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A blockchain use case in food distribution: Do you know where your food has been?
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International Journal of Information Management journal homepage: www.elsevier.com/locate/ijinfomgt
A blockchain use case in food distribution: Do you know where your food has been? Daniel Bumblauskasa, , Arti Manna, Brett Duganb, Jacy Rittmerb ⁎
a b
Department of Management, University of Northern Iowa, Postal Address: 262 Curris Business Bldg., Cedar Falls IA 50614-0125, United States Bytable Inc., United States
ARTICLE INFO
ABSTRACT
Keywords: Blockchain Supply Chain Management Technology & Innovation Food Distribution
This paper aims to explain the implementation of blockchain technology in the production and supply chain delivery system for eggs from farm to consumer by a company based in the Midwestern USA. One of the primary research questions answered is how blockchain can be utilized and applied to more accurately and transparently move goods through global supply chains. This company is at the forefront of developing such systems for use in industry, and a use case for egg distribution is detailed. The goal is to track products from farm to fork using blockchain and internet of things (IoT) enabled technologies. By creating traceable and transparent supply chains for food, consumers can attain the information they need to make informed choices about the food they buy and the companies they support. For stakeholders in the food supply chain, having traceability and transparency builds better relationships with their customers, increases efficiency, and reduces the risk and cost of food recalls, fraud, and product loss. The blockchain technology and this business are creating a case for fixing and transforming the world’s food system.
1. Introduction, Background, and Purpose Blockchain is growing in notoriety as are use case applications of the technology. One of the most common and familiar examples of contemporary uses for blockchain technology is in cryptocurrency, such as bitcoin, and finance (Fosso Wamba, Kamdjoug, Robert, Bawack, & G Keogh, 2018; Tapscott & Tapscott, 2017). While the use of the distributed ledger in supply chains is relatively young (Abeyratne & Monfared, 2016), the number of applications for the distributed ledger are seemingly endless. Tapscott and Tapscott (2016) go so far as to say “[Blockchain is] the tech. most likely to change the next decade of business…Blockchain is the first native digital medium for value, just as the Internet was the first native digital medium for information.” Much like the Internet when it was originally developed, blockchain has been “slow to engender any significant momentum within the Information Systems (IS) and Information Management (IM) literature (Hughes et al., 2019, p. 114). Blockchain use cases often tie in other new upcoming technologies, such as remote sensing technology and artificial intelligence (AI), to collect and parse data. In this specific use case, customized sensor networks tracking location, time, temperature, and humidity levels report data to a blockchain. Within the blockchain, information is tied
digitally to each individual product, creating a digital record to prove provenance, compliance, authenticity, and quality. This information follows the product throughout the supply chain and is accessible to every stakeholder. Calls for integrating blockchain are also becoming more prevalent (Senyo, Liu, & Effah, 2019). With distributed ledger technology, consensus between multiple nodes is required to alter data, so no single party in the supply chain can alter existing information. Since the majority of information being verified and uploaded is by sensor networks, it becomes much more difficult for a party to be dishonest about where the product is coming from, whether it is within compliance or any certifications and claims associated with it. Pun et al. discuss this topic as it relates to deceptive practices in blockchains (Pun, Swaminathan, & Hou, 2018). There are many interesting use cases emerging in the academic and industrial communities. One area gaining some attention is food distribution, and more specifically the cattle, egg, and poultry production industries. Two of the co-authors of this article founded a Midwestern start-up company working in this area. There are other organizations developing blockchain solutions for food traceability companies, such as IBM's FoodTrust (DeCastro, 2018), SAP's cloud enterprise and Leonardo blockchain solution (Perez, 2018), and Cargill's blockchain Honeysuckle White ® turkeys (Cargill, 2018). Governments in states like
Corresponding author E-mail addresses: [email protected] (D. Bumblauskas), [email protected] (A. Mann), [email protected] (B. Dugan), [email protected] (J. Rittmer). ⁎
https://doi.org/10.1016/j.ijinfomgt.2019.09.004 Received 30 March 2019; Received in revised form 9 September 2019; Accepted 12 September 2019 0268-4012/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Daniel Bumblauskas, et al., International Journal of Information Management, https://doi.org/10.1016/j.ijinfomgt.2019.09.004
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Colorado and Wyoming are in some cases “aggressively pursuing blockchain tech companies (Evans, 2018),” with one example from Wyoming being BeefChain (DeCastro, 2018; Evans, 2018).
marked differences in adoption behaviors between Indian and United States-based professionals. In the Indian context, they found trust between supply chain stakeholders influenced the behavioral expectation, whereas, in the USA context, facilitating conditions were a major influencer. Schuetz and Venkatesh (2019) also discuss the challenges in adopting blockchain technologies in India. Kamble, Gunasekaran, and Sharma (2019) identified and established relationships between the enablers of blockchain technology and the agriculture supply chain in the Indian context. Kamble et al. (2019) identified thirteen such enablers from literature and evaluated these enablers using a combination of Interpretive Structural Modeling (ISM) and Decision-making Trial and Evaluation Laboratory (DEMATEL) methodology. They found the following to be the most significant enablers – traceability, auditability, immutability, and provenance. Other studies have analyzed the implementation of blockchain technology in an organizational context and found blockchain usage enables organizations to protect sensitive information and eliminate institutional intermediaries (Ying, Jia, & Du, 2018). Behnke and Janssen (2019) analyzed and identified boundary conditions for sharing assurance information to improve traceability. They recommend standardization of traceability processes and interfaces, joint platform and independent governance for as the key boundary conditions for successful implementation of blockchain in food supply chains. Traditional consensus algorithms used in blockchain for data consistency in distributed networks (Zheng, Xie, Dai, Chen, & Wang, 2017) can be problematic. Instead, focusing on consensus between nodes to emphasize efficiency, speed, security, and fairness should be considered. The Byzantine Generals (BG) Problem (Zheng et al., 2017) and BFT Nodes (Vukolić, 2015) are also important considerations in the foundations of blockchain-enabled design. In this article, we present a use case for egg distribution. One other lab concurrently developing a use case is the Purdue University Blockchain Lab, which recently partnered with Fresh Legend in China for bean product traceability as presented at the Production and Operations Management Society 30th Annual Conference 2019 in Washington DC (no published citation available, https://pomsmeetings.org/conf-2019/).
