The Road to Accountable and Dependable Manufacturing
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The Road to Accountable and
Dependable Manufacturing
Jan Pennekamp
Communication and Distributed Systems, RWTH Aachen University, Aachen, Germany
Roman Matzutt
Communication and Distributed Systems, RWTH Aachen University, Aachen, Germany
Salil S. Kanhere
School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
Jens Hiller
Communication and Distributed Systems, RWTH Aachen University, Aachen, Germany
Klaus Wehrle
Communication and Distributed Systems, RWTH Aachen University, Aachen, Germany
Abstract—In manufacturing, advances from the IoT foster the vision of a highly dynamic and
interconnected Industrial IoT. However, business-driven use cases mandate different levels of
security, privacy, accountability, and verifiability alike. Blockchain technology addresses these
requirements and thereby enables previously unforeseen collaborations. The authors emphasize
the need for active research at the intersection of IoT, CPS, and blockchain.
M ANUFACTURING is expected to signif- with dynamically evolving and flexible short-term
icantly benefit from recent advances in the areas relationships, we identify a new research pillar
of Internet of Things (IoT) and Cyber-Physical (P3) that enables accountable and dependable
Systems (CPS). Particular development directions dataflows for stakeholders without any trusted
include establishing highly-dynamic business re- or previous relationships (v). In this article, we
lations and creating interconnected production focus on the research pillars P1–P3 that consider
environments, even for short-lived collaborations, multiple stakeholders in collaborative processes.
through increasing degrees of automation based
Such industry-driven settings mandate special
on (sensor) data [1]. Concepts of the Industrial
needs that traditional solutions in the IoT can-
IoT (IIoT) or Internet of Production (IoP) [2] ex-
not satisfy. These aspects encompass improved
plicitly target to implement these improvements.
accountability and verifiability to deal with un-
Research mainly evolves around three existing certainty concerning the origin [3] and reliability
pillars (P0–P2): (P0) CPS and site-related im- of data [4], but also security and privacy re-
provements (œ) with limited external influences, quirements have to be considered as information
(P1) extended data sharing along the supply leakage can have tremendous consequences in
chain (Õ), e.g., to reduce the bullwhip effect, highly competitive environments [2]. We envi-
and (P2) secure industrial collaborations across sion that the consequent integration of blockchain
supply chains (Ö), e.g., to reduce ramp-up costs. technology provides these desired features by de-
To achieve P1 and P2 not only with today’s sign. Its tamperproofness offers verifiability and
(established) long-term trust but also in settings reliability once information has been recorded on
Computer Published by the IEEE Computer Society © 2020 IEEE
1
Authors’ version of a manuscript that was submitted for publication in Computer.Feature
the blockchain. Similarly, blockchains are decen- Information Sharing along Supply Chains (P1
tralized and thus well-suited for securing interac- Õ)
tions among mutually distrustful parties. Finally,
the extensible nature of blockchain technology Traditionally, supply chain data sharing was
enables scalability features, such as sidechains driven by large companies dictating their require-
or sharding [5], as needed for solutions across ments to all suppliers. In this setting, information
different use cases and domains. was collected in data sinks accessible by single
(large) players [4], e.g., automotive manufactur-
Given that research at the intersection of IIoT
ers. Furthermore, due to privacy concerns, data
and blockchain is still in its infancy, we identify
is usually shielded from external stakeholders,
three key research areas. We discuss blockchain-
for example, even rather insensitive information,
specific research questions for the industrial set-
such as delivery schedules or shipment tracking,
ting, which mainly evolve around the general
is retained locally. Today, additional data is only
scalability of proposed solutions and the privacy
shared under the promise of large financial im-
of participants. Similarly, we identify a lack
pacts despite production data being expected to
of manufacturing-specific solutions that integrate
improve manufacturers’ productivity and overall
blockchains to improve accountability in this do-
product quality [2].
