Revolutionizing business using computer vision

Revolutionizing business using computer vision

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.

Retail

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, 

ZENVAS
ZENVAS Dashboard

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.

Insurance

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.

Manufacturing

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.

Conclusion

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.

Source: https://www.zensar.com/blogs/2020/01/revolutionising-business-using-computer-vision/

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admin April 7, 2020 0 Comments

Artificial Intelligence in Manufacturing

Artificial intelligence (AI) seems to have travelled from sci-fi fantasy to boardroom hyperbole at warp speed in no time at all. Not only does AI make it possible to have a future working environment that involves robotics, but it is also very relevant in the context of manufacturing and can be of great value to organizations today. 
    
AI will help manufacturers stay competitive, reduce costs, optimize capital employed and provide a better environment for their employees and service to their customers.

Aspects of the supply chain that are being revolutionized by AI 
Large manufacturing enterprises are beginning to use AI to make material purchasing and allocation decisions. As the economy has evolved around shorter invention cycles, globalization, and sustainability, and mass personalization, both manufacturers and distributors have to become more agile, which means manufacturing resource planning (MRP) can be a daily process for many companies.

Factories of the future, are taking MRP to the next level. Machine learning models suggest changes to planning parameters, lead times, and inventory stocking levels, and predict quality issues and down chain disruptions to both lead-time and price insulating the end customer and supporting their expectation of immediate gratification. 
   
AI will also help set better expectations, for manufacturers on delivery dates and volumes based on capacity along with planned and unplanned downtime. And AI can help companies decide what to do with spare capacities, like producing seasonal items early that can be wholesaled to retail outlets at a lower cost later in the year. 

Industries with the highest AI adoption
  
Typically, as supervised AI requires good data and good training, most early success will come in industries that use common data. For example, all logistics companies use the same types of the road map, weather, and traffic data. Likewise, virtually all retailers use the same Universal Product Code (UPC) to identify products, which mean AI techniques applied to UPC data benefits a large number of customers, and this will drive AI companies to build solutions for retailers. In other words, industries adopt AI when the solutions begin to emerge. So, industries with high-quality data that is readily available will have AI solutions ready sooner than others. 

Impact of AI on the manufacturing workforce 
Many modern manufacturing facilities run with very few employees already and this is why manufacturing productivity is actually far higher than most people realize. In the near term, autonomous vehicles may, for example, replace fork-lift drivers. Better computer vision software can make it easier for fewer staff to ensure quality on a mass scale. But these basic automation trends have been in place for some time. The next wave of automation will probably impact roles that analyze data and recommend actions to optimize the business, from design to operations and service. If AI systems can analyze images and correlate data from many sources, then it’s possible for a computer to design the next popular clothing line or at least predict which lines will be successful.  
  
Product design will also be heavily disrupted by AI as we start seeing generative design tools where the problem is defined and all of the problem space is explored computationally, amplifying our cognitive abilities. Augmented collaboration will be the working environment of the future. The human-robot collaboration will be more gesture-based, where workers will be doing the things humans are good at-perception, awareness and decision making-while robots will be doing the things they are currently good at, repetitive tasks with precision. 

Emerging trends in AI
AI will impact manufacturing in various ways. The continued improvement in computer vision (CV) has long been used for quality assurance by detecting product defects in real-time. But now that manufacturing involves more data than ever-coupled with the fact that plant managers do not want to pay staff to enter data-AI with computer vision can streamline how data gets captured.
 
The second area that AI will impact is with the Internet of Things (IoT) or cyber-physical systems. IoT is remarkable in that the basic technology is being deployed rapidly even though the outcomes and security aspects haven’t really been thought through. All in all, IoT is an incoming tsunami of data that AI can use to reason over and evolve. This will help augmented generative design processes where products are reimagined in ways more akin to evolution. 

In conclusion, AI is not yet a turn-key solution. You don’t buy it off the shelf and use it, you infuse it in everything you do to augment your business and products and unlock potential for future business growth. 