2. Literature review Interest in the use of blockchain is growing across multiple industries because of its inherent uniqueness in ensuring transaction integrity across multiple entities. As previously mentioned, the most common example is cryptocurrency use in financial applications (Fosso Wamba et al., 2018; Tapscott & Tapscott, 2017). However, some nonfinance industries where widespread acceptance and experimentation with blockchain technology is being observed are transportation, origin-to-consumer, pharmaceutical, legal and regulatory (Perez, 2018). There are also examples emerging of blockchain from the healthcare industry (Grover, Kar, & Davies, 2018; Sodhro, Luo, Sangaiah, & Baik, 2018), the chemical industry (Takhar & Liyanage, 2018), and in big data applications (Chae, 2019). Prior literature has shown that there are many benefits of blockchain technology, with one benefit being identity management. Blockchain can be used in the supply chain to store and share data with other parties, e.g., suppliers, customers, etc., or to compare the data received with other node data, or outside data, for verification. Blockchain can provide supply-chain wide visibility to who is performing what actions, at what location, and at what time (Alam, 2016; Kshetri, 2018). Blockchain technology can also be used to eliminate intermediaries and auditors, enabling lower costs and increased efficiency (Koetsier, 2017; Kshetri, 2018; Tonnissen & Teuteberg, 2019). Additionally, blockchain technology not only provides transparency but also helps create distributed and immutable records leading to traceability of inputs (Boschi, Borin, Cesar Raimundo, & Batocchio, 2018). The traceability mechanism of blockchain technology also helps in fraud prevention across the supply chain (Biswas, Muthukkumarasamy, & Tan, 2017). Some of these benefits of blockchain technology are clearly visible in the retail industry where companies like Walmart are working towards utilizing blockchain technology for tackling food safety in the supply chain (Kamath, 2018) and also eliminating third parties to build more transparency in food production (Polansek, 2019). For food safety and quality blockchain initiatives are taking place in China (Pan, Pan, Song, Ai, & Ming, 2019), South America, and Central America, where Walmart is collaborating with IBM (Burkitt, 2014; IBM, 2017). These IBM implementations of blockchain utilize Hyperledger Fabric, which supports modular architecture and plug-and-play components such as consensus and membership services (IBM, 2017). This implementation enables all the users to have a shared view of truth at any given point, in addition to efficient data capture, management, and control. This, in turn, enables trust among all parties across the entire food supply chain. According to McDermott (as cited in Kamath, 2018), “The trust it delivers enables more efficient and complete sharing of the critical data that drives enterprise transaction (p. 48)”. This also enables transparency in the supply chain which in turn helps solve one of the biggest issues in food safety – accurate traceability for products in the food supply chain, esp. contaminated food, to avoid economic loss of millions of dollars (Hodge, 2017). In supply chain literature, multiple cases have been analyzed to understand the impact of blockchain technology on key supply chain objectives such as cost, quality, transparency, trust, risk reduction, sustainability and flexibility (Aste, Tasca, & Di Matteo, 2017; Fosso Wamba, 2018; Kshetri, 2018). The use of blockchain technology enhances transparency and accountability (Kshetri, 2018) and increases cooperation between supply chain members (Aste et al., 2017). To understand the adoption behavior at the individual level, Queiroz and Fosso Wamba (2019), utilized the unified theory of acceptance and use of technology (UTAUT) model across the USA and India. They found
3. Design Methodology 3.1. Use Case Design One use case for blockchain traceability in the food supply chain is currently in testing with Bytable Inc., a blockchain food traceability company conducting a pilot project to track eggs from farm to consumer. Consumers are able to scan a QR code on product packaging and use carton information to access data collected throughout the supply chain. This use case is a proof of concept (PoC) with plans to bring the resulting product to market in early 2020. 3.2. Use Case Project Background Project discussion and planning began in April 2018 when Bytable Inc. partnered with a specialty egg brand for organic, free-range, and pasture-raised eggs in the Midwestern region of the United States. The egg packer collects products from approximately 100 small farms in the region before cleaning, processing, grading, packaging, and distributing approximately 100,000 eggs to retailers per week. The egg processor in this case study was already using internal traceability software on processing equipment, which collects data from human and manufacturing equipment input and stores it in an on-site server. Application development began in January 2019 and the PoC was completed in late February 2019. Primary stakeholders in this use case were farms, the egg packer, test retail stores, consumers and Bytable Inc. 2
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Table 1 Important Use Case Criteria. Considerations Feasibility Operational Compatibility Performance Privacy Assurance Relevance to Stakeholders
Many considerations were taken in the execution of this use case and were relevant to determining how the solution was implemented. Ease of implementation with available technologies and services and ease of integration with existing stakeholders’ processes and infrastructure. The solution must be implemented and used without slowing down the facility’s production or decreasing operational efficiency. The system needed to be responsive, easy to use, accessible, and fast enough to be used in day to day operations. Sensitive information such as home addresses needed to be kept private for certain stakeholders. Assurance that data integrity could be maintained and audited. Information also needed to be reliable in order to mitigate potential safety risks related to a particular batch of eggs. Stakeholders needed motivation for being part of the use case. A business case value should be present for each actor.
3.3. Issues Explored and Project Motivations
3.3.2. Condition Monitoring for Compliance and Product Quality Egg producers and processors are required by law to maintain temperature levels below 45 °Fahrenheit (7.2 °C) for unpasteurized egg products. They are not required by law to monitor humidity levels, but this is essential for ensuring consistently high product quality. Bytable Inc. uses temperature and humidity sensors to track date, time, location, and proximity of other sensors in key points throughout the egg supply chain to contribute data to products’ blockchain records without human input. Hypothetically, by increasing automation and the amount of data collection, the risk of input error is lowered and the integrity of existing data is increased by association. Data is collected into a single product record, makes temperature compliance and humidity monitoring easier, and ensures quick and easy digital access to all data for a product.
3.3.1. Compliance in the Food Industry Historically in the United States, food supply chain stakeholders needed only enough traceability for federal regulators to trace products “one-step-forward and one-step-back” per the Bioterrorism Act of 2002 (U.S. Customs and Border Protection, 2014). With the passage of the Food Safety Modernization Act (FSMA) in 2011 (U.S. Food and Drug Administration, 2018), the United States Food and Drug Administration is able to require mandatory recalls and full traceability for high-risk products. This implementation is a challenge for food businesses of all sizes, as it requires extremely diligent and time-consuming bookkeeping and labeling by all members of a facility, and necessitates a reliance on technology rarely seen before in the food industry.
Fig. 1. The Flow of Assets and Data Within the System. 3
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3.3.3. Food Safety and Recall Impact Food recalls are becoming more frequent and more severe, resulting in rising concern from consumers and industry professionals alike. The Stericycle Recall Index (Stericycle Expert Solution, 2019) showed a dramatic spike in units recalled since 2012, with 31.3% of recalls being a result of potentially deadly bacterial contamination. The impact of these recalls is felt profoundly by food businesses, with the average recall costing $10 million in direct costs alone, which does not include the cost of litigation, lost sales, loss of company value, or brand damage (Food Marketing Institute & Grocery Manufacturers Association, 2011).
snapshot of the PoC architecture. The PoC blockchain layer was implemented using Hyperledger Sawtooth v1.0 and custom Smart Contracts1, (see Appendix A for PoC definition superscripts) known as ‘Transaction Processors’ by Sawtooth language (Olson et al., 2018). Hyperledger Sawtooth is a permissioned blockchain (Olson et al., 2018) and was used to manage recording data to the ledger and accessing transaction history data. Validators2 and smart contracts were deployed across several servers inside of Docker3 containers. A substantial amount of custom software and hardware was developed around Sawtooth including a custom REST4 API5, client interfaces (front-end applications written in React6), microservices7 (serverless88 cloud processes written in NodeJS9), embedded software (written in the C programming language) and Internet of Things10 (IoT) sensors (modified open-source microcontrollers). This extra technology was developed to improve ease stakeholder adoption and facilitate authenticated and permissioned automated data exchange.
3.3.4. Food Fraud and Rising Ethics Concerns Consumers place a high (and growing) value on ethical practices for egg production, with 66-74% of people paying attention to the label on egg, meat, and dairy products about how the animal was raised (Spain, Freund, Mohan-Gibbons, Meadow, & Beacham, 2018). On average, consumers are willing to pay $.79 more for a carton of eggs if the welfare of the animals was guaranteed by a trusted welfare certification (Spain et al., 2018). Additionally, farmers are transitioning to produce more specialty eggs to meet growing consumer demand, with hundreds of grocery stores, restaurants, and other food businesses committing to switching to cage-free eggs by 2025 (Shanker, 2018). With consumer demand for information on where their egg products are coming from and the standards they were raised with, specialty egg packers are placing high importance on using traceability data to create consumer-facing transparency for their egg products.
3.5.2. Blockchain Consensus Hyperledger Sawtooth uses a consensus mechanism called Proof of Elapsed Time (PoET) (Olson et al., 2018; PoET 1.0 Specification, 2017). This is important because blockchain consensus is often associated with high environmental impact, e.g., energy usage, and poor performance. The developers of Sawtooth aim to implement “pluggable consensus” or the ability to change consensus algorithms, (Olson et al., 2018) but this feature is limited as of this writing. PoET requires specialized hardware that supports Intel Software Guard Extension (SGX) to provide Byzantine fault tolerance11,12 (BFT) but can be simulated when BFT is not required and SGX support is not available. SGX allows code to be executed in regions of memory called an enclave which is inaccessible to all users and processes (even those with the highest privileges). PoET
3.4. The Relevance of Blockchain to Use Case Blockchain is anticipated to be relevant for this use case, and to food and agriculture in general, because of the ability to share immutable data between supply chain stakeholders and automate agreements and the exchange of trusted information between multiple actors. All stakeholders agreed transparency, information sharing, and improved information management could improve safety, product quality, and brand engagement from consumers while improving operational efficiency with product tracking. Table 1 provides a summary of important criteria in the use case. Note these criteria can be applied to any context and not limited to only food and egg distribution.
1 Smart Contract(s). A program existing on a blockchain, usually executing when transaction data is submitted to the blockchain. Typically these programs are validating a transaction in some way. 2 Validator(s). A Hyperledger Sawtooth component that facilitates transaction processing, peer-to-peer network communication, and peer-to-peer consensus. It also facilitates interconnectivity between the nodes REST API and transaction processors. See more at: https://sawtooth.hyperledger.org/docs/ core/releases/1.0/introduction.html. 3 Docker. Containerization software developed by Docker, Inc. to allow creation of a consistent run-time environment for software across multiple different types of operating systems and hardware. See more at: https://www. docker.com/why-docker. 4 REST. (REpresentational State Transfer) is a commonly used architectural design pattern for Application Programmer Interfaces(API). 5 API. Application Programming Interface. Typically refers to a set of tools, procedures, routines and or protocols for facilitating software development. In this context it refers to software services hosted on remote endpoints and the interfaces and protocols implemented to access them. 6 React. A popular Javascript library for developing software user interfaces for web and mobile applications. See more at: https://reactjs.org/ 7 microservice(s). A small, lightweight, loosely coupled software-service. Implemented commonly when using the microservices architectural design technique. 8 Serverless. Refers to serverless computing. Where a cloud-based compute service provides a temporary run-time environment for code execution in response to a triggering event. 9 NodeJS. A Javascript runtime environment. Commonly used for server-side programming. See more at: https://nodejs.org/en/about/ 10 Internet of Things. A generalized term for describing network connected hardware devices capable of remote configuration and communication. 11 Byzantine fault tolerance. Named because of the Byzantine Generals Problem, used to describe the tolerance of the class of all possible failures resulting from the Byzantine Generals Problem. 12 Byzantine Generals Problem. A problem described as: A commanding general must send an order to his n - 1 lieutenant generals such that IC1. All loyal lieutenants obey the same order (Lamport, Shostak, & Pease, 1982).
