by Prudence Calabrese, MAI and Ron McFarland, Ph.D.
Artificial Intelligence in the Manufacturing Supply Chain
Individually, Artificial Intelligence (AI) and blockchain are two disruptive technologies. Each offers advantages to a wide array of technology, financial, healthcare, business, and the manufacturing supply chain sectors. AI provides the promise of building software and machines that are capable of performing tasks that involve more computation and applied intelligence. In the meantime, blockchain can be considered a new file system for storing information in an encrypted form on a distributed ledger format (Marr, 2018).
Combined, AI and blockchain will lead to both a technical and economic revolution, especially in terms of the manufacturing supply chain. Gartner (2018) indicates that four percent of CIOs have implemented both AI systems and blockchain technologies at their organizations and another 46 percent developing plans to do so. In terms of the manufacturing sector, manufacturing contributes to about 11.7% of the GDP (Mire, 2019), and the push to infuse AI and Blockchain technology in the manufacturing supply chain to support efficiencies has only increased (Osbourne, 2017).
There is significant media attention given to the combination of AI and blockchain, especially in terms of the supply chain, as exemplified by TedTalk videos by visionaries and venture capitalists. As an example, the TedTalk video by Markus Mutz entitled “How supply chain transparency can help the planet” discusses a viable use of AI combined with blockchain technologies (Mutz, 2020).
Blockchain technology uses a decentralized, distributed, immutable ledger to store encrypted data across a network of computers that can support manufacturing. Blockchains initiated as the backbone of cryptocurrencies, like Bitcoin but are now being used for a variety of other applications, including in the areas of finance, retail, real estate, and health care. They use a combination of cryptography and a distributed network to create a record of every transaction presented to the network, as well as all-important activities surrounding that transaction (Ghanchi, 2019).
Blockchains offer significant benefits. The distributed network database is immutable; it cannot be tampered with. Since every block holds a different piece of encrypted data, the blockchain helps to guarantee trust and veracity of the data. Data is transparent in a blockchain; all nodes can examine every transaction and trace transactions. Because of the use of cryptography, blockchains provide privacy of data and security. However, technology does present challenges. The development of a blockchain for specific use can be expensive. There may be issues integrating such a network with existing databases and systems. Due to the Wild West nature of the technology, there may be legal issues and complications in some areas like healthcare and finance due to a lack of regulation and compliance (Ghanchi, 2019).
Beyond decentralized currencies, blockchain technology has found a home in Supply Chain Management. Supply chains utilize data systems that are involved in the logistics of trading. Using a blockchain can inform clients when data is recorded, stored, and processed within the network. Supply Chain Management can benefit from elimination in shipping delays, a reduction in errors and fraud, and improved management (Quibell, 2019).
Smart contracts and Ethereum have moved blockchain technology into its second generation. Smart contracts allow the transfer of anything of value — money, shares, property — in a transparent transaction that avoids the services of a middleman. They define the penalties and rules around a transaction agreement in the same manner that a traditional contract does, but a smart contract automatically enforces any conditions (Rosic, 2017).
Artificial Intelligence (AI) is a term that is used to describe an attempt to create intelligent manmade computers and machines. Alan Turing is credited with the beginning concept of AI in 1950 when he wrote about the potential development of “thinking machines” that could think and respond at the level of a human being. His Turing Test, where a computer would need to mimic human reasoning to complete a series of puzzles in order to be considered intelligent, has become a litmus test of sorts in the field of computer science (Harris, 2018). Several years later, John McCarthy coined the term “Artificial Intelligence,” and described it as a way to get the computer to do things that mimic human intelligence (Shubhendu & Vijay, 2013).
AI algorithms are developed to make decisions; sometimes these are made in real-time. Unlike machines that can only respond in predetermined or mechanical ways, AI processes use a variety of data which could include sensors or remote inputs, analyze the collected data, and respond based on insights revealed within the data. AI systems can learn and adapt as they analyze data. Self-driving and semi-autonomous vehicles use the experience of other vehicles in the vicinity to let operators know about road construction, congestion, accidents, potholes, and other barriers to efficient travel (West & Allen, 2019).
