Article | December 2, 2021
The world of manufacturing is continuously evolving in the 21st century, and companies have to combat competition, altering consumer demands, and unexpected events to be able to deliver in today’s experience. Global connectivity, innovation, and disruption are all reshaping the manufacturing industry, but a world-class business platform can help companies transform operations digitally to keep up with an evermore digitized world. The factory of the future will allow manufacturers to enhance production through the convergence of information technology with factory operations, combining the effectiveness of the virtual world with the materiality of the physical world to lower costs, increase flexibility, and better meet customer expectations.
The factory of the future functions on four dimensions: resource planning, manufacturing planning, planning and optimization, and manufacturing operations. Resource planning involves defining and simpulating the plant layout, flow, assets, and resources needed to efficiently develop products in a safe environment. Normal production change requests can be quickly validated by using 3D virtual experience twin technology. This technology could also quickly pivot operations to alternative products in the case of disruptive events. Manufacturing planning enriches the resource and product definition by defining and validating a process plan and creating work instructions that meet production goals.
Digital visualization of resource and process changes can also help speed up time-to-production in any scenario no matter the location by leveraging the cloud. Planning and optimization of supply chains across planning horizons will help manufacturers gain visibility with planning and scheduling by having the ability to model, simulate, and optimize alternative supply and production plans to reduce disruptions. Lastly, manufacturing operations management can transform global production operations to attain and maintain operational excellence. Manufacturers can create, manage, and govern operational processes on a global scale while maintaining operational integrity to meet altering demands.
For the factory of the future to come about successfully, there needs to be connected technology and shared data. Technology has to be adaptable with robotics and equipment that can be reconstructed to house changes and new products. An AI-powered product demand simulation is necessary to maintain agility and boost productivity. A versatile, cross-functional workforce with the ability to explicate data and function well in AR environments is also required along with smart factory technology such as wearable sensors and virtual prototypes. Through all this, the factory of the future can connect technologies across the product life cycle while optimizing the workforce and increasing sustainability.
Although achieving the factory of the future has several benefits, creating a feasible factory of the future plan can be challenging. In 2018, only 12% of companies had a mature factory of the future plan. One of the main challenges that companies face is a lack of internal skills to devise digital solutions. However, this can be combated by carefully considering how you can utilize digital technologies to deliver improved performance, resiliency, and flexibility. It is easier to begin with small steps and to collaborate with a partner who could support your efforts to build toward your desired transformation goal. It is important to always be prepared by evaluating your next steps, industry trends, and progress metrics. It is also crucial to focus on the people, process, and technology you’re using to have a successful transformation journey.
Manufacturing with the factory of the future can provide savings in a wide range of categories. For example, it can reduce virtual vehicles build time by 80%, increase on-time performance of industrial equipment by 45%, and reduce modular construction time of construction, cities, and territories by 70%. Leading the transformation of the manufacturing space towards the direction of the factory of the future will allow manufacturers to work smart and better meet the needs of the end consumers.
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 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 | October 27, 2021
Technologies in the manufacturing industry are upscaling daily. Manufacturers are keen to embrace the latest manufacturing trends to improve their manufacturing process, total production rate, and product quality at their factories. Manufacturing technology advances have also boosted production speed while retaining product quality.
“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, VP Market Innovation and Project Business Evangelist at Adeaca.
Apart from manufacturing technology developments, we will look at new manufacturing business trends in this article, which will help you achieve maximum customer engagement and a positive relationship with your target consumer groups.
So, let's see some of the top manufacturing business trends that are assisting the industry in improving its business processes.
Manufacturing Business Trends: 2022
Manufacturers must adopt a business procedure that focuses on the target consumer group. Also, incorporating social responsibility and technology into company procedures would be beneficial.
Here are five ways manufacturing leaders are becoming more communicative and results-oriented in their manufacturing and consumer experience strategies.
Deliver a One-of-a-kind Digital Experience
Every industry's target demographic is now online. Manufacturers must use digitalization to interact with their target consumer group to be noticed and remembered. Maintain an active presence on all popular digital platforms used by your target demographic. Post your new products, business strategy, or get genuine customer feedback on your brand and products. Engage your target audience and keep them informed of your progress in making their lives easier.
