دانلود مقاله مدیریت زنجیره تامین بر اساس خوشه بندی نوسانات CBDC
خلاصه
1. مقدمه
2. داده ها و روش
3. برنامه ریزی تولید برای زنجیره تامین تولید بر اساس مدل GARCH
4. برنامه ریزی تولید برای زنجیره تامین تولید بر اساس مدل یادگیری ماشین
5. نتیجه گیری
منابع مالی
مشارکت های نویسندگی
اعلامیه های منافع رقابتی
قدردانی
منابع
Abstract
1. Introduction
2. Data and methodology
3. Production planning for manufacturing supply chain based on GARCH model
4. Production planning for manufacturing supply chain based on machine learning model
5. Conclusion
Funding
Authorship contributions
Declarations of Competing Interest
Acknowledgments
References
چکیده
یک ارز دیجیتال بانک مرکزی (CBDC) که توسط بانک انگلستان راه اندازی شده است، می تواند مشاغل را قادر به پرداخت مستقیم الکترونیکی کند. می توان استدلال کرد که پرداخت دیجیتال در برنامه های کاربردی مدیریت زنجیره تامین مفید است. با این حال، پذیرش CBDC در زنجیره تامین می‌تواند آشفتگی جدیدی ایجاد کند زیرا ارزش CBDC ممکن است نوسان داشته باشد. بنابراین، این مقاله در نظر دارد با کاهش عدم قطعیت ارزش CBDC، برنامه تولید زنجیره تامین تولید را بر اساس مدل خوشه‌بندی نوسانات بهینه کند. ما هم مدل GARCH و هم مدل یادگیری ماشین را برای به تصویر کشیدن خوشه‌بندی نوسانات CBDC اعمال می‌کنیم. به طور تجربی، ما از شاخص خشک بالتیک، بیت کوین و نرخ ارز به عنوان متغیرهای اصلی با دوره نمونه از 2015 تا 2021 برای ارزیابی عملکرد دو مدل استفاده کردیم. بر این اساس، ما نشان می‌دهیم که مدل یادگیری ماشین ما به طور قابل توجهی از مدل GARCH بهتر است. در نتیجه، نتیجه ما نشان می‌دهد که عملکرد شرکت‌های تولیدی را می‌توان از طریق کاهش عدم قطعیت CBDC تقویت کرد.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
A Central Bank Digital Currency (CBDC) launched by the Bank of England could enable businesses to directly make electronic payments. It can be argued that digital payment is helpful in supply chain management applications. However, the adoption of CBDC in the supply chain could bring new turbulence since the CBDC value may fluctuate. Therefore, this paper intends to optimize the production plan of manufacturing supply chain based on a volatility clustering model by reducing CBDC value uncertainty. We apply both GARCH model and machine learning model to depict the CBDC volatility clustering. Empirically, we employed Baltic Dry Index, Bitcoin and exchange rate as main variables with sample period from 2015 to 2021 to evaluate the performance of the two models. On this basis, we reveal that our machine learning model overwhelmingly outperforms the GARCH model. Consequently, our result implies that manufacturing companies’ performance can be strengthened through CBDC uncertainty reduction.
Introduction
Cryptocurrencies, represented by Bitcoin, have exhibited a significant impact on the current financial system (Easley et al., 2019, Zhang and Li, 2020, Huynh et al., 2021, Jin et al., 2021, Prat and Walter, 2021), especially after the outbreak of COVID-19 (Guo et al., 2021, Diniz-Maganini et al., 2021) and Fintech development (Le et al., 2021, Chaudhry et al., 2022). As a result, to confront and replace such an emerging private digital currency system, the Bank of England announced the launch of a Central Bank Digital Currency (CBDC), which served as a new form of digital currency issued by the Bank of England and can be used by households and businesses (BenDhaou and Rohman, 2018, Vessio, 2021).
Since CBDC is still under development, copious of scholars advocate the adoption of blockchain techniques into CBDC systems, which can yield a number of benefits, such as increased payment safety and information transparency (Sun et al., 2017, Sethaput and Innet, 2021). Moreover, several countries have already signified the adoption of blockchain in their CBDC payment systems (Xu and Zou, 2021). On the other hand, bitcoin is an exceedingly representative digital coin based on the blockchain technique (Hughes et al., 2019, Jiang et al., 2022). Consequently, we use bitcoin to depict blockchain-based digital currency, which aligns with the future development trend of digital currency like CBDC.
Conclusion
In summary, the main issue for this paper to accommodate is to deliver a sensible production plan based on the volatility clustering model regarding manufacturing companies with the application of CBDC in the supply chain. We unravel the fact that CBDC volatility can generate considerable effect on the supply chain management for manufacturing companies. Consequently, manufacturing companies formulate the production plan based on volatility clustering model can be helpful in the reduction of uncertainties from both CBDC and BDI. Our framework is based on the volatility clustering model, and we schedule company-related activities into low-volatility periods. Under such a framework, we can thereby reduce the overall uncertainty levels faced by manufacturing companies, as both CBDC and BDI uncertainties can be lessened through rescheduling.
To capture the feature of CBDC volatility, we meld both BTC volatility and GBP volatility in the rescheduling model, where BTC represents the digital currency side of CBDC and GBP represents the sovereign currency side of CBDC. We first used the GARCH model as the volatility clustering model for production planning, where the model performance is inferior. We argue that if we can consider volatilities in both time series simultaneously and filter the noise from the time series, the model performance could be enhanced. Hence, we adopt the DBSCAN technique, which is a type of machine learning approach to achieve these two goals, and the performance actually has been improved. Thus, we can conclude that manufacturing companies’ production plans can be strengthened through uncertainty reduction. We combine BTC volatility with GBP volatility to reveal the CBDC volatility effect on SCM, and we schedule the production plan within those low volatility periods for both BTC and GBP accordingly. On this basis, our model verifies that the enhanced production plan can help companies to have better cost and revenue management, as it reduces costs and increases revenue.