A Review of Smart Contract Blockchain Based on Multi-Criteria Analysis: Challenges and Motivations

A smart contract is a digital program of transaction protocol (rules of contract) based on the consensus architecture of blockchain. Smart contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications. A smart contract is the central aspect of a blockchain that facilitates blockchain as a platform outside the cryptocurrency spectrum. The development of blockchain technology, with a focus on smart contracts, has advanced significantly in recent years. However, research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an identical state. This paper extensively reviews smart contracts based on multi-criteria analysis, challenges and motivations. Therefore, implementing blockchain in multi-criteria research is required to increase the efficiency of interaction between users via supporting information exchange with high trust. Implementing blockchain in the multi-criteria analysis is necessary to increase the efficiency of interaction between users via supporting information exchange and with high confidence, detecting malfunctioning, helping users with performance issues, reaching a consensus, deploying distributed solutions and allocating plans, tasks and joint missions. The smart contract with decision-making performance, planning and execution improves the implementation based on efficiency, sustainability and management. Furthermore, the uncertainty and supply chain performance lead to improved users’ confidence in offering new solutions in exchange for problems in smart contacts. Evaluation includes code analysis and performance, while development performance can be under development.     

A Distributed Privacy Preservation Approach for Big Data in Public Health Emergencies Using Smart Contract and SGX

Security and privacy issues have become a rapidly growing problem with the fast development of big data in public health. However, big data faces many ongoing serious challenges in the process of collection, storage, and use. Among them, data security and privacy problems have attracted extensive interest. In an effort to overcome this challenge, this article aims to present a distributed privacy preservation approach based on smart contracts and Intel Software Guard Extensions (SGX). First of all, we define SGX as a trusted edge computing node, design data access module, data protection module, and data integrity check module, to achieve hardware-enhanced data privacy protection. Then, we design a smart contract framework to realize distributed data access control management in a big data environment. The crucial role of the smart contract was revealed by designing multiple access control contracts, register contracts, and history contracts. Access control contracts provide access control methods for different users and enable static access verification and dynamic access verification by checking the user’s properties and history behavior. Register contract contains user property information, edge computing node information, the access control and history smart contract information, and provides functions such as registration, update, and deletion. History contract records the historical behavior information of malicious users, receives the report information of malicious requestors from the access control contract, implements a misbehavior check method to determines whether the requestor has misbehavior, and returns the corresponding result. Finally, we design decentralized system architecture, prove the security properties, and analysis to verify the feasibility of the system. Results demonstrate that our method can effectively improve the timeliness of data, reduce network latency, and ensure the security, reliability, and traceability of data.     

Contract design for cooperative product service system with information asymmetry

This paper studies how to effectively provide product service system (PSS) in the supply chain with asymmetric information for service-oriented manufacturing (SOM). With a PSS, the manufacturer chooses the product quality, and the retailer, who possesses private cost information, is responsible for adding necessary value-added service based on the basic product. The analytical underpinnings of our work are provided by the principal-agent paradigm. In the paper, we develop three types of contracts and conduct a detailed comparison among the three contract models. With these studies, we find that: (i) the wholesale price contract is dominated by the other two contracts, (ii) The retail price maintenance contract can always generate a higher profit for the manufacturer and a higher welfare for the consumers, whereas the franchise fee (FF) contract can always generate a higher profit for the retailer and (iii) The FF contract can always stimulate more service from the retailer. Our theoretical and numerical results show that there are significantly different effects when the three types of contracts are utilized in the supply chain for SOM with asymmetric information, and which of them is the most attractive will depend largely on the power structure of the supply chain and/or their cost information types.     