main. We discuss scenario-driven research direc-
tions that close this gap and realize fast, versa- This situation is unsatisfactory as it fails to
tile, accountable, and dependable manufacturing address several desired aspects. Especially re-
enabled by blockchains. Furthermore, we discuss garding legislation, today’s landscape cannot reli-
arising socio-economic challenges. Particularly, ably provide (long-term) verifiability of relevant
new legal frameworks will need to take into information [6], e.g., provenance data for parts
account the increased usage of external data, po- in the aerospace industry or associated mainte-
tentially in safety-critical applications. First and nance protocols. Although additional processes
foremost, however, we want to raise awareness are often in place, counterfeit or non-fair trade
on how to establish trust into the authenticity products are, occasionally, still entering legitimate
and correctness of data on the blockchain as supply chains [7]. To improve the reliability of
a foundation for interorganizational data sharing (received) data, we envision technical solutions
within the IIoT. that minimize the room for manipulations and
provide an efficiently verifiable certification for
each individual product. Furthermore, a unified
MOTIVATION & POTENTIALS approach could improve governmental oversight,
which is especially desirable for safety-critical
Manufacturing is expected to compile vast products or food chains [8].
amounts of process and product data in the near
future [2]. Consequentially, we have to deal with Another insufficiency stems from the lacking
associated big data challenges that stand out due identifiability of root causes of manufacturing
to virtually infinite volumes of available sensor or product failures [6]. Currently, accountability
data and the increased need for high-frequency is mostly limited to contractually-bound stake-
sensing [1]. However, big data also provides holders. If not explicitly contractually negotiated,
opportunities when properly extracting its encap- individual untrusted suppliers may remain pas-
sulated knowledge [1]. Regarding manufacturing, sive or even behave adversely for their benefits,
this potential has previously been neglected for e.g., when covering up incidents. Simultaneously,
lack of globally available process information, missing feedback to estimate the lifetime or the
and even data sharing along the supply chain fit of a product, which both might depend on
was limited. Figure 1 illustrates the data sharing the application, hinders the implementation of
along (Õ) and across (Ö) supply chains, which improvements. To overcome such limits, acces-
we detail hereafter based on two fine blanking sible production and usage data can provide in-
lines. sights [2].
2 ComputerDataflow (P1 ⇄)
Automotive Fine Blanking Manufacturer Dataflow (P2 ⇅)
Lubricant Supplier
Automotive Assembly
Material Supplier
Tool Manufacturer Aerospace Fine Blanking Manufacturer Aerospace Assembly
Supply Chain Flow
Figure 1: Manufacturing engulfs both dataflows along the supply chain (P1 Õ)and across supply
chains (P2 Ö). Suppliers (here: for lubricants, material, and tools) support manufacturers who
themselves provide subsequent assembly lines with production data. Similarly, manufacturers exchange
process information (here: fine blanking lines), the processed material, and their interplay. A currently
non-existing relationship between both assembling companies could be non-existent due to the
untrusted environment (P3 v). We adapted the figure from our analysis of dataflows in an Internet of
Production [2].
Foundations for Expanded Secure Industrial unexplored.
Collaboration Across Supply Chains (P2 Ö)
In addition to the marginal data sharing along Ad-Hoc Relationships in Untrusted
supply chains (P1 Õ), data exchanges across Environments (P3 v)
supply chains (P2 Ö)are basically non-existing When considering relationships with previ-
in today’s manufacturing landscape [1]. While ously unaffiliated and thus untrusted compa-
manufacturers gather usage data from their cus- nies (P3 v), several additional use cases emerge.
tomers (in centralized data silos), virtually no Along supply chains (P1 Õ), identifying the
knowledge exchange happens between different ideal supplier for a component is simplified when
operators of (identical) machines [2]. For ex- the utilization of relationships among previously
ample, experiences with used machine config- unaffiliated parties is improved. Similarly, ex-
urations or information about the (expectable) changing information with companies in related
production quality can reveal interesting insights domains across supply chains (P2 Ö)is currently
into newly configured manufacturing processes. hindered by a lack of trust between the in-
Hence, all knowledge is retained locally without volved stakeholders. We expect that more use
global availability, despite potentially tremendous cases surface once the first steps towards secure
benefits [2]. industrial collaboration have been taken as busi-
To improve productivity and to decrease costs, nesses are naturally cautious when sharing sen-
companies could, for instance, share ideal ma- sitive and valuable details, especially production
chine configurations for their workpieces, e.g., and product data [2]. Furthermore, we observe
within their fine blanking line, without revealing that currently no uniform standardization for data
all details to the machine supplier. Furthermore, sharing exists, which especially hinders flexible
this information exchange may reduce ramp-up relationships as company-specific adjustments are
times of new manufacturing processes by deriv- required for each new partner [4].
