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Michael Taylor

Innovation

Five Simple Digital Applications That Are Changing Manufacturing

When first starting out, use a staged approach that best fits your work environment

Published: Thursday, December 24, 2020 - 13:03

Digital applications in manufacturing are not only becoming increasingly accepted; they are expected. However, for smaller manufacturers, the process of making this switch can be daunting. Initial expenses, as well as the cost of training employees, is enough to stop the process altogether.

But beginning the process of “going digital” doesn’t have to be overwhelming. With a little guidance and education, all manufacturers can start to implement digital manufacturing concepts in a staged approach that best fits their individual work environments. Here are our top five recommendations for digital applications that can help you get started.

1. Digital performance management

Since 2010, the percentage of business that is conducted digitally has grown from 4 percent to almost 12 percent, and that trend is expected to continue. Finding an integrated way to analyze both business and IT metrics is key to optimizing the experience of this growing enterprise. Enter data performance management.

The ability to gain real-time, multidepartmental insights through data performance management is a simple way to increase the effectiveness of your business decisions—and increase revenue and customer loyalty. With a simple way to measure metrics, as well as a common language for all departments, the facilitation and dissemination of information is simplified, increasing productivity. Using tools such as a digital dashboard that pulls production information, including machine operating data and production output, into one place is a simple way to launch your own digital performance management platform. This will vary based on production line.

2. Predictive maintenance

Anyone with experience in the manufacturing industry understands the need for ongoing preventive maintenance. Keeping all machines in top condition is key to avoiding downtime and increasing productivity. However, digital manufacturing has introduced a new concept: predictive maintenance.

Predictive maintenance is similar to preventive maintenance with one specific difference: Instead of being prompted by variables such as time or routine readings, predictive maintenance seeks to anticipate when a future product might fail. Using actual equipment condition (often determined by installing monitoring equipment or software to collect data) vs. age or service recommendations, the goal is to stay ahead of the game by letting operators see the current condition of their equipment so they can schedule a service appointment to prevent any critical machine failure.

There are three primary components to a successful predictive maintenance program. First, you must gather the right data to be analyzed for your machinery. This will vary based on your needs. Second, your analysis of the problem must be framed accurately. Finally, the evaluation, both initially and ongoing, must be accurate to predict when maintenance will be needed.

3. Yield, energy, and throughput optimization

At its very core, yield, energy, and throughput optimization is the continual examination of data to calculate the best and most efficient process. Implementing a yield, energy, and throughput optimization program checks all the boxes. From improved efficiency, to increased yield, to overall energy-use reduction, this process will deliver ROI quickly. So where do you start?

First, use the data you already have. For example, examine how long it should take to produce your individual units. Once you have that information, identify an expert consultant you can trust to assist you in selecting the appropriate software that can develop and monitor your individual algorithms. The last step is to monitor the information that your software provides and begin to develop an initial pilot program to increase efficiency.

4. Automation and robotics

Implementing automation—and especially the use of robots—can sound like a daunting endeavor, but the truth is that it is becoming more common in the manufacturing industry. In addition to lower operating costs, introducing this digital application can increase production output, improve quality of production, and enhance worker safety.

Before you make the switch to a more automated environment, there are two key factors to consider.

Employee training: Even with automation and robotics doing the production work, employees will still need to know how to operate and maintain the machines. This could require training for current employees, as well as the possibility of hiring personnel with required certifications and experience.

Up-front investment: In the long run, moving to automation lowers operating costs. However, there is usually an up-front investment. Making the choice to implement automation will often require planning and budgeting for the system before you make the jump, although some innovators are offering interesting alternatives that include hardware rental and leasing programs.

What are some of the best initial projects to consider for automation? We recommend short cycle-time machine tending, low-speed material handling (<40 ppm) and simple, repetitive, consistent applications.

5. Digital quality management

Implementing a digital quality management system comes with many benefits. Similar to digital performance management, digital quality management takes the key performance indicators (KPI)s you are currently analyzing and puts them into a digital application. By reducing human interaction through standardizing the quality decision making, and therefore reducing the possibility of human error, your quality management will experience higher efficiency, a reduction in costs, and an improved ability to trace—and mitigate—errors.

When you decide to move from manual to digital quality management, there are some best practices to consider.

Upgrade your methodologies: It can be tempting to continue quality management the way it has always been done. But to truly get the most out of your investment, don’t just digitize these existing methodologies; define them. Take this opportunity to examine what you are measuring and its relevance to your desired outcomes. Or maybe explore what additional metrics you can track with a digital platform. Optimize the use of all your capabilities.

Use best practices: When looking at your digital quality management outcomes, be sure that any potential production change uses easy-to-configure solutions, and that these are based on best practices. A digital platform may be able to give you more detailed information quicker than before, but it doesn’t replace years of industrial knowledge and experience.

Employee buy-in and adoption: With all newly implemented technologies, user adoption is critical. But this may be doubly true in digital quality management. Getting your team members to buy in prior to implementation is crucial to success.

We hope that breaking down the implementation of these digital applications helps clarify the process. If you are interested in learning more, or have any questions, connect with the experts at your local Manufacturing Extension Partnership (MEP) center.

First published Dec. 4, 2020, on NIST's Manufacturing Innovation Blog.

 

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About The Author

Michael Taylor’s picture

Michael Taylor

Michael Taylor is a mechanical engineer and project manager in the Extension Services Division at the National Institute of Standards and Technology Manufacturing Extension Partnership (NIST MEP). His broad manufacturing background spans small to large production facilities in a manufacturing engineer, advanced process development, product development and test engineer capacity. His focus at NIST MEP is advanced manufacturing technology and industry 4.0. He facilitates the MEP National Network Industry 4.0 Working Group and received his bachelor of science degree in mechanical engineering from the University of New Haven, and a professional master’s degree in project management from the The George Washington University.