Who imagined that the technology we see only in Sci-Fi movies would become a reality of the manufacturing industries?
Manufacturers have embraced relevant technologies and digitalization for a much better, faster, and efficient manufacturing process. The products we use in our daily lives are manufactured in high-tech smart factories.
As the adoption of smart factories grows exponentially, yet another disruptive technology — Artificial Intelligence (AI) — has caught every manufacturer’s attention.
AI is seeing an incredible adoption rate among all the major manufacturing industries due to the following trends:
1 Cobot Integration
Manufacturers investing in AI integrated COBOTS have witnessed significant improvement in the overall efficiency (OEE) of the manufacturing process. For instance, the Automotive Industry manufactures, processes, and assembles heavy and hazardous automobile parts and equipment; when laborers are employed to engage with such equipment, the risk of their safety is inevitable; this also may impact the efficiency of the manufacturing process. AI-backed COBOTS Integration makes for an excellent choice; as COBOTS take over the complex and potentially hazardous manufacturing processes, human safety is assured and manufacturing efficiency improves drastically.
2 Generative Design
Innovation is integral to achieve competitive advantage. Customers look for products with novelty and unique features in addition to the quality of the product. However, the design engineers need not spend days in brainstorming innovative ideas anymore; AI’s Generative Design capability requires the design engineer to only give relevant inputs such as the desired height, weight, dimension, material, a key feature, etc. for the product and he will have a plethora of innovative designs at his disposal. AI’s Generative Design has certainly made product innovation much easier and seamless.
3 Predictive Maintenance
This is one of the most crucial benefits of Artificial Intelligence. Most manufacturers incur heavy operational costs due to minor glitches in the manufacturing process. Predictive Maintenance is an intelligence forecast of what could go wrong in a production line and enables the manufacturer to take corrective measures before the entire manufacturing process is disturbed. Predictive Maintenance helps in cost reduction and is conducive to a highly efficient manufacturing process.
4 AI-backed Vision System
Manufacturers have realized that defect detection by Machine Vision that uses a golden template to capture defects is not yielding desired results. AI Vision System is gaining immense popularity among the manufacturers of all the major industries. AI Vision easily captures microscopic defects on the surface and presents the manufacturer with meaningful findings. The accuracy and precision of AI Vision Systems for defect detection is significantly high as compared to the traditional Machine vision. AI Vision Systems certainly plays a crucial role in quality assurance.
Artificial Intelligence is groundbreaking technology. If used in the right way, it will bring multiple benefits to the manufacturers and looking at its increasing adoption rate, in all fairness it seems AI is here to stay; here to disrupt!
Every automotive – from the kid’s bikes to massive cranes, is a product of an intriguing yet complex automotive manufacturing process so the automotive manufacturers know the importance of a seamless manufacturing process to deliver good quality and safe automobiles.
However, the manufacturers face a lot of challenges in manufacturing automobiles; we have listed some of them below:
1 Quality Assurance
Automotives are manufactured in volumes. For instance, a truck or a road roller is a massive product with multiple components. Checking every component for its quality is a taxing process; if the components are not inspected thoroughly, it will affect the quality of the automotive; which could impact the safety of the vehicle as well. Quality assurance for every automotive is certainly a major hurdle for automotive manufacturers.
2 Employee safety
In an automotive manufacturing facility, labourers are involved in lifting, moving, or carrying machines and heavy equipment; it is not only laborious but also puts their safety at risk as they work with hazardous automotive components. Automotive manufacturers often find it challenging to manufacture heavy machines and equipment for automotive whilst considering the safety of their employees.
3 Shortage of skilled labour
The availability of labourers is humongous, but how many out them have the skills to improve manufacturing efficiency? Not many. Lincode Labs catered to a well-known automotive manufacturer to address a similar problem; the manufacturer incurred a huge loss because of export of false parts in CKD (complete knocked down kit) as the labourers were unskilled to identify the right parts. They mistook the wrong parts for the right ones for the fact that the parts looked identical with a very minor variation in their appearance.
4 Innovation and novelty
To achieve a competitive advantage, it is important to bring innovation and novelty to the product. However, since automotive manufacturing is a complex process; the manufacturers are mostly involved in ensuring that the whole process is smooth and error-free, leaving very little time to think about innovation.