ing machine parameters from readily available In the context of accountable and dependable
information (cf. Figure 1). Consequentially, non- manufacturing, we also have to address privacy
competing companies can cooperate and jointly and safety [9]. Appropriate means are not yet
assemble a shared knowledge base in a give-and- available, or they are not proven or tested in
take manner or offer their valuable data for sale. manufacturing [1]. A major milestone to establish
As of today, a lot of expected potential is still trust can be achieved by providing accountability,
September 2020
3Feature
verifiability, and transparency for all actions and digital ownership of property, coupons, or stock-
traded information. Consequentially, blockchains marketing shares through a cryptocurrency’s
are a promising tool to establish trust in mutually blockchain, users can tie assets to blockchain
distrustful manufacturing markets and to eventu- transactions. Beyond that, notary services
ally allow for interorganizational data sharing and immutably attest the existence of documents by
novel applications. storing a cryptographic hash on a blockchain,
a tamperproof identifier to which owners can
THE INFLUENCE OF BLOCKCHAINS subsequently refer to.
Blockchain systems have matured consider-
ably since their introduction through Bitcoin in Process Automation Smart contracts [5] re-
2008. Initially created for the decentralized, yet alize the automated execution of transactions
secure, management of digital currency, the po- once the blockchain’s state satisfies their one-time
tential of blockchains for larger and more diverse programmable conditions. This tamperproof pro-
tasks was quickly identified across academia and grammability allows for transparent automation
industry. of global processes. While Ethereum popular-
ized blockchain-based smart contracts, business
applications are commonly built using consor-
The State of Blockchain Integration
tium blockchains, e.g., created through Hyper-
We now reiterate impactful milestones and
ledger Fabric or the Ethereum-compatible Quo-
applications of distributed ledger technology to
rum. Beyond the banking sector, insurers pro-
assess its current level of integration into business
cess insurance claims without human interaction
processes and to identify areas where blockchains
through smart contracts. An increased demand
have been applied successfully.
for blockchain-based process automation sparked
the creation of Blockchain-as-a-Service solutions,
Financial Origins Bitcoin paved the way for e.g., offered by Microsoft Azure, IBM, and Ama-
global financial transactions without banks as zon Web Services. These services lower the bar-
intermediaries. Besides inspiring numerous com- rier for creating blockchain-backed architectures,
parable cryptocurrencies, the banking sector also but also introduce an infrastructure provider as a
noticed the potential of blockchains to improve new centralized entity.
transactions between financial institutes. This de-
velopment yielded major blockchain-based inter- Internet of Things Advances in process au-
bank networks, e.g., the Ripple payment and tomation proliferated the vision of coupling au-
exchange network or JP Morgan’s Interbank tonomous IoT devices with blockchains. The
Information Network. Furthermore, blockchains main advantages of blockchain-based IoT infras-
promise to provide better, i.e., more direct, cus- tructures lie in the immutable and decentralized
tomer experience at lower costs due to more auto- IoT-based sensing of physical environments in
mated, disintermediated processes. Especially in conjunction with the accountable recording of
scenarios where participants are known, and their actuation events. If seized well, these capabilities
majority is trusted, consortium blockchains are can significantly simplify applications for smart
seen as key enablers for shaping new transaction cities, e.g., smart microgrids [11] or vehicular
processes in highly distributed applications, e.g., networks [12]. Here, blockchains aid trust man-
accounting in supply chains. agement and access control to sensed data alike.
Digital Assets One of the first non- Supply Chain Blockchains may be used as
cryptocurrency applications of blockchains an architectural pillar for reshaping supply
was the establishment of digital assets and notary chains [13], [7], [6], [14], especially due to
services. While dedicated solutions, such as improved financial transactions, asset manage-
Namecoin, were launched quite early, numerous ment, process automation, and data manage-
such services piggyback on existing blockchains, ment. However, smooth integration is still lack-
commonly Bitcoin [10]. Particularly, to transfer ing [9]. TrustChain [8] or ProductChain [3] al-
4 Computerready tackle important issues of supply chain Open Blockchain-Inherent Challenges (L1)
deployments, such as reputation-based trust man- As groundwork for more scenario-specific re-
agement among suppliers and provenance track- search, we identify blockchain-induced research
ing for customers. Still, holistic, all-encompassing areas that surface when relying on blockchains
approaches to improve supply chains based on for accountable and dependable manufacturing.
distributed ledgers are yet to come.
