smart digitization can standardize these practices across all manufacturing operations, giving organizations a global best practices standard to measure standard work adherence, clear defects more quickly, and improve auditability.
  • Perform inspection and monitoring: Operators who are trained on maintenance processes can carry out maintenance tasks independently and without error. With smart skills management and AI-enhanced workforce development, organizations can reduce training time and provide individualized guidance and support to workers when and where needed.
  • Standardize visual maintenance: Incorporate visual aids that help operators better understand equipment and labeling. For example, written procedures could contain a diagram showing how fluids should flow in a particular machine. Continuous learning and personalized insights via connected worker solutions are able to take this one step further and integrate things like instructional videos, interactive diagrams, and even remote experts into the flow of work to improve operational excellence and productivity.
  • Work towards continuous improvement: It’s imperative to strive for continuous improvement in maintaining machinery. Operators who are constantly learning and evolving are more productive and empowered with better decision-making capabilities through actionable, AI-driven insights.

Learn more on how to implement autonomous maintenance and the seven steps involved, or get in touch with us for a personalized demo to see Augmentir’s Autonomous Maintenance solution in action.

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How to implement PM

According to Forbes, when implemented correctly, preventive maintenance ensures that upkeep is performed at a set time to prevent unexpected machine deficiencies. Smart, connected frontline worker solutions are able to improve preventative maintenance procedures through smart communication, scheduled notifications and improved collaboration.

Eight steps for implementing preventive maintenance:

  • Establish project scope: Gauge which machinery will be inspected and which maintenance tasks are needed to be done at specific intervals.
  • Pinpoint upkeep requirements: Set requirements for which tasks are crucial for each piece of equipment. Tasks could vary from lubrication and calibration to inspections and part replacements.
  • Create maintenance schedule: Create a set schedule for carrying out PM tasks that’s based on equipment requirements, production schedules, and planned downtime.
  • Allocate worker responsibilities: Assign which tasks each maintenance worker is expected to fulfill.
  • Provide necessary resources: Give staff the proper tools, equipment, and supplies to execute PM tasks (e.g., lubricants, replacement parts, testing equipment, etc.).
  • Define metrics: Establish metrics for gauging the efficiency of PM (e.g., downtime, equipment reliability, maintenance costs, etc.).
  • Create training programs: Hands-on training and how-to instructions can help maintenance workers better understand how to perform upkeep tasks.
  • Monitor performance and adjust: Measure how well your PM efforts are doing and revise if necessary. This may mean updating procedures, adjusting maintenance schedules, or creating more training opportunities.

All of these steps are able to be standardized and optimized through connected worker solutions. Augmentir’s suite of connected worker tools delivers in-line training and support at the point of work, provides a searchable database to allow workers access to knowledge when and where needed, gives workers individualized guidance and support, connects teams for better collaboration, and more. This approach helps standardize and optimize maintenance processes and notifications as well as training, offering a better, more efficient adoption process for both frontline workers and management from start to finish, and giving everyone the proper tools for successful manufacturing operations.

 

If you are interested in learning for yourself why companies are choosing Augmentir to help digitize and optimize their autonomous and preventive maintenance programs – reach out to book a demo.

 

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How an Asset Hierarchy Can Help with Asset Maintenance

Learn about what an asset hierarchy is and how it can help with asset maintenance and equipment reliability.

An asset hierarchy outlines all of a business’s top equipment, machines, and components visually to help the business plan, execute and track maintenance activities. Asset hierarchies are usually in the shape of a pyramid, similar to an organizational chart. And since every operation is different, it’s likely you won’t have the same hierarchy as your competitor.

The benefits of an asset hierarchy include accurate maintenance planning, faster failure root cause analysis, and improved cost tracking. By implementing an asset hierarchy in conjunction with a frontline operations system, such as a connected worker solution, manufacturers can benefit by dramatically improved maintenance planning and execution. This article answers the following questions to help you learn more:

asset hierarchy improves maintenance

What is an asset hierarchy?

An asset hierarchy is an index of your most critical equipment, machines, and parts to better understand how these assets work together and monitor their maintenance needs. For example, building and maintaining your manufacturing business’s hierarchy can help you track and identify root causes of failure in your equipment.asset hierarchy and taxonomy - iso standard

This taxonomy is often represented as a pyramid, based on the ISO 14224 standard, which was developed for the collection and exchange of
reliability and maintenance data for equipment. Initially developed for the Petroleum, Petrochemical, and Natural Gas industry, this taxonomy for equipment and failure data can apply to any manufacturing environment, and has become the de-facto standard for every other industry.

Asset hierarchies are typically built and maintained within an organization’s EAM (Enterprise Asset Management) or CMMS (Computerized Maintenance Management System), which tracks asset maintenance and condition data, as well as maintenance schedules. Increasingly, EAMs, CMMS, and asset hierarchy information are being integrated with digitized frontline operations systems to improve maintenance planning and execution.

Pro Tip

It’s not enough to simply define your asset hierarchy with your EAM or CMMS. Innovative manufacturing companies are now extending this by integrating their asset hierarchies with connected worker solutions, which help digitize and optimize the actual work being done by frontline maintenance teams, improving maintenance execution.

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Better organization of equipment can also help workers understand how the action of one affects the other to solve any potential problems. This is another benefit of integrating your asset hierarchy with a connected worker solution. In a nutshell, strong hierarchies are a solid foundation for proper maintenance management and reliability.

What is asset maintenance?

While maintenance is generally synonymous with repair, in effective manufacturing facilities, maintaining equipment can prevent the need for repairs. Asset maintenance is an umbrella term for everything that goes into keeping your assets in tip-top shape.

