=Paper= {{Paper |id=Vol-2397/paper12 |storemode=property |title=(How) Can Blockchain Contribute to the Management of Systemic Risks in Global Supply Networks? |pdfUrl=https://ceur-ws.org/Vol-2397/paper12.pdf |volume=Vol-2397 |authors=Gilbert Fridgen,Marc-Fabian Körner,Johannes Sedlmeir,Martin Weibelzahl |dblpUrl=https://dblp.org/rec/conf/wirtschaftsinformatik/FridgenKSW19 }} ==(How) Can Blockchain Contribute to the Management of Systemic Risks in Global Supply Networks?== https://ceur-ws.org/Vol-2397/paper12.pdf
(How) Can Blockchain Contribute to the Management of
      Systemic Risks in Global Supply Networks?

                 Gilbert Fridgen1,2, Marc-Fabian Körner1, Johannes Sedlmeir2,
                                    and Martin Weibelzahl1,2
    1
        FIM Research Center, University of Bayreuth, Wittelsbacherring 10, Bayreuth, Germany
         2
           Project Group Business & Information Systems Engineering of the Fraunhofer FIT,
                              Wittelsbacherring 10, Bayreuth, Germany


                          gilbert.fridgen@uni-bayreuth.de,
                              marc.koerner@fim-rc.de,
                        johannes.sedlmeir@fit.fraunhofer.de,
                         martin.weibelzahl@uni-bayreuth.de



           Abstract. Even though globalization has led to larger, faster, and more efficient
           supply chains, at the same time the new worldwide interconnection has also re-
           sulted in major challenges with respect to hidden systemic risks. In particular,
           there is a lack of a holistic perspective on the entire supply network. This missing
           global view prohibits the anamnesis and management of underlying risks.
           Against this backdrop, in this paper we discuss the potential contributions of
           Blockchain technology to systemic risk management in global supply chains and
           networks. Given the increasing number of recent initiatives of businesses in the
           context of Blockchain, we argue that Blockchain technology can lower the hurdle
           for the use of secure multiparty computation. Ultimately, it may be possible to
           implement a corresponding monitoring mechanism for systemic risks without (i)
           the need of a central authority and (ii) revealing competition relevant, confiden-
           tial information to other supply network participants.

           Keywords: Systemic Risks, Supply Networks, Blockchain, Secure Multiparty
           Computation.


1          Introduction

   With the steady progression of globalization, supply networks expand globally and
operate across borders. To address the growing global competition, companies are con-
tinuously increasing efficiency and speed, resulting in reduced inventory levels and
just-in-time production [1]. In the past decades, digitalization has successfully contrib-
uted to expanding and managing the resulting complexity of modern supply network
structures. However, while digitalization has helped to speed up business processes,
systemic risks have simultaneously increased, since failures may rapidly propagate




