Datalynq's Market News - March 2023 Update
Data Analytics and AI, How Components Are Changing Everything from Healthcare to Manufacturing - March 24, 2023
Digitalizing your organization is for more than just tech industries. Artificial intelligence (AI), machine learning, big data, predictive analytics, and more are changing how organizations operate in the 21st century. After the 2020-2022 chip shortage, development in AI and its capabilities has resumed improving rapidly. Each improvement brings better benefits through enhanced accuracy and faster results.
Healthcare, notorious for its complex and niche fields, benefits immensely from the inclusion and aid of AI. Predictive analytics and AI, in general, partnered with human staff, through studies have proven how efficiently work can be done when operating in tandem.
Predictive Analytics is a Great Tool for Manufacturing and Precision Medicine
Technology truly is a beautiful thing. Each innovation benefits everyone, from an organization's production lines right down to the consumer. Every industry saves time and cost while increasing efficiency with the aid of digital tools. Any business sector, from retail to healthcare, can operate more smoothly with the aid of AI and the many tasks it can accomplish
A rising star within AI is predictive analytics. While it is not necessarily a new feature, its aid in numerous industries has recently risen in popularity and further implementation. That includes healthcare.
While AI, in general, is not new to medicine, predictive analytics, a great tool for manufacturers, is now on the rise. Predictive analytics identifies patterns and trends through data analysis, thereby providing alerts and possible solutions to human decision-makers. The biggest boon of predictive analytics in the semiconductor manufacturing supply chain is sorting through mountains of data quickly and accurately. The semiconductor supply chain, a large and usually complicated chain of original chip manufacturers (OCMs), original equipment manufacturers (OEMs), distributors, clients, and more, produces proverbial oceans of data daily by each member within it.
Predictive analytics is not limited to supply chain forecast management. It can also be utilized to predict when robotic assembly tools will go down for maintenance. This ensures that operations can be accurately scheduled around that time frame. Predictive analytics in chip manufacturing can also quickly forewarn manufacturers of possible risks in the form of sole source components. Sole source components face higher prices, longer lead times, and obsolescence, as there are no form-fit-function (FFF) alternates to replace them in the future.
With the boon predictive analytics provides to manufacturing, is it a surprise it has also become an excellent medical assistant? Predictive analytics in precision medicine can provide personalized treatment plans and associated risks of a patient’s genetic history, lifestyle, and environmental data. It can alert providers to specific conditions and diseases, such as cancer or heart disease, for patients long before symptoms appear. Successfully aiding in proactively treating patients long before malignant tumors or illnesses strike. Does that sound familiar?
Healthcare is just as, if not more complex, than the semiconductor supply chain. The amount of data the program must sort through to accurately predict future events and outcomes is immense and only continues to grow. Luckily, most developments within AI are happening at the same rapid pace that both medicine and technology have set, making it sensible to integrate predictive analytics into an organization so it can learn these advancements as they’re developed.
AI is Being Utilized to Detect Cancer
Pattern recognition within AI has been a function steadily perfected over decades. It’s a vital component of most AI systems, as machines can easily identify patterns in data. Once it recognizes a pattern, AI can make decisions or predictions using specific algorithms. This crucial component of AI can be utilized in many fields and industries.
Radiology is not a new industry for AI. In 2018 in an article published by the National Institutes of Health, a team of doctors examined the relationship between AI and radiology. Specifically, the authors discussed the general understanding of AI and how it can be applied to image-based tasks like radiology. Recent advances in AI-led doctors realize that deep learning algorithms could lead AI models to exceed human performance and reasoning with complex tasks, as current AI models can already surpass human performance in narrow task-specific areas. In some regions of medicine, such as radiology, the early detection of cancer can make a big difference in the patient's mortality.
In the 2018 article, the authors noted that using AI in mammography, a particularly challenging area to interpret expertly, could help identify and characterize microcalcifications in tissue. In 2023, AI is being used to help successfully identify breast cancer in mammogram screenings. This AI is utilizing an advanced form of pattern recognition to assist radiologists in analyzing the images’ details.
