Cloud model for new energy vehicle supply chain management based on growth expectation

The goal of supply chain management is to enhance output levels through the integration and collaboration of supply chain members. As such, identifying high-quality cooperative members is critical. However, many decision-making models for supply chain management neglect the consideration of growth expectations for these members, resulting in an unstable decision-making process. Therefore, it is essential to incorporate growth expectations into the decision-making process. To reduce ambiguity, we propose using cloud theory to quantify growth expectations and establish a cloud model of growth expectations. Our study underscores the importance of considering growth expectations when selecting supply chain cooperative members. By utilizing the cloud model of growth expectations, we provide a more comprehensive decision-making approach that enables decision-makers to assess the suitability of potential cooperative members and select the best member based on a multi-attribute decision-making process. We demonstrate the effectiveness of our method through case studies, which ensures its practicality and usability in real-world applications. Ultimately, our method offers a more efficient means of selecting cooperative members, which is expected to enhance output levels and increase supply chain efficiency.

Automated summary

The goal of supply chain management is to improve output levels by integrating and collaborating with supply chain members. However, many decision-making models neglect the consideration of growth expectations for these members, leading to an unstable decision-making process. To address this issue, we propose using cloud theory to quantify growth expectations and establish a cloud model. Our study emphasizes the importance of considering growth expectations when selecting supply chain cooperative members. By utilizing the cloud model, we provide a comprehensive decision-making approach that enables decision-makers to assess the suitability of potential cooperative members and select the best member based on a multi-attribute decision-making process. Case studies demonstrate the effectiveness of our method, ensuring its practicality and usability in real-world applications. Ultimately, our method offers a more efficient means of selecting cooperative members and enhancing supply chain efficiency.