5 Longer cycle time
Automotive manufacturing has longer cycle time as compared to other industries. Multiple components have to be produced, assembled, and inspected at every production stage. Automotive manufacturers have the added pressure of delivering the product at the right time despite the longer cycle time.
While the challenges in automotive manufacturing are inevitable; it should not necessarily impact the manufacturing process, especially in this digital age where manufacturers can scale up the automotive manufacturing process with relevant technologies like Artificial Intelligence (AI).
Benefits of AI in Automotive Manufacturing
AI for quality assurance
As compared to the traditional machine vision that detects defects using only a golden template at a fixed image orientation, AI-backed computer vision technology captures microscopic defects, irrespective of the image orientation. For instance, the traditional machine will miss out on a black mark on a black surface (black-on-black) but AI vision is capable to capture this exception seamlessly; such capabilities are extremely crucial for quality assurance.
AI ensures human safety
AI not only sends alerts about a possible danger the labour may be exposed to while he works with heavy, hazardous equipment, it also eliminates the need for humans to work with such equipment. The process of moving, lifting, and inspecting heavy automotive equipment is automated; making it much safer and efficient.
AI helps in innovation
AI’s Generative Design capability significantly helps in innovation. With basic inputs like height, weight, width, size, etc. the manufacturer will have plenty of ideas to innovate to the manufacturing process that delivers good quality, innovative and safe automotive.
AI reduces cycle time
AI vision system monitors every stage of the production cycle, automates the most complex manufacturing processes, and predicts defects and errors in the assembly line beforehand. AI helps in reducing cycle time, increases production capacity, and improves the delivery rate.
Choose Lincode’s AI Vision System
Lincode’s AI Visual Inspection System is a state-of-the-art, Industry 4.0 solution that enables manufacturers to identify surface defects, identify and measure individual components, and generate real-time data about products on the production line. With AI and deep learning, we can significantly reduce false-positives and false-negatives generated by traditional machine vision and capture untrained defects.
Any company that earns goodwill due to its quality and timely delivery of goods knows the importance of having a flawless manufacturing process; ultimately, the quality of goods invariably depends on it.
Manufacturers, irrespective of their industries, must always look to improve the manufacturing efficiency. We have listed 4 main ways to successfully enhance the efficiency of your manufacturing process.
1. Invest in the right technology
Investing in the latest, relevant technologies will scale up the manufacturing process. For instance, if a fabric manufacturer employs a labourer to check for knots on a piece of fabric before it is moved to the next part of the production, the quality of the manufacturing process may be compromised if some knots remain unnoticed by the labour. If the same task is performed by robust technology, all the knots will be detected within a short span and the overall manufacturing efficiency will improve significantly.
2. Reduce product cycle time
To keep up with the demand, manufacturers must ensure that good quality product are delivered to the market at the right time. This solely depends on how efficient the manufacturing process is. Manufacturers must plan and strategize the workflows to deliver the maximum output within a short product cycle time. Here’s an example: A steel manufacturer is aware that manual inspection of steel pipes is a time-consuming and inefficient process. They must consider replacing manual inspection with a better vision system to speed up the process; this shortens the product cycle time and improves production capacity without impacting the efficiency of the process.
3. Automate complex manufacturing process
Employee ‘A’ has skills that can be utilized to improve the efficiency of process ‘A’; however, if ‘A’ is employed to work on process ‘B’, which may be complex, hazardous, and laborious. In this case, the efficiency of both the manufacturing processes ‘A’ and ‘B’ will be affected. Manufacturers must consider using apt technologies to automate the complex manufacturing process for better efficiency. The employees may be engaged in projects where they can use their skills optimally.
4. Track data frequently
Improving manufacturing efficiency is certainly not a one-time job. It is a continuous process. The only way to constantly upgrade your manufacturing process is to keep a tab on the data, the errors, costs incurred, improved rate of efficiency, etc. By tracking data frequently, the manufacturer will know the exact glitches in the process and will be able to take corrective measures at the right time.