The AI used is called computer-assisted detection (CAD). Studies have shown that CAD helps review images, assess breast density, and flag high-risk mammograms that radiologists might have missed. It also flags technologists for mammograms that need to be redone. A study published last year found that CAD was just as effective, if not more so, than a human radiologist in a faster period.
One doctor who spoke with the New York Times stated, "AI systems could help prevent human error caused by fatigue, as human radiologists could miss life-threatening cancer in a scan while working long hours.” While doctors and AI development teams agree that AI can never replace doctors, AI-human teams reduce the workload of radiologists by having an automated system quickly and accurately provide a second opinion.
This partnership is true for all industries that incorporate AI into their organization. The goal of utilizing AI shouldn’t be to replace human staff but to aid them in accomplishing goals faster and more accurately. Continued advances in AI show that it is versatile and can be trained to perform numerous tasks, from cancer detection to even product defects. Implementing AI into your organization can be a time and cost-effective strategy.
Why Case Management is Necessary for Any Design Strategy- March 10, 2023
The digital tools we use are only getting smarter. Machine learning is quickly becoming part of organization workflows, but it shouldn’t stop there. Machine learning can provide a strong foundation for several manufacturing operations, decreasing costs while improving production line efficiency.
Component case management is an easily overlooked strategic aspect in the electronic component industry. For many original equipment manufacturers (OEMs), the only element given any strategic overview is sourcing and when to schedule orders. But none of that matters if you aren’t investing in a tool that can aid you in successfully managing components necessary for your products from start to finish.
Component case management should be done during the initial design phase so that risks are discovered and mitigated long before they become a problem.
Why You Need Component Case Management
The traditional reactivity method in response to component obsolescence, shortages, and other disruptions is no longer applicable in a post-pandemic supply chain. Proactivity concerning component management is necessary for the supply chain of today. What was solved through simple communications with suppliers is no longer applicable in the complex, global supply chain that is continuing to diversify. Strategizing for component risk factors must be done as early as the design phase.
Otherwise, one might lose costs and time reacting to problems through damage control, much like the automotive industry had to during the pandemic.
Manufacturing case management is a dynamic process that assesses, plans, implements, coordinates, monitors, and evaluates to improve outcomes, experiences, and value. In the case of electronic component manufacturing, case management is actively pre-planning for scenarios, the expected downtime, and the cost for the preplanned response. That could be anything from component obsolescence impacting products and planning a last-time-buy (LTB) before a manufacturer ceases production to complete product redesign around a component.
Component case management is usually used to plan a documented strategy to mitigate and resolve the issue in preparation for component obsolescence. However, OEMs can use case management for many problems beyond obsolescence management. Documenting plans that coordinate strategies to resolve these issues lead to better efficiency in resolving them. Likewise, for many defense OEMs, these documents on case management are required.
Datalynq offers case management for users that aids in identifying issues you may have with parts, opening a case on these problems, and taking action to resolve them. When you open a case in Datalynq, you can add the expected impact date, the case status, the government case number if acquired, the number of days production will be impacted, the impact rate of logistics and repairs, and more. Once you’ve initiated a case within Datalynq, all your pertinent case information is documented in an audit trail.
You can also add information for potential resolutions, their cost, the summary of the mitigation plan, and even the confidence of how this mitigation strategy is expected to work. These documents provide full transparency to government agencies, like the Department of Defense (DoD), which require it. For other OEMs, visibility into product case management helps smooth the resolution process and can be shared easily with the necessary departments to implement such a significant change.
If you want to see how easily Datalynq’s case management system can improve manufacturing processes effectively, Datalynq’s 7-day free trial lets you take control.
