Tag Archives: Artificial Intelligence

 

Labor shortages, supply chain disruption, and technological change have been cause for concern for executives in the manufacturing industry the last few years. As 2024 draws to a close, business leaders are looking ahead to the coming year. What will manufacturing be facing in 2025?

Here are five trends and challenges we’re expecting for the manufacturing industry in 2025 and advice on how to handle each issue.

1. Digital transformation

It’s not that AI and technology are coming for people’s jobs. It’s about this technology being able to streamline how the job gets done, adding speed, quality, and efficiency to the process. The 2024 Manufacturing and Distribution Pulse Survey Report by Citrin Cooperman found 43% of leaders in manufacturing are currently implementing advanced tech programs and policies in their organizations.

It’s involving AI and Machine Learning to optimize processes and outcomes, the Internet of Things (IoT) which will use smart technology to have machines communicate their own glitches and needs for maintenance, and robotics and automation for tasks like assembly.

The end goal is to increase predictive maintenance, optimize processes, ramp up quality control and provide real-time data for better decision making.

What manufacturing should do:

At USC, we help clients use AI, Machine Learning, and Predictive Analytics to optimize their workflows, processes and demand forecasting. Companies should be using these techniques now, if they’re not already. It’s also crucial to upskill existing employees to be able to work with the new technologies. That’s a win-win for manufacturing companies and their workforce. Higher skilled employees are happier, more effective, and more loyal to the company.

2. Talent

Workforce development, skills gaps and employee retention will be the top issues in regard to talent in 2025. It has been estimated that 1.9 million manufacturing jobs could go unfilled over the next decade if talent challenges aren’t solved. The old guard, long term, experienced employees that executives rely on to get the job done are retiring without a strong pipeline of younger workers to take their place. In addition, the labor force itself is concerned with flexibility, hours, pay, child care and more.

But there’s also the issue of skills. A new study by Deloitte and the Manufacturing Institute found that the need for roles requiring higher-level skills, including technical, digital and soft skills are growing at a rapid rate.

What manufacturing should do:

Working with local trade schools, community colleges and even high schools to offer internships and apprenticeships is a great way to build the talent pipeline.

Also, offering current employees training in digital skills, as well as soft skills like leadership and management training, will provide the company with higher-skilled workforce. This will create a sense of loyalty and pride in the employee knowing the company is investing in them with an eye toward the future.

3. Sustainability

The focus on sustainability is everywhere. Manufacturers are feeling increased pressure to become greener, and as a result are implementing environmental, social and governance strategies.

There is governmental pressure because of tighter environmental standards, but there is also pressure coming from consumers who increasingly want and seek out goods that are manufactured with “clean” methods.

What manufacturing should do:

Continuing to investigate efficient technologies like solar and wind, and making investments in machinery and other assets that are more energy efficient, will be crucial in the coming year and beyond. It will help lower operating costs while satisfying the demand from consumers.

4. Supply chain

Supply chain disruption that plagued just about every business on the planet during the pandemic has eased to a great extent, but challenges are still out there. Lead times for materials is still high, and the cost of transportation and logistics is weighing on companies’ bottom lines.

Shipping delays and uncertainties are a big part of the problem, with headlines nearly every day of yet another cargo ship being attacked at sea.

Then there’s the issue of labor shortages all along the supply chain, both in foreign countries and the U.S., with labor strikes slowing down delivery and labor shortages of truck drivers adding to the snarl.

What manufacturing should do:

It’s extremely challenging for companies to combat labor shortages and shipping delays in their supply chains, but smart demand forecasting and considerations like reshoring supply sources can help. In addition, establishing a strong Sales, Inventory, and Operations Planning (SIOP) program will optimize your supply chain.

5. Tariffs

With a new administration may come new global trade policies, and it’s not just the U.S. that held elections in 2024. Many countries around the globe are restructuring leadership. Ongoing U.S.-China trade tensions will certainly intensify as a result of the tariffs the new administration is proposing, driving up the cost of materials for manufacturers.

What manufacturing should do:

Many manufacturers are ordering supplies and materials now, before the new administration takes over. Stocking up now, in case of major price hikes later.

This issue goes hand in hand with supply chain disruption and is one more reason to consider reshoring and nearshoring of supplies and materials.

The Outlook

Despite ongoing challenges, 2025 looks bright for manufacturers to grow their businesses. Adapting operations to be sustainable and incorporating advanced technology with an upskilled workforce to manage it, business leaders will enjoy major improvements to productivity, their supply chain, and customer satisfaction.

At USC Consulting Group, we’re here to help manufacturing companies become more productive and profitable with standardized operating procedures, enhanced management operating systems, SIOP improvements, and other strategies to find opportunities for greater efficiencies, increased throughput and bottom line results. Contact us today to have your operations humming in 2025.

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Factors ranging from the weather to celebrities’ social media posts can spur the public’s demand for particular products. Those spikes can cause supply chain constraints company leaders aim to avoid. It is better when corporate teams can predict what people will want and get those products far enough in advance to cater to everyone wishing to buy them. To achieve this, businesses are using AI to strengthen their supply chains. Here’s how…

Managing Demand While Selling Diverse Product Assortments

Demand planning is especially complicated when retailers sell huge varieties of goods within a large category. Such was the case with one of Canada’s largest electronics retailers. People go there to purchase everything from phone chargers to televisions.