3.5. Proof of Concept The proof of concept was built to track data about free-range eggs which were collected at a variable number of farms and followed through the packaging operations at a packing facility. Eggs were collected from refrigerators located on the farm then transported to the packing facility to be stored, washed, graded, packed, and shipped to retail stores across the country. The goal of this proof of concept was to capture as much data from this process as possible and gauge its usefulness to both the business and to consumers. The other primary objective was to prove eggs in a carton can be traced back to a small group of suppliers and to a window of production time. Data was captured at each stage of the product life-cycle and made searchable by date and time information on the end of the egg carton. Consumers were made aware of the possibility to search for information by QR code stickers on the carton. Cartons were stickered in Iowa City, Iowa and in the Denver, Colorado metropolitan area. In order to better understand how data is captured in this use case, Fig. 1 illustrates a simplified view of the flow of data and assets between actors within the system. 3.5.1. Architecture Multiple components and technologies were required to track information and make it accessible. Fig. 2 provides a summarized 4
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Fig. 2. PoC High-Level Architecture.
works by allowing each validator to request a wait time from the enclave. The wait time is randomly generated and the validator with the shortest wait time claims the role of leader and is allowed to add the block to the blockchain. The enclave and lottery mechanism provides a secure, efficient, and relatively fast mechanism for block validation consensus.
third-party sensors on the farm. Please note sensor data was not used in the consumer-facing application for this PoC. (2) The Packaging Facility: For data capture within the packaging facility, Bytable Inc. relied on integrations with internal traceability software, processing hardware, and data entry from system operators.
3.5.3. Authentication and Permissioning To ensure only certain stakeholders were allowed to access sensitive information such as precise farm location, a proxy authentication server and REST API were implemented. This provided different stakeholders with differing levels of read access to the blockchain data. Devices and users with a valid private key were able to directly interface with the blockchain through a proxy microservice, while stakeholders accessing the ledger without a key were required to interact through a separate public proxy client service with fewer privileges. Furthermore, the blockchain network was implemented as a private network, meaning transactions submitted to the network and nodes13 participating in consensus are limited and regulated by an on-chain network policy. Data points were captured at three locations:
The data captured at these two points created a record of transactions on the blockchain including collection location and time, farm name, temperature and humidity history, transit departures and arrivals, processing and packaging time, egg type, certification data, batch quantity, best-by date, brand, color, product labels, and any possible supplier overlaps during processing and packaging times. (3) The Consumer Scan: For the initial PoC, Bytable Inc. collected data from consumers scanning products from four specialty retailer locations in Iowa City, Iowa and the Denver, Colorado metro area. Stickers were placed on select free-range egg cartons between midFebruary and mid-March 2019. The web application containing traceability data was accessible to consumers via a scannable QR code on product packages and requested input of data printed on the end of the carton to access traceability data for a particular carton. Website analytics within the web application collected data about the number of users accessing the site, their behavior, time spent on the site, visits per user, and other general data about their interactions on the web application. Figs. 3–8 provide images of the web application.
(1) The Farm: Data was captured where the eggs are collected when the collector logged the egg type and pickup time. Temperature, location, and humidity data were captured by Bytable Inc. and the
13 node(s). A Hyperledger Sawtooth Validator on or connecting to Sawtooth network. See more at: https://sawtooth.hyperledger.org/docs/core/releases/1. 0/architecture/permissioning_requirement.html.
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Figs. 3 and 4. Web application display for customer data input from end of egg carton.
• Identification of the establishment
4. Results and Findings 4.1. Results of Proof of Concept
This use case was originally planned to imitate certain aspects of the European system with codes directly on each egg. A key difference between the U.S. and the EU is that commercially available eggs are washed during the grading process in the U.S. Egg marking prior to washing would not be feasible as the markings are likely to be washed off or made illegible. Equipment and process constraints made marking individual eggs impractical for the initial use case and the decision was made to trace eggs using carton data for the first iteration of the project. The solution outlined here could be made easily transferable to a system like the EU’s, and egg markings could easily be associated with blockchain records. The minimum requirements for marking eggs by the EU would not be enough to trace an egg back to its pack time and date and would either need to be combined with information from the carton or would need unique information added to the existing code. Adding extra information to these codes is a practice already in use by producers who would be able to rapidly integrate into a blockchain application similar to this use case. Combining egg markings with distributed ledger technology could be a sensible way for future traceability systems to be implemented. Traceability down to the individual egg would be achieved more easily with unique markings and easily referenceable records. Distributed ledger technology could also be a promising means of combating existing issues with marked egg systems like code forgery. Records could be accessed more intelligently to determine if a particular egg marking is likely to be a forgery or incorrect.
The proof of concept resulted in above-expected consumer engagement and interest in further development from stakeholders. During tests at the retail level, cartons were reliably traced back to the supplier and date they were collected. The proof of concept began with the goal of 5-10% scan rates and average usage of 1 minute, both of which were exceeded by a significant margin. Of 171 cartons stickered, 46 unique users accessed the web application for a scan rate of 21.2%. On average, the time each unique user spent on the web app was 2 minutes and 48 seconds. These results are detailed in Fig. 9. 5. Use Case Applicability to Europe While this use case study was performed in the United States, it is important to understand how it could be applied to other markets and systems. In Europe, policy surrounding egg marking is considered by some to be more advanced than in the United States. In the European Union, each egg’s shell is marked with a code containing critical tracking information. Non-exempt countries and facilities must mark each egg with a code representing a minimum of the following:
• Farming method • Member state
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Figs. 5 and 6. Web application display for product summary page and traceability data of eggs.
6. Discussion
capture data and analytics relating to the impact of their implementation.
While the potentials for practical use of blockchain technology are still being understood, the outcomes of this study show promise. Despite many challenges and barriers to overcome, the results indicate that consumer demand and the need for improved traceability may drive adoption within certain segments of the food industry. The study also indicated in certain environments, blockchain implementations can be possible without immense effort from stakeholders. In this section, we will evaluate the contributions, implications, and limitations of the study.
6.2. Implications for Practice Blockchain technology could be used to supplement food traceability in new and existing supply chains. Many current solutions are siloed and traceability only exists internally for specific stakeholders within the supply chain. In other cases, traceability does not exist at all. In this study, it was possible to add blockchain technology onto existing systems and technologies without significant process or behavior change by supply chain stakeholders. Adding blockchain technology is possible in practice, but scenarios exist where poor processes and primitive technologies exist among stakeholders. In these scenarios, it is likely that major technology upgrades and process standardization will be required before the implementation of blockchain technology is possible. Because of these barriers relating to lack of industry-wide process standardization and necessary technology adoption, it is likely that blockchain technology can only benefit supply chains where traceability is well-practiced internally by each stakeholder.
6.1. Contributions This study was developed to understand if blockchain technology could be a value add to food businesses and support the claim that blockchain can improve food traceability. The authors compiled relevant research in the field of blockchain traceability of food and agricultural products, then leveraged them to develop a practical approach towards implementation with a specialty egg brand. Furthermore, this is the first study known by the authors to use blockchain technology to track eggs from farm to consumer in major markets and capture traceability and engagements data at nearly every step of the supply chain. The consumer-facing captured data element presents an opportunity for further exploration of consumer interaction with blockchain traceability data. Finally, the implementation methods highlighted in the study, such as the use of IoT sensors and integration into existing systems, provide a framework for further studies to
6.3. Limitations and Future Research Direction The case study represented here followed the implementation of a blockchain traceability solution for the egg industry. This implementation is early and there are still many limiting factors regarding implementing blockchain solutions in the food supply chain. This study
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Figs. 7 and 8. Web application display for producer information, certifications, and practices.
was performed with a specialty egg brand in the United States with substantial internal traceability and data tracking already existing in their facilities. While the authors partnered with a European egg traceability solution during implementation, more information is needed to understand the differences and difficulties of implementation in other parts of the world, among other food products, and even among individual supply chains’ egg production methods.
There is also a high barrier to technology and process adoption. This study was performed partially by integrating with existing traceability technologies and processes, which will not exist in all supply chains and will be a significant barrier in less wealthy parts of the world. Furthermore, there are no common interfaces or standards in regards to food traceability, and much work and collaboration are needed before seamless widespread adoption is possible. Additionally, there are
Fig. 9. A comparison of the quantity of egg cartons stickered and unique site visitors.
