The classical competitive firm is continuously searching for new ways to maximise profit. For this purpose there exist several methods to identify sources of losses and to calculate profitability or productivity of a production unit. However, the existing methods do not provide adequate information of how to calculate the profit potential related to changes in technology and process parameters. This research question is crucial in order to be able to target the development and investment efforts effectively. Therefore, there is a need to carry out this research work. As a conclusion of the research, a novel profit potential calculation method is developed in this dissertation. This study applies a constructive research approach to develop a novel profit potential calculation method suitable for web manufacturing processes. The theoretical framework is the profit function, which is further developed to form the web profit model. The profit model is optimised with respect to technology and process related input parameters to form the profit potentials. Then, new concepts: profit potentials at different optimisation levels of increasing complexity are presented. Sensitivity and uncertainty calculations are included in the method and the method is applied to two web process case examples. Method application is presented as a continuous process and the principles of the method application to other products or processes are presented. The findings show that the created profit potential method can be applied to web process profit potential analysis. Based on the profit potentials found it can be claimed that this novel method combined with new technology assessment, brings out significant potential to improve the profitability of the web processes. The accuracy and scope of the calculation can be further improved by model development. To ensure continuous profit growth, the novel method application can be implemented as a continuous process. In addition, this novel method can be applied to different types of products and processes and also optimal resource planning by introducing new parameters and models.
|Award date||13 Nov 2015|
|Place of Publication||Espoo|
|Publication status||Published - 2015|
|MoE publication type||G4 Doctoral dissertation (monograph)|