As we mention the ways to improve the manufacturing process, we know it is impossible to incorporate them manually, which is why we emphasize leveraging relevant technologies to upgrade the process.
In this digital age, it is imperative to use technologies designed for industrial applications. However, manufacturers often ponder over what technology best suits their industry. We believe Artificial Intelligence (AI) is just the right technology for every manufacturing industry.
AI for Manufacturing Industries
AI is a disruptive technology. It has essential capabilities to enhance manufacturing by leaps and bounds. AI will effortlessly capture microscopic defects on the surface for quality assurance, reduce product cycle time with action detection and predictive analytics, improve employee productivity and safety by automating complex and hazardous manufacturing processes, and provide real-time data tracking. Read our blog on AI in manufacturing to know more about its industrial application.
The limitations in Manual Inspection system and Machine Vision system have led manufacturers of all the major industries to open up to alternative and better vision systems. Our previous blog on the Importance of AI in manufacturing has entailed how the AI vision system has found its niche in the manufacturing industry; AI has drastically scaled up the operational efficiency of manufacturing processes as compared to other vision systems.
However, manufacturers have some myths about the industrial application of AI that may hold them back from deploying the technology. So, we have debunked 4 main myths about AI in manufacturing:
Myth 1: AI is suited for certain industries to perform high-tech tasks only
Fact: AI is suited for all the manufacturing industries, irrespective of its size. AI performs all the functions that are essential to improve the manufacturing process. AI can be used for multiple tasks; right from detecting microscopic defects to tracking human/machine movement for safety and security reasons, AI is not limited to perform only specific tasks. AI can always be trained to perform any task, as required by the manufacturer.
Myth 2: AI always needs expensive hardware installation and factory set-up
Fact: AI is effortlessly compatible with normal IP cameras. It is the traditional Machine Vision technology that requires expensive hardware installation and yet, offers limited functions. AI does not need a specific factory set-up to give adequate results. It functions perfectly well in any manufacturing facility; irrespective of the lighting, vibrations, and dust.
Myth 3: AI will replace workers
Fact: It does not replace workers. It improves employee productivity and makes the factory a safer place by automating complex, hazardous, and time-consuming processes. Workers can be spared from the laborious manufacturing process and be employed in other critical production processes where they can make optimum use of their skills and efficiency.
Myth 4: AI helps in quality assurance only
Fact: AI is not limited only to quality assurance. AI’s Predictive Maintenance and Action Detection capabilities stringently monitor the production process, predict and detect errors at the right time so the entire production process is not largely affected. Therefore, other than quality assurance, AI reduces operational costs, shortens cycle time, and improves production capacity.
AI is a disruptive technology and its benefits go way beyond the traditional machine system. Today, customers expect timely delivery of quality goods to the market; this purely depends on the efficiency of the manufacturing process, and what better than a robust AI technology to amplify the overall operational efficiency with a seamless manufacturing process?
Trust in Linocode’s AI Computer Vision solution
Lincode’s AI computer Vision Technology, LIVIS, is a patent-pending technology that offers AI-based Computer Visual Inspection, Dimension Measurement, High-Speed Optical Character Recognition, Precise Object Detection, Systematized Object Tracking, and more. To deploy our solution, contact us at firstname.lastname@example.org
What would the world be without manufacturing industries? The products we use in our daily lives; steel, fabric, plastic, pharmaceuticals, etc. are an outcome of an extensive manufacturing process. If there is a single glitch in the manufacturing process, the final product will lose its market standing; therefore, investing in technologies to monitor every stage of the manufacturing process is a necessity for every manufacturing industry today.
Machine Vision System
While Machine Vision technology was popularly used among all the major manufacturing industries, the technology could not keep up with the pace of increased customer expectation for product quality, due to the following limitations:
The technology used a golden template to detect surface defects and errors and was rigid towards image orientation; as a result, Machine Vision could only detect the obvious defects and could not capture the microscopic defects.
Machine Vision requires the installation of expensive hardware and had limited functionality
The training process and deployment cycles were time-consuming and costly
Machine Vision technology was not flexible to the manufacturer’s change in requirements
Manufacturers of various industries now seek a technology, much stronger and better than a Machine Vision system to scale-up the overall manufacturing process.