                                                   89
within fast-responding (supply) networks [2–5]. A prominent example for such propa-
gating effects are the floods in Thailand in 2011: After several tropical storms and
heavy rainfall, Thai manufacturers of hard disks were forced to shut down their pro-
duction temporarily [6, 7]. In an ex-ante unexpected intensity, this finally affected the
global production of notebooks and digital video recorders via different intermediate
manufacturers [8]. In fact, an ex-post investigation revealed that the involved manufac-
turers have had a significant market share at that time. As this example demonstrates,
an exogenous local event has led to a large global supply network disruption, where
rising prices for end customers heavily influenced markets all around the world. There-
fore, managing systemic risks is a major challenge in times of increased interconnection
and complexity of supply networks [9].
   As highlighted above, various suppliers and manufacturers that are worldwide dis-
tributed and connected characterize today's supply networks. However, there is no cen-
tral institution that could take over the anamnesis, diagnosis, or therapy of underlying
systemic risks. Given this lack of a global perspective and control, not only researchers
but also customers and managers are well aware of the significant challenges posed by
cascading risks and failures [10–13]. Ultimately, the results of the described develop-
ments may have highly negative consequences for end consumers, e.g., in form of rising
prices or decreasing welfare.
   On the other hand, Blockchain technology has caused a sensation with its first and
most popular application to date, the Bitcoin, for almost ten years [14]. The Blockchain
architecture unconditionally focusses on decentralization and hence for example ena-
bles a currency system with equal parties, i.e., without any central institution or inter-
mediaries [15, 16]. Its key properties concerning forgery protection, transparency of
rules, neutrality, and the already mentioned decentralization make Blockchain technol-
ogy highly relevant for cross-organizational workflow management and particularly for
applications in logistics and supply networks.
    Against this background, it is conceivable that for the first time all relevant players
of a supply network can meet on a common system with a uniform way of communi-
cation and a spirit of cooperation in competition (“coopetition”). In principle, such a
meeting would make it possible to collect all relevant data in order to identify systemic
risks with comparatively little effort. However, members of a supply network may still
hesitate to share their typically confidential data. Given this potential hesitation, this
paper discusses the opportunities of Blockchain technology for managing systemic
risks in global supply networks by using secure multiparty computation. Of course, the
latter technology has been known for quite some time, but practical applications in the
supply sector have not been observed, yet. In this paper, we argue that with the presence
of new Blockchain infrastructures, secure multiparty computation has the potential to
derive different risk-related metrics of a supply network. In particular, for computing
such metrics, inputs from various supply network participants can be used without any
company gaining additional information except the final result.
   This paper is organized as follows: We will first describe main Blockchain-related
developments in supply networks in Section 2. Based on these developments, Section
3 will subsequently discuss the opportunities of Blockchain to address the challenges




                                            90
of systemic risks by being an economic enabler for secure multiparty computation. Fi-
nally, the paper concludes with a summary in Section 4.


2      Distributed ledger technologies and the rise of Blockchain-
       based initiatives in supply networks

   Although Blockchain is the most commonly used term for the technology under con-
sideration in this paper, we will place Blockchain in the more general context of “Dis-
tributed Ledger Technologies” (DLT). DLT is a collective term for distributed data-
bases within a peer-to-peer network that typically employs a combination of crypto-
graphic methods on the technical side and principles from game theory as economic
incentives in order to create consensus between the participants [17]. Consensus refers
to a commonly accepted definition of what the rules are, e.g., “append-only” or “no
double spending”. Such rules can then enforce immutability of data in the Blockchain,
facilitate digital money (cryptocurrencies), or joint execution of scripts – so-called
smart contracts – in a trusted way without the need for an intermediary [18, 19]. In
DLT-based architectures, usually the same data is stored on every single node, resulting
in complete transparency of the data in the ledger. In general, the concrete design of
distributed ledgers can take various forms depending on reading or writing permission
(permissioned vs. permissionless), efficiency, or the degree of centralization (public vs.
private).
   A special type of DLT is Blockchain technology. The latter employs a specific, linear
data structure of blocks that are linked by inserting the hash-value of the previous block
into each block. In fact, the first and most prominent representative of a distributed
ledger application, namely the Bitcoin network for the well-known cryptocurrency [14],
is a Blockchain. However, the number of applications of DLT has increased rapidly in
the last years, as researchers and practitioners consider them to have a radical potential
not only for cryptocurrencies, but also for various other areas [20], e.g., the energy
sector [21] or general supply networks [22–24]. Since most of the applications so far
have the structure of a Blockchain, the latter is the more popular term, and hence we
will also mainly use the word “Blockchain” in this paper – even though most statements
are also true for DLT in general.
   The generic idea behind the use of Blockchain is the implementation of an IT archi-
tecture that ensures manipulation security and transparency of rules without the need
of a trusted intermediary. In other words, Blockchain technology can facilitate so-called
“neutral platforms”. It could therefore also take on the role of a coordinating, trusted
central authority that currently does not exist in global supply networks [16].
   In this context, logistics and supply networks have long strived for improved digi-
talization, automation, and coordination, which is only possible if the relevant players
agree to participate on some kind of common platform. However, participants may hes-
itate to entrust competition-relevant information (e.g., data on their suppliers or cus-
tomers) not only to rivals, but also to a central institution – regardless of whether such
an institution is represented by a government or by a private company. In particular,
even if all participants in the network were to agree that a central authority would make




