Article | December 14, 2021
The manufacturing industry has evolved to new heights of innovation, productivity, and excellence with digital transformation. Manufacturing digitalization has made operational procedures more skilled, accurate, and time-savvy.
“Many companies simply are not willing to change or think they are done once they make a change. But the truth is technology; consumer demands, the way we work, human needs and much more are constantly changing.”
Michael Walton, Director, Industry Executive (Manufacturing) at Microsoft
With a CAGR of 19.48 percent between 2021 and 2026, the digital transformation in the manufacturing market is expected to reach USD 263.93 billion by 2026. Manufacturing plants adopt digital technology to improve, automate, and modernize processes as part of Industry 4.0.
So, what are the key benefits of digitalization for manufacturers? This article will elaborate on the top five benefits of digital manufacturing transformation.
How to Define Digital Manufacturing?
Manufacturing digital transformation involves integrating digital technologies into processes and products to improve manufacturing efficiency and quality. Manufacturing's digital transformation aims to increase operational efficiency and reduce expenses. The digital transformation techniques ensure product quality. It also makes work more efficient, safe, and stress-free.
What Is Included in Manufacturing Digitization (Industry 4.0)?
Industry 4.0 is the digitalization of manufacturing. Cyber-physical systems, IoT, and cloud computing are current trends in manufacturing automation and data exchange. Connected devices, cloud computing power, and the modern emphasis of lean, efficient operations enable Industry 4.0 to construct advanced and innovative smart factories.
Industry 4.0 includes design, sales, inventories, scheduling, quality, engineering, customer and field service.
Five Benefits of Digital Transformation in Manufacturing
Manufacturing organizations can benefit from digitalization in a variety of ways. It can help make the work more efficient, decentralized, and secure. It further creates new business opportunities and attracts new talent to the industry. Additionally, integrating products into a digital ecosystem increases their value and appeal. Let’s dig deeper into each of the five key benefits.
Technology is an invaluable companion in reducing the manufacturing company's expenses in the future. The incorporation of digital technology results in the transformation of procedures and the digitization of documents, resulting in overall process optimization. Therefore, a reduction in labor costs might be expected as a result of the elimination of unnecessary expenditures.
Additionally, digitization enables businesses to assess and estimate expenses considerably more precisely, ensuring that budgets stay on track. Additionally, it eliminates andsubstitutes inefficient jobs within processes, significantly increasing their efficiency. This efficiency is translated into time savings, which results in a substantially more cost-effective manufacturing process.
Manufacturing digital transformation allows organizations to supervise manufacturing remotely, allowing production to continue uninterrupted. In rare cases like Covid-19, digitalized businesses have not had to cease or even slow down production. These systems can work without interruptions for much longer than any worker.
Digitalization also boosts methodology flexibility and reactivity. For example, if a production plant has a problem, an automatic alert is generated, and the issue is resolved regardless of the day, time, or presence.
Improved Operational Efficiency
Smart product connectivity allows devices to connect and communicate with each other (M2M). This connectivity enables decentralized decision-making. Many duties no longer require an employee to be physically present. New manufacturing and production models minimize boring, risky activities while increasing accuracy, efficiency, and responsiveness.
Transforming businesses through digital means making better decisions based on real-time data. Training, changes, and repairs are no longer issues due to reduced frequency and automation.
New Business Opportunities
New digital technologies enable the manufacture of previously unviable products and services, generating new revenue streams. Also, new services (innovation or reorientation) are launched considerably faster. Companies may utilize big data and AI to experiment, anticipate trends, and predict about new advancements. These technologies can help organizations become more eco-friendly and create products that are less detrimental to our environment.
Attracts New Talent
Professionals with fundamental talents in this complicated and disruptive environment are drawn to digitalizedorganizations that are up-to-date with trends and processes. Also, if the change is managed well, it will lead to higher profitability, increasing employee satisfaction. Human motivation, along with excellent digital technologies, will reflect in the company's production and profitability.
Dusseldorf@Germany: The Deloitte Digital Factory
The digital factory in Dusseldorf provides a flexible setting for innovative workshops and training, bringing together the old and new worlds of supply chain and industrial operations to provide a seamless experience. Specific use case examples, as well as the digital solutions sector, will motivate and encourage businesses to get on their digital transformation journeys, making use of the most up-to-date technologies in the process.
Manufacturing digitalization has a lot to offer the industry, and many manufacturers are capitalizing on this new phase of the industrial revolution by incorporating cutting-edge technologies into manufacturing and business operations. As said previously, the benefits of digital transformation in the manufacturing business are increasing the importance of digitalization in the industry. Transform your traditional manufacturing operating processes with these new manufacturing trends and observe the results that other benefitting manufacturing businesses have achieved.
Why is digitalization vital in manufacturing?
Manufacturing process digitization improves overall business performance. But the results are seen across the factory. Digital transformation improves working conditions for employees and streamlines daily operations.
How are digitization and digitalization different?
Digitalization is a transformation of data and processes. Digitalization is the use of digital technologies to collect data, identify patterns, and make better business decisions.
How digital technologies are applied in manufacturing?
Digital manufacturing technologies enable the integration of systems and processes across all stages of production, from design to production and beyond.
Article | January 4, 2022
Lead generation for manufacturers has always been tough due to a lack of knowledge about new technology and trends that can help them generate more business leads. There are still a lot of small and mid-sized manufacturing companies that rely on antiquated manufacturer lead generation methods.
