Tag Archives: New tech

Blockchain, Provenance, Traceability & Chain of Custody

This is an article written by John G. Keogh.

Here are my answers to questions posed over the past few months online and in industry and regulator dialogue. As some of my points can be successfully argued from different angles, this is intended to create the dialogue and not limit it. Your comments and perspectives are valuable and I look forward to this discussion.

Question 1 : Do I need a Blockchain for effective Food Recall? 

No. In a closed supply chain with limited exchange partners you don’t need a blockchain to execute a rapid recall of an unsafe product. Any GS1-standards based technology platform can be used to rapidly trace (backward) and track (forward) a consumer packaged product if the product has a data carrier (barcode) and/or batch/lot # attached. Check out the GS1 global office website or your country GS1 organization as they have a traceability and product recall standard and guidelines on how to execute recall effectively.

In the USA, industry standards body GS1 has partnered with GMA and FMI and has a nationwide, cloud-based Rapid Recall Express platform in operation for almost 10 years. There are similar industry-driven, national recall platforms in place in CanadaAustralia and New Zealand which align to regulations and helps protect consumers and reduces industry risks. GS1 South Korea has a ‘stop-sale’ process in place with multiple government regulators for about 10 years. If any of the regulators determine a product is unsafe, the regulator sends a GS1-centric message to the retailers HQ. Within 30 minutes of receiving the regulators alert, all points of sale (cash registers) in the country are blocked and the ‘stop-sale’ process is enacted. I have seen this in action and it’s amazing. The stop-sale process is quickly followed by the formal recall process. This globally unique process reduces the risk of consumer harm and helps to protect the brand at the same time.

Blockchain is helpful for a recall use case when you have multiple exchange partners across multiple countries and using disparate technologies (see Q2). The opensource and purpose-built blockchain data protocol from OriginTrail is very useful in this scenario because it enables GS1-standards based interoperability between multiple blockchains and legacy. As the below slide from OriginTrail indicates, today we have many data silos and interoperability is crucial to address both traceability, transparency and to execute a rapid recall. Origin Trail will be the first to advise that without first addressing data governance (accurate and standardized data) blockchain will not work as intended.

Disclaimer: I advise the Origin Trail board on industry standards, transparency and trust

Question 2: Are current food regulations driving the need for Blockchains?

Yes. Regulations are generally non-prescriptive and in the food chain they call for a “1-up/1-down” traceability. In complex, multi-party supply chains this is costly, time-consuming and can lead to (preventable) illness and death. In the Walmart Mango use case, it took almost 7 days to execute a mock recall based on 1-up/1-down approach and 2.2 seconds using their specific Blockchain configuration. Blockchain technology is helpful in complex, multi-country, multi-exchange party supply chains that already have good data governance and industry data standards (GS1) in place. A standards-based blockchain enables linkages to be made between the exchange parties and permits sharing of product master data, transactional data and event data – the unhindered flow and visibility of this data is what we call transparency.

I have adapted and use the following diagram to explain the success of the Walmart model in context of theoretical and practical applications of transparency and trust using technology. In this model, the below-the-line RMT indicates regulation mediated transparency. You will note that this is based on mistrust – so are strong contracts that buyers put in place with suppliers. The alternative is what Walmart achieved with voluntary trust-building with strategic transparency and identification based trust enabled by technology – what I call TMT or Technology Mediated Transparency.

Question 3: Can Blockchain guarantee Food Safety and Food Authenticity?

No. Blockchain is oversold as a guarantee of food safety, food authenticity and anti-counterfeit in general. The only legitimate and legal way to guarantee food safety and authenticity is through analytical testing of the product itself – we cannot track the outer package or container and claim the food is safe and authentic. On-pack security features (forensic, covert or overt) help in fraud detection but forensic evidence is required for successful conviction in food fraud cases.

