Following the design science methodology, a recommender system has been created with the research objective of finding a novel approach to the bundling problem in order to generate data-driven insights identifying cost potentials across an organization. In this study, a concept that has been implemented in business-to-business marketing at IBM is taken over to procurement in the automotive industry to provide decision support. Thereby, this work builds on information processing theory to utilize artificial intelligence technologies, i.e., natural language processing and supervised learning to augment the skills of buyers, whereby design principles were derived for information technology providers and procurement organizations in private and public settings worldwide. As a key finding, overall, Mini Batch K-means was the most performative model among the selected clustering algorithms. Furthermore, through actively making use of purchasing requisition data typically available in enterprise resource planning systems, the bundling generator artifact can lead to significant improvements in strategic planning and commodity management resulting in better utilization of buyer’s capacities, tender designs, and thus procurement’s value contribution in the transformation toward industry 4.0. The empirical study contributes to the literature on bundling and spend analysis, which has predominantly relied on historical data. By incorporating requisition data, which poses inherent challenges of precision and information-richness, this work expands this traditional approach with a forward-looking perspective.

遵循设计科学方法，创建了一个推荐系统，其研究目标是找到解决捆绑问题的新方法，以便生成数据驱动的见解，识别整个组织的成本潜力。在本研究中，IBM 在企业对企业营销中实施的概念被运用到汽车行业的采购中，以提供决策支持。因此，这项工作建立在信息处理理论的基础上，利用人工智能技术，即自然语言处理和监督学习来增强买家的技能，从而为全球私人和公共环境中的信息技术提供商和采购组织推导出设计原则。作为一项重要发现，总体而言，Mini Batch K-means 是所选聚类算法中性能最佳的模型。此外，通过积极利用企业资源规划系统中通常可用的采购申请数据，捆绑生成器工件可以显着改进战略规划和商品管理，从而更好地利用买方的能力、招标设计，从而更好地利用采购的价值贡献。向工业 4.0 转型。实证研究为捆绑和支出分析的文献做出了贡献，这些文献主要依赖于历史数据。通过整合申请数据，这对精确性和信息丰富性提出了固有的挑战，这项工作以前瞻性的视角扩展了这种传统方法。