However, the demand for those two examples is very different. Many consumers buy several phone chargers per year, such as if they want one for each main room in a home or have forgotten to pack the item before going on a trip. However, most TVs last several years, and people only buy them once the ones they have break or otherwise no longer meet their needs. Plus, many shoppers are more likely to buy those big-ticket items during the holiday season than at other times.

The Canadian retailer uses AI and machine learning technologies to get data-driven demand insights that shape inventory and supply-chain-related decisions. Its leaders have already noticed several benefits. For example, demand planning has become more automated, and those involved can receive detailed reports highlighting potential business risks and impacts.

Additionally, supply chain employees can address slow-moving inventory, plan more enticing promotional offers and reduce stockouts. Another aspect of the AI solution evaluates various supply chain scenarios and gives prescriptive recommendations to prevent unwanted consequences. These examples show how AI can support workers in their roles and increase productivity.

A common misconception about AI is that it will replace human staff. One study found job loss from automation and other advanced technologies was a worry for 42% of respondents. However, besides assisting them with the tasks they already know, artificial intelligence can expand their skills, encouraging them to use new platforms and tools that make demand planning easier.

Streamlining Demand Planning Processes for Better Productivity

Demand planning processes vary depending on what the brand sells, the size of its supplier network, its budget and more. However, no matter how organizations handle them currently, AI can pinpoint opportunities to streamline the work for better overall outcomes.

One example comes from a multinational consumer goods enterprise offering diapers, detergent, personal grooming products and other household staples. Leaders hoped to improve current demand planning by bringing artificial intelligence into the workflow. Initial data inputs for the project included bill-of-materials information for 5,000 products and 22,000 components. Additionally, users imported various types of supporting supply chain details into the system, including specifics about vendors, warehouses and manufacturing plants.

The technology then compiles all that information to give real-time or trend-based insights. Besides providing live inventory data, the AI product can generate supply projection reports that indicate future needs while highlighting possible supply chain disruptions. Knowing about potential issues sooner gives employees the information to act confidently and prevent or mitigate those problems.

The tool was also a significant productivity booster for the consumer goods firm. For example, supply chain queries used to take more than two hours to complete but now occur immediately. Additionally, although it formerly needed more than 10 people to verify the data, the technology can do that without human oversight. Such improvements substantiate studies showing AI can make people 20%-45% more productive depending on various factors.

Running Supply Chain Simulations Before Key Events

Even though some periods of increased demand are impossible to predict, most supply chain managers can anticipate others with near certainty. For example, Black Friday is one of the biggest shopping days of the year in the United States. Additionally, late summer drives sales of bedding sets, reasonably priced furniture and school supplies as students prepare for college.

Demand planning is essential for giving supply chain professionals the necessary information to source and move the products customers will want most during those hectic periods. Since artificial intelligence can process large quantities of information quickly, users could feed details such as social media mentions, customer service email or chat data, and sales figures into tools to determine which factors make some products more or less desirable.

The leaders of one multinational American retailer used AI to determine what customers would want before Black Friday arrived. The goal was to learn those details before shoppers even consciously expressed a desire to buy specific items. While using the artificial intelligence platform, retail staff entered data about shopping and customer trends, seasonal factors and more. The resulting output steered supply chain decisions and helped address issues that might ordinarily cause Black Friday disruptions.

The retailer has also added AI to its daily supply chain workflows, relying on the technology to anticipate demand cycles and unexpected traffic peaks. Some businesses use complementing technologies such as digital twins to get similar results. These tools enable people to predict bottlenecks and investigate potential actions before pursuing them in real life.

Making Demand Planning More Manageable

Demand planning is tricky and requires a thoughtful approach from people who combine their expertise with trustworthy data. However, these examples show how purposeful AI applications can assist with this all-important aspect of supply chain operations, increasing the likelihood of satisfied customers and profit.

*This article is written by Jack Shaw. Jack is a seasoned automotive industry writer with over six years of experience. As the senior writer for Modded, he combines his passion for vehicles, manufacturing and technology with his expertise to deliver engaging content that resonates with enthusiasts worldwide.

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By integrating their Management Operating Systems (MOS) with AI and IoT, mining and metals companies can significantly enhance their operational capabilities, leading to better asset management, increased productivity, and ultimately, improved financial performance.

Utilizing IoT devices, such as sensors and connected equipment, to continuously collect data on various aspects of their operations, including equipment performance, environmental conditions, and production metrics, this real-time data is fed into the MOS, providing a comprehensive and up-to-date view of operations. The collected data is then analyzed by AI algorithms within the MOS to generate insights, identify patterns, and predict outcomes, allowing for proactive management of assets and operations, such as predicting equipment failures or optimizing production schedules.

A key aspect of any MOS is to assist management in decision making. Integrating AI with MOS enables real-time decision support, where AI provides recommendations or automates decision-making processes based on the analysis of IoT data. This helps managers make more informed decisions quickly, improving responsiveness to changing conditions. Additionally, AI allows the MOS to simulate different operational scenarios and predict their outcomes. This capability helps managers evaluate the potential impact of different decisions before implementing them, reducing risks and optimizing outcomes.

