The continuous digitization of production facilities, the emergence of modern methods of data processing and advancements in artificial intelligence increase the potential for decision-making processes in industrial control. An ever increasing number of sensors, digital (global) links through data spaces, and integrated supply chain models result in an abundance of input data that can be considered in decision-making processes. At the same time, the objectives of industrial control problems extend beyond mere profit maximization, as shifting societal and political frameworks demands the consideration of ecological sustainability and demographic changes. Subsequently, the complexity of decision-making processes in the industrial context growth analogously, prompting questions regarding meta-decisions regarding which input data to consider and how to weigh different objective functions. In this article, we review current challenges of decision-making in the industrial context and derive a meta-framework supporting both researchers and practitioners in the design of decision-making processes and the associated decision spaces.