AI has become a welcome addition to the decision-making process. The automation of manufacturing and business processes has become widespread; AI systems take in enormous amounts of data and present it in ways that are easy to understand using a combination of linguistic, social, computing, and mathematical knowledge. The technology is being used extensively in the areas of Big Data, Precision Manufacturing, and Supply Chain Management (Fattal, 2018).
AI and Blockchain Synergy
Scientists and developers are investigating how blockchain and AI technologies can be combined to enhance each other. Both technologies act upon data in distinctly different ways but integrating blockchain and AI may enhance AI’s potential while boosting blockchain’s architecture. Blockchain may also aid in making AI more understandable; the ledger could record all information and variables through which decisions are made through machine learning (Banafa, 2019).
How AI Enhances Blockchain
AI systems could drastically change how blockchain networks are managed, making them more efficient. Bitcoin transactions often take hours or even days to confirm; this is caused by the decentralized nature of the cryptocurrency as miners need to group transactions into blocks, and any increase in the number of transactions creates a backlog. AI may be able to reduce the computing power required to confirm transactions (Mathew, 2019).
Blockchains use state-of-the-art encryption, but the addition of deep learning technologies alongside a decentralized network may make applications even more secure. Additionally, the scalability of a blockchain could be enhanced with decentralized machine learning. AI could decipher a more efficient use of resources that can minimize the costs involved in the blockchain network (Ghanchi, 2019).
Hardware is another area in which AI could assist blockchain technology. Miners continuously upgrade their hardware as the network grows. AI could be used to develop more efficient and less expensive mining solutions. AI has the potential to fill the talent gap as blockchain implementation increases; virtual agents using AI systems could create additional ledgers and handle new tasks better than human employees (Bharadwaj, 2019).
AI systems can be developed to advance blockchains. Deep learning can analyze what is occurring on a blockchain; patterns and problems in the stored data sets can be discerned. AI can look for normal behavior and detect what may be out of the ordinary. This kind of monitoring can keep the blockchain more efficient, reliable, and secure (Walch, 2019).
How Blockchain Enhances AI
AI utilizes incredible amounts of information to analyze and make insightful decisions. Blockchain can be used as a way of securely and transparently storing data. Unlike centralized data storage solutions, blockchain networks are a distributed ledger; they create an enormous database of all transactions. The network constantly verifies the information across nodes. In the event a hacker manipulates data at one site, the remaining nodes will invalidate the false transaction. The immutability of data that blockchains provide could be a big advancement in Big Data analytics and in AI systems (Mathew, 2019). This data security can be important in areas such as mission-critical military data. AI with blockchain could work together to enable alerts about any potential disasters (Ghanchi, 2019).
AI systems that make decisions based on a large-scale set of data may be difficult to understand; one of the largest issues in deep learning is that decision-making sequences can be impossible to understand. A blockchain ledger that contains all of the data records used in a decision could help deconstruct how the machine came to its conclusion and provide a roadmap for developing more efficient insights or correcting issues. AI systems are often characterized as being a “black box” which can be difficult to penetrate as opposed to the built-in transparency of a blockchain. Blockchains can record every step of every decision process an AI undertakes in a non-reputable way, which allows for an examination of the process and any subsequent correction (Schmelzer, 2019).
Blockchains may also be used as a central brain in sharing models and data across multiple machine learning systems. With multiple AI systems connected through a blockchain, it may be possible to share all of the data and decisions across systems on the network. Such a network could result in an AI potentially greater than the sum of its parts due to the wealth of data arriving from a vast variety of areas (Schmelzer, 2019).
