The main focus of the thesis was to find opportunities for startups in the field of industry 4.0, specific to the steel industry. Industry 4.0 describes a future state which has integration across the value and process chain as its main characteristic. The development towards this state and the associated technologies provide new economic opportunities for market participants who have the relevant expertise in information technology as well as domain knowledge and network in the industry. A literature review provided the necessary overview on what industry 4.0, the associated technologies and its applications are, as well as challenges associated with implementing them. Furthermore, a detailed review on specific applications in the steel industry helped to get a more concrete understanding of digitalization in metallurgical process chains. Furthermore, the role of startups in corporate technology and innovation management as well as types of corporate-startup collaboration was investigated. For the entrepreneurial effort the lean startup approach and the inherent build measure learn feedback circle was used. Based on the initial hypothesis that digitalization creates opportunities for startups in the steel industry, three preliminary business models were conceptualized: first, the data analytics collaboration platform was intended to simplify remote collaboration between data analysts and domain experts on sight. Second, predictive maintenance software was intended to predict component malfunction and give recommendations on when to engage in maintenance activity, in order to increase the overall economic performance of the mills. Finally, the sales solution was meant to digitalize the selling process and automate repetitive tasks, in order to free up sales staff for launch of new products and product development efforts. The ideas were discussed with interview partners from steel companies, equipment providers to the steel industry and independent experts in specific areas, with the main learning that the complexity of the field almost prevents conceptualizing software if a direct and continuous access to domain experts is missing. Accordingly, the preliminary business models were only met with moderate enthusiasm. Nevertheless, predictive quality analytics was identified as a highly promising area, as it was mentioned and explained by multiple interview partners. Identifying correlations and causalities between process parameters and product quality can help to make necessary adjustments to the process, and improve the overall economic performance of the companies. Finally, a revised business model was drafted. Project-based data analytics is meant to support the steel companies case by case and provide them with the necessary machine learning and advanced data analytics expertise in their quality improvements. This has an immediate benefit for the steel companies and helps the startup to build expertise and a brand in this field. The revised business model was drafted before the final interviews while the feedback from the interview partners was optimistic, which was also a main reason why it was chosen as the final outcome.