Machine Learning Transforms Manufacturing
As part of the greater umbrella of artificial intelligence (AI), machine learning is “the use and development of computer systems that can learn and adapt without following explicit instructions by using algorithms and statistical models to analyze and draw inferences from patterns in data.” Machine learning is an intelligent program that learns through studying data to predict, detect, and provide analyses through its algorithm.
Machine learning usually comes to mind when people think of AI, as it is often utilized in natural language processing (NLP) chatbots to better imitate human speech. The popular ChatGPT possesses some machine learning in its program as its algorithms are pre-trained by data. This pre-training then aids in generating text, whether small chat box responses or entire articles, close to human speech.
Beyond chatbots, machine learning is a rapidly expanding field thanks to its endless potential in many applications. Any industry can utilize machine learning, from retail to healthcare. It is particularly helpful in manufacturing applications. OEMs can use machine learning within manufacturing for quality control through defect detection, automation of repetitive work in production lines, and customization of products.
Utilizing training algorithms to help identify product defects from images and other data sources can help reduce the cost of quality control while improving inspection accuracy. Along those same lines, machine learning can be paired with another subset of AI called predictive analytics. Together these programs can detect, predict, and forecast when automated production lines need maintenance and how long they’ll be nonoperational.
The repetitive nature of production lines makes machine learning the perfect tool for automation. Assembly line robots can run off machine learning algorithms trained to perform many tasks, from welding to part fabrication. Automation with machine learning cuts down on operational costs while increasing efficiency. Automating production lines also frees human staff from tedious but necessary simple tasks so they can put their time and attention into more innovative projects.
Customizing products with automated production lines trained through machine learning is far easier. Time spent customizing products through manual labor and individualized assembly lines would no longer be necessary. Machine learning can abide by the data that comprises these custom designs without incurring additional costs of individualized production lines. It simply needs to be told when and how to do it before it starts creating.
Machine learning technologies will continue to be implemented far into the future beyond the small forays of ChatGPT into general workflows. With how competitive the current global supply chain is, reducing operating costs and time to manufacture products will give companies an edge many hesitate to take advantage. It’s time to get started.
Data is Transforming the Semiconductor Supply Chain - February 24, 2023
The world is becoming more connected. As it does the amount of data that these connections produce grows too. The global supply chain produces a significant amount of data every year but still needs more visibility for industry members to be able to glean all the critical insights within it. To manage this information, data analytics and artificial intelligence are vital to collect important supply chain information.
As data-driven tools become more advanced, so does the information it delivers to users. To stay competitive and keep further disruptions from impacting the world’s supply chain, original equipment manufacturers (OEMs) must adopt these tools. If not, manufacturers might miss some important red flags that warn of future disruptions, like those brought on by the pandemic.
Semiconductor Supply Chain Issues Are Negated by Data Analytics and AI
It should be no surprise that data-driven analytics and artificial intelligence (AI), among other digital tools, help mitigate supply chain disruptions. After experiencing unexpected events derailing plans over the last several years, supply chain managers are eager to keep history from repeating. Rohit Tandon, the managing director and global AI and analytics services leader at Deloitte, explained the only way to prevent future disruptions is to know what they are and plan accordingly.
“The Covid-19 pandemic vividly illustrated unexpected events' impact on global supply chains. However, AI can help the world avoid similar disruptions in the future.” Tandon said, “AI can predict various unexpected events, such as weather conditions, transportation bottlenecks, and labor strikes, helping anticipate problems and reroute shipments around them.”
The supply chain is a complicated beast that needs dedicated monitoring, which today only a program can sufficiently manage. AI and other machine learning algorithms accomplish this by crunching through massive amounts of data generated daily by the electronic component supply chain. The data, far too much for a team to sort through and analyze as quickly as AI can, is growing in specificity and amount each year.
This increased transparency can lead to improvements in operating efficiency, which, in turn, boosts working capital management with fewer supply disruptions. “Manufacturers that are using AI for visibility,” said Tandon. “Can better respond to potential disruptions to avoid delays and pivot if needed…organizations can leverage data analytics for deeper insights across the supply chain.”
