Manufacturing Economy

Secure Audit Trails in Distributed Manufacturing Supply Chains

Background: Finetool Machine with Coils. Image: © WZL | Christian May & Semjon Becker

Preamble

This article is based on the Ph.D defense held by Anton Shirobokov. Anton worked with the WZL for approximately 6 years before undertaking a new challenging role with Robert Bosch GmbH in June 2018.


Abstract

The possibilities and challenges of the Industrial Internet of Things have been investigated for years at the Laboratory for Machine Tools and Production Engineering WZL of RWTH Aachen University under the topic of Internet of Production (IoP). The IoP focuses on interdisciplinary optimization approaches along with the entire value chain, especially in the areas of data acquisition, machine learning and artificial intelligence. This article evaluates whether and how the vision of IoP can be extended to a Manufacturing Economy. Distributed ledgers in general, and IOTA in particular, were examined for this purpose. Subsequently, IoP potential use cases for the area of manufacturing technologies were outlined, but it turned out to have greater potential in manufacturing economy.


“Digital is the main reason just over half of the companies on the Fortune 500 have disappeared since year 2000.”

— Pierre Nanterme, CEO of Accenture


1. Introduction

The Industrial Revolution is a rapid and dramatic social-economic change caused by the adaptation of a new technology [GEHR15]. Industrial revolutions are rapid developments that bring about dramatic change. The First Industrial Revolution shifted society from an agricultural to an industrial model with advances in transport and early mechanization by steam power. That was the beginning of the machine work, see Fig. 1.

Fig. 1: Industrial development through the perspective of revolutions [GEHR15]. Image: © WZL | Anton Shirobokov

The Second Industrial Revolution took us one step further: electricity has enabled mass production of cars, railways and telegraphs, which opened the age of mobility. The Third Industrial Revolution introduced digitization through the invention of the computer and the internet. These inventions help in connecting people and value chains. It has brought a new level of efficiency and automation which is transforming several economic sectors, displacing powerful established companies and overthrowing established business models.

The convergence of new technologies is accelerating so fast and we don’t want to miss the opportunities it brings. Today we are on the verge of the Fourth Industrial Revolution — combining the physical (from the first and second industrial revolution) and the digital (from the third industrial revolution). Identical to the previous industrial revolutions, machines will also play a dominant role. The key difference is that they are now becoming smart but not yet at self-awareness level.

(Industrial) Internet of things

The Internet of Things is the network of all physical devices and systems that are connected to the Internet. It enables interaction and data exchange between devices and people. The IoT market has grown significantly in the past and will continue to grow exponentially in the future. Recent studies assume that by 2025 for every person there will be 10 to 12 connected devices [STAT18]. Smart cities and the industrial IoT sector are the main drivers of the IoT market with a market capitalization of around USD 267 billion in 2020, see Fig. 2.

Fig. 2: Growth and market capitalization of the Industrial Internet of Things market [STAT18]. Image: © WZL | Anton Shirobokov

Internet of Production

A direct application of the IIoT approach to production engineering is currently not sufficiently feasible, as there are many more parameters, but much less available data compared to other big data application domains. Modern production is characterized by vast amounts of data. However, this data is neither easily accessible, interpretative, nor connected to gain knowledge. With the Internet of Production (IoP) the WZL and RWTH have the objective to enable a new level of cross-domain collaboration by providing semantically adequate and context-aware data from production, development and usage in real-time on an appropriate level of granularity. The central scientific approach is the introduction of Digital Shadows as purpose-driven, aggregated, multi-perspective and persistent datasets. The Cluster of Excellence (CoE) will design and implement a conceptual reference infrastructure for the Internet of Production that enables the generation and application of Digital Shadows. For the realization of the IoP, Aachen’s highly renown researchers in production engineering, computer science, materials engineering and further necessary disciplines team-up to solve interdisciplinary challenges, like the integration of reduced production engineering models into data driven machine learning for cross-domain knowledge generation and context-adaptive action. The IoP will be leveraged by the production engineers in order to support a new way of more holistic working on — and with — systems by developing and advancing engineering tools, methods and processes. Therefore, an integrated development for the entire production technology is required.

