Tag Archives: AI

 

In industrial operations, Maintenance, Repair, and Operations (MRO) functions are essential but often overlooked as a source of inefficiency. While MRO spend typically accounts for less than 10% of a company’s total procurement, it can represent up to 80% of its transactional activity. The root of this imbalance?

Poor data.

Bad MRO data is more than a technical issue, it’s a strategic liability. Duplicate part numbers, inconsistent naming conventions, missing attributes, and obsolete records all combine to slow down procurement, increase errors, and inflate costs. The problem is so pervasive that some studies estimate up to 26% of all purchase orders require rework due to bad data.

How Poor Data Inflates Transaction Costs

Economists refer to the hidden friction in business processes as transaction costs-the overhead incurred not from the value of goods themselves, but from the effort required to find, negotiate, and manage those goods.

In MRO, transaction costs are driven by three main issues:

These costs don’t just reduce efficiency, they erode trust in systems and waste valuable labor hours across procurement, maintenance, and finance teams.

The Power of a Clean, Structured MRO Dataset

Fixing these problems starts with building a clean, enriched, and standardized MRO data foundation. That means:

When data is standardized and machine-readable, organizations can streamline part identification, automate procurement, and unlock powerful analytics. They can also reduce inventory costs, prevent emergency orders, and improve supplier performance tracking.

Automating the Solution with AI

The scale and complexity of MRO datasets make manual data cleansing and classification impractical. That’s where AICA’s Product Data Intelligence platform plays a pivotal role.

AICA automates the process of data classification, cleansing, and enrichment by applying domain-specific machine learning models trained on millions of MRO records. It can:

The result is a reliable, structured material master that integrates directly into ERP, EAM, and procurement systems, serving as a “single source of truth” for all MRO items.

How USC Consulting Group and AICA Work Together

We’ve partnered with AICA enabling us to deliver rapid, scalable improvements in material master quality for our clients, unlocking cost savings, productivity gains, and strategic sourcing capabilities.

Together, USC and AICA help organizations move beyond firefighting and toward a future of proactive, data-driven operations, where every transaction is faster, smarter, and more reliable.

Start your product data transformation today, get in touch with us to find out more.

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Smart factories are redefining how supply chains operate, making production lines faster, more accurate, and better connected than ever. At the heart of this transformation is the integration of advanced technologies that allow systems to communicate in real time. This connection between machines, software, and data is key to improving decision-making, reducing waste, and boosting operational efficiency.

Core Technologies Powering Smart Factories

Several technologies work together to bring smart factory environments to life. Industrial Internet of Things devices collect data across every part of a facility, from machine performance to product quality. This data is then sent to centralized platforms powered by artificial intelligence and machine learning, which analyze it and trigger automated responses. These insights help supervisors predict maintenance needs, monitor production bottlenecks, and allocate resources more efficiently.

Meanwhile, robotics and automation streamline repetitive tasks. Machines that once operated in isolation now sync with surrounding equipment, creating smooth transitions between steps in production. This reduces downtime and manual errors. Cloud-based platforms allow managers to access performance data from any location, supporting more agile responses across the entire supply chain.

The Importance of System Compatibility

Integrating these technologies successfully depends on strong infrastructure and compatible systems. Manufacturers must ensure that legacy equipment can communicate with newer software platforms or be updated without major disruption. Choosing hardware that supports open protocols allows easier integration across departments and vendors.

Reliable connections are also essential. Sensors, programmable controllers, and real-time data platforms must work in coordination without lags or breakdowns. This is where cable assembly manufacturers play a subtle yet critical role. High-quality cables and connectors provide the physical support necessary for transmitting data quickly and accurately throughout a smart factory.

Transforming Supply Chain Agility

As smart factories mature, their benefits ripple throughout the supply chain. Real-time visibility allows businesses to respond faster to supply shortages or changes in demand. Integrated technologies help eliminate silos across logistics, procurement, and production planning. The result is a more flexible, cost-effective operation that meets customer needs faster.

Smart factory integration is not just a technological upgrade. It is a strategic step toward building a connected supply chain that can anticipate challenges and adapt quickly. Companies that invest in the right tools and connections will be better positioned to lead in an increasingly data-driven economy.

The accompanying infographic provides a breakdown to building a smart factory:

Building Smart Factories - Integrating Technologies for Efficiency infographic

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Poor asset management can result in significant financial losses beyond the cost of replacement, with reputational damage, compromised data, and operational disruption a few of many risks involved. Effective asset management, on the other hand, is essential for businesses across industries, with advantages that include increased productivity, elevated asset utilization, and minimal downtime, The Enterprise World highlights. For organizations that aim to enhance their asset management process, the perfect balance of key strategies is a must — especially when the goal is to better manage the return and disposal of physical assets.