Even better, these tools are designed to improve demand prediction and support data sharing with customers and partners. So, everyone benefits from the insights. The increased transparency also helps fortify supply chain resilience and build trust in the output of analytics and AI processes. As these tools develop, it becomes easier to identify trends and patterns to guide customers through market conditions years into the future. It can point out design risks when using specific components if they are a sole source, EOL preventing costly future redesigns or increased shortage risk.
The most crucial factor to consider is finding a tool that provides the most accurate data so that when information is shared, it helps, not hinders. The most capable market intelligence tool that combines real-time market data and predictive analytics with other management algorithms is Datalynq.
Cyber-Manufacturing Improves the Global Supply Chain
The CHIPS and Science Act are making OCMs approach manufacturing in the U.S. a little differently. The U.S. needs more skilled labor to become a chip-manufacturing powerhouse. However, there are two solutions to that problem.
At the start of 2023, tech giants in Silicon Valley began laying off staff in massive waves, thanks to consumer demand slowdown. As a result, the talent pool of skilled candidates increased. This influx of experienced labor is not as large as the talent pools inside India and Vietnam, but their experience with Silicon Valley’s tech leaders can kill two birds with one stone. First, they can help support the new facilities in research, development, and manufacturing with their technical expertise. Secondly, they have the expertise to properly manage a cyber-manufacturing line efficiently.
What is cyber-manufacturing? It refers to a modern manufacturing system that offers an information-transparent environment to facilitate asset management, provide reconfigurability, and maintain productivity. The real-world application of cyber-manufacturing is utilizing automation, artificial intelligence (AI), the Internet of Things (IoT), and other data-driven analytics to provide transparency. Most industry experts believe that the modern manufacturing model further embraces automation tools for production lines to save on labor expenses. The future workforce with more competitive OEMs will be tech-savvy academics that utilize predictive analytics to forecast downtime and maintenance on machines.
Covid-19 showed how dangerous unplanned and unexpected failures complicated the global supply chain. Further developments in cloud and quantum computing through AI and machine learning are expected to lead to additional visibility of production line health. Vulnerabilities will be easier to spot, and the costs to maintain this type of production will be lower in the long run.
When more manufacturers embrace cyber-manufacturing, the greater resilience of the world supply chain develops. The less unpredictable events occur on automation lines, the less likely it will impact other manufacturers and suppliers further down the chain. The best way to get started is through digital tools that support case management for design components. Datalynq does this task effectively and only grows in accuracy.
Data Can Manage Your Stock Better, But Sole Sources Can Undo That Step - February 10, 2023
Too much stock, too little stock, and no stock because the sole source manufacturer cannot meet demand or is otherwise impacted. Many of us have dealt with that over the past several years. Unexpected demand highs and lows paired with disruptions turned buying stock into a gamble. No matter what original chip manufacturers (OCMs) and original equipment manufacturers (OEMs) tried, everyone wound up with the short end of the stick.
But what else can you do? In the face of unpredictable consumer demand and weather, how can you accurately predict what will be short one day and excessive the next? Fortunately, there are digital tools available that can do all that and more.
Predictive Analytics is the Key to Prevent Backlogs and Excess
Over the last few years, manufacturers have experienced one of two things—inventory backlogs or excess stock. Many of us have been without supply-demand stability for a while, and we might be in for one or two more years. Why?
At the pandemic's start, automotive original equipment manufacturers (OEMs) cut chip orders. In response, original chip manufacturers (OCMs) cut capacity for automotive chips. Chip capacity is determined by order amount, and with no orders, there was no reason to keep capacity for automotive chips. Only automotive demand spiked long before automotive OEMs thought it would and no OCMs had the chips to meet the growing demand.
Since the start of the pandemic, many personal electronics and other white goods OEMs continued to make large orders, or double order products, from eager consumers. OCMs, as a result, increased chip capacity for them. Then in July 2022, demand quickly dropped as recession fears mounted.