Example 1. WINE bottle recycling

There is a known underground industry that trades in used wine bottles. A hotel or restaurant worker may be incentivized to collect and sell empty vintage wine bottles for hundreds of dollars each. They are re-filled and re-sold for thousands of $, often with fake security features. According to a 2017 Forbes article, an estimated 30,000 bottles of fake imported wine are sold in China every hour. Solution providers are making technology advances and offering security features that create obstacles on the bottle itself including tamper-evident features and fraud alerts for multiple scans of the serialized identifier. Despite the technology improvements and their utility, the only way to legally guarantee the wine is genuine is through forensic testing of the wine bottle contents against the reference samples taken from the harvested crop, or the final blended mix. The storage of reference samples by harvested batch may be a regulatory requirement in some regions.

Example 2. Commingling of fresh fruit and vegetables

Colorful vegetables for sale at the Central Market of Hoi An, Vietnam

Fresh fruits and vegetables may be commingled with products from multiple, geographically dispersed suppliers which increases the risks related to quality, safety, authenticity and provenance. For example, a product may claim to be organic but might have 50% non-organic mixed in to complete the order. The role of blockchain and other technologies in this scenario is limited because human behaviour is the variable. Risk reduction strategies will vary and depend on the context and culture. They can draw on combinations of 1) incentivized behaviour to reduce cheating 2) training on a food safety culture 3) effective food safety practices 4) farm and supply chain auditing 5) industry supply chain standards 6) technology solutions and 7) analytical science. The latter, analytical science being the most critical for evidence.

Question 4: Can Blockchain deliver a guarantee of Food Provenance?

European flags on minced meat. International meat trade

No. This is confusing I know. Provenance refers to geographic source or origin and is determined by forensic science not software, GPS or hardware (see below traceability). Let me share a hypothetical example; lets say we have potatoes and carrots in Vietnam that go to market as ‘product of Vietnam’. In one possible scenario, bad actors could roll the veggies in dampened local dirt to enhance the illusion of being a local product. When the product is forensically tested, both the veggie species, and their carbon fingerprint proves they are indigenous to, and were grown in a particular region of China. This is food fraud and classified as an economically motivated adulteration where a cheaper product is sold as a more expensive premium local product. Blockchain, IoT, stickers/logos or barcodes on bundles of products will not solve this because human behaviour is the variable.

Analytical laboratories can address these issues as part of a regular audit of suppliers and supply chains. Similarly, forensic testing can determine if fish were wild caught or farmed. Companies doing exceptionally well at this today include Perth-based Source Certain and New Zealand-based Oritain, to name a few.

Question 5: What’s the difference between provenance, traceability and chain of custody?

Even the experts get these confused. Let me explain how I see it. Provenance is defined above as geographic source or origin and it is guaranteed only through the results of forensic testing of it’s carbon fingerprint. You will hear experts or software companies say they ‘track provenance’. In many cases what they really mean is classic supply chain traceability or in some cases, chain of custody. Classic traceability includes the source of the materials and is best interpreted as the ‘business or logistics source’. In my opinion, we should not call it tracking provenance as we are not necessarily tracking the true geographic source or origin per-se, we are tracking physical ‘movement’ from a business or logistics source through the supply chain. This draws an important distinction between classic product traceability and forensic product traceability of the geographic source or origin as defined by forensic testing of the products carbon fingerprint.

To help the discussion and align on terminology, see below definitions of food traceability extracted from Olsen and Borit (2013).

CODEX: Traceability is defined in the Codex Alimentarius Commission Procedural Manual (FAO/WHO, 1997) as “the ability to follow the movement of a food through specified stage(s) of production, processing and distribution ”.