Fig. 2b: The Vision of the Internet of Production. Image: © WZL

Machine Economy

In a machine economy, self-monitoring and autonomous machines, devices and systems will be able to order services such as maintenance, organize their own production and make decisions with the trust of their owners [RAJA17]. These services are initially provided jointly with people, but increasingly also by other machines, see Fig. 3.

Fig. 3: The definition of the machine economy and its long-term effects [RAJA17]. Image: © WZL | Anton Shirobokov

Industrial companies will try to avoid on buying expensive equipment and machinery, instead there will be a kind of Uber-isation of self-managed assets that share their services in a decentralized ecosystem. Machine subscription models and real-time leasing will be widespread. Machines are increasingly becoming independent market participants and independent financial actors with their own bank accounts and payment systems. These machines will be built to avoid inconvenient human interaction in turn of creating new challenging fields for people in this new market.

Six pillars define a machine economy, see Fig. 4: Machines and systems must be digitized with the aid of sensors that make machine states visible and enable machine-to-machine (M2M) communication in both directions, transmission and reception. With the help of artificial intelligence, these machines can work alone in a decentralized sharing economy in which the operators no longer define value through ownership [RÜTH17]. The backbone of such a machine economy is any distributed ledger technology that enables trustworthy data exchange and smart contracts between the devices.

Fig. 4: Pillars of machine economy [RÜTH17]. Image: © WZL | Anton Shirobokov

Intermediate conclusion: Introduction and Machine Economy

  • Smart, connected, and autonomous cyber-physical systems emerge as a result of the fourth industrial revolution
  • Autonomous machine-to-machine (M2M) transactions give a rise to the machine economy
  • Data is a major resource in the machine economy: data is the new oil
  • Distributed ledger technology (DLT) is the data backbone of the machine economy

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2. Distributed Ledger Technologies (DLT)

Ledgers, the basis of accounting, are as old as writing and money. Their medium was clay, wood sticks, stone, papyrus and paper [BAUE18a]. After the normalization of computers in the 1980s and 1990s, paper files were digitized, often by manual data entry, see Fig. 5.

Fig. 5.: What is a ledger? [BAUE18a] Image: © WZL | Anton Shirobokov

These early digital books mimic the cataloging and accounting of the paper-based world, and one could say that digitization was applied more to the logistics of paper documents than to their production. Paper-based institutions remain the backbone of our society: money, seals, signatures, invoices, certificates and the use of double-entry accounting.

Computing power and breakthroughs in cryptography, along with the discovery and use of new algorithms, have enabled the creation of distributed ledgers. In its simplest form, a distributed ledger is a database that is kept and updated independently of each participant (or node) in a large network. The distribution is unique: Data records are not communicated to different nodes by a central authority, but are set up and maintained independently of each node. That is, every single node in the network processes every transaction, comes to its own conclusions and then votes on those conclusions to ensure that the majority agrees with the conclusions thus called consensus.

Once this consensus is reached, the distributed ledger is updated and all nodes maintain their own identical copy of the ledger. This architecture allows a new dexterity as a system of recording that goes beyond a simple database.

From Data Silos to Distributed Ledgers

Distributed ledgers are a dynamic form of media and have properties and capabilities that enable us to formalize and secure new kinds of relationships in the digital world [YAFF17]. The core of this new type of relationship is that the costs of trust (previously incurred by notaries, lawyers, banks, regulators, governments, etc…..) are avoided by the architecture and quality of the distributed ledger, see Fig. 6.

Fig. 6: Data silos vs. distributed ledger. Image: © WZL | Anton Shirobokov

The limited automated interoperability between the data silos is compensated by the human interface between the silos. The invention of distributed ledgers represents a revolution in the way information is collected and communicated. It applies to both static data (registry) and dynamic data (transactions). Distributed ledgers allow users to go beyond simply storing a database and redirect energy to the way we use, manipulate and extract the value of databases — less to maintain a database than to manage a recording system. DLTs are not built using new technology. They are based on a unique orchestration of three existing technologies.