Source: https://www.epicor.com/en-in/blogs/manufacturing/artificial-intelligence-in-manufacturing/

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admin March 30, 2020 0 Comments

An Introduction to Quality Control in Manufacturing

“Quality is Everyone’s Responsibility”

Edward Deming, considered to be a “God” in the eyes of the Japanese business community for his influence on their manufacturing sector after WWII is attributed as the source of the now somewhat obvious quote above. His philosophy was one that wanted us to see manufacturing as a “system,” not a series of bits and pieces of additive work.

The idea was that by applying appropriate principles of management consistently, quality could be increased AND costs reduced by reducing waste, improving customer satisfaction, less staff churn, and less litigation. His philosophy remained relatively unknown in the US until the 1980’s, when it became clear that Japanese manufacturers – who took this approach to heart in the 1950’s – were going to be succeeding at bumping the US off its perch in many key industries due their superior quality and cost management techniques, including the “just-in-time” manufacturing that the Japanese auto industry made into a household term.

How They Do It

Deming’s philosophy was summarized by some of his Japanese followers in the 1970s with the following “a”-versus-“b” comparison:

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(a) When people and organizations focus primarily on quality, defined by the following ratio, quality tends to increase, and costs fall over time.

(b) However, when people and organizations focus primarily on costs, costs tend to rise, and quality declines over time.

One might even say this is a modern version of the Ben Franklin wisdom of being “penny-wise, pound foolish” applied to manufacture. The key takeaway here is that a focus on quality is NOT a path to higher cost, but rather that the path to lower costs is a healthy obsession with quality.

Quality Has Many Names

In the last 20-30 years, there have been many “systems” created and rolled out that was supposed to be the final word in quality. Undoubtedly, there will be more coming our way in the following decades as well. For today, we’ll take a brief look at the four major success stories in the evolution of quality.

Total Quality Management (TQM). TQM is an entire management system focused on people their focus on increasing customer satisfaction while continually reducing costs. It uses scientific methods for assessing quality, its associated costs and constraints to implementing improvement. TQM also requires a total systems approach where all functions, processes, and departments, and all employees at all levels, are integral to ensuring success – be it in manufacturing or delivery of services. Learning and adaptation to continual change are essential for achieving success.

Six Sigma. Coined by Motorola as its methodology for improving business processes by minimizing defects, Six Sigma refers to a statistical measurement of “only 3.4 defects per 1 million opportunities.” To produce a defect (or virtually zero). As an organizational approach, it means that companies make decisions based on data, seek roots of problems, define defects based on customer requirements, and track leading indicators of problems to prevent them from happening.

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Lean Production. Lean production, which can be credited to the Japanese adoption of Deming’s teachings, refers to the continuous flow of products or services to the customer at the moment it is needed and to the customer’s specifications. It focuses on increasing productivity and quality while reducing inventory and shortening lead time from floor to customer. Its principles include workplace safety, order, and cleanliness; just-in-time production; built-in Six Sigma quality; empowered teams; visual management to track performance and provide immediate feedback on a daily or even hourly basis; and continual pursuit of perfection.

International Standards Organization Quality Management Standards. The International Standards Organization (ISO) has developed a series of quality management standards that support quality philosophy. Specifically, it has developed a set of five such standards, ISO 9000–9004. The American National Standards Institute (ANSI) and the American Society for Quality Control (ASQC) developed the ANSI/ASQC Q9000–Q9004. Also, specific standards also exist for automotive, aerospace, and telecommunications industries and environment management. These standards have been revised over the years, and organizations must continually address these revisions. Organizations competing in the global market must achieve the quality levels dictated by these standards.

TQM Approach

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The importance of quality both as a means of ensuring customer satisfaction and reducing cost has become increasingly recognized over the past thirty years. The philosophy has shifted from Quality Control to Quality Assurance to Zero Defects and beyond, while standards such as ISO 9000 have sought to level on the practice.  Total Quality, in fact, means that quality is all-pervasive through a company’s products, processes, procedures and systems and thinking – and is practised by all.

The question ‘What is quality?’ may be debated at length, and there are many definitions. For now, let’s assume it primarily means ‘Giving the customer what he/she wants,’ and ‘consistency.’

Consider a manufacturing process. There are some ways it may endeavour to ensure the customer gets what he/she wants. 