ISO: Traceability defined in ISO 9000 and ISO 22005. ISO 9000 (ISO, 2000) as “The ability to trace the history, application or location of that which is under consideration”

The ISO 22005 (ISO, 2005 ) definition is word for word the same as the ISO 9000 definition, but ISO 9000 is a standard for quality management systems in general whereas ISO 22005 is a specific standard for traceability in the food and feed chain. ISO 22005 adds that “Terms such as document traceability, computer traceability, or commercial traceability should be avoided. ”

For all these ISO definitions (ISO 8402, ISO 9000, ISO 22005), there is an additional clause which states that when relating to products, traceability specifically entails “the origin of materials and parts, the processing history, and the distribution and location of the product after delivery”.

EU General Food Law (EU, 2002) defines traceability as “The ability to trace and follow a food, feed, food producing animal or substance intended to be, or expected to be incorporated into a food or feed, through all stages of production, processing and distribution ”.

 

The net-net, traceability includes the material origin. A brief note: within a supply chain, physical products are tracked-forward but traced-backwards and this bi-directional capability is generally referred to as traceability. The chart below is unpublished and from my academic research. It shows the nuances of information, product and assurance flows.

 

Chain of Custody (CoC)

CoC or cumulative tracking was an active discussion in pharmaceuticals in the early to mid 2000’s but seems to have lost some favour. CoC is critically and legally important in highly regulated sectors. For example in weapons, explosives, transport of bulk money, works of art etc. where exact time stamps of the product physical movement, locations and details of all transactions including the parties in physical custody must be tracked and registered. This is similar to a FedEx package delivery where very detailed information is available and signatures are required for acceptance from one party to another. This accumulation of data along the supply chain is sometimes referred to as similar to a ‘Russian doll’.

Example: Pharmaceuticals and Tobacco

Pharmaceuticals and tobacco are two sectors that are highly regulated to protect against many issues including illicit trade, counterfeit, human health and safety etc. What this means is that every dispensing unit of a drug and every pack of cigarettes must be globally and uniquely identified with a serial number and tracked at every stage in it’s supply chain (to the point of dispensing for drugs and to the last point before purchase for tobacco. Note, drugs are tracked to prescriptions and patients, tobacco is not tracked to smokers).

In the (old) chart below from GS1, CoC is represented by cumulative tracking in comparison to 1-up/1-downcentralized database control for closed networks and distributed databases; which we noted more than 15 years ago and is now similar to the current blockchain dialogue. The latest version of the various traceability models can be found in the GS1 Global Traceability Standard (2017).

Disclaimer: I was previously a senior vice president at GS1 Canada and Director of Product & Consumer Safety at GS1 Global office.

Food is regulated of course but not to the extent above that it requires serial number specificity (lot size 1). Generally, food is tracked by lot, batch or date code and a can of soda will have the same global trade item number (GTIN) as the same soda product next to it. The GTIN, while globally unique and aligned to the brand is not a serial number and is referred to as a product family or class code. With the increase in food fraud, there is now growing momentum to add a second data carrier to a food product with a serialized identifier and links to a product web page or product authentication tools. Note, date carrier is a ‘family name’ for all barcodes and RFID tags. Regulations may suggest the ‘data to be carried’ and the brand owner will then select the appropriate data carrier.

To visualize how a GTIN works in a food chain today, see the chart below from GS1 which can be found in the 2017 version of the Global Traceability Standard

BREAKING NEWS

On August 13th 2018, GS1 released a new standard called the GS1 Digital Linkstandard which will enable connections to all types of B2B and B2C information. This new standard is the foundational bridge between physical products and their digital twins.

That’s it for this post – your comments, feedback and opinions are highly valued and very important. Keep an eye out for upcoming posts on topics related to transparency, trust, credence, anti-counterfeit, traceability, product recall, blockchain, provenance and many more.

About the Author:

John G. Keogh is a sought-after speaker, advisor and researcher. Operating at the intersection of the Public + Private sectors globally, he provides confidential advisory, research & interventions across policy, operations, strategy and technology.

John holds a PG Dip. and an MBA in General Mgmt. He has an MSc (distinction) in Business and Management Research into Supply Chain Transparency and Consumer Trust. He is currently a part-time, associate researcher at Henley Business School, undertaking doctoral (DBA) research into food chain transparency and consumer trust. John plans to publish an ebook “Food Chain Transparency – what executives need to know” in 2018.