Features of Distributed Ledgers

A distributed ledger is a chain of time-stamped, cryptographically secured, immutable blocks of consensus validated digital data that exist in multiple, synchronized, geographically distributed copies [BAUE18b, BAUE18c]. The technology behind distributed ledger can be applied in the following areas: a) Ensure data immutability, b) Establish digital identity, c) Serve as a platform, and d) Serve as a system of record, see Fig. 7:

Fig. 7: Features of a distributed ledger [BAUE18b, BAUE18c]. Image: © WZL | Anton Shirobokov

a) Ensure data immutability

The main characteristic of DLT database is that it has a history of itself. For this reason, they are often described as immutable or unchangeable. Strictly speaking, it would be a massive effort to change an entry in the database, because all the data that comes after that would have to be changed on every single node. In a way, it works best as a record management system than just a database [WIKI18a].

Fig. 8: Digital fingerprints using cryptographic hash functions. Image: © WZL | Anton Shirobokov

b) Establish digital identity

The digital identity of a distributed technology is fulfilled by using cryptographic keys. The combination of a public and a private key create a strong digital identity reference based on ownership. A public key is known to the public, like a mail box, a private key is how you express your consent to interactions, like a mail box key. Cryptography is the key to make DLT secure and strong [WIKI18b].

Fig. 9: Analogy for public private key encryption and digital signatures. Image: © WZL | Anton Shirobokov

c) Serve as a system of record

DLTs are an innovation in the collection and distribution of information. They are suitable for recording both static data (registry) and dynamic data (transactions), which makes them a further development of recording systems. In the case of a registry, the data can be stored in three different ways [BAUE18c]:

  • Unencrypted data — can be read by anyone participating in the DLT and is completely transparent.
  • Encrypted data — can be read only by the participants with a decryption key. The key allows access to the data and can prove who added the data and when.
  • Hash data — can be displayed next to the function that created it to indicate that the data has not been tampered with.
Fig. 10: How public private key encryption works. Image: © WZL | Anton Shirobokov

Hashes are usually performed in combination with the original off-ledger stored data. Digital fingerprints, for example, are often hashed into the ledger, while most of the information can be stored offline.

d) Serve as a platform

The first DLT-based platform was a cryptocurrency, but recently smart contracts have come to the foreground. Most times smart contracts are considered to be programs which control DLT assets, executed over interactions on the DLT.

Types and Data Structures of DLTs

The best known distributed ledger technology is probably the blockchain. A very good analogy for a blockchain is a book. Each block represents a page in a book, the chain of blocks represents the book binding and a transaction within a block represents an entry in a line on this page. With the difference that another block can only be attached to the blockchain if a distributed decentralized consensus (validation) has been reached, see Fig. 11. In book printing, the bookbinder assembles the pages and assures the validation centrally. Validation ensures that the order of the book pages or blocks is correct.

Fig. 11: Types of DLTs: Blockchains and DAGs [TAMK18]. Image: © WZL | Anton Shirobokov

As in book printing, the validation of blocks is very time-consuming and energy-intensive. The pages of the book have to be pressed together under high pressure for considerable time windows until finally all the pages hold together and the blocks become a chain. In blockchain world, this process is called Proof-of-Work (PoW) and refers to the process by which the validation process can be calculated by making external computing resources available. As compensation for the resulting computing effort, the computer that first reaches a consensus receives financial compensation, usually in the form of the technology’s own crypto currency.

Other than blockchain, there exist other distributed ledger technologies based on a directed acyclic graph (DAG). Technically speaking, a blockchain is also a one-dimensional DAG, but since the term blockchain has already established itself so strongly, blockchain and DAG are also used in this article for simplicity. DAG is then understood to be at least a two-dimensional graph. In contrast to the blockchain, DAG does not combine transactions into blocks, but attaches them directly to structure. The result is not a one-dimensional line but a graph that grows in width and length. Unlike blockchain, DAG implementation has no blocks with a fixed number of transactions require to be attached, except a single transaction. Thus lowered the resources needed to do PoW. Because it’s not worth paying a compensation for the resources spent during PoW transactions are often feeless (e.g: IOTA). Feeless transactions lead to a significantly better scalability for transaction throughput. [TAMK18].

