Authors Joël Vogt (Research Engineer) & Dominique Guinard (CTO & Co-Founder)

As part of our contribution to the EU-funded research project, TagitSmart, the EVRYTHNG Labs team have been working on environment-sensitive product tags connected to the Internet of Things.

That is: low-cost tags that can record data about the environment they are deployed in.

TagItSmart & Environment-sensitive, web-enabled tags

TagitSmart is an EU-funded IoT initiative that’s developing a new technology for ink-based, serializable tags.  assign a specific value to a product; a number or URL for example.

Once a traditional barcode (1D or 2D) is printed, the data in it, such as a product code or url  cannot be changed or updated.

TagItSmart example: Thermochromic ink printed as a QR code. This tag contains two indicators. “ON” indicates that the tag has started measuring, and “Above High Limit” indicates if the temperature limit has been exceeded.

In contrast, an environment-sensitive tag is a barcode with values that can automatically change when certain environmental conditions change. These tags will be able to recognize their current and past environmental exposure (such as temperature fluctuation) to update physical products that they are attached to with dynamic digital information.

These types of “sensitive” barcodes are not new. For example, these technologies are already being used in cold-chain and food-safety applications. What is new, however, is the convergence of environment-sensitive tags with open IoT technologies.

Supply-chain visibility and brand protection as use cases

Item-level identification of each physical and digital object is at the core of the IoT and environment-sensitive tags.

Being able to put information into context is powerful. The more that is known about an object’s physical context, such as its environment, the more useful the object becomes to decision makers when analyzing  products and business processes.

If this sounds a bit abstract, let us look at two use cases — supply chain visibility and brand protection — to develop an understanding for when environment-sensitive tags could be used.

Supply-chain visibility

Item-level supply-chain visibility is needed to monitor cold-chains for  food-safety and ‘keepability’, to ensure that consumers are only being sold food that is safe for consumption.  With growing focus on food waste, this is a booming area – according to Supply Quarterly¹, the market for cold chain monitoring is expected to reach $6.2 billion by 2022.

It’s also necessary in healthcare to make sure drugs are being kept within a specific temperature range during transportation and storage.

Brand protection

Despite high-tech anti-counterfeit measures, counterfeiting  is still on the rise. The Economist² quote a 2016 OECD³ report that fake goods account for 2.5% of all global trade, a total of $461 billion. And counterfeiters aren’t just harming the perfume and apparel industries.

Increasingly, counterfeiters have begun to target pharmaceuticals, plane parts, children’s toys, and beverages. Through their actions, counterfeiters are introducing products to the market that are unsafe and potentially  pose a risk to people’s lives.

Product authenticity through product passports

Environment-sensitive tags provide the basis to build product passports — labels that provide a certificate of authenticity by storing the “fingerprint” of each product which is a combination of digital data in the cloud with local data about the product’s interactions with the physical environment. They can visibly change when a product expires or can show the environmental conditions a product was stored in, making tampering much more difficult.

Like your personal passport, a product passport will allow you to verify the authenticity of each and every  product in a tamper-proof manner by combing physical properties that make them hard to copy and by leaving a digital trace that a counterfeiter cannot replicate. The collective histories of similar products also opens up the possibility of using machine learning algorithms for fraud detection.

The combination of physical and digital information therefore makes it harder and considerably more expensive, for counterfeits to copy products.


To wrap up, environment-sensitive tags, as proposed by TagItSmart, combined with identifiers that are connected to the Web through an IoT platform such as EVRYTHNG, have the potential to address customer needs. We focused on specific use cases in this blog, but others will rapidly emerge too.   Transparency, authenticity and visibility have always proved difficult for manufacturers, but the technology is there now to make these things considerably easier.



Author Iker Larizgoitia Abad (Research Engineer/Program Manager)

We’re very excited to announce that EVRYTHNG and Recycl3R have established a new partnership to transform the way that consumer packaged goods are recycled.

By combining the capabilities of EVRYTHNG’s Smart Products Platform with the recycling information service developed by Recycl3R, CPG brands and retailers can now boost consumer engagement and trust, by providing clear recycling information to consumers through smart packaging, and then by rewarding good recycling habits.

At the same time, thanks to item-level tracking and business intelligence, brands can get new insights through the product lifecycle, from sales and distribution to use and disposal.

The product was purchased, long live the product

It’s no surprise that many companies (especially those in the FMCG business) spend a significant amount of their annual turnover in consumer engagement strategies. However, winning trust and loyalty has proven to be a difficult task for brands, as they have little direct connection with their end consumers after purchase.


