The simulation experiments performed with the SCENNET21 program, with artificial neural networks implemented as functions, were to identify how the development of 14 individual thematic areas forming part of the research field M reflecting the manufacturer’s point of view and of the field P corresponding to the approach of a consumer expecting a product with the required functional properties influences the occurrence – with specific probability – of each of the three alternative scenarios.
By applying artificial intelligence tools to solve a research issue proposed in such a way, a solution can be searched immediately in a group containing thousands of solutions, similar to an optimum solution satisfying the user. A neural network trained using the implemented reference data acquired through expert surveys generates the multivariant forecasts of future events.
There are 3 relevant development trends of the thematic areas analyzed, i.e., a growth trend, a trend stabilized at the current level, and a falling trend, taken into consideration. It is possible to identify the occurrence probability of the relevant development trends of the thematic areas analyzed with the one defined at the beginning by each user probability value wherein each of the three alternative macroscenarios of future events occurs. The user, while performing a computer simulation, may set a particular numerical value of a chosen macroscenario’s probability and also search a solution for its extreme values, i.e., a maximum or minimum value.
The results are shown in Fig. 11, of examples of three simulation experiments carried out with reference to the research field M as charts generated with the SCENNET21 program, concerning a pessimistic scenario with the probability value set, respectively, as 10 % (Fig. 11a), 20 % (Fig. 11b), and 30 % (Fig. 11c). Percentages of probability that a growth trend, a trend stabilized at the current level, and a falling trend occurs are provided on the axis of abscissa.

The relevant, analyzed thematic areas are provided on the axis of coordinates, i.e., laser technologies in surface engineering (M1), PVD technologies (M2), CVD technologies (M3), thermochemical technologies (M4), technologies of polymeric surface layers (M5), technologies of nanostructural surface layers (M6), and other surface engineering technologies (M7), respectively.
The results of computer simulations, performed using neural networks, indicate a predicted leading role of the development of the nanostructural surface layer technologies (M6) and laser technologies (M1) against the entire research field M. For the areas of M6 and M1, the probability of a growth trend in all the analyzed cases maintains on the highest level of the analyzed cases and is slightly decreasing as the probability of a pessimistic variant of future events grows. Note also that a probability of a falling trend for such thematic areas is zero, meaning that such areas cannot be degraded. In accordance with the results of the simulations, the degradation is not possible, in either of the PVD technology (M2) or of polymer technologies of surface layers (M5) for which the probability value of a growth trend in all the three analyzed cases is similar and is maximum approx. 70–73% for both thematic areas each time. A relationship between the probability value of a growth trend for other surface engineering technologies (M7) and the probability value of a pessimistic scenario at a macroscale is directly proportional, meaning that the worse is the general situation, the faster is the progress of this technology group against the research field, i.e., it is recommended that instead of it, more promising technologies develop more intensively, i.e., M6, M1,M2, andM5. The probability value of a growth trend for the CVD technology (M3) maintains at the maximum level of, respectively, 64 %, 60 %, and 57 % for a pessimistic macroscenario occurring with, respectively, 10 %, 20 %, and 30 % probability. This inversely proportional dependency coupled with the values of a stabilized and falling trend indicates that the existing dynamics of slight growth is maintained in the area’s importance. The development of classical thermochemical treatment technologies is most predictable. As the probability of a pessimistic scenario rises (from 52 % to 69 %), the probability of the M4 trend stabilized at the existing level is growing substantially. Both these values, probability values of a rising and falling trend of such technologies, reflect the actual situation and future strategic position of these technologies with respect to the research field M. Thermochemical treatment, although not an avant-garde or extremely developmental treatment nor an extremely developmental technology, considering the economic calculus and widespread applications, has an important role in the current economy, and forecasts show this status quo will maintain for the next 20 years.
As regards the research field P, chosen for presentation have been the examples of results of simulation experiments concerning a dependency between development trends of the relevant thematic areas and an optimistic macroscenario of future events having the occurrence probability of 10 % (Fig. 12a), 20 % (Fig. 12b), and 30 % (Fig. 12c). The occurrence probability values of the relevant development trends (a growth trend, a stabilized trend, and a falling trend) of the individual thematic areas were applied in per cents on the axis of abscissa, similar as for the research field M. The following thematic areas subjected to simulation investigations are provided on the axis of coordinates, respectively: biomaterials surface engineering (P1); metallic structural materials surface engineering (P2); nonmetallic structural materials surface engineering (P3); tool materials surface engineering (P4); steel surface engineering for the automotive industry (P5); surface engineering of glass, micro-, and optoelectronic elements and photovoltaic elements (P6); and polymer materials surface engineering (P7).

The results of the simulation experiments performed reveal that certain and rapid development of biomaterials surface engineering (P1), functional materials (P6), and tool materials (P4) as signified takes place with 30 % probability. The probability of a falling trend of the areas P1, P6, and P4 was determined as zero, similar as for nonmetallic structural materials surface engineering (P3) and polymeric materials (P7). As regards nonmetallic structural materials surface engineering (P3), an inversely proportional dependency is observed between the probability value of a growth trend of such technologies and a probability value of an optimistic macroscenario. This means that in most optimistic variant of events, the development of the most promising areas, i.e., P1, P6, and P4, is more important than the development of the area P3. As it is not possible that the importance of the thematic area P7 will decrease and as there is no clear regularity in changes of the growth and stabilized trend, depending on the probability value of an optimistic macroscenario, this signifies that the predicted dynamics of  changes in such area is stabilized at the existing, good level. A directly proportional dependency between the probability value of a growth trend and the probability value of an optimistic macroscenario of future events can be observed also with reference to steel surface engineering for the automotive industry (P5). This shows that this area will develop in the future in connection with predictable further development of the car industry. Despite the fact that traditional steel is more and more often replaced by other materials, e.g., light metal alloys (Mg, Al) or polymeric materials, the position of steel in the automotive industry is basically unthreatened in particular owing to the rapid development of new grades of steel. High-manganese austenitic steels of the TRIP type (transformation-induced plasticity) should be mentioned when discussing such steels, characterized by a unique combination of strength and plastic properties where a martensite TWIP (twinning-induced plasticity) transformation is induced in a cold plastic deformation, characterized by intensive mechanical twinning during plastic deformation, and of the TRIPLEX type characterized by a three-phase structure: an austenitic and ferrous structure with dispersive carbides. There is no clear regularity observed of changes in the growth and stabilized trend with regard to metallic structural materials surface engineering (P2) and approximate probability values of a falling trend for individual probability values of an optimistic macroscenario. This signifies the predicted, stabilized dynamics of changes in the area at the existing level. The manufacturing technologies of metallic structural materials (P2), which are not avant-garde or extremely developmental, have their certain place ensured among the important materials surface engineering technologies as they are so widespread in industry and as they often cannot be replaced by other solutions at economically reasonable costs.
The selected results presented concerning a pessimistic scenario for the research field M and a pessimistic scenario for the research field P, being only an example of much broader simulation investigations, allow to answer the question of how growth, stabilization, or decline in the importance of the analyzed thematic areas influences the occurrence, with particular probability, of each of the alternative scenarios in the nearest 20 