Scalability Permissionless blockchains tradi-
tionally struggle with limited scalability in terms
Useful Properties for Diverse Applications of transaction throughput, transaction latency, and
Even today’s limited integration of blockchain storage requirements. For instance, Bitcoin fa-
technology into business processes highlights that mously has a low transaction rate of only 3.5
distributed ledgers have proved to provide valu- transactions per second as its 10-minute inter-
able foundations for various domains, applica- block delay requires users to wait for an hour
tions, and use cases. Particularly, we highlight to safely accept payments [5]. Even though con-
that blockchain technology provides desirable sortium blockchains can utilize more efficient
contributions to flexible collaborations and es- consensus algorithms [15], recording large num-
pecially to applications involving supply chains. bers of events on-chain still remains challeng-
First, the decentralized nature of blockchain ap- ing. Solutions may aggregate multiple events
plications suits the highly distributed and het- into single or few (on-chain) transactions, similar
erogeneous environments created by collabo- to micropayment channels that boost transaction
rating companies and supply chains. Second, throughputs in today’s cryptocurrencies. Further-
blockchains can provide data integrity and verifia- more, applying sharding schemes [5] to consor-
bility even if collaborators are partially distrusting tium blockchains may improve their transaction
each other. As part of this process, recorded data throughput as these schemes target to partition
is kept on a tamperproof ledger. Finally, estab- the network and to distribute the responsibility
lished measures to keep track of digital assets for transaction processing.
and to prevent double-spending enable the pub- Another scalability issue is the ever-increasing
lic, transparent traceability of products or their storage requirement to operate blockchains. For
components. However, the decentralization and instance, heavily-utilized blockchains today ac-
immutability of blockchains creates issues that cumulate hundreds of Gigabytes of historical
were not present in traditional business processes. data. This problem is aggravated in the context
Next, we thus dive into resulting challenges that, of supply chain applications once suppliers are
once tackled, will help realize suitable full-stack required to tie their reports for other contrac-
solutions for improving business processes via tors immutably to the blockchain. Pruning strate-
distributed ledgers. gies have been proposed to unburden blockchain
nodes from storing historic transaction data that
has become obsolete meanwhile [16]. However,
OPEN RESEARCH AREAS applications relying on blockchain-extrinsic data
We identify three layers of open research cannot immediately seize this potential since what
areas that we illustrate in Figure 2: (L1) yet constitutes obsolete data has to be defined on
unaddressed challenges for the use of blockchain a per-application basis. Again, also partitioning
technology in manufacturing, (L2) new opportu- data storage across the network with sharding
nities for a fast, versatile, accountable, and de- schemes can reduce per-node storage require-
pendable manufacturing enabled by blockchains, ments. Overall, future research needs to assess
i.e., scenario-driven challenges, and (L3) socio- the need for long-term data availability to allow
economic challenges stemming from immutably for efficient and scalable solutions.
recorded production data and highly flexible
cross-company collaborations. We consider these Efficiency Wide-spread adoption of
layers to be highly relevant when shaping the blockchain technology in supply chains
future of interconnected manufacturing. necessitates an efficient operation of the
September 2020
5Feature
L3:
Socio-Economic
Challenges
Legal Frameworks Access & Transparency
(Governmental Oversight) (Platform Openness)
L2: P1 ⇄ P1 ⇄ P2 ⇅ P1 ⇄ P2 ⇅ P3 ❖
Reliable Efficient & Dependable Dynamic
Scenario-Driven
Product Information Collaboration Distributed Markets
Challenges
• Accountability along the supply chain • Granularity of data sharing • Trade-off privacy vs. verifiability
Questions
Questions
Questions
Research
Research
Research
• Correctness of available information • Private accountable billing of companies • Fairness of data sharing
• Tamperproofness of measurements • Keeping automation with more flexibility • Maintaining a data catalogue
• Untampered digital processing • Granting access to external companies • Privacy-preserving bidding platform
• Linking of physical goods and its data • Dynamic digital factories • Measuring the value of data
TrustedStore
(Trustworthy Information Store)
L1:
Blockchain-Inherent
Challenges
Blockchain size Operational costs Persisted garbage Sensitive metadata
Transaction throughput Workload on nodes Outdated data Information leakage
Distributing responsibility Environmental impact Correctness of information Verifiability & transparency
Scalability Efficiency Immutability Privacy
Figure 2: We group research towards accountable and dependable manufacturing into three layers.
