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This condition is introduced as an incentive to efficiency for member enterprises and, analogously, as a disincentive to opportunistic behavior. In this step, the recovery of alignment costs P All must be distributed; these are the costs incurred by some members to align themselves with the interests of the chain.

At the end of this step we have the Chain Profit value PC that represents the revenue of the chain after the payment of efficient production costs PL and the alignment costs P All. This negotiation need not be held at each market supply, but quotas can be reviewed periodically or on demand by member enterprises.

The distribution of profits must be preceded by a phase of negotiation between the groups in the Request of Tender phase. What needs to be negotiated is what share of the PC profit should be assigned to the various resource k group providers. Said G k this quota, it must be. There is no need for bargaining within the same resource provider group; in fact, it is sufficient to assume as the opening bargaining quota, within the group, the request of the most demanding member, and as the minimum closing quota the request of the least demanding member.

Generally, it will not be necessary for bargaining to take place with every supply of the chain: where satisfactory agreements are reached, these can be maintained for more supplies. The results of the project presented in the final public event highlighted both the power of vertical integration and the benefits of Revenue Sharing. In the eyes of today, the simulations that were carried out give also solid ground to the desirability and potential of the technological enhancement of Revenue Sharing through blockchains and smart contracts.

In fact, they provide evidence that, in traditional technological configurations, Revenue Sharing does work effectively, but only if implemented as an additional component of vertical integration, in which the dominant partner takes on the role of manager of trust and coordination. The agri-food chain was the focus of the project and the production process of a IV gamma product semidried tomatoes was tested.

The supply chain was composed of four raw material suppliers two tomato suppliers, a gas supplier and a pack supplier , a semidried tomatoes producer, and to simplify the approach all the retailers were collected in one single virtual subject. The semidried tomatoes producer, Ortoreale s. In the experimentation phase a set of data about production and transportation of a semidried tomatoes supply was collected: fix production costs aggregated data , variable production costs aggregated data , production time, production range related to variable costs , holding costs, fixed and variable logistic costs, warehouse capacity.

The experimentation phase was developed starting from a market demand of 36 pallets of semidried tomatoes, equal to a total amount of 5, trays of IV gamma product. To reach this amount and minimize supply chain costs the raw material partners had to provide 4, kg of tomatoes, 5 l of gas and 10 kg of packaging materials. Figure 2 shows in detail the return of each partner from the lot of supply tested in the project. Figure 2. These results can be compared with the wholesale approach as in the graph in Figure 3 , where historical data were used for the comparison.

This case shows also quite well the co-opetitive scenario fostered by the application of Revenue Sharing. Figure 3. Qian et al. This imbalance seriously jeopardizes the sustainability of the entire sector. The study was carried out in the North China's Inner Mongolia Autonomous Region, where most of the dairy production for the whole China takes place through two of the country's largest dairy enterprises, Mengniu and Yili.

The aim of the introduction of Revenue Sharing in the management of this dairy supply chain is to increase its overall profits in order to benefit farmers and manufacturers without affecting the profits of the retailers. This is indeed achieved by applying a three-stage version of the Revenue Sharing model provided in Giannoccaro and Pontrandolfo with an increase of overall profitability of However, the practical implementation beyond the experimental level of this RS-based dairy supply chain model poses the usual problems of coordination and administrative control that can be effectively addressed with intelligent smart contracts.

In this way, the negotiating power and the consequently higher returns of stronger participants can be constrained within the acceptable boundaries indicated by the authors of the study, without impairing the overall win-win effect deriving from the adoption of Revenue Sharing. By contrast, if the stronger participants took over the role of controllers and coordinators their advantage could grow to a far larger extent.

Hence, the indications we have from these results are that Revenue Sharing, if disengaged from the centralized control intrinsic to vertical integration, could give even more effective and wide-ranging results, by transferring the benefit of the approach to large-scale business ecosystems through the algorithmic management of relationships of trust and coordination, thus eliminating power asymmetries and balancing the role and weight of the participants in the decision-making processes for supply terms and conditions.

A number of analytical models related to the benefits of blockchain technology in the support of supply chains provide criteria confirm the validity of this direction Korsten, ; Liu et al. In the next section we show how these findings can be concretized in a software architecture for Revenue Sharing that hinges on state-of-the-art blockchain technology. Real-world supply chains rarely come in a simple structure where the service or the product is supplied, produced, and delivered on a single level.

In most business practices, supply chains are layered over multiple levels involving stakeholders both downstream, toward end-customers, and upstream, toward raw material suppliers. The upstream refers to all the activities needed to collect the materials or inputs required to create a product or deliver a service. Conversely, the downstream process involves processing the materials or inputs collected during the upstream stage into a finished product or a delivered service, up to the actual sale of that product Min et al.