Photo by Martin Adams on Unsplash

Interview – Founder of SensoMind, Rufus Blas

We love the new technologies here at MyFoodTrust, of course in relation to improving the current lack of transparency. Last week we talked to Daniel from Bext360, and their use of blockchain and AI. Today we focus on AI again, which we find super interesting as a tool for food transparency, so it was a no brainer to do a interview with Rufus from SensoMind.

Read here, how SensoMind have applied AI to create a system to detect anomalies in food products and what role AI will play in creating transparency in food supply chains in the future.

Can you start with telling us a little about yourself and SensoMind?Hi, my name is Rufus. I’ve been involved in AI ever since I studied at MIT in 2004 at their Artificial Intelligence Lab. I hold both a PhD and an MBA and have a passion for innovation management and entrepreneurship. Previously I worked a lot with perception for self-driving vehicles in agriculture. I founded Sensomind with my partner in 2016 in order to democratize AI and get it out to the masses.  We’ve built our own set of tools around top AI products such as Googles Tensorflow which we thought at the time were too much targetting data scientists and not enough the engineers that are out in the field today. Our core competencies lie in analysis of complex sensor data. This is available in abundance in manufacturing so is one reason why we have gotten into this industry.

Rufus Blas
Sounds interesting, but can Sensomind’s AI technology be applied on food?
We’ve been working extensively with food manufacturing customers where our technology can be used for quality monitoring and sorting of food products. Most of our solutions are based on optical sensors (Such as cameras and multi-spectral imaging). Vision technology has been around in the food industry for 10-20 years but it’s been very difficult to apply it to food products with organic shapes and high variety. Examples include monitoring breads, meat, and fruits & vegetables. With AI you can teach the system just be showing it examples which opens up for completely new applications. An example can be automating the cutting of meat.  The price of a final product has a large influence on the cutting being done correctly and it can be very difficult using traditional computer vision to recognize exactly where to cut.
And in relation to that, can Sensomind’s technology help tackle the problem of, e.g. food contamination or unapproved enhancements/additives in food?
So we have a system to detect anomalies which can for example detect contaminants. In the food industry we have used this to detect contaminants such as bone fragments, metal, plastic, and other objects which shouldn’t be there. Unlike a human operator, our system never tires. Unapproved additives is difficult to detect using traditional color cameras so here we work with spectrometers or multi-spectral cameras. Using traditional computer vision an engineer would normally sit and try to make a model for different additives based on a pre-conceived notion of what to look for. AI allows a more statistic and data-driven approach which reduces the chance of unapproved additives making it through the production undetected.
In your opinion, what role does new technologies, e.g. AI, play in creating transparency in food supply chains?
Supply chains are notoriously difficult to model because of large amounts of often poor quality or missing data. AI is really good at crunching numbers and extracting meaningful informations from poor quality and multi-source data (including images, text, numbers, etc). AI can help piece together the information about specific products which would be impossible to model by hand.
If someone was interested in learning more about the work you do, where can the find more? 
The obvious thing would be to contact me. Check out our website (sensomind.com). We have a number of international projects going so location is often not a big issue.
A big thank you to Rufus, and great to hear of the use of AI in the food supply chain. Here at MyFoodTrust, we are always looking for how new technologies can enhance transparency and traceability.
So if you know of any startups, please let me know!
Have a great day.
© MyFoodTrust 2018

Blockchain as a food supply chain

How to improve trust in supply chains – by blockchain

Introduction 
The main purpose of this blog post is to state how Blockchain Technology influence the role of trust and how it might solve the challenges in tracking and tracing products throughout its supply chain, by identification of opportunities with blockchain as a platform of traceability, information and documentation sharing regarding Extra Virgin Olive Oil (EVOO). The case partner was COOP Trading. This blog post is an executive summary of a master thesis on the matter.

