Article | December 8, 2021
The new manufacturing industry outlook for 2022 is what businesses desire. Due to COVID-19, the sector has seen several ups and downs in recent years. But the industry overcame the most difficult situation by adopting innovations as their working hands.
But all this upgrading and digitalization in manufacturing isn't for everyone. Some manufacturers may struggle with this change, while others may not. So, taking into account all industry segments, we have compiled a list of potential manufacturing challenges for 2022.
“Many companies simply are not willing to change or think they are done once they make a change. But the truth is that technology, consumer demands; the way we work, human needs and much more are constantly changing.”
– Michael Walton, Director, Industry Executive (Manufacturing) at Microsoft
The summary of manufacturing industry challenges and industry outlook for 2022 are presented in the stats below.
According to the National Association of Manufacturers (NAM), four million manufacturing jobs will likely be needed over the next decade, and 2.1 million will likely go unfulfilled unless we motivate more people to pursue modern manufacturing occupations.
According to PTC, 70% of companies have or are working on a digital transformation plan.
According to Adobe, 60% of marketers feel technology has increased competitiveness.
The statistics show that while digitalization facilitates the process, it also poses several challenges that must be addressed in the coming years. Let's explore what obstacles manufacturers may face in 2022.
The Manufacturing Industry Challenges in 2022
The manufacturing business has had a difficult few years as a result of the current economic downturn, and 2022 may not be even that smooth. Thought, technology, and current trends make the operations of upscale manufacturers easier, but not everyone is on the same page.
Let's look at some of the manufacturing challenges that businesses will face in the next year.
Skilled Labor Shortage
The manufacturing industry is facing a workforce shortfall as a skilled generation prepares to retire. Industry experts say that by 2025, there will be between 2 and 3.5 million unfilled manufacturing jobs. As a result of the advancement of new technologies, manufacturing organisations are finding themselves with fewer personnel. They do, however, require individuals with a diverse range of abilities, such as mathematicians and analytic thinkers, to accomplish the tasks with precision.
Specific manufacturing tasks have been automated to save time and money. Industry has adopted machine sensors to capture large amounts of data. With this kind of innovation, the industry's job structure is changing and the desire to hire an untrained or trainable workforce is slowly fading in the industry. However, using augmented reality and virtual reality, manufacturers can easily train personnel for the job and save money.
Lack of Ability to Mine Data
Manufacturing is progressively using IoT. The majority of businesses have already installed or are planning to install Internet of Things machines. These smart machines let businesses collect data to improve production and conduct predictive maintenance. But getting data is a simple task. The difficult aspect is analyzing and aggregating data.
Despite possessing the machines, most companies lack the systems to analyze and retrieve the data recorded by the systems. In this way, the industries are missing a vital opportunity. The industry must improve data mining capabilities to make better decisions in real-time.
Using IoT for analytics and predictive maintenance is critical. Monitoring technologies can help the sector examine data quickly. It can also help predict an asset's maintenance period. As a result, the industry will move from replacement to predict and fix.
Self-service Web Portals That Is Extremely Detailed and Precise
Manufacturing businesses usually strive for on-time order delivery and optimum revenue. However, consumer self-service, which has been in the industry for a long time, has never proven to be a simple walk for clients. Clients are frequently required to pick up the phone and contact manufacturers in order to track their orders and receive delivery estimates. This is hardly the service one would expect from a manufacturer, even more so in today's digital era.
The term customers in manufacturing include partners, end-users, and subcontractors. These three clients have distinct requirements and concerns about collaborating with the manufacturer. Companies can better serve their customers if their partner and end-customer portals are linked to a central hub which we can mention as self-service web portals.
All of the information and updates they need about their orders will be available to them through this new system. They can track, accept and amend their tasks. They'll also use the self–service portal to contact the manufacturer.
In this way, manufacturers can better serve their customers. A system like this will ensure that all parties have access to timely information in a digital format.
Meeting the Deadline for the Project
Product launch timelines are extremely demanding, tight, and stringent. Every project in the assembly line is about cost, time, and quality. Ultimately, these projects are rigorous and well-controlled. Manufacturers who fail to meet deadlines risk losing millions in potential revenues and sales.