Effects of information transparency and cooperation on supply chain performance: a simulation study

This research explores the effect of information transparency and cooperation among the front nodes of supply chains. Although published works in the supply chain (SC) domain underline the need for information transparency and cooperation for competitiveness of firms, the majority among these are conceptual or empirical. As such, the domain requires fresh effort towards analytical and simulation based research for validation. In the present work, simulation is used as the research methodology. The simulation platform is developed in ARENA®, which is based on simulation language SIMAN. It has the capability to model multi-product, multi-echelon supply chains. Cooperation is depicted in the form of demand pooling and accelerated delivery of consignments from cooperating SC nodes, when stock-out contingency occurs at a particular node. The modelling also explores the effects of full, partial and zero level of information transparency on the SC performance. The simulation results suggest that these initiatives improve SC service time performance. It is also observed that more advantages from cooperation and information transparency are obtainable when the SC is leaner. The studies also highlight that such SC initiative that motivates local optimisation at the nodes is counter-productive to SC wide performance.     

Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak

An intertwined supply network (ISN) is an entirety of interconnected supply chains (SC) which, in their integrity secure the provision of society and markets with goods and services. The ISNs are open systems with structural dynamics since the firms may exhibit multiple behaviours by changing the buyer-supplier roles in interconnected or even competing SCs. From the positions of resilience, the ISNs as a whole provide services to society (e.g. food service, mobility service or communication service) which are required to ensure a long-term survival. The analysis of survivability at the level of ISN requires a consideration at a large scale as resilience of individual SCs. The recent example of coronavirus COVID-19 outbreak clearly shows the necessity of this new perspective. Our study introduces a new angle in SC resilience research when a resistance to extraordinary disruptions needs to be considered at the scale of viability. We elaborate on the integrity of the ISN and viability. The contribution of our position study lies in a conceptualisation of a novel decision-making environment of ISN viability. We illustrate the viability formation through a dynamic game-theoretic modelling of a biological system that resembles the ISN. We discuss some future research areas.     

Real Estate Management via a Decentralized Blockchain Platform

Blockchain technology is one of the key technological breakthroughs of the last decade. It has the ability to revolutionize numerous aspects of society, including financial systems, healthcare, e-government and many others. One such area that is able to reap the benefits of blockchain technology is the real estate industry. Like many other industries, real estate faces major administrative problems such as high transaction fees, a lack of transparency, fraud and the effects of a middleman including undue influence and commissions. Blockchain enables supporting technologies to overcome the obstacles inherent within the real estate investment market. These technologies include smart contracts, immutable record management and time-stamped storage. We utilize these key properties of blockchain technology in our work by proposing a system that has the ability to record real estate transactions in a private blockchain, using smart contracts. The immutability of the blockchain ledger and transactions can provide a safe space for the real estate business. Blockchain technology can also assist the authentication process by hastening background checks. Personal digital keys are provided to parties that are involved in a contract, thus minimizing the risk of fraud. We also discuss the rationale behind the advantages of using a blockchain in this manner, and how we selected the consensus mechanism for our proposed system.     

Ripple effect quantification by supplier risk exposure assessment

Supply chain (SC) disruptions are considered events that temporarily change the structural design and operational policies of SCs with significant resilience implications. The SC dynamics and complexity drive such disruptions beyond local event node boundaries to affect large parts of the SC. The propagation of a disruption through a SC and its associated impact is called the ripple effect. Previous approaches to ripple effect modelling have mainly focused on estimating the likelihood of a disruption; our study looks at the disruption consequences. We develop a new model to assess the ripple effect of a supplier disruption, based on possible maximum loss. Our risk exposure model quantifies the ripple effect, comprehensively combining features such as financial, customer, and operational performance impacts, consideration of multi-echelon inventory, disruption duration, and supplier importance. The ripple effect quantification is validated with simulations using actual company data. The findings suggest that the model can be of value in revealing latent high-risk supplier relations, and in prioritising risk mitigation efforts when probability estimations are difficult. The performance indicators proposed can be used by managers to analyse disruption propagation impact and to identify the set of most critical suppliers to be included in the disruption risk analysis.     

