Blockchain in the Manufacturing Industry — Key Use Cases
While blockchain technology is believed to disrupt the financial sector, research and empirical evidence about its application in the manufacturing industry is relatively scarce. With this paper, we provide an analysis of challenges and benefits that blockchain technology holds for three different use cases in the manufacturing industry: (1) smart maintenance, (2) dynamic leasing, and (3) quality assurance. — Author: Johannes Schwab
Download the article as a PDF file.
Blockchain technology became popular by providing the technical foundation
for the cryptocurrency Bitcoin and is since then believed to transform many
aspects of the financial services sector (Cong & He, 2019). Nevertheless, the
possibilities of this innovative technology reach far beyond the facilitation of
payments. With the creation of touring-complete blockchains such as
Ethereum and Hyperledger, a broader and more flexible range of applications
was made possible (Nærland et al., 2017). They have the potential to affect a
wide range of sectors besides the financial industry. Especially in conjunction
with Industry 4.0 and the Internet of Things (IoT), blockchain technology
might also play an essential role in overcoming current challenges in the
manufacturing industry (Babich & Hilary, 2020).
With a 30% compound annual growth rate, the Boston Consulting Group expects the industrial IoT (IIoT) segment to be one of the fastest growing in the IoT market (Lorenz et al., 2019). Smart factories making use of IoT will allow for a higher degree of automation, lower costs, and enhanced efficiency (Kusiak, 2017). As hard- and software required to establish inter-machine connectivity tend to get more efficient and affordable (Almada-Lobo, 2016) also small and medium-sized companies can benefit from this innovation, leading to a widespread implementation of smart manufacturing solutions throughout the manufacturing industry.
However, these innovations also entail myriad of challenges that have to be addressed from a conceptual and technological perspective. Machine-to-machine communication in smart factories enabled by IoT raises concerns about data integrity, privacy, and data management (Whitmore et al., 2014). Equipped with innovative features, blockchain technology has the potential to serve as a backbone framework for the IIoT, providing data integrity, security, and transparency without involving a trusted third party (Lao e al., 2020).
Smart factories and the IoT
The IoT. Combining the concepts and technologies inherent in Industry 4.0,
the picture of a smart factory is drawn. This vision depicts a fully autonomous
factory where machines communicate with each other and make decisions in
the absence of human interaction based on data received from various
sensors. One of the leading technologies believed to enable smart factories is
the IoT. With the rise of IoT, borders between the physical world and the
internet will vanish (Ng & Wakenshaw, 2017). With relatively inexpensive
technologies such as radio-frequency identification (RFID) tags, physical
objects can be uniquely identified in a virtual system and real-time statements
about their status, location, and other properties can be made (Ng &
Wakenshaw, 2017). Known as the IIoT, this concept is expected to have a
major impact on the manufacturing industry. However, certain crucial
challenges arise with the implementation of this concept.
Privacy and security. To connect machines and other objects, a tremendous amount of data is collected from all kinds of sensors and other measuring devices. This data reveals very critical and intimate insights into the business
and production processes of a company and must be treated with the utmost
confidentiality. Therefore, crucial privacy and security issues arise (Whitmore
et al., 2014). Considering machines and other devices base their autonomous
decisions on this information suggests the distorting impact forged data could
Interoperability. A further hurdle arising on the path towards broad IoT
adoption is the interoperability of different IoT devices and IT systems within
a company or a consortium of partner companies. Different standards across
companies and devices might obstruct efficient communication and data
exchange (Zhao et al., 2019).
Effective maintenance of machines has an important effect on a firm’s performance and is therefore a crucial managerial challenge as stated by de Jonge and Scarf (2020). They also find that firms increasingly recognize the benefits of planning maintenance measures proactively instead of just reacting to occurred incidents. Nevertheless, many companies still leave the potential of machine data unused (Bokrantz et al., 2020) and therefore also lack automation in maintenance processes. According to Bokrantz et al. (2020), one of the main features of so-called smart maintenance is datadriven decision-making. With the availability of IIoT, this can be made possible based on data provided by smart machines. Sensors and other measurement technologies integrated into the equipment could, for instance, track erosion and the status of specific parts and inform the machine operator in real-time about their insight.
Data about the degree of utilization of machinery, past incidents as well as conducted maintenance can be saved in a digital logbook (Aleshi et al., 2019). Sharing this logbook could help the manufacturer or an external mechanic to get a better overview of the state of the machine and to react faster and more targeted in case of an outage. It becomes obvious that the success of smart maintenance heavily depends on data-driven decision-making. Therefore, great attention has to be paid to how data is collected, processed, stored, and distributed over a network of participants. Blockchain as the underlying technology of the use cases is especially interesting due to the advantages it provides over centralized systems when implemented in intercompany transactions.
