Article | October 13, 2021
The electronics manufacturing business is adopting new technologies to create smart electronics manufacturing products for its consumer base. Next-generation technologies are shaping the future of the manufacturing industry by enabling it to create technologically advanced and user-friendly products. Matt Mong, one of the manufacturing industry's leading professionals, stated in an interview with Media7,
“Be Different. Don’t position your product in an existing category. Instead, create your category and make the competition irrelevant and obsolete.” – Matt Mong, VP Market Innovation and Project Business Evangelist at Adeaca.
The year 2022 will be a year of advancement and development for the electronics manufacturing industry.
So, manufacturers are eager to embrace new technologies and produce more innovative, more user-friendly goods that become part of consumers' daily lives and meet their needs. To make the manufacturing process manageable and deliver advanced products, we will look at the top five trends flourishing in the electronics manufacturing industry.
Top Five Electronics Manufacturing Industry Trends
Future manufacturing technologies are transforming the electronics manufacturing industry's processes and products. Let's look at the top electronics manufacturing industry trends for 2022, which will propel the sector to new heights of technological advancement.
Utilizing the Benefits of the Internet of Things
The Internet of Things is being used in both the manufacturing process and the products themselves. It enables electronic manufacturing products and processes to become more intelligent and performance-driven to fulfill business and customer needs.
In electronics manufacturing, the Internet of Things (IoT) enables businesses to solve common production challenges such as product quality issues, changing demands, and a complex global supply chain. As a result, it increases productivity and efficiency while reducing human effort.
Industrial units may gather and analyze real-time data and processes using IoT-based sensor systems. Additionally, it assists organizations in managing data and transforms traditional manufacturing into an intelligent manufacturing unit.
Using an ERP System to Maintain the Company's Competitive Edge
ERP (Enterprise Resource Planning) is a centralized management system for all operational and business activities. The software automates all manufacturing processes and enables the electronics manufacturing sector to achieve higher precision throughout the manufacturing process and product delivery.
ERP has the potential to boost productivity, improve efficiency, decrease expenses, and increase profitability. ERP enables electronics manufacturers to forecast, plan, modify, and respond to changing market demands. By using an ERP system in your manufacturing unit, you may expand your business and increase revenue.
Making Use of Big Data
The electronics manufacturing industry benefits from the use of big data to make critical business decisions. It aids in the integration of previously isolated systems to provide a comprehensive view of industrial processes. It also automates data gathering and processing, allowing for more excellent knowledge of each system individually and collectively.
Big data also assists manufacturers in discovering new information and identifying trends, allowing them to optimize operations, improve supply chain efficiency, and find variables that impact manufacturing quality, volume, or consistency. In addition, big data assists the electronics manufacturing industry in keeping up with the rapidly changing digital world.
Using AR and VR to Create Consumer-friendly Goods
AR and VR are future manufacturing technologies that are changing electronics manufacturing products and driving growth. Robotics is a crucial usage of virtual reality in electronics production. Manufacturers may use powerful virtual reality software to design goods. This implementation of virtual reality software reduces production errors and saves time and money.
AR in electronics manufacturing allows product developers to generate interactive 3D views of new products before production. AR and VR are part of Industry 4.0, the digital revolution of conventional electronics production units.
Adoption of 3D Printing on a Wide Scale
One of the essential advantages of today's electronics 3D printing is that companies can quickly prototype PCBs and other electrical devices in-house. In addition, 3D printing has simplified the electronics manufacturing process, and it is currently being utilized to manufacture multilayer printed circuit boards. It uses material jetting technology to spray conductive and insulating inks onto the printing surface.
Let's look at an example of an analogy that worked for Jinzhenyuan - The Electronic Technology Co. Ltd., managed by Mr. Huang Runyuan, Jinzhenyuan's General Manager, and based on the concept of Industry 4.0. (Reference: Forbes)
Jinzhenyuan - The Electronic Technology Co. Ltd. Takes a Significant Step Forward with Industry 4.0
Jinzhenyuan - The Electronic Technology Co. Ltd., formed in 2012, sells its products globally. In addition, it manufactures cellphones, computers, cars, and a variety of other consumer electronics. Due to changing market needs, the firm planned to upgrade its production facility to industry 4.0 by the end of 2017 to participate in smart manufacturing.
The company increased production efficiency, shortened production cycles, and cut costs due to the digital revolution. Today, Jinzhenyuan is regarded as a model of digital transformation in the community in which it works. Let’s observe the statistics for Jinzhenyuan following the deployment of Industry 4.0.