Structural quantification of the ripple effect in the supply chain

In recent years, remarkable advancements have been achieved in quantitative analysis methods for supply chain design (SCD). Typically, cost or service level optimisation has been included in the objective functions. At the same time, supply chain managers face the ripple effect that arises from vulnerability, instability and disruptions in supply chains. This research aimed to quantify the ripple effect in the supply chain from the structural perspective. The research agenda of this study includes issues of integrating operability objectives as new key performance indicators, e.g. resilience, stability, robustness into SCD decisions. The research is based on a simultaneous consideration of both static structural properties of SCD and execution dynamics subject to uncertainty and disruptions. Due to high dimensionality of real SCD problems, such integration can hardly be implemented in only one model. In this study, an original two-model multi-criteria approach is proposed in order to assess the potential ability of an SCD to remain stable and resilient. This modelling approach is based on a combined application of a static and a dynamic model. A multi-criteria approach relies on the analytic hierarchy process method. The results of this research can be used as an additional quantitative analysis tool in order to select an SCD. An additional application of the developed method is that it can be used at the control stage in order to adapt supply chain execution subject to the achievement of desired economic performance.     

Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach

The ripple effect can occur when a supplier base disruption cannot be localised and consequently propagates downstream the supply chain (SC), adversely affecting performance. While stress-testing of SC designs and assessment of their vulnerability to disruptions in a single-echelon-single-event setting is desirable and indeed critical for some firms, modelling the ripple effect impact in multi-echelon-correlated-events systems is becoming increasingly important. Notably, ripple effect assessment in multi-stage SCs is particularly challenged by the need to consider both vulnerability and recoverability capabilities at individual firms in the network. We construct a new model based on integration of Discrete-Time Markov Chain (DTMC) and a Dynamic Bayesian Network (DBN) to quantify the ripple effect. We use the DTMC to model the recovery and vulnerability of suppliers. The proposed DTMC model is then equalised with a DBN model in order to simulate the propagation behaviour of supplier disruption in the SC. Finally, we propose a metric that quantifies the ripple effect of supplier disruption on manufacturers in terms of total expected utility and service level. This ripple effect metric is applied to two case studies and analysed. The findings suggest that our model can be of value in uncovering latent high-risk paths in the SC, analysing the performance impact of both a disruption and its propagation, and prioritising contingency and recovery policies.     

Impact evaluation of supply chain initiatives: a system simulation methodology

To maintain enterprise competitiveness, enterprises require appropriate initiatives to efficiently manage the product, information and cash flows among different supply chain (SC) partners. Co-operation, postponement, vendor managed inventory, transhipment, logistic consolidation etc., are some of the typical initiatives for performance improvement in SC. In an industrial scenario, two or more of these SC initiatives are undertaken simultaneously for superior enterprise performance on multiple performance dimensions. These factors render the analytical SC models inapt and simulation modelling becomes a suitable methodology to study SC performance that can accommodate the complexities present. The impact studies of these initiatives are difficult because these require exploitation of flexible and dynamic modelling elements which are analytically complex and mathematically intractable. We address these issues in the present research by developing a simulation platform to undertake studies with an industrial SC scenario. The challenges to explore SC performance under multiple initiatives are the major motivations for the present research. The novelty of this research lies in the development of a multi-product, multi-echelon SC simulation platform for realistic and industrial SC scenario encompassing the capability to accommodate multiple SC initiatives simultaneously. It is based on the building block approach using Arena®. We undertake SC performance studies under the application of several important SC initiatives. The results are encouraging and they underline interesting research areas to explore further.     

Information management strategies and supply chain performance under demand disruptions

The operations management literature presents inadequate comprehensive understanding on information management strategies of mitigating supply chain disruption risks. By using control theory modelling and simulation, this study compares the disruption mitigation effects of three information management strategies. From the aspect of stability, the existing stability boundaries are revised by a new method in a two-echelon case. It shows that supply chains (SC) with popular information management strategies are not evidently more stable than traditional ones. From the aspect of disruption recovery time, an innovative two-echelon swiftest response problem under these information management strategies is formulated and solved. Results show that a collaborative planning, forecasting and replenishment (CPFR) SC with complete SC information performs the best. However, in a later operational risk mitigation test, an information sharing (IS) SC with partial information has the smallest bullwhip effect. From the aspect of demand amplification and frequency response, an innovative frequency–response plot of order amplification is proposed in a time-continuous SC with moving average forecasts. It implies the best frequency response for concurrently mitigating both operational and disruption risks coming from a CPFR SC. But for a certain SC structure there is still a balance between mitigating bullwhip effect and quick response. Moreover, it also implies that anti-bullwhip should exist in a certain condition, as realised in our numerical experiments.     

