Tag Archives: AI

In the rapidly evolving landscape of modern business, artificial intelligence (AI) is a pivotal force reshaping how companies operate. Integrating AI into business processes offers a profound opportunity to enhance efficiency, drive innovation, and gain a competitive edge. However, successful AI adoption requires more than technological investment; it demands a strategic approach encompassing education, collaboration, and ethical considerations. Businesses can effectively harness AI’s potential to revolutionize their operations and achieve sustainable growth by focusing on these key areas.

Maximizing AI Integration Through Strategic Partnerships

Collaborating with technology partners can significantly boost your efforts to integrate AI into your business operations. By forming strategic alliances, you can tap into specialized insights and expertise crucial for navigating the complexities of AI implementation. For instance, a bilateral collaboration with a tech firm can streamline data integration processes, ensuring your AI systems function efficiently. Engaging with multiple partners in an AI-driven ecosystem allows for sharing knowledge and resources, which is essential for overcoming challenges like stakeholder coordination and data management.

Leveraging AI Education for Business Growth

Deepening your understanding of AI through education can be transformative for your business. By exploring computer science degrees online, you can build your skills in AI, IT, programming, and computer science theory. This knowledge is vital in today’s competitive market. Online learning offers the flexibility to manage your business while advancing your education, making it an ideal choice for busy entrepreneurs. As AI becomes more integral to business operations, having a robust foundation in these areas can provide a significant advantage, allowing you to innovate and streamline processes.

Analyzing Data for Strategic Business Growth

Integrating AI-driven data analytics into your business operations can transform decision-making by extracting actionable insights from extensive datasets. As the augmented analytics market expands, businesses adopting these tools can gain a significant competitive advantage. Utilizing AI and machine learning, business intelligence platforms can reveal trends, discover new revenue opportunities, and preemptively address potential challenges. This approach enhances operational efficiency and drives innovation in product development and customer engagement. As more large organizations embrace these technologies, incorporating AI into your business strategy is essential for sustained success.

Mastering AI ROI in Business Operations

Measuring the return on investment (ROI) for AI initiatives can be complex, requiring a nuanced approach beyond traditional financial metrics. Unlike conventional IT projects, AI initiatives demand a comprehensive evaluation of strategic and operational impacts. It’s essential to consider the immediate costs, such as data acquisition and computational resources, and the long-term benefits, like improved decision-making and enhanced market positioning. To effectively gauge AI ROI, align your AI projects with your organization’s broader goals and continuously assess their influence on productivity and customer experience. Doing so ensures that your AI investments achieve their intended objectives and provide substantial value to your business.

Optimizing AI Integration with Scalable Storage

Adopting scalable data storage solutions is essential to successfully integrate AI into your business operations to accommodate growing data needs. As AI systems become more advanced, they demand extensive data to operate efficiently, making scalable storage indispensable. Technologies like NVMe and Optane offer the low latency and high throughput necessary to support these data-heavy processes, ensuring your AI applications run seamlessly. Moreover, consumption-based Storage-as-a-Service (STaaS) models are expected to replace a significant portion of enterprise storage capital expenditure by 2028, providing a flexible and cost-effective way to manage data growth.

Harnessing AI for Enhanced Business Operations

Integrating artificial intelligence into your business operations can significantly elevate the quality of your products and services, providing a competitive advantage. AI technologies excel at analyzing large datasets to uncover patterns and insights that might be missed by human analysis, leading to innovations in product design and service delivery. For example, AI-driven analytics can deepen your understanding of customer preferences, enabling you to tailor offerings precisely to their needs. Additionally, AI can automate quality control processes, ensuring consistent product standards and minimizing defects.

Promoting Ethical AI Literacy in Your Organization

To foster a culture of responsible AI usage within your organization, your team must enhance ethical AI literacy. Educating employees about the moral implications and potential biases in AI systems empowers them to make informed decisions and underscores the importance of transparency and accountability. This knowledge helps mitigate risks associated with AI errors and ensures fairness in AI-driven choices, such as those affecting promotions or job evaluations. Encouraging this literacy can lead to a more inclusive workplace as employees become more aware of how AI can inadvertently perpetuate discrimination if not correctly managed.

Incorporating AI into business operations goes beyond a technological upgrade—it’s a strategic transformation. By emphasizing education, fostering partnerships, and prioritizing ethical practices, businesses can seamlessly integrate AI to boost efficiency and drive innovation. While AI adoption may be complex, a well-planned approach can lead to significant advancements, streamlined operations, and a stronger position in the market, paving the way for long-term success in an increasingly digital world.

Partner with USC Consulting Group to transform your operations and achieve sustainable success through expert process improvement and hands-on implementation.

*This article was written by Dean Burgess. Dean runs Excitepreneur, which celebrates the achievements of entrepreneurs. He understands that there are many types of entrepreneurs, and strives to provide helpful information to assist them in achieving their particular idea or goal.

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As technology continues to improve, large companies and supply chain manufacturers have more opportunities to expand their businesses and reach more customers with their products. With this power comes responsibility, calling for transparency in supply chains.

Reliable tracking systems must be implemented to enhance supply chain transparency and ensure businesses and customers get the accurate information they need. Real-time visibility platforms (RTVP), new technology and optimized data collection can create more visible supply chains.

What Is a Real-Time Visibility Platform?

Real-time visibility allows for the tracking and monitoring of products and goods, from pickup to delivery. With real-time data, all steps involved in the supply chain process are receivable, making it much easier for large companies to provide honest product and transportation details.