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1. The process can make a good product but is unreliable, and defects escape into the market. If customers complain, their complaints are resolved. This is costly and may harm reputation.

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2. Inspectors detect bad product at the end of the process and repair/reject it to protect the customers. This is costly and frustrating; deliveries will be delayed or costly buffers of finished goods required.

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3. Defects are returned to the source for rectification or rework. Costs remain, as in (2). There are delays in identifying problems so causes may not be apparent.

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4. Defects are detected at the source; causes will be more obvious. Delivery remains erratic, downstream customers are kept waiting, but at least they’re not adding value to the defective product.

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5. Defects are prevented. Through improving products and processes, we can assure delivery without incurring rectification costs.

There are a number of things which we have to accept if we wish to achieve the scenario depicted in (5):

  • The customer knows what they want. We have to ask the right questions to define the specification. Market research may involve anticipating consumer needs; we need feedback from customers. We need data.
  • We need materials and equipment capable of achieving what is expected, and products designed not only for the market but for manufacture. We rely on those responsible for Design, Engineering, and Sourcing to provide what is required.
  • We have to accept responsibility for our own actions. People need to be trained, directed and motivated. We look to Personnel and Training for their support.
  • We are all part of a team which is the Business; a team which is our particular Division or Department; and a team which consists of those with whom we work on a day-to-day basis. We can take pride in what we collectively achieve for our customers and shareholders, and satisfaction in playing our part as individuals. 
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TQM primarily addresses the business as a whole, developing a state of mind consistent with the above. Three major divisions of a manufacturing company may be considered as illustrated left.

While GK is consistent with this, and the same disciplines prevail in all three divisions, the prime focus for GK is Manufacturing and its immediate support areas: 

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Six Sigma is a similar approach which uses the same tools and techniques, ‘ re-labelling’ some and according to the title ‘Black Belts’, etc. to the facilitators. It’s simple (but not easy!) goal is to achieve six sigma capabilities of all business processes – a 3.4 ppb defect rate.

Source: https://www.newcastlesys.com/blog/an-introduction-to-quality-control-in-manufacturing

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admin March 20, 2020 0 Comments

Sales Engineers for Chennai

Lincode Labs is an innovation first company on AI, ML, Deep learning. Helping Manufacturers Quality Check their products efficiently using computer vision. 

Responsibilities:

  • Establish and maintain a deep understanding of the overall product portfolio and the competitive landscape.
  • Lead technical discovery and prepare/deliver technical presentations explaining our products to prospects and customers.
  • Create and deliver powerful presentations and demos to clients that clearly communicate the uniqueness of the value proposition.
  • Successfully manage and execute technical proof of concepts (POCs), on-site or remote.
  • Responsible for representing the product to customers and at field events such as conferences, seminars, etc.
  • Evangelize Lincode products to prospects, customers, and partners via presentations and product demos.
  • Convey feature input and customer requirements to Product Management teams.
  • Partnering with sales executives to plan, prepare and execute on strategic deals in complex sales cycles.
  • Collaborate with sales teams to understand customer requirements and provide sales support.
  • Respond to technical objections and articulate the value and return on investment delivered.
  • Liaise with the Engineering, Product, Marketing, and Sales teams to provide consultative technical expertise for all customer needs.
  • Effectively communicate & build confidence with customers across teams (Engineering, Product, Marketing, and Sales).
  • Engage in and oversee the development of customer proposals, design and delivery, ensuring all expertise, information and recommendations are concisely defined

Requirements:

  • 2-3 years of Sales Engineering experience.
  • Worked previously with Machine Learning/ Computer Vision Companies- preferred
  • Min qualification- Graduates
  • Knowledge about installing Industrial cameras
  • Excellent presentation, written and verbal communication skills to communicate professionally.
  • Self-motivated with strong interpersonal and problem-solving skills.
  • Ability to work well in a highly dynamic team.

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admin March 19, 2020 0 Comments

Artificial intelligence in manufacturing: Optimization of additives consumption

Artificial intelligence and machine learning, in particular, is the new, powerful tool for minimizing consumption of additives and energy in almost every industrial process. Today, traditional manufacturing companies are actively beginning to implement machine-learning models to save costs.