By focusing on operational efficiency, AI models integrated into the MOS can optimize processes in real-time by adjusting operational parameters based on current conditions and historical data, leading to improvements in ore and metal recovery, energy efficiency, and overall productivity. AI can also be used to analyze data on resource usage and availability, helping the MOS to optimize the allocation of resources such as labor, equipment, and materials, leading to cost savings and improved operational efficiency.

When approaching enterprise asset management and predictive maintenance models, integrating AI and IoT with the MOS, companies can enhance their predictive maintenance capabilities. AI algorithms analyze sensor data from IoT devices to predict when maintenance is needed, helping to prevent unexpected equipment failures and reduce downtime. This assists the MOS to automatically schedule maintenance activities based on AI predictions, ensuring that maintenance is performed only when necessary and that it is coordinated with other operational activities.

The use IoT and AI integration helps the MOS to optimize inventory levels by predicting demand for spare parts and materials based on operational data, thus reducing inventory costs and ensuring that critical components are available when needed. By having AI analyze data across the supply chain, assisting the MOS to optimize logistics, reduce lead times, and minimize costs associated with the procurement and transportation of materials.

Integrating Management Operating Systems with AI and IoT in the mining and metals industry offers substantial benefits, but it also comes with several challenges and potential pitfalls.

USC partners with your organization and coaches your people to significantly impact performance outcomes and accelerate Operational Excellence

For more than 55 years, USC has been working with clients to address the challenges and avoid the pitfalls when developing, enhancing and deploying their management operating systems.

As technology enablers, like AI and IoT, are deployed, we help clients to address the challenges through careful planning and a strong focus on change management, including employee involvement.  By proactively identifying and mitigating the pitfalls, mining and metal companies can successfully integrate AI and IoT with their MOS, unlocking the full potential of these technologies for improved asset management and operational efficiency.

Integrating AI and IoT into MOS often requires close coordination across different departments, such as IT, operations, and maintenance. Misalignment or lack of communication between these departments can lead to project delays and failures. The complexity of integrating AI and IoT, projects can often experience timeline and budget overruns. Effective project management is critical to keep the implementation on track and within budget.

Mining and metal operations often have data scattered across different systems and departments. Integrating this data into a unified MOS that can effectively leverage AI and IoT is challenging, particularly if the data is stored in incompatible formats or is not standardized. AI systems require high-quality, accurate data to function effectively. Inconsistent, incomplete, or inaccurate data can lead to poor AI performance, resulting in unreliable predictions or insights. Ensuring that data from IoT devices is processed in real-time is crucial for effective AI-driven decision-making. However, high latency in data transmission or processing can lead to delays, reducing the effectiveness of AI in making timely decisions.

Many companies often face a skills gap when it comes to AI, IoT, and data analytics. There may be a shortage of in-house expertise required to manage and maintain these advanced technologies effectively, so having a partner can assist in compressing the time it normally takes cleanse data and align MOS processes. Employees accustomed to traditional methods may resist adopting new technologies, especially if they perceive AI and IoT as threatening their jobs or making their roles redundant. Effective change management and training programs are essential to address this issue.

Companies that have integrated their Management Operating Systems with AI and IoT are experiencing several quantifiable benefits across various aspects of their operations. These benefits are often measurable in terms of improved safety (30-50% reduction in safety incidents), cost savings (10-40% reduction in maintenance costs), and an increased productivity (5-15% increase in productivity and 10-20% improvement in operating efficiency), just to name a few. By leveraging these technologies effectively, mining and metal companies can achieve substantial improvements across their entire value chain.

USC helps you tackle key challenges

Do you want to understand how a MOS can integrate your mine and operational planning, while helping you to safely increase performance site wide? Contact us today.

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Remember when artificial intelligence (AI) was a glimmer on the horizon? And then ChatGPT stormed onto the scene and people were convinced every job out there was soon going to be replaced by a bot? Now it turns out, not so much.

As awesome (and we don’t use that word lightly) as AI is, it’s only as good as the data it has to work with. At USC Consulting Group, we’re finding this is especially true when we’re using AI for predictive analytics. AI doesn’t like variation, and there can be a lot of that in manufacturing processes.

Here’s a look into this issue and how to handle it.

A short primer into AI and predictive analytics

AI is a broad term describing computer systems that perform intelligent tasks, like reasoning, learning, problem solving, and more. Not so obvious is predictive analytics, which is the ability to forecast future outcomes using AI based on data. You’re already familiar with it, to a certain degree. If you’ve ever had a recommendation from Netflix based on what you’ve watched in the past, that’s it. In a nutshell.

Netflix’s use of predictive analytics created a seismic shift in consumer expectations. This technology also has the potential to transform operating procedures and processes for many industries.

It’s extremely powerful when dealing with processes in which multiple predictors are influencing outcomes. It has the ability to tell us which path to take in order to achieve a desired outcome, even when process patterns and trends are changing.

It means greater precision and accuracy, speed and increased efficiency, the holy grails for any manufacturer.

But there is a fly in this cyber ointment.

Variation.