Figure 1: EVRYTHNG and Recycl3R are powering the last stage of  the product lifecycle.

At EVRYTHNG we give a unique digital identity, called an Active Digital Identity™, to every single item, and help revolutionize product lifecycle management. Defining how to reach out to consumers throughout the last phases of the lifecycle — from purchase to recycle — is key for differentiation and has huge potential for brands, producers, and retailers. This potential comes from providing insights on various types of interactions happening with products, and by prompting consumers to recycle and engage in socially responsible consumption.

Recycle, reuse, reduce … it’s complicated

Let’s face it, recycling is not as easy as it should be. The recycling scene is fragmented, and regulations are typically not uniform or standard, even within the same region. Brands do not have a unified way of communicating with consumers about how to recycle, making it hard for individuals to engage in the task. Information about packaging and the corresponding recycling schemes is often obscure and not intuitive.

This is where EVRYTHNG and Recycl3R come into it.  We’ve created a solution for the recycling ecosystem, involving brands, retailers, councils and consumers, which leverages advancements in technology – specifically with connected packaging – to make recycling easier, and more transparent.

Pilots and Next Steps

We’re launching our solution with brands, retailers, and other industry partners in three locations; Merseyside in the United Kingdom and Logroño and Palma de Mallorca in Spain, where the solution will be tested throughout 2018.

The goal of these pilots is to test the service, with consumers following three simple steps:

  • Scan their shopping receipts to import their products into a mobile recycling app.
  • Sort their items at home. Through the mobile app, consumers can access information on where, when, and how to recycle the different parts of each product they bought based on their local schemes.
  • Recycle their items in street bins or preferred recycling centers. Consumers can then register where they are recycling by scanning or tapping identity tags on recycling units, earning them rewards.


Take a look at this quick video  to see how it will work.

We’ll bring you progress and insights from the pilots in an upcoming blog, and in the meantime, if you want to join our pilot program, please get in touch.


This partnership and the work towards the solution has been made possible thanks to the TagItSmart research project (, where EVRYTHNG is taking active part as the Smart Products Platform provider.




Author Niall Murphy (Co-founder and CEO)

EVRYTHNG’s vision is every product in the world connected to the Web, delivering value for consumers, brands, manufacturers and retailers with data-driven intelligence, insight and applications.  Our mission is to enable one trillion products to be online with digital identities in the cloud by 2020.

Today we can report that accomplishing this mission just got a major turbo-boost.  

Trillions of products around the world use the barcode. In fact over 5 billion scans happen every day in retail locations across the planet.  The BBC named the GS1 barcode as one of “the 50 things that made the world economy.”  Now, GS1 has kicked-off a process to enhance and extend the standard barcode.

This enables every product to have a web address, using standard GS1 product identifiers, and makes it possible for the same code on a product, in the form of a QR code or NFC tag, to work with both retail points of sale AND smartphones for direct-to-consumer applications.  With billions of iOS and Android smartphones now able to automatically read and scan NFC and QR codes, brands can tap into a huge global audience who can interact with physical products. Millions of people already scan proprietary smart codes from WeChat, Snapchat, Facebook, Amazon and others.

The time for mass-scale product digitization is now.

EVRYTHNG is co-chairing the GS1 working group on a new specification, called GS1 URI, working alongside some of the largest consumer product brands and retailers in the world.  In January GS1 issued a call to action whose mission objective is for “GS1 identifiers to become linkable for the first time, enabling users to link to them or from them on the web”.   Their short-term goal is: “to reduce the need for multiple codes on packs, while ensuring that we develop a glide path with industry toward a future where a single 2D barcode could serve the needs of all parties.”

EVRYTHNG expect a new standard – giving web addresses to products using GS1 identifiers – to be completed by mid-2018.  This puts the industry at an inflection point that can see the world’s consumer products connecting to the Web at massive scale.

Why web-enable consumer products?

Consumers benefit with access to richer product information and digital services on every product. Like, collecting loyalty points, buying or reordering the item, or checking its provenance or authenticity in real-time.

Brands, retailers and manufacturers are able to generate enormous efficiencies with data from and about their products throughout the lifecycle, and connect directly with their customers and consumers via their ‘brand in the hand’.

To learn more about what this means and how to digitize your products, check out our new white paper.