OEMs tried to cancel orders, but it didn’t work. Many OEMs are left with six months of stock and no product demand.
And there are many other instances of lacking data, market visibility, and historical trend analysis that led to frustration from either backlogs or excess stock. The result is lost costs and time, production stalls, delayed product development, and more. It is imperative to prevent either scenario from taking place. How are OEMs expected to stay on top of market trends and more if disruptions make time scarcer than the components themselves?
Predictive analytics is the solution.
Predictive analytics is exactly what you think. It predicts customer demand and forecasts future market trends. Rather than manually tracking inventory data, predictive analytics takes mountains of real-time data to forecast demand according to market trends, weather patterns, and other variables to determine future demand needs.
Datalynq, a market intelligence tool that utilizes predictive analytics, uses component sales data from a global, open-market and machine learning to deliver insights on future part availability. You’ll never be caught off-guard when making strategic orders months or years out. It’s time to stop being caught by surprise when Datalynq is ready to use.
How the Shortage Reminded Us of the Danger of Sole Sources
The 2020-2022 semiconductor shortage was a rough ride. The shortage’s easement has finally arrived, but there are still areas of chip scarcity that could continue for a few more years–especially with automotive components. The effects are extensive and could last for years into the future. While there are ways, we can mitigate such devastating effects through predictive analytics and market intelligence, these methods will be worthless if one simple step isn’t taken.
It’s preventing sole source components in your product designs.
What are sole sources? A sole source for a component entails it has no form-fit-function (FFF) alternates and is not manufactured by more than one OEM. Sole source components are an inherently dangerous design risk. The reason is simple and one that the shortage proved over and over throughout its course. There is no backup if bad weather, logistics issues, geopolitical strife, raw material shortages, and more impact the supply chain for sole source components. A manufacturer must wait for the stock to become available again or, if lucky, buy excess inventory from another.
Once a sole source enters obsolescence, manufacturers have no choice but to redesign. As there usually are no alternate components that mimic the form, fit, and function well enough, manufacturers need more wiggle room. Redesigns themselves are time-consuming and costly during normal market conditions. In a period of shortage, those prices shoot up.
Sole source components are usually far costlier than multi-source components. As sole source components are unique and scarce by nature, their prices are generally far higher than non-sole source components. While predictive analytics can warn manufacturers of upcoming shortages and price trends rising, it doesn’t do much to ease either challenge for sole sourced parts.
Another misstep manufacturers can make when trying to avoid sole source components in their BOMs is not identifying who manufactures the alternates and where they’re based. While a component might have existing alternates, they could all be produced by the same manufacturer. Despite the alternates making it a little easier to secure stock, if the OCM is impacted, OEMs, contract manufacturers (CMs), and others still wind up with no stock.
Another concern when sourcing components and preventing sole source is failing to identify whether alternate components are active. If the alternates for a component are all inactive, then it’s no better than having no existing alternates.
The more resilient your BOM is the easier it will be to mitigate minor and major supply chain disruptions. You need a tool to measure your BOM and design risk for sole source components.
Datalynq’s Multi-Source Availability Risk Score uses real-time data to assess numerous attributes, from the number of unique manufacturers to active FFF and DIR alternates. It then breaks the information into a brief, easy-to-understand window for quick, decisive decisions. Take back control of your product design now to prevent tomorrow’s problems with help from Datalynq.
Component Obsolescence is About to Make Waves - January 27, 2022
The market is in a precarious position. The shortage is easing but not over, and a chip surplus is beginning but only for some. Outside of chip excess and scarcity, the global supply chain is still in an odd flux. Automakers want the capacity for legacy nodes to increase while chipmakers cut what is no longer in demand.
What that means is component obsolescence, and a lot of it is just around the corner.
