Industry 4.0 initiatives can help traditional manufacturing industry cope with increasing global competition. Such solutions facilitate transparency, automation as well as business process transformation. This paper elaborates on a collaboration with a medium-sized manufacturing company. We highlight the design, evaluation and roll-out of an escalation management system with integrated data-driven decision support. We do so by applying an action design research process. Thereby, our study focuses on the system design concerning the creation of business value.

The system leverages state-of-the-art machine learning algorithms for disruption type classification and escalation handling duration prediction. These predictions can be embedded in an integrated planning procedure leveraging diverse organizational data sources (e.g., personnel availability, production plans) to instantiate a prescriptive analytics solution. Combined with informative analytics insights, this allows the proposed system to generate significant business value by reducing escalation durations. In the long run, the transformational business value enabled by the system is likely to exceed the automational business value. This highlights the special importance of tight integration of industrial analytics applications within business processes.

工业4.0计划可以帮助传统制造业应对日益加剧的全球竞争。这样的解决方案有助于提高透明度，自动化程度以及业务流程的转换。本文详细阐述了与一家中型制造公司的合作。我们重点介绍具有集成数据驱动决策支持的升级管理系统的设计，评估和推出。我们通过应用动作设计研究过程来做到这一点。因此，我们的研究集中在与创造商业价值有关的系统设计上。

该系统利用最新的机器学习算法进行中断类型分类和升级处理持续时间预测。这些预测可以嵌入到利用各种组织数据源（例如，人员可用性，生产计划）的集成计划程序中，以实例化说明性分析解决方案。结合信息丰富的分析见解，这可以使建议的系统通过减少升级持续时间来产生可观的业务价值。从长远来看，系统支持的转型业务价值可能会超过自动化业务价值。这突出了在业务流程中紧密集成工业分析应用程序的特别重要性。