Get started now for free

EVRYTHNG has invested heavily in making it quick and easy for brands to digitize products at scale. This week at the GS1 Global Forum in Brussels we unveiled new tools to connect products with their GS1 identifiers and launch consumer and supply chain applications. Available from today:

  • QuickStart Online Tool: Create unique digital identities for your products using existing GS1 identifiers in minutes. Check out the video here, and get started with our easy onboarding now.
  • Instant Consumer Experiences: Out-the-box, EVRYTHNG provides optimized mobile experiences for Product Information, SmartLabel™ (food and beverage information) and SAC (apparel sustainability information), or you can use our dynamic redirect tool to provide instant consumer access to other digital experiences.
  • GS1 URI:  You can now use a GS1 URI when creating QR codes, which puts the GTIN and the URL into the same code. The crucial first step towards the end of multiple codes on a product, leading to simpler, more cost-effective packaging.
  • Packaging integrations: Working with our packaging partners, including WestRock and Crown in the food, beverage and household goods sectors, and Avery Dennison RBIS in the footwear and apparel market, we have standardized the process for embedding GS1 identifiers, linked to digital identities, into products ‘at source’.

We have more exciting product developments coming out over the next weeks, stay tuned.

In the meantime, to find out more, visit  or to get started today visit

Welcome to the next-generation of consumer products!



Author Dominique Guinard

Product codes are set to change, and we’re calling on brands to have their say.

GS1, the global standards organisation, is launching a working group on a new standard called the ‘GS1 URI’.  In their call to action, GS1 describe the twin objectives:

“The short-term goal of this work is to reduce the need for multiple codes on packs, while ensuring that we develop a glide path with industry toward a future where a single 2D barcode could serve the needs of all parties.”

What this means and why it matters

Today products are usually identified by a 1D barcode also known as a GTIN. 1D GTINs are fine when you stay within the supply chain, however they generally can’t be used by consumer apps or smartphones.

So if a consumer wants to scan their purchase to find out about the product’s provenance, manufacturers often have to connect consumers to such information via the scan of an additional code on the product (typically a QR code) which contains the address of a web page where product or marketing information is available.  The new GS1 URI Standard proposed would mean manufacturers will no longer have to add an additional code.

This may seem a small change, but it’s actually a major milestone in product identification.

Interested?  Here’s how you can get involved

To make this a reality we hope our customers or other manufacturers can get involved with the working group, either as an active contributor or in a more passive overseeing role, to make sure the initiative gets inputs from the voices that matter.  The working group is available for GS1 members organisations who can follow this simple three step process to register and ideally join the kick off call on Thursday, 18 January or one of the following weekly calls.

All products BornDigital

At EVRYTHNG we’ve long advocated the use of QR codes containing URLs (Web identities) as a way to drive digital experiences directly from products and it has worked very well for a number of our customers. So we’re excited by GS1’s move which is a major step in this direction: the working group will look into standardizing product URLs leading to a world where every single product will embed a single universal code useable in the supply chain but also beyond, to create a unique and dynamic link between consumers and products.

We have already joined this group and will play an active role in helping to shape this milestone. We’d love you to get involved too!

Authors Joël Vogt (Research Engineer) & Dom Guinard (Co-founder and CTO)


What is machine learning?

Machine learning is to data scientists what mining automation is to gold diggers. That is to say, it is now profitable to extract gold from large piles of rubble and sand that were previously considered too expensive, or even impossible, to manually process.

Machine learning is ushering in a paradigm shift in the way software is designed and developed. Traditional software engineering is essentially coders writing step by step how a machine is supposed to transform data. These instructions are written using a programming language, which is then translated into a program that a machine can execute.  When we talk about machine learning, a software engineer’s work is to describe what the problem is as a ‘machine learning model’ and let a machine learning algorithm automatically discover how to best tune the model, based on the training data. Take as an analogy the work of a business consultant. A business consultant can either define a business process model top down, based on industry best practices, regulations and personal experience. Or she can observe informal processes within an organisation, conduct interviews and then summarise her findings as one or more business process models, based upon these learnings.

Why now?

While machine learning has been around as a very active research field for a while, it is only recently being adopted by the wider industry, after game changing success stories by Internet giants like Google, Microsoft and Facebook. But why are we now only seeing a wide adoption of machine learning?

First, there is a caveat: for a machine learning algorithm to perform well, it needs a lot of data, which is now increasingly available thanks to the Internet. The more data you throw at it,  the more accurately a machine learning algorithm can learn a representation. The second reason is affordable specialized processors, initially GPUs (Graphical Processing Unit) that were developed for gaming, that could crunch through these huge datasets in reasonable time. Thirdly, machine learning frameworks like Keras or Tensorflow are now available and greatly facilitate the development, training and deployment of very powerful machine learning solutions.

