Joe Vernon Bio Lean Computer Vision: The Next Step in Supply Chain, Inventory, and Manufacturing The greatest advantage of CV is its ability to count and categorize inventory Published: Wednesday, June 8, 2022 - 12:03 Comment Rss Send Article (Must Login) Print Author Archive T he pandemic has had many consequences for manufacturing companies, the most prevalent being supply chain disruptions. In light of these, it’s paramount that organizations establish robust and reliable operations to ensure that productivity targets are met, especially as consumer demands continue to rise regarding speed, accuracy, and quality. To keep up with the rapidly accelerating and challenging economy, many in the manufacturing sector have deployed computer vision (CV) technologies. Coupled with artificial intelligence (AI) it’s possible to train computers to interpret, identify, and classify objects in the visual world using digital images, cameras, videos, and machine learning. CV in light of the pandemic CV allows manufacturers to track and monitor productivity, worker safety, and inventory levels more precisely. Maintaining inventory efficiency in light of supply chain shortages is no small task and requires a large amount of time, manpower, and resources. Although inventory management software and hardware such as barcode scanners, hand-held terminals, and RFID scanners have reduced manual effort and saved time, CV enables the next level of accuracy by providing real-time inventory accountability throughout the operation. During the pandemic, stockouts were abundant, creating downtime, production-order rescheduling, and other operational disruptions. CV improved inventory tracking and stock measurements, and greatly reduced manual workarounds, inventory-adjustment procedures, and associated downtime. By using CV, manufacturers have gained considerable benefits: In less than a decade, the accuracy rates for object identification and classification have risen from 50 percent to 99 percent, making CV more precise and faster than humans. Applications of CV The possibilities of CV are extensive, from spotting counterfeit bills and identifying cancer in patients’ brains and livers to detecting early signs of disease in crops and recognizing leaks in pipelines. For the manufacturing industry, the greatest advantage of CV is its ability to count and categorize inventory from images and videos via machine learning. One example is the inventory monitoring of raw materials. Businesses can use CV to capture images at specific intervals to identify the inventory levels of various items and stream those data in real time to systems controlling the manufacturing process. This alerts the company if its inventory levels go below a set threshold and triggers a “digital kanban” replenishment response. CV has proved effective as a means to conduct continuous digital inventory counting at receipt, storage, and all related movements. A distribution center or bulk storage area can use CV to count boxes and remove the need for routine stock-taking. Similarly, when raw materials are moved during the receiving or shipping process, CV can process the data from cameras and video feeds at different angles to automatically count the inventory and even cross-validate with shipping or receiving orders to avoid miscounting penalties. Beyond inventory optimization, CV detects defects more accurately and reliably than the human eye. By gathering real-time data and using machine learning algorithms, CV can compare a product based on the predefined quality standards to determine if any flaws are present. Already, manufacturers have improved defect-discovery rates up to 90 percent with CV. In the same vein, CV enables predictive maintenance to decrease the chances of a costly equipment malfunction. Typically, humans must conduct manual and routine machine checkups. But by implementing CV, manufacturers can constantly monitor the health of their equipment and have engineers preemptively correct deviations when they are noticed. This proactive monitoring saves manufacturers money, time, and labor. Although there are many capabilities possible through CV in manufacturing, one of the standout benefits is automated product assemblage. Consider that almost 70 percent of the Tesla manufacturing process is automated via 3D modeling designs, which CV then uses to guide the assembly process. CV allows most of Tesla’s vehicle creation to be automated, from monitoring to directing robotic arms and assisting employees. Strategies for deploying CV Although CV has the potential to revolutionize a manufacturing business’s output and boost revenue, decision-makers should first evaluate the organization’s readiness from a technology and operations perspective. For instance, would the CV solution be hosted on-premise or in the cloud? As for part codes in the warehouse, is there a mix that would necessitate one’s operations team to employ part code identification and segregation? Physical considerations are also important—CV is only as effective as the data it captures—so the visibility for warehouse cameras must be clear. After considering those factors, manufacturers should run a financial analysis. CV leverages camera systems, allowing many businesses to save expenses when deploying the solution because they can use their preexisting CCTV cameras. However, businesses without an extensive CCTV setup will need to install additional ones. Likewise, companies should determine if they need an implementation partner to help them. After calculating costs, manufacturers can measure this figure against the impact of the CV solution, such as savings from automatic counting, error reduction, and a lower incidence of stockouts. CV as part of Industry 4.0 From a competitive standpoint, manufacturing companies that do use CV will see significant gains over their competitors that do not. CV is an essential part of Industry 4.0 and larger shifts occurring in manufacturing, including IoT, cloud computing, and AI. Indeed, CV might be the next stage of manufacturing evolution, just as the assembly line, electricity, and steam power were in their day. Quality Digest does not charge readers for its content. We believe that industry news is important for you to do your job, and Quality Digest supports businesses of all types. However, someone has to pay for this content. And that’s where advertising comes in. Most people consider ads a nuisance, but they do serve a useful function besides allowing media companies to stay afloat. They keep you aware of new products and services relevant to your industry. All ads in Quality Digest apply directly to products and services that most of our readers need. You won’t see automobile or health supplement ads. Our PROMISE: Quality Digest only displays static ads that never overlay or cover up content. They never get in your way. They are there for you to read, or not. So please consider turning off your ad blocker for our site. Thanks, Quality Digest Discuss ( 0 ) Hide Comments Comment About The Author Joe Vernon Joe Vernon is principal of business consulting at EPAM Systems, where he specializes in supply chain processes, technology, and strategy. As a supply chain transformation expert with more than 25 years of experience, he combines knowledge of best-in-class technologies with process redesign, creative solutioning, optimization strategy, and manufacturing and distribution operations. His consulting experience spans multiple sectors, including retail, consumer packaged goods, healthcare, and telecommunications. He has a bachelor’s degree in biology and liberal arts from Cornell College.