We know surprisingly very little about most of the products we eat every day. Before even reaching the end consumer, products travel through an often-vast process flow of retailers, distributors, transporters, storage facilities, and suppliers, yet in almost every case these journeys remain unseen. This can lead to fraud of adulteration and tampering with the products we consume everyday. Which the The Danish Veterinary and Food Administration action team found, by adulterated EVOOs at Dagrofa and Dansk Supermarked. Out of the 35 tested bottles, only 6 could be classified as EVOO.

Challenges
The identified challenges from the gathered data, were the difficulty to qualify trust as it’s very ambiguous of what it entails, but is key to have an effective supply chain. Regarding the actual process of EVOO, the law requirement of only knowing “one step back, one step forth” of where the product came from, the lack of interoperability of systems along the supply chain, formats rangning from paper slips, oral communication to large ERP systems. The low traceability and documentation sharing hinders an effective supply chain, especially when fraudulent behaviour seems a great concern.

Results
One of the outcomes where what kinds of trust might be influenced by blockchain. Contract trust, predictability and dependability was chosen from 21 different kinds (Seppänen 2005). After a workshop with COOP Trading employee’s, they deemed contract trust as a central aspect of trust in a supplier out of the 21. It was found that blockchain and smart contracts inherent qualities that might qualify the technology to accomplish a form of digital trust, by managing one of the approaches to measure trust, contract trust.
The outcome for COOP Trading was conceptual UML blockchain design, illustrating the possibilities of enhanced traceability, information, documentation sharing along the supply chain of EVOO. The challenges depicted was information quality, legal implications and digital trust.

  • With information quality, is the issue with garbage in, garbage out as data transferred to the blockchain needs to be truthful and of high quality for the blockchain platform to work. This might be solved by RFID tags to get quality data.
  • Legal implications is the current legislation challenging greater traceability and information sharing, due to contractual bindings between buyer/supplier (FPA), and on blockchain application legislation as it is highly unregulated.
  • With digital trust would be a form of calculative trust, that one can place trust in a technology to handle what is to be expected of it, and thereby handle aspects of trust.

The takeaway
Blockchain have great opportunities to influence the role of trust, by developing a form of digital trust, and be a platform for greater traceability, product information and documentation sharing among supply chain participants. With any new technological improvements it should sprout internally, teaching management of the possibilities, internal meetings and identify other areas where the technology can be applied in the future. Take time to do a simple test, gain knowledge and grow from there.

If this has your interest, raised some questions or just got you curious for more, please contact me. I have a 12 page summary that gives a lot more detail, and of course the 109 page long full thesis on the matter, if you´re really into it.

Looking forward to hear from you.

Kristoffer Just

Below is the illustrations made, first the current supply chain of EVOO and then with blockchain as a platform.

Current


Blockchain supply chain

© 2018 Kristoffer Just Petersen

What is Blockchain? And how does it work?

The Genesis

The blockchain technology was invented by a person under the alias Satoshi Nakamoto, to support the cryptocurrency Bitcoin (Nakamoto 2007). For the first time it was possible for many users to trade values with each other over the Internet without the need for a third party or intermediary – typically a bank – to verify the transaction. A blockchain is a ledger of facts, replicated across several computers assembled in a distributed peer-to-peer network. Or put simply, a chain of blocks (Beck 2017). Anyone participating in a blockchain can review the entries in it; users can update the blockchain only by consensus of a majority of participants. Once entered into a blockchain, information can never be erased (Nakamoto 2007: 2).

Blocks are an order of facts in a network of non-trusted peers, similar to how Uber’s technology intermediates between suppliers and consumers of transportation. Facts are grouped in blocks, and there is only a single chain of blocks, which then is replicated in the entire network. Each block has a reference to the previous block, through the hashing cryptography that links the blocks. Some of the nodes in the chain create a new block with pending facts. They, in the case of bitcoin miners, compete to see if their local block is going to become the next block in the chain for the entire network, called proof of work. Then this block is sent to all other nodes in the network. All nodes run a check on that to see if the block is correct, then add it to their copy of the chain, and try to build a new block with new pending facts (Nakamoto 2007: 3).

