“Smart Manufacturing” is one of those terms where you can go online and find about ten different definitions. Without being too specific, it refers to data-driven decision making in manufacturing, including automated processes and project prioritization. Smart manufacturing is also fully integrated manufacturing systems that respond to changing demands (supply and/or customer-based) and apply these changes to production occurring on the shop floor.
Smart manufacturing utilizes automated processes, the Industrial Internet of Things (IIoT), optimized production functions, and many other manufacturing breakthroughs. So what cutting-edge technology is essential to smart manufacturing?
Here are five things that define smart manufacturing:
1: Data accessibility and digital twins
Manufacturers embraced data-driven decision making more than a decade ago. The accessibility and ease of use of many digital systems paved the way for organizations to begin using data to make decisions about prioritizing jobs, managing inventory levels, machine usage, and staffing. Planning ahead and forecasting based on real-time data had a huge impact on profit margins while improving quality standards. Now predictive analytics are taking data usage one step further by using historical or archived information. These analytics feed machine learning models that can predict machine downtime or inventory shortages before they occur - all based on previous patterns.
Beyond building algorithms and implementing machine learning models, companies also lean on accessible data to build digital twins. A digital twin is very popular in the field of manufacturing as it allows manufacturers to test out strategies in a virtual environment and determine if those strategies are viable or not. Creating a virtual model of an asset, process, or system using sensor data and algorithms does not interrupt current manufacturing runs or delay completion time. Such predictive models are ideal for companies looking to run their machines at maximum capacity.
2: Data Accuracy Matters
Real-time data is great to use for planning and prioritizing, provided that the data is accurate. Yet how do you gauge accuracy? New processes aimed at reducing human error, like barcoding and scanning, have a demonstrated a proven impact on the accuracy of the data used for planning purposes. When companies made the switch to barcoding and scanning, a majority of them saw an immediate improvement in inventory count accuracy.
Mobile technology took scanning to a whole new level by providing companies with an easy to use solution for scanning purchase order receipts and inventory counts. Mobile apps, like Bezlio, implement barcode scanning tech into the smartphone and tablet interfaces familiar to most people. The result is shorter training time than on other devices and data that is more accurate because it was not entered manually.
Barcoding also eliminates the stacks of paper that accumulate with manual counts and purchase order receipts. The elimination of the lag time to enter receipts and counts combined with removing the possibility of human error, not only speeds up the entering process, but also results in better, more accurate data.
3: System Compatibility and Guided Implementation have become the norm
With manufacturing organizations embracing digital transformation and automation, data service providers have quickly realized that their systems need to work with other systems in order to be successful. Cross platform compatibility has become more popular as it allows companies to select vendors that serve their needs. Use cases across an industry can be similar, but companies often have small nuances based on customer mix and annual and quarterly goals. Many companies who want digital solutions lack the bandwidth to invest in dev teams and data science experts. So those companies are now turning to systems with out-of-the-box data analysis systems to help sort, filter, and develop data into actionable insights.
Many data and data system providers offer fully guided implementation, which is more comprehensive than a training session. Guided implementation, whether administered on an in-person basis or through the cloud, installs the system alongside your other information systems and layers more than one type of data together. This layering of data gives a thorough picture of the manufacturing environment, taking into consideration inventory levels, planned machine maintenance, and historic manufacturing data. This combined data has a dramatic impact on operations and profitability, all depending on your goals.
4: the emergence of blockchain
Blockchain is still emerging in manufacturing and will continue to have an enormous impact on how manufacturers operate in the future. Right now, blockchain is primarily used in financial systems, but companies continue to explore applications in manufacturing. Industries that navigate enforced rules and regulations stand to gain the most from implementing blockchain solutions. Look for blockchain to be tapped for maintaining quality control from the development of raw materials all of the way through the manufacturing process. Industries currently developing blockchain include solar energy, food and beverage, fishing, shipping, healthcare, mining, and aviation. As the technology advances and becomes more widely used, more industries will tap blockchain to enhance their manufacturing ecosystems.
5: Automation
Automation and industrial robots with artificial intelligence are another hallmark of smart manufacturing. Robots have been used in manufacturing for more than a decade and continue to advance in technology and capabilities. Now these task performing robots are connected to a sensor network and respond to the sensor’s data - meaning they can change what they’re doing based on real-time information. The mechanism responsible for such responsive robots? Artificial intelligence. The automotive industry has already benefited immensely from these types of breakthroughs. Look for networks of these robots to synchronize their activities in a collaborative way in the future.
Automation has been used in manufacturing since its inception and artificial intelligence, as it continues to grow and be refined, is playing an even larger role across all types of manufacturing processes. The industrial robot market is forecasted to grow to more than $71B by 2023, with a CAGR of 9.60%.
NEXT STEPS
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