Podjetje, projekt, oddelek, zaposlen posameznik, pa tudi družba in narava so procesi. Procesi spreminjajo svoje deležnike, njihove vire in medsebojne odnose. Namen prispevka je predstaviti univerzalni model procesov kot teoretično podlago tehnologije digitalnih dvojčkov, na katerih temelji digitalna transformacija. Superstruktura tovrstnih procesov se odrazi v strukturiranem matematičnem programu, ki omogoča celovito optimizacijo poslovanja. Iz te povezave sledi naravna metodologija uvajanja upravljanja z učinkovitostjo podjetja (corporate performance management) ali celovite optimizacije poslovanja (enterprise wide optimization) podjetja. V prispevku predstavimo proces izdelave analiz za upravljanje z učinkovitostjo podjetja. Za ta proces izdelamo model analize stroškov in koristi, ki ilustrira navedeni proces, obenem pa v konkretnih primerih omogoča oceno finančnih parametrov investicije v upravljanje z učinkovitostjo podjetja.

The management of fishing fleets is an important factor in the sustainable exploitation of marine organisms for human consumption. Therefore, regulatory services monitor catches and limit them based on data. In this paper, we analyze North Atlantic Fishing Organization (NAFO) data on North Atlantic catches to direct the effectiveness of fishing stakeholders. Data on fishing time (month and year), equipment, location, type of catch, and, for us, the most interesting, data on the fishing effort are given, and their quality is analyzed. In the last part, The Principal Component Analysis for individual activities, among which fishing stakeholders can decide, is performed on a selected data sample. The complexity of the connections between the set of observed activities is explained by new uncorrelated variables - principal components - that are important for achieving the expected fishing catch. We find that the proportions of variance explained by the individual principal components are low, which indicates the high complexity of the topic discussed.

Since December 2019, SARS-CoV-2 infections have altered many aspects of our societies. Citizens were faced with circumstances to which even experts and scientists did not yet know the answers and were applying the scientific method to make daily steps of progress towards better understanding the threat and how to contain it. Within a year, several vaccines were produced to protect individuals from the virus, thereby resolving the most important medical problem. However, not just medical issues call for the application of the scientific method. The management of epidemics also can, and in fact should, benefit significantly from a science-based approach. The novel complexity of the situation left us torn between permissive and authoritarian approaches of containment, and it is still subject to debate what works best and why. In our contribution, we model the emerging complexity of the epidemics and propose a scientific-based data driven approach that aims to aid the decision makers in their focus on the most relevant issues and thus helping them to make informed and consistent decisions. The resulting monitoring and control system, termed COVID-19 vigilance, helps with risk assessment and communication during regional COVID-19 outbreaks. The system is based on the Cynefin decision complexity framework and the universal process model, and it uses several mathematical models that describe epidemic spreading. Different future scenarios are used to predict the impact of realistic, optimistic, and pessimistic outcomes, in turn allowing for a more efficient communication of involved risk.