As mentioned in the previous section, a collective agreement among all interested parties to adopt a common revenue sharing criterion is an essential prerequisite of our approach. The management of revenue sharing increases the operational complexity, which is already complicated by the multilevel nature of supply chains, where it is a matter of coping, among other things, with the challenge represented by the logistics of geographically distributed stakeholders, as well as with disruptions due to delays or changes in previously defined conditions.

To deal effectively with all this and take advantage of the revenue sharing approach, we propose Intelligent Smart Contracts in direct technological continuity with Blockchain and Smart Contracts. Intelligent Smart Contracts not only manage the complexities in the implementation of the revenue sharing algorithm in multilevel supply chains, but also contribute to significant improvements in efficiency, transparency, security, traceability and streamlined integration among the different levels of the supply chain.

In particular, we adopt the Ethereum 2 implementation of the blockchain technology as the core back-end engine see component EthereumBlockchain in Figure 4 , on which to record all the effects of the transactions involved, from the establishing of the supply chain, up to the distribution of revenues. The blockchain also supports calculations of optimal shares, implemented in Solidity 3 , while a Web-based client component WebClient in Figure 4 incorporating a DApp written with the Web3.

It is assumed that the Web client will maintain a reference to some Ethereum node through which the blockchain can be accessed and transactions executed on an Ethereum Virtual Machine. The interaction events generated by the WebClient through the Browser and interpreted by the We b3A pp component are communicated to a component responsible to enable the execution of tasks as requested by the overall process CoordinationEngine. Both front and back-end engines can refer to a suitable repository containing, among others, the documents describing the actual production of the goods involved in the smart contract DocumentStore.

The document store can be implemented in a distributed way, with no need for a central database, for example using the IPFS InterPlanetary File System infrastructure, as indicated in Figure 4 , where reference is made to some generic IPFS node. Communication between the Web application, the blockchain implementation and the concrete databases will occur over the HTTP protocol, typically exploiting the JSON communication framework.

Figure 4. A deployment diagram depicting the overall architecture of the proposed platform. The approach to the construction of the application takes inspiration from tools such as Lorikeet and Caterpillar that derive Solidity implementations from models of the contract realized with the Business Process Modeling Notation BPMN Tran et al.

In our approach, we define a Use Case model to identify the actors and their interactions with the system, and the BPMN model to define the overall coordination and message exchange to be realized. For the domain model see the UML class diagram in Figure 5 , we consider the kind of information required to provide an integral view of the supply chain, taking into consideration the tree-like structure induced by the composition of the product. Specific categories of sub-products can be provided by different suppliers, at different costs and in different quantities, and one participant can produce different categories of sub-products, thus appearing at different nodes.

Each contribution of a participant to the overall contract can be described in terms of the type of product provided, the quantity to be provided and the price. Depending on the state of the contribution, e. Similarly, the product to be provided may have been required or delivered.

Only once the final product, for which the contract was established, is sold at a final price, will the revenues be calculated. Hence, we assume that only one participant is in charge of originating the contract, and setting the final price. Figure 5. A class diagram depicting the domain model of contracts on supply chains. In the treatment below, we assume that all interested parties run an implementation of the WebClient , obtained by the developers, and that the endpoints for interacting with the blockchain implementation of the supply chain are made known to them in due time.

The platform supports use cases for two types of actors, that we call Originator and Supplier see Figure 6 , roles which can be taken by any Participant registered in the system. A person or organization that initiates the whole process by requesting the supply of a product with specifications.

This actor defines the main conditions that the system will later transfer into a new Intelligent Smart Contract. Any originator can initiate and participate in different smart contracts at the same time. A person or organization that has the possibility of providing either product or service within an existing Intelligent Smart Contract.

When placing a bid, suppliers provide competitive specifications for the sub-products they can offer to the contract. Any supplier can participate in different smart contracts at the same time. In certain cases, a supplier can start a subcontracting activity, thus acting as a sort of originator for a sub-branch of the supply chain.

Figure 6 shows the use cases for the originator and supplier actors. Both types of actor can perform their use cases, according to the coordination mechanism, through their accounts on the Web application, which also gives access to information concerning their active and past smart contracts. With respect to a specific smart contract, each participant can play only one role. Actors' actions are executed according to a process model, described at high-level by the BPMN diagram of Figure 7 , and detailed in the subsequent diagrams depicting various sub-processes.