L1: Blockchain-inherent challenges that concern the properties of blockchain technology which is
expected to serve as an underlying key component of our envisioned TrustedStore.
L2: Scenario-driven challenges that can be grouped into three main research directions that each focus
on a specific research pillar, i.e., along supply chains (P1 Õ), across supply chains (P2 Ö), and
situations with insufficient trust between stakeholders (P3 v).
L3: Socio-economic challenges that have an impact on underlying collaborations and improvements.
To offer viable solutions for accountable and dependable manufacturing, research must consider and
tackle all layers and their individual research challenges.
infrastructure. To this end, any proposed requirements of the overall system. The main
architecture must take the deployment and bottleneck of traditional blockchains is the
operation costs into account, with a special focus redundant execution of various tasks, such
on computing overhead for securely keeping as verifying digital signatures or maintaining
data on-chain. Improvements in efficiency a local state [5]. This redundancy not only
mainly originate from more fundamental lines increases costs but also creates a potentially
of research, e.g., advances in authentication, avoidable environmental impact. Solutions, such
distributed consensus, or secure communication. as sidechains or sharding [5], that distribute the
Yet, a proper integration of these advances workload without lowering security guarantees
into a full blockchain-based architecture is will help to reduce the operating costs. While
mandatory to seize this potential for efficient these concepts are primarily being researched for
data management and to not undermine any public settings, the envisioned high-frequency
6 Computerutilization and large volumes of data call for tors may be inferred, putting affected parties at
similar developments for consortium blockchains. a disadvantage against competitors, e.g., during
price negotiations or when company acquisition
Immutability Recording events immutably de- is imminent. A key challenge for sustainable
spite the presence of adversaries eager to alter consortium blockchains will be carefully gauging
history is arguably the blockchain’s key achieve- the desired level of point-to-point collaborations
ment. Thus, storing non-financial, application- and consequently tackling arising trust barriers
specific data on-chain or referencing such data through both trust and data management.
through on-chain fingerprints, has become a fre-
quent proposition [10]. However, this immutabil- Scenario-Driven Research Directions (L2)
ity has also proved to create further issues On top of the blockchain-inherent challenges,
than only impacting the long-term scalability of further research directions may lead to a fast, ver-
blockchains, e.g., distributing and storing un- satile, accountable, and dependable blockchain-
wanted blockchain data can cause legal liabil- backed manufacturing (cf. Figure 2). Research
ity [16]. While the prevalence of known identi- into (i) reliable product information will en-
ties within consortium blockchain mitigates such sure the availability of high-quality data along-
risks, different stakeholders may nevertheless be side all production steps of a supply chain (P1
in conflict about the value of recorded data, e.g., Õ), ranging from tamperproof sensing to se-
whether data is outdated or when unknown raw cure blockchain storage. Based on this reliable,
data formats pollute the shared storage. Overall, high-quality information more (ii) efficient and
the quality of recorded information becomes more dependable collaborations can form in the fu-
important as participants should be able to rely ture that will increasingly affect dataflows across
on data that is recorded by other parties that supply chains (P2 Ö). Ultimately, (iii) dynamic
exhibit varying individual levels of trust. Today, a distributed markets allow for flexible sharing of
link between a physical (product) property and its data and advertising services, especially when
digital data is missing, which limits the consensus stakeholders without any trusted or previous rela-
algorithms’ ability to verify claimed events before tionships intend to collaborate (P3 v). This way,
persisting them on-chain, e.g., sensor readings collaborators can efficiently foster fast, versatile,
from inaccessible, remote environments. Correct- and dependable business relations.
ing identified errors is trivially possible by over-
writing data in a new transaction, but implies Reliable Product Information Today, large-
a more complex transaction processing by all scale production and supply chains (P1 Õ) are
parties. Hence, further research is required to opaque regarding processes and the origin of
explore the trade-off between data availability processed goods [4]. Consequentially, failure root
and data utility as well as data verifiability and causes and other issues cannot be tracked down
efficient corrections. efficiently, creating massive administrative over-
heads [6], [14], e.g., hampering legal investiga-
Privacy Tightly related to the individual data tions, causing over-dimensioned product recalls,
value for different stakeholders involved in the or an inefficient lookup of compatible spare parts
consortium blockchain is the notion of data pri- for repairs or assembling bigger workpieces. Sim-
vacy, which applies not only to traditional privacy, ilarly, feeding back information from mid-term
e.g., storing and trading customer data, but to or long-term field experience into manufacturing
information leakage in general [16]. On the one processes for improvements is hard [2].
