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How AI and Robotics are Revolutionizing Manufacturing

Tech and humans working together, one of the ways how AI and robotics are revolutionizing manufacturing
Global Logistics, Global Trade Daily, Uncategorized

How AI and Robotics are Revolutionizing Manufacturing

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New technologies constantly bring improvement and offer an edge to those who adopt them sooner than competitors. The same, naturally, applies to the field of manufacturing. As such, let’s go over how AI and robotics are revolutionizing manufacturing so you can use them! 

The rise of smart factories

Smart factories are at the forefront of the improvements in American manufacturing. These state-of-the-art facilities are revolutionizing the industry by integrating cutting-edge AI and robotics technologies. This technological revolution is boosting manufacturing and making it more accessible in every single way, making this a pivotal step towards a more promising future.

AI-powered predictive maintenance

One of the main driving forces behind why AI and robotics are revolutionizing manufacturing is predictive maintenance. By harnessing the capabilities of artificial intelligence, manufacturers now predict equipment failures before they occur, minimizing downtime and maximizing productivity. Whereas traditional maintenance approaches often lead to reactive repairs, causing costly disruptions. Moreover, with AI, real-time data analysis and machine learning algorithms identify early warning signs of potential malfunctions, enabling proactive maintenance. This shift from reactive to predictive maintenance saves manufacturing companies time, resources, and expenses. 

Collaborative robots (Cobots) in manufacturing

Collaborative robots, or Cobots, are another reason AI and robotics revolutionize manufacturing. Unlike traditional industrial robots that require safety barriers and isolation, Cobots are designed to work safely alongside human workers. With built-in safety features such as force-limiting technology, Cobots detect human presence and respond by slowing down or stopping. These user-friendly robots can be easily programmed and reprogrammed for different tasks, reducing the need for specialized skills. So, Cobots take on repetitive, mundane, and demanding tasks, freeing human employees to focus on complex aspects of manufacturing. 

AI-driven quality control

AI-driven quality control is transforming manufacturing by automating and enhancing the accuracy of product inspections. Traditional quality control processes often rely on manual checks, which can be time-consuming and prone to human error. However, with AI, manufacturers can employ sophisticated algorithms to analyze vast amounts of data and accurately detect defects. Computer vision systems with this tech can identify even the slightest imperfections in real-time. That ensures that only products meeting the highest standards reach the market. Integrating AI into quality control processes not only boosts product consistency but also reduces waste and associated costs. Finally, by streamlining quality assurance, the tech enables manufacturers to deliver superior products, increasing customer satisfaction. 

Supply chain optimization with AI

Supply chain optimization with AI is revolutionizing supply chain management by streamlining and improving the efficiency of the entire process. Traditional supply chain management involves numerous complexities, including inventory management, logistics, and demand forecasting, which is challenging to handle manually. However, with the power of the new tech, companies can now leverage advanced algorithms to analyze vast amounts of data in real time, enabling more accurate demand predictions and better decision-making. AI-driven systems can also optimize inventory levels, reducing excess stock while ensuring products are available when needed. Moreover, AI can optimize route planning and transportation, reducing shipping costs and delivery times. 

AI-enhanced design and prototyping

AI-enhanced design and prototyping are revolutionizing product development by accelerating innovation and optimizing design iterations. Creating and testing new concepts in traditional design and prototyping can be time-consuming and resource-intensive. However, with the integration of AI, designers can leverage machine learning algorithms to analyze vast amounts of data and generate creative design solutions. AI helps designers explore various possibilities, considering materials, performance, and aesthetics. Additionally, AI can predict potential design flaws and suggest improvements, leading to faster and more efficient prototyping. This synergy of human creativity and AI-driven optimization results in higher-quality products and reduced time-to-market. Furthermore, AI benefits 3D printing and rapid prototyping technologies, making the iterative process even more seamless. 

Robotics and autonomous material handling

Robotics and autonomous material handling are transforming the logistics and warehousing industry by revolutionizing how goods are moved and managed. Traditional material handling can be labor-intensive and time-consuming, but with robotics, companies can deploy automated systems that efficiently handle tasks like picking, packing, and transporting goods. Autonomous mobile robots equipped with sensors and AI can easily navigate complex warehouse environments, reducing the need for manual intervention. These robots can also work collaboratively, optimizing workflows and improving operational efficiency. Moreover, AI-powered algorithms analyze real-time data to optimize inventory storage and replenishment, reducing costs and improving accuracy. 