Fig. 12: Distinction of DLTs by data structure and access type [TAMK18]. Image: © WZL | Anton Shirobokov

In addition to the data structure, DLTs are also defined by their accessibility. The most popular blockchain implementation is the Bitcoin. It is an open source DLT network, which allows everyone to participate in the network (known as permissionless). Having permissionless system resulting in countless advantages such as less risks of being hacked by malicious participants whilst still enabling people to access the content. For that reason blockchain implementations exist to regulate access, e.g. to protect sensitive data of certain institutions from misuse (permissioned).

This permissionless feature can also be found on the DAG side and the best known permissionless DAG implementation is the Tangle network by IOTA Foundation Berlin, Germany. While the regulated counterpart is Hashgraph.

In theory all DLTs are basically suitable for a machine economy, the goals of the IOTA Foundation and the implementation fineness of the Tangle seem to be particularly suitable for the Industrial Internet of Things. The fact that no transaction costs are incurred with the Tangle and a comparatively network scaling can be achieved better than with blockchain-based systems, the author believes that IOTA is more advantageous at the time of report generation or decision making.


Intermediate conclusion: Distributed Ledger Technologies

  • Distributed ledger technology (DLT) is a novel approach for gathering, and storing of transactional data
  • DLT is based on the usage of special data structures (blockchain or DAG), cryptography, and peer-to-peer network architecture which are orchestrated by an algorithm
  • Transactions in DLT are transparent, reliable and incorruptible
  • DLTs have special functions: immutability, identity, platform, records
  • IOTA is a permissionless distributed ledger developed specifically for IoT and machine economy

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3. IOTA in a Nutshell

At the end of 2016, the execution rate of IP traffic was around 1.2 zettabytes per year — enough data to fill over 9 billion of the iPhone’s highest available storage capacity at the time [CISC17]. Over the next five years, global IP traffic is expected to increase by fivefold, with monthly IP traffic reaching an 31 gigabytes per capita in 2021. But there is one big problem. In the same period, broadband speed expected only to double, and the electromagnetic spectrum is a fundamental limit for wireless communication. The global data pipelines will be overloaded. It will not be possible for all these devices to continuously connect to centralized cloud silos for all the data they will produce, nor will it be possible for the analytics engines in these clouds to react to the actuators to respond to the data in real time. This is where ’fog’ and ’mist’ computing come into play. The resources like memory, storages, and bandwidth must be distributed across the whole landscape, which immediately raises the question of how this can be done in practice with all the bureaucracy when there are 10, 100 or even 1000 actors involved in this new machine economy. This mystery was the reason for the foundation of IOTA. Through feeless transactions, these devices can share these technological resources locally in real time over a distributed network, eliminating central points of failure, relieving resource infrastructure. The objective of IOTA development was therefore to create a transactional backbone of the machine economy for data and monetary streams, with the purpose, to fulfill their four requirements of a secure, lightweight, affordable and scalable technology, see Fig. 13.

Fig. 13: Features of IOTA ‘s Tangle. Image: © WZL | Anton Shirobokov

In an IOTA economy, people, cars, robots or other IoT devices exchange data with the IOTA Peer-to-Peer (P2P) network. Each IOTA node of this P2P network executes a copy of IOTA’s Tangle, which allows you to attach your own and third-party unconfirmed transactions. The design of the tangle allows any operator to operate an IOTA Full or Light node on almost any device. Currently, the IOTA Foundation recommends providing at least 4 cores, 6 GB of memory and 40 GB of storage, requirements that probably exceed those of any top of the line smartphones. Of course IOTA supports remote PoW for delegation in case resources are limited.

In order for a transaction to be accepted by the tangle one has to confirm two previously unconfirmed transactions and perform a PoW. This makes the new transactions a tip. A tip transaction is an unconfirmed transaction waiting to be validated and confirmed, see Fig. 14. The premise is that as more transactions occur, the network will be able to scale faster since there are more verifications being performed in parallel.

Fig. 14: From IOTA node to bundle hash. Image: © WZL | Anton Shirobokov

A transaction consists of a bundle of information, such as the transaction ID, a message to send, the recipient’s address, a value if someone wants to transfer financial resources/money with the transaction, a tag to summarize messages or transactions, a timestamp, and much more necessary to participate in a validation process.

