Article | July 28, 2021
Rex Moore Group, Inc. is a Top50 electrical contractor delivering unmatched integrated electrical solutions. As an early adopter of Lean manufacturing principles, Rex Moore has created a company-wide culture of continuous improvement that drives significant value to their clients. The firm contracts and performs both design/build and bid work for all electrical, telecommunications, and integrated systems market segments.
Rex Moore has a full-service maintenance department to cover emergency and routine requirements for all facilities, whether an existing facility or one that has been recently completed by the company. The ability to negotiate and competitively bid various forms of contracts including lump-sum, fixed fee, hourly rate, and cost-plus work as a prime contractor, subcontractor, or joint venture is enhanced with Project Business Automation (PBA) from Adeaca. This solution permits the company to propose work only if they are in a position to be competitive in the marketplace and provide excellent service with fair compensation.
Rex Moore used Adeaca PBA as a construction management software for builders and contractors to integrate and facilitate its business processes in its ERP system. Together with Microsoft Dynamics, PBA integrated processes across the company on a single end-to-end platform. This allowed the company to replace 15 different applications with a single comprehensive system, eliminating the costs and inefficiencies associated with multiple systems and silos of information.
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.
"name": "What is the future of manufacturing?",
"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 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 14, 2021
Do manufacturing businesses require Business Intelligence (BI)? The answer is YES. Manufacturing is one of the most data-intensive businesses, producing massive amounts of data ranging from supply chain management to shop floor scheduling, accounting to shipping and delivery, and more.
All of this information would go to waste if not properly categorized and utilized. Scrutinizing and analyzing your data with business intelligence will help you become a more efficientand productive organization. Your organized data can show you where the gaps or inefficiencies are in your manufacturing process and help you fix it.
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 at Microsoft
BI has the potential to improve the operations of an organization and transform it into an organized one. According to Finances Online research, more than 46% of organizations are already employing a BI tool as a significant part of their company strategy, and according to Dresner Advisory Services research, 8 in 10 manufacturers who use BI for analytics have seen it function successfully.
How Manufacturing Operations Are Improving with Business Intelligence?
As revealed by the BI statistics above, we can see that business intelligence is critical in manufacturing. To further illustrate how business intelligence supports the manufacturing industry, let's look at some of the business intelligence benefits that are making a difference in the manufacturing industry.
Advances Operational Efficacy
While modern enterprises create massive amounts of data, not all of this data is relevant. Today's business intelligence solutions take all of the data from your organization and transform it into an easily comprehensible and actionable format. It enables you to minimize or fix errors in real-time. Additionally, it helps you to forecast raw material demand and assess procedures along the supply chain to ensure maximum efficiency.
Allows for the Analysis and Monitoring of Financial Operations
Business intelligence solutions provide insight into sales, profit, and loss, raw material utilization and can usually assist you in optimizing resources to increase your return on investment. Understanding your cost-benefit analysis, BI enables you to manage production costs, monitor processes, and improve value chain management.
Assists in the Management of Your Supply Chain
Manufacturing companies engage with various carriers, handling these multiple processes can be complicated. BI enables manufacturing companies to have more accurate control over shipments, costs, and carrier performance by providing visibility into deliveries, freight expenditures, and general supplies.
Contributes to the Reduction of Inventory Expenses and Errors
Overstocks and out-of-stocks are substantial barriers to profitability. Business intelligence can assist you in tracking records over time and location while identifying issues such as product faults, inventory turnover, and margins for particular distributors.
Determines the Efficiency of Equipment
Several factors can cause inefficient production. For example, errors with equipment due to improper installation, maintenance, or frequent downtime can reduce production. So, to keep industrial operations running well, one must monitor these factors.
Manufacturers can maintain their machines' health using data analytics and business intelligence. It provides real-time information about your production lines' status and streamlines production procedures.
How Business Intelligence Helped SKF (SvenskaKullagerfabriken) to Efficiently Plan Their Future Manufacturing
SKF is a key supplier of bearings, seals, mechatronics, and lubrication systems globally. The company posses its headquarter in Sweden and has distributors in over 130 countries.
Due to SKF's extensive worldwide reach and product diversity, they constantly need to forecast market size and demand for their products to modify their future manufacturing. Generally, SKF experts developed and kept their forecasts in traditional and intricate excel files. However, the efforts of maintaining and reconciling disparate studies were excessively high. As a result, SKF used require days to generate a simple demand prediction.
Later, SKF integrated its business data assets into a single system by utilizing business intelligence in production. Following that, they could swiftly begin sharing their data and insights across multiple divisions within their firm. They are now able to aggregate demand estimation fast and does not face cross-departmental issues about data integrity for the vast number of product varieties they manufacture.
This intelligent data management enabled SKF to plan their future production operations efficiently.
Business intelligence in manufacturing makes a big difference in the organization's entire operations. Given the benefits of business intelligence in manufacturing, a growing number of manufacturers are implementing it in their operations.
According to Mordor Intelligence, Business Intelligence (BI) Market was worth USD 20.516 billion in 2020 and is anticipated to reach USD 40.50 billion by 2026, growing at a 12% compound annual growth rate throughout the forecast period (2021-2026).
Hence, we may say that the business intelligence is crucial for manufacturing and is booming, thanks to its enormous potential and the numerous benefits it provides to various businesses.
Why is business intelligence so important in manufacturing?
Organization intelligence may assist businesses in making better decisions by presenting current and past data within the context of their business. Analysts can use business intelligence to give performance and competitive benchmarking data to help the firm run more smoothly and efficiently.
What value does BI add to manufacturing?
Business intelligence solutions provide insight into sales, profit, and loss, raw material utilization and can usually assist you in optimizing resources to increase your return on investment. Understanding your cost-benefit analysis enables you to manage production costs, monitor processes, and improve value chain management.
What is business intelligence's key objective?
Business intelligence is helpful to assist corporate leaders, business managers, and other operational employees in making more informed business