AI for demand forecasting

Another way AI and robotics revolutionize manufacturing is through demand forecasting, which is particularly helpful when dealing with global logistics. Traditional forecasting methods often rely on historical data and manual analysis, making adapting to dynamic market changes challenging. However, with AI-powered algorithms, businesses can analyze vast amounts of real-time data from multiple sources, including social media, weather patterns, and economic indicators, to make more accurate predictions. That enables companies to optimize inventory levels, reduce stockouts, and avoid excess inventory, leading to cost savings and improved customer satisfaction. Moreover, AI can identify demand patterns and seasonality, helping companies plan for peak periods and ensure timely product availability. 

Human-machine collaboration in manufacturing

Human-machine collaboration in manufacturing is reshaping the industry by fostering a harmonious partnership between human workers and machines. Rather than replacing humans, advanced technologies augment human capabilities and transform how work is done. Human workers contribute their creativity, problem-solving skills, and emotional intelligence, while machines handle repetitive, dangerous, or physically demanding tasks with precision and efficiency. This collaboration streamlines production processes, enhances productivity, and ensures consistent quality. So, companies are investing in reskilling and upskilling their workforce to adapt to new roles in this evolving landscape. 

Conclusion on how AI and robotics are revolutionizing manufacturing

With everything we covered on how AI and robotics are revolutionizing manufacturing, it’s obvious that the future is bright. So, the only thing left is to integrate this new tech into your business!

Author Bio

Alexandra Foster is a logistics expert at Bravo Moving California, passionate about optimizing supply chains and embracing human-machine collaboration. With years of experience in global logistics, she’s a strong advocate for sustainability and AI-driven solutions in the industry. Through her insights and expertise, Alexandra aims to inspire efficient and innovative transportation and supply chain management practices.

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Demand for Robot Vision System is Anticipated to Reach US$ 7,236.2 million by 2033

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The global robot vision system market is likely at US$ 2,690.4 million in 2023 and expected to grow at a CAGR of 10.4% during the forecast years of 2023-2033. In order to improve job efficiency and replace human labor, the market for robots is rising and is anticipated to rise more during the coming year.

The market is expanding as a result of increased adoption of machine vision systems and the use of cognitive humanoid robots in end use industries such as automotive industry and others.

Machine direction frameworks, which are critical to the engine chassis marriage process, utilize 2D and 3D machine vision frameworks to build the exactness and speed of gathering robots and robotized material taking care of gear. The main categories of applications can be seen in robotics, dimensional gauging activities, assembly verification, flaw identification, paint job verification, and code reading. However the specific uses depend on the type of vehicle or model being manufactured.

These robotic vision systems are beginning to gain favor with manufacturing companies all over the world, particularly in settings where precise execution of repetitive tasks like inspection is required. In both dangerous areas and fast-moving production lines, they play a crucial part. These system provide a number of important advantages, including improved productivity, decreased machine downtime, and tighter process control.

Key Takeaways from Market Study

  • The global robot vision system market is projected to reach the valuation US$ 7,236.2 million by 2033.
  • The market witnessed 7.2% CAGR between 2018 and 2022.
  • 2D type of vision system will dominate the market with US$ 1,794.5 million valuation in 2023.
  • By application sector, inspection will dominate the market with 26.3% market share in 2023.
  • Based on region, demand for robot vision system is expected to increase in North America & East Asia with an impressive CAGR of 18.2% and 43.1%, respectively during the forecasted period.

“The Plethora of Advantages Provided by the Robot Vision System Are Ought to Increase Due to the Technological Advancements” says a Fact.MR analyst.

Market Development

To solve concerns with quality vision systems are receiving more funding from automakers. Automotive manufacturers and component suppliers are rapidly utilizing the technology for a range of tasks, such as adhesive dispensing, bin selecting, material handling, error-proofing, inline welding analysis, surface inspection, robotic guiding, and traceability. Increasing desire for personalization, tightening labor markets, and cost pressures are a few of the key factors influencing the usage of vision systems in the automotive sector.

More Valuable Insights on Offer

Fact.MR, in its new offering, presents an unbiased analysis of the global robot vision system market, presenting historical market data (2018-2022) and forecast statistics for the period of 2023-2033.

The study divulges essential insights on the market on the basis of vision (2D, 3D), end use industry (aerospace, automotive industry, consumer electronics, food & beverages, logistics & warehousing, manufacturing, pharmaceutical, semiconductor, others) and application (depalletizing, inspection, measuring, navigation, product assembly & sorting) across six major regions (North America, Latin America, Europe, East Asia, South Asia & Oceania, and the Middle East & Africa).