AI doesn’t like it and – low and behold – that means humans are necessary in this process in order for predictive analytics to achieve its potential.

What is variation?

When we’re talking about manufacturing processes, what exactly does variation mean?

In manufacturing, variation is the difference between an actual measure of a product characteristic and its target value. Excessive variation often leads to product discard or rework.

When you’re dealing with high process variation and instability, it degrades efficiency, consistency and ultimately, profits. A key manufacturing performance objective is the establishment of stable and predictable processes that limits variation – minimum variation around target values.

A main focus for USC Consulting Group is to identify the root causes of variation and address them. Generally, it boils down to people, components and materials.

Some examples to causes of variation include:

It can be one of these factors, several, or something else. But whatever it is, it’s impeding our ability – and the bot’s – to predict outcomes.

Minimizing variation with our Customized Quality System (CQS)

Every situation is different. The cause of variation on one manufacturing line isn’t going to be the same on another. USCCG assesses and evaluates client processes, then applies a customized approach using a series of tools, techniques and methods that is most applicable in addressing the causes of variability. This customized approach enables USCCG to address variability in an efficient manner. We call it our Customized Quality System (CQS).

We review processes from “the cradle to the grave” and identify the highest-impact operations, then drill down to the tasks and steps within those operations until we uncover the culprits.

Although every situation is different, the general roadmap includes:

Removing variability through our CQS not only has an immediate impact on improved product conformance but also paves the way for AI to do its job in predictive analytics, i.e., we want predictions with minimum variability.

It’s just one way USC Consulting Group is using the human touch to make sure AI is up to the job.

Read more about this in our free eBook, “AI and Machine Learning: Predicting the Future Through Analytics.”

AI and Machine Learning - Predicting the Future Through Data Analytics eBook

 

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Global supply chains are intricate networks that span multiple countries and continents, involving a multitude of processes, from procurement to distribution. The complexity is further compounded by varying local standards and regulations, making standardization a critical need.

The United Nations Standard Products and Services Code (UNSPSC) provides a universal classification framework that is essential for streamlining these complex processes and facilitating seamless international operations.

Benefits of UNSPSC

UNSPSC serves as a global language for businesses, ensuring that products and services are categorized consistently regardless of where they are produced or consumed. This standardization is vital for global trade, as it simplifies communications between suppliers and buyers, enhances spend analysis and reporting capabilities, and improves procurement efficiency.

By adopting UNSPSC, companies can ensure more accurate demand forecasting and inventory management, which are crucial for maintaining the flow of goods and services across global markets.

AICA’s Automated Approach to UNSPSC

Data management and cleansing specialist AICA offers a SaaS platform that leverages advanced AI and ML technologies to automate the UNSPSC classification process. This automation is driven by AI models trained on extensive datasets, significantly increasing accuracy and reducing errors commonly seen in less sophisticated systems.

The process of manually classifying products into UNSPSC codes is a task that traditionally requires substantial time investment. For instance, cataloguing a single product into the UNSPSC framework manually takes approximately 10 minutes. Classifying 10,000 products would, therefore, require about 69 days. Thus, manually classifying products consumes a significant amount of time, representing a substantial opportunity cost.

However, AICA’s platform automates this process and assigns the classified items with an accuracy score. Items that receive a quality score lower than 93% are flagged for review by our subject matter experts.

Here’s a breakdown of the time savings:

Thus, by using AICA’s system, a task that would normally take over 69 days of continuous work can be reduced significantly to only a few.

This methodology not only speeds up the classification process but also ensures a high level of accuracy and reliability, allowing businesses to deploy resources more effectively and enhance overall productivity in the supply chain.

Universal Relevance

The relevance of UNSPSC and AICA’s technological solutions extends across various critical sectors, including Manufacturing, Mining, and Aerospace and Defense. These industries face unique challenges such as managing complex assemblies, complying with strict regulatory standards, and handling high-value inventories.

UNSPSC codes help standardize component classifications, making it easier to track and manage parts across global supply chains. For these sectors, the ability to accurately classify and analyze product data can lead to more strategic sourcing and better risk management.

Conclusion

For global enterprises aiming to improve their supply chain operations, adopting AICA’s UNSPSC-classifying technologies offers a transformative opportunity. By integrating our solutions, companies can benefit from enhanced data accuracy, improved operational efficiency, and a competitive edge in the global market.

*This article is written by USCCG’s strategic partner, AICA Data. AICA is a data cleansing and management specialist that optimizes your product and services data with AI to provide faster, more accurate, and cost-effective solutions. To find out more about AICA’s services – visit their website here.

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It’s no secret that manufacturing and supply chain organizations are constantly in pursuit of a greater degree of efficiency. This is the key to remaining competitive in both increasingly contentious markets.

It’s also no secret that attaining a higher degree of efficiency is harder than it looks. Supply chain organizations have faced disruption from multiple angles, with decentralized distribution, competitors with a higher level of digitalization, and the deglobalization of trade causing them to fall behind. Similarly, manufacturers are attempting to ride out the silver tsunami and the resulting gap in team member experience while doing so.

