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 | January 12, 2022
Real-time manufacturing analytics enables the manufacturing base to increase its efficiency and overall productivity in a variety of ways. Production data is an effective means of determining the factory's efficiency and identifying areas where it might be more productive.
“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”
– Geoffrey Moore, an American Management Consultant and Author
Creating a product-specific data collection may assist you in determining and visualizing what needs to be improved and what is doing well. In this article, we'll look at why manufacturing data collection is vital for your organization and how it may help you improve your operations.
Why is Manufacturing Data Collection so Critical?
Visibility is the key benefit that every manufacturer gets from manufacturing data collection. By collecting real-time data, or what we refer to as "shop floor data," manufacturers better understand how to assess, comprehend, and improve their plant operations. Manufacturers can make informed decisions based on detailed shop floor data. This is why having precise, real-time production data is critical.
“According to Allied Market Research, the worldwide manufacturing analytics market was worth $5,950 million in 2018 and is expected to reach $28,443.7 million by 2026, rising at a 16.5% compound annual growth rate between 2019 and 2026.”
For modern manufacturers, the advantages of data collection in manufacturing are numerous. The manufacturing industry benefits from production data and data-driven strategy in the following ways.
Substantial reduction in downtime by identifying and addressing the root causes of downtime.
It increases manufacturing efficiency and productivity by minimizing production bottlenecks.
A more robust maintenance routine that is based on real-time alerts and machine circumstances.
Improvements in demand forecasting, supplier scoring, waste reduction, and warehouse optimization reduce supply chain costs.
Higher-quality goods that are more in line with customers' wishes and demands depending on how they are utilized in the current world.
So, after looking at some of the significant benefits of real-time manufacturing analytics, let’s see what type of data is collected from production data tracking.
What Sorts of Data May Be Collected for Production Tracking?
Downtime: Operators can record or track downtime for jams, cleaning, minor slowdowns, and stoppages, among other causes, with production tracking software. In the latter scenario, downtime accuracy is optimized by removing rounding, human error, and forgotten downtime occurrences. The software also lets you categorize different types of stops.
Changeovers: Changeovers can also be manually recorded. However, changeovers tracked by monitoring software provide valuable data points for analysis, considerably reducing the time required for new configurations.
Maintenance Failures: Similar to downtime classification, the program assists in tracking the types of maintenance breakdowns and service orders and their possible causes. This may result in cost savings and enable businesses to implement predictive or prescriptive maintenance strategies based on reliable real-time data.
Items of Good Quality: This is a fundamental component of production management. Companies can't fulfill requests for delivery on schedule unless they know what's created first quality. Real-time data collection guarantees that these numbers are accurate and orders are filled efficiently.
Scrap: For manufacturers, waste is a significant challenge. However, conventional techniques are prone to overlooking scrap parts or documenting them wrong. The production tracking system can record the number and type of errors, allowing for analysis and improvement. Additionally, it can capture rework, rework time, and associated activities.
WIP Inventory: Accurate inventory management is critical in production, yet a significant quantity of material may become "invisible" once it is distributed to the floor. Collecting data on the movement and state of work in progress is critical for determining overall efficiency.
Production Schedule: Accurate data collection is essential to managing manufacturing orders and assessing operational progress. Customers' requests may not be fulfilled within the specified lead time if out of stock. Shop floor data gathering provides accurate production histories and helps managers fulfill delivery deadlines.
Which Real-time Data Collection Techniques Do Manufacturers Employ?
Manufacturers frequently employ a wide range of data collection techniques due to the abundance of data sources. Manual data collection and automated data collection are two of the most common data collection methods. Here are a few examples from both methods:
IoT: To provide the appropriate information to the right people at the right time with the correct shop floor insight, IoT (Internet of Things) sensor integration is employed.
PLC: The integration of PLC (Programmable Logic Controller) is used to measure and regulate manufacturing operations.
HMI: It can provide human context to data by integrating line HMI (Human Machine Interface) systems (such as individual shop terminals like touch screens located on factory floor equipment).
SCADA: Overarching management of activities with SCADA (Supervisory Control and Data Acquisition) systems.
CNC and Other Machines: Integrating CNC and other machines (both new and older types) to keep tabs on production efficiency and machine well-being is a must these days.