How can it help the IoT and supply chains?

What could we do with machine learning that couldn’t be done before? Automated business workflow and rules? Well if you know the rules that governs your data then you don’t need machine learning. A rules engine such as EVRYTHNG’s powerful Reactor™ is a better fit! Data exploration? Check out our latest dashboard widgets and query tools!

Machine learning and in particular, deep learning, lets you extract insights from massive amounts of data when visualizations become too complex and writing rules practically impossible because of the number of permutations.

The IoT is generating data at an unprecedented rate and this is only the beginning. Machine learning frameworks will help distill information from vast data pools containing unstructured, semi-structured or well structured data, and can be used in the following example use cases:

Gray market detection: Gray market can be seen as a classification task. By classifying products by their expected market, a machine learning algorithm can learn the context, route, etc. of products in each class. Products that are purchased in a place other than their intended market are considered as sold on the gray market.

Product authenticity: We can use machine learning to add further intelligence to scans of physical products (known as THNGS). This is done by training a machine learning model on the context of product scans of authentic and counterfeits products. Once deployed, each scan will be result in a probability of authenticity.

Replenishment: The EVRYTHNG platform makes it easy to train a predictive machine learning model on appliance telematics data coming from a collection of similar appliances, for example coffee machines or washing machines. We are able to predict when to reorder coffee beans based on the vibration and duration of the coffee machine in use. Because the accuracy of machine learning improves with more data,  this collective “knowledge” will yield overall a more reliable, personalised reordering service. Furthermore, our platform can act as a mediator between the coffee machine and the supplier. With a simple Reactor™ rule, we can reorder coffee from our favourite supplier when they are only two days of coffee beans supplies left, ensuring that we always have coffee at the office. You don’t need machine learning to predict what happens when software engineers are deprived of coffee!

Preventive maintenance: This is similar to the replenishment use case. The difference being that we use the Reactor™ to dispatch a maintenance notification to a person and that we leverage third-party data sources, such as the current weather report, to augment operational data from appliances. Read our appliance telematics blog to learn more about how we make this a reality.

Applying machine learning to detect gray markets

Let’s focus on one of these use cases specifically.  Customers often come to us with seemingly simple questions, such as: can you tell me if a product is being sold on the gray market or not?

The only way for brands to detect gray market problems is by having visibility over their supply chain: product digitization and item-level traceability is a must. This is an area in which EVRYTHNG specialize – our platform makes it easy to integrate different information systems and write apps and analytics tools to gain insights from vast data pools.  The EVRYTHNG Labs team have looked at ways to use deep learning to help our customers make even more use of the vast amount they manage in our platform.

If each step in the supply chain is logged, determining parallel imports should be straightforward, just look for products that were bought where they were not supposed to be sold. Unfortunately, this only works in theory, because it would require every step to be recorded and it would require every party in the supply chain to use the same ‘vocabulary’ which is often far from being the case. So in many cases the data is either wrong, or missing. This is common among many supply chains which are only partially instrumented. Could we possibly go through millions of records and figure out the correct destination of every product? No, but our newly developed machine learning feature could!

Figure 1: our parallel import neural network for gray market detection

This gray market problem was essentially a multiclass classification problem. Each expected market is a class. We developed a 1D convolutional neural network. The picture above shows the model architecture. Next, we trained the model with full records that included ‘expected market’. Training the model means the machine learning algorithm had to correctly classify records by expected market, based on values such as stock keeping unit (SKU), reseller or where the product was collected. After each iteration, the machine learning algorithm tunes the model parameters to improve accuracy.
The result was a trained neural network built upon Keras and Tensorflow that proved to accurately find the actual destination of a product (in 94% of the cases), hence helping to detect parallel imports.

Naturally, it is essential to keep the human in the loop. We do this for example by predicting the value of every new record, irrespective of whether this value is missing. This way users can see how well the model is performing, and it will help improve the model over time.

Getting started with machine learning for your products

After over a year of research in this space we are now in the process of introducing machine learning features into our platform. Rather than offering raw machine learning capabilities, our productization approach is to ‘pre-package’ trained networks that can be used to achieve specific goals. The idea being that our customers can activate these trained networks within a few clicks to start learning from the incoming data. For instance, as explained above, to detect gray market or product authenticity, or enhance automatic replenishment and preventive maintenance.

We’ll announce soon when our new machine learning capabilities are generally available, but in the meantime, if you’d like to trial these exciting features please contact us.