Although most industrial processes have been studied in detail for decades, recent progress in the area of artificial intelligence, in particular machine learning, has opened new horizons for further optimization. A degree of uncertainty is part of any complex process. Due to the uncertainty inherent in chemical processes, technologists have to use extra quantities of costly additives to cover any deviation in processing. Current average suboptimal values give us an opportunity to optimize by replacing them with exact values.

Artificial intelligence lets us build a model that takes into account data coming from different sources: initial material composition, a quality check of raw materials, readings from hundreds of sensors. The trained model accurately predicts parameters of the final product, and based on this prediction calculates exact amounts of additives needed to achieve the requested parameters for each particular batch.

Uses of artificial intelligence in the manufacturing industry.

There’re thousands of different industrial processes, which can benefit from AI and machine learning technology. Here is the shortlist of heavy-industries, that leverage machine learning to reduce the usage of input materials today:

  • Ore beneficiation
  • Gas processing
  • Oil refinery
  • Chemical industry
  • Plastics industry
  • Glass manufacturing
  • Semiconductor manufacturing
AI - Artificial Intelligence

BitRefine offers technology that increases the efficiency of the process without heavy capital investments. To gain an additional 5-10% of cost savings, our clients don’t need to redesign their production lines or install a higher grade of equipment. 

We use existing historical logs, collected from particular client’s process, to build a machine learning model that is able to predict properties of the final product based on a given set of initial and intermediate parameters. On the basis of this model, we deploy either fully automated or recommender decision module that minimizes amounts of input materials, chemicals and energy while keeping requested quality property of the product.

Source: https://bitrefine.group/industries/big-data-manufacturing/93-industries/manufacturing/manufacturing-solutions/255-artificial-intelligence-in-manufacturing-consumption

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admin March 19, 2020 0 Comments

Importance of Quality Control and Quality Assurance

Quality is an important factor when it comes to any product or service. With the high market competition, quality has become the market differentiator for almost all products and services. Quality control is essential to building a successful business that delivers products that meet or exceed customers’ expectations. It also forms the basis of an efficient business that minimizes waste and operates at high levels of productivity. A quality control system based on a recognized standard, such as ISO 9001 published by the International Organization for Standardization, provides a strong foundation for achieving a wide range of marketing and operational benefits.

Therefore, all manufacturers and service providers out there constantly looking for enhancing their product or service quality. In order to maintain or enhance the quality of the offerings, manufacturers use two techniques, quality control and quality assurance. These two practices make sure that the end product or the service meets the quality requirements and standards defined for the product or the service.

There are many methods followed by organizations to achieve and maintain the required level of quality. Some organizations believe in the concepts of Total Quality Management (TQM) and some others believe in internal and external standards. The standards usually define the processes and procedure for organizational activities and assist to maintain the quality in every aspect of organizational functioning.

When it comes to standards for quality, there are many. ISO (International Standards Organization) is one of the prominent bodies for defining quality standards for different industries. Therefore, many organizations try to adhere to the quality requirements of ISO. In addition to that, there are many other standards that are specific to various industries.

Since standards have become a symbol for products and service quality, the customers are now keen on buying their product or the service from a certified manufacturer or a service provider. Therefore, complying with standards such as ISO has become a necessity when it comes to attracting the customers.

Every organization that practices QC needs to have a Quality Manual. The quality manual outlines the quality focus and the objectives of the organization. The quality manual gives quality guidance to different departments and functions. Therefore, everyone in the organization needs to be aware of his or her responsibilities mentioned in the quality manual.

Quality Assurance is a broad practice used for assuring the quality of products or services. There are many differences between quality control and quality assurance. In quality assurance, a constant effort is made to enhance the quality practices in the organization. Therefore, continuous improvements are expected in quality functions in the company.

When it comes to our focus, we understand that quality control is a product-oriented process. When it comes to quality assurance, it is a process-oriented practice. When quality control makes sure the end product meets the quality requirements, quality assurance makes sure that the process of manufacturing the product does adhere to standards. Therefore, quality assurance can be identified as a proactive process, while quality control can be noted as a reactive process.