Germany Takes Steps to Decrease Obsolescence Risks During Future Disruptions
In an article by Supply Chain Connect in 2018, two years before the 2020-2022 shortage, industry leaders described obsolescence as a supply chain threat. The reason is simple to understand. Managing component obsolescence takes time, money, and logistics to handle. Obsolescence in electronic components is a persistent and never-ending challenge.
It is not a matter of “if” obsolescence will occur. It is a matter of when.
Component obsolescence is an inherent complication with only a few solutions. One can procure a sizeable last-time purchase of components as they enter the end-of-life (EOL) stage. Another solution requires finding form-fit-function (FFF) alternates to replace the obsolete component. Sometimes last-time purchases or FFF alternates are unavailable, especially if the components are from a sole supplier or used in specific industry devices. That requires a redesign.
It is a lot easier to redesign a phone than it is a defibrillator. The latter requires time-consuming and costly testing to ensure it follows stringent regulations. Medical devices, among other products, must have every part approved. Even replacing a component with an FFF alternate is costly.
Obsolescence becomes much more complicated when the global supply chain is in a period of shortage or excess. If mitigating a natural part of a component’s lifespan is considered a supply chain threat far before the impact of the shortage occurs, it is pertinent to take proper steps to minimize the effects. To aid in preventing such far-reaching consequences, Germany is fortifying its resilience against such profound risks.
“The German Electronics Design and Manufacturing Association (FED) and the Component Group Deutschland (COGD) signed a cooperation agreement at Electronica, Munich,” Evertiq reported. “The agreement ensures coordinated representation of interest with political decision-makers and networking in research and development.”
Further, both organizations plan to develop training courses and lectures to better inform others of proactive and strategic obsolescence management. The goal is to bring attention to obsolescence by creating long-term strategies.
“Efficient, proactive obsolescence management starts in the design. If risky components or materials are used at this early stage, the subsequent effort required to correct the problem is all the greater.” Dr. Wolfgang Heinbach, an honorary chairman of the COGD board, said this on COGD’s collaboration with FED and the importance of obsolescence management.
Current geopolitical uncertainties and other imponderables will make the effects of obsolescence risks more significant and frequent. These challenges apply beyond electronic components and their raw materials and software products.
“We have now reached a point where obsolescence can pose a significant risk not only to individual companies,” Dr. Heinbach warned, “but also, worst case, to entire sections of our national economy.”
2023 Obsolescence Outlook, What Are the Challenges?
The shortage might be easing, but the supply-demand balance is not here. With automotive components still scarce and advanced chips piling up, the global supply chain is still a year away from stabilization. As excess stock builds, manufacturers across the supply chain are moving into the next stage.
That stage is inventory correction. As consumer demand deteriorates, so does the need for chips. Increased capacity for specific chips will be limited without demand funding as original component manufacturers (OCMs) cut back production. This new limited capacity brings a worrying fact to the table.
A lot of components are about to enter obsolescence.
The semiconductor market worldwide lost $240 billion in value last year. Excess stock is forecasted to be a problem throughout 2023 until late Q3 and possibly early Q4. In late 2022, TSMC had ten of its top clients cancel orders. These orders were supposed to get them through 2023 alone when product demand was still high. Many original equipment manufacturers (OEMs) are now stuck with six months’ stockpiles.
Now it isn’t. In the face of production stalls and another year of revenue losses, many OCMs are digesting what inventory they can. OCMs will cut capacity to avoid losses. With this limited capacity and no demand, plenty of advanced chips will become obsolete, possibly before product demand picks back up.
Innovation doesn’t stop. TSMC, as an example, has already begun production of its 3nm nodes. Samsung’s plan puts 1.4nm into total production by 2027, beginning with 3nm in 2024 and 2nm in 2025. TSMC is considering price cuts on their 3nm during this inflationary period to attract more buyers and increase capacity. Other components enter obsolescence to make room.
Overcoming obsolescence will be complicated by the current factors impacting the supply chain, including shortages, excess inventory, macroeconomic pressure, inflation, and more. Being proactive now by finding FFF alternates, sourcing another supply elsewhere, and even redesigning existing projects if necessary while early enough, will make the difference. Datalynq will help mitigate obsolescence’s effects starting now.
