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Recent technological advances brought about by Industry 4.0 significantly impact maintenance management by improving failure prediction and automating decision making. Cyber-physical systems (CPS) and Internet of Things (IoT) integrate computational and physical processes and are the enabling technologies for predictive maintenance. It surpasses not Industry 4.0 driven policies with the capability of predicting unpredictable failures through automated fault diagnostics and prognostics performed on big data for which machine learning becomes more common due to increased system complexity. The implementation of CPS and IoT is challenging and associated costs are high, however, benefits as flexibility and adaptability are undeniable. Predictive maintenance is the overall preferred policy for randomly failing, critical (and expensive) assets, for being profitable in the long term. As a systematic review of Industry 4.0 and its technologies seems to be lacking, the thesis’ first aim is to present one coherent, yet conceptual, overview of the topic. It then focuses on giving a general outline of predictive maintenance and a framework for its successful implementation that may guide business managers.

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This thesis investigates the value proactive maintenance policies, CBM and PdM 4.0, deliver and how the value can be captured. The aim is to close the gap between research and practice by giving a structured overview of the returns of proactive maintenance policies and compare literature findings with a survey study. The survey is conducted at Belgian manufacturers and aims to highlight the areas of agreement and disagreement between research and practice. We found that the decision on the implementation of proactive maintenance policies depends on both the necessity and the capacity of the organization. Necessity increases when safety and quality requirements are high. As for the capacity, we found that IT capabilities and infrastructure are important, but also management support and strategic alignment between different departments. Although both literature and our survey indicate that only a small number of firms are applying proactive maintenance policies, especially predictive maintenance 4.0, significant benefits are realized on the four maintenance value drivers, being cost reductions, increased uptime, extended asset lifetime and SHEQ risk reductions. The goal is to convince management about the value of proactive maintenance so as to encourage the implementation when necessity is proven to be high.

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In this thesis, the current literature on software pricing models is reviewed. New market trends such as software-as-a-service and open-source software are firstly discussed, revealing the challenging conditions for software vendors to adapt to these pricing models. Afterwards, the literature on software pricing strategies is reviewed, making a distinction between cost-based pricing and value-based pricing. Numerous strategies are analyzed and industry examples are given, this to further explain the differences between these two strategies. In the next section, the most important software pricing models are presented and compared to each other along with the important elements of these models like piracy, network effects and switching costs. This analysis revealed that protection of the software may not be beneficial, as network effects can cause a significantly improved diffusion. Another finding was that a drug-pusher’s strategy, where the software vendor offers its software for a lower pricing initially to be able to cash-in later, is preferable either through upgrades or other revenue streams related to the userbase. Lastly, the business case of Microsoft Windows is used to discuss how their pricing helped them to become the leader in their market and what challenges they face towards the future.

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Performance based contracts (PBC's) are increasingly being used to incentivise service providers to meet predetermined performance criteria. Literature on PBC’s in the service industry is fragmented and mostly lacks conceptualization and a holistic view. This paper seeks to fill that void by taking stock of the literature and developing a general framework for PBC’s in the service industry. The applicability of this framework is tested on two specific service sectors: electricity distribution and the health care sectors. The main finding from the case studies is that even within sectors PBC design and implementation methods differ significantly depending on contextual factors, such as country, regulation and industry organization. Notwithstanding these differences, substantial common ground was found by categorizing the sector-specific literature on the general framework. As such, this general framework offers useful guidelines for other sectors and a better understanding of the different design and managerial aspects of PBC’s.

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This thesis bundles the literature that exists on demand learning models in the context of revenue management. When a seller of a good or a service is faced with unknown demand, it is in his best interests to forecast or model future demand. By making use of demand learning models, the seller is able to learn about demand over time and model what demand may look like. During this process, he is faced with the exploration-exploitation trade-off. Learning requires the sacrifice of short term revenue, which is traded off against the possible gains in long term revenue. A grouping and an analysis of models that are based on the same basic assumptions is provided, along with the identification of a best performer for a specific setting based on T-period regret. Finally, a flow chart is presented that serves as a tool for the reader to identify which models are best suited given a specific situation.