AI-backed Computer Vision System
AI-backed Computer Vision System has begun to disrupt all the major manufacturing industries. Manufacturers who augment their factories with AI-backed Computer Vision System have experienced the following advantages:
The machine vision’s capability to capture microscopic defects is limited. Computer Vision takes multiple images of a product on full speed conveyors and effortlessly captures microscopic defects on various product surfaces irrespective of the image orientation, enabling the manufacturer to take corrective measures at the right time, enhancing the overall product quality.
Reduction of Operational Costs
Here’s an instance: A steel manufacturer discovers that an entire batch of the product has minute chipped corners. The vision system he invested in failed to detect the chipped corners. Imagine the loss of time, money, material, and efforts the manufacturer will have to bear to produce another batch, all over again!
AI Computer Vision detects errors at the right time and saves the organization from spending a fortune on remediating measures. According to an interesting article, with the help of machine learning, one of the esteemed company managed to save USD 1 billion in 2017!
Shorter Production Cycles
As production cycles get shorter, the production capacity becomes higher. When the production cycle is not bound to time and labour-intensive processes and is stringently monitored for defects and errors, the production cycle becomes faster and more efficient. Manufacturers can produce quality goods within a reasonable time frame and are ready to deliver them to the market just when the demand is at a peak.
Improved Employee Productivity
When manufacturers choose AI Computer Vision over the flawed Human Vision or Machine Vision, the manpower at the factory can be put to better use. While AI Computer Vision accurately inspects the product for quality assurance, the labourers are spared from this strenuous process and can be employed in other processes of manufacturing that require adequate human intervention.
The Future of AI Computer Vision System
The AI in the computer vision market was valued at USD 2.37 billion in 2017 and is expected to reach USD 25.32 billion by 2023, at a CAGR of 47.54% during the forecast period. 2017 has been considered as the base year, and the forecast period is from 2018 to 2023.*
Lincode’s AI Computer Vision Solution
Lincode’s AI Visual Inspection System is a state-of-the-art, Industry 4.0 solution that enables manufacturers to identify surface defects, identify and measure individual components, and generate real-time data about products on the production line. With AI and deep learning, we can significantly reduce false-positives and false-negatives generated by traditional machine vision and capture untrained defects.
Reach out to us at email@example.com to know more about our solution. *source:https://bit.ly/2KGS2cL | https://bit.ly/3eYxkTK
Issey Miyake, a renowned Japanese fashion designer, known for technology-driven clothing designs, once said: “There are no boundaries for what can be fabric.” The statement fits rightly in this day and age where customers are extremely quality conscious. If they spot one little hole, a slub or a loose thread on the fabric, they will not shy away from expressing negative reviews about the brand.
Fabric manufacturers are opting for smart manufacturing with the latest technologies, digitalization, and innovation to manufacture supreme quality of the fabric, but the desired quality of the fabric is not achieved. So, to produce the ultimate quality of the fabric, manufacturers must enhance their smart manufacturing with disruptive technology like Artificial Intelligence (AI).
Let’s tell you about 3 interesting ways AI helps fabric manufacturers in quality assurance:
1. Captures microscopic defects
The defects on fabric surfaces can be so intricate, that even machine vision cannot capture it, let alone its visibility to the human eye. Defects such as a minute hole, knot, missing thread, miss-pick, slub, negligible colour anomalies, etc. can only be spotted with a technology that goes beyond the human and machine vision. AI-backed computer vision technology can capture such microscopic defects on the fabric surface at full conveyor speed. The defects are spotted well-in-time and enable the manufacturer to take corrective measures right before the fabric is passed on to other stages of production.
2. Pattern inspection
Fabric manufacturers know the importance of bringing a variety of apparels to the market; so every fabric has different patterns. Weaving, knitting, braiding, finishing, and printing, is unique to each fabric piece. AI inspects the pattern on the fabric to check for the quality of the fabric. For instance, AI-based pattern inspection shows a certain weaving pattern may potentially harm the fabric. The manufacturer may then choose an alternative weaving pattern or restructure the existing one. Pattern inspection enables the fabric manufacturer to produce a variety of fabric without compromising on the quality.