Security. Current IoT devices and applications are prone to data getting changed, forged, or otherwise misused by cyber attackers (Zhao et al., 2019). In the case of a blockchain, however, the data and therefore also the created maintenance log are by design stored immutably, meaning they can hardly be changed once validated and stored on the chain (Zhang et al., 2019). Furthermore, as data is distributed for verification and storage between the participants every full node holds an identical copy of the blockchain and can transparently stay informed about the current state of the machine (Cong & He, 2019). In contrast to a centralized platform where data is stored with a single entity, a blockchain system stores data at every node. Thus, if cybercriminals or even the machine operator him-/herself tries to forge data, it would not be enough to simply attack one entity. Another important component of security is identity management which is especially crucial in the IoT to distinguish different devices (Whitmore et al., 2014). Only if machines and other devices in a network can be identified uniquely, they can be associated with their actions such as machine-to-machine payments. This is also crucial to allow the assignment of rights to certain machines. Blockchain provides this kind of identification functions allowing to uniquely identify nodes in the network and the respective transactions they were involved in without a central server (Lao et al., 2020). As stated by Babich and Hilary (2020, p. 9):
Blockchain has the potential to provide a decentralized identity management with strong security features.
Trust/Automation. According to Pereira et al. (2019), the security features of blockchain combined with a robust consensus mechanism could contribute to shifting trust from third parties to executable code. This executable code can be introduced in the form of smart contracts, which act on predefined conditions. Yermack (2017) mentions that smart contracts ensure that both contract parties fulfill their promise and hence overcome moral hazard problems like strategic default. Combining the trust participants put into blockchain technology and the automation features of smart contracts, new forms of automation are enabled. As illustrated in Figure 1, in case of planned or unplanned maintenance, the machine could autonomously react within a predefined smart contract and for instance call a mechanic, assign an external service provider to execute repairs, or order respective replacements if they are not in stock (Babich & Hilary, 2020). As no human intervention is needed, this would reduce the time until a mechanic or spare part arrives at the production plant and hence also decrease downtime as well as the opportunity cost associated with production shutdown.
Disintermediation. Additionally, blockchain renders third-party platforms obsolete as consensus can be found through the inherent consensus protocol and therefore contributes to disintermediation (Pereira et al., 2019). According to Gulati (1995), transaction costs are mainly driven by the possibility for opportunistic behavior which can be eliminated using smart contracts that act automatically and according to predefined code. Pereira et al. (2019) also state that disintermediation does not only achieve cost efficiency, it also impedes a third party’s control over the data since the maintenance log is distributed over the different participating nodes.
Catalini and Gans (2020) show that extensive control over data by an intermediary or centralized platform can have negative consequences such as high switching cost, censorship or fraudulent behavior. As data recorded in the maintenance log can give insights into production processes and utilization this information has to be treated highly confidential and should therefore only be within the control of participating parties.
Transparency. Another advantage of a digital and trusted maintenance log is that the history of the respective machine can be proven reliably which mi ht influence a potential buyer’s decision in case of a resale of the machinery (Babich & Hilary, 2020). In addition to the maintenance history, various certificates could be stored on the blockchain and be attached to the digital twin of the respective machine.
First applications of blockchain in maintenance operations can be found in the aviation sector. Honeywell Aerospace, for instance, created a digital marketplace called GoDirect Trade selling used aircraft parts using blockchain to store the history of the parts in a trustful manner to protect market participants (Kearney, n.d.).
Dynamic leasing does not only follow current trends towards performance or condition-based contracting, it can also provide significant benefits for the operating company and the machine manufacturer. Dynamic leasing describes the idea that monthly leasing rates are adjusted based on predefined parameters such as hours of usage or conducted maintenance measures. As an increasing number of machines and factories in Industry 4.0 will be equipped with sensors (Lasi et al., 2014), technically, this model is already feasible. What might suppress the broader application of this business model is the need for close relationships in performance-based contracts (Hypko et al., 2010) and missing trust in the correctness of the machine data. Blockchain technology has the potential to establish the required confidence and trust for both parties to enter into a condition-based contract (Cole et al., 2019).