Source: https://www.parekhplast.com/blog/importance-of-quality-control-and-quality-assurance/

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admin March 18, 2020 0 Comments

QUALITY CONTROL IN MANUFACTURING

In manufacturing, quality control is a process that ensures customers receive products free from defects and meet their needs. When done the wrong way, it can put consumers at risk. For example, the recent defect found in Takata airbags resulted in the biggest automotive recall in history. The recall includes almost 69 million airbag inflators and may cost billions of dollars. The recall will last until the end of 2019 and take until 2020 to resolve.

Major recalls like these can be prevented through effective quality control in manufacturing. Some common tools used to support quality control include:

  • Statistical process control (SPC) monitors and controls quality by tracking production metrics. It helps quality managers identify and solve problems before products leave the facility.
  • Six Sigma uses five key principles to ensure products meet customers’ needs and have zero defects.

When supported by lean tools like Total Productive Maintenance (TPM), 5S, and Kaizen, most if not all defects can be eliminated.

Benefits of Using Quality Control in Manufacturing

Customers expect and demand high-quality products. When customers receive quality products you will:

  • Increase customer loyalty
  • Gain repeat business
  • Gain new customers from referrals
  • Maintain or improve your position in the market
  • Improve safety
  • Reduce liability risks
  • Contribute to overall positive branding of your product

Manufacturers with quality control procedures in place are far less likely to face product recalls or place customers at risk from poorly made products. The cost associated with these recalls can be steep. A testament to this is the Takata recall, which is estimated to cost the company between $7 and $24 billion.

Discover how you can avoid costly recalls and support your quality control system with TPM. Graphic Products’ Best Practice Guide to Total Productive Maintenance (TPM) will help you on the road to total quality. Improve quality, eliminate defects, and increase your profits.

Incorrect Implementation of Quality Control in Manufacturing

Quality control in manufacturing can be a little tricky. Often, it is done at the end of the production process, only catching defects after the fact.

Effective quality control is more involved and should include two levels:

  • Operators monitor the manufacturing process and ensure that there is little variation.
  • Engineers routinely monitor the product design for issues. When a problem is found, it is immediately fixed.

By monitoring products at the end of production as well as reviewing the products’ design, companies can solve problems more efficiently, saving time and money.

Quality Assurance with Quality Control

Quality assurance streamlines production and helps to ensure that the final products meet the company’s quality criteria. It ensures that the processes used to design, test, and produce products will be done correctly.

In manufacturing, quality assurance approaches, like ISO 9001, help manage and improve many processes, including:

  • Acquiring raw materials
  • Purchasing third-party components and sub-assemblies
  • Designing and using inspection procedures
  • Complying with production processes
  • Responding to defects

For every business, quality assurance is different. However, ISO 9001 works for businesses both large and small and can be adapted for almost any need. It provides the means for creating a lasting quality assurance program, ensuring that everything, from raw materials to inspection procedures are of the highest quality. Issues and defects from poor quality materials or third-party components are all but eliminated.

Quality Control, QA and Lean Manufacturing

Lean manufacturing tools can bolster your company’s quality program. Lean revolves around improving quality and safety while increasing efficiency and profits. Some powerful lean manufacturing tools that can bolster your quality system include:

  • TPM improves product quality by eliminating downtime, defects, and accidents. TPM accomplishes this through comprehensive maintenance programs and operator training.
  • Kaizen helps eliminate problems at their source by empowering workers to find and solve problems on a daily basis.
  • 5S helps organize and standardize the workplace. Take control with the 5S System Best Practice Guide, by Graphic Products. Improve procedures and eliminate errors in your facility.

While every facility has different needs and may require a different lean tool, using lean to support quality control is essential. Procedures will be simplified, and the number of errors will be reduced.

Learn more about Kaizen and how it can help improve quality with this helpful video.

How to Implement Quality Control in Manufacturing

To implement an effective quality control program, first, create and document your approach to quality control. This includes:

  • Defining the quality standards for each product
  • Selecting the quality control method
  • Defining the number of products/batch that will be tested
  • Creating and training employees for quality control
  • Creating a communication system for reporting defects or potential issues.