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Lauren Fiochetta Joins Tompkins Robotics as Senior Marketing Manager

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Tompkins Robotics, a global leader focused on the robotic automation of distribution and fulfillment operations, has named Lauren Fiochetta to the role of Senior Marketing Manager. In this new role, she will develop and implement omnichannel marketing campaigns designed to attract and qualify customers across a wide range of industries, according to Tompkins Robotics President and CEO Mike Futch.

“We are excited to welcome Lauren to the team at Tompkins Robotics at a time when the warehouse and retail automation industry is poised for dramatic growth,” said Futch. “She is a marketing powerhouse with extensive B2B experience and an existing track record of success in the logistics and robotic automation space.”

Prior to joining Tompkins, she held marketing management roles for global manufacturing companies in the logistics, industrial materials and beverage industries, directing data-driven marketing campaigns encompassing advertising, public relations, social media and a variety of other digital marketing channels.

Fiochetta graduated from Westminster College in Utah with a Bachelors Degree in Business Administration.

About Tompkins Robotics

Tompkins Robotics is a global leader focused on the robotic automation of distribution and fulfillment operations. Our primary system, tSort, consists of autonomous mobile robots that sort a wide range of items and parcels to consolidation points for order fulfillment, store replenishment, returns, parcel distribution – virtually any process in the supply chain. tSort is a truly modular, scalable, and portable robotic sortation system that helps build world-class supply chains while providing unmatched flexibility and throughput.  Tompkins Robotics solutions maximize performance, making our clients more agile, adaptable, profitable, and successful in todays dynamic marketplace.  For more information, visit https://tompkinsrobotics.com.

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Archives, Education, Global Trade Daily, Technology

How Machine Learning Has Improved Production Factories’ Robotics

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Machine learning, robotics, and manufacturing automation have the potential to disrupt and transform our global economy in the upcoming years. The increased use of robots that are powered by machine learning and artificial intelligence in manufacturing and warehousing means there is a massive rise in efficiency and productivity. Machine learning is quickly improving the capability and competency of robots in production and automated manufacturing. Flexible and large training datasets have led to a marked improvement in several areas. Let’s take a close look at some of them.

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  1. Safety: The use of machine learning in robotics is gradually improving the safety standards of automated workspaces. 2D and 3D image datasets are being used for enhancing the environmental perceptions of all industrial robots. You can get reliable and fast object detection for making sure that powerful machines can avoid human beings and obstacles.
  2. Quality: Better image labeling in the field of robotics, is improving the capability of machines to identify faults and other defects in products that are coming fresh out of assembly lines. Computer vision-enabled cameras in robotics are capable of spotting defects that are not visible to the human eyes. Apart from that, AI-powered inspections may be carried out frequently without dropping the fault detection rates.
  3. Longevity: Machine learning-enabled systems are also being deployed that can be used to carry out the maintenance of other structures and machines. There is a regular use of visual datasets featuring pictures that are properly labeled with examples of wear for training models. These training models are used to spot possible defects in machinery or mechanical problems before there is a catastrophic failure. This type of preventive ML surveillance can improve the lifespan of several vital pieces of equipment.
  4. Product development: One of the more common uses of machine learning is product development. Both things viz. design of new products and the improvement of existing ones require the use of extensive data analysis to achieve the best results. ML solutions help collect and analyze a large amount of product data for understanding consumer demand and uncover hidden flaws to identify newer business opportunities. 

This not only helps in identifying existing product designs but can also develop superior quality products that can develop newer revenue streams for your business. Software developing has played a great deal of a role when it comes to product development, many companies have reached the top through analyzing and fitting software usage to their needs, there are many companies that can do a software business plan and help you achieve the things that your firm is striving for product wise.

  1. Cybersecurity: The solutions using machine learning depend on data, network, and tech platforms for both cloud and on-premise functions effectively. Security of these kinds of data and systems is crucial and machine learning plays a vital role in better regulation of important digital platforms and other info. Machine learning is capable of streamlining the way users will access sensitive data and the type of application they can use. It can also streamline the way you can connect with it. It is extremely useful for businesses to protect their digital assets by detecting anomalies fast and immediately triggering corrective action.

Use of machine learning for robotics in production factories

The various advancements made possible by artificial intelligence and machine learning for robotics have been used in several industries. There are many production factories out there that use AI-driven machinery for production. Some robotics arms that are trained with visual datasets can act as pickers for distribution warehouses. This raises the speed at which items can get moved away from a place. ML-powered robots are being used in automobile factories while using bounding boxes, for identifying vehicles, while they are moving in an assembly line. It allows the cars to avoid possible collisions in a crowded production environment.