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limitations regarding the ability to verify data logged within blockchain solutions, mitigation of potentially dishonest stakeholders, and human error - these issues have no universal solutions. Regarding distributed system implementation, this study was performed with few stakeholders compared to many conventional or more complex supply chains. Implementations of larger distributed systems will also prove difficult as system size and complexity is required to increase. Another question that will need to be answered is whether distributed ledger alternatives to blockchain will emerge, such as Dr. Leemon Baird’s Hashgraph (and virtual voting). There is an immediate need for ERP interfaces more specific to the [food] industry requiring partnerships for integration. Other concerns are the use and infringement of patents, copyrights, and trademarks for software (i.e., intellectual property). The mainstream media and press have also latched on to the energy consumption of the distributed ledger which was prominently covered in the May/June MIT Technology Review (2018). This was a “proof of concept study,” in which Bytable Inc. is not using proof of work for consensus, thus at least partially alleviating the excessive energy usage concerns. Hardware and software (readers, GPS devices, and sensors) integrated with distributed applications are designed to be low power and energy-efficient. Tags do not require a power source (RFID or NFC) and are used for other applications as well, such as cattle identification. Devices can be powered and charged with renewable [e.g., solar] energy, but only when necessary as this does increase cost. In addition, the longevity of battery life (and capacitors) is an engineering challenge (http://www.engineeringchallenges.org/) of concern in blockchain-enabled devices. Zinc-air batteries could be one possible future technology. Food initiatives such as the University of Cambridge TIGR2ESS program (https://www.globalfood.cam.ac.uk/ ) are also exploring these types of applications for sustainable food supply in regions such as India. The next step for the use case is to bring the technology to several product lines for the egg distributor, including full integration with new facility machinery and software, automation of the generation of data for consumers to access via packaging, and building a data-access system for the egg distribution company to manage and track their eggs internally with ease. In addition to this project, the authors are currently collaborating with a UK university and Irish industry partner to scale this approach in the EU (the project is active and on-going).
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Accessed 30 March 2019 http://wyomingbusiness.org/news/branding-barbed-wireand-blockchain/10967. Food Marketing Institute, & Grocery Manufacturers Association (2011). Capturing Recall Costs: Measuring and Recovering the Losses. Grocery Manufacturers Association. October (Accessed 29 March 2019) https://www.gmaonline.org/file-manager/ images/gmapublications/Capturing_Recall_Costs_GMA_Whitepaper_FINAL.pdf. Fosso Wamba, S. (2018). Continuance Intention in Blockchain-Enabled Supply Chain Applications: Modelling the Moderating Effect of Supply Chain Stakeholders Trust. OctoberEuropean, Mediterranean, and Middle Eastern Conference on Information Systems. Cham: Springer38–43. Fosso Wamba, S., Kamdjoug, K., Robert, J., Bawack, R., & G Keogh, J. (2018). Bitcoin, Blockchain, and FinTech: A Systematic Review and Case Studies in the Supply Chain. Production Planning and Control Forthcoming. https://papers.ssrn.com/sol3/papers. cfm?abstract_id=3281148 Accessed 29 March 2019. Grover, P., Kar, A. K., & Davies, G. (2018). Technology enabled Health”–Insights from twitter analytics with a socio-technical perspective. International Journal of Information Management, 43, 85–97. Hodge, S. (2017). Can blockchain technology transform safety standards in the global food supply chain? Supply Chain Digital. Accessed 20 May 2019 https://www. supplychaindigital.com/technology/can-blockchain-technology-transform-safetystandards-global-food-supply-chain. Hughes, L., Dwivedi, Y. K., Misra, S. K., Rana, N. P., Raghavan, V., & Akella, V. (2019). Blockchain research, practice and policy: Applications, benefits, limitations, emerging research themes and research agenda. International Journal of Information Management, 49, 114–129. IBM (2017). Hyperledger–open source blockchain for business – IBM Blockchain. IBM Cognitive Advantage Reports. Accessed 20 May 2019 www.ibm.com/blockchain/ hyperledger.html. Kamath, R. (2018). Food traceability on blockchain: Walmart’s pork and mango pilots with IBM. The JBBA, 1(1), 3712. Kamble, S. S., Gunasekaran, A., & Sharma, R. (2019). Modeling the blockchain enabled traceability in agriculture supply chain. Internation Journal of Information Management.. https://doi.org/10.1016/j.ijinfomgt.2019.05.023. Koetsier, J. (2017). Blockchain Beyond Bitcoin: How Blockchain Will Transform Business in 3–5 Years. Accessed 29 March 2019 https://www.inc.com/john-koetsier/howblockchain-will-transform-business-in-3-to-5-years.html. Kshetri, N. (2018). Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80–89. Lamport, L., Shostak, R., & Pease, M. (1982). The Byzantine Generals Problem. ACM Transactions on Programming Languages and Systems, 4(3), 384. MIT Technology Review (2018). May/JuneMIT Technology Review, 121, Cambridge, MA (USA): Massachusetts Institute of Technology3. Olson, K., Bowman, M., Mitchell, J., Amundson, S., Middleton, D., & Montgomery, C. (2018). Sawtooth: An Introduction. 1-6. Accessed 29 March 2019 https://www. hyperledger.org/wp-content/uploads/2018/01/Hyperledger_Sawtooth_WhitePaper. pdf. Pan, X., Pan, X., Song, M., Ai, B., & Ming, Y. (2019). Blockchain technology and enterprise operational capabilities: An empirical test. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2019.05.002. Perez, G. (2018). Blockchain: A Study Rooted in Reality. SAP and Medium. Accessed 21 January 2019 https://medium.com/sap-innovation-spotlight/blockchain-studyeab4e6dcaa43. PoET 1.0 Specification (2017). PoET 1.0 Specification - Sawtooth v1.0.5 Documentation. Intel Corporation. Accessed 29 March 2019 https://sawtooth.hyperledger.org/docs/ core/releases/1.0/architecture/poet.html#introduction. Polansek, T. (2019). Walmart creates Angus Beef Supply Chain, Cutting Out Meat Processors. Reuters. Accessed 20 May 2019 https://www.reuters.com/article/us-walmart-beef/ walmart-creates-angus-beef-supply-chain-cutting-out-meat-processors-
7. Conclusions In this article, a use case has been presented for the deployment of blockchain in food distribution, specifically related to egg supply chains. A literature review of blockchain concepts and applications is provided. This work furthers the research conversation on how blockchain can be utilized and applied to more accurately and transparently move goods through supply chains. Further tests will be done to track consumer engagement, including testing on new product displays as the tests were conducted on labels that were not visible from the aisle at some of the retail locations. Several opportunities for improved tracking methods and supply chain analytics were discovered during implementation. New blockchain client applications are being actively developed as a result. These applications will be included in the next phase of the project, which includes expansion across several egg product lines to be distributed widely across the United States. As the potential of blockchain technology enters into the next stage of implementation and use, it is becoming clear that this could be the start of revolutionary food supply chain tools which will allow consumers to really know where their food has been. References Abeyratne, S. A., & Monfared, R. P. (2016). Blockchain ready manufacturing supply chain using distributed ledger. International Journal of Research in Engineering and Technology, 5(9), 1–10.
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Daniel P. Bumblauskas, Ph.D., is an Associate Professor of Management and the Hamilton / ESP International Fellow of Supply Chain and Logistics Management at the University of Northern Iowa, a visiting professor at the University of Washington, and holds a courtesy appointment at the University of Missouri where he previously held a faculty appointment. Dan conducts research, teaches, and consults on various areas related to operational excellence and business development. Dan has published over 45 peerreviewed journal articles and conference proceedings, including publications in journals such as Expert Systems with Applications, IEEE Transactions on Industry Applications and Business Process Management. He earned a B.S. in Industrial Engineering and Economics from Iowa State University, a master of liberal arts in general management from Harvard University, and received his M.S. and Ph.D., both from Iowa State University, in Industrial Engineering. Prior to his faculty appointments, Dr. Bumblauskas was previously employed in industry by ABB Inc. and Sears Holding Corporation and currently serves as a Vice President of PFC Services, Inc., a consulting firm based in Marietta, Georgia.