We only consider the actions of the tasks on the blockchain associated with the supply chain, indicated here as SuChBlc and assume that the associated DocumentStore is updated consistently with information on the interactions executed via the Web application. In particular, we consider that an intelligent smart contract is executed through four sub-processes: Initialization; definition of the smart contract BuildUpSc ; computation of optimal prices and shares according to the algorithms described in Section Algorithmic Supply Chain Coordination and storage of the result in the form of matrices, memorized on the blockchain MatrixDeployment ; and actual execution of production, sale and revenue sharing Closure.

Figure 7. A high-level view of the process associated with an intelligent smart contract. Figure 8 details the initialization process, as performed by an originator. This actor defines the requests and set up a new Smart Contract on a blockchain, on which the criteria for participation and conditions for fulfillment will be published e. Any new contact virtually defines a number of shares that suppliers bid to own. The number of Smart Contract shares each supplier owns will represent how much profit they will make by the end of this contract.

All the interested suppliers are made aware of the publication of the new Smart Contract. Figure 8. The sub-process for originating a contract and setting up the blockchain. The Appendix provides a series of snapshots from an implementation of Web interfaces for the originator and the suppliers within Revenue Sharing contracts.

The texts in the interfaces are in Italian, as this prototypical implementation is meant to be used by Italian SMEs. Once criteria are published, interested parties, becoming aware of the call, can produce their offers, as shown in Figure 9. This might entail offline negotiations, or the originator may select beforehand the possible participants, but offers to participate need to be performed through the Web application in any case, so that they can be documented and maintained through the blockchain.

Suppliers might recursively start a call for sub-providers, which however will participate in the same contract, i. The system receives bids at the different levels recursively until the deadline for receiving bids. The final list of suppliers is defined by matching the received bids to the selection criteria provided by the originator in the new Smart Contract request hence, it is described as the business rule task AwardOffers in Figure 9.

In Figure 10 , we summarize the steps through which the matrix resulting from the previous sub-process is computed, according to the algorithm of Section Algorithmic Supply Chain Coordination, and published on the blockchain. Finally, Figure 11 offers a synthetic view of the sub-process leading to the actual production. The task ProduceAll will lead to loading on the DocumentStore all the relevant documents, e.

Once the actual sales are performed task Sell , the algorithm on the blockchain is executed, based on the saved matrices hence it is again described as a business rule task, ComputeRevenues. The computed shares, transformed from percentages of a virtual quota to actual currencies, are then distributed task DistributeRevenues , and the corresponding transactions recorded.

Figure 11 also shows the interactions of these tasks with the blockchain. The model also incorporates methods not discussed here for the sake of simplicity to recover from exceptions, where for example quotas of production have to be shifted from one supplier to another.

The implementation of the Web client is derived from both the process model and the deployed supply chain Smart Contract. In particular, a view in the We b3 application is created for each task in the process, while the coordination engine authorizes the execution of tasks according to the model. Constraints on the actions allowed in the different views derive from the different roles of originator and supplier assumed by users at the different layers in the supply chain.

Suitable mechanisms for access control make sure that only relevant parts of the stored documents are available to different users. For example, an originator should be enabled to retrieve all the documents pertaining to its immediate relationship with an immediate supplier, but not those pertaining to the interaction of the supplier with its sub-providers, except for pieces of information which might be relevant to checking whether specific constraints are complied with, e.

The radically evolutionary outlooks opened by blockchain technology for supply chain management have been a source of experimentation and discussion in recent years. The interest in this direction cuts indeed across the whole spectrum of possible chains, and includes co-opetitive business eco-systems -target of our contribution—as well as vertically integrated systems characterized by dominant companies.

In both cases, the main objective is bringing transaction costs down. In one instance such reduction entails distributing larger profits to a larger business community, thus increasing the common good; in the other, it means increased returns to dominators by virtue of further cutting supply costs and capillary control on the overall functioning of the chain. Effective examples of vertically dominated supply chains that have been strengthened through blockchain technology are the TradeLens platform, jointly developed by IBM and shipping giant Maersk 5 , and the supply chain of distribution giant Walmart, where blockchain technology, also powered by IBM 6 , is exploited to track provision disruptions and ill-functioning, such as sources of unsafe food.

Clearly, these are private blockchains, where the validation of transactions is strictly managed by the dominant company, which is thus even further strengthened in its role of manager of trust. Therefore, the algorithmic consensus and the digital trust that follows from it, which are key aspects of public blockchains, play no role here, while the specific feature from blockchain technology that gets exploited is the ability to maintain historical transaction logs for control and audit.

We must also mention those applications that, without setting themselves the aim of redefining the economics and organization of supply chains, leverage blockchains just to trace and notarize the various elements and steps contributing to final products within supply chains, so as to endow end users with greater transparency and verifiability on these very products, with the expectation of beneficial effects on brand value and customer loyalty.