Conclusion

The use of machine learning in production and other related processes can provide a significant rise in the efficiency of your manufacturing. This also leads to the development of newer business opportunities. Nowadays manufacturers wish to know how machine learning is useful for resolving specific business issues such as tracing production defects back to some specific steps taken undertaken in the production process. You can also achieve lesser waste with better identification of the presence of faulty components in the earlier stages of the production process. However, newer generations of machine learning must have access to a better quality of training data at a scale desired.

 

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ePicker Enters Robotic Market with Lift Truck Powered by Anantak

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Leveraging the latest in AI and Mobile Robotics, ePicker Launches Autonomous Pallet Mover Designed to Make Automation Accessible to All

ePicker, an elite material handling equipment provider, has teamed with Anantak, a leading robotics company, to power warehouse vehicles such as pallet movers, tuggers, stackers, and forklifts.  ePicker’s autonomous pallet mover, powered by Anantak, aims to make warehouse automation attainable and widely accessible.  The first vehicle from the collaboration uses a combination of lasers, 3D cameras, vehicle telemetry, as well as a proprietary self-driving software package to allow the unit to complement existing workflows instead of replacing them.

The autonomous pallet mover delivers its value like most mobile robots by taking on the task of shuttling material long distances, but sets itself apart with on-board intelligence and how little effort is needed to deploy, maintain, and scale for any operation.  Units will be available for delivery in August 2022, but ePicker is actively taking orders this week at MODEX.

Traditionally, when mobile robotics are deployed, there are significant changes required to existing processes and infrastructure to make them run seamlessly.  The autonomous pallet mover allows for rapid adoption by intentionally keeping a human in the loop at the front-end of the process, and then engaging onboard autonomy to allow for safe, reliable transport and drop off to destinations within a facility.

This strategy allows for straightforward implementation that lets facility leaders quickly automate simple workflows. As workflows get more complex, or the number of robots increases, the fleet management capabilities expand, including a cloud-based platform that allows for remote support and over-the-air updates to keep operations running smoothly.  ePicker’s autonomous pallet mover also supports its customers by ensuring extended costs are limited by offering simple repair and service solutions that are easy to implement and cost effective.

Manuj Naman, founder and CEO at Anantak made known some of the highlights of their technology adding that it doesn’t need a lot of programming, it grows with you, becomes customized to your needs and learns as it operates.

Jason Bratton, President for ePicker also made known some of the features of epicker adding that it has lithium and traditional powered vehicles that help fill a gap in the warehouse, and the addition of Anantak technology will give their customers another excellent option to meet the needs that require consistent and repetitive routes.

Features of the autonomous pallet mover include manual pick, autonomous drop off, in-line or in-grid mapping that can be done by the user and obstacle avoidance. The first autonomous pallet mover can automatically map any footprint changes and includes onboard diagnostics, showing users step-by-step how to solve serviceability issues on-screen. These components break the barrier between technology and accessibility, making it user-friendly for customers and giving them more autonomy.

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Tompkins Robotics Continues to Enhance Automated Sortation Process with New tSort3D System

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In keeping with the belief to deliver adaptive, flexible, and portable solutions, Tompkins Robotics introduces the new tSort3D. tSort3D is modular, allows customers to implement quickly, and the system can grow and change as their operational needs evolve.  The tSort3D greatly multiplies the destination density and volume of the sortation process.

A tSort3D system is mated with the Tompkins Robotics tSort solution for item sortation loading and routing the items to tSort3D modules for order consolidation.  The system is ideal for fulfillment of items for customer e-commerce orders and other fulfillment flows such as store replenishment and reverse logistics.  The system can be deployed in a scalable fashion in sites as small as the backroom of retail store and up to very large Distribution and Fulfillment Centers.

tSort3D allows 6 to 8 times the sort destinations in the same space as other traditional automated sortation solutions, provides for thousands of sort destinations, volumes up to 20,000 an hour, and facilitates a single, very large batch pick.  These capabilities far exceed other dense sortation systems on the market today. This solution solves a pressing need in distribution and fulfillment operations that no previous automation solution fully addressed.

tSort3D can handle the widest range of products compared to other automated sortation solutions on the market. The tSort3D uses a tray as the carrier, while other solutions use a cross belt. Tompkins Robotics unique tray design ensures that round, cylindrical, and oddly shaped items are compatible with their system.

In addition, tSort3D can handle wider, taller, and deeper products than other dense, robotic sorters on the market. The system can continuously track items, orders, and order status to provide real time updates to an operator. tSort3D provides a much less labor-intensive process from picking through to order delivery to packing for many product flows and products.