32% improvement in total production efficiency
33% cost reduction
41% decrease in R&D to production cycles
51% reduction in substandard parts rate – from 3,000 to 1,500 per million
The electronics manufacturing sector is on the verge of a digital revolution that will improve the production process efficiency and cost-effectiveness. Many of the world's biggest firms, like Apple, Microsoft, Hitachi, and Saline lectronics, are developing future agile factories to keep up with the world's digital transformation. Future manufacturing technology will help your manufacturing company make the manufacturing process more efficient and boost the business revenue.
What are the future electronics technologies?
Smart grid solutions, wearable technology devices, prefabricated goods, the Internet of Things, and robots are some of the future electronics innovations that will propel the business forward.
Is the supply chain benefiting from new technology trends?
Yes, supply chain management benefits from smart technology as well. Trucks equipped with cutting-edge technologies can get real-time data on the weather and road conditions ahead of time. It contributes to the supply chain process's reduction of possible risks.
Which manufacturers are implementing the industry 4.0 concept in their factories?
Whirlpool, Siemens, Hirotec, Tesla, Bosch, and Ocado, among others, have turned their traditional factories into digitally smart ones that incorporate all of the cutting-edge technology necessary to improve and optimize the production process.
"name": "What are the future electronics technologies?",
"text": "Smart grid solutions, wearable technology devices, prefabricated goods, the Internet of Things, and robots are some of the future electronics innovations that will propel the business forward."
"name": "Is the supply chain benefiting from new technology trends?",
"text": "Yes, supply chain management benefits from smart technology as well. Trucks equipped with cutting-edge technologies can get real-time data on the weather and road conditions ahead of time. It contributes to the supply chain process's reduction of possible risks."
"name": "Which manufacturers are implementing the industry 4.0 concept in their factories?",
"text": "Whirlpool, Siemens, Hirotec, Tesla, Bosch, and Ocado, among others, have turned their traditional factories into digitally smart ones that incorporate all of the cutting-edge technology necessary to improve and optimize the production process."
Article | December 7, 2021
Machine learning in manufacturing is becoming more widespread, with businesses like GE, Siemens, Intel, Bosch, NVIDIA, and Microsoft all investing heavily in machine learning-based ways to enhance manufacturing.
Machine learning is predicted to expand from $1 billion in 2016 to USD 9 billion by 2022at a compound annual growth rate (CAGR) of 44% throughout the forecast period, according to Markets & Markets.
The technology is being utilized to cut labor costs, achieve better transition times, and increase manufacturing speed.
“I advocate business leaders get to know more about what AI can do and then leverage AI in proofs of concept.”
– Michael Walton, Director and Industry Executive, Microsoft speaking with Media 7
Machine learning can help enhance manufacturing processes at the industrial level. This can be achieved by assessing current manufacturing models and identifying flaws and pain factors. Businesses can rapidly address any difficulties to keep the manufacturing pipeline running smoothly.
Let us explore how machine learning is transforming manufacturing operations.
How Machine Learning Is Transforming Manufacturing Operations
“The greatest benefit of machine learning may ultimately be not what the machines learn but what we learn by teaching them.”
- Pedro Domingos
Machine learning in manufacturing is revolutionizing manufacturing operations and making them more advanced and result-oriented, so let's have a look at how this is unfolding.
Allows for Predictive Maintenance
Machine learning provides predictive maintenance by forecasting equipment breakdowns and eliminating wasteful downtime. Manufacturers spend far too much time correcting problems instead of planning upkeep. In addition to enhancing asset dependability and product quality, machine learning systems can forecast equipment breakdown with 92% accuracy. Machine learning and predictive analytics increased overall equipment efficiency from 65% to 85%.
Increases Product Inspection and Quality Control
Machine learning is also utilized for product inspection. Automated inspection and supervision using ML-based computer vision algorithms can discriminate between excellent and bad products. These algorithms simply need excellent samples to train; therefore a fault library is not required. However, an algorithm that compares samples to the most common errors can be built. Machine learning reduces visual quality control costs in manufacturing. Forbe's says AI-powered quality testing can boost detection rates by up to 80%.
Logistics-related Tasks Are Automated
To run a production line, industrial companies need considerable logistics skills. The use of machine learning-based solutions can improve logistics efficiency and save expenses. Manual, time-consuming operations like logistics and production-related documentation cost the average US business $171,340 annually. It saves thousands of manual working hours every year to automate these everyday procedures. Using Deep Mind AI, Google was able to lower its data center cooling bill by 40%.