3. Action detection
Fabric manufacturing has longer assembly lines. If the assembly lines are not well monitored, the quality of the product will go down invariably and the production capacity will be affected. AI Action Detection stringently monitors the assembly line and promptly reports the fault in assembling. Most often, quality-degrading defects are detected only after the completion of production. AI Action Detection inspects the fabric at the production stage and ultimately only the desired quality of the fabric is produced.
Lincode’s AI-backed Computer Vision technology for Fabric Industry
We are proud to be the pioneers in textile-based visual inspection. Our knowledge and experience with the textile/ fabric industry have enabled us to design a robust, AI-backed Computer Vision solution, LIVIS, ideally suited for the fabric industry for extensive quality assurance.
A global conglomerate, headquartered in Mumbai, India, is a major player of the Textile industry. The company trusted in Lincode’s AI Computer Vision Solution for fabric surface detection. Our solution assisted in quality assurance by inspecting the fabric and detecting microscopic defects such as Hole, Slub, Broken pick, Stain, and Pinhole with high precision and accuracy.
Digital transformation has revolutionized the steel manufacturing industry. While Steel manufacturers are readily embracing and experimenting with digitalization, they are willing to upheaval the steel manufacturing process even further with Artificial Intelligence (AI).
Artificial Intelligence has become the nucleus of the steel manufacturing industry. AI has helped businesses achieve high ROI by transforming the toilsome and flawed steel manufacturing process to an effortless and friction-less one. AI is a powerful tool and is paramount to the steel manufacturing industry, owing to the 5 main reasons listed below.
1 Quality Control
As it is humanly impossible to track every stage of the production cycle for quality assurance, manufacturers opted for machine vision, which again had its own limitations; lack of data tracking, installing expensive hardware, inspection restricted towards specific image orientation, etc. AI-backed vision systems aid quality control and assurance by identifying, analyzing, predicting, and eliminating defective products with high accuracy and microscopic precision at every stage of production.
2 Predictive Maintenance
Good maintenance ensures a significant reduction in operational costs and enhances the overall production efficiency. Predictive maintenance is one of the most important functions of AI, it is a technique wherein AI collects data from various manufacturing sources and applies intelligent algorithms to predict defects and anomalies beforehand. *According to a report by Mckinsey, AI-derived predictive maintenance can create a value of about$500 billion to $700 billion across the world’s businesses!
3 Maximize Cobot efficiency
Cobots or Collective Robots have become a popular adoption in the steel manufacturing industry. Cobots are basically robots working in unison with humans to reduce the human effort and boost employee productivity resulting in a multi-fold increase in efficiency with AI integrated COBOTS. The complex and hazardous processes and operations in manufacturing that were earlier performed by human are now being taken over by AI-based cobots leading to much better, faster and satisfactory performances without risking the safety of employees.
4 Streamlined Supply Chain
AI not only aids in frictionless manufacturing of steel and steel products, but it also streamlines the supply chain process for efficient delivery of the product. AI streamlines the supply chain process by solving complexities of logistics and generating best supply chain strategies by analyzing demand forecast, identifying multiple supply channels, optimizing logistics network, etc.
5 Generative Design
An ultimate step to product innovation is to use generative design. For instance, a steel manufacturer needs to manufacture steel chairs. He gets in touch with the design engineer, who uses AI-based generative design by giving simple inputs like height, width, strength, size, etc. Thousands of design options are now readily available to the manufacturer within a short time. AI Generative design enables the manufacturer to gain a competitive advantage due to product novelty and quality.
Manufacturers embedding AI in the steel production process achieve ultimate operation efficiency, a significant reduction in costs, improved employee productivity and enterprise-class quality of steel products that passes quality assurance standards and regulations without much ado.
Alain Dehaz rightly quotes: “Technology, through automation and artificial intelligence, is definitely one of the most disruptive sources.”
So, it is safe to say, AI technology is here to disrupt and steel manufacturers must take complete advantage of it.