A typical IOTA transaction

Imagine two machines in a common hall that are running autonomously. Those machines have to perform different production steps that cannot be standardized or automated due to process fluctuations and a large variety of products. Those two machines were not procured through traditional method, but instead leased via pay per use model. In order for the machine complete their activities, ‘Alice’ negotiates a price with ‘Bob’ depending on the activity to be performed. see Fig 15.

Fig. 15: Overview of an IOTA transaction. Image: © WZL | Anton Shirobokov

IOTA transactions are encrypted and it is based on Trinary logic. Each encrypted transaction is 2673-trytes large. Once decoded, the transaction object consists of the following elements [SCHI18]:

- hash: string: 81-trytes unique transaction (tx) hash
- message: string: 2187-trytes signature message. If values are spend, the message contains the signature of the private key. If no values are spend, either the message is empty or contains a arbitrary input
- address: string: 81-trytes address, either of the recipient or the sender
- value: integer: tx value in iota
- timestamp: integer: time and date of the tx
- currentIndex: integer: the index of this tx in the bundle
- lastIndex: integer: the total number of tx in the bundle
- bundle: string: 81-trytes bundle hash to group bundle's tx
- trunk: string: 81-trytes hash of the first tx that was approved with this tx
- branch: string: 81-trytes hash of the second tx that was approved with this tx
- nonce: string: 81-trytes hash. The nonce is required for the tx to be accepted by the network. It is generated by doing PoW.

In our example, a transaction object (TR0) looks like this: Machine Alice and machine Bob have agreed on a value of 62 iota. This sum must be paid by machine Bob to machine Alice, for which machine Alice carries out a production step called C2123.

Getting the meta infos and transaction number

In this case the message field corresponds to the signature of the private key (message:Signature part 1). Since this is an outgoing transaction, the address field contains the address of the sender Bob (address: Address Bob). The value field is logically -62, since Bob sends money and does not receive it (value: -62). For simplicity’s sake, any string is used as a tag ( tag: for service C2123). The timestamp results from the day and the date of the transaction (timestamp: 16.05.2018 11:22) [LULO18].

Machine Bob would like to give an additional message to Machine Alice. However, since the signature of the private key is already stored in the message field, it cannot store any further messages there. Therefore, Machine Bob must initiate another transaction TR1 of zero value (value:0), which he can submit with the first transaction TR0 of -62 iota. In the message field of TR1 machine Bob can send any text (message: Signature part 2).

On the other hand, machine Alice receives only one single transaction TR2, namely the one with a credit of +62 iota (value:+62). However, the address field of TR2 contains Machine Alice’s address (value: Address Alice), since she is the recipient of the +62 iota.

In summary, 3 transactions must therefore be carried out. The value of lastIndex is therefore 2 in all three transactions (because counting starts at 0, 1, 2). The currentIndex of TR0 for +62 iota is 0, the currentIndex of TR1 for the additional message is 1 and the currentIndex of TR2 is 2.

Calculating the bundle hash and signing

The Bundle hash can only be calculated from the above fields. The Kerl hash function and sponge/squeeze constructor are used for the calculation. This means that the above fields (address, value, tag, timestamp, currentIndex and lastIndex) of all CurrentIndex are successively absorbed in exactly this order. As a result, the Bundle hash is squeezed out. The Bundle hash is identical for all three transactions ( bundle: Bundle hash for TR0, TR1, and TR2) [HOPE18].

Although for didactic reasons we have already discussed above, which content comes into the message fields, the signature for the message field can only be calculated with the bundle hash. Thus, the message fields are filled in only now (TR0(-62 iota): message: Signature part 1 = signed(AddressPrivateKey,BundleHash, TR1(0 iota): message: Signature part 2 = Not signed message string).

Selecting tips, trunk and branch

The Monte Carlo Markov Chain algorithm is used to get two tips of the tangle. Simply put, these are the transaction hashes of two previous transactions that need to be confirmed. This allows you to attach your own transaction to the tangle. The tips are distributed to sender and receiver branches and the trunk hashes reference themselves if necessary, if additional messages are sent, see Fig. 15. For more information, please refer to the IRI Handbook [IRIA18].

