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This thesis develops innovative frameworks to address strategic decision-making challenges across diverse business domains. The first study investigates optimal business model strategies for mechatronic firms, where the integration of digital control and physical products creates new avenues for value capture. By employing a Stackelberg game framework, the analysis identifies the conditions under which firms should adopt selling versus renting strategies for the digitally controlled functionalities of mechatronic products, as well as the conditions under which the firm’s profitability aligns with consumer welfare.
The second study addresses the practical challenges faced by traditional retailers in estimating demand elasticity under fixed pricing constraints. By leveraging bundle discounting to generate pseudo price points, a multinomial logit model is employed to compare the effectiveness of direct versus bundling discount strategies. The results highlight how consumer perceptions in allocating discounts impact the precision of demand estimators, offering actionable insights for environments where price experiments are limited.
In the third study, an algorithmic approach is developed to optimize risk-weighted assets (RWA) for financial institutions operating under Basel III regulations. By formulating the optimization process as a large-scale integer programming problem and incorporating advanced pre-processing techniques, the proposed method significantly reduces computational complexity and improves capital optimization. This framework significantly frees up the bank’s capital reserve and enhances its investment flexibility.
Together, these studies demonstrate how advanced optimization and estimation methods can be applied to diverse areas—from innovative, technology-enabled business models and demand estimation to financial risk management—providing a comprehensive toolkit for strategic decision-making in business.