“Marketing is VERY important to any company, although I generally see it being justified by the number of web hits or ‘leads’ that come in” – John Hays, Director of Sales at BALYO
Allow your clientele to interact with your products digitally. To be a part of the new digital revolution in any industry, create a new digital business model.
Initiative for Ecosystem Partnership
An ecosystem partnership is a network of enterprises working together to provide a product or service to meet changing market needs. A partner ecosystem can generate customer-ready solutions faster. It also helps firms to co-create value. This value is demonstrated in extraordinary customer and partner experiences. The B2B ecosystem partners work together to bring mutual benefits to their companies.
Revenue Generation via Data Monetization
Data monetization allows industrial CIOs (Chief Information Officers) to monetize their digital products and services. Rapid digitization in manufacturing generates massive data. CIOs may monetize and distribute data across ecosystems. CIOs can leverage information as a resource to generate new services or business models. This ensures revenue even when external reasons like supply chain issues or human resource shortages interrupt the firm.
Using the Equipment as a Service (EaaS) Approach
EaaS, or Pay-Per-Use, is defined as: A business model where equipment is rented rather than sold, with remote diagnostics and predictive maintenance solutions offered by the vendor.
Using Eaas reduces capital expense, improves data reliability, and lowers operating costs. As a result, producers can undertake all production-related tasks with precision.
Bosch RexRoth CytroBox – a Perfect Example of EaaS
The global equipment-as-a-service market is estimated to develop at an 11.5 percent CAGR from 2021-2027. (OpenPR)
The RexRoth Cytrobox from Bosch is an example of EAAS. This hydraulic power unit converts electrical power into hydraulic fluid pressure and flow to move and force a machine. They are widely utilized in presses and tooling equipment.
It can handle up to 33 kW in a small space. Its exceptionally flexible; its unique design allows it to run efficiently and quietly. In addition, modern automation and sensor packages allow easy integration into modern machine designs.
Benefits of Bosch RexRoth CytroBox
It provides data insights during the long lifecycles
Using this hydraulic power unit on a lease can save a lot of money which cost $100.000
It requires heavy maintenance cost as per its type of usage that can be avoided with the EaaS approach
Shifting the Emphasis from B2B to B2C
Many firms are moving their attention from B2B to B2C to understand their target consumer better. This new strategic approach helps producers identify market needs and gain real-time feedback on their products. This method helps producers increase profit margins while also controlling the product's interaction with the intended audience.
The latest manufacturing trends will take you to the cutting edge of manufacturing. The manufacturing developments in 2022 will boost the total manufacturing market in the coming years, allowing manufacturers to generate more business revenue.
What is the manufacturing industry's future?
Industry 4.0 is rapid technological progress in production and is transforming the worldwide manufacturing industry. According to bccresearch's market research, the global manufacturing and process control market is predicted to increase from $86.7 billion in 2020 to $117.7 billion in 2025, a CAGR of 6.3 percent.
What is the industry 4.0 technology in the manufacturing industry?
IoT, industrial internet of things (IIoT), Cyber-physical systems (CPS), cloud computing, artificial intelligence, big data, machine learning, robotics, virtual reality, augmented reality, and additive manufacturing or 3D printing are some technologies that are used in industry 4.0 factories.
What are the current technology trends in the manufacturing industry?
AI, robots, 3D printing, and the like are all the latest manufacturing trends in manufacturing technology. Additionally, enterprise resource planning (ERP), cloud computing, and machine vision all play a significant part in advanced manufacturing.
"name": "What is the manufacturing industry's future?",
"text": "Industry 4.0 is rapid technological progress in production and is transforming the worldwide manufacturing industry. According to bccresearch's market research, the global manufacturing and process control market is predicted to increase from $86.7 billion in 2020 to $117.7 billion in 2025, a CAGR of 6.3 percent."
"name": "What is the industry 4.0 technology in the manufacturing industry?",
"text": "IoT, industrial internet of things (IIoT), Cyber-physical systems (CPS), cloud computing, artificial intelligence, big data, machine learning, robotics, virtual reality, augmented reality, and additive manufacturing or 3D printing are some technologies that are used in industry 4.0 factories."
"name": "What are the current technology trends in the manufacturing industry?",
"text": "AI, robots, 3D printing, and the like are all the latest manufacturing trends in manufacturing technology. Additionally, enterprise resource planning (ERP), cloud computing, and machine vision all play a significant part in advanced manufacturing."