                                           91
sense to coordinate and monitor the network, this authority would possess a central
market role and thus a considerable amount of market power. Finally, not only eco-
nomic, but also political considerations might suggest a refusal of such a potential mo-
nopolist.
   Already today, a non-negligible number of consortia and initiatives – often either
consisting of or being supported by global players – aim at employing Blockchain to
pursue the latter goal have been formed. These initiatives try to tackle practical prob-
lems in operation and management of modern supply networks with the help of Block-
chain-based solution approaches, e.g., problems related to missing data integration, lim-
ited information about the manufacturing process, or the huge effort with respect to
necessary paperwork [16, 22]. Ultimately, with the described initiatives, the involved
companies aim at realizing positive effects on the efficiency of their supply networks,
on product quality, and on customer confidence [25]. For example, IBM and Maersk
created the so-called “TradeLens” initiative in 2018 to implement a Blockchain infra-
structure within a global supply network. Furthermore, also Walmart implemented a
Blockchain-based supply network platform to trace its pork and mangos for tackling
food scandals [25, 26]. Given these well-known initiatives, further and more advanced
Blockchain-based neutral platforms and new ecosystems are expected to evolve in the
coming years.


3      Secure Multiparty Computation, its relation to Blockchain,
       and the corresponding potential for managing systemic risks

    As described in the previous section, companies usually keep their suppliers and
customers in the supply network secret and hesitate to give corresponding information
to their competitors. One of the main reasons is that information asymmetries in supply
networks are often an integral part of the business secret and therefore provide the foun-
dation for profitability of companies. In particular, companies may also not be willing
to give such information to a trusted central institution even if the resulting, aggregated
information on the general state of the network would be highly relevant for individual
decision making.
   Against this backdrop, secure multiparty computation (SMC), which has already
been a subject to research since the 1970s, provides the ability to perform computations
which use data inputs from different participants without distributing the inputs among
the participants or having to disclose any of them to a third party. The following exam-
ple, which is inspired by [27], illustrates the basic idea behind secure multiparty com-
putation by a simplified sketch of secure addition: Let us assume three involved com-
panies denoted by A, B, and C. The associated private numbers of the companies are a,
b, and c. In order to compute their sum by means of SMC, company A first generates a
random number r in a sufficiently large range and gives 𝑟 + 𝑎 to company B. In turn,
company B adds its own number 𝑏 and passes the result to company C, which then adds
𝑐 and arrives at 𝑟 + 𝑎 + 𝑏 + 𝑐. This subtotal is subsequently forwarded to company A,
which is the only company which knows 𝑟. Company A can then subtract 𝑟 from the
last subtotal and gets the desired result a+b+c. Finally, A communicates this sum to




                                            92
the other companies. Note that this protocol makes sure that no single company can
draw any conclusions about the others’ individual inputs. Consequently, none of the
three companies gets any additional information apart from the final sum a+b+c. Also,
no central authority is needed to perform the protocol. Figure 1 summarizes this simple
example for secure addition among the three companies.




                Figure 1: Secure addition among three companies A, B, and C

   Even though the example is quite simple, it gives an illustrative way of describing
the main functioning of SMC. In its standard version, “curious-but-honest” participants
are assumed. More advanced problems often employ further mechanisms such as per-
muting the roles of A, B, and C in order to detect potential misbehaviour by checking
whether the result is the same for each permutation. For even more enhanced security,
such as ruling out collusion among a subset of the participants or ensuring tap-proof
information exchange, cryptographic methods can be employed.
   Academic literature already suggests several metrics for measuring systemic risks in
supply networks: Among the most common examples is the “betweenness centrality”
[28, 29]. The latter metric is calculated as a weighted sum of market shares of a specific
good along shortest paths (with respect to suitable metrics) in a network. By using an
appropriate secure multiparty computation protocol, such metrics can be computed in
complex supply networks, too [30]. However, it remains to be analyzed how much in-
formation about the network can be reconstructed from the explained quantities such
as betweenness centrality. In particular, the extent to which the results of a SMC pro-
tocol should be published needs to respect the degree of anonymity in the network or
the severity of a systemic risk.
   From a technical perspective, it is not necessary to have a Blockchain architecture
set up in order to perform SMC protocols. Rather, a network is required in which the
involved participants can meet and exchange data, ideally securely. Up to now, no such




