@phdthesis{Defregger2009, author = {Defregger, Florian}, title = {Revenue management for manufacturing companies}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:824-opus-559}, school = {Katholische Universit{\"a}t Eichst{\"a}tt-Ingolstadt}, year = {2009}, abstract = {Revenue management has proven successful in service industries. This dissertation tries to answer the question if revenue management can also be applied successfully to manufacturing companies. For this purpose, a survey was conducted which showed that there is significant potential for revenue management in the steel, aluminium and paper industries. Furthermore, a number of mathematical decision models were developed and solved by heuristic procedures which showed that revenue management can improve profits for manufacturing companies substantially.}, subject = {Revenue Management ; Stochastisches Entscheidungsmodell ; Industriebetrieb}, language = {en} } @phdthesis{Ling2023, author = {Ling, Kunxiong}, title = {Data aggregation and sampling procedures for usage profiling and customer-centric automotive systems engineering}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:824-opus4-8026}, school = {Katholische Universit{\"a}t Eichst{\"a}tt-Ingolstadt}, year = {2023}, abstract = {Specifying or deriving customers' needs into detailed requirements becomes essential for holistic customer-centricity in automotive systems engineering, relying heavily on simulation models. However, with the increase of system complexity, customer diversity, and difficulty of customer data acquisition, it becomes challenging to specify model inputs that represent individual customer usage behavior across the whole product lifecycle. To let these challenges be tackled, this dissertation addresses the problem of customer usage profiling to support decision-making in the context of automotive systems engineering. First, two fundamental data engineering procedures are investigated, including data aggregation for feature reduction and sampling for representing the customer fleets with a few selected reference customers. After data preprocessing, a method for preparing the model inputs from aggregate customer data, i.e., usage profiling, is developed. Usage profiling applies meta-heuristics, synthesizing sufficiently representative from aggregate fleet data. Furthermore, a decision support system is developed to deploy the aggregation, sampling, and usage profiling into model-based automotive system engineering processes. As both data and various models are connected, digital twinning is applied. Using real-world fleet data, an evaluation case study for determining lifetime requirements indicates that the methodology is plausible and capable of consolidating customer-centricity into automotive systems engineering processes.}, subject = {Kundenprofil}, language = {en} }