Automation is already impacting both industries for the better, providing accurate analytics, monitoring and limiting resource expenditure, and removing manual tasks from employee dockets. But newer technological innovations promise to be a massive boon for both industries, optimizing operations, further streamlining decision-making, and enhancing productivity. Digital twins technology offers insights that revolutionize traditional manufacturing and supply chain management – and we’re about to break down exactly how.

What is Digital Twins Technology?

A common misconception that surrounds the topic of digital twins technology is that it’s just another form of 3D modeling – a sensor, a software platform, or a particularly creative application of artificial intelligence (AI).  Digital twins are, in fact, none of these things.

Digital twins are an amalgamation of technologies that work in tandem to record, model, and simulate projects in real time. The technologies involved in this process will range according to organizations’ capabilities and needs but often include sensors, augmented reality tools, modeling software, and AI. Far from a simple model, digital twins technology tests, records, and reports key data points to leadership, unlocking agile decision-making on an unprecedented level.

Let’s quickly break down some of the use cases for digital twins in supply chain and manufacturing organizations:

Manufacturers in particular will see a massive value-add from digital twins technology, as it can be used to:

While it’s not the most buzzed about technological innovation on the market, digital twins are certainly one of the more useful types of technology for manufacturers and supply chain organizations.

Digital Twins, Your Network, and Expanding Your Infrastructure

Digital twinning also has implications for your network, especially if you’ve already made the switch from copper to fiber. Employing digital twins technology necessitates a high capacity for data transference, as a large quantity of data will be consistently transferred to your single source of truth. While switching from copper to fiber can somewhat fill that need, depending on your network’s capacity and the quality of the components within, you may find that your current network doesn’t adequately support your data-transmitting needs.

Taking the step to convert to a dark fiber network is one possible solution, as dark fiber networks grant a robust, scalable network infrastructure that is entirely customizable according to need. Organizations that need to expand their bandwidth while also maintaining network security and consistent uptime may consider switching to dark fiber, as it is a high-capacity, consumer-controlled network that can effectively replace inferior infrastructure overnight.

Another option is actually using digital twins technology to replicate and reinforce your network. Creating a network digital twin allows you to connect tasks with network performance, granting you control over all facets of your network’s lifecycle. Similarly to how digital twinning allows you to identify bottlenecks and potential impediments to swift service throughout your operations, network digital twinning replicates those benefits for your network.

Either option will allow you to boost your network’s performance while also granting you a greater degree of visibility into and control over said network. This is key when using a technology like digital twins, which can consume quite a bit of bandwidth, as it allows you to reap the benefits of this technology without any unintended consequences.

Digital twins technology can empower manufacturers and supply chain organizations to drive efficiency, regaining a competitive edge in markets overrun with disruptions. With the right solution and the infrastructure to support it, you’ll find efficiency, customer satisfaction, and profits spike.

*This article is written by Ainsley Lawrence. View more of Ainsley’s articles here.

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Warehouse operations are critical to any manufacturing business. From holding inventory to delivering items, the process must be as swift and efficient as possible. Earlier practices such as document management and communication have been a significant step, but growth and progression in the supply chain call for more.

The rise of the Internet has been a key event in improving warehouse operations. As technology progresses, there are even more ways to optimize the supply chain, and ensure every item or employee is included.

The Need to Streamline Warehouse Operations

Warehouse operations offer many opportunities for error while meeting tight deadlines. Brand owners must recognize these areas for improvement and see what can be done to reduce mistakes. Streamlining translates to more accurate and faster processing, which equates to higher customer satisfaction.

Warehouse operational efficiency also translates to long-term time and cost savings. Next-gen technology can streamline warehouse operations using fewer minutes and dollars resulting in increased productivity.

Remember to include workers when integrating these new electronics. Forty-two percent of workers fear job loss from automation and new technologies. However, the reality is humans are responsible for tool management and strategy execution. Train them to work with these items rather than against them.

Vital Next-Gen Technologies in the Warehouse

Some facilities may incorporate multiple next-gen technologies, while others only incorporate one. The most important factor is to assess what works best for a specific set of operations and makes sense investment-wise.

Automation and Robotics

Certain warehouse operations are rather repetitive. It can be the same cycle of picking out a product, packing it, adding a shipping label and sending it off. Automating these processes with robots can take care of these mundane tasks, shifting focus to more pressing concerns in the facility.

Smaller establishments can still find ways to introduce automation. For example, installations like conveyor belts move items along the facility. Automated labeling machines can transfer the necessary information.

Certain equipment can also improve staff safety. For example, about 70 worker fatalities occurred in forklift-related accidents across different sectors. Self-operating forklifts simplify warehouse transportation and prevent hazardous contact.

Blockchain Technologies

Blockchain technology is a key database streamlining data storage and information sharing. Warehouse management entails plenty of information about product quantity and delivery. Many parties — like suppliers, manufacturers and distributors — are involved.

The blockchain ensures information is accessible and interconnected. What’s ideal about this next-gen ledger tech is it keeps data under wraps. Each block is secure in nature because it requires verification and permission.

Thus, blockchain technology is ideal for various financial transactions. If a distributor pays a manufacturer for production, they should process the transaction through this network. It has a suitable layer of encryption while executing those actions.