Due to rigidity and stringent control, companies are less able to change project scopes or make adjustments as projects develop. The majority of initiatives begin with a design commitment. As new facts or change criteria emerge, adjustment flexibility decreases. This can be aggravating for a team that expects high-quality results. Deadlines are always a constraint.
Effective Business Digital Marketing Strategy
An industry's key digital transformation challenges are driving leads, sales, and MRR through digital channels. Many manufacturing organizations struggle to efficiently use marketing channels like paid media, enterprise SEO, local SEO, content strategy, and social media. In our opinion, one of the most significant issues these organizations have is their digital experience, website design, and overall brand presentation. They can't ignore them if they want to keep enjoying the manufacturing revival.
Visibility of the Supply Chain
Manufacturers must respond to the growing demand from customers for greater transparency. In order to meet customer demand across the customer experience and product lifecycle, they must first understand that precise and real-time visibility throughout the supply chain is essential.
All details must be taken into consideration by the manufacturers. They must be aware of any delays in the arrival of products on the market. Keeping abreast of such developments would give them a leg up in terms of adjusting or rectifying the situation.
Manufacturing industry challenges have long been a part of the industry. However, industry leaders and professionals have always confronted and overcome any challenges that have come their way. The year 2022 will also be a year of achievements, setting new records, and growth for the manufacturing industry, since it will be a year in which it will develop solutions to all of the aforementioned challenges.
What is the future of manufacturing?
Manufacturers should start using AI, block chains, and robotics today. The combination of these new technologies will reshape manufacturing. A new workforce capable of augmenting these technologies is developing and will become the future of manufacturing.
How will automation affect manufacturing in 2022?
When applied properly, automation can greatly assist manufacturing. These benefits include shorter production times, faster and more efficient work than human labor, and lower production costs.
How is the manufacturing industry’s market likely to upsurge in the future?
According to BCC Research, the global manufacturing and process control market is expected to grow at a CAGR of 6.3 percent from $86.7 billion in 2020 to $117.7 billion in 2025.
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"text": "Manufacturers should start using AI, block chains, and robotics today. The combination of these new technologies will reshape manufacturing. A new workforce capable of augmenting these technologies is developing and will become the future of manufacturing."
"name": "How will automation affect manufacturing in 2022?",
"text": "When applied properly, automation can greatly assist manufacturing. These benefits include shorter production times, faster and more efficient work than human labor, and lower production costs."
"name": "How is the manufacturing industry’s market likely to upsurge in the future?",
"text": "According to BCC Research, the global manufacturing and process control market is expected to grow at a CAGR of 6.3 percent from $86.7 billion in 2020 to $117.7 billion in 2025."
Article | December 13, 2021
Lean manufacturing is a growing trend that aims to reduce waste while increasing productivity in manufacturing systems. But, unfortunately, waste doesn't add value to the product, and buyers don't want to pay for it.
This unusual method pushed Toyota Motor Corporation's industry to become a leading Toyota Production System (TPS). As a result, they are now efficiently producing some of the world's top cars with the least waste and the quickest turnaround.
The majority of manufacturers are now using lean management. According to the 2010 Compensation Data Manufacturing report, 69.7% of manufacturing businesses use Lean Manufacturing Practices.
Lean tools are the ones that help you in implementing lean practice in your organization. These lean tools assist in managing people and change while solving problems and monitoring performance. Lean Manufacturing technologies are designed to reduce waste, improve flow, improve quality control, and maximize manufacturing resources.
What Are the Five Best Lean Manufacturing Tools and How Do They Work?
There are roughly 50 Lean Manufacturing tools available in the market. This post will describe 5 of them and their value to your business and its developments.
The 5S system promotes efficiency by organizing and cleaning the workplace. To help increase workplace productivity, the system has five basic guidelines (five S's). The five Ss are Sort, Set, Shine, Standardize, and Sustain.
5S improves workplace efficiency and effectiveness by:
Sort: Removing unnecessary material from each work area
Set: Set the goal of creating efficient work areas for each individual
Shine: Maintaining a clean work area after each shift helps identify and resolve minor concerns
Standardize: Documenting changes to make other work areas' applications more accessible
Sustain: Repeat each stage for continuous improvement
5S is a lean tool used in manufacturing, software, and healthcare. Kaizen and Kanban can be utilized to produce the most efficient workplace possible.