Completed orders can be removed individually or as a batch of up to 24 orders.  The system greatly enhances the productivity and capacity of an operation while taking up less space, deploying in less than half the normal required time, and costing less. In addition, the system is very flexible having modular, scalable configurable and portable abilities that are unique and not found in other solutions.

For additional information or to arrange a demonstration of the tSort3D please visit Tompkins Robotics Booth # B7240 at MODEX. If you would like to schedule an individual time to discuss, please contact Uriah Bullard at ubullard@tompkinsrobotics.com.  Tompkins Robotics looks forward to sharing how tSort3D can dramatically increase the number of sort destinations, reduce space, provide greater productivity and capacity, and drive a larger upstream batch pick process- all while maintaining extreme flexibility for your operations.

About Tompkins Robotics

Tompkins Robotics is a global leader focused on the robotic automation of distribution and fulfillment operations. Our primary system, tSort, consists of autonomous mobile robots that sort a wide range of items and parcels to consolidation points for order fulfillment, store replenishment, returns, parcel distribution – virtually any process in the supply chain. tSort is a truly modular, scalable, and portable robotic sortation system that helps build world-class supply chains while providing unmatched flexibility and throughput. With three models, tSort; tSort Plus; tSort Mini; and two sortation methods, tilt trays and cross belts, Tompkins Robotics systems handle the broadest range of product on the market – as small as a penny to up to 40.

They are shipping every type of RAD unit to a wide range of vertical markets and end users, indicating that where there are labor, performance or cost challenges there is likel
Archives, Global Trade Daily, International Trade, News

Robotic Assistance Devices Shipping Large Quantity of Units as Fiscal Year End Approaches

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Artificial Intelligence Technology Solutions, Inc., (OTCPK:AITX), announced that its wholly owned subsidiary Robotic Assistance Devices, Inc. (RAD) is expected to ship a minimum of 18 units this week as it continues its progress towards completion of the fiscal quarter and year.

They are shipping every type of RAD unit to a wide range of vertical markets and end users, indicating that where there are labor, performance or cost challenges there is likely a RAD solution. It’s significant to note that these units were sold through an ideal mix of channel partners and direct to the end user.

Nine months ago, They took possession of their REX (‘RAD Excellence Center’) and now it’s humming with activity. The REX is RAD’s 30,000 sq. ft. manufacturing complex located near Detroit, Michigan. The devices they are shipping out of the REX this week include at least one of every solution RAD has to offer, from ROSA to ROAMEO.

The company confirmed that scheduled to ship this week are 2 SCOT towers, being deployed at a significant regional airport that typically handles more than 15 million passengers per year, 5 ROSAs destined for a public park in San Francisco, 2 AVA units heading to a distribution center for a well-known global retailer, 1 Wally HSO for professional office use, and the second of 2 ROAMEOs for a leading theme park operator.

During his weekly video message to nearly 20,000 subscribers, Steve Reinharz, CEO of AITX commented that the fear of being first is being replaced by the fear of missing out. He elaborated that the security industry has been very conservative in how quickly it adopts new technologies. He said that there is a shift taking place right now and RAD is perfectly positioned to take advantage of it.

The company also noted that the identities and locations of several of the devices shipping this week are expected to be revealed in the near future. Hundreds of thousands of people will soon see SCOTs and ROSAs at airports, ROSAs at a public park, and ROAMEOs at theme parks. These public-facing RAD units will mean incredible visibility for the company as they approach the end of the company’s fiscal year.

Robotic Assistance Devices (RAD) is a high-tech start-up that delivers robotics and artificial intelligence-based solutions that empower organizations to gain new insight, solve complex security challenges, and fuel new business ideas at reduced costs. RAD developed its advanced security robot technology from the ground up including circuit board design, and base code development. This allows RAD to have complete control over all design elements, performance, quality, and the user’s experience of all security robots whether SCOT™, ROSA™, Wally™, Wally HSO™, AVA™, or ROAMEO™.

Read about how RAD is reinventing the security services industry by downloading the Autonomous Remote Services Industry Manifesto.

CAUTIONARY DISCLOSURE ABOUT FORWARD-LOOKING STATEMENTS
This article contains “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E the Securities Exchange Act of 1934, as amended and such forward-looking statements are made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995. Statements in this news release other than statements of historical fact are “forward-looking statements” that are based on current expectations and assumptions. Forward-looking statements involve risks and uncertainties that could cause actual results to differ materially from those expressed or implied by the statements, including, but not limited to, the following: the ability of Artificial Intelligence Technology Solutions to provide for its obligations, to provide working capital needs from operating revenues, to obtain additional financing needed for any future acquisitions, to meet competitive challenges and technological changes, to meet business and financial goals including projections and forecasts, and other risks. Artificial Intelligence Technology Solutions undertakes no duty to update any forward-looking statement(s) and/or to confirm the statement(s) to actual results or changes in Artificial Intelligence Technology Solutions expectations.