Proof-of-Work (PoW)

In addition to the actual transaction data, a transaction also consists of a nonce. A nonce is a random pseudo number from cryptography used to validate transactions. The proof of work is thus carried out by iteratively finding a certain pattern. IOTA’s proof-of-work algorithm is comparable to hashcash approach, see Fig. 16. A random nonce is hashed and converted to trits using the hash function curl. If the last 13 trits are 0, the pattern has been fulfilled and the PoW is complete. If the hashed nonce deviates from this pattern, a new nonce must be advised and the process starts again. Once the nonce is found, the transaction hash can also be determined and the bundle is complete. IOTA nodes then check the nonce, verify the transactions and attach them to the Tangle. Once transactions are validated by other transactions they are immutable.

Fig. 16: How to find a nonce. Image: © WZL | Anton Shirobokov

Development of further technology layers

Three developments are to be highlighted here, as they open up special possibilities in the course of the machine economy: Masked Authenticated Messaging, Flash Channels, and Data Marketplaces, see Fig. 17:

Fig. 17: MAM, Flash Channels and Data Marketplaces extend IOTA functionality. Image: © WZL | Anton Shirobokov

Masked Authenticated Messaging

Masked Authenticated Messaging (MAM) is a way to send encrypted messages over the tangle. Three types are supported (public, private, and restricted) [HAND17].

  • In public mode, the messages are broadcast similar to a radio broadcast. Possible use cases are announcements of devices or persons, with the advantage that they are now immutable and their data integrity can be checked.
  • In private mode, only the recipient can decrypt the MAM stream.
  • In restricted mode, an authorization key is added to the private mode. This means that access can be given and revoked to particular subscribers.

Flash Channels

Flash is a bidirectional off-tangle payment channel that enables instant, high-throughput transactions. Essentially, they provide an opportunity for parties to trade at high frequency without waiting for every transaction in the public IOTA network to be confirmed. Instead, only two transactions will ever take place on the IOTA main network: opening and closing transactions on the Flash channel. An off-tangle approach reduces overhead per transaction to a negligible level by creating signed transactions outside the tangle and opening a fee-less transaction model for instant token streaming [FREI17].

Data marketplace

The biggest obstacle to achieving the size targeted by Big Data is the fact that the vast majority of data remains enclosed in data silos. Data silos do not or very rarely pass on their data outside their own closed environment. This leads to enormous data loss, often over 99% of the data is lost [MANY15b].

Fig. 18: Oil rigs only store and analyze 1 % of data. Image: © WZL | Anton Shirobokov

However, siloed data can contain very valuable information when it flows freely into data streams that form an open and decentralized data lake (data marketplace) accessible to any compensator [SONS17].


Intermediate conclusion: Demystifying IOTA

  • IOTA is an open-source DLT developed as a transactional backbone for IoT and machine economy
  • IOTA is the only permissionless DLT enabling feeless monetary and data transfer
  • The basic transaction layer of IOTA is stable and working
  • Further technological layers of the IOTA protocol are being actively developed

Go up to the Table of Content!

4. Overview of IOTA use-cases in manufacturing

How does IOTA fit into WZL’s vision of the Internet of Production or the fourth Industrial Revolution in general? Is it a logical step to integrate DLT as part of 4th Industrial Revolution in integrating artificial intelligence approaches to cyber-physical systems? As of this moment IOTA is the most suitable option for the monetization of data transactions, or for a P2P network when they are capable to run as digital identity, immutable data and as a system of records, see Fig. 19. One can theorize IOTA as a handshake protocol and as an immutable ledger for the backbone of machine-to-machine transactions.

Fig. 19: What’s next? Image: © WZL | Anton Shirobokov

Business Layer

Nevertheless, the question whether DLTs are suitable for a business or an use case is not a trivial matters. Based on a study by PricewaterhouseCoopers, six characteristics must be fulfilled for DLTs to be meaningfully integrated [PWC16], see Fig. 20.