Next, you will need to create procedures for handling defects. Consider the following:

  • Will batches be rejected if defected items are found?
  • Will there be further testing and potential repair work involved?
  • Will production be halted to ensure that there are no more defective products created?
  • How will new product versions be handled?

Finally, use a method like 5-Whys to identify the root cause of the defect, make any needed changes, and ensure your products are defect-free.

Source: https://www.graphicproducts.com/articles/quality-control-in-manufacturing/

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admin March 17, 2020 0 Comments

Automation and AI’s Role in Manufacturing Industry

AI and Automation, the terms that bring excitement and fear simultaneously on the face of any manufacturer today, post a pertinent question to the manufacturing Industry, Is the future exciting or scary, the answer to which will be discovered in coming years but will it be adventurous? For sure. From history, we have learnt that change is inevitable and it should be accepted ecstatically. Now to understand the ongoing changes we need to look at the basics behind AI and automation.

Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science, AI research is defined as the study of “intelligent agents”; any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term AI is applied when a machine mimics cognitive functions that humans associate with other human minds, such as learning and problem-solving.

Automation fundamentally means the technology by which a process or procedure is performed without human assistance. Automation or automatic control is the use of various control systems for operating equipment, such as machinery, processes in factories, boilers and heat treating ovens, switching on telephone networks, steering and stabilization of ships, aircraft, and other applications and vehicles with minimal or reduced human intervention.

AI

Role of AI and Automation in Manufacturing:

Manufacturing

“Artificial intelligence isn’t a scary future it’s the amazing present”, said well-known Yale University professor, David Gelernter. Imaging an AI-powered machine with an IQ of 5000, as compared to average human IQ of 100. We do not have the vaguest idea of what it would mean and how powerful it can be.

Artificial intelligence in manufacturing is no more the thing of the future, it is the thing of today. We are accustomed to using AI in our everyday life with Apple’s Siri and Amazon’s Echo. AI has made tremendous progress with the help of improved processing, algorithms, and a lot of data. With advanced Machine learning, all this data can be analysed and critical insights can be gained, helping future projects keeping user behaviour in mind.

Next-gen AI-powered industries will work on lean inventories, reduced product glitches, cheap labour cost, shortened unplanned downtimes and increased production speed. With AI in manufacturing, we are fundamentally talking about network-connected factories, where design team, production line, and quality control are highly integrated into an intelligent machine, producing useful insights.

Next-gen AI-powered industries will work on lean inventories, reduced product defects, cheap labour cost, shortened unplanned downtimes, and increased production speed.

Automation and artificial intelligence help in doing the repetitive task with a high level of accuracy which was unimagined with human ability. It will help us in working in dangerous environments which were previously not imaginable due to higher chances of loss of human life. Future machines will have voice and image recognition that will be used to perform complex tasks which were earlier not possible without human intervention.

AI is no more the thing of future it is the thing of today. We are using so much of AI in today’s everyday life.

Use Case for AI and Automation in Manufacturing:

Manufacturing automation involves a complicated and detailed oriented approach to produce a material, hence following are the use cases for the industry.

AI and Automation are hot topics of manufacturing companies of the future. With AI and automation powered technologies, the manufacturer can improve efficiency, fasten processes, and even optimize operations. It can reduce the production cost by 20% of which 70 % comes from improved resource productivity.

Transportation and logistics companies are at the forefront of AI and Automation adoption. Companies in emerging nations are more enthusiastic about these benefits, while industrialized nations have a different view.

AI and automation will transform the value chain from end-to-end. Operations of the organization will be most heavily affected by this change. These factors augment existing levers that help in improving productivity.

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Adoption of AI and manufacturing automation will significantly alter the composition of the workforce, e.g., all the quality control task that requires intensive human support will be heavily supported.

Currently, factories automate processes and machinery through a fixed approach. In future, AI and automation in the manufacturing industry will together formulate an approach to make intelligent decisions in unexpected situations. In the current scenario, robots cannot select an item from a bin of unsorted parts, but in future AI-supported robots will be able to do the same.