idUSKCN1S021A. Pun, H., Swaminathan, J. M., & Hou, P. (2018). Blockchain Adoption for Combating Deceptive Counterfeits. Kenan Institute of Private Enterprise Research Paper No. 18-18. Accessed 6 June 2019 https://papers.ssrn.com/sol3/papers.cfm?abstract_id= 3223656. Queiroz, M. M., & Fosso Wamba, S. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70–82. Schuetz, S., & Venkatesh, V. (2019). Blockchain, adoption, and financial inclusion in India: Research opportunities. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2019.04.009. Senyo, P. K., Liu, K., & Effah, J. (2019). Digital business ecosystem: literature review and a framework for future research. International Journal of Information Management, 47, 52–64. Shanker, Deena (2018). Companies Are Rushing to Meet Cage-Free Egg Deadline. Bloomberg: Bloomberg.com. March Accessed 29 March 2019 www.bloomberg.com/news/ articles/2019-03-08/clock-is-ticking-as-companies-scramble-to-produce-cage-freeeggs. Sodhro, A. H., Luo, Z., Sangaiah, A. K., & Baik, S. W. (2018). Mobile edge computing based QoS optimization in medical healthcare applications. International Journal of Information Management. Spain, C. V., Freund, D., Mohan-Gibbons, H., Meadow, R. G., & Beacham, L. (2018). Are They Buying It? United States Consumers’ Changing Attitudes toward More Humanely Raised Meat, Eggs, and Dairy. Animal, 8, 128. https://doi.org/10.3390/ ani8080128. Stericycle Expert Solution (2019). Recall Index. Accessed 29 March 2019 https://www. stericycleexpertsolutions.com/product-recall-index/. Takhar, S. S., & Liyanage, K. (2018). Blockchain application in supply chain chemical substance reporting. Proceedings of 22nd Cambridge International Manufacturing Symposium September. Tapscott, D., & Tapscott, A. (2016). The impact of the blockchain goes beyond financial services. Harvard Business Review, 10. Tapscott, A., & Tapscott, D. (2017). How blockchain is changing finance. Harvard Business Review, 1. Tonnissen, S., & Teuteberg, F. (2019). Analysing the impact of blockchain-technology for operations and supply chain management: An explanatory model drawn from multiple case studies. Internation Journal of Information Management. https://doi.org/10. 1016/j.ijinfomgt.2019.05.009. U.S. Customs and Border Protection (2014). The Bioterrorism Act. Available: https://www. cbp.gov/trade/priority-issues/import-safety/bioterrorism Accessed 29 March 2019. U.S. Food and Drug Administration (2018). FDA Food Safety Modernization Act (FSMA). Accessed 29 March 2019 https://www.fda.gov/food/guidanceregulation/fsma/. Vukolić, M. (2015). The quest for scalable blockchain fabric: Proof-of-work vs. BFT replication. International Workshop on Open Problems in Network Security. Springer112–125. Ying, W., Jia, S., & Du, W. (2018). Digital enablement of blockchain: Evidence from HNA group. International Journal of Information Management, 39, 1–4. Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2017). An overview of blockchain technology: Architecture, consensus, and future trends. 2017 IEEE International Congress.
Arti Mann is an Assistant Professor of Management at the College of Business, University of Northern Iowa. Her research interests span business value of information systems; the role of information technology in innovation; and the use of spatial analysis and econometrics techniques from regional economics and geography. Her work has appeared in Journal of Management Information Systems, Decision Support Systems, and Applied Geography among others, and various academic conference proceedings. Her graduate degrees are from Marquette University (M.S.) and Arizona State University (Ph.D.).
Brett Dugan is the CTO and Cofounder of Bytable Inc., a company focusing on traceability and market access solutions for sustainable and regenerative food businesses. He specializes in data architecture, blockchain and other distributed ledger technologies, and distributed systems. Brett holds a B.S. in Computer Science from the University of Northern Iowa. Jacy Rittmer is the CEO and Cofounder of Bytable Inc., a company focusing on traceability and market access solutions for sustainable and regenerative food businesses. She specializes in user interface and experience design, web development, and oversees product and business development at Bytable. Jacy holds a B.A. in Biological and PreMedical Illustration from Iowa State University.
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Contents lists available at ScienceDirect
International Journal of Information Management journal homepage: www.elsevier.com/locate/ijinfomgt
A blockchain use case in food distribution: Do you know where your food has been? Daniel Bumblauskasa, , Arti Manna, Brett Duganb, Jacy Rittmerb ⁎
a b
Department of Management, University of Northern Iowa, Postal Address: 262 Curris Business Bldg., Cedar Falls IA 50614-0125, United States Bytable Inc., United States
ARTICLE INFO
ABSTRACT
Keywords: Blockchain Supply Chain Management Technology & Innovation Food Distribution
This paper aims to explain the implementation of blockchain technology in the production and supply chain delivery system for eggs from farm to consumer by a company based in the Midwestern USA. One of the primary research questions answered is how blockchain can be utilized and applied to more accurately and transparently move goods through global supply chains. This company is at the forefront of developing such systems for use in industry, and a use case for egg distribution is detailed. The goal is to track products from farm to fork using blockchain and internet of things (IoT) enabled technologies. By creating traceable and transparent supply chains for food, consumers can attain the information they need to make informed choices about the food they buy and the companies they support. For stakeholders in the food supply chain, having traceability and transparency builds better relationships with their customers, increases efficiency, and reduces the risk and cost of food recalls, fraud, and product loss. The blockchain technology and this business are creating a case for fixing and transforming the world’s food system.
1. Introduction, Background, and Purpose Blockchain is growing in notoriety as are use case applications of the technology. One of the most common and familiar examples of contemporary uses for blockchain technology is in cryptocurrency, such as bitcoin, and finance (Fosso Wamba, Kamdjoug, Robert, Bawack, & G Keogh, 2018; Tapscott & Tapscott, 2017). While the use of the distributed ledger in supply chains is relatively young (Abeyratne & Monfared, 2016), the number of applications for the distributed ledger are seemingly endless. Tapscott and Tapscott (2016) go so far as to say “[Blockchain is] the tech. most likely to change the next decade of business…Blockchain is the first native digital medium for value, just as the Internet was the first native digital medium for information.” Much like the Internet when it was originally developed, blockchain has been “slow to engender any significant momentum within the Information Systems (IS) and Information Management (IM) literature (Hughes et al., 2019, p. 114). Blockchain use cases often tie in other new upcoming technologies, such as remote sensing technology and artificial intelligence (AI), to collect and parse data. In this specific use case, customized sensor networks tracking location, time, temperature, and humidity levels report data to a blockchain. Within the blockchain, information is tied
digitally to each individual product, creating a digital record to prove provenance, compliance, authenticity, and quality. This information follows the product throughout the supply chain and is accessible to every stakeholder. Calls for integrating blockchain are also becoming more prevalent (Senyo, Liu, & Effah, 2019). With distributed ledger technology, consensus between multiple nodes is required to alter data, so no single party in the supply chain can alter existing information. Since the majority of information being verified and uploaded is by sensor networks, it becomes much more difficult for a party to be dishonest about where the product is coming from, whether it is within compliance or any certifications and claims associated with it. Pun et al. discuss this topic as it relates to deceptive practices in blockchains (Pun, Swaminathan, & Hou, 2018). There are many interesting use cases emerging in the academic and industrial communities. One area gaining some attention is food distribution, and more specifically the cattle, egg, and poultry production industries. Two of the co-authors of this article founded a Midwestern start-up company working in this area. There are other organizations developing blockchain solutions for food traceability companies, such as IBM's FoodTrust (DeCastro, 2018), SAP's cloud enterprise and Leonardo blockchain solution (Perez, 2018), and Cargill's blockchain Honeysuckle White ® turkeys (Cargill, 2018). Governments in states like
Corresponding author E-mail addresses: [email protected] (D. Bumblauskas), [email protected] (A. Mann), [email protected] (B. Dugan), [email protected] (J. Rittmer). ⁎
https://doi.org/10.1016/j.ijinfomgt.2019.09.004 Received 30 March 2019; Received in revised form 9 September 2019; Accepted 12 September 2019 0268-4012/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Daniel Bumblauskas, et al., International Journal of Information Management, https://doi.org/10.1016/j.ijinfomgt.2019.09.004
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Colorado and Wyoming are in some cases “aggressively pursuing blockchain tech companies (Evans, 2018),” with one example from Wyoming being BeefChain (DeCastro, 2018; Evans, 2018).
marked differences in adoption behaviors between Indian and United States-based professionals. In the Indian context, they found trust between supply chain stakeholders influenced the behavioral expectation, whereas, in the USA context, facilitating conditions were a major influencer. Schuetz and Venkatesh (2019) also discuss the challenges in adopting blockchain technologies in India. Kamble, Gunasekaran, and Sharma (2019) identified and established relationships between the enablers of blockchain technology and the agriculture supply chain in the Indian context. Kamble et al. (2019) identified thirteen such enablers from literature and evaluated these enablers using a combination of Interpretive Structural Modeling (ISM) and Decision-making Trial and Evaluation Laboratory (DEMATEL) methodology. They found the following to be the most significant enablers – traceability, auditability, immutability, and provenance. Other studies have analyzed the implementation of blockchain technology in an organizational context and found blockchain usage enables organizations to protect sensitive information and eliminate institutional intermediaries (Ying, Jia, & Du, 2018). Behnke and Janssen (2019) analyzed and identified boundary conditions for sharing assurance information to improve traceability. They recommend standardization of traceability processes and interfaces, joint platform and independent governance for as the key boundary conditions for successful implementation of blockchain in food supply chains. Traditional consensus algorithms used in blockchain for data consistency in distributed networks (Zheng, Xie, Dai, Chen, & Wang, 2017) can be problematic. Instead, focusing on consensus between nodes to emphasize efficiency, speed, security, and fairness should be considered. The Byzantine Generals (BG) Problem (Zheng et al., 2017) and BFT Nodes (Vukolić, 2015) are also important considerations in the foundations of blockchain-enabled design. In this article, we present a use case for egg distribution. One other lab concurrently developing a use case is the Purdue University Blockchain Lab, which recently partnered with Fresh Legend in China for bean product traceability as presented at the Production and Operations Management Society 30th Annual Conference 2019 in Washington DC (no published citation available, https://pomsmeetings.org/conf-2019/).