Is there any product that does not require at least some amount of plastic? Hardly any! In fact, for a lot of products, plastic is an integral manufacturing material. Take automobiles, for instance, to produce an automobile, high-performance plastic such as Polypropylene, Poly-Vinyl-Chloride (PVC), Polyethylene (PE), etc. are used in volumes. This means, if the quality of the plastic used in producing the automotives is defective, the quality of the entire product will be compromised.
Given how plastic is the choice of material for most industries, the manufacturing process to produce plastic should be subject to stringent vision inspection for quality assurance. Manufactures are going beyond the conventional Human and Machine vision system to augment their smart factories with Artificial Intelligence (AI) based Computer Vision technology.
Benefits of AI Computer Vision for Plastic Industry
1 Highly accurate defect detection
While Machine Vision detects only obvious defects, a robust technology like AI Computer Vision identifies, analyzes, predicts, and eliminates microscopic defects such as scratch, dirt, colour & discolours identifications, patches, flash, crack, missing parts, short-fills, vacuum voids, and other anomalies on both matte and glossy surface of the plastic, irrespective of the image orientation.
2 Shorter Cycle time
Each product undergoes various process; assembling, painting, welding, etc. If done manually, the production process becomes laborious and time-intensive. The cycle time for each station becomes longer. AI computer Vision inspects every process with high precision and identifies issues in the process at the right time, making cycle time shorter and improving the production capacity.
3 Real-time data tracking
The Machine Vision system stores data in a raw image form, making it inconvenient to access. AI Computer Vision not only provides the user with meaningful findings but, also stores the data in a binary format. It is easy to access and track real-time data and the user will get a detailed report on all the findings, including the defects he wants to know about in particular. The report can be availed anytime, anywhere.
4 Flexible to manufacturers’ needs
The manufacturer may not want all the functions of the solution. For instance, a plastic manufacturer may not want surface defect detection. He may only want to measure a specific metal cable that protrudes out of their pipes. AI Computer Vision system is modular and can be trained to perform specific actions.
Smart factories in the plastic industry are highly digitalized and the combination of production, communication, and information technologies have improved the manufacturing process significantly. It’s about time smart factories experience a radical transformation by deploying AI-backed Computer Vision technology over Human and Machine vision to achieve a desirable quality of products, reduce operational costs, and improve employee productivity.
AI Computer Vision solution by Lincode Labs
Lincode’s AI computer Vision Technology, LIVIS, is a patent-pending technology that offers AI-based Computer Visual Inspection, Clear-Cut Dimension Measurement, High-Speed Optical Character Recognition, Precise Object Detection, Systematized Object Tracking, and more.
Connect with us at firstname.lastname@example.org to know more about deploying our solution.
Artificial intelligence in manufacturing is part of a larger trend towards fully automated production. With the development of “smart factories”, AI systems have the potential to transform the way companies run their production lines, enabling greater efficiency by enhancing human capabilities, providing real-time insights and facilitating design and product innovation.
A New Industrial Revolution
Manufacturing has come a long way since the Industrial Revolution of the 1800s when water and steam-powered machines were used to help workers for the first time. By the 1960s, Industry 3.0 — the third industrial revolution — was well underway, with General Motors unveiling the first industrial robot in 1961. However, early industrial robots were limited in scope, programmed to perform only a single task at a time.
Now, the manufacturing industry is at the latest stage of its evolution: Industry 4.0.
Industry 4.0 refers to the use of automation and the exchange of data and encompasses technologies such as the Internet of Things, cloud computing — and artificial intelligence.
A scenario: during production on the factory floor, a sensor detects a malfunctioning piece of equipment. This data is transmitted via cloud computing, which immediately flags up the defect and automatically requests a replacement. This example of real-time analysis and action can significantly increase efficiency across the entire production line.
7 Ways Artificial Intelligence can Impact Manufacturing:
Optimising production processes
Artificial intelligence can help to increase efficiency on the production floor by automating manual or repetitive tasks. Robotics is an area where this is already in practice, with robots being used to perform physical tasks such as assembly, lifting and packaging. Using industrial robots in this way removes the need for humans to perform routine, manual tasks, enabling workers to focus on more complex operations.
Last year, researchers at Siemens unveiled their two-armed robot, capable of manufacturing prototypes without needing to be programmed. The robot is able to decipher different CAD models, removing the need to programme its processes. Further developments in this space could see the future of production becoming fully automated.