Welcome to 2023 From Datalynq! - January 13, 2023
Welcome to the new year! We have many exciting adventures planned for 2023, starting with this new blog dedicated to market news. This blog will bring you the latest market news, obsolescence management, component lead times, and more by updating biweekly.
As a digital market tool, Datalynq condenses large amounts of data across the supply chain and millions of parts into one easy window. After the twists and turns over the past year, it’s hard to keep track of if you are used to the traditional way of storing information on spreadsheets. That’s why we’re ready to give you a crash course in why digitalization and digital tools will be key in coming out of 2023 on top.
Datalynq and Market News
The supply chain suffered setbacks over the last few years. Now, it’s in a tumultuous state of extremes. The automotive industry can’t get enough parts to keep production lines open. Consumer electronics manufacturers are up to their necks in components they no longer need. While no significant disruptions are affecting the global supply chain now, it would only take one to throw this delicate balance into chaos.
That’s where Datalynq comes in. It replaces traditional spreadsheets with information that’s easy to digest. Datalynq’s market intelligence monitors fluctuations in the electronic components industry. It makes obsolescence management easy, lead time tracking a breeze, and preparing for future component risks. Excel spreadsheets and hours of phone tag chases are no longer needed.
Datalynq accomplishes this with its Market Availability Score rating system that supplies a comprehensive view of component obtainability based on information compiled from historical trends, suppliers, and market forecasts. The Design Risk Score rating system also uses insights from supply data, lead times, and product lifecycles to determine the risks of designing a part in-house. These ratings are then scored from 1-to-5 for easy legibility, with five being the worst and one the best.
Technical data for components are at your fingertips and readily available on Datalynq, so you can ensure the part is the best fit for any project. Along with pricing and inventory trends, users will always make the most informed choice with Datalynq’s aid. It doesn’t take long to learn how to use it, either. With straightforward analysis, users only need a few minutes to understand how to best optimize their projects and supply chain.
Digitalization is the Way to Go
It’s time to invest in digitalization. Manufacturers like Qualcomm are encouraging OEMs, CMs, ODMs, and EMS providers to invest in digital tools as the new year begins. Whether through automation, artificial intelligence (AI), machine learning, or other digital tools, the theme of 2023 should be expanding your digital footprint. Why? The more you invest in implementing these tools, the more you’re bound to benefit.
Digital tools make it easier to prevent production stalls, navigate shortages, and strategize for component obsolescence. Machine learning can help companies with their algorithms called predictive analysis. These algorithms can predict disruptions by analyzing historical trends and market data.That amount of data would be far too time-consuming and tedious for human analysts to predict as accurately in the short time it takes machine learning tools.
AI can give users greater supply chain visibility, which many lacked in early 2020 when decreasing consumer demand at the beginning of the pandemic led to canceled orders en masse. This visibility helps users make more informed demands and keeps miscommunication from happening as it did in early 2020. It took a few months for that demand to turn and skyrocket sharply, leading to the 2020-2022 chip shortage.
Automation of processes is imperative beyond production lines. Automating processes save time and give it back to staff to focus on more critical task. OCMs like Qualcomm are taking steps to automate semiconductor manufacturing. As the process is labor-intensive, automation is necessary to avoid production stalls if another pandemic occurs–which previously kept staff home and unable to continue producing chips.
These are just a few of the ways to begin your digital journey. It’s not a one-size-fits-all solution, as different tools are more beneficial to some than others. The industry is becoming far more reliant on data, especially within the EV industry. To create better EVs, quickly analyzing mountains of data to design better features will be imperative to stand apart from the competition. This is also true for consumer electronics, medical, industrial, and other industries.
While that may sound like a monumental task to begin the transformation, there are easy and quick tools to get you started. Datalynq is one of those tools.