                                           93
system of relevance with the purpose of SMC has been formed in practice. The advent
of Blockchain-based platforms can significantly lower the barrier to establish and uti-
lize SMC applications in supply networks. First examples are already being tested on
Hyperledger Fabric, which is the Blockchain IT-architecture behind TradeLens [31]. It
is therefore conceivable that the addressing of systemic risks in supply networks may
soon become a realistic scenario.
    To sum up, Blockchain may provide the basic infrastructure on which companies
can (pseudonymously) identify themselves and exchange data under a certain degree of
standardization. Given current Blockchain initiatives, there is a realistic chance of es-
tablishing decentralized and far-reaching networks where SMC protocols can be exe-
cuted to compute critical risk metrics. Taking on the task of a trustworthy central au-
thority, the latter metrics may then be used to monitor the risks of global supply sys-
tems. In this respect, Blockchain in combination with SMC may have the potential of
better managing and regulating entire supply networks without pillorying individual
companies.


4       Conclusions

   Being a catalyst for globalization, digitization allows to trade faster across borders
and to operate global supply networks more efficiently. With a growing global interde-
pendency and interconnection, there is an increasing threat of systemic risks at the same
time. Ultimately, such risks may result in failures that spread faster and more exten-
sively in modern supply networks than ever before.
   As we argue in this paper, distributed ledgers like Blockchains in combination with
secure multiparty computation may have the potential to tackle the challenges of de-
tecting and managing systemic risks in large supply networks. In particular, Blockchain
technology could take on the role of a central authority, which does currently not exist
in global supply networks, and grant access to data that is relevant for an anamnesis,
diagnosis, or therapy of systemic risks.



References

1.   Manuj, I., Mentzer, J.T.: Global Supply Chain Risk Management. Journal of Business Lo-
     gistics 29, 133–155 (2008)
2.   Mertens, P., Barbian, D.: Die Wirtschaftsinformatik der Zukunft–auch eine Wissenschaft
     der Netze? HMD Praxis der Wirtschaftsinformatik 51, 729–743 (2014)
3.   Buhl, H.U., Penzel, H.-G.: The Chance and Risk of Global Interdependent Networks.
     Business & Information Systems Engineering 2, 333–336 (2010)
4.   Fridgen, G., Stepanek, C., Wolf, T.: Investigation of exogenous shocks in complex supply
     networks – a modular Petri Net approach. International Journal of Production Research 53,
     1387–1408 (2015)
5.   Mertens, P., Barbian, D.: Researching “Grand Challenges”. Business & Information Sys-
     tems Engineering 57, 391–403 (2015)