2. Literature review Interest in the use of blockchain is growing across multiple industries because of its inherent uniqueness in ensuring transaction integrity across multiple entities. As previously mentioned, the most common example is cryptocurrency use in financial applications (Fosso Wamba et al., 2018; Tapscott & Tapscott, 2017). However, some nonfinance industries where widespread acceptance and experimentation with blockchain technology is being observed are transportation, origin-to-consumer, pharmaceutical, legal and regulatory (Perez, 2018). There are also examples emerging of blockchain from the healthcare industry (Grover, Kar, & Davies, 2018; Sodhro, Luo, Sangaiah, & Baik, 2018), the chemical industry (Takhar & Liyanage, 2018), and in big data applications (Chae, 2019). Prior literature has shown that there are many benefits of blockchain technology, with one benefit being identity management. Blockchain can be used in the supply chain to store and share data with other parties, e.g., suppliers, customers, etc., or to compare the data received with other node data, or outside data, for verification. Blockchain can provide supply-chain wide visibility to who is performing what actions, at what location, and at what time (Alam, 2016; Kshetri, 2018). Blockchain technology can also be used to eliminate intermediaries and auditors, enabling lower costs and increased efficiency (Koetsier, 2017; Kshetri, 2018; Tonnissen & Teuteberg, 2019). Additionally, blockchain technology not only provides transparency but also helps create distributed and immutable records leading to traceability of inputs (Boschi, Borin, Cesar Raimundo, & Batocchio, 2018). The traceability mechanism of blockchain technology also helps in fraud prevention across the supply chain (Biswas, Muthukkumarasamy, & Tan, 2017). Some of these benefits of blockchain technology are clearly visible in the retail industry where companies like Walmart are working towards utilizing blockchain technology for tackling food safety in the supply chain (Kamath, 2018) and also eliminating third parties to build more transparency in food production (Polansek, 2019). For food safety and quality blockchain initiatives are taking place in China (Pan, Pan, Song, Ai, & Ming, 2019), South America, and Central America, where Walmart is collaborating with IBM (Burkitt, 2014; IBM, 2017). These IBM implementations of blockchain utilize Hyperledger Fabric, which supports modular architecture and plug-and-play components such as consensus and membership services (IBM, 2017). This implementation enables all the users to have a shared view of truth at any given point, in addition to efficient data capture, management, and control. This, in turn, enables trust among all parties across the entire food supply chain. According to McDermott (as cited in Kamath, 2018), “The trust it delivers enables more efficient and complete sharing of the critical data that drives enterprise transaction (p. 48)”. This also enables transparency in the supply chain which in turn helps solve one of the biggest issues in food safety – accurate traceability for products in the food supply chain, esp. contaminated food, to avoid economic loss of millions of dollars (Hodge, 2017). In supply chain literature, multiple cases have been analyzed to understand the impact of blockchain technology on key supply chain objectives such as cost, quality, transparency, trust, risk reduction, sustainability and flexibility (Aste, Tasca, & Di Matteo, 2017; Fosso Wamba, 2018; Kshetri, 2018). The use of blockchain technology enhances transparency and accountability (Kshetri, 2018) and increases cooperation between supply chain members (Aste et al., 2017). To understand the adoption behavior at the individual level, Queiroz and Fosso Wamba (2019), utilized the unified theory of acceptance and use of technology (UTAUT) model across the USA and India. They found
3. Design Methodology 3.1. Use Case Design One use case for blockchain traceability in the food supply chain is currently in testing with Bytable Inc., a blockchain food traceability company conducting a pilot project to track eggs from farm to consumer. Consumers are able to scan a QR code on product packaging and use carton information to access data collected throughout the supply chain. This use case is a proof of concept (PoC) with plans to bring the resulting product to market in early 2020. 3.2. Use Case Project Background Project discussion and planning began in April 2018 when Bytable Inc. partnered with a specialty egg brand for organic, free-range, and pasture-raised eggs in the Midwestern region of the United States. The egg packer collects products from approximately 100 small farms in the region before cleaning, processing, grading, packaging, and distributing approximately 100,000 eggs to retailers per week. The egg processor in this case study was already using internal traceability software on processing equipment, which collects data from human and manufacturing equipment input and stores it in an on-site server. Application development began in January 2019 and the PoC was completed in late February 2019. Primary stakeholders in this use case were farms, the egg packer, test retail stores, consumers and Bytable Inc. 2
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Table 1 Important Use Case Criteria. Considerations Feasibility Operational Compatibility Performance Privacy Assurance Relevance to Stakeholders
Many considerations were taken in the execution of this use case and were relevant to determining how the solution was implemented. Ease of implementation with available technologies and services and ease of integration with existing stakeholders’ processes and infrastructure. The solution must be implemented and used without slowing down the facility’s production or decreasing operational efficiency. The system needed to be responsive, easy to use, accessible, and fast enough to be used in day to day operations. Sensitive information such as home addresses needed to be kept private for certain stakeholders. Assurance that data integrity could be maintained and audited. Information also needed to be reliable in order to mitigate potential safety risks related to a particular batch of eggs. Stakeholders needed motivation for being part of the use case. A business case value should be present for each actor.
3.3. Issues Explored and Project Motivations
3.3.2. Condition Monitoring for Compliance and Product Quality Egg producers and processors are required by law to maintain temperature levels below 45 °Fahrenheit (7.2 °C) for unpasteurized egg products. They are not required by law to monitor humidity levels, but this is essential for ensuring consistently high product quality. Bytable Inc. uses temperature and humidity sensors to track date, time, location, and proximity of other sensors in key points throughout the egg supply chain to contribute data to products’ blockchain records without human input. Hypothetically, by increasing automation and the amount of data collection, the risk of input error is lowered and the integrity of existing data is increased by association. Data is collected into a single product record, makes temperature compliance and humidity monitoring easier, and ensures quick and easy digital access to all data for a product.
3.3.1. Compliance in the Food Industry Historically in the United States, food supply chain stakeholders needed only enough traceability for federal regulators to trace products “one-step-forward and one-step-back” per the Bioterrorism Act of 2002 (U.S. Customs and Border Protection, 2014). With the passage of the Food Safety Modernization Act (FSMA) in 2011 (U.S. Food and Drug Administration, 2018), the United States Food and Drug Administration is able to require mandatory recalls and full traceability for high-risk products. This implementation is a challenge for food businesses of all sizes, as it requires extremely diligent and time-consuming bookkeeping and labeling by all members of a facility, and necessitates a reliance on technology rarely seen before in the food industry.
Fig. 1. The Flow of Assets and Data Within the System. 3
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3.3.3. Food Safety and Recall Impact Food recalls are becoming more frequent and more severe, resulting in rising concern from consumers and industry professionals alike. The Stericycle Recall Index (Stericycle Expert Solution, 2019) showed a dramatic spike in units recalled since 2012, with 31.3% of recalls being a result of potentially deadly bacterial contamination. The impact of these recalls is felt profoundly by food businesses, with the average recall costing $10 million in direct costs alone, which does not include the cost of litigation, lost sales, loss of company value, or brand damage (Food Marketing Institute & Grocery Manufacturers Association, 2011).
snapshot of the PoC architecture. The PoC blockchain layer was implemented using Hyperledger Sawtooth v1.0 and custom Smart Contracts1, (see Appendix A for PoC definition superscripts) known as ‘Transaction Processors’ by Sawtooth language (Olson et al., 2018). Hyperledger Sawtooth is a permissioned blockchain (Olson et al., 2018) and was used to manage recording data to the ledger and accessing transaction history data. Validators2 and smart contracts were deployed across several servers inside of Docker3 containers. A substantial amount of custom software and hardware was developed around Sawtooth including a custom REST4 API5, client interfaces (front-end applications written in React6), microservices7 (serverless88 cloud processes written in NodeJS9), embedded software (written in the C programming language) and Internet of Things10 (IoT) sensors (modified open-source microcontrollers). This extra technology was developed to improve ease stakeholder adoption and facilitate authenticated and permissioned automated data exchange.