AI-Driven Smart Contracts
Smart contracts reside on a blockchain where they monitor contract provisions and trigger transactions when conditions are met. One of the first smart contract transactions involved the shipment of 88 bales of cotton between Texas and Qingdao in China. This event used an Internet of Things (IoT) device that utilized GPS to track the location of the cotton in transit; each time the location was scanned, a notation was sent to the blockchain. Once the bales reached their destination, payment was released. The transaction required no invoices or payments by a human being (Wass, 2016).
Adding AI to a smart contract blockchain network could add a level of analysis that would make smart contracts more effective. An AI system could analyze past transactions and negotiations to see how users set up contract conditions in the past and suggest clauses and conditions most likely to secure an agreement. AI could analyze past smart contracts and identify areas and variables that were not considered and add them to future contracts (Blockchain Consortium, 2019).
One of the issues surrounding adding AI to smart contracts is that of security. For example, Ethereum-based smart contracts allow any user to write code for a contract that can be executed on a blockchain. AI could be integrated to automate the transactions in a more dynamic and efficient way. However, Ethereum has not been particularly resilient to hacking attacks. In June 2016, a distributed autonomous organization (DAO) developed using the Ethereum network was hacked. A DAO is a set of code that connects smart contracts together and work as a mechanism for governance. This hack stole $50 million from the network and was the first of several large exploits to hit the smart contract token provider (Mylrea, 2018).
Blockchain Data Management Systems
Microsoft is developing a new blockchain tool designed to make AI less fearful for its corporate clients. Organizations are leery about putting their trust into a system where deep learning algorithms are applied in a variety of ways to vast sets of data. Microsoft’s Azure Blockchain Data Manager promises to add a layer of trust and transparency to Big Data manipulation (Allison, 2019).
The Blockchain Data Manager connects data on the blockchain and joins it with other applications; transaction data from smart contracts or network nodes can then be sent to other data storage areas. AI can be implemented in these databases. In the case of Supply Chain Management, IoT-related data can be utilized. Microsoft claims that its new data manager is “ledger agnostic.” It could be used with multiple blockchains of varying types, though the company has primarily worked within the Ethereum system (Allison, 2019).
A test of the Azure Blockchain Data Manager conducted by Icertis, a cloud-based platform for contract management, was promising. The test involved a series of smart contracts that included some kind of liability limitation. The data from the blockchain was delivered to an AI system, which predicted the risk level for the end-user. The integration allowed the Icertis team to see exactly what data the AI used to reach its decision (Allison, 2019).
Supply Chain Management
AI and Blockchain offer not only to support for the supply chain and manufacturing sectors but will spur new innovation. The manufacturing industry, as a part of the supply chain segment, cites benefits due to AI combined with blockchain (Osborne, 2017). Similar to other sectors, the manufacturing sector is facing a shortage of skilled workers, especially over the next decade (Mire, 2019). The industry in the United States will call for 3.5 million new jobs, however it is estimated that only 2 million jobs will be filled due to the lack of skills training (Mire, 2019). To support manufacturing and the supply chain, the use of AI and Blockchain will support the continued globalization of manufacturing and the resulting increase in demand for skilled labor (Mire, 2019).
Supply chains for manufacturers are quite complex and can make transparency and accountability quite a challenge. Add in the infusion of logistics of building and shipping and the inclusion of IoT (Internet of Things) technology in manufacturing and logistics, accurate analytics and verification become difficult, if not impossible to manage (Osbourne, 2017). The combination of AI and blockchain technologies can provide a viable method to manage and control the issues within a supply chain and can support the streamlining of industrial processes for the SMB or large enterprise (Osbourne, 2017). Since transactional and contract data is held in a blockchain shared across notes, technology can be implemented to continually verify and update data across the highly-complex evolving networks for the manufacturing supply chain. In conjunction with blockchain, AI can be integrated to coordinate, project, and support planning for the manufacturing supply chain (Osbourne, 2017). Mire (2019) indicates ten use cases for the combination of Blockchain and AI in manufacturing, as described below:
- Supply Chain Auditing: Auditing can be just-in-time and related databases can be readily updated.