AI systems will also be able to optimise manufacturing processes by monitoring every stage of the production cycle, such as lead times and quantities used. In the case of additive manufacturing, machine learning algorithms can be used to predict the fill rate of machine builds, thereby optimising production planning.
Safer working environments
One area of robotics that has come to the forefront in recent years is the notion of “cobots” — collaborative robots designed to work safely with humans. Small and lightweight, cobots offer an entry point for companies seeking to adopt robot technologies, as they are considerably less expensive and easier to programme than traditional industrial robots.
Cobots can help to create safer working environments by performing more dangerous and physical tasks, leaving workers free to work on more complex tasks and avoid injury. In time, machine learning algorithms will be able to improve the capabilities of factory robots so that they can better interact with and take instructions from humans.
One problem with many autonomous robotic systems is the rule-based approach, in which robots are programmed for a task, and are unable to react to changes or unexpected actions. Machine learning overcomes this challenge, by analysing vast amounts of data to identify meaningful patterns. From this, the system is able to continually learn and improve without needing to be programmed for one, singular tasks. The integration of AI systems and sensors could have significant implications for worker safety: for example, a robot would be able to recognise a dangerous situation and take preemptive measures to prevent injury.
One great way to improve production efficiency is by accurately forecasting and predicting demand. AI-powered systems can be immensely useful for this, as they are capable of testing many different models and possible outcomes. Machine learning algorithms can use data to discover meaningful patterns and provide real-time insights. Manufacturers can use these insights to predict demand and determine which products to prioritise accordingly.
Artificial intelligence is creating new possibilities for production — generative design being a good example. Used by the likes of Airbus and New Balance, generative design software enables engineers to generate hundreds, if not thousands, of design possibilities. Designers and engineers can then choose the outcomes that best suit their needs.
In this case, artificial intelligence is able to solve key manufacturing and engineering challenges by creating new design solutions that would otherwise be impossible or inconceivable. This form of “co-creation” between humans and technology will enable manufacturers to create new, innovative products and provide services that meet customer needs with less time and at a lower cost.
Simplified supply chains
According to a recent study, companies are spending 6,500 hours per year on average on manual processes related to supply chain management activities. This includes processing paper invoices, responding to suppliers and chasing up purchase order numbers. By automating many of these routine tasks, the time spent could be slashed significantly.
But artificial intelligence can take this one step further, by optimising supply chain planning processes. Using machine learning technology, manufacturers can potentially identify patterns of demand for various products, including key variables like market behaviour, political or socio-economic developments, for example. This could help forecast future market demand, having an impact on the way raw materials are sourced and help manufacturers make key financial and recruitment decisions.
Optimising the entire decision-making process along the supply chain in this way can also help to speed up delivery and balance supply and demand.
Vital to any production operation is the availability of functioning tooling equipment. Being able to predict and prevent equipment failure or malfunction is therefore highly beneficial for a smooth and efficient production process. However, the servicing of production equipment is generally based on a fixed schedule, regardless of the current operating status, wasting valuable labour time, and raising the risk of unexpected equipment failures.
Manufacturers are therefore increasingly recognising the importance of predictive maintenance solutions — for example, using sensors to track the condition and performance of equipment. In time, predictive maintenance can eventually evolve into machine learning systems being able to analyse vast amounts of data to predict future malfunctions. This would significantly increase efficiency and help reduce maintenance costs related to expensive replacement parts.
Much of the future of manufacturing will lie in mass customisation. As consumers increasingly expect personalised products, manufacturers will need to find ways to meet this demand without affecting efficiency.
With traditional, mass production approaches, customisation is neither cost-effective nor time-efficient. The emergence of technologies like additive manufacturing, however, turns this on its head. Advances in artificial intelligence and additive manufacturing will help manufacturers meet demand by making products that are relevant for their customers. It will also help to share data along the value chain to create a more responsive customer service and faster deliveries.
Driving Production Efficiency with AI
Developments in technology, such as cloud computing, big data and machine learning, has significant implications for the way products are manufactured. Artificial intelligence is the logical next step in this evolution and will play a key role in helping to achieve better productivity, efficiency and visibility across manufacturing operations.
