Semiconductor supply chains are described by significant demand fluctuation that increases as one moves up the supply chain, the so-called bullwhip effect. To counteract, semiconductor manufacturers aim to optimize capacity utilization, to deliver with shorter lead times and exploit this to generate revenue. Additionally, in a competitive market, firms seek to maintain customer relationships while applying revenue management strategies such as dynamic pricing. Price change potentially generates conflicts with customers. In this paper, we present KnowGraph-PM, a knowledge graph-based dynamic pricing model. The semantic model uses the potential of faster delivery and shorter lead times to define premium prices, thus entail increased profits based on the customer profile. The knowledge graph enables the integration of customer-related information, e.g., customer class and location to customer order data. The pricing algorithm is realized as a SPARQL query that relies on customer profile and order behavior to determine the corresponding price premium. We evaluate the approach by calculating the revenue generated after applying the pricing algorithm. Based on competency questions that translate to SPARQL queries, we validate the created knowledge graph. We demonstrate that semantic data integration enables customer-tailored revenue management.
翻译:半导体供应链被描述为需求大幅波动,这种波动随着向供应链上移动而增加,即所谓的 " 牛鞭效应 " 。为了抵消这种波动,半导体制造商力求优化能力利用,缩短周转时间,并利用这一机会创造收入。此外,在有竞争力的市场中,公司在应用动态定价等收入管理战略的同时,力求保持客户关系。价格变化可能会与客户产生冲突。在本文中,我们介绍知识图形动态定价模型 " KnowGraph-PM " 。语义模型利用更快交货和较短周转时间的潜力来确定溢价价格,从而根据客户概况增加利润。知识图表使得与客户有关的信息(例如客户类别和地点)能够整合到客户订购数据。定价算法是作为SPARQ查询实现的,它依赖客户概况和命令行为来确定相应的溢价。我们通过应用定价算法来计算产生的收入来评估这一方法。我们根据能力问题,向SPARQL查询,验证所创建的知识图表。我们证明,语义数据整合有助于客户定制收入管理。