Abstract for: Understanding the interplay between exploration and exploitation in the data-driven servitization of manufacturing firms

Data-driven servitization is meant to be the imperative for manufacturers in an age in which data becomes the new oil. The analysis of the accumulated customer usage and process data unveils new insights for process improvements and innovative offerings. Nevertheless, too often manufacturers struggle to use data analytics appropriately. They fail to balance adaptability and efficiency which either leads to unmet customer needs or leads away from operational excellence. Thus, a crucial trade-off has to be made between exploitative data analytics as an input for the delivery management and explorative data analytics as an input for the development management to be viable in both the short- and the long-term. To model this trade-off, the general theories of management cybernetics and organizational ambidexterity are combined with specific findings in the field of data-driven servitization. To consolidate the several theses and to investigate the resulting organizational dynamics a formal model is deduced which is validated against empirical findings. The resulting contributions are twofold: First, different policies for the trade-off between exploitative and explorative data analytics can be evaluated based on cybernetic performance measures. Second, the existing literature is condensed into a system of differential equations which is characterized by consistence and parsimony.