As in smart maintenance, to enable efficient interactions with and between machines, a digital twin has to be set up (Huang et al., 2020) which is tied to the respective data on the blockchain. The data, in this case, is information collected about leasing rate relevant parameters such as hours of production per day. This data is collected by sensors at the machine level and will immediately be signed and sent to the blockchain network for validation. Due to predefined rules the data is validated by the nodes and appended to the blockchain where it can hardly be tampered or changed (Biais et al., 2019). The nodes within this network storing the entire blockchain can comprise those of the manufacturer of the machinery acting as lessor and those of the operator of the machinery acting as lessee. Smart contracts can be implemented to calculate the current leasing rate. Due to the predefined rules encoded in the smart contract, certain sensor information such as an exceeded threshold of production hours can trigger the smart contract to increase the leasing rate. In the same way, a planned maintenance inspection that was not adhered to can also raise the payment.
Digital money. Full automation and trust into the process can only be reached if the payment process is also being automated and no human intervention is required. For the automation of the payment process a digital money can be used which enables fast transactions and does not require trusted third parties such as banks to transfer values. Smart contracts can be used to issue a payment when predefined conditions are met (Kolb et al., 2020).
Powered by an appropriate form of digital money, the machine would be able
to pay and receive funds autonomously via smart contracts as stated by
Stöcker (2017). He also explains that a machine could then act as a fully
automated profit center, getting paid for its services and, on the other hand,
settle expenses such as the leasing rate and maintenance expenditure. The
machine would have its own profit and loss statement and cash flows which
enables the operatin company to observe the machine’s profitability directly.
This way the lessor can better assess the economic feasibility of dynamic
leasing models and evaluate adoptions.
Security/Trust. Without a blockchain or a trusted third party, the lessor,
which in this case is also the manufacturer, would have to be on site to check
the parameters that flow into the calculation of the leasing rate. As in the
blockchain-based process data is immediately signed and sent to the
blockchain network, it is assured from an early stage that data cannot be corrupted or changed. Furthermore, as both participants store a full history
of the blockchain on their nodes, they can easily and with certainty look up
and verify historic parameters and leasing rates. The immutable ledger of
leasing rate relevant data creates trust between the two parties (Cole et al.,
2019) as it assures the manufacturer and the customers that the parameters
have not been tampered with malicious intent.
Automation. As the machines are now capable of communicating and
interacting with the manufacturer directly, human interaction can be
minimized, and administrative efforts are reduced. That is, once the system
and smart contracts are set up, the leasing rate calculation as well as the
payment process can be conducted automatically. Nevertheless, automation
in general can also be achieved without blockchain technology. However,
according to Babich and Hillary (2020), blockchain enables increased
coordination between all parties or machines involved in an automated
process and furthermore decreases potential credibility problems.
Transparency. With blockchain technology the lessee and the lessor both
have the complete and identical history of sensor and transaction data stored
in their IT system. This trusted set of data could then easily be used by the
lessor to observe the machine’s production properties and run analyses to
identify potential pain points. This also benefits the machine user if error
sources can be detected and eliminated efficiently.
Information asymmetry. According to Sharpe and Nguyen (1995), a
significant risk for the lessor is the salvage value in case the machine has to
be re-leased or is sold after the lease contract has ended. They explain that
under an operating or true lease the lessor holds all risks associated with the
ownership of the machine such as the uncertainty of the salvage value. As this
salvage value also depends on the physical deterioration of the machine, the
history of usage saved on the blockchain can simplify the re-sale or re-lease
of the machine for the manufacturer. On sale the former owner could hand
over the physical asset as well as the digital twin of the machine (Huang et al.,
2020) and therefore decrease the information asymmetry between the
involved parties. The salvage value could therefore be estimated more reliably
and possibly enable higher prices paid for the used equipment.
Product quality is an important factor for manufacturers and their reputation.
Blockchain technology can offer benefits for quality management as well as
anti-counterfeiting policies in many ways.
Quality control. Jraisat and Sawalha (2013) found that quality control should not only be applied on the firm level but span throughout the whole supply chain. Two important factors for quality control in supply chains pointed out in their paper are communication between the members of the supply chain and the quality of information the supply chain members share. With this in mind, it becomes evident that tracking products along their way in the supply chain is an integral part of quality assurance which can be facilitated by blockchain technology.