Preventative maintenance as a front line defense

For companies that manage assets like physical tech equipment, a strategy that focuses on maintenance is essential in order to maintain assets that are in optimal working condition at all times. Further advantages include asset longevity, reduced maintenance costs over time, and greater operational efficiency. Due to the value that preventative maintenance can bring, businesses must consider the strategy as a valuable front line defense in an asset’s life cycle.

While manual, routine inspections are crucial to an effective asset maintenance strategy, technology now plays an indisputable role in predictive maintenance and asset management as a whole. Artificial intelligence (AI) is just one technology that is gaining traction in asset management. For example, AI algorithms can process large amounts of data in order to predict maintenance issues and generate optimal schedules for maintenance — all the while factoring in an asset’s previous maintenance data. This can prove to be particularly beneficial for organizations that possess a significant number of assets (such as many laptop computers). When combined with efforts like manual asset tracking, businesses can rest easy in knowing that everything is accounted for, in good condition, and up-to-date on routine maintenance.

A streamlined return process

Effective equipment tracking is essential for any business with physical assets. Today, equipment management endeavors go well beyond a simple spreadsheet, and will typically involve the combined use of both physical tracking options (like barcodes or equipment tags). Software is another essential element, as asset management software provides a deeper look into the valuable details associated with physical assets. This technology will not only provide an overview of the assets that a business has, but details in regard to maintenance history and location (to highlight a couple of insights).

Businesses that have a well-defined asset tracking approach can easily overlook the complex nature of certain parts of the asset management process. For example, the return process can often involve several kinks along the way, such as employees that fail to send back company equipment, or inefficient shipping which can result in untimely delays. As such, effective equipment tracking does extend to a successful equipment return process. This is especially crucial for companies that routinely ‘loan out’ technology to their employees — a lost laptop is just one asset that can create a ripple effect for a business. In addition to lost productivity and the cost of replacement, the company may also have to shoulder the cost associated with a data breach, a factor that further underlines the importance of an effective return process. In addition to clear instructions, it’s essential that a business has a strategy in place to streamline the return. In addition to a trustworthy equipment management system and staying on top of paperwork (such as custom fees, etc.), aspects like consistent tracking updates throughout the shipping process can make a major difference.

Asset disposal can be a sustainable process

The integration of technology can elevate an asset management strategy by enabling businesses to continuously optimize maintenance schedules and elevate the return process. In turn, companies can minimize the downtime of assets, and benefit from equipment that will go the mile. When assets are truly no longer useful, however, businesses must abide by an effective disposal strategy. In many cases, there are sustainable approaches that can underline further advantages, such as environmental benefits or supporting the local community.

In some cases, a business may wish to donate old equipment (like computers, printers, etc.) to schools in order to support the local community. Businesses may wish to explore other options as well, such as the ability to refurbish and sell their equipment. Recycling old tech is another solution, and can be a sustainable option for equipment that is truly at the end of its life cycle. Before old tech leaves the hands of a business, however, it’s crucial to gain a full understanding of the laws, regulations, and the additional considerations that are involved. For example, businesses that wish to recycle their tech will need to comply with e-waste disposal laws. Other tasks are equally as necessary, such as a thorough asset inventory, the proper and secure destruction of any sensitive information (including GDPR compliance where applicable). Enlisting the help of a certified and professional Information Technology Asset Disposition (ITAD) provider can be a great option that can help guide a business through the process.

Poor management of physical assets brings to light a number of stark consequences for a business, from unnecessary financial losses to lessened efficiency overall. A balance between technology driven solutions and smart considerations, however, can make for an elevated management process even where asset return and disposal are concerned.

*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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Operational excellence is the pursuit of enhanced efficiency and effectiveness in business processes. Traditionally, companies relied on established methods to optimize their operations. However, artificial intelligence (AI) and data science now augment traditional practices, leading to innovations in Lean manufacturing.

Lean manufacturing remains foundational to operational excellence. Its principles — such as continuous improvement, process optimization, and employee engagement — help organizations adapt to changing market demands. For instance, companies that implement Lean practices can respond swiftly to customer needs, reduce lead times, and improve product quality.

Sticking to lean principles is crucial; they not only enhance flexibility and engagement among employees but also position companies better to manage supply chain disruptions and fluctuations in demand. By embracing these methodologies, businesses can achieve long-term growth and get ahead of the competition.