Internet of Things

The Internet of Things (IoT) is a flexible alternative to blockchain technology. By employing this network, a warehouse can generate connections between products and machines through sensors and software. If one product is removed, the system will detect it and send an update.

The IoT enables warehouses to receive real-time data about the movement of their shipments. This cuts down the slower steps in inventory management and prompts communication between devices so all parties in the supply chain can stay up to date.

It is possible to fuse both next-gen technologies in warehouse operations. The blockchain establishes trust, while the IoT improves connectivity, refining the process of sharing information among multiple parties.

Artificial Intelligence

Multiple industries are utilizing artificial intelligence (AI) in business processes. While most people find its use helpful in customer service, 40% of business owners use AI for inventory management and 30% for supply chain operations. Warehouses can use their programs to collect and organize data in the long run.

AI can also generate different presentations and reports based on the data it receives. Manufacturers with multiple facilities can upload their information and send a prompt to receive specific information about their inner workings.

AI can also provide business recommendations on streamlining operations with predictive analytics. However, these programs’ output depends on the data set given, and there are limits to the predictions they can make depending on the amount of variation.

The next best thing to do with this output is to conduct a comprehensive data analysis. Use the information to set metrics for evaluation in the future. If one area is faltering, make actionable decisions to influence processing in the facility.

Cybersecurity

As effective as next-gen technologies in warehousing are, new problems arise. The Identity Theft Resource Center found supply chain attacks impacted more than 10 million people in 2022. Each facility and its streamlined performance are vulnerable to these cyber threats.

Focus on preventive measures to maintain the order of operations. Investing in a firewall adds a layer of protection to warehouse information. Add intrusion detection systems to alert business owners of any breaches.

Physical security installments can also protect warehouses. For example, surveillance cameras log who accesses company computers during and outside active hours. Biometric technology is also a good touch for tracking and access control.

Optimize Warehouse Operations with Digitalization

Speed and effectiveness are crucial in warehouses. Next-gen technologies have made great strides in equipping facilities with these attributes, so take advantage of them to strengthen operations.

*This article is written by Jack Shaw. Jack is a seasoned automotive industry writer with over six years of experience. As the senior writer for Modded, he combines his passion for vehicles, manufacturing and technology with his expertise to deliver engaging content that resonates with enthusiasts worldwide.

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Automobiles are becoming smarter thanks to advancing technologies and manufacturing practices. Nowadays, vehicles can communicate with each other, people and networks to increase safety due to artificial intelligence (AI) and machine learning (ML). What if cars on the road could communicate with everything? This technology is in the works through vehicle-to-everything (V2X) communication. Here’s a guide on V2X, its benefits and what it means for auto manufacturing.

The Role of V2X Communication

Automotive communication systems date back nearly half a century as manufacturers started designing systems to let vehicles communicate with each other in the 1970s — a concept known as vehicle-to-vehicle (V2V). V2V is still evolving, with Volvo and FedEx experimenting with automated platooning in Europe and pairing groups of trucks to follow each other on the highway.

Other types of vehicular communication include:

V2N will be critical as researchers continue improving 5G. With this system, cars will send information across networks through LTE and 5G. Experts say about 90% of American mobile connections by 2030 will be through 5G.

V2X in Auto Manufacturing

V2X is a critical technology because it combines all types of vehicular communication into one system. With this advanced mechanism, cars will be more intelligent than ever and could establish themselves as better drivers than humans. Auto manufacturers are trying to accomplish this feat with self-driving vehicles, but the industry hasn’t reached fully autonomous operations yet.

Improving V2X is essential in the race for self-driving vehicles, as this technology lets cars see and understand the world around them. An autonomous car or truck must be able to react quickly to traffic jams, emergency vehicles passing, animals crossing the road and other sudden events. Vehicles could work together and make the streets safer, thus creating a safer environment for autonomous machines.

V2X Applications

V2X offers opportunities to integrate all these technologies into one machine. This vehicular communication system exists in limited numbers currently but could soon make its way into more automobiles.

More recently, Toyota successfully tested its V2X technology in collaboration with Orange. The automaker equipped a vehicle with V2X capabilities and credited 5G and edge computing for its test track accomplishments. V2X technology warned drivers of emergency vehicles, helped them avoid collisions and accurately positioned the car.

What Are the Manufacturing Implications of V2X Communication?

V2X presents an incredible opportunity in the automotive industry to make cars smarter. What does this technology mean for manufacturing? Here are four implications to see as this concept evolves into the mainstream.

Advancing Technologies

Incorporating V2X in all auto manufacturing would make car assembly more advanced due to the AI and ML necessary for building. While some vehicles are simplistic with minimal technology features, these machines require onboard units and other devices to meet V2X’s needs. This change will require employees to understand the technology and how to include it inside the vehicles.

Standardization Needs

Automakers use vehicular communication technology like V2V, but these concepts only work with machines from the same manufacturer. For V2X’s success, auto manufacturers must standardize this technology so cars can connect seamlessly despite the logo on the front. Collaboration must also include semi-chip manufacturers, software developers and other professionals involved in advanced automotive technology.

