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5 Ways Manufacturers can make countership

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5 Ways Manufacturers can make  countership

Product enhancements in counterfeit products have created a negative impact on world markets. As India is concerned, though it has tremendous business potential, as a significant market for counterfeit products.

Counterfeiting is spread across all sectors / industries such as pharma, retail, FMCG, and others. Ti toothpaste for aspirin and high-end handbags, fake anywhere.

Problems arise if the company fails to calculate this market and collects the last user data, and can not see the distribution network.

False markets in India have reached up to 40,000 RTS in the regulated sector. The market to cheat the world is around $ 1.6 trillion, and spend around $ 204 billion annually to tackle this problem.

Here are five ways, manufacturers can use technology to counterfeit counterfeiting.

Technology Based on Blockchain

Blockchain has generated many inventors and expectations among people. It’s a great concept and solves the purpose of securing transactions and detail contracts between participation.

However, to protect such needs or transactions it needs to be supported by proof of work. General blockblocks without proof-works are made as fragile systems, so the use of cost blocks can not be economically applied so as to be used for practical cases except for significant product ticket sizes ($ 10K or more) and all parties ready to be ready for overhead technology.

Also, blockchain itself alone can not solve the problem of copying the code for verification. You need to use technologies like AI, and other smarter ways to find duplicate code decoding.

There are personal blocks that can cope with proof work with some of the consequences of calculus, but will be excessive and unnecessary overhead technology using blockchain for product validation purposes in most cases.

Reinforcing the problem of copying the validation code by a counterfeiter should be handled as an additional blockchain of the technology.

RFID is a very expensive solution.

The RFID reader is required to read the code installed on the RFID chip, but anyone reading can read and modify the code.

The label can be read only by reading RFID, so it is difficult for the buyers to check the original product. But there are some NFC tags that many smartphones can read, but they also face problems falsified by counterfeiters.

Micro Tags

Micro-tag is mainly used in Pharmaceuticals. These tags are very small, can be watched and can be punched on the pill. Simplification solutions quickly tag micro, but specialized equipment is needed to relieve the pill so ultimately the user can not think about it.

Authentication QR code uses smartphones

This solution is very popular. Many settlement providers offering QR labels / labels. The tag can be scanned using any smartphone that is easier for end users to check the original product.

Common users are directed to the API or website products in the marker. The lack of a QR code is that people can copy and forge fake products, and counterfeit products will get validation, no matter what you write using 1024 DPI or how to print more or more paper-level.

It is only a QR code that will be converted to alphanumeric code which is then used by the backend to validate.

However, the QR code used in the tracking and monitoring system is not capable of capturing counterfeiting unless supported by some system monitors or AIs to check for Anomalies and other features.

The moderator solution provides a scanned QR code after purchasing the tag. So my buyer can not check the authenticity of the product before making a purchase.

AI supports Smart Technology

AI makes the tag provide a weak and effective solution. The solution is to use semi-open code algorithmically arranged. The buyer can see the opening part of the tag to check the product of originality with a certain probability.

After they purchase the product, they can open a delightful and scannable section to be supposed to be original for the product.

In the backend, technology that delayed and detected systems based on AI’s anomalies always saw the activity of scan protected tags and open tags.

Once the protected label is scanned, all matching open tags will be rejected. The system also improves the RED flag for abnormal pattern scanners or anomalies.

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