Blockchain-driven operation strategy of financial supply chain under uncertain environment

Abstract

The emergence of blockchain creates a new possibility to solve the fraudulent problem of financial supply chain. We construct a game model to verify the strategic choice of the financial supply chain in an uncertain environment. We derive the equilibrium results and investigate the strategic choice of blockchain service for the financial supply chain. We also study the product price, product quantity, financing interest rate, and supply chain risk transmission, respectively. Specifically, when the blockchain is not considered, the financial model of supply chain led by core enterprises depends on firms’ reputation. The retail and wholesale prices increase when fraud occurs or consideration payment increases. Besides, the market stability reduces price performance. In the blockchain environment, the strategic choices are divided into two cases: when choosing the core enterprise model and the third-party service model, the third-party service model is the equilibrium strategy; when choosing the third-party service model and the platform model, the platform model is the equilibrium strategy.

KEYWORDS:

• Supply chain finance
• blockchain service
• market uncertainty
• strategic choice
• risk transmission
• game theory

Acknowledgements

We appreciate editors and reviewers that spent time on the original manuscript.

Supporting information

We submit an electronic E-Appendix to provide supporting information for this study.

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and its E-appendix.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 CSCC, as the internet-based supply chain financial service platform, has some advantages in the industrial side and fund side, which has become the first choice of central enterprises, state enterprises, and financial enterprises.

2 There are many causes that the production activity of enterprises has stagnated, in addition to the influence of COVID-19, including unstable quantity, disturbing, or supply disruption. In this study, we use the Covid-19 as an example to show the influence in the uncertain environment, which also supports the model setting.

3 From a mathematical perspective, $\mathrm{Var}\left(e\right)=E\left(e^2\right)-(E\left(e\right){)}^2$ holds, where $e\in \left[0,1\right]$. Therefore, $E\left(e^2\right)<E\left(e\right)$ and $\mathrm{Var}\left(e\right)<E\left(e\right)-(E\left(e\right){)}^2$, where $E\left(e\right)\in \left[0,1\right]$. Let the function as $y=E\left(e\right)-(E\left(e\right){)}^2$, we can obtain the $y$ as a quadratic function. Therefore, the function value $y$ reaches max value $1/4$, where $E\left(e\right)=1/2$. In order to make the result monotonically increasing from function, we take the value $\mu =E\left(e\right)\in \left[0,\frac{1}{2}\right)$ to satisfy $E\left(e\right)-(E\left(e\right){)}^2$ reaches max value $1/4$.

4 To show the rationality of the demand function, we show an example that blockchain technology promotes the increase of semiconductor demand. Specifically, blockchain technology will help simplify supply chain problems, reduce costs and improve efficiency. The semiconductor-manufacturing process with artificial intelligence can increase the output of grain chips. This kind of blockchain traceability will improve the company’s business operation, speed up the product to the market, and reduce the cost as much as possible. Semiconductor companies can also use blockchain to create, expand and manage technology-based collaboration and redefine future business models (OF week 2018).

5 Such behavior may lead to the inconsistency between the actual revenue and the revenue originally designed by the contract, which may lead to potential fraud.

6 A model and T model have different features in the financial activity, which has different leaders. According to some example in practice, the main business department in the A model are manufacturer, retailer, and financing institution. The retailer needs to financing service in A model. The T model is the opposite with A model. Specifically, the third-part service provider, as the leader in the supply chain, has some advantages, including funds, service, and capital advance payment. In this base, third-part service providers will contract the financing needs of different enterprises to achieve better service value. In this way, the enterprise, such as a retailer, could reduce some non-essential capital expenditure.

7 In internal operation of e-commerce platform (e.g. Alibaba), it can monitor the authenticity of the transaction background, the certainty of capital flow, the closure of business and the self-compensation of capital. In this way, there is no core enterprise, and there is no process of pledge of goods due to the platform enterprise can control risks. In this sense, there is no core enterprise. However, this does not mean that there is no leader in the supply chain system. In reality, it is more often the e-commerce platform, as the whole system (e.g. Alibaba) plays the role of the core firm, which is the aggregation of SEMs for providing financing and service.

8 The T model and D model have different features in the service level of blockchain technology and supply chain structure according to commercial practice. Although two models have a support blockchain service by the third-party platform, it needs to decide service level according to the changes of supply chain structure due to optimal principle by the third-party platform.

9 In practice, commercial fraud is the use of improper methods, including concealing facts and publishing false information to obtain the benefit of personal or enterprise and various material benefits in the process of market transactions, investment, and services. In our research, it means that the probability of deception in the supply chain, which also means the loss of profit.

10 Due to many funds of technical investment, the T model and D model of the third-party platform needs to consider the investment cost of blockchain service with rational principles according to the changes of supply chain structure, which also shows the different levels of blockchain service in both models.

11 In our study, the three models of supply chain finance are derived from the existing management practices. Therefore, the choice of fund demands should be based on management practice. For example, in the A model, the retailer has financing needs, which from the liquor supply chain. In the other two models, enterprises with financing needs are adjusted to platforms, such as EASCS and Alibaba, respectively. If we consider the change of financing needs in different scenarios, it is the influence of the leading party in the supply chain.

12 For manufacturer, the enterprise profit has profit from selling the product to a retailer. The retailer has the profit of selling products. The platform is obtained by selling the product to downstream enterprise, except the financing cost and blockchain investment cost. The profit of financial institution can be divided into two parts, such as product market or financing market.

Additional information

Funding

This research was supported by the National Natural Science Foundation of China [grant number 71774019], the Fundamental Research Funds for the Central Universities [grant number 3132022608], and the Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University [grant number 0143210271].

Notes on contributors

Huida Zhao

Huida Zhao is currently a PhD student at the School of Maritime Economics and Management, Dalian Maritime University. His main research directions include supply chain management, blockchain economy, and maritime operations management. He has published academic papers in the International Journal of Production Economics, Annals of Operations Research, and Computers & Industrial Engineering.

Jiaguo Liu

Jiaguo Liu is currently a Professor and Head of the Department of Management Business Administration at the School of Maritime Economics and Management, Dalian Maritime University. His research interests include Supply Chain Management, Maritime Operations Management. He has published more than 120 peer-reviewed journal and conference publications, such as Transportation Research Part B, Transportation Research Part E, European Journal of Operational Research, International Journal of Production Economics, International Journal of Production Research, Transport Policy, Ocean & Coastal Management, Ocean Engineering, Maritime Policy & Management. His research has been supported by the National Science Foundation of China and the National Development and Reform Commission.

Guoqing Zhang

Guoqing Zhang is a Professor in Industrial Engineering, and the director of supply chain and logistics optimisation research lab at the Department of Mechanical, Automotive & Materials Engineering, University of Windsor. He received the Ph.D. degree in management sciences from City University of Hong Kong in 2000. His recent research interests include supply chain management and optimisation, logistics, warehouse management, algorithms design and development, operations research, data-driven optimisation, and intelligent decision support systems. He has published over 70 articles on those areas in journals such as Operations Research, Eur. J. Oper. Res., IIE Transactions, Transport Res B-Meth, and E-Log, Omega, Int. J. Prod. Res., Int. J. Prod. Econ., Comput Oper Res., and so on. Dr Zhang has provided consulting to several world-leading companies on Automotive, Energy, Airline, and food industries. Dr. Zhang and his team won the first place in the Canadian Operations Research Society Practice Prize competition in 2015.