One of the most challenging aspects of shop floor management is determining what to measure and what to overlook. The National Institute of Standards and Technology recently conducted a study on assisting manufacturing operations in determining which data to collect from the shop floor.Additionally, you may utilize the manufacturing data set described above to obtain information from your manufacturing facility and use it strategically to improve operations, productivity, efficiency, and total business revenue in the long term.
What is manufacturing analytics?
Manufacturing analytics uses operations and event data and technology in the manufacturing business to assure quality, improve performance and yield, lower costs, and optimize supply chains.
How is data collected in manufacturing?
Data collection from a manufacturing process can be done through manual methods, paperwork, or a production/process management software system.
Article | May 5, 2022
A consistent stream of marketing qualified leads is vital for B2B manufacturing enterprises. This is because manufacturing companies rely on a daily stream of orders to be productive and offer sufficient work for their personnel. Indeed, the manufacturing business model is contingent upon the sales process remaining active. Even one week without new leads can have a detrimental effect on your business's revenue and the livelihood of your personnel.
Manufacturing businesses can generate new leads in numerous ways. Of course, the particular strategies you choose will vary according to your target audience. There may be some diversity in your strategy for potential leads. This article will discuss three manufacturing lead generation strategies that can assist you in generating more qualified leads for your business.
Why Is Lead Generation Critical for B2B Businesses?
B2B lead generation is critical to sales development since it contributes to the development and conversion of demand for a product. Without a lead generation process, your team would be unable to create prospects, leaving your sales funnel empty.
This would imply that your sales representatives would be without leads to contact, nurture, and close. Operating a sales force without a lead pipeline impairs your ability to forecast sales and earnings, which has repercussions throughout your business.
The development of leads is a primary objective for management and executive teams in general. This can have a significant influence on revenue.
Producing consistent volumes of high-quality leads can provide your sales team with additional opportunities to close and revenue-generating opportunities. As a result, it is an acute component of client acquisition and revenue growth operations.
So how can you increase your business's lead generation in order to maintain growth in sales? Let us ascertain.
How Can Your Manufacturing Company Get More Qualified Leads?
Turn Website Visitors into Leads
Just because you manufacture something doesn't mean you shouldn't try to generate leads online. Manufacturing websites tend to be quite industrial, with lots of information and requirements on each page. Even if this is the case, make it simple for people to contact you. This can be done with a simple “Contact Us” button on each page. Never let these valuable website visitors depart without providing you with their email address.
Consider a web-to-lead form that links with your company's CRM program. Visitors that fill out this form will be added as new leads in your CRM. You'll be alerted by email so you can follow up quickly.
Provide Free Samples to Qualified Leads
You can't just send free samples to everyone who visits your site — you'd go bankrupt! If your company's products are small, you could give free samples to verified potential customers. Displaying this offer prominently on your homepage will encourage visitors to register.
Include fields on your web-to-lead form that capture the lead's industry and/or company size. Your sales team can then target the most appropriate prospects to send samples. Inquire about the products' satisfaction with these leads. A gift with customized service may be the secret to turning leads into customers.
Collaborate with E-Commerce Vendors
Another possibility is for manufacturers to drop ship white-labelled products or sell wholesale to individual drop-shippers. Many enthusiastic drop shippers are looking for things to offer on e-commerce sites. By working with these merchants, you can quickly get your goods to those who wish to sell or buy them.
This is a less traditional “lead generation” strategy that takes advantage of modern e-commerce. The early setup costs may be high for this collaboration, but the potential return on investment for your company is significant.
Lead generation for manufacturers is a vital component of maintaining a healthy sales funnel. While the manufacturing business may face certain unique lead generation challenges, there are still numerous approaches to generating new qualified leads for follow-up.
Using the guidelines outlined above, you should be well on your way to developing a lead generation plan that generates consistent results for your business.
What is B2B lead generation?
B2B lead generation is a technique used by B2B marketers to organically attract new clients to their business. This strategy comprises a variety of inbound marketing methods aimed at increasing exposure, awareness, and interest among prospects within a defined target demographic.
What are the four steps of lead generation?
Lead generation includes four steps: awareness, education, sampling, and conversion. This framework can be used to design the optimal end-to-end lead generation process for every new market, product or service you establish.
What comes first: the opportunity or the lead?
A lead is a sales prospect. An opportunity is a specific sales contract with an estimated financial value. The opportunity record will be linked to the lead or contact record of the potential client.