Conclusion:

Industrial companies should use the following approach to implement AI and automation in the present day scenario:

  1. Analyse the current scenario – Once the company identifies the pain points, it should evaluate itself against its competitors. Repository of topics and benchmarks will be required for the same.
  2. Identify the enablers – Once use cases are built, stakeholders should discuss in-depth to prioritize the implementation, keeping financial and non-financial benefits in mind. Then the company should develop the final approach of implementing AI and automation in the industry
  3. Test and scale – The organization should test the MVP and then quickly deploy the solution over multiple iterations

AI and automation will become the most important lever in increasing productivity of next-generation manufacturing plants. An organisation needs to put an infrastructure in place to quickly raise its game to remain relevant in the coming years.

Source: https://www.hcltech.com/blogs/automation-and-ai-role-manufacturing-industry

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admin March 16, 2020 0 Comments

Smart Factory and Its benefits on the manufacturing industry

New Levels Reached with Smart Factory

Today you need not expand the 4 walls of your factory in order to achieve more. Imagine increasing your output with the same limited factors, now that is smart. A smart factory is a highly digitized and connected production facility that relies on smart manufacturing. Thought to be the so-called factory of the future is still in its infancy, the concept of the smart factory is considered an important outcome of the fourth industrial revolution, or Industry 4.0.

A factory is truly smart when all machinery and equipment are able to improve processes through automation and self-optimization. The benefits also extend beyond just the physical production of goods and into functions like planning, smart maintenance supply chain logistics, and even product development.

smart factory

Used by manufacturing companies, a smart factory works by employing technology such as artificial intelligence (AI), robotics, analytics, big data and the internet of things (IoT) and can run largely autonomously with the ability to self-correct.

However, the operations take place inside the four walls of the factory. And all IIoT (industrial internet of things) devices make the process more seamless. The structure of a smart factory can include a combination of production, communication technologies and information, some with the potential for integration across the entire manufacturing supply chain.

Smart factories rely on smart manufacturing, which connects the plant to other entities in the digital supply network, enabling more effective supply chain management. They also rely on digital manufacturing, which uses a digital twin to connect a product digitally at all stages in its lifecycle.

Any technological change boosts the overall efficiency in any condition, especially in industry 4.0 dawning the rise of smart industry. With the rapid pace of technology, human-machine interfaces and better analytics, more businesses are expected to look for ways to create their own smart factory endeavours.

One of the key components in any smart industry is the maintenance system. A smart industry implies IIoT powered smart maintenance which is predictive maintenance. In contrast to preventive maintenance, this method of maintenance is much more sophisticated. Predictive maintenance (PdM) techniques are designed to help determine the condition of operating equipment in order to predict when maintenance should be performed. The chances left for the breakdown in preventive maintenance are no more.

The most immediate benefits of smart factories are

  1. Leaner Process
  • Maximum savings on order management cost
  • Significant reduction in material handling cost
  • Lowest inventory holding cost
  • Highest availability
  1. Maximum flexibility

Smart Factory Logistics Systems are engineered to suit different manufacturing environments and production setups. This ensures maximum operational flexibility.

  1. Improved predictability

Infinite Uptime’s IDE (Industrial Data Enabler) calculates mechanical data through edge computing to deliver predictive maintenance.

  1. Increased agility

Using advance embedded sensors technology, Smart Factory Logistics Systems automatically recognize manufacturing demand fluctuation. This will allow the supply chain to respond with better agility.

  1. Proven Productivity

Based on the value-stream-mapping methodology, Smart Factory Logistics Advisory provides expertise for continuous and sustainable productivity improvement.

Today, smart factories have only seen their genesis. With the current technological force, it promises a great degree of advancement.

Source: https://infinite-uptime.com/blog/smart-factory-benefits-manufacturing/

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admin March 13, 2020 0 Comments

Industry 4.0: How Advancements in Manufacturing Technology Are Transforming the Industry


In the United States, economic growth has been a bit sluggish in the past decade. When you look at global economic growth, there’s one common factor with all that causes the economy to grow.

It’s in manufacturing.