Fig. 20: Characteristics of feasible DLT use-cases. Image: © WZL | Anton Shirobokov

If the following conditions apply, then DLTs have a strong potential to help your business:

  • Multiple participants need views of common information, thus, they share a common data set
  • Multiple participants take actions that need to be recorded and change the data, thus, a decentralized update policy is needed
  • Participants need to trust that the actions that are recorded are valid, thus, they need some kind of a data verification platform
  • Removal of central authority record keeper intermediaries has the potential to reduce cost (e.g. fees) and complexity (e.g. multiple reconciliations)
  • Participants need to act on time, they work at time sensitive tasks, thus, reducing delays has business benefits (e.g. reduced settlement risk, enhanced liquidity)
  • Transactions created by different participants depend on each other

Use-Case Layer

Based on the PwC characteristics we can define variety of use cases, all of them with a unique benefit. If we combine PwC characteristics and the technology stack of what IOTA is offering: immutability, identity, platform, system of record [DIET17], see Fig. 21.

Fig. 21: How to derive DLT use-case. Image: © WZL | Anton Shirobokov

In addition to those technology stack, one is looking for Business cases that require the sharing of a common data set that is also updated together, whose entries must be verified, which increases the interaction of the participants, and leads to an acceleration of your business as well as enables streamlining. Thus resulting in many benefits in manufacturing such as: Asset sharing, M2M communication, Data marketplace, Distributed manufacturing, Supply chain tracking, Digital product memory, Verification of spare parts, Quality documentation.

Example: Market place for immutable and auditable data

The idea of a data marketplace seems questionable at first glance. But if we deep dive into the reasoning behind the data marketplace you will understand why it is important. Imagine working in production engineering, using the various range of measurement tools for quality features to control your processes daily. Many of these measurements are frankly pointless because they have already been performed by the entity before you in the supply chain. Since you don’t have access to those quality control data result, it means not only that you have to repeat the measurement, but you also have to purchase equipment for the measurement such as building an air-conditioned measuring room. This may be necessary for some of them, but small and medium-sized companies in particular could benefit from sharing data along the value chain. It can speed-up the whole process and better quality control which also benefits the downstream processes [SONS17].

Fig. 22: Potentials of Data marketplaces. Image: © WZL | Anton Shirobokov

Especially these days, when artificial intelligence in general and deep machine learning in particular show unprecedented potential, access to a value chain-spanning data lake would massively improve one’s own processes. Unprecedented process patterns could be uncovered and implicit knowledge made visible. Not only this could lead to improved products and processes, but also completely new business models could also emerge. For this to happen, a secure data marketplace that offers immutable and auditable data in real time is needed.

It does not matter whether the data itself is stored in the tangle or only a signature of it, whether the data was stored locally, in the cloud or on portable drive. With the data signature referenced in the tangle, you can always be sure the integrity of the data [PRIT18], see Fig. 23. This means you don’t have to worry about the reliability of data when using distributed storage or computing services.

Fig. 23: Storage and computations as a service [PRIT18, SONS17]. Image: © WZL | Anton Shirobokov

Example: Supply Chain

Documented certificates of origin for an item can help to establish that the item has not been altered or forged, reproduced, stolen. Certificates of origin helps in assigning the work to a well-known artist, and a documented history can verify the proof of ownership. In manufacturing supply chains, there is currently a lack of trust and transparency along the up- and downstream. However, a digital twin shared by a DLT could ensure data integrity and enable secure audit trails. Thereby, a digital twin is a digital model of a real-life process, product, machine, or service with a unique immutable identity, see Fig. 24.

Fig. 24: Digital twins in DLT based Supply Chains. Image: © WZL | Anton Shirobokov

Example: Fujitsu’s Proof of concept

Fujitsu established a supply chain with two objectives. In one hand, a Component Audit Trail was implemented, i.e. only authentic components, that have past all manufacturing steps in the supposed order, were accepted and passed on by the robots, see Fig. 25.

Fig. 25: Fujitsu’s Proof of Concept. Image: © WZL | Anton Shirobokov

This ensured a proof of authenticity and proof of provenance and data access from anywhere. While in the other hand, an Auditable Robot Lifecycle was established through immutable storage of sensor data in a MAM stream for the complete lifecycle of each robot and the monetisation of the recorded data.


