Effective asset management is crucial for reducing costs related to the acquisition and upkeep of assets. Companies must smartly plan and execute maintenance activities to ensure asset reliability and longevity. Informed decisions on the type and timing of maintenance significantly impact operational efficiency and cost savings. The thesis explores various aspects of asset management. Chapter 1 introduces the topic and outlines the thesis. Chapters 2, 3, and 4 focus on optimizing maintenance planning with incomplete information, while Chapter 5 addresses broader decision-making surrounding retrofitting and upgrading for power plants. Chapter 6 concludes the thesis and suggests areas for future research. Chapter 2 covers maintenance planning for HVAC units in trains, inspired by a case from Dutch Railways. The study considers how environmental factors influence the condition of HVAC units and how inspections and maintenance interventions can consider this. Three numerical experiments are conducted: comparing different policies, evaluating the value of complete information, and assessing the effect of using a stochastic model. Results show that incorporating environmental factors can lead to over 10% cost savings. Chapter 3 examines maintenance planning for a multi-component system with a single sensor based on a case study of a Canon Production Printing printer. The sensor provides partial condition information, which aids maintenance decision-making. The research finds that an optimal policy with a maximum of three decision regions can be developed under specific conditions. This helps visualize the maintenance policy. Chapter 4 focuses on sewer pipe inspection, maintenance, and replacement optimization using data from Breda, in collaboration with Rolsch Assetmanagement. The study finds that traditional periodic schedules may not always be cost-effective. Instead, inspection, maintenance, and replacement decisions should be based on condition data and failure costs, emphasizing that maintenance can extend asset life and reduce overall replacement costs. Chapter 5 analyzes power plant upgrading and retrofitting decisions, simulating various factors such as fuel prices, electricity prices, and carbon pricing. A case study using Texas electricity data demonstrates that increasing carbon prices can lead to technology trapping, where producers switch to less desirable but still carbon-emitting technologies like combined cycle gas turbines (CCGTs) and effectively get stuck with that technology. The study advises policymakers to carefully design carbon pricing and related measures to achieve effective technology transitions. Overall, the thesis underscores the importance of data-driven, context-specific maintenance decisions in asset management, offering valuable insights for both practitioners and policymakers.