But it has gradually become clear that the technique has much broader applications than just acting as the backbone of Bitcoin. One of the key elements is the ledger, which is a database of the content of the blockchain – whether it is bitcoin transactions, intelligent smart contracts, or something else (Boye 2016).

Blockchain is a type of electronic ledger created to ensure that once a party transfers a digital asset, he cannot transfer it to anyone else, prevent double spending. Unlike other ledgers, blockchain is owned by its participants, and decisions about what it records are subject to participant consensus.

Recording accuracy is ensured by duplication: every participant has a copy of the ledger. Discrepancy-resolution mechanisms ensure that all copies reflect an identical history. Though permissions can be managed with a fair degree of control, by default any permitted participant can view all transactions. Thus together with immutability, notarization and assured provenance, transparency is a core blockchain attribute (1).

There are many ways of applying a blockchain technology, in short, either as a public blockchain, a private blockchain, or as a consortium blockchain. A public blockchain is a blockchain that anyone in the world can read, through which anyone in the world can send transactions, and include transactions if they are valid, i.e. Bitcoin (Buterin 2015). A fully private blockchain is a blockchain where write permissions are kept “centralized” to one or few institutions, i.e. banks (Buterin 2015). A consortium blockchain is a blockchain where the consensus process is controlled by a pre-selected set of nodes. An example, is a consortium of 15 financial institutions, each of which operates a node and of which 10 must sign every block in order for the block to be valid. A consortium blockchain can be altered to fit the need of the one using it, ex that the R3 consortium want different “rules”, than the Hyperledger consortium or Ethereum Alliance (Buterin 2015; R3; Hyperledger).  

Public blockchains can offer advantages that a private blockchain and consortium simply cannot, and vice versa. The take-away with the different ways of adopting blockchain technology, in relation to COOP Trading, is what they want to gain from a blockchain solution, who should be a part of it, who should have read and write permissions and what data can’t be shared. One must have a high due diligence in order to research the possibilities and challenges with a blockchain solution.     

Factbox:

“A block is the ‘current’ part of a blockchain which records some or all of the recent transactions, and once completed goes into the blockchain as permanent database. Each time a block gets completed, a new block is generated. There is a countless number of such blocks in the blockchain. The blocks are linked to each other (like a chain) in proper linear, chronological order with every block containing a hash of the previous block.” (Investopedia)

Finally, blockchain isn’t simply a secure collective database. In addition to transactions, it also records and executes simple programs.

The idea of pre-programed conditions, interfaced between users, and then broadcasted to everyone, is called a smart contract. A contract is a promise that signing parties agree to make legally-enforceable. Proponents of smart contracts claim that many kinds of contractual clauses can be partially or fully self-executing, even self-enforcing, or both. The aim of smart contracts is to provide security, which is superior to traditional contract law and to reduce other transaction costs associated with contracting (Tapscott 2016: 105-108). Buterin explains it as: “(…) then we can cut costs to near-zero with a smart contract.” (Parker 2016).

Blockchain smart contracts may also influence, or be influenced by, product movements. For instance, a positive QA test indication can release a part for assembly. However, today that role is played by ERP systems. Blockchain technology doesn’t necessarily add value in such traditional operations management tasks (1).

Factbox:

“An asset or currency is transferred into a program and the program runs this code and at some point it automatically validates a condition and it automatically determines whether the asset should go to one person or back to the other person, or whether it should be immediately refunded to the person who sent it or some combination thereof.” (BlockGeeks)

  1. http://www.sdcexec.com/article/12247812/supply-chain-finance-on-the-blockchain-enables-network-collaboration

© 2018 Kristoffer Just Petersen