Due to a number of contributing economic, political, and market factors across the globe, there’s a significant revolution that’s underway within the manufacturing industry. The revolution is commonly referred to as Industry 4.0.

The first industrial revolution (1.0) involved the mechanization of production converting water to steam power. The second industrial revolution (2.0) took place 30 years later when the first electricity-powered assembly line introduced mass production. The third industrial revolution (3.0) started in the late 1960s when the first programmable logistic controller (PLC) enabled production automation through the use of electronic and IT systems.

Industry 4.0 refers to the combination of several major technology innovations, all maturing at the same time, that is expected to significantly shift the landscape of the manufacturing industry. These technologies – advanced robotics, artificial intelligence, sophisticated sensors, cloud computing, and big data analytics – all exist in manufacturing today in some form, but as they integrate with one another, the physical and virtual worlds will interlink and transform the industry. 

Advanced Manufacturing Technology

The key objective of Industry 4.0 is to drive manufacturing forward: to be faster, more efficient, and customer-centric while pushing beyond automation and optimization to discover new business opportunities and models.

By embedding modern technology into manufacturing, you essentially achieve Industry 4.0 objectives. In terms of business technology, it goes beyond transactions like accounts payable or receivable. It’s bigger than that. It goes beyond applications and software versions. Gone are the days of having to buy adapters and different layers of business technology to maintain and manage. And if the letters “ERP” make you cringe, it’s for good reason. ERP comes with a lot of administrative baggage and frankly dabbles in manufacturing. With the advent of cloud computing, there is a system that allows you to do more than just replace existing functionality. You can transform the way you do business and figure out how you’re going to beat the competition.

The other critical area where technology makes a big impact is with your workforce. Communication and collaboration are what will help you retain your best people. They need to talk about what is happening in the “manufacturing moment” to find the solutions that work. To attract quality candidates, you need to establish an environment that is similar to our everyday modern lives. The days of cubicles, tethered desktop computers, and physically being present on the shop floor are gone. Young workers want to run everything—including business—from mobile devices.

Stepping Stones to Industry 4.0

  1. Cloud Computing. A single instance, multi-tenant environment scales with your business. Companies from startups to multinational global corporations are running on the same set of manufacturing software codes and the same database technology. This is the platform on which manufacturers need to build their Industry 4.0 environment.
  2. Industrial Internet of Things (IIoT). Leading manufacturing companies are automatically receiving goods into the facility and the next thing a human does is drive the truck out the door — which may be changing in upcoming years once vehicle automation is a reality. Everything in between receiving and shipping is done today through automation. It’s all done through PLC integration and controls, which is highly efficient, higher product quality, and better customer satisfaction. IIoT connects devices from the shop floor and to ERP.
  3. Agility and Sequencing. Similar to just-in-time (JIT) inventory strategy, sequencing is where components and parts arrive at a production line, in a specific configuration, at the exact time the product is required for the customer’s specific product configuration. This method of manufacturing will be a requirement for all manufacturing companies across the industry spectrum.
  4. On-Demand Manufacturing. Consumer preference and demand patterns are starting to drive how the companies are going to be providing products to the market. In order to stay competitive, manufacturing companies need to be able to react and rapidly change the production process to align with the evolving demand patterns of their customer base.   These variable demand patterns will be flowing through the enterprise business systems straight down to the shop floor manufacturing technology. The shop floor technology will then auto-configure the manufacturing production lines so the specific products can be manufactured. The days of large manufacturing plants making the same part or product are gone. It’s going to be localized manufacturing, supporting customers within a very specific region. You’re going to need to be able to manufacture what the customer demands, based upon agility and sequencing.

If you want to thrive in the business of manufacturing, you have to be agile. You have to adapt to customer demand, and you have to look at what it takes to be a leader. If you want to know where your company is on the path to Industry 4.0, we’ve developed a maturity model that can help identify that for you. You can use the model to plot out a path to move through your Industry 4.0 journey which will set you on course to get you ahead of your competition.

Source: https://www.plex.com/blogs/industry-4-0-advancements-in-manufacturing-technology.html

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admin March 11, 2020 0 Comments