Cybersecurity Risks

Integrating technology comes with cybersecurity risks, so automakers must ensure their V2X technology has robust security features to protect drivers. Otherwise, operators risk crashes, theft and other unwanted outcomes. One way to safeguard V2X-integrated vehicles is implementing security requirements with third parties to minimize the risk of data breaches.

Supply Chain Visibility

V2X technology can help auto manufacturers with their supply chain visibility — a critical component considering the modern economic climate. With advanced communication devices, automakers can help fleet owners with logistics management and increase transparency with suppliers. For instance, V2X’s enhanced route optimization can reduce lead time for parts, making manufacturing more efficient.

What Advantages Does V2X Communication Bring?

V2X communication is beneficial because it lets the auto industry take another step toward autonomous vehicles. What other advantages does this sector reap? Here are a few positive takeaways from V2X technology.

Driver Safety

With V2X communication, car operators can feel safer on the road. Vehicles communicate with each other to know when hazards lie ahead on the road or changing weather conditions. This benefit is even more pronounced with long-haul trucks, considering their role on America’s highways.

V2X technology in semi-trucks would let logistics professionals use autonomous trucks and reduce accidents and losses. Experts say driverless trucks perform up to 30% better than those with operators, so V2X would go a long way in promoting safety.

Environmental Benefits

Advancing vehicular communication technology also benefits the environment by cutting emissions. The transportation sector is responsible for 29% of all emissions, so reducing this output is essential. V2X can help the environment by mitigating traffic congestion, thus reducing idle time and wasted fuel in cars.

Smart City Integration

Rising urban populations mean cities will need to manage their energy grids better. V2X technology lets vehicles communicate charging needs and reduce strain on the grid. For instance, EVs could select optimal charging times — such as off-peak hours — to help the city’s energy grid and optimize efficiency.

Using V2X Communication for an Autonomous Future

Research on autonomous vehicles has surged as automakers race to be the first to debut fully self-driving cars. Reaching this level of driverless operations requires V2X devices that combine the best aspects of vehicular communication technology. These advanced mechanisms have implications for manufacturers and benefits for drivers, so the future has a lot of potential for this corner of the automotive industry.

This article is written by Jack Shaw. Jack is a seasoned automotive industry writer with over six years of experience. As the senior writer for Modded, he combines his passion for vehicles, manufacturing and technology with his expertise to deliver engaging content that resonates with enthusiasts worldwide.

Auto Parts Supplier Revs Up Its Production Process {case study}

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Since the industrial revolution, every technological advancement has been viewed through the lens of its effect on jobs. Will I be obsolete? Can a machine do my job better than I can? Are the bots coming for me? If my skills are rendered obsolete, what will I do?

The plain truth is, sometimes machines can do the job better, faster or more efficiently than a human can. Think of the advent of the sewing machine. Even your grandmother’s old Singer model is a whole lot faster, more precise and efficient than she is working with a needle and thread. The art and craft of sewing isn’t lost or obsolete, but for sheer volume and exact replication, you can’t beat the machines.

What’s happening now with artificial intelligence (AI) in manufacturing is a little bit like that. People on all levels of the manufacturing chain want to know if AI is taking over.

The answer is no. Don’t think of it as a takeover. Think of it as more of a transformation. It’s already happening, and it’s not all bad.

AI’s current impact on manufacturing

Artificial intelligence is seeping into the manufacturing workplace in a couple of important ways.

Automation: Much like the sewing machine and indeed all of the industrial revolution, AI has the power to automate repetitive tasks previously done by humans. Operating machinery, tasks on the assembly line, even inspecting products for defects – all of these things are increasingly being automated.

Efficiency: AI can help us optimize processes and procedures, leading to greater efficiency on the line and as a whole.

New job creation. Yes, you read that right. Whereas AI may reduce the amount of jobs focused on repetitive tasks, it is also creating jobs that we haven’t seen before in the manufacturing realm, including specialized programmers, engineers, and technicians. It means companies will need people with different skill sets, and the savvy employers will dig in and train the people they already have to take on these new roles.

Predictive analytics

At USC Consulting Group, we’ve already been using AI with some of our manufacturing clients, specifically in the area of predictive analytics. We spell it all out in our eBook, “AI and Machine Learning: Predicting the Future Through Data Analytics,” but here is the gist of it in a nutshell.

By now, we all know what AI is — computer systems that perform intelligent tasks, like reasoning, learning, problem solving, decision making, and natural language processing, among others.

Machine learning is a subset of AI. It is, technically, a set of algorithms that can learn from data. Instead of having to be programmed, the computer learns on its own based on data.

Predictive analytics is one output of machine learning. It is the ability to forecast future outcomes based on data. It’s like having a crystal ball that’s informed by vast amounts of complex algorithms and data.

You’re already familiar with predictive analytics but may not know it. You know how Amazon suggests an item for you to buy based on past purchases, or Netflix queues up new shows based on what you’ve already watched? That’s predictive analytics in action.

Much like Netflix’s use of predictive analytics created a seismic shift in consumer expectations, this technology also has the potential to transform operating procedures and processes for many industries.

The benefits of using AI in predictive analytics are many, including:

Bottom line: AI needs us

AI is a powerful tool we’ve used at USCCG to help our clients achieve greater efficiency, productivity, and profits.