hand, blockchains may disclose sensitive busi- To overcome these limitations, manufacturing
ness secrets [13], such as capabilities of produc- needs a reliably accessible, tamperproof informa-
tion machines or process details, e.g., required tion store that links clearly identifiable products
temperatures or metal alloys, both directly and to their physical state in a verifiable manner.
indirectly. On the other hand, meta-information For example, the transportation of fresh produce,
such as the frequency of transactions between which must uphold a mandated cold chain, re-
two collaborators or key performance indica- quires the container’s temperature to be con-
September 2020
7Feature
tinually monitored such that tricking sensors is ternatively, companies store raw data in globally
infeasible [8]. distributed certified data stores and prove such
First, this process requires measures to storage to the TrustedStore. Overall, decoupling
achieve a tamperproof gathering of physical-state the storage of large amounts of raw data from
information. Here, we identify tailored machine derived insights and key properties ensures the
learning mechanisms for anomaly detection as immutability and availability of rich raw data
promising research area. Such a machine learning while keeping reasonable loads for globally main-
algorithm can base on the following data: (i) Us- tained infrastructures.
ing multiple sensors allows for cross-checking Second, a tamperproof digital processing of
gathered data, e.g., sensors redundantly monitor- gathered data ensures that original sensor read-
ing the container from different vantage points ings enter the blockchain-backed TrustedStore
can increase tamper resilience as already subtle correctly. This way, data can be collected even
monitoring inconsistencies could unveil manipu- from untrusted or hostile environments, e.g.,
lations. (ii) Similarly, different sensor types and to realize new collaborations without sufficient
measuring methods further increase the range for trust levels. Tamperproof sensors can provide
sensing correlation to detect anomalies regard- this form of dependable data gathering and pro-
ing the coherence of real-world physical effects. cessing [17]. Such devices combine traditional
As sensor nodes cheapen and allow for long- sensors, e.g., RFID scanners, or temperature or
lasting battery-based operation, these solutions humidity sensors [18], with trusted computing
are also becoming increasingly economically vi- mechanisms, such as hardware security modules
able. (iii) Further, high sampling rates also im- (HSMs). These security-enhanced sensors are
prove tamper resilience, as more readings are able to immediately hand over data to HSMs for
available to identify inconsistencies. Overall, the processing, thereby minimizing the attack surface
gathered data provides promising input for a for tampering. Ultimately, the HSM uploads the
machine learning-based anomaly detection. sensor readings to the local storage and stores
Still, storing these large amounts of raw data their fingerprints on the TrustedStore. From this
(i–iii) in globally replicated tamperproof storages point on, the reliably-sensed data is persisted
such as the blockchain remains challenging. In- immutably.
stead, we envision a combination of mid-term Assuming mechanisms for tamperproof sens-
local storages maintained by companies and a ing and blockchain inclusion, we finally must
long-term distributed information store. In this clearly link these readings to the respective phys-
deployment model, companies store their raw ical products, e.g., via camera tracking, RFID
production data locally and signal its availability tags, imprints, or other markings. Importantly,
on-chain via fingerprints. Further, the blockchain this identification must also be tamperproof, using
stores (small-sized) insights that result from anal- suitable mechanisms as described before.
yses of the locally stored raw data. Likewise, In summary, this research will yield a reliably
this storage happens in a certified manner, overall accessible, tamperproof TrustedStore for produc-
creating a trustworthy information store, which tion data to establish accountability along any
we refer to as TrustedStore. To ensure that com- supply chain. Beyond aiding legal investigation,
panies fully preserve raw data locally, certified managing product recalls, and optimizing parts
service providers (verifiers) periodically check if utilization, this TrustedStore can further serve as
local stores match with the TrustedStore, so that a medium to foster collaborations among well-
misbehavior can be detected in a timely manner known and novel companies alike.