Trust management for e-transactions

There has been enormous increase in transactions and cooperative-computing services on the internet. This is both a technical and a social phenomenon. Transactions and services over the internet have global reach and users, known or unknown to the service provider, might be interested in availing access or participating in the cooperative transaction in a distributed manner. Thus, it is very important for service providers to identify and establish trustworthiness of potential collaborators, which they do by writing contracts (e.g. access control, security policies; the words contract and policy are used interchangeably) without violating the privacy and confidentiality laws that prevail across geographical boundaries. But as the system becomes complex and dynamic, contractual incompleteness arises since it becomes cumbersome to mention potentially large set of outcomes of the user's choice of action. Trust plays a crucial role in the design of optimal contracts; not all the relevant, valuable information on the user's choice of action is incorporated in the equilibrium contract. It may also be noted in that traditional transactions, the notion ofseeing is believing plays a vital role. However, in e-transactions, this is not the case. The challenge is to see how in such a scenario trust can indeed be generated. Note that the presence of trust facilitates cooperative behaviour and allows for exchange to occur in situations where its absence would preclude trade. In this paper, we shall present a comparative analysis of various approaches of trust management in practice that integrates technology with other factors. We shall also bring out the relative deficiencies and how these issues are tackled in our ongoing work that facilitates execution of optimal contracts.     

A multi-objective model for risk mitigating in supply chain design

The goal of this study is to recognise various factors for responsive SCs that affect supply risk and model their impact on SC design and operation. We propose a conceptual model for SC responsiveness that encompasses practices such as flexibility, agility, internal integration, and visibility. This conceptual model is utilised to build up a multi-objective, multi-period SC design and operation model. A heuristic algorithm is developed to find the supplier, product, period, and production rate for the numerical problem. The improved genetic algorithm (GA) produces solutions with more accuracy in considerably less time than a traditional GA. Finally, an approach to prioritise the objective functions is developed that allows managers to focus on specific objective functions more than optimum values. This approach provides risk-averse, responsiveness-oriented, cost-effective managers the capability to set priorities based on their policies.     

Coordinating a three-layer supply chain with uncertain demand and random yield

The paper considers a three-layer supply chain involving one raw-material supplier, one manufacturer and one retailer. The market demand is assumed to be stochastic and productions at the raw-material supplier and manufacturer are subject to random yield. The centralised model is studied as the benchmark case. The decentralised model is solved and Nash equilibrium solutions are obtained. It is shown that buyback contract fails to coordinate such a supply chain. However, a composite contract framed combining buyback, and sales rebate and penalty contracts is shown to coordinate the supply chain. Numerical examples are provided to illustrate the developed models.     

不同供应链合同的最优订货策略及适用性比较

针对由一个制造商和一个零售商组成的单一易逝品供应链,在随机库存问题报童模型基础上,推导了回购、收入分享和数量折扣合同下,零售商的最优订货策略、零售商和制造商的期望利润,给出了不同供应链合同的适用条件。通过一个皮衣生产、销售供应链的案例,对不同供应链合同下的最优订货策略、合同适用性及原因进行了分析、比较。为理解不同供应链合同的适用性提供了数理依据和案例解释。     

Investigating the link between transaction and computational costs in a blockchain environment

The research and thinking pertaining to blockchain have thus far focused on cryptocurrency and Bitcoin. However, there is increased interest in using the technology to solve operational challenges in manufacturing and service supply chains. In this study, we introduce a new implication of using blockchain technology and propose two unique contributions. First, we introduce the notion of computational costs (measured in units of gas) as an essential mechanism for completing operational transactions in the blockchain environment. Second, we discuss the use of smart contracts and their influence on operational transactions. To investigate the link between blockchain transaction and computational costs, this study uses an experimental methodology. We develop and implement a fully functional virtual public blockchain to store, validate, and maintain transactions. The methodology provides a process to measure the computational costs, frequency, and intensity of transactions. This research contributes to conceptual research on the blockchain implementation paradigm. Its novelty stems from the identification of computational costs for operational transactions and use of an experimental methodology. This research provides managers an insight into the design of smart contract transactions in a supply chain from a cost perspective.     