In this article, we'll discuss some proven methods for generating leads that can help you increase your company's sales and improve your overall return on investment.
"Don’t think of your lead as "LEADS." Instead, think of them as people who are frustrated because understanding and buying your product is too hard. Your job is to make it easy for them to learn about your product and get started. "
—Andy Pitre, VP of Product, HubSpot
So let's get started.
Factors That Must Be Aligned to Drive New Business Leads
Landing pages are mini-websites embedded into your site. To deliver an experience that speaks directly to the user's intent, marketers use landing pages. Create unique landing pages for every industrial lead generation campaign.
To generate quality leads, you need to have a strong call to action (CTA). CTAs are usually buttons that allow users to submit information, visit a landing page, or download an e-book or white paper.
Your CTA must be compelling. Give your prospects a reason to click and remove any barriers to the click. CTAs should be bold, high-contrast, and easily identifiable.
Without forms, it is impossible to collect leads. Therefore, visitors will be asked to fill out forms that collect their contact information in exchange for an offer.
Every lead contains a transaction. A company provides a valuable service in exchange for a prospect's contact information. You won't get many leads unless you provide value to your potential customers.
This offer comes in the form of an e-book, consultation, coupon code, or whitepaper. To generate more leads, make compelling offers.
The above components can't exist if you don't have a website. If you want to emphasize your business, you need to generate quality leads. Why? Because most of today's B2B buyers are millennials, who are highly tech-savvy and increasingly conduct their supplier research online.
How to Generate Manufacturing Leads: 5 Effective Techniques
Create a Website That Converts
"The lead generation process starts by finding out where our target market ‘lives’ on the web."
– Wayne Davis
Your website should be a trusted source of quality leads. Unfortunately, many companies lose sight of this and develop websites that are cumbersome and difficult to browse, causing a rift between you and your potential customers.
Don't expect people to behave if your site doesn't encourage them to act. Your website should have clear "call to action" buttons and lead capture options that allow visitors to contact you in a simple and pleasant way.
Additionally, there are website optimization tools available, including PageSpeed Insights, Hotjar, Google Optimize, and WAVE. All of the above-mentioned website optimization tools provide complimentary services. So, this is the most accessible and most reliable approach to generating web leads.
Make Descriptive Infographics
Infographics are a great way to provide prospects with helpful information while motivating them to contact you. Most importantly, infographics establish your company as a thought leader and unconsciously identify you with industry authority.
According to recent surveys,
Nearly 41% of respondents indicated that infographics and illustrations are the most effective solutions for achieving their business objectives.
On social media, infographics receive three times the number of shares as other types of content.
Articles that included infographics received 72% more views than standard articles.
A great infographic has a great design. If your organization lacks an in-house graphics team, you can simply locate a freelance designer to create a stunning infographic for you.
Your infographics are ageless pieces of material that can be reused for smart marketing and lead generation manufacturing.
Here's an example of an infographic from Digital Marketing Philippines from 2019 that received over 1,600 shares and is jam-packed with data and text. In this way, you can see how the company may have gotten its leads from these 1600 shares, each of which is a potential customer.
Host an Event
According to Bizzabo,
86% of senior management (Senior Managers, Executives, and Board Members) believe that in-person events are critical to the success of their company.
The majority of B2B marketers (97%) believe in-person events have a significant impact on achieving business goals.
According to Marketing Charts,
68% of B2B marketers agree that in-person events aid in lead generation for manufacturers, while case studies help with lead conversion and acceleration.
Improve manufacturing lead generation through events that can occur both online and offline. Organize any kind of event to attract your target market while collecting contact information. Use live events to interact with consumers in real-time, answering questions, addressing objections, learning about your audience, and guiding prospects through the sales funnel. Consider webinars, workshops, seminars, meetups, and conferences.
Create a Value-Packed Newsletter
"Content is the fuel for your lead generation efforts."
– Dayna Rothman
According to the Content Marketing Institute,
31% of B2B marketers believe email newsletters are the most effective way to nurture leads.
81% of B2B marketers report that email newsletters are their most frequently used form of content marketing.
Create a must-read newsletter that invites interested prospects to engage and remain connected with your company. This enables you to stay top-of-mind with consumers and promote your products and services to move prospects along the sales funnel.
The newsletter can have new blog entries, product or service updates, special deals, upcoming events, and recommended reading from other thought leaders.
Incorporate SEO into Your Marketing Plan
According to Intergrowth,
61% of B2B marketers say SEO and organic traffic generate more leads than any other type of marketing.
Search engine leads convert at a rate of 14.6%, while outbound leads (cold calling, direct mail, etc.) convert at a rate of 1.7%.
Manufacturers are no exception when it comes to the importance of search engine optimization (SEO). As a result, you may generate many leads with the correct marketing approach and a profound grasp of SEO for manufacturers.
When we consider the statistics explained earlier in this article, we can see the benefits of all of the techniques mentioned and how they can help you increase your lead generation and, as a result, your overall business ROI.
Thus, we can refer to the above-mentioned lead generation techniques as sales-driven strategies that will assist you at every lead generation and conversion stage. Improved lead generation for manufacturers enables businesses to reach a vast customer base and generate more high-quality leads.
What is a high-quality lead in manufacturing?
Leads of high quality have an increased propensity to convert into paying consumers. The more qualified your leads are, the more likely they will buy your product or service.
What are the lead generation challenges?
Making accurate data, shaping conversations, and converting qualified leads into sales are some of the significant challenges in manufacturing lead generation.
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.