Creates More Business Opportunities
Machine learning is frequently used in the production process. Substantial data analysis is required to create new items or improve existing products. Collection and analysis of huge amounts of product data can help find hidden defects and new business opportunities. This can help improve existing product designs and provide new revenue streams for the company. With machine learning, companies can reduce product development risks by making smarter decisions with better insights.
Protects Company’s Digital Assets
On-premise and cloud-based machine learning systems require networks, data, and technological platforms to function. Machine learning can help secure these systems and data by restricting access to vital digital platforms and information. Humans’ access sensitive data, choose applications, and connect to it using machine learning. This can help secure digital assets by immediately recognizing irregularities and taking appropriate action.
Harley Davidson's Sales Climbed by 40% Using Albert – The ML & AI-Powered Robot
Today, traditional marketing is harder to break through. It's easy to see why Albert (an AI-powered robot) would be a good fit for Harley Davidson NYC. Thanks to machine learning and artificial intelligence, robots are producing news stories, working in hotels, controlling traffic, and even running McDonald's.
Albert works well with social media and email marketing. It analyzed which customers are more likely to convert and modifies the personal creative copies on its own for the next process.
Harley-Davidson is the only company to employ Albert in its business. The company evaluated customer data to find prior consumers who made purchases and spent more time browsing the website than normal. Albert used this data to categorize customers and scale up test campaigns.
Using Albert, Harley-Davidson's sales climbed by 40% and leads increased 2,930%, with half coming from high-converting ‘lookalikes' detected by AI and machine learning.
The groundbreaking benefits of machine learning are the pillars of machine learning applications in manufacturing. Machine learning in manufacturing helps enhance productivity without compromising quality. According to Forbes, Amazon has automated warehouse logistics picking and packing using a machine learning system. With Kiva's help, Amazon's typical ‘click to ship' time dropped from 60-75 minutes to 15 minutes. So, industry leaders are seeing fantastic outcomes, and machine learning in manufacturing is the future.
How is machine learning used in manufacturing?
Machine learning is used in manufacturing to improve product quality and uncover new efficiencies. It unquestionably aids in the identification and removal of bottlenecks in the manufacturing process.
Which two forms of machine learning are there?
Machine learning is divided into two forms: supervised and unsupervised. In supervised machine learning, a machine learning algorithm is trained using data that has been labeled. Unsupervised ML has the advantage of working with unlabeled data.
What is a machine learning model?
A machine learning model is a file that can recognize patterns. In order to learn from a set of data, you must first train a model using an algorithm.
"name": "How is machine learning used in manufacturing?",
"text": "Machine learning is used in manufacturing to improve product quality and uncover new efficiencies. It unquestionably aids in the identification and removal of bottlenecks in the manufacturing process."
"name": "Which two forms of machine learning are there?",
"text": "Machine learning is divided into two forms: supervised and unsupervised. In supervised machine learning, a machine learning algorithm is trained using data that has been labeled. Unsupervised ML has the advantage of working with unlabeled data."
"name": "What is a machine learning model?",
"text": "A machine learning model is a file that can recognize patterns. In order to learn from a set of data, you must first train a model using an algorithm."
Article | December 8, 2021
A digital twin is a virtual model of an object or system that comprises its lifecycle. It is updated with real-time data and aids decision-making through simulation, machine learning, and reasoning for the production system.
IoT sensor data from the original object is used to create a digital twin of the system. This cloud-connected data allows engineers to monitor systems and model system dynamics in real-time.
Modifications can be tested on the digital twin before making changes to the original system.
Considering that digital twins are supposed to replicate a product's complete lifecycle and are used throughout the production process, it's not unexpected that digital twins have become prevalent in all stages of manufacturing.
“More than a blueprint or schematic, a digital twin combines a real-time simulation of system dynamics with a set of executive controls,”
– Dr. Daniel Araya, consultant and advisor with a special interest in artificial intelligence, technology policy, and governance
Companies will increasingly embrace digital twins to boost productivity and decrease expenses. As per recent research by Research and Markets, nearly 36% of executives across industries recognize the benefits of digital twinning, with half planning to implement it by 2028.So how does this digital twin technology benefit modern manufacturing? Let's have a look.
How the Digital Twin Drives Smart Manufacturing
Digital twins in manufacturing are used to replicate production systems. Manufacturers can develop virtual representations of real-world products, equipment, processes, or systems using data from sensors connected to machines, tools, and other devices.
In manufacturing, such simulations assist in monitoring and adapting equipment performance in real-time. With machine learning techniques, digital twins can predict future events and anticipate potential difficulties.
For maintenance, digital twins allow for quick detection of any problems. They collect real-time system data, prior failure data, and relevant maintenance data. The technique employs machine learning and artificial intelligence to predict maintenance requirements. Using this data, companies can avoid production downtime.
Digital Twin and Artificial Intelligence (AI) in manufacturing
Using digital twins and AI in production can enhance uptime by predicting potential failures and keeping equipment working smoothly. In addition, there are significant cost savings in the planning and design process as digital twins and AI can be used to replicate a specific scenario.
Maintenance is another area that has seen significant progress with the use of digital twin manufacturing. A Digital Twin powered by AI can predict when a piece of equipment will fail, allowing you to arrange predictive maintenance that is not simply taking information from OEM manuals but can significantly cut maintenance expenses along with reducing downtime.
Using the digital twin, it is feasible to train virtual workers in high-risk functions, similar to how pilots are trained using flight simulators. It also frees up highly skilled workers to upgrade the plant and streamline operations.
General Electric Created the Most Advanced Digital Twin
General Electric Company (GE) is a multinational business based in Boston that was founded in 1892. It has developed the world's most advanced digital twin, which blends analytic models for power plant components that monitor asset health, wear, and performance with KPIs (Key Performance Indicators) determined by the customer and the organization's objectives. The Digital Twin is powered by PredixTM, an industrial platform built to manage huge amounts of data and run analytic algorithms. General Electric Company provides extra "control knobs" or "dimensionality" that can be utilized to improve the operation of the system or asset modeled with GE Digital Twin.
Given the numerous advantages of digital twin manufacturing, the potential for digital twins to be used in manufacturing is virtually endless in the near future. There will be a slew of new advancements in the field of digital twin manufacturing. As a result, digital twins are continually acquiring new skills and capabilities. The ultimate goal of all of these enhancements is to create the insights necessary to improve products and streamline processes in the future.
What is a digital twin in manufacturing?
The digital twins could be used to monitor and enhance a production line or perhaps the whole manufacturing process, from product design to production.
How digital twin benefit manufacturers?
Using digital twins to represent products and manufacturing processes, manufacturers can save assembly, installation, and validation time and costs.
What is a digital thread?
A digital twin is a realistic version of a product or system that replicates a company's equipment, controls, workflows, and systems. The digital thread, on the other hand, records a product's life cycle from creation to dissolution.
"name": "What is a digital twin in manufacturing?",
"text": "The digital twins could be used to monitor and enhance a production line or perhaps the whole manufacturing process, from product design to production."
"name": "How digital twin benefit manufacturers?",
"text": "Using digital twins to represent products and manufacturing processes, manufacturers can save assembly, installation, and validation time and costs."
"name": "What is a digital thread?",
"text": "A digital twin is a realistic version of a product or system that replicates a company's equipment, controls, workflows, and systems. The digital thread, on the other hand, records a product's life cycle from creation to dissolution."
Article | December 21, 2021
Consumer demand has shifted dramatically in recent years, and manufacturers are trying to adapt to this shift. To maintain high product quality, minimize costs, and optimize supply chains, manufacturing analyticshas become essential for manufacturers.
Manufacturing analyticsis the process of gathering and analyzing data from various systems, equipment, and IoT devices in real-time to get essential insights.
As technology takes over and enhances many of the processes we used to handle with manual labor, we are freed up to use our minds creatively, which leads to bigger and better leaps in innovation and productivity.
– Matt Mong
Manufacturing analyticscan assist in maintaining production quality, boost performance with high-profit returns, decrease costs, and optimize supply networks.
This article will outline manufacturing analyticsand present a list of possible application cases. It will also highlight the benefits of manufacturing analyticsfor any shop floor or factory.
Manufacturing analytics: An Overview
With manufacturing analytics, we can streamline and speed up the entire process. Data interchange and automation helps in speeding up the production process. Manufacturing analyticsuses predictive manufacturing, big data, Industrial IoT, network virtualization, and machine learningto produce better scalable production solutions.
Manufacturing analyticscollects and analyses data from many sources via sensors embedded in machinery to identify areas for improvement. Data is collected and presented in an easy-to-understand structure to illustrate where difficulties emerge throughout the process.
In short, manufacturing analyticscollects and analyses large volumes of data to reveal insights that might improve performance. Users can also obtain automated business reports to reply in real-time.
Why Manufacturing analytics is Vital for Leading Businesses
There are numerous benefits of manufacturing analyticsthat drive any company’s production and overall manufacturing business growth. The benefits of manufacturing analyticsfall into three distinct categories as below.
It reduces the overall cost: Analytics may save a significant amount of money if used more efficiently. Labor costs are also reduced due to automation and semi-autonomous machinery. Similarly, preventive and prescriptive maintenance programs may save money while enhancing productivity.
It boosts profits for businesses: Manufacturers can respond swiftly to changes in demand using real-time insights in production, inventory management, and demand and supply forecasting. For example, assume the data indicates that they are approaching their maximum capacity. In such instances, they can increase over time, increase capacity, modify procedures, or tweak other production areas to adapt and maintain delivery times.
Other unforeseen benefits: There are several advantages to the increased capabilities enabled by manufacturing analytics. These benefits include lower energy use, safer environmental practices, fewer compliance failures, and more customer satisfaction.
Five Real-world Applications of Manufacturing Analytics
A machine's analytics uses aggregate data from real-time detectors to anticipate when it needs to be replaced or functioning irregularly. This process helps predict machine failure or equipment defects.
Analytics can assist in determining a plant's capacity and how many products are produced by the unit in every production cycle, which is helpful in capacity planning. In addition, analytics may help determine the ideal number of units to create over time by considering capacity, sales predictions, and parallel schedules.
Predictive analytics solutions can automate maintenance requests and readings that shortens the procedure and reduce maintenance expenses.
Product development is an expensive process in manufacturing. As a result, businesses must invest in R&D to develop new product lines, improve existing models, and generate new value-added services.
Earlier, this approach was in place by repeated modeling to get the finest outcome. This approach can now be modeled to a large extent, with the help of data science and technologically superior analytics. Real-world circumstances can be replicated electronically using "digital twins" and other modeling approaches to anticipate performance and decrease R&D expenses.
Many factors that might help in the plan significant capital expenditures or brief breakdowns can be explained using historical data and a few high-impact variable strategies. For example, consider the seasonality of products like ice cream. As a result, historical market data and a few high-impact factors can help explain numerous variables and plan major capital expenditures or short-term shutdowns.
In addition to demand forecasting, predictive analytics incorporates advanced statistical techniques. With predictive analytics, a wide range of parameters, including customer buying behavior, raw material availability, and trade war implications, may be taken into consideration.
Warranty support may be a load for many manufacturers. Warranties are frequently based on a "one-size-fits-all" approach that is broader. This approach introduces uncertainty and unanticipated complications into the equation.
Products may be modified or updated to decrease failure and hence expense by using data science and obtaining information from active warranties in the field. It can also lead to better-informed iterations for new product lines to minimize field complaints.
Managing Supply Chain Risks
Data may be recorded from commodities in transit and sent straight from vendor equipment to the software platform, helping to enable end-to-end visibility in the supply chain.
Manufacturing analyticsallows organizations to manage their supply chains like a "control tower," directing resources to speed up or slow down. They may also order backup supplies and activate secondary suppliers when demand changes.
Businesses should adapt to changing times. Using analytics in manufacturinghas altered the business industry and spared it from possible hazards while boosting production lines. Industry 4.0's route has been carved. Manufacturing analyticsis the key to true Industry 4.0, and without it, the data produced by clever IoT devices is meaningless. The future is data-driven, and success will go to those who are ready to adopt it. The faster adoption, the sooner firms go ahead of the competition.
How can data analytics help manufacturers?
Data analytics tools can help manufacturers analyze machine conditions and efficiency in real-time. It enables manufacturers to do predictive maintenance, something they were previously unable to accomplish.
Why is data so crucial in manufacturing?
Data helps enhance manufacturing quality control. Manufacturers can better understand their company's performance and make changes by collecting data. Data-driven manufacturing helps management to track production and labor time, improve maintenance and quality, and reduce business and safety concerns.
What is Predictive Manufacturing?
Predictive manufacturing uses descriptive analytics and data visualization to offer a real-time perspective of asset health and dependability performance. In addition, it helps factories spot quality issues and takes remedial action quicker by eliminating the waste and the cost associated with it.