Traceability as a means to investigate supply chain sustainability: the real case of a leather shoe supply chain

In recent years, the growing attention to environmental challenges has shown that these issues are becoming of more and more interest to both research and industry. Companies are expected to ensure their products are fully traceable and more sustainable, which requires the involvement of all of the actors in the production network. According to this aim, this study proposes a structured approach that uses the traditional traceability concept as a means to identify the main information needed to assess environmental impacts along the whole supply chain (SC). The proposed approach is composed of four main steps: (i) SC modelling to identify all stakeholders and their inter-relations, (ii) data sharing to collect all relevant data, (iii) data elaboration to calculate performance at different levels of detail and (iv) result interpretation to optimise the SC. The distributed implementation of the approach at different SC steps represents a useful means to practically realise a sustainable SC management. A case study involving a leather shoe SC is used to demonstrate the effectiveness of the approach in identifying criticalities, supporting the selection of the most appropriate suppliers and correctly setting a management strategy towards the optimisation of internal and external traceability and environmental sustainability performances.     

The impact of contract parameters on the supply chain performance under different power constellations

Reporting forecast data is a common method used to improve the functioning of supply chains (SCs) and to reduce supply shortages. Customers tend to report the maximum possible demand as a forecast if restrictions are missing. Such a forecast is useless for suppliers. Hence, special contracts are needed to enhance the value of forecast data and therefore the cooperation between SC partners. In this paper, such a contract is presented. It encourages the customer to report a more realistic forecast. Deviations from the reported forecast are punished in different ways: If the customer reported too much and wants to release less than what was reported, he has to pay a penalty. On the other hand, the customer has the flexibility to purchase more than reported to meet the demand on his outlet but at the cost of an additional fee. This paper analyses how different contract parameters affect the performance of the SC, in particular when the bargaining power of customer and supplier is not equally distributed. Results show that the supplier and therefore the SC is better off if the supplier leaves the contractual cost parameters untouched but hides the true value of flexibility, especially when the customer is less powerful than the supplier.     

Blockchain technology and its relationships to sustainable supply chain management

Globalisation of supply chains makes their management and control more difficult. Blockchain technology, as a distributed digital ledger technology which ensures transparency, traceability, and security, is showing promise for easing some global supply chain management problems. In this paper, blockchain technology and smart contracts are critically examined with potential application to supply chain management. Local and global government, community, and consumer pressures to meet sustainability goals prompt us to further investigate how blockchain can address and aid supply chain sustainability. Part of this critical examination is how blockchains, a potentially disruptive technology that is early in its evolution, can overcome many potential barriers. Four blockchain technology adoption barriers categories are introduced; inter-organisational, intra-organisational, technical, and external barriers. True blockchain-led transformation of business and supply chain is still in progress and in its early stages; we propose future research propositions and directions that can provide insights into overcoming barriers and adoption of blockchain technology for supply chain management.     

A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0

Smart factories Industry 4.0 on the basis of collaborative cyber-physical systems represents a future form of industrial networks. Supply chains in such networks have dynamic structures which evolve over time. In these settings, short-term supply chain scheduling in smart factories Industry 4.0 is challenged by temporal machine structures, different processing speed at parallel machines and dynamic job arrivals. In this study, for the first time, a dynamic model and algorithm for short-term supply chain scheduling in smart factories Industry 4.0 is presented. The peculiarity of the considered problem is the simultaneous consideration of both machine structure selection and job assignments. The scheduling approach is based on a dynamic non-stationary interpretation of the execution of the jobs and a temporal decomposition of the scheduling problem. The algorithmic realisation is based on a modified form of the continuous maximum principle blended with mathematical optimisation. A detailed theoretical analysis of the temporal decomposition and computational complexity is performed. The optimality conditions as well as the structural properties of the model and the algorithm are investigated. Advantages and limitations of the proposed approach are discussed.