They are shipping every type of RAD unit to a wide range of vertical markets and end users, indicating that where there are labor, performance or cost challenges there is likel
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Fast Company Names Seegrid as 4th Most Innovative Robotics Company in the World

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Leading Autonomous Mobile Robot Provider Recognized for Transforming the Global Supply Chain with Intelligent Automation Solutions

Seegrid Corporation, the leader in autonomous mobile robots (AMRs) for material handling, has been named to Fast Company’s prestigious list of the World’s Most Innovative Companies for 2022, placing then #4 globally in the robotics category. The publication assembles the annual list to honor businesses that are thriving in today’s ever-changing world and making the biggest impact on their industries and culture. Serving the world’s largest manufacturing, e-commerce, and logistics brands, Seegrid was recognized by Fast Company for its industry-defining approach to delivering complete, connected material handling automation solutions.

Seegrid’s innovative autonomy technology, Seegrid IQ, fuses data from cameras, LiDAR, and machine learning models with the company’s proprietary 3D computer vision system. This proprietary technology collects a high density of information, then prioritizes and filters the data to enable mobile robots with a human-like understanding of industrial environments.

Seegrid IQ enables Seegrid Palion™ AMR models to safely move thousands of pounds of material while working collaboratively alongside humans.

Jim Rock, Chief Executive Officer at Seegrid had this to say “I am incredibly proud of Seegrid’s collective ability to solve complex material handling challenges. We are committed to delivering mobile automation solutions that safely bring transformational change to the world’s supply chain.”

Fast Company’s editors sought out the most groundbreaking businesses across the globe and industries. In the last year, Seegrid introduced three new AMR models, launched Fleet Geek™ analytics software, and invested millions into new equipment and tools to help drive its research and development initiatives. Seegrid earned recognition as the #1 AMR provider in the US and #1 market leader in tow tractor AMRs worldwide from Interact Analysis, an international market research authority for the supply chain automation industry. The company’s Palion AMR fleet has driven seven million autonomous miles in customer production environments without a single safety incident.

Fast Company selects businesses who are creating the future today with some of the most inspiring accomplishments of the 21st century. Of this same mindset, Seegrid continuously advances its breakthrough robotics technology pioneered by world-renowned roboticist Dr. Hans Moravec, the company’s founder and Chief Roboticist. Blue Labs, a dedicated in-house research group of world-class automation experts, many with Ph.D. level expertise in robotics and computer vision systems, is solely focused on the rapid advancement of mobile automation technologies. One such advancement includes the company’s first autonomous lift truck, Seegrid Palion Lift AMR, set to be unveiled this month at MODEX, the largest manufacturing and supply chain trade event.

As part of its commitment to ensuring all customers realize the full benefits of automation, Seegrid offers its customers options to purchase equipment outright, as well as various leasing and subscription models.

Fast company Deputy editor David Lidsky had this to say”The world’s most innovative companies play an essential role in addressing the most pressing issues facing society, whether they’re fighting climate change by spurring decarbonization efforts, ameliorating the strain on supply chains, or helping us reconnect with one another over shared passions”.

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Archives, Global Logistics, Global Trade Daily, Trucking, Warehousing

Investing in Supply Chain Technology Will Give You a Competitive Edge

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The COVID-19 pandemic brought about many changes, some of which were more unexpected than others. Anyone with a stake in the logistics industry saw unprecedented supply chain shortages and disruptions that impacted everyone. Materials foundered in warehouses with no one to transport them to factories for manufacturing. Completed products collected dust because no drivers were available to carry them to their final destination. It even threw a wrench in Christmas 2021, making it harder for consumers to get their hands on artificial trees.

Some of these issues have begun to fade, but there are still challenges facing the supply chain industry. How can investing in new supply chain technologies give companies a competitive edge?

The Last Mile Is Evolving

While 2020 wasn’t the first year where e-commerce and online orders started to take precedence over physical storefronts, adding a global pandemic to the mix made having that option more essential. It allowed people to stay home as much as possible while still ensuring they had everything they needed or wanted throughout the lockdowns. Consumers have grown accustomed to fast delivery, but their definition of fast is different from what most might typically find in the logistics industry.