Ngai et al. (2014) describe how RFID technology can be applied to aircraft
parts to track them throughout the supply chain. They mention that each part
can be assigned a unique RFID tag and ID to track it throughout every step in
its lifetime. Blockchain can be used to complement this technology. When a
respective RFID tag is scanned, the data could immediately be sent to the
blockchain and assigned to the digital twin of the product identified by the
unique product ID inherent in the RFID tag. This would allow every
participant in the network to transparently see a tamper-proof and therefore
trusted movement history of a certain product according to George et al.
(2019). They apply this concept to a food supply chain and aim to create a
food quality index for meat. They do not merely track the food along the
supply chain but also include other quality measures such as the storage
temperature or humidity.
A similar concept could be imagined for other products with certain quality
requirements along the supply chain. Predefined upper and lower bounds
could tell the receiver of the product if it is of sufficient quality. The full nodes
of the network could comprise every participant within the supply chain
starting from the source of commodities over the logistics firm up to the enduser, which would allow for high visibility of the finished product (Wang et al., 2019). Figure 2 illustrates these ideas and shows how a blockchainenabled quality tracking system could work.
A report published by the Boston Consulting Group about the “Factory of the
Future” sees blockchain as an enabler to simplify quality checks (Küpper et
al., 2019). According to the authors, every step along the production process
including quality checks could automatically be assigned to the specific
product’s di ital twin and be sent to the blockchain. This would enable the
manufacturer or buyer to transparently check the quality of the product in the
sense that the product went through all necessary production steps and
quality checks and furthermore fulfills the stipulated specifications.
Counterfeiting protection. Another important aspect of preserving the
quality standards of a firm is to assure that no counterfeits are being
distributed in the market which could be harmful to the own brand.
Conducting supply chain expert interviews, Wang et al. (2019) found that
knowing about the provenance of a product is of high value as fraudulent
parts can cause severe consequences for instance in the aircraft sector. They
state that an immutable, transparent, and distributed ledger of transactions
based on blockchain technology can enable product provenance and therefore
help mitigating counterfeit risk. If every step from the sourcing of the material
up to the finished good is traced transparently on the blockchain and assigned
to the digital twin, a customer can be more confident that the physical product they buy is not a forgery (Wang et al., 2019).
Transparency. Without blockchain technology, partners in the supply chain
have to rely on declarations by their supplier or perform extensive and time-consuming status queries about a variety of processes via a pull mechanism as stated by Chang et al. (2019). They also state that data in current systems is exchanged between various databases aggravating real-time tracking. However, a traceability system for food based on Hyperledger Fabric showed that blockchain-based systems could significantly increase the visibility and traceability of products in the supply chain (Hyperledger, n.d.). This so-called IBM Food Trust Project in cooperation with Walmart decreased the time to determine the origin of mangos from seven days to 2.2 seconds (Hyperledger, n.d.)
Security/Trust. It becomes evident that for more extensive supply chains
with various participants, quality assurance is a challenge every involved
party contributes to. This requires communication, information sharing, and
trust between the supply chain members (Jraisat & Sawalha, 2019).
Blockchain technology can establish multilateral relations between untrusted
parties (Schmidt & Wagner, 2019). Trust in this use case is established by the
immutability of data entries meaning the participants can be assured no entry
has been tampered (Beck et al., 2016). As the data entries also get timestamped, one can also be sure about the sequential order of the entries and the lifespan of the product within the production or supply chain.
Automation. The created trust can be beneficial to automate processes. As
described above, quality checks on the physical product can be executed
automatically and the specifications consequently can be recorded in a
trusted manner on the blockchain, making inbound quality checks by the
receiver unnecessary (Küpper et al., 2019). This could allow for further
automation as machinery could accept goods autonomously based on the
quality measures saved with the digital twin of the product. Blockchain
technology can result in less paper-based, manual transactions throughout
the supply chain and along quality checks and therefore enhance process
efficiency (Chang et al., 2019).
Interoperability. An additional advantage which refers to all three use cases
is interoperability. Many IoT applications lack interoperability with systems
of other manufacturers or even other business units within the same company
as different platforms or data formats are used. Especially when working with an extensive supplier network it is crucial that every participant can interact
with the system, meaning the data uploaded by one party needs to be
accessible and readable by other participants or IoT enabled machines. The
interconnecting role of blockchain technology in the manufacturing industry
is illustrated in Figure 3. Nevertheless, interoperability between different
blockchains can be challenging as further outlined in the next section.
Risks and challenges of blockchain technology
Compared to other technologies, blockchain is a relatively young and at least
for its implementation in the IoT or manufacturing not prevalent technology
(Schmidt & Wagner, 2019). It becomes evident that there are also substantial
risks, challenges, and prerequisites that need to be addressed and evaluated
in the light of the respective use case.