3.3.4. Food Fraud and Rising Ethics Concerns Consumers place a high (and growing) value on ethical practices for egg production, with 66-74% of people paying attention to the label on egg, meat, and dairy products about how the animal was raised (Spain, Freund, Mohan-Gibbons, Meadow, & Beacham, 2018). On average, consumers are willing to pay $.79 more for a carton of eggs if the welfare of the animals was guaranteed by a trusted welfare certification (Spain et al., 2018). Additionally, farmers are transitioning to produce more specialty eggs to meet growing consumer demand, with hundreds of grocery stores, restaurants, and other food businesses committing to switching to cage-free eggs by 2025 (Shanker, 2018). With consumer demand for information on where their egg products are coming from and the standards they were raised with, specialty egg packers are placing high importance on using traceability data to create consumer-facing transparency for their egg products.
3.5.2. Blockchain Consensus Hyperledger Sawtooth uses a consensus mechanism called Proof of Elapsed Time (PoET) (Olson et al., 2018; PoET 1.0 Specification, 2017). This is important because blockchain consensus is often associated with high environmental impact, e.g., energy usage, and poor performance. The developers of Sawtooth aim to implement “pluggable consensus” or the ability to change consensus algorithms, (Olson et al., 2018) but this feature is limited as of this writing. PoET requires specialized hardware that supports Intel Software Guard Extension (SGX) to provide Byzantine fault tolerance11,12 (BFT) but can be simulated when BFT is not required and SGX support is not available. SGX allows code to be executed in regions of memory called an enclave which is inaccessible to all users and processes (even those with the highest privileges). PoET
3.4. The Relevance of Blockchain to Use Case Blockchain is anticipated to be relevant for this use case, and to food and agriculture in general, because of the ability to share immutable data between supply chain stakeholders and automate agreements and the exchange of trusted information between multiple actors. All stakeholders agreed transparency, information sharing, and improved information management could improve safety, product quality, and brand engagement from consumers while improving operational efficiency with product tracking. Table 1 provides a summary of important criteria in the use case. Note these criteria can be applied to any context and not limited to only food and egg distribution.
1 Smart Contract(s). A program existing on a blockchain, usually executing when transaction data is submitted to the blockchain. Typically these programs are validating a transaction in some way. 2 Validator(s). A Hyperledger Sawtooth component that facilitates transaction processing, peer-to-peer network communication, and peer-to-peer consensus. It also facilitates interconnectivity between the nodes REST API and transaction processors. See more at: https://sawtooth.hyperledger.org/docs/ core/releases/1.0/introduction.html. 3 Docker. Containerization software developed by Docker, Inc. to allow creation of a consistent run-time environment for software across multiple different types of operating systems and hardware. See more at: https://www. docker.com/why-docker. 4 REST. (REpresentational State Transfer) is a commonly used architectural design pattern for Application Programmer Interfaces(API). 5 API. Application Programming Interface. Typically refers to a set of tools, procedures, routines and or protocols for facilitating software development. In this context it refers to software services hosted on remote endpoints and the interfaces and protocols implemented to access them. 6 React. A popular Javascript library for developing software user interfaces for web and mobile applications. See more at: https://reactjs.org/ 7 microservice(s). A small, lightweight, loosely coupled software-service. Implemented commonly when using the microservices architectural design technique. 8 Serverless. Refers to serverless computing. Where a cloud-based compute service provides a temporary run-time environment for code execution in response to a triggering event. 9 NodeJS. A Javascript runtime environment. Commonly used for server-side programming. See more at: https://nodejs.org/en/about/ 10 Internet of Things. A generalized term for describing network connected hardware devices capable of remote configuration and communication. 11 Byzantine fault tolerance. Named because of the Byzantine Generals Problem, used to describe the tolerance of the class of all possible failures resulting from the Byzantine Generals Problem. 12 Byzantine Generals Problem. A problem described as: A commanding general must send an order to his n - 1 lieutenant generals such that IC1. All loyal lieutenants obey the same order (Lamport, Shostak, & Pease, 1982).
3.5. Proof of Concept The proof of concept was built to track data about free-range eggs which were collected at a variable number of farms and followed through the packaging operations at a packing facility. Eggs were collected from refrigerators located on the farm then transported to the packing facility to be stored, washed, graded, packed, and shipped to retail stores across the country. The goal of this proof of concept was to capture as much data from this process as possible and gauge its usefulness to both the business and to consumers. The other primary objective was to prove eggs in a carton can be traced back to a small group of suppliers and to a window of production time. Data was captured at each stage of the product life-cycle and made searchable by date and time information on the end of the egg carton. Consumers were made aware of the possibility to search for information by QR code stickers on the carton. Cartons were stickered in Iowa City, Iowa and in the Denver, Colorado metropolitan area. In order to better understand how data is captured in this use case, Fig. 1 illustrates a simplified view of the flow of data and assets between actors within the system. 3.5.1. Architecture Multiple components and technologies were required to track information and make it accessible. Fig. 2 provides a summarized 4
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Fig. 2. PoC High-Level Architecture.
works by allowing each validator to request a wait time from the enclave. The wait time is randomly generated and the validator with the shortest wait time claims the role of leader and is allowed to add the block to the blockchain. The enclave and lottery mechanism provides a secure, efficient, and relatively fast mechanism for block validation consensus.
third-party sensors on the farm. Please note sensor data was not used in the consumer-facing application for this PoC. (2) The Packaging Facility: For data capture within the packaging facility, Bytable Inc. relied on integrations with internal traceability software, processing hardware, and data entry from system operators.
3.5.3. Authentication and Permissioning To ensure only certain stakeholders were allowed to access sensitive information such as precise farm location, a proxy authentication server and REST API were implemented. This provided different stakeholders with differing levels of read access to the blockchain data. Devices and users with a valid private key were able to directly interface with the blockchain through a proxy microservice, while stakeholders accessing the ledger without a key were required to interact through a separate public proxy client service with fewer privileges. Furthermore, the blockchain network was implemented as a private network, meaning transactions submitted to the network and nodes13 participating in consensus are limited and regulated by an on-chain network policy. Data points were captured at three locations:
The data captured at these two points created a record of transactions on the blockchain including collection location and time, farm name, temperature and humidity history, transit departures and arrivals, processing and packaging time, egg type, certification data, batch quantity, best-by date, brand, color, product labels, and any possible supplier overlaps during processing and packaging times. (3) The Consumer Scan: For the initial PoC, Bytable Inc. collected data from consumers scanning products from four specialty retailer locations in Iowa City, Iowa and the Denver, Colorado metro area. Stickers were placed on select free-range egg cartons between midFebruary and mid-March 2019. The web application containing traceability data was accessible to consumers via a scannable QR code on product packages and requested input of data printed on the end of the carton to access traceability data for a particular carton. Website analytics within the web application collected data about the number of users accessing the site, their behavior, time spent on the site, visits per user, and other general data about their interactions on the web application. Figs. 3–8 provide images of the web application.
(1) The Farm: Data was captured where the eggs are collected when the collector logged the egg type and pickup time. Temperature, location, and humidity data were captured by Bytable Inc. and the
13 node(s). A Hyperledger Sawtooth Validator on or connecting to Sawtooth network. See more at: https://sawtooth.hyperledger.org/docs/core/releases/1. 0/architecture/permissioning_requirement.html.
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Figs. 3 and 4. Web application display for customer data input from end of egg carton.
• Identification of the establishment
4. Results and Findings 4.1. Results of Proof of Concept
This use case was originally planned to imitate certain aspects of the European system with codes directly on each egg. A key difference between the U.S. and the EU is that commercially available eggs are washed during the grading process in the U.S. Egg marking prior to washing would not be feasible as the markings are likely to be washed off or made illegible. Equipment and process constraints made marking individual eggs impractical for the initial use case and the decision was made to trace eggs using carton data for the first iteration of the project. The solution outlined here could be made easily transferable to a system like the EU’s, and egg markings could easily be associated with blockchain records. The minimum requirements for marking eggs by the EU would not be enough to trace an egg back to its pack time and date and would either need to be combined with information from the carton or would need unique information added to the existing code. Adding extra information to these codes is a practice already in use by producers who would be able to rapidly integrate into a blockchain application similar to this use case. Combining egg markings with distributed ledger technology could be a sensible way for future traceability systems to be implemented. Traceability down to the individual egg would be achieved more easily with unique markings and easily referenceable records. Distributed ledger technology could also be a promising means of combating existing issues with marked egg systems like code forgery. Records could be accessed more intelligently to determine if a particular egg marking is likely to be a forgery or incorrect.
