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Performance improvement is taken as the primary goal in the asset management. Advanced data analysis is needed to efficiently integrate condition monitoring data into the operation and maintenance. Intelligent stress and condition indices have been developed for control and condition monitoring by combining generalized norms with efficient nonlinear scaling. These nonlinear scaling methodologies can also be used to handle performance measures used for management since management oriented indicators can be presented in the same scale as intelligent condition and stress indices. Performance indicators are responses of the process, machine or system to the stress contributions analyzed from process and condition monitoring data. Scaled values are directly used in intelligent temporal analysis to calculate fluctuations and trends. All these methodologies can be used in prognostics and fatigue prediction. The meanings of the variables are beneficial in extracting expert knowledge and representing information in natural language. The idea of dividing the problems into the variable specific meanings and the directions of interactions provides various improvements for performance monitoring and decision making. The integrated temporal analysis and uncertainty processing facilitates the efficient use of domain expertise. Measurements can be monitored with generalized statistical process control (GSPC) based on the same scaling functions.

Zarządzanie przedsiębiorstwem wodociągowym. Uwarunkowania funkcjonowania i współczesne koncepcje zarządzania . P. Chudziński, Ed. Warszawa: PWE, 2018, pp.185-202. [4] R. Brouwer, C.M. Ordens, R. Pinto, M.T. Condesso de Melo. (2018, May). „Economic valuation of groundwater protection using a groundwater quality ladder based on chemical threshold levels”. Ecological Indicators. Vol. 88, pp. 292-304. DOI: 10.1016/j.ecolind.2018.01.041. [5] D. Carstens, R. Armer. (2019, Feb.). „Spatio-temporal analysis of urban changes and surface water quality”. Journal of Hydrology. Vol

actually do? An observational study of entrepreneurs’ everyday behavior in the start-up and growth stages. Entrepreneurship Theory and Practice , vol. 36(5), pp. 995-1017, 2012. [20] Müller S. D., Påske N., Rodil L.: Managing Ambidexterity in Startups Pursuing Digital Innovation. Communications of the Association for Information Systems, vol. 44, pp. 273-298, 2019. [21] Perlow L.A., Okhuysen G.A., Repenning, N.P.: The speed trap: Exploring the relationship between decision making and temporal context. Academy of Management journal , vol. 45(5), pp. 931-955, 2002. [22