Cost. Blockchain technology and smart contracts generally have the potential
for cost savings associated with opportunistic behavior, as the protocol as well
as the conditions that trigger a smart contract are defined in advance (Pereira
et al., 2019). Nevertheless, Pereira et al. (2019) also identify downsides
concerning cost. They state that blockchain systems compared to centralized
systems have higher storage costs as data is saved multiple times and higher
verification costs as data needs to be verified by multiple nodes. According to
the authors, this cost is even higher if the respective assets are not purely
digital as a continuous link needs to be established to the offline world. A certain infrastructure of sensors and RFID tags is required to establish the
link between the blockchain and the physical world. Kumar et al. (2019) also
state that blockchain compared to existing inter-organizational systems
comes with high setup and overhead cost which can only be justified upon
careful use case analyses has been undertaken.
Data acquisition. Data or transactions which are stored on the blockchain
are by design stored immutably and distributed which creates trust in the
integrity of the information (Kolb et al., 2020). However, blockchain
technology cannot prevent data from getting manipulated right at the
acquisition stage (Schmidt & Wagner, 2019). According to Babich and Hilary
(2020), this represents the so-called “ arba e in, arba e out” problem They
state the key issue to be the link between the physical asset and the
information saved about it on the bloc chain, the product’s di ital twin If,
for instance, sensors used to implement dynamic leasing are corrupted, they
will provide inaccurate data which will then be processed through smart
contracts and potentially lead to miscalculated leasing rates.
Source code errors. As all three aforementioned use cases make use of
smart contracts another important challenge is the design of these smart
contracts themselves. Typically, smart contracts require to be complete
contracts meaning every eventuality needs to be included in the smart
contract’s code ( ereira et al , 2019) Accordin to Kumar et al. (2019), it is
challenging to translate every contract correctly into computer-executable
language. It becomes clear that smart contracts are limited if more complex
or highly fragmented customization is needed but can unfold huge benefits
used with repetitive and formal contracts (Pereira et al., 2019).
Storage capacity. Another possible limitation is the preciousness of storage
capacity. As blockchain, by design, stores the entire chain of information or
transactions, the needed storage capacity is steadily increasing over time (Lao
et al., 2020). Additionally, the other nodes in the network hold a replication
of the data multiplying the storage requirements for the whole network by the
number of nodes as expressed by Kumar et al. (2019).
To reach consensus, new transactions need to be distributed to the network and after validation the nodes also have to communicate the outcome of the validation process generating a high number of messages exchanged throughout the network (Lao et al., 2020). This moreover elucidates another
precious resource to the system which enables this frequent communication
between the nodes, the bandwidth (Lao et al., 2020). Thinking about quality
assurance as an example, we see that, depending on the number of products
and updates per day, storage requirements can quickly reach an enormous
level if every possible data point and pictures of the product are saved on the
To address the storage challenge, only critical data and data needed as input
for smart contracts should be stored on-chain (Chang et al., 2019). Kumar et
al. (2019) mention a solution where additional data is stored off-chain while
its hash is stored on-chain. This way, data storage can be saved but changes
in the off-chain data can still be observed by comparing the hash value to the
hash value stored on the blockchain. Again, taking quality assurance as an
example, important data such as the specifications of the product should be
stored on-chain whereas additional data like a picture of the product could be
saved centralized on the manufacturer’s server If the manufacturer tries to
tamper data on his server, the hash value will change and other participants
in the network such as the receiver could detect this change and request the
recovery of the original data.
Technological uncertainty. As blockchain is a relatively young technology,
especially in manufacturing, investments in the technology at this early stage
can be perilous (Schmidt and Wagner ,2019). They point out that other DLTs
are currently developed with characteristics and features which could make
them more desirable than blockchain in certain use cases. The blockchain
alternative IOTA, for instance, is considered especially promising for the
usage in IoT and Industry 4.0 as it provides high scalability and is lightweight
enough to be implemented within IoT devices (Popov & Lu, 2019). Until the
concepts are technologically mature, it remains unclear which technology will
prevail and investments could be risky. Additionally, many firms are still not
sure about the functionalities and benefits of the technology. Even though
blockchain is a trustless system there needs to be a certain trust established
in the technology itself for broad adoption (Babich and Hilary, 2020).