But here’s the thing about that. It’s a tool. And it’s only as good as the data we supply. Any variation, and there can be skewed results.

As we all know, life is not a data set. Variation is happening all around us, all the time, even in projects where we need great precision.

That’s why the bots are never going to replace humans. They need us as much as we need them. At USCCG, we have more than 50 years of experience making process improvements, finding hidden opportunities for efficiency, creating leaner systems and helping companies thrive. For the next 50, AI will be one tool we use to help achieve that.

Read more about this innovative technology, including a specific case study about how AI works in practice, in our eBook, “AI and Machine Learning: Predicting the Future Through Data Analytics.”

AI and Machine Learning - Predicting the Future Through Data Analytics eBook

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The automotive industry is driving automation by having the largest number of robots working in factories around the world — operational stock hit a new record of about one million units, according to the International Federation of Robotics (IFR). With the prevalence of automation rising in the automotive industry, the benefits associated with its use in manufacturing cannot be understated. With advantages that work to bring productivity and efficiency all around, advancements in technology such as the integration of artificial intelligence underline the many innovative applications to come.

Exploring the current advantages of automation

“The automotive industry effectively invented automated manufacturing,” notes Marina Bill, the President of the IFR. “Today, robots are playing a vital role in enabling this industry’s transition from combustion engines to electric power. Robotic automation helps car manufacturers manage the wholesale changes to long-established manufacturing methods and technologies.” The IFR goes on to highlight the recent density of robots in the automotive industry — in the Republic of Korea, 2,867 industrial robots per 10,000 employees were in operation in 2021, while Germany had 1,500 units followed by the United States with 1,457 units.

Automation plays a variety of roles in automotive manufacturing, including taking on tasks such as screw driving, windshield installation, and wheel mounting. Automate highlights one example of a valuable role that automation plays in the manufacturing process, via an automated vehicle floor plug insertion system developed by FANUC for General Motors. As a result, the system effectively helps relieve workers from “the ergonomic strain of the manual process and improves production time.” Apart from assembly, Robotics and Automation News notes additional uses for automation in manufacturing include car painting, welding, polishing and material removal, and quality inspection. Regarding the benefits, automation in automotive manufacturing is known to have a wide variety of advantages that heighten productivity in immense ways — including lowering costs, improving accuracy and safety, and amping up efficiency.

Increasing automation highlights a productive future

According to CBT News, automakers are “likely to introduce more robots and other forms of automation over time.” Currently, CBT notes that many robots on production lines are called ‘cobots,’ as they work alongside workers in order to complete tasks that are physically demanding or more challenging to do — Ford, for example, has “at least 100 of these cobots across two dozen of their plants around the world.” Automakers are already planning for increased automation in the future in order to achieve various goals. Tesla is a pioneer regarding factory automation and robots; Elon Musk, for example, has said that introducing more automated equipment at Tesla as part of a goal to cut the costs of making future models by 50%, according to CBT News.

To further underline the presence of automation in auto manufacturing, a 2021 article from The Korea Economic Daily Global Edition highlights the use of robots and artificial intelligence (AI) by Kia Corp., South Korea’s second-largest automaker. According to the article, the company had released a video “showing a highly automated production line of the all-electric mid-size crossover utility vehicle (CUV) at a smart factory powered by artificial intelligence and robot technology.” Crossovers have risen in popularity in the US, with the vehicle featuring an SUV-style body based on a car (rather than a truck platform), therefore using unit-body construction. Today’s crossovers offer a variety of features, with top-rated crossovers offering those such as a spacious interior and a smooth engine.

Innovation foreshadows advancements to come

In addition to simply expanding automation efforts throughout auto manufacturing, ‘smart manufacturing’ employs technology in addition to automation. Also called Industry 4.0, RT Insights notes that data-driven decision-making and predictive maintenance are just the beginning of the advantages associated with smart manufacturing, with benefits extending to areas such as energy efficiency and supply chain optimization. “The resulting factors of having a smart manufacturing set-up are efficiency, production optimization, trackability, quick turnaround during downtime, safer working conditions, and responsible manufacturing,” notes Mobility Outlook.

AI and machine learning (ML) are both components that are driving the future of smart manufacturing, with Mobility Outlook explaining that AI systems analyze data sets and historical records of Internet of Things (IoT) devices. As a result, AI can identify patterns and trends which would otherwise go unnoticed by workers. ML algorithms, on the other hand, can “learn from data, make predictions, and make suggestions to improve manufacturing processes.” Predictive maintenance can also make a major difference in the future of automotive manufacturing, with the analysis of data allowing for minimized repair costs and proactive maintenance. Furthermore, Mobility Outlook highlights the value of quality control systems powered by AI — with this technology, defects can be detected in real-time, allowing for waste reduction and improved product quality across the board.

Automation brings a variety of benefits to automotive manufacturing. While automakers are already making use of the technology, technological advancements like AI are driving the future of ‘smart manufacturing,’ effectively foreshadowing a range of advantages to come.

*This article is written by Lottie Westfield. Lottie spent more than a decade working in quality management in the automotive sector before taking a step back to start a family. She has since reconnected with her first love of writing and enjoys contributing to a range of publications, both print and online.

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