One recent survey found that 96% of consumers equate “fast” delivery with “same-day” delivery. Barely half of the retailers offer that delivery option, but that consumer definition means it is essential to shorten the amount of time those last-mile deliveries take. Supply chain innovations and new technologies can help bridge the gap between what the consumer perceives as fast and the reality that defines the logistics industry as it currently stands.

Artificial Intelligence (AI) Is Making Its Mark

The logistics industry as a whole generates massive amounts of data every single day. Supply chain data, consumer information, manufacturing details, and everything in between gets collected and stored. This data is often stored where companies can access it, but it’s usually just a mish-mash of numbers in its raw form. Making sense of that information is often beyond what even the most skilled business owner can manage – at least on their own.

Experts anticipated that more than half of companies in the supply chain industry were planning to begin investing in artificial intelligence systems for their companies by the end of 2021. This is a 15% increase year-over-year for this industry alone. In the long run, AI will likely add trillions in value to the industry in the coming years. This trend is picking up speed, but there is still plenty of time for existing companies to get in on the ground floor and adopt it before it transitions from niche to necessity.

Changing Best Practices With Robotics and Automation

Robotics and automation is a field that often earns a lot of negative attention and press because of its threat to human jobs. People are afraid that robots will steal jobs, and this sort of hidebound attitude has often led to industries that are otherwise on the cutting edge of their field shunning advances. In supply chain operations, experts expect robotics and automation to grow steadily over the next five years, especially in any situation where a robot can take over a dangerous or high-risk task.

Introducing robotics and automation can help companies overcome existing problems, especially regarding functionality and fulfillment. Warehouses that still rely on manual picking methods aren’t going to keep up with the growing demand when competing against companies that have already purchased and implemented automated picking systems.

Removing Humans From Some Equations

A lack of skilled workers, especially in the trucking industry, has presented a unique challenge for those working with supply chains. Having all the materials in the world doesn’t mean much when no one is available to haul those materials from warehouse to factory or from factory to consumer. There was a shortage of 80,000 truck drivers by the end of 2021, and experts estimate that will double by 2030.

The technology is almost ready for self-driving trucks that could help offset this growing labor shortage. They will never fully replace the need for human drivers, but they could help fill in the gaps while the trucking industry makes the necessary changes to rebuild its ranks. The demand for skilled drivers will never disappear, especially when the weather turns sour. Still, these self-driving alternatives could help ensure materials and finished products promptly reach their destinations.

Warehouse Optimization Is Key

Warehouse layout options haven’t changed much in the last few decades, but change is a necessity if companies are hoping to keep up with the increased demand. Warehouse optimization is essential to manage the increasing number of orders. Often, something as simple as rearranging the inventory so the most frequently purchased items are closer to the picking and packing stations can help, but that isn’t always enough.

Warehouse management systems – software designed to sort through and manage all the data a warehouse produces – are one piece of the puzzle. These, when paired with the artificial intelligence and machine learning systems mentioned above, can create a network that will increase productivity and supply chain efficiency. Robotics and automation will also play a role, removing some of the human element, especially in regards to inventory management and picking orders. The goal here isn’t to eliminate human workers entirely, but to make their jobs easier through AI and other advanced supply chain technology so they can carry them out more efficiently.

Overcoming Supply Chain Challenges in the Future

No one could have anticipated the challenges that arose during the COVID-19 pandemic. Now, companies need to work to recover from those challenges and overcome any new problems that might occur in the future. New technologies can help give companies an edge in an already ultra-competitive industry. For those that haven’t already started considering these changes, now is a perfect time – the calm between storms – to begin researching how it could work for them.

The demand for e-commerce and the supply chains to support it isn’t going to go away anytime soon. Now is the time to start adopting these new technologies so companies can start getting ahead of the competition.

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Archives, Commentary, Global Trade Daily

Taking The Mystery Out Of AI: 4 Ways It Makes Life Easier

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Artificial intelligence is significantly impacting the world, yet there’s still a great amount of mystery – and misconceptions – about it.

The public’s imagination has been heavily shaped by science fiction, with the term AI evoking images of robots like WALL-E, C3PO from Star Wars, and David from Stephen Spielberg’s movie A.I. Scientists and technologists refer to this kind of humanlike AI as “general artificial intelligence.” General AI attempts to mimic the kind of abstract thought and typical problem-solving skills seen in humans.

I say attempts because there is currently no existing general AI system even approaching the sophistication of the human brain. The technology is nowhere close to creating a system capable of abstract thought or general intelligence. But “applied AI” is rapidly becoming a mainstream technology, improving the efficiency and profitability of businesses in many industries.