and appropriately acted upon (legally). As the
amount of data renders full-blown checks imprac- Efficient and Dependable Collaboration
ticable from remote locations and on-site checks Established business relations with trust in place
involve high costs, they have to happen only can increase their efficiency with a dependable
rarely. In between, verifiers remotely request data TrustedStore. This claim especially holds for
for randomly selected fingerprints to frequently, dataflows across supply chains (P2 Ö) that could
yet economically, check for data availability. Al- improve the productivity in manufacturing qual-
8 Computerity [2]. Additionally, sharing workpiece data, required granularity of sharing data to achieve
production machine schedules, and states in a these envisioned benefits. As business secrets are
timely manner enables close collaborations, ac- potentially at risk when providing information
cumulating companies into digital factories with to external, partially trusted collaborators [2],
production efficiencies similar to single, multi- companies have to make informed decisions when
factory companies. Rich information flows allow trading off efficiency and profit for data privacy.
for a cross-company allocation of machine time
and flexible handling of process deviations [2], Dynamic Distributed Markets Ultimately,
e.g., by automatically reallocating machine ca- we envision (distributed and transparent)
pacity in case of delays. Here, the TrustedStore blockchain-based bidding platforms that realize
enables trustworthy tracking methods for work- fast, versatile, yet dependable markets for goods,
pieces along the full (multi-factory) supply chain. services (e.g., machine rentals), and configuration
As a result, problems can easily be tracked, and knowledge, especially fostering collaborations
clearly assigned responsibilities motivate partic- between–previously unknown and potentially
ipants to comply with their obligations. Most untrusted–business partners (P3 v). Today’s
basically, this information allows for detecting business relations typically evolve over long
infringements early on, e.g., misconfiguration or periods and trust builds up slowly or is enforced
maintenance backlogs. through complex contracts. Blockchains can
Beyond supply chain management, Trusted- largely substitute social trust through technical
Stores simplify the billing of goods or ma- guarantees and thus foster the establishment of
chine usage (Manufacturing-as-a-Service) [2]. Es- new business relations. Furthermore, a distributed
pecially with production environments shifting TrustedStore allows for efficient automation,
from generic mass production to individual prod- e.g., the allocation of machine time, achieving
ucts, companies require verifiable and highly au- high utilization even in adaptive manufacturing
tomated payment processes to keep administrative processes. Consequentially, manufacturers can
burdens at a reasonable level. Even pay-as-you- generate profit even from short-time business
go contracts for cost-efficient machine usage in relations for single workpieces, which would
adaptive production are conceivable where cus- otherwise be uneconomical and incur high risks.
tomers pay only for the resources and energy Customers can search for the best-matching
required to create the requested (potentially low- offer and benefit from reasonable prices due to
quantity) workpieces. Thereby, high degrees of increased market competition. Especially smaller
automation enable manufacturers to maintain a manufacturers can profit from low-barrier mar-
high utilization as multiple customers can share ket access to appeal to customers and business
single machines with almost no downtime. partners and easily increase (domain) knowledge
Managing data from mid-term and long-term through the TrustedStore.
field experience on the TrustedStore promises However, the realization of these distributed
further benefits. In contrast to the previously markets faces a big challenge, i.e., the potential
discussed less sensitive product data, the process disclosure of business secrets. For example, big
data considered here is more valuable and, thus, companies could exploit the TrustedStore’s infor-
must be protected accordingly. Nowadays, infor- mation to suppress competitors, e.g., by engaging
mation on product life cycles, required mainte- in well-informed price dumping. Thus, a funda-
nance intervals, or production quality variations is mental research question is how to match business
exclusively accessible to the manufacturer. Using partners based on desired capabilities and quality-
the TrustedStore, such data becomes accessible guarantees without requiring manufacturers to re-
to current and prospective machine users alike veal too sensitive information up front. Promising
(cf. Figure 1). Here, the TrustedStore provides building blocks for such a privacy-preserving
evidence of data correctness. Data of individual catalog are known from privacy-preserving com-
machines further facilitates reselling as prior us- puting. However, they require extensive research
age and output quality become assessable. to fit the desired scenario of privacy-preserving
Research has to answer questions on the bidding platforms for manufacturing.
September 2020
9Feature
Such mechanisms must realize fair data shar- the corresponding trade-off between verifiability
ing, i.e., participants must not obtain detailed and privacy. On the one hand, broad access
information about other participants, especially to information increases transparency such that
competitors, without providing said information customers can obtain information more easily.
themselves. To this end, mechanisms to assess the Research must reveal which information is nec-
value of data can provide measures to rate-limit essary, e.g., to alleviate the required trust from
or charge participants with extraordinary usage today’s slowly forming business relations via
patterns. technical measures to ease collaboration without
pre-established trust. Legal entities may further
Socio-Economic Challenges (L3) demand access, e.g., to discover cartels.