Relevant among such sectors, in which blockchain-based boosting of supply chains for notarization purposes has been implemented, are agri-food e. From the existing literature on the subject, Korpela et al.

Treiblmaier defines a broad methodological framework to measure the impacts and organizational advantages from applying blockchain technology to supply chains where, in addition to Transaction Cost Economics, evaluation criteria are obtained from Positive Agency Theory Jensen and Meckling, which measures companies' competitive advantage according to their ability to establish partnerships with trustworthy agents , Resource-based View Theory Wernerfelt, which identifies companies' ability to access and protect scarce resources as a key advantage and Network Theory Johanson and Mattsson, which considers networking, namely companies' ability to dynamically establish advantageous business partnerships, as a key advantage.

Our approach concerns not just the improvements in transaction costs deriving from the systematic adoption of digital trust made available by the blockchain, but also the optimal management of resources as implemented in the intelligent smart contract of Revenue Sharing, as well as the possibility of dynamically creating new supply agreements between partners that may lack former business relationships; it thus appears assessable also within this wider evaluation grid.

Liu et al. Therefore, these contributions complement our own, which provides concreteness, in the form of implementation guidelines, for such formal demonstrations. Specifically, Choi considers the performance of an abstract Revenue Sharing model operating on blockchain in the fashion industry with monetary transfers between participants made in cryptocurrency, with the consequent disintermediation of the bank entities traditionally in charge of this task.

Beside bank disintermediation, digital trust also plays a role in the model as a risk mitigation factor, and both of them are weighted against a number of assumed overheads for deploying Revenue Sharing on blockchain in particular, the costs of adding transactions to the blocks. It follows from these assumptions that Revenue Sharing on blockchain is highly rewarding compared to traditional supply chains burdened by high banking fees, while performing non-inferiorly in the case of low banking fees.

Hayrutdinov et al. Along similar lines, Liu et al. Korsten demonstrates the performance of Revenue Sharing as a smart contract on the blockchain in the presence of risk averse participants. Berg et al. This vision in turn aims at resurrecting the Electronic Markets Hypothesis put forward by Thomas Malone and his associates Malone, ; Malone et al. That is, the Electronic Market Hypothesis envisages very slender companies because they are dedicated only to products and services for which they possess the key skills, while, in the face of near-zero transaction costs, anything else they need they get it from other companies, thus inexorably tilting the pendulum toward the buy option of the make-or-buy dilemma.

However, the Electronic Markets Hypothesis has translated into reality only to a very limited extent, principally because it did not adequately take into account the need of companies to fend themselves from incorrect or opportunistic behaviors from untested business partners.

Fact is that, as easy as it has become, by virtue of a global IT infrastructure, to establish relationships between companies on an unprecedented scale so as to pool key skills, rather than disperse resources and energies in secondary activities, in the end distrust still prevails over the attractiveness of doing business together. If anything, those who have benefited from the new set-up are the usual big players, which may have found convenient trimming down governance overheads by outsourcing secondary functions; at the same time, they have put further bonds over minor partners through information technology, while protecting themselves against misbehaviors through suitable legal fencing, which they can easily afford, and even more through their financial power, which leaves little room to smaller parties to play against the rules.

But, as argued in Berg et al. Our Intelligent Smart Contracts for Revenue Sharing can be a substantial step in this direction, as they couple digital trust with a ready-to-wear fair cooperative process model for enterprises participating in a supply chain.

Intelligent Smart Contracts for Revenue Sharing can be seen as a flexible form of planning, as they provide cost indicators that must be used in order to achieve optimal performance within plans for building and delivering products in the context of current market constraints, and then execute the plans defined from such premises. There has been recently a resurgence of interest for planning in economy Phillips and Rozworski, ; The Economist, , a notion until recently dismissed and even ridiculed as a relic of the socialist economies of the twentieth century.

This interest stems indeed from the observation that colossuses of capitalist economy such as Walmart and Amazon make extensive and effective use of planning to squeeze costs and boost revenues in their logistics and supply chains. Without the need for major institutional upheavals, it could therefore be a panacea against excessive concentration of decision-making power and over-privileged access to economic and financial resources.

Intelligent Smart Contracts for Revenue Sharing can also be seen as Decentralized Autonomous Organizations DAOs , a concept that has been widely discussed within the Blockchain Community and has a significant case history of implementations. This definition fits perfectly with Revenue Sharing, since the intelligent smart contract that implements it is an autonomous organization that lives on the Internet and hires humans, in the form of companies rather than individuals, to perform the complex task of working together on a product or service.