Regulation. Additionally, legal uncertainty can expose blockchain
investments to substantial risk. As a relatively new technology, the legal
enforceability of smart contracts in case of a dispute might be uncertain. According to Mik (2017), smart contracts can be handled as ordinary and
enforceable contracts but each case must be considered individually, and no
statement can be made for all smart contracts. To tackle this issue, especially
with more complex contracts, smart contracts could be used as an extension
to written physical contracts. Regarding, for instance, dynamic leasing, the
manufacturer and operator of the machine should clarify what happens if
sensors fail to transmit the correct production time and therefore a wrong
leasing rate is calculated.
Blockchain design. Many different blockchain designs and consensus
protocols exist, and further designs are currently under development. As
outlined by Babich and Hilary (2020), determining a technical design that
provides an optimal level of verifiability, privacy, and efficiency is a major
challenge. It becomes evident that every technological design has its own
challenges and features that need to be balanced according to the specific use
case. The main technological challenges of different blockchain designs are
described in the so-called “ calability trilemma” by itali Buterin, stating
that a single system can hardly satisfy all of the three characteristics security,
decentralization, and scalability simultaneously (Lao et al., 2020).
The use cases introduced in this paper require a high degree of privacy and
security as confidential operational data is exchanged but also scalability is
needed. According to the scalability trilemma, a blockchain that fulfills these
needs has to accept shortcomings concerning decentralization. Hyperledger
Fabric, as an example, satisfies these needs at the cost of decentralization as
only authorized nodes can join the network (Kolb et al., 2020). However, for
most B2B blockchain implementations, decentralization is subordinated to
scalability and security and permissioned blockchains like Hyperledger
Fabric are therefore well-suited (van Laar, 2019). Discussing the scalability
trilemma and the arising challenges in detail is beyond the scope of this paper
and is therefore condensed as the challenge to find an appropriate blockchain
Blockchain interoperability. Even though blockchain technology can be
beneficial for interoperability of different IoT devices, the compatibility with
other blockchains can become a challenge. According to Schmidt and Wagner
(2019), firms are likely to be participants in different blockchain ecosystems,
which might be problematic if cross-blockchain interoperability is required.
If, for instance, the quality of a product shall be tracked, and the involved
parties already joined different blockchain ecosystems having different
designs, the communication between them could be obstructed. Table 1
summarizes the challenges of blockchain technology and provides an
overview of possible mitigants.
We find that blockchain technology can be utilized as the underlying
technology to facilitate data storage and exchange between participants. This
form of data exchange holds significant advantages compared to traditional
systems such as enhanced security, transparency, privacy, and
interoperability overcoming current challenges of the IoT in these areas. We
also found that smart contracts can support the automation of processes in
the manufacturing industry, thereby further increasing operational and cost
efficiency. Combined with the trust-building feature of the technology, it can
enable new forms of intercompany interaction and collaboration.
Nevertheless, the technology also faces challenges which need to be addressed in order to promote a more widespread implementation of blockchain technology in the manufacturing industry. However, especially in the light of Industry 4.0, where manufacturing companies tend to be more
interconnected, blockchain can be a promising technology. Building on this
form of data exchange and storage, the implementation of use cases such as
smart maintenance, dynamic leasing, and quality assurance can be facilitated.
If you like this article, we would be happy if you forward it to your colleagues or share it on social networks. If you are an expert in the field and want to criticize or endorse the article or some of its parts, feel free to leave a private note here or contextually and we will respond or address.
Do you want to learn more about how blockchain will change our world?
- Blockchain knowledge: We wrote a Medium article on how to acquire the necessary blockchain knowledge within a workload of 10 working days.
About the Author
Johannes Schwab is pursuing his Master of Finance at Frankfurt School of Finance & Management. In the course of his bachelor’s thesis, he dealt with the application of blockchain technology in the manufacturing sector. You can contact Johannes via mail (firstname.lastname@example.org) or LinkedIn
Aleshi, A., Seker, R., & Babiceanu, R. F. (2019). Blockchain model for enhancing aircraft maintenance records security. Proceedings of the 2019 IEEE International Symposium on Technologies for Homeland Security. https://doi.org/10.1109/hst47167.2019.9032943.
Almada-Lobo, F. (2016). The industry 4.0 revolution and the future of manufacturing execution systems (MES). Journal of Innovation Management, 3(4), 16–21. https://doi.org/10.24840/2183-0606_003.004_0003.
Babich, V., & Hilary, G. (2020). OM forum — Distributed ledgers and operations: What operations management researchers should know about blockchain technology. Manufacturing & Service Operations Management, 22(2), 223–240. https://doi.org/10.1287/msom.2018.0752.