Why applied AI is faster but not yet smarter than us 

Current AI systems, for all their computational ability, do not have the ability to understand and analyze context the way human brains do. For example, we can see a barking dog and instantly determine the threat level. Sufficiently advanced AI can recognize the dog but may be unable to determine the breed, its physical agility, and whether it has been encountered before. Unlike humans, AI cannot connect dots and judge context to solve problems creatively.

But AI systems can make decisions far more rapidly and accurately than humans. The strengths and limitations of the current generation of artificial intelligence make it most applicable for solving fit-for-purpose business problems. (Fit-for-purpose is the concept in which a product or service is adequate for the purpose for which the consumer selected it.) AI systems are designed to handle a specific task while operating within imposed contextual constraints. These fit-for-purpose systems and tools are known as applied AI.

There are four primary business applications of applied AI in industry. Here is a look at each and how they create efficiencies and value:

Automation

Automation saves countless man hours and resources, as computers can process

data in a fraction of the time it takes humans. Some of these processes require decision-making. This is where AI comes into play. Most business processes involve a structured set of inputs, and decisions are made based on defined policies and guidelines. The variables in the equations are well known and operate within a narrow context. Algorithms can make these decisions rapidly and accurately.

These algorithms allow much of the workload that companies do to be offloaded onto automated systems. That advancement provides conveniences for consumers in the age of online shopping. Capital One Shopping, for example, offers customers a browser add-on claiming to save money by automatically comparing prices, applying promo codes and providing deal alerts.

Banks use AI to automate the loan process. Relevant financial records are collected automatically, validated, and analyzed. The system can give a recommendation on the loan before a lender ever sees the application. This may sound frightening and impersonal, but these systems actually assess loans more accurately and fairly than humans. They look at a borrower’s credit history, credibility, liabilities, and other factors and make an impartial decision. Quite often, these individual metrics are also calculated by AI. Credit scores, for example, are calculated automatically.

Insights

The data produced by automated business processes holds valuable insights. These insights can be about almost anything; they may reveal something about a business process, unlock untapped opportunities, or even allow companies to make predictions about the future.

The data analysis that leads to insights is mostly automated. Given the volume of data that companies now work with, we need these automated AI systems to find the signal in the noise. AI looks for patterns and makes decisions based on training and defined guidelines. The exponential power of these systems can be seen in machine learning.  IBM Watson, for example, can predict when an elevator is going to fail. AI systems are able to return insights about insights into their own operation, and this allows them to improve their data sets and algorithms. As a result, AI systems draw insights that the designers may not have ever considered or been looking for.

Personalization

Insights have many applications, but one that is rapidly transforming industries is personalization of user engagement. The insights drawn from consumers, users, and other stakeholders can be used to improve their experiences by engaging with them in a personalized manner.

There are many ways companies can use data to personalize the experience for the stakeholders they serve. Google maps knows through habits a driver’s route and daily schedule and will give them an outlook for the day based on the current data. Retail companies have their websites feature inventory designed to appeal to the specific viewer. Online ads are micro-targeted at individuals based on insights into their specific consumer behavior. Social media platforms also customize news feeds to increase user engagement. Search engines provide results that are tailored to the user’s location, demographics, search habits, online shopping history, and other data.

Sensing

Sensing produces a kind of insight that deserves special mention due to its revolutionary potential. The exponential explosion in data and computing power now allows us to better recognize patterns as they are forming and predict how they will develop, which in turn allows us to actually sense trends as they form and develop.

This ability has huge business applications. Consider the sudden rise and success of TikTok. ByteDance, the Beijing-based parent company that developed the app, hit it big by filling an emerging niche for teens who could use a platform for recording and sharing short videos. TikTok noticed the trend among teens, built a dedicated app, and marketed it to the emerging user base as the trend developed. TikTok is now worth billions of dollars and is changing the social media landscape.

Many companies using applied AI have built fortunes and improved the world for billions of people. They did so by leveraging exponential technology and riding the development curve. We can likely achieve higher levels of AI-powered efficiency and improvement in all industries.

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Rajeev Ronanki (https://rajeevronanki.com/) is the president of digital platforms at Anthem Inc., and the ForbesBooks author of You and AI: A Citizen’s Guide to AI, Blockchain, and Puzzling Together the Future of Healthcare. Before joining Anthem, Ronanki was a partner at Deloitte Consulting, where he spearheaded a myriad of technological healthcare innovations. Ronanki is a frequent contributor to Forbes and other publications. He earned a bachelor’s degree in mechanical engineering from Osmania University and a master’s in computer science from the University of Pennsylvania.