For example, asset maintenance in manufacturing machinery may mean frequent inspections to prevent breakdowns and repairs. Your space as a whole relies on this type of maintenance to ensure everything is running smoothly, from equipment to everyday production processes.

Lastly, this term makes daily manufacturing processes more productive to manage. That’s because effective asset management tells you where assets are located, how they are used, and when changes were made to them.

How does an asset hierarchy improve asset maintenance?

An asset hierarchy and asset maintenance work in conjunction with one another. This visual tool gives workers a better idea of what each asset is and the dependencies between them.

Knowing what each asset is can help you schedule preventative inspections and tasks. If any problems arise, you can more easily identify all the working parts, find the root cause and fix it.

 

Augmentir’s AI-powered asset management software helps you simplify the operations and maintenance of your facility by integrating your asset hierarchy and maintenance data within a frontline operations system. Through Augmentir, organizations can benefit from a complete view of asset management, all through a visual mobile interface. Each asset contains a complete view of:

  • Kanban board for all asset activities
  • Work and maintenance procedures
  • Skills required for operation and maintenance
  • Collaboration related to the asset
  • Associated documentation
  • CIL/Standard Work schedule
  • History of all activities on the asset

Asset management with Augmentir

Augmentir’s asset management capabilities include an out-of-the-box autonomous maintenance solution, which gives equipment operators more control over equipment cleaning, inspections, and lubrications (CIL) to improve CIL completion rate, resulting in minimized machine downtime.

Request a live demo today to learn why companies are choosing Augmentir to help standardize and digitize their maintenance activities.

 

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AI: Revolutionizing How Humans Work in Manufacturing

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

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

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

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

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

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

connected enterprise

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

How Centerlining Improves Efficiency in Manufacturing

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

Centerlining in manufacturing is a methodology that uses standardized process settings to assure that all shop floor operations are carried out consistently.

For example, in manufacturing, it pinpoints which machine settings are needed to execute a given process and ensures operators implement those settings to avoid any defects on the shop floor. This works to decrease product and procedure discrepancies by improving machine efficiency.

centerlining in manufacturing

The type of machine configurations that can be centerlined to create quality goods that meet customer expectations range from temperature, speed, and pressure settings to the proper alignment of guard rails. When applied to a procedure, centerlining can substantially increase the number of sellable items, secure uniform product quality, and decrease production costs.

In a nutshell, employing a successful centerlining process can help optimize plant operations and reduce mistakes in product creation.

Learn more about how centerlining can improve everyday operations, and how to centerline a manufacturing process to yield the best output, in the following sections:

Centerlining methodology

Centerlining works by using specific machine settings per product (pressure, speed, temperature, etc.) to ensure processes are carried out the same way during each assembly line run.

Using the right centerline settings also has a side benefit: it lets operators identify problems as they happen. If workers know which process variables are triggering production delays, they can better control them to boost product quality output.

This can be achieved by creating a statistical process control chart to see which variables are causing interruptions to the assembly line and make any needed changes to the process. Creating a chart can also help workers identify procedures that are affecting the development of goods to ensure continuous improvement.

Centerlining goes hand in hand with total productive maintenance (TPM), a method which utilizes equipment, machine operators, and supporting processes to boost the quality and safety of production protocols.

How manufacturing efficiency can be improved by centerlining

Standardizing the appropriate machine settings can make everyday operations run more smoothly. For example, centerlining the requirements for each product can streamline changeovers, allowing workers to quickly reset their equipment and not lose time when switching to a new product run. This can prevent costly mistakes and reduce waste throughout the shop floor.

It also guarantees that all processes are completed in the same manner. Consistency helps ensure quality, especially when operators are setting up equipment for a production run. Failing to configure the right settings can increase the time for product changeovers and cause product deficiencies.

How to centerline a manufacturing process

Centerlining in manufacturing is a great way to troubleshoot product and procedure variations, oversee operations, and carry out statistical analysis to boost quality assurance and control.

Learn how to centerline a process by following the four steps below.

Step 1: Determine key process variables

It’s crucial to spot process variables that have the greatest effect on product quality to minimize any defects. Potential variables can include pressure, temperature, density, mass, and more.

Step 2: Identify machine settings for each variable

Then, look at which centerline settings can be applied to each process to ensure the creation of quality goods. Again, you’ll want to determine what has worked well in the past and use a statistical process control chart to set variable limits.

Important things to consider are: when the process has worked, which setting was best suited for that procedure, and how the two worked in conjunction with one another.

Step 3: Assess variable impact on production process and product

After you’ve identified the appropriate machine settings, it’s time to monitor how each variable impacts the production process and final product creation. Start by analyzing which assembly line runs yielded the highest production rate, factoring in things like equipment idle time, scrapped parts, rework, etc., to gauge what works and what needs improvement.

It’s vital that you have accurate, clear data to analyze. We recommend digitizing your centerlining process and results to correctly quantify the performance of each variable.

Step 4: Ensure centerline settings are always applied

Lastly, make sure that all operators are aware of and educated on how to best implement a centerlining process so that the right settings are applied each time. Failure to do so can result in mistakes and product deficiencies down the line. It’s best to provide all the necessary resources, steps, and training from the get-go to avoid costly errors. Digital work instructions and connected worker tools are a great way to ensure that operators are properly equipped to perform centerlining procedures.

At this stage, your manufacturing firm should have the proper reporting techniques to evaluate product quality against centerline procedures.

Interested in learning more?

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

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