T he pandemic has had many consequences for manufacturing companies, the most prevalent being supply chain disruptions. In light of these, it’s paramount that organizations establish robust and reliable operations to ensure that productivity targets are met, especially as consumer demands continue to rise regarding speed, accuracy, and quality.

To keep up with the rapidly accelerating and challenging economy, many in the manufacturing sector have deployed computer vision (CV) technologies. Coupled with artificial intelligence (AI) it’s possible to train computers to interpret, identify, and classify objects in the visual world using digital images, cameras, videos, and machine learning.

CV allows manufacturers to track and monitor productivity, worker safety, and inventory levels more precisely. Maintaining inventory efficiency in light of supply chain shortages is no small task and requires a large amount of time, manpower, and resources. Although inventory management software and hardware such as barcode scanners, hand-held terminals, and RFID scanners have reduced manual effort and saved time, CV enables the next level of accuracy by providing real-time inventory accountability throughout the operation.

During the pandemic, stockouts were abundant, creating downtime, production-order rescheduling, and other operational disruptions. CV improved inventory tracking and stock measurements, and greatly reduced manual workarounds, inventory-adjustment procedures, and associated downtime. By using CV, manufacturers have gained considerable benefits: In less than a decade, the accuracy rates for object identification and classification have risen from 50 percent to 99 percent, making CV more precise and faster than humans.

The possibilities of CV are extensive, from spotting counterfeit bills and identifying cancer in patients’ brains and livers to detecting early signs of disease in crops and recognizing leaks in pipelines. For the manufacturing industry, the greatest advantage of CV is its ability to count and categorize inventory from images and videos via machine learning.

One example is the inventory monitoring of raw materials. Businesses can use CV to capture images at specific intervals to identify the inventory levels of various items and stream those data in real time to systems controlling the manufacturing process. This alerts the company if its inventory levels go below a set threshold and triggers a “digital kanban” replenishment response.

CV has proved effective as a means to conduct continuous digital inventory counting at receipt, storage, and all related movements. A distribution center or bulk storage area can use CV to count boxes and remove the need for routine stock-taking. Similarly, when raw materials are moved during the receiving or shipping process, CV can process the data from cameras and video feeds at different angles to automatically count the inventory and even cross-validate with shipping or receiving orders to avoid miscounting penalties.

Beyond inventory optimization, CV detects defects more accurately and reliably than the human eye. By gathering real-time data and using machine learning algorithms, CV can compare a product based on the predefined quality standards to determine if any flaws are present. Already, manufacturers have improved defect-discovery rates up to 90 percent with CV.

In the same vein, CV enables predictive maintenance to decrease the chances of a costly equipment malfunction. Typically, humans must conduct manual and routine machine checkups. But by implementing CV, manufacturers can constantly monitor the health of their equipment and have engineers preemptively correct deviations when they are noticed. This proactive monitoring saves manufacturers money, time, and labor.

Although there are many capabilities possible through CV in manufacturing, one of the standout benefits is automated product assemblage. Consider that almost 70 percent of the Tesla manufacturing process is automated via 3D modeling designs, which CV then uses to guide the assembly process. CV allows most of Tesla’s vehicle creation to be automated, from monitoring to directing robotic arms and assisting employees.

Although CV has the potential to revolutionize a manufacturing business’s output and boost revenue, decision-makers should first evaluate the organization’s readiness from a technology and operations perspective. For instance, would the CV solution be hosted on-premise or in the cloud? As for part codes in the warehouse, is there a mix that would necessitate one’s operations team to employ part code identification and segregation? Physical considerations are also important—CV is only as effective as the data it captures—so the visibility for warehouse cameras must be clear.

After considering those factors, manufacturers should run a financial analysis. CV leverages camera systems, allowing many businesses to save expenses when deploying the solution because they can use their preexisting CCTV cameras. However, businesses without an extensive CCTV setup will need to install additional ones. Likewise, companies should determine if they need an implementation partner to help them.

After calculating costs, manufacturers can measure this figure against the impact of the CV solution, such as savings from automatic counting, error reduction, and a lower incidence of stockouts.

From a competitive standpoint, manufacturing companies that do use CV will see significant gains over their competitors that do not. CV is an essential part of Industry 4.0 and larger shifts occurring in manufacturing, including IoT, cloud computing, and AI. Indeed, CV might be the next stage of manufacturing evolution, just as the assembly line, electricity, and steam power were in their day.

Quality Digest does not charge readers for its content. We believe that industry news is important for you to do your job, and Quality Digest supports businesses of all types.

However, someone has to pay for this content. And that’s where advertising comes in. Most people consider ads a nuisance, but they do serve a useful function besides allowing media companies to stay afloat. They keep you aware of new products and services relevant to your industry. All ads in Quality Digest apply directly to products and services that most of our readers need. You won’t see automobile or health supplement ads. Our PROMISE: Quality Digest only displays static ads that never overlay or cover up content. They never get in your way. They are there for you to read, or not.

So please consider turning off your ad blocker for our site.

Joe Vernon is principal of business consulting at EPAM Systems, where he specializes in supply chain processes, technology, and strategy. As a supply chain transformation expert with more than 25 years of experience, he combines knowledge of best-in-class technologies with process redesign, creative solutioning, optimization strategy, and manufacturing and distribution operations. His consulting experience spans multiple sectors, including retail, consumer packaged goods, healthcare, and telecommunications. He has a bachelor’s degree in biology and liberal arts from Cornell College.

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