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Produtrak
Using machine learning
to recognize products at
the disposal site
Who are we
Synthetic Data is a leading Big Data solutions
developer company from Spain
The company has had projects in Tel...
What is ProduTrak?
Produtrak is a project being undertaken by Synthetic Data which uses machine
learning to recognize prod...
How does it work?
Produtrak works by learning the shape, color, etc of a target product
To that end, the product is photog...
How does it work?
Applications
1 – Analise consumption patterns
Being able to know how long a product took to be completely consumed, where
...
Challenges
By far the biggest challenge in this model is the proper identification of products.
This, because:
1 – Product...
Conclusions
1 – Produtrak completes the lifecycle analysis of a product. If we could before know
the lifecycle only until ...
alcosta@syntheticdata.eu
+34 622.630.783
Thank you for your time
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Alvaro Costa "Produtrak - використання машинного навчання для дослідження продуктів в архівах"

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Alvaro Costa "Produtrak - використання машинного навчання для дослідження продуктів в архівах"

  1. 1. Produtrak Using machine learning to recognize products at the disposal site
  2. 2. Who are we Synthetic Data is a leading Big Data solutions developer company from Spain The company has had projects in Telefónica, Banco Santander, Mercedes-Benz (Munich), Alkol Biotech (UK), etc Its focus is IoT + Big Data + AI, which it sees as an indivisible unit Its founder, Al Costa, is the author of book on Big Data and professor at Madrid´s EOI- Escuela de Organización Industrial Here, we developed a spin-off called “Produtrak” which we Will demonstrate here for technical and investment purposes
  3. 3. What is ProduTrak? Produtrak is a project being undertaken by Synthetic Data which uses machine learning to recognize products at the disposal site. The idea is to identify where they originated in order to find metrics such as the amount of time it takes for customers to use the product, where they prefer to purchase them, how do they use it, etc. Thus, it is able to provide manufacturers with useful data on the complete lifecycles of their products from the time they leave the store shelf to the time it is disposed of. For this, Produtrak is able to identify the product as it is being disposed using machine learning libraries and training models.
  4. 4. How does it work? Produtrak works by learning the shape, color, etc of a target product To that end, the product is photographed in different positions, with different amounts of liquid, dirty, label worn off, etc. A minimum of 300 pictures is required for a proper identification The system works with a videocamera attached on a support on top of the belt carrying waste of recycling centers, identyfing products as they pass below In these, each container is identified and thus it is able to backtrack each product to the precise spot where it was dumped. To that end, Productrak is speaking with leading recycling companies in Spain such as Ecoembes in order to use our technology on their operations to offer this service to consumer product companies
  5. 5. How does it work?
  6. 6. Applications 1 – Analise consumption patterns Being able to know how long a product took to be completely consumed, where it ended up in comparison to where it was purchased, how deteriorated was its packaging when disposed, etc allows for a better understanding of consumer patterns and thus a better product 2 – Identify sales opportunities A supermarket which does not carry a product (for example, a shampoo) which is ending up in a nearby dumpster is missing out on a sales opportunity 3 – Substitute use of plastic Plastics are becoming an increasing source of marine contamination. By identifying the amount of shelf time, it is possible to replace it with paper-based packaging options
  7. 7. Challenges By far the biggest challenge in this model is the proper identification of products. This, because: 1 – Product label may be worn off or just inexistent 2 – Product may be too dirty or deformed 3 – Product may be covered by others 4 – Product may be in a position not previously trained 5 – Packaging could be transparent (PVC, glass) and thus hard to see 6 – Product could be similar to a competitor (se pics below)
  8. 8. Conclusions 1 – Produtrak completes the lifecycle analysis of a product. If we could before know the lifecycle only until the product is sold, now we know it until it is consumed and disposed of 2 – To this, only through optical recognition with machine learning we are able to monitor 24x7 many dumping sites 3 – The data collected can then be matched with data from the stores which actively sell the product 4 –The end result is knowing how is a product really consumed in order to improve it, lower its production costs, and compete better in the marketplace
  9. 9. alcosta@syntheticdata.eu +34 622.630.783 Thank you for your time

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