Beyond the outlined technical measures to On the other hand, information stored on
realize accountable and dependable manufactur- a (semi-)public blockchain must not subvert
ing, we also briefly discuss overarching socio- privacy-legislation. Specifically, granting broad
economic challenges (cf. Figure 2). access to information may put business se-
crets and privacy at risk. Furthermore, reason-
Legal Frameworks Legislation currently fails able freedom of action for market participants
to cover blockchain-based smart contracts and must be maintained. For example, adequate mea-
analyses have to show whether general rules sures must prevent customers from exploiting the
suffice to enable the envisioned business rela- knowledge of a participant’s low machine utiliza-
tions. Especially when considering global supply tion to achieve an uneconomic price. In the end,
chains, also different legal frameworks and multi- socio-economic research must develop guidelines
national agreements must be taken into account. for blockchain-based platforms that do not only
To realize the desired accountability, legal frame- optimize cost but lead to a healthy ecosystem with
works must further clarify the responsibility for incentives for high quality, economically healthy
the accuracy of information in a TrustedStore. companies, and employee well-being.
An exemplary question is whether manufacturers F UTURE manufacturing will be driven by ex-
should be responsible only for the data they pro- citing advances stemming from the combination
vide or whether they should also be responsible of IoT and blockchain technology to implement
for consistency checks on the received data. a dependable and accountable ecosystem. We
In terms of privacy, all systems must comply identified relevant future use cases for both supply
with local as well as multi-national rules for data chain-related and unrelated aspects that should
privacy, such as the GDPR, including the right significantly improve the utilization of manufac-
to erasure of previously recorded data. Thus, an turing data (cf. Figure 1). In particular, research
extensive analysis has to show which data is safe must address open challenges on different layers,
to be stored on-chain, and systems must prevent ranging from system-specific blockchain ques-
the inclusion of data that falls under the right to be tions to overarching socio-economic challenges
forgotten or provide mechanisms for data removal (cf. Figure 2). Regardless, we believe that most
without undermining the desired goals. effort must be invested in scenario-driven tasks to
Furthermore, several third-party services that enable trustworthy information stores, i.e., Trust-
use the available data are conceivable, e.g., uti- edStores, in competitive, business-driven, and po-
lizing individual usage data to offer improved tentially distrustful industry environments. Fortu-
maintenance for all customers. To this end, legal nately, smaller advances are already achievable in
frameworks have to clarify who owns the data increments, and as such first changes should be
on the blockchain and who is allowed to process realizable in the near future.
which data in which way. Similar questions also
arise for any derived knowledge. ACKNOWLEDGMENT
Funded by the Deutsche Forschungsgemein-
Access and Transparency Before realizing schaft (DFG, German Research Foundation) un-
immutable TrustedStores, research must work out der Germany’s Excellence Strategy – EXC-2023
the access requirements for different entities and Internet of Production – 390621612.
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FURTHER READING P. Niemietz, M. Rudack, S. Knape, A. Ep-
We provide references to further reading ma- ple, D. Trauth, U. Vroomen, T. Bergs,
terial related to this article for an overview into C. Brecher, A. Bührig-Polaczek, M. Jarke,
related work and today’s relevant research chal- and K. Wehrle, “Towards an Infrastructure
lenges. In particular, our selected literature pro- Enabling the Internet of Production,” in 2019
vides additional insights into challenges (1) and IEEE International Conference on Industrial
application areas (2–4) of blockchain technology Cyber Physical Systems (ICPS). IEEE, 2019,
as well as supply chain-specific research (5–6). pp. 31–37.
Finally, we include literature on the envisioned 8) Challenges in Big Data:
Internet of Production (7) and associated chal- A. Oussous, F.-Z. Benjelloun, A. A. Lah-
lenges when processing big data (8). cen, and S. Belfkih, “Big Data technologies:
1) Blockchain challenges: A survey,” Elsevier, 2018, Journal of King
Z. Zheng, S. Xie, H.-N. Dai, H. Wang and Saud University-Computer and Information
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portunities: A Survey,” Inderscience, 2018,
International Journal of Web and Grid Ser-
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2) Financial blockchain applications:
Y. Guo and C. Liang, “Blockchain applica-
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