- 3D Printing Design Rights: The rights to 3D print a model can be verified and tracked.
- Lowering barriers to entry: Distributed manufacturing and other methods to track smaller production runs can be supported.
- Reducing systemic Failures: With rapid synchronization of data in the manufacturing process, systemic issues can be quickly identified and addressed.
- Improving Trust in Products: The blockchain ledger can serve to track product attributes including (but not limited to) source of manufacturing (including specific devices used), date/time, and other related measures.
- IoT Device Authentication: For security and compliance measures, IoT devices and the use of these devices in the manufacturing process can be verified.
- Better Tracking of Maintenance: Maintenance records and maintenance updates from manufacturers can be readily implemented.
- Securing Critical Data Logs: Due to the encryption of the blockchain and as a part of cybersecurity efforts in the manufacturing organization, blockchain can support the encryption and monitoring of critical log files.
- Local, Direct-to-Consumer Platforms: Given the detail provided in a blockchain, local and direct-to-consumer platforms can be supported.
- Production Part Approval Process (PPAP) and Sourcing of Materials: This aspect of the manufacturing process is more readily supported with blockchain (Mire, 2019).
As an example, Denver-based Bext360 has integrated AI systems and blockchain technology to assist supply chain efficiency and transparency in the mineral, timber, coffee, and seafood industries. The company has developed an AI that analyzes resources and crops and predicts future availability and growing patterns; the blockchain they employ follows the production of all goods in all stages from growth to finished and delivered product (Bext360, 2019). Bext360’s AI is integrated with its mobile apps, private blockchain, and IoT devices. It measures coffee bean quality, for example, using sensor-heavy machines that sort the coffee beans and assesses quality based on how ripe and large the fruits are. The data is placed on the company’s blockchain, where buyers can examine shipments and place bids. The blockchain keeps down overhead costs and makes auditing financials easier (Hackett, 2017).
IBM has entered the AI-Blockchain Supply Chain Management world with its Sterling Supply Chain Suite. They estimate that one-third of all supply chains will be using some kind of AI systems by the end of 2020, and one-third will be utilizing blockchain technology by the end of 2021. Their new suite promises to allow for start-to-end supply chain visibility across all data sources and systems, to predict and resolve any chain disruptions, and to sort through collected data and gain new insights. Customers using IBM’s AI-integrated blockchain product include Master Lock and Lenovo (IBM, 2019).
Artificial Intelligence and blockchain technology are two systems that manage data in different ways. Combining the two into a more powerful technology may provide synergies that enhance each individual aspect, especially in the supply-chain manufacturing segment. The advent of Big Data has proven blockchain to be a strong and viable data storage solution; blockchains ensure that data is anonymous, private, and secure, making them a perfect data storage system for sensitive data. Adding AI to the transparency and privacy of blockchain can unlock valuable insights that remain hidden in the data.
This is an emerging field, and working solutions employing AI and blockchain are still few and far between. Larger corporations such as Google and IBM are at the forefront of these advances. Academic and corporate researchers are developing solutions in a number of industries that could revolutionize the way that data is stored, analyzed, and decided upon, especially for the supply chain and manufacturing segment.
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About the Authors
Prudence Calabrese (to be updated)
Dr. Ron McFarland, CISSP, PMP is a Cyber Security Analyst at CMTC. He received his doctorate from Nova Southeastern University’s School of Engineering and Computer Science and a post-doc graduate certificate and Masters of Science in Cyber Security Technologies from the University of Maryland (Global Campus). He also holds multiple security certifications including the prestigious Certified Information Systems Security Professional (CISSP) certification and several Cisco certifications. He is a guest blogger at the Wrinkled Brain Network (https://wrinkledbrainnetwork.com/ ), a blog dedicated to Cyber Security and Computer Forensics. Dr. McFarland can be reached at his University of Maryland email is: email@example.com