Beck, R., Czepluch, J. S., Lollike, N., & Malone, S. (2016). Blockchain–The gateway to trust freecryptographic transactions. Proceedings of the Twenty-Fourth European Conference on Information Systems. https://aisel.aisnet.org/ecis2016_rp/153.
Biais, B., Bisière, C., Bouvard, M., & Casamatta, C. (2019). The blockchain folk theorem. The Review of Financial Studies, 32(5), 1662–1715. https://doi.org/10.1093/rfs/hhy095.
Bokrantz, J., Skoogh, A., Berlin, C., Wuest, T., & Stahre, J. (2020). Smart Maintenance: An empirically grounded conceptualization. International Journal of Production Economics, 223, Article 107534. https://doi.org/10.1016/j.ijpe.2019.107534.
Catalini, C., & Gans, J. S. (2020). Some simple economics of the blockchain. Communications of the ACM, 63(7), 80–90. https://doi.org/10.1145/3359552.
Chang, S. E., Chen, Y. C., & Lu, M. F. (2019). Supply chain re-engineering using blockchain technology: A case of smart contract based tracking process. Technological Forecasting and Social Change, 144, 1–11. https://doi.org/10.1016/j.techfore.2019.03.015.
Cole, R., Stevenson, M., & Aitken, J. (2019). Blockchain technology: Implications for operations and supply chain management. Supply Chain Management: An International Journal, 24(4), 469–483. https://doi.org/10.1108/scm-09-2018-0309.
Cong, L. W., & He, Z. (2019). Blockchain disruption and smart contracts. The Review of Financial Studies, 32(5), 1754–1797. https://doi.org/10.1093/rfs/hhz007.
de Jonge, B., & Scarf, P. A. (2020). A review on maintenance optimization. European Journal of Operational Research, 285(3), 805–824. https://doi.org/10.1016/j.ejor.2019.09.047.
George, R. V., Harsh, H. O., Ray, P., & Babu, A. K. (2019). Food quality traceability prototype for restaurants using blockchain and food quality data index. Journal of Cleaner Production, 240, Article 118021. https://doi.org/10.1016/j.jclepro.2019.118021.
Gulati, R. (1995). Does familiarity breed trust? The implications of repeated ties for contractual choice in alliances. Academy of Management Journal, 38(1), 85–112. https://doi.org/10.5465/256729.
Huang, S., Wang, G., Yan, Y., & Fang, X. (2020). Blockchain-based data management for digital twin of product. Journal of Manufacturing Systems, 54, 361–371. https://doi.org/10.1016/j.jmsy.2020.01.009.
Hyperledger. (n.d.). How Walmart brought unprecedented transparency to the food supply chain with Hyperledger Fabric. The Linux Foundation Hyperledger. https://www.hyperledger.org/wpcontent/uploads/2019/02/Hyperledger_CaseStudy_Walmart_Printable_V4.pdf.
Hypko, P., Tilebein, M., & Gleich, R. (2010). Benefits and uncertainties of performance‐based contracting in manufacturing industries. Journal of Service Management, 21(4), 460– 489. https://doi.org/10.1108/09564231011066114.
Jraisat, L. E., & Sawalha, I. H. (2013). Quality control and supply chain management: A contextual perspective and a case study. Supply Chain Management: An International Journal, 18(2), 194–207. https://doi.org/10.1108/13598541311318827.
Kearney, K. (n.d.). Four ways blockchain will change aircraft maintenance. Honeywell International Inc. https://aerospace.honeywell.com/en/learn/aboutus/blogs/2019/08/four-ways-blockchain-will-change-aircraft-maintenance.
Kolb, J., AbdelBaky, M., Katz, R. H., & Culler, D. E. (2020). Core concepts, challenges, and future directions in blockchain. ACM Computing Surveys, 53(1), 1–39. https://doi.org/10.1145/3366370.
Kumar, A., Liu, R., & Shan, Z. (2019). Is blockchain a silver bullet for supply chain management? Technical challenges and research opportunities. Decision Sciences, 51(1), 8–37. https://doi.org/10.1111/deci.12396.
Küpper, D., Ströhle, J., Krüger, T., Burchardi, K., & Shepherd, N. (2019). Blockchain in the factory of the future. Boston Consulting Group. https://www.bcg.com/dede/publications/2019/blockchain-factory-future.
Kusiak, A. (2017). Smart manufacturing. International Journal of Production Research, 56(1– 2), 508–517. https://doi.org/10.1080/00207543.2017.1351644.