Much has been said on the possibility of AI and automation replacing human workers, but this isn’t necessarily the case. AI will not replace human intelligence; rather, it will support and enhance the role of humans by eliminating repetitive, manual tasks and the possibility of human error. Workers could then be retrained to perform more complex tasks.
Manufacturers will need to operate flexible manufacturing processes, meaning that they must be able to adapt rapidly to exploit new technologies and be responsive to ever-changing customer needs and market landscape.
Computer Vision is a field of Artificial Intelligence which enables computers to interpret and analyze the visual world with better efficacy. It has gained immense popularity in the past few years in dynamic industries such as retail, insurance and manufacturing. These industries are leveraging machine vision to enhance their customer experience, reduce time and efforts and achieve better quality assurance.
It is well acknowledged that the retail industry is at the forefront of leveraging computer vision. This would help improve customer experience and provide relevant data and insights to retailers. With the increasing popularity of eCommerce, businesses are evolving to offer customer delight by leveraging computer vision for the personalized and streamlined in-store shopping experience. Computer vision allows retailers to speed up business operations like shelf management, payments and data collection.
Let’s talk about some integral computer vision solutions that Zensar has built for our retail customers.
Facial Recognition System
Every retail store has cameras for security reasons. These cameras can be used to recognize faces and identify frequent customers and new customers. This identification can help retailers to give discounts to increase brand loyalty and to attract new customers. The simplest way of attracting new customers is by providing the basis of the most suitable recommendations for their purchase history. To put this to use,
Reverse Image Search
Customers often come across something that they want to buy, but somehow; do not have relevant information about it. Object recognition technology can be used to recognize such products and provide contextual information about it. It can also direct the user to the same/similar product. ‘Try an image search’ option has got wide acceptance by customers in many popular e-commerce sites. Zensar has advanced expertise in ‘reverse image search’ feature for clothes recognition that can be used by e-retailers and can be extended to cover other object types as well.
Claim Processing in Insurance is a time-consuming process and relies a lot on human intervention. After a claim has been filed, a human adjuster visits the workshop (in case of asset damage) or the place where the damage occurred (in case of a home insurance) to inspect the damage, validate claim and coverage, evaluate the claim amount and approve payment followed by the finance department initiating payment.
Computer vision can play a vital role in eliminating the roadblocks in faster processing of claims by doing automatic damage detection and assessment.
Car Damage Assessment
This in-house solution fastens the claim processing for car damage by doing auto-detection of damaged parts and auto-assessment of the severity of damage to estimate the claim amount. The user can log in using his/her credentials on the app. The details of the user such as name, policy number and vehicle number get populated from the guidewire. The user can then use the photo claim option to take pictures of damaged cars. The AI engine analyzes those images, identifies the damaged parts of the car and assesses the severity of the damage. Based on this assessment, the claim amount is evaluated. If the user is satisfied with the estimates, he/she can submit the claim for processing to the guidewire.
Roof Damage Assessment
This solution is a part of the home insurance claim processing and identifies the part of the roof which is damaged due to hailstorms or any other natural calamity. The pictures are taken using drones and assessed using computer vision algorithms. Watch this video to learn more.
Quality assurance is the most expensive activity in production and manual inspections are carried out for the same. Computer vision makes it possible to spot minor defects that are not visible to the human eye. According to Forbes, AI can improve manufacturing defect detection rate by 90%. Surface Imperfection Detection is a quality assurance task which is mandatory to guarantee the quality of a manufactured item.
Steel Defect Identification
Defect Identification on steel sheets is one such step in reducing the manual efforts in quality check. Surface defects on steel sheets are not identifiable by human eyes and require the use of high-frequency cameras to detect the same. We have built a solution to localize and classify those defects in four categories using computer vision algorithms.
Artificial Intelligence is disrupting business and society in a pivotal way. Computer vision is enabling a multitude of industries like retail, insurance, manufacturing, etc. to achieve enhanced customer delight and satisfaction. Our ‘Living AI’ philosophy, compels us to empower businesses to provide better services to their customers.