The proof of concept resulted in above-expected consumer engagement and interest in further development from stakeholders. During tests at the retail level, cartons were reliably traced back to the supplier and date they were collected. The proof of concept began with the goal of 5-10% scan rates and average usage of 1 minute, both of which were exceeded by a significant margin. Of 171 cartons stickered, 46 unique users accessed the web application for a scan rate of 21.2%. On average, the time each unique user spent on the web app was 2 minutes and 48 seconds. These results are detailed in Fig. 9. 5. Use Case Applicability to Europe While this use case study was performed in the United States, it is important to understand how it could be applied to other markets and systems. In Europe, policy surrounding egg marking is considered by some to be more advanced than in the United States. In the European Union, each egg’s shell is marked with a code containing critical tracking information. Non-exempt countries and facilities must mark each egg with a code representing a minimum of the following:
• Farming method • Member state
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Figs. 5 and 6. Web application display for product summary page and traceability data of eggs.
6. Discussion
capture data and analytics relating to the impact of their implementation.
While the potentials for practical use of blockchain technology are still being understood, the outcomes of this study show promise. Despite many challenges and barriers to overcome, the results indicate that consumer demand and the need for improved traceability may drive adoption within certain segments of the food industry. The study also indicated in certain environments, blockchain implementations can be possible without immense effort from stakeholders. In this section, we will evaluate the contributions, implications, and limitations of the study.
6.2. Implications for Practice Blockchain technology could be used to supplement food traceability in new and existing supply chains. Many current solutions are siloed and traceability only exists internally for specific stakeholders within the supply chain. In other cases, traceability does not exist at all. In this study, it was possible to add blockchain technology onto existing systems and technologies without significant process or behavior change by supply chain stakeholders. Adding blockchain technology is possible in practice, but scenarios exist where poor processes and primitive technologies exist among stakeholders. In these scenarios, it is likely that major technology upgrades and process standardization will be required before the implementation of blockchain technology is possible. Because of these barriers relating to lack of industry-wide process standardization and necessary technology adoption, it is likely that blockchain technology can only benefit supply chains where traceability is well-practiced internally by each stakeholder.
6.1. Contributions This study was developed to understand if blockchain technology could be a value add to food businesses and support the claim that blockchain can improve food traceability. The authors compiled relevant research in the field of blockchain traceability of food and agricultural products, then leveraged them to develop a practical approach towards implementation with a specialty egg brand. Furthermore, this is the first study known by the authors to use blockchain technology to track eggs from farm to consumer in major markets and capture traceability and engagements data at nearly every step of the supply chain. The consumer-facing captured data element presents an opportunity for further exploration of consumer interaction with blockchain traceability data. Finally, the implementation methods highlighted in the study, such as the use of IoT sensors and integration into existing systems, provide a framework for further studies to
6.3. Limitations and Future Research Direction The case study represented here followed the implementation of a blockchain traceability solution for the egg industry. This implementation is early and there are still many limiting factors regarding implementing blockchain solutions in the food supply chain. This study
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Figs. 7 and 8. Web application display for producer information, certifications, and practices.
was performed with a specialty egg brand in the United States with substantial internal traceability and data tracking already existing in their facilities. While the authors partnered with a European egg traceability solution during implementation, more information is needed to understand the differences and difficulties of implementation in other parts of the world, among other food products, and even among individual supply chains’ egg production methods.
There is also a high barrier to technology and process adoption. This study was performed partially by integrating with existing traceability technologies and processes, which will not exist in all supply chains and will be a significant barrier in less wealthy parts of the world. Furthermore, there are no common interfaces or standards in regards to food traceability, and much work and collaboration are needed before seamless widespread adoption is possible. Additionally, there are
Fig. 9. A comparison of the quantity of egg cartons stickered and unique site visitors.
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limitations regarding the ability to verify data logged within blockchain solutions, mitigation of potentially dishonest stakeholders, and human error - these issues have no universal solutions. Regarding distributed system implementation, this study was performed with few stakeholders compared to many conventional or more complex supply chains. Implementations of larger distributed systems will also prove difficult as system size and complexity is required to increase. Another question that will need to be answered is whether distributed ledger alternatives to blockchain will emerge, such as Dr. Leemon Baird’s Hashgraph (and virtual voting). There is an immediate need for ERP interfaces more specific to the [food] industry requiring partnerships for integration. Other concerns are the use and infringement of patents, copyrights, and trademarks for software (i.e., intellectual property). The mainstream media and press have also latched on to the energy consumption of the distributed ledger which was prominently covered in the May/June MIT Technology Review (2018). This was a “proof of concept study,” in which Bytable Inc. is not using proof of work for consensus, thus at least partially alleviating the excessive energy usage concerns. Hardware and software (readers, GPS devices, and sensors) integrated with distributed applications are designed to be low power and energy-efficient. Tags do not require a power source (RFID or NFC) and are used for other applications as well, such as cattle identification. Devices can be powered and charged with renewable [e.g., solar] energy, but only when necessary as this does increase cost. In addition, the longevity of battery life (and capacitors) is an engineering challenge (http://www.engineeringchallenges.org/) of concern in blockchain-enabled devices. Zinc-air batteries could be one possible future technology. Food initiatives such as the University of Cambridge TIGR2ESS program (https://www.globalfood.cam.ac.uk/ ) are also exploring these types of applications for sustainable food supply in regions such as India. The next step for the use case is to bring the technology to several product lines for the egg distributor, including full integration with new facility machinery and software, automation of the generation of data for consumers to access via packaging, and building a data-access system for the egg distribution company to manage and track their eggs internally with ease. In addition to this project, the authors are currently collaborating with a UK university and Irish industry partner to scale this approach in the EU (the project is active and on-going).
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7. Conclusions In this article, a use case has been presented for the deployment of blockchain in food distribution, specifically related to egg supply chains. A literature review of blockchain concepts and applications is provided. This work furthers the research conversation on how blockchain can be utilized and applied to more accurately and transparently move goods through supply chains. Further tests will be done to track consumer engagement, including testing on new product displays as the tests were conducted on labels that were not visible from the aisle at some of the retail locations. Several opportunities for improved tracking methods and supply chain analytics were discovered during implementation. New blockchain client applications are being actively developed as a result. These applications will be included in the next phase of the project, which includes expansion across several egg product lines to be distributed widely across the United States. As the potential of blockchain technology enters into the next stage of implementation and use, it is becoming clear that this could be the start of revolutionary food supply chain tools which will allow consumers to really know where their food has been. References Abeyratne, S. A., & Monfared, R. P. (2016). Blockchain ready manufacturing supply chain using distributed ledger. International Journal of Research in Engineering and Technology, 5(9), 1–10.
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Daniel P. Bumblauskas, Ph.D., is an Associate Professor of Management and the Hamilton / ESP International Fellow of Supply Chain and Logistics Management at the University of Northern Iowa, a visiting professor at the University of Washington, and holds a courtesy appointment at the University of Missouri where he previously held a faculty appointment. Dan conducts research, teaches, and consults on various areas related to operational excellence and business development. Dan has published over 45 peerreviewed journal articles and conference proceedings, including publications in journals such as Expert Systems with Applications, IEEE Transactions on Industry Applications and Business Process Management. He earned a B.S. in Industrial Engineering and Economics from Iowa State University, a master of liberal arts in general management from Harvard University, and received his M.S. and Ph.D., both from Iowa State University, in Industrial Engineering. Prior to his faculty appointments, Dr. Bumblauskas was previously employed in industry by ABB Inc. and Sears Holding Corporation and currently serves as a Vice President of PFC Services, Inc., a consulting firm based in Marietta, Georgia.
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Arti Mann is an Assistant Professor of Management at the College of Business, University of Northern Iowa. Her research interests span business value of information systems; the role of information technology in innovation; and the use of spatial analysis and econometrics techniques from regional economics and geography. Her work has appeared in Journal of Management Information Systems, Decision Support Systems, and Applied Geography among others, and various academic conference proceedings. Her graduate degrees are from Marquette University (M.S.) and Arizona State University (Ph.D.).
Brett Dugan is the CTO and Cofounder of Bytable Inc., a company focusing on traceability and market access solutions for sustainable and regenerative food businesses. He specializes in data architecture, blockchain and other distributed ledger technologies, and distributed systems. Brett holds a B.S. in Computer Science from the University of Northern Iowa. Jacy Rittmer is the CEO and Cofounder of Bytable Inc., a company focusing on traceability and market access solutions for sustainable and regenerative food businesses. She specializes in user interface and experience design, web development, and oversees product and business development at Bytable. Jacy holds a B.A. in Biological and PreMedical Illustration from Iowa State University.
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