It therefore operates as a combination of algorithmic automation and human activity. In Buterin's organizational taxonomy, in fact, the relevant parameters are automation at the center, that is, in the control room, and automation at the edges, that is, during the execution of specific tasks. Traditional human organizations lack automation both at the center and at the edges.

Automation at the edges under human control is exemplified by robots without decision-making autonomy as used in manufacturing assembly lines. Automation both at the center and at the edges points in the direction of Strong Artificial Intelligence, which is at present a still unfinished construction. DAOs are organizations in which automation resulting from smart contracts resides at the center, in the automated management of processes and procedures composed by tasks whose execution is left to humans active at the edges.

Figure A quadrant of organizations. Future work will be along two lines, one technological and the other organizational. Technologically, it is a matter of identifying the key features in the existing blockchain platforms that suit best the performance of intelligent smart contracts for Revenue Sharing. We shall start here from the existing experience based on the Solidity prototype running on Ethereum, by building on existing indicators for blockchain-based supply chains like those in Litke et al.

Organizationally, the aim is to further refine the existing model, by introducing tokenization systems to facilitate Revenue Sharing transactions and defining bank credit methods geared to the supply chains of peer participants, so as to relieve especially those stakeholders who, being at the beginning of the supply chain, most heavily bear the burden of early exposure to costs.

The answer here is likely to lie once again in the blockchain itself, in that, as shown in Omran et al. In this article, we have provided a framework to exploit blockchains and smart contracts as enabling technologies for an innovative type of supply chain management, aimed at achieving higher levels of collaboration between the companies participating in the chain. We have then illustrated a specific instance of an intelligent smart contract, both from an algorithmic and an architectural standpoint, as given by Revenue Sharing, a methodology for supply chain management that can greatly boost the profitability of the supply chain for the benefit of all participants, but must be fully automated to achieve its full potential.

An RS-based intelligent smart contract takes advantage of the digital trust guaranteed by the blockchain to replace human trusted third parties in coordinating the various parties in the management of a supply chain, thus making it much easier and less expensive to set up supply chains based on peer-level participation.

In addition, it defines and executes plans for bringing the products of the supply chain to the market with the highest returns and fairly distributes the revenues from the proceeds of sale to all participants. The net result will be the greater good that follows from better business ecosystems, with a fairer share of returns among participating companies, greater collaboration and communication between participants, better quality of results, and overall benefits that will extend to the community of stakeholders at large.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher. RP originated the proposal of a blockchain-based implementation, wrote sections Introduction, Blockchains, Intelligent Smart Contracts and Digital Trust, Discussion and Future Work, and Conclusions and coordinated the writing of the whole article. EA had the original intuition for cooperative supply chains.

All authors collaborated in revising the whole text. Work partially supported by Sapienza, project Consistency problems in distributed and concurrent systems, — The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. We thank Claudio Facciabene and Riccardo Garbo for providing a basis for the implementation of the Web application.

Albrecht, M. Bannon, S. Berg, C. Blockchain technology as economic infrastructure: revisiting the electronic markets hypothesis. Blockchain Bitran, G. The need for third-party coordination in supply chain governance. MIT Sloan Manag. Google Scholar. Cachon, G. Supply chain coordination with revenue-sharing contracts: strenghts and limitations. Chen, J. On channel coordination under price-dependent revenue-sharing: can eBay's fee structure coordinate the channel? Choi, T. Supply chain financing using blockchain: impacts on supply chains selling fashionable products.

Coase, R. The nature of the firm. De Kok, A. Di Ciccio, C. Blockchain support for collaborative business processes. Spektrum 42, — Ferrucci, D. Building watson: an overview of the deepQA project. AI Mag. Garriga, M. Blockchain and cryptocurrencies: a classification and comparison of architecture drivers.

Concurrency Comput. Giannoccaro, I. Supply chain coordination by revenue sharing contracts. Gong, D. Revenue sharing or profit sharing? An internet production perspective. Hayrutdinov, S. Coordination of supply chain under blockchain system-based product lifecycle information sharing effort. Jensen, M. Theory of the firm: managerial behavior, agency costs and ownership structure. Johanson, J. Interorganizational relations in industrial systems: a network approach compared with the transaction-cost approach.

Inter Organ. Korpela, K. Korsten, K. A blockchain-based supply chain contract-an analysis to the development of a supply chain contract on a blockchain M. Eindhoven: University of Eindhoven. Litke, A. Blockchains for supply chain management: architectural elements and challenges towards a global scale deployment. Logistics 3, 1—5. Liu, L. Research on risk avoidance and coordination of supply chain subject based on blockchain technology.

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