Lao, L., Li, Z., Hou, S., Xiao, B., Guo, S., & Yang, Y. (2020). A survey of IoT applications in blockchain systems: Architecture, consensus and traffic modeling. ACM Computing Surveys, 53(1), 1–32. https://doi.org/10.1145/3372136.
Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242. https://doi.org/10.1007/s12599-014-0334–4.
Lorenz, M., Hunke, N., Brown, J., Buchner, T., & Ludwig, M. (2019). The machinery maker’s guide to the IIoT galaxy. Boston Consulting Group. https://www.bcg.com/dede/publications/2019/machinery-maker-guide-industrial-internet-of-things-iiot-galaxy.
Mik, E. (2017). Smart contracts: Terminology, technical limitations and real world complexity. Law, Innovation and Technology, 9(2), 269–300. https://doi.org/10.1080/17579961.2017.1378468.
Nærland, K., Müller-Bloch, C., Beck, R., & Palmund, S. (2017). Blockchain to rule the waves — Nascent design principles for reducing risk and uncertainty in decentralized environments. Proceedings of the International Conference on Information Systems. https://core.ac.uk/download/pdf/145226366.pdf.
Ng, I. C. L., & Wakenshaw, S. Y. L. (2017). The internet-of-things: Review and research directions. International Journal of Research in Marketing, 34(1), 3–21. https://doi.org/10.1016/j.ijresmar.2016.11.003.
Ngai, E. W. T., Cheung, B. K. S., Lam, S. S., & Ng, C. T. (2014). RFID value in aircraft parts supply chains: A case study. International Journal of Production Economics, 147, 330– 339. https://doi.org/10.1016/j.ijpe.2012.09.017.
Pereira, J., Tavalaei, M. M., & Ozalp, H. (2019). Blockchain-based platforms: Decentralized infrastructures and its boundary conditions. Technological Forecasting and Social Change, 146, 94–102. https://doi.org/10.1016/j.techfore.2019.04.030.
Pólvora, A., Nascimento, S., Lourenço, J. S., & Scapolo, F. (2020). Blockchain for industrial transformations: A forward-looking approach with multi-stakeholder engagement for policy advice. Technological Forecasting and Social Change, 157, Article 120091. https://doi.org/10.1016/j.techfore.2020.120091.
Popov, S., & Lu, Q. (2019). IOTA: Feeless and free. Blockchain IEEE. https://blockchain.ieee.org/technicalbriefs/january-2019/iota-feeless-and-free.
Schmidt, C. G., & Wagner, S. M. (2019). Blockchain and supply chain relations: A transaction cost theory perspective. Journal of Purchasing and Supply Management, 25(4), Article 100552. https://doi.org/10.1016/j.pursup.2019.100552.
Sharpe, S. A., & Nguyen, H. H. (1995). Capital market imperfections and the incentive to lease. Journal of Financial Economics, 39(2–3), 271–294. https://doi.org/10.1016/0304- 405x(95)00830–8.
Stöcker, C. (2017). Automating machine transactions and building trust in the 4th industrial revolution. Medium. https://blog.iota.org/automating-machine-transactions-andbuilding-trust-in-the-4th-industrial-revolution-d3219a157396.
van Laar, S. (2019). A shortcoming of blockchain: The scalability trilemma. Medium. https://medium.com/@s_van_laar/a-shortcoming-of-blockchain-c588ea8e6db2.
Wang, Y., Singgih, M., Wang, J., & Rit, M. (2019). Making sense of blockchain technology: How will it transform supply chains? International Journal of Production Economics, 211, 221– 236. https://doi.org/10.1016/j.ijpe.2019.02.002.
Whitmore, A., Agarwal, A., & Da Xu, L. (2014). The internet of things — A survey of topics and trends. Information Systems Frontiers, 17(2), 261–274. https://doi.org/10.1007/s10796- 014–9489–2.
Yermack, D. (2017). Corporate governance and blockchains. Review of Finance, 21(1), 7–31. https://doi.org/10.1093/rof/rfw074.
Zhang, R., Xue, R., & Liu, L. (2019). Security and privacy on blockchain. ACM Computing Surveys, 52(3), 1–34. https://doi.org/10.1145/3316481.
Zhao, S., Li, S., & Yao, Y. (2019). Blockchain enabled industrial internet of things technology. IEEE Transactions on Computational Social Systems, 6(6), 1442–1453. https://doi.org/10.1109/tcss.2019.2924054.