AI and Automation in Operational Excellence

AI-driven automation is revolutionizing business operations by improving efficiency and innovation. By integrating intelligent algorithms, organizations can streamline processes and reduce manual intervention, enabling employees to focus on higher-value tasks. For instance, predictive analytics allows companies to anticipate customer needs and align production schedules accordingly, minimizing waste and maximizing output — core tenets of Lean manufacturing.

Strategic AI approaches, such as machine learning for demand forecasting, empower businesses to adapt swiftly to market fluctuations. Companies like Amazon use AI to optimize inventory management, ensuring products are available when needed while reducing excess stock. Similarly, AI-powered chatbots improve customer service by providing instant support, increasing engagement and convenience.

Moreover, automating routine tasks both accelerates operations and fosters a culture of ongoing improvement. As employees embrace AI tools, they are encouraged to find opportunities for innovation. Ultimately, these AI-driven strategies position organizations to thrive in a competitive landscape, exemplifying the synergy between technology and Lean principles.

Leveraging Data Science to Identify Inefficiencies

Data science plays an instrumental role in analyzing and improving business operations by using vast amounts of data to uncover patterns, trends, and insights. By employing statistical methods and algorithms, businesses can identify inefficiencies within their processes, leading to data-driven decision-making.

The synergy between data science and AI amplifies this effect. AI algorithms can quickly analyze complex datasets, enabling predictive analytics that foresee customer behavior and operational challenges. For example, machine learning models can optimize supply chains by predicting demand fluctuations, which helps reduce costs and improve service delivery.

Together, these technologies encourage a proactive approach to performance optimization. Businesses can continually refine their operations, respond agilely to market changes, and ultimately maximize customer satisfaction. Therefore, integrating data science with AI not only helps in identifying inefficiencies but also drives growth and competitive advantages.

Integrating Lean Practices with AI and Data Science

Lean practices focus on eliminating waste and improving efficiency, while technology-driven strategies leverage AI and data science to enhance operations. The integration of these methodologies allows companies to create a robust operational framework that is both agile and efficient.

Organizations can employ AI for real-time data analysis to support Lean initiatives. This enables quicker identification of process bottlenecks and focal areas for improvement. When combined with data science, businesses can employ predictive analytics to anticipate customer demands accurately, facilitating proactive decision-making.

Companies like Coca-Cola and Unilever have successfully harnessed advanced technologies such as AI and data analytics to streamline operations. Coca-Cola utilizes AI to optimize its supply chain and enhance customer engagement, while Unilever employs machine learning for demand forecasting, allowing for better inventory management. Both organizations demonstrate how integrating advanced technologies can lead to improved efficiency and responsiveness in a dynamic market.

Real-World Applications: Reducing Waste and Streamlining Processes

To enhance supply chain efficiency, organizations can leverage AI, data science, and Lean methods to identify and eliminate key sources of waste. For instance, AI-driven analytics can uncover overproduction by predicting demand more accurately, allowing companies to align their manufacturing with customer needs. Data science can optimize inventory levels, reducing excess stock and storage costs by implementing just-in-time inventory systems.

Additionally, Lean principles advocate for minimizing motion waste by redesigning workplace layouts and streamlining processes. Using motion studies can identify unnecessary movements in warehouses, enabling the creation of more efficient workflows.

By addressing common sources of supply chain waste, such as waiting time, overprocessing, and poor route planning, organizations can create a waste-resistant distribution chain. Route optimization software improves transportation efficiency, reducing fuel costs and delivery delays. Collectively, these strategies not only cut costs but also enhance customer satisfaction and employee morale, fostering a more effective and responsive supply chain.

Conclusion

The evolution of operational excellence has increasingly integrated AI, data science, and Lean practices, creating a framework for sustainable growth and competitive advantage. This enables organizations to use real-time data analytics, enhancing decision-making and facilitating agility in operations. AI can predict customer demand more accurately, minimizing overproduction and optimizing inventory levels, while Lean principles focus on eliminating waste and streamlining processes.

The benefits of this integration are profound: reduced costs, improved efficiency, and maximal customer satisfaction. By harnessing advanced technologies, companies can identify process bottlenecks and enhance supply chain efficiency, positioning themselves adeptly in a dynamic market environment.

Looking ahead, the future of operational excellence will see deeper integration of emerging technologies, fostering a culture of continuous improvement. Organizations that embrace this will improve their operational capabilities and innovation, ensuring they remain competitive in an increasingly complex business landscape.

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

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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

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.

Leveraging AI and IoT in Your MOS Feature Image

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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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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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