A supply chain network architecture is a key element of designing and modeling a supply chain to better understand the cost and time associated with the distribution of products with available resources and market locations. Due to the large size of combinations for product design and supplier choices; descriptive, predictive and prescriptive analytics are needed to design, control and then improve a supply chain network. Current study is the first instance in the supply network management field using linguistic summarization (LS), a descriptive analytics tool generating natural language-based summaries of raw data with the help of fuzzy sets. This study has developed a LS method for revealing information from a realistic complex network of a bike supply chain, and it produces network description phrases by using fuzzy set theory to model linguistic/textual terms. The truth degree of generated summaries is calculated by fuzzy cardinality-based methods instead of scalar cardinality-based methods to overcome inherent disadvantages. The results of the study are interpreted in two ways: word clouds are used for single objective cases, and list of sentences that exceed a threshold value are used for bi-objective cases. LS-based findings, explanations and strategic decisions are directed at decision support to increase supply network performance, efficiency and sustainability.

供应链网络体系结构是设计和建模供应链以更好地了解与具有可用资源和市场位置的产品分配相关的成本和时间的关键要素。由于产品设计和供应商选择的组合规模很大；设计，控制和改善供应链网络需要描述性，预测性和规范性分析。当前的研究是使用语言汇总（LS）在供应网络管理领域的首次研究，LS是一种描述性分析工具，可以借助模糊集生成基于自然语言的原始数据汇总。这项研究开发了一种LS方法，用于从现实的自行车供应链复杂网络中揭示信息，并使用模糊集理论对语言/文字术语进行建模，从而生成网络描述短语。通过基于模糊基数的方法而不是基于标量基数的方法来计算生成的摘要的真实度，以克服固有的缺点。研究结果的解释有两种方式：词云用于单个目标案例，而超过阈值的句子列表则用于双目标案例。基于LS的发现，解释和战略决策旨在为决策提供支持，以提高供应网络的性能，效率和可持续性。研究结果的解释有两种方式：词云用于单个目标案例，超过阈值的句子列表用于双目标案例。基于LS的发现，解释和战略决策旨在为决策提供支持，以提高供应网络的性能，效率和可持续性。研究结果的解释有两种方式：词云用于单个目标案例，而超过阈值的句子列表用于双目标案例。基于LS的发现，解释和战略决策旨在为决策提供支持，以提高供应网络的性能，效率和可持续性。