                                              94
6.  Haraguchi, M., Lall, U.: Flood risks and impacts: A case study of Thailand’s floods in
    2011 and research questions for supply chain decision making. International Journal of
    Disaster Risk Reduction 14, 256–272 (2015)
7. Abe, S.: Impact of the Great Thai Floods on the International Supply Chain. Malaysian
    Journal of Economic Studies 51, 147–155 (2017)
8. Chongvilaivan, A.: Thailand's 2011 flooding: Its impact on direct exports and global sup-
    ply chains (2012)
9. Helbing, D.: Globally networked risks and how to respond. Nature 497, 51 (2013)
10. Little, R.G.: Controlling cascading failure: Understanding the vulnerabilities of intercon-
    nected infrastructures. Journal of Urban Technology 9, 109–123 (2002)
11. Watts, D.J.: A simple model of global cascades on random networks. Proceedings of the
    National Academy of Sciences 99, 5766–5771 (2002)
12. Lorenz, J., Battiston, S., Schweitzer, F.: Systemic risk in a unifying framework for cascad-
    ing processes on networks. The European Physical Journal B 71, 441 (2009)
13. Buldyrev, S.V., Parshani, R., Paul, G., Stanley, H.E., Havlin, S.: Catastrophic cascade of
    failures in interdependent networks. Nature 464, 1025 (2010)
14. Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash system (2008)
15. Schweizer, A., Schlatt, V., Urbach, N., Fridgen, G.: Unchaining Social Businesses –
    Blockchain as the Basic Technology of a Crowdlending Platform. ICIS 2017 Proceedings
    (2017)
16. Fridgen, G., Radszuwill, S., Urbach, N., Utz, L.: Cross-Organizational Workflow Manage-
    ment Using Blockchain Technology - Towards Applicability, Auditability, and Automa-
    tion. Hawaii International Conference on System Sciences 2018 (HICSS-51) (2018)
17. Ferdinando M. Ametrano: Bitcoin, Blockchain, and Distributed Ledgers: Between Hype
    and Reality. SSRN (2016)
18. Peter H, M.A.: Blockchain-applications in banking & payment transactions: Results of a
    survey. European financial systems 2017: Proceedings of the 14th International scientific
    conference, 141–149
19. Davidson, S., Filippi, P. de, Potts, J.: Disrupting Governance: The New Institutional Eco-
    nomics of Distributed Ledger Technology. SSRN (2016)
20. Beck, R., Müller-Bloch, C.: Blockchain as radical innovation: a framework for engaging
    with distributed ledgers as incumbent organization (2017)
21. Mylrea, M., Gourisetti, S.N.G.: Blockchain for smart grid resilience: Exchanging distrib-
    uted energy at speed, scale and security. In: Proceedings 2017 Resilience Week (RWS).
    Chase Center on the Riverfront/Wilmington, DE, Wilmington, DE, 18-22 September 2017,
    pp. 18–23. IEEE, Piscataway, NJ (2017)
22. Korpela, K., Hallikas, J., Dahlberg, T.: Digital Supply Chain Transformation toward
    Blockchain Integration. Hawaii International Conference on System Sciences 2017
    (HICSS-50) (2017)
23. Tian, F.: An agri-food supply chain traceability system for China based on RFID & block-
    chain technology. In: Yang, B. (ed.) 2016 13th International Conference on Service Sys-
    tems and Service Management (ICSSSM), pp. 1–6. IEEE, Piscataway, NJ (2016)
24. Nærland, K., Müller-Bloch, C., Beck, R., Palmund, S.: Blockchain to Rule the Waves -
    Nascent Design Principles for Reducing Risk and Uncertainty in Decentralized Environ-
    ments. ICIS 2017 Proceedings (2017)




                                              95
25. Kamath, R.: Food Traceability on Blockchain: Walmart’s Pork and Mango Pilots with
    IBM. The JBBA 1, 3712 (2018)
26. Hackius, N., Petersen, M.: Blockchain in logistics and supply chain : trick or treat? Pro-
    ceedings of the Hamburg International Conference of Logistics (HICL), 23 (2017)
27. Schneier, B.: Applied Cryptography. Protocols, Algorithms and Source Code in C. John
    Wiley & Sons Incorporated, New York (2015)
28. Newman, M.: Networks: An Introduction. Oxford University Press, Oxford (2010)
29. Kim, Y., Choi, T.Y., Yan, T., Dooley, K.: Structural investigation of supply networks: A
    social network analysis approach Journal of Operations Management, 194–211 (2011)
30. Zare-Garizy, T., Fridgen, G., Wederhake, L.: A Privacy Preserving Approach to Collabo-
    rative Systemic Risk Identification: The Use-Case of Supply Chain Networks. Security
    and Communication Networks (2018)
31. Benhamouda, F., Halevi, S., Halevi, T.: Supporting Private Data on Hyperledger Fabric
    with Secure Multiparty Computation. 2018 IEEE International Conference on Cloud Engi-
    neering (IC2E), 357–363 (2018)




                                             96