Intermediate conclusion: IOTA use-cases in manufacturing

  • High-potential applications of DLT / IOTA should leverage the inherent strengths and could be applied for use-cases involving interactions between multiple independent parties relying on the same data basis
  • DLT / IOTA enable the emergence of a secure data marketplace for machines and humans where the integrity and auditability of data is ensured
  • The properties of DLT / IOTA make it suitable for a deployment of digital twins which enable secure audit trails in decentralized systems
  • DLT and IOTA are novel technologies and their applicability in the manufacturing context is largely not investigated

Go up to the Table of Content!

5. The WZL x GCX x IOTA Use Case

The subject of this Proof of Concept (PoC) is an industrial fineblanking machine type XFT 2500 speed from Feintool AG. Fineblanking is a metal cutting process designed for mass production, e.g. for the manufacturing of brake calliper carriers or belt straps. Fineblanked components thus often perform safety-relevant tasks, e.g. in automobiles, see Fig. 26.

Fig. 26: WZL’s Fineblanking press. Image: © WZL | Daniel Trauth

The goal of this PoC is to extract production data of fineblanked components from the machine control in real time, secure data persistance through the tangle and retrieve proof of integrity via a web-based frontend, see Fig. 27.

Fig. 27: WZL’s Proof of Concept. Image: © WZL | Anton Shirobokov

500 transactions are stored in the Testnet using the Tag WZL9GCX9IOTA9POC9IIOT999999, see Fig. 28. Click here to see a randomly chosen transaction.

Fig. 28: Test transactions on the Tangle. Image: © WZL | Anton Shirobokov & Semjon Becker

If you want a detailed report on the WZL x GCX x IOTA use case, check our Status reports on medium.com


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6. Conclusion

  • The rapid development of machine intelligence and connectivity lead to the emergence of machine economy where the data is the most valuable resource
  • Distributed ledger technology (DLT) is an innovation enabling the integrity and auditability of data in decentralized systems
  • IOTA is a DLT developed as a transactional backbone for the machine economy
  • From technological and business standpoints high-potential industrial use-cases of DLT and IOTA are the industrial data marketplace and audit trails in decentralized supply chains

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Acknowledgement

The authors thank the German Research Foundation for the funds of the fineblanking press.


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References

  • BAUE18a: Bauerle N: What is a Distributed Ledger? 2018.
  • BAUE18b: Bauerle N: What Are the Applications and Use Cases of Blockchains? 2018.
  • BAUE18c: Bauerle N: What Can a Blockchain Do? 2018.
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  • HAND17: Handy, P: Introducing Masked Authenticated Messaging, 2017.
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  • LULO18: Lu, L: In-depth explanation of how IOTA making a transaction, 2018.
  • IRIA18: IRI Api: getTransactionsToApprove() https://iota.readme.io/v1.2.0/reference#gettransactionstoapprove, 2018
  • MANY15a: Manyika J, Chui M, Bisson P, Woetzel J, Dobbs R, Bughin J, Aharon D: The internet of things: mapping the value beyond the hype. McKinsey Global Institute, 2015.
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  • PWC16: PwC/Long Finance: Chain reaction: How blockchain Technology Might Transform Wholesale Insurance, 2016.
  • RAJA17: Rajasingham D: Welcome to the machine-to-machine economy. Opportunities and challenges in a connected world. Commonwealth Bank of Australia, 2017.
  • RÜTH17: Rüther M: Project Twin of Things — Digital Twin. Lighthouse Machine Economy, Innogy Innovation hub, 2017.
  • SCHIE18: Schiender D: IOTA Guide — Bundles, 2018.
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  • STAT18: https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/
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  • WIKI18a: Wikipedia.org, https://en.wikipedia.org/wiki/Digital_signature, 2018.
  • WIKI18b: Wikipedia.org, https://en.wikipedia.org/wiki/Public-key_cryptography, 2018
  • YAFF17: Yaffe L: Using the Blockchain to Bridge Data Silos, 2017.

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