Just-In-Time (JIT) manufacturing
Just-in-time manufacturing allows manufacturers to produce products only after a customer requests them. This reduces the risk of overstocking or damaging components or products during storage.
Consider JIT if your company can operate on-demand and limit the risk of only carrying inventory as needed. JIT can help manage inventory, but it can also hinder meeting customer demand if the supply chain breaks.
With Kaizen, you may enhance seven separate areas at once: business culture, leadership, procedures, quality, and safety. Kaizen is a Japanese word, means "improvement for the better" or "constant improvement."
“Many companies 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 at Microsoft
The idea behind Kaizen is that everyone in the organization can contribute suggestions for process improvement. Accepting everyone's viewpoints may not result in significant organizational changes, but minor improvements here and there will add up over time to substantial reductions in wasted resources.
Kanban is a visual production method that delivers parts to the production line as needed. This lean tool works by ensuring workers get what they need when they need it.
Previously, employees used Kanban cards to request new components, and new parts were not provided until the card asked them to. In recent years, sophisticated software has replaced Kanban cards to signal demand electronically. Using scanned barcodes to signify when new components are needed, the system may automatically request new parts.
Kanban allows businesses to manage inventory better, decrease unnecessary stock, and focus on the products that must be stored. To reduce waste and improve efficiency, facilities can react to current needs rather than predict the future.
Kanban encourages teams and individuals to improve Kanban solutions and overall production processes like Kaizen. Kanban as a lean tool can be used with Kaizen and 5S.
PDCA (Plan, Do, Check, Act)
Plan-Do-Check-Act (PDCA) is a scientific strategy for managing change. Dr. W. Edwards Deming invented it in the 1950s; hence, it is called the ‘Deming Cycle.’
The PDCA cycle has four steps:
Problem or Opportunity: Determine whether a problem or an opportunity exists
Do: Make a small test
Examine: Look over the test results
Act: Take action depending on results
How Nestlé Used the Kaizen Lean Manufacturing Tool
Nestlé is the largest food corporation in the world, yet it is also a company that practices Lean principles, particularly the Kaizen method. Nestlé Waters used a technique known as value stream mapping, which is frequently associated with Kaizen. They designed a new bottling factory from scratch to guarantee that operations were as efficient as possible. Nestlé has been aiming to make ongoing changes to their processes to reduce waste and the amount of time and materials that can be wasted during their operations.
Lean manufacturing techniques enable many businesses to solve their manufacturing difficulties and become more productive and customer-centric. In addition, useful lean manufacturing tools assist companies in obtaining the anticipated outcomes and arranging their operations in many excellent ways to meet buyer expectations. Hence, gather a list of the top lean manufacturing tools and choose the best fit for your organization to maximize your ROI and address the performance issue that is causing your outcomes to lag.
What are the standard tools in lean manufacturing?
Among the more than 50 lean manufacturing tools, Kaizen, 5S, Kanban, Value Stream Mapping, and PDCA are the most commonly used lean manufacturing tools.
How to Select the Best Lean Manufacturing Tools for Your Business?
Choosing a lean manufacturing tool begins with identifying the issue or lag in your organization that affects overall productivity and work quality. To select the lean device that best meets your company's needs, you must first grasp each one's benefits and implementation techniques.
What is included in a Lean 5S toolkit?
The lean 5S toolbox contains some essential items for achieving the goal. It comes with a notepad or tablet, a camera, a high-quality flashlight, a tape measure, and a stopwatch.
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"text": "Choosing a lean manufacturing tool begins with identifying the issue or lag in your organization that affects overall productivity and work quality. To select the lean device that best meets your company's needs, you must first grasp each one's benefits and implementation techniques."
"name": "What is included in a Lean 5S toolkit?",
"text": "The lean 5S toolbox contains some essential items for achieving the goal. It comes with a notepad or tablet, a camera, a high-quality flashlight, a tape measure, and a stopwatch."
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 | 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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"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?",
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