Real-time visibility platforms are the software tools and technologies that make this data possible. RTVP gathers data based on live updates on product location and status.

What Technologies Play a Part in Real-Time Visibility?

Like many things today, real-time visibility would not be possible without technology. As systems continue to grow, so does the potential for full transparency.

Many technologies work together to make real-time visibility possible. The following are some of the tools used to streamline processes and maximize supply chain transparency:

1. GPS

GPS technology has become an integral part of the real-time visibility process and enables RTVP to track objects for accurate data. GPS technology uses satellite signals and signal reception to capture the location of items, roads and buildings, and it sends this data back to our devices.

GPS technology does wonders for the transportation industry. By accurately tracking trucks and other transportation vehicles, we can watch products travel from point A to point B and make decisions based on their location.

2. AI

Artificial intelligence has entered many realms of society, including the supply chain. Fortunately, AI makes RTVP possible for various transparency purposes.

AI considers all factors and works alongside humans to enhance decision-making and efficiency, leading to a faster, safer and more honest supply chain. AI also powers advanced analytics to help humans and businesses analyze real-time data, making it applicable to all industries.

3. Internet of Things (IoT)

IoT creates a robust network that allows data to flow freely and improves connection and communication between different devices. Through this created network, IoT can narrow down specific items and points of data to share information and even make decisions.

From quantity to fulfillment, IoT processes data through algorithms that contribute to accurate, real-time visibility.

4. Blockchain

Blockchain acts as a safety tool for RTVP, “blocking” hackers and other forms of data manipulation. There are four main types of blockchain: public, private, hybrid and consortium. Each form creates a securely shared network of data that allows parties to communicate.

Blockchain allows equal access for all parties, and there is no single network owner. This provides for ethical, open movement throughout the supply chain and adds transparency to traditional supply chains.

Why Implement a Real-Time Visibility Platform?

Real-time visibility platforms provide endless benefits to supply chain industries. RTVP acts as a high-functioning network of technology and intelligence to help businesses identify areas for improvement and solve problems in all areas — from ethical to logistical.

With RTVP, there is no shortage of possibilities. The following are four benefits of implementing a real-time visibility platform:

1. Ensure Customer Satisfaction

Without RTVP’s technology, product location and safety are unknown to businesses and customers. When companies implement a real-time visibility platform, “the unknown” is eliminated. With access to knowledge such as when and where their goods will arrive, customers know exactly what to expect.

RTVP shares information with customers they previously did not have access to, such as ETAs and tracking details. The more customers know, the happier they will be!

2. Speed up Reaction Time

RTVP gives companies the power to detect precisely when and where disruptions occur, from departure to arrival. If an issue arises along the way, businesses know in real time, allowing them to immediately develop a direct course of action.

Companies can mitigate risks with transparent access to data, which reduces wasted time spent planning strategies and reacting to issues. RTVP allows for the tracking of delays, traffic, congestion, weather and anything else that could pose a potential threat.

3. Reduce Costs

Implementing a real-time visibility platform provides many financial benefits. Companies have access to trucks and products, allowing them to see available capacity and, in turn, utilize all available space. This creates sustainable, efficient transportation and also cuts costs.

Many industries are now dealing with labor shortages, rising material costs and an ever-changing risk landscape. With RTVP, industries can detect traffic, weather conditions and other risks to product transportation, reducing the risk of unnecessary financial burdens. With saved time and money, industries can focus on solving more significant issues.

4. Improve Relationships

RTVP uses technology to improve relationships between all parties involved. With transparency and proper information sharing, people can access honest details, avoiding the risk of being blindsided or misinformed.

Transparency positively impacts shippers, managers, workers, clients and customers, resulting in better collaboration and more satisfied people. RTVP also enhances communication, allows for honest lane sharing and improves handoffs and business interactions.

Use RTVP for Supply Chain Transparency

RTVP allows supply chain leaders and manufacturers to stay ahead of their industry and interact with advanced modern technology. Businesses can work hand in hand with real-time data to make their processes more efficient and keep customers satisfied. With RTVP,  transparency is possible.

***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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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

• Ensure the right resources are at the right place to minimize lost time – enabling safe and disciplined execution
• Optimize mine planning and scheduling across all planning horizons – delivering detailed and accurate plans
• Identify potential roadblocks proactively during mine planning and solve complexity during the planning process
• Control quality of work at the point of execution by identifying off specification and enabling in-shift correction
• Enhance your ability to cluster & centralize scarce human expertise, allowing all sites to benefit from their expertise

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:

• Poor product design
• Poorly designed processes
• Unfit operations
• Unsuitable machines/equipment
• Untrained operators
• Variability from incoming vendor material
• Lack of adequate supervision skills
• Changing or inadequate environmental conditions
• Inadequate maintenance of equipment
• Inadequate or changing environmental conditions

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:

• Carefully defining the problem
• Selecting the right team
• Objectively identifying high-impact operations
• Drilling down into the tasks within those operations
• Brainstorming possible causes on those high-impact tasks
• Recommending and implementing deeply focused corrective actions
• Controls so it doesn’t happen again

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.”

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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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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:

• Greater precision and accuracy. Yes, humans do the programming. But AI can analyze mountains of data and identify complex patterns people might miss.
• AI can analyze vast amounts of data in a snap. This helps companies make decisions faster.
• Increased efficiency. All of this accuracy and speed leads to greater efficiency, output and ultimately, profits.

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.”

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