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.
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.
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Article | November 1, 2021
Manufacturing processes will undergo significant transformation in 2022 as a result of advanced manufacturing technology. The changing manufacturing industry is full of digitization to improve processes, products, productivity, and business revenue.
Manufacturers must realize the advantages of advanced manufacturing and learn to enhance their production processes to increase their manufacturing plant's productivity. Once you understand the heart of your business and where to integrate technology, you're halfway to success.
The manufacturing industry's top professionals have also recognized the technology shift and are supporting upskilling in the manufacturing business. Mr. Matt Mong has said in his interview with Media7,
“Once you start to look at yourself in the right way and realize that projects are at the core of your business, it is easy to see how you should use technology to support your business.” – Matt Mong, VP Market Innovation and Project Business Evangelist at Adeaca
Importance of Advanced Manufacturing
Adopting innovative manufacturing technologies has several advantages that grow its acceptance and relevance among manufacturers across industries. The following are the most significant advantages of advanced manufacturing.
Amplifying the product and process quality
Improvement in productivity
Encourages new ways to innovation
Decreases production time
Considering the factors above that contribute to the production process becoming more efficient, productive, and revenue-generating, advanced manufacturing will continue to dominate the manufacturing business in 2022.
Top Five Technologies That Are Advancing Manufacturing in 2022
To better understand the current trends in manufacturing technology, let us comprehend the technologies that are advancing the manufacturing industry. Additionally, discover how these technologies help manufacturers to advance their manufacturing processes.
Cloud computing in manufacturing is used for marketing, product development, inventory management, and productivity management. It stores software and commercial data via internet connections. Cloud computing is becoming a prevalent technology because it enables manufacturers to improve communication and manage the entire production cycle effectively.
According to MarketsandMarkets' study, the global cloud computing market will also increase to 832.1 billion dollars by 2025, growing at a 17.5 percent annual rate in the following years.
Benefits of Cloud Computing
Fewer technical issues when compared to traditional software use
Cost savings because no in-house servers are required
Cloud computing enables easy scalability for your developing firm
Cloud computing provides centralized control and access from any device
How did McDonald's use AWS cloud computing services to transform their customer service?
Additive manufacturing enables the fabrication of lighter, more sophisticated designs that would be impossible or prohibitively expensive to fabricate using conventional dies, molds, milling, and machining techniques. Rapid prototyping is another area where additive manufacturing thrives.
As a result, it is gaining a grip in the manufacturing business. According to Statista's market research, the additive manufacturing industry is worth approximately 12.6 billion US dollars in 2020 and is predicted to increase at a compound annual growth rate of 17% until 2023.
Benefits of Additive Manufacturing
Reduces material waste
Simplifies manufacturing processes
Additive manufacturing enables on-demand production
Increases supply chain flexibility
The finished product can be made close to the end customer
Robots are used in manufacturing to automate repetitive tasks, resulting in a more efficient assembly line. Humans and robots work together on a variety of things as well during the production of various products. Many jobs are hazardous or necessitate many supplies, which could be harmful to the human workers themselves. So, in such instances, robots play a significant role in executing manufacturing operations.
Robotics' increasing application in production is transforming it into a necessary component of industrial processes in the future. According to Mordorintelligence's study, the robots industry was worth USD 27.73 billion in 2020 and is predicted to reach USD 74.1 billion by 2026, growing at a CAGR of 17.45 percent between 2021 and 2026.
Benefits of Robotics
Increased productivity when compared to traditional manufacturing
Consistent speed and quality during goods production
Increased workplace safety for all employees at the manufacturing plant
Aids in more efficient use of floor space
Internet of Things
The Internet of Things allows devices to interconnect with one another and exchange data. The IoT connects assets to processes, systems, and people in manufacturing. This enables improved process integration, increased efficiency, and the advancement of manufacturing to the next transformation phase, Industry 4.0.
According to Mordorintelligence's market research, the global IoT market is estimated to reach USD 1,386.06 billion in 2026, up from USD 761.4 billion in 2020, a CAGR of 10.53 percent over the forecast period of 2021 to 2026.
Benefits of Internet of Things
It contributes to increased energy efficiency by identifying and optimizing underperforming devices
It monitors all processes and parameters to determine when to replace specific components
It improves product quality by analyzing and correcting issues at any stage of the manufacturing process
It reduces downtime by detecting and alerting production line personnel to problems
It enables more informed decision-making by unlocking
Virtual reality enables plant managers to mimic manufacturing processes and assembly line layouts to discover potentially harmful scenarios. Additionally, virtual reality can immerse an employee in a future workstation and then capture their movement to assess task feasibility and proficiency. As a result, the usage of virtual reality in manufacturing is increasing daily and is widely seen as the industry's future.
According to market research firm markets and markets, the worldwide virtual reality industry is estimated to rise by USD 20.9 billion by 2025, expanding at a 27.9 percent compound annual growth rate from 2020 to 2025.
Benefits of Virtual Reality
Enables a more thorough examination of the product's design
Enables the gathering of feedback on future items before they are launched in the real world
In recent years, advanced manufacturing technology has helped the manufacturing industry become more efficient, precise, and goal-oriented. As a result, manufacturers are eager to upgrade their current manufacturing facilities to state-of-the-art facilities. As a result, manufacturing companies would become more competitive and an integral industry component if they implement cutting-edge technologies.
How do you define advanced manufacturing?
Product and process improvement through inventive use of new technology is called advanced manufacturing.
Which sectors are utilizing cutting-edge technology?
Aerospace, Medical, Electronics, Transportation, Energy, and Consumer Product Production Companies are the top businesses or sectors that use cutting-edge technologies with sophisticated manufacturing.
What is the difference between conventional manufacturing and advanced manufacturing?
Traditional manufacturing adds value to attain the goal. Traditional manufacturing adds value to attain the goal. But, on the other hand, advanced manufacturing covers production methods in specific industries like aerospace, medical, pharmaceutical, etc.
Article | January 4, 2022
So much emphasis has been placed on features, advantages, and benefits; too little attention has been paid to delivery dates. The best automation solution on paper means nothing if it cannot be delivered in 2022. Selling the sexy sizzle of new, clever, even remarkable AGVs means nothing if manufacturers and distribution centers cannot take delivery of the product until 2023. Throughout industrial manufacturing and distribution the lead time from many AGV manufacturers is more than a year. That means product ordered in Q1 2022 will not be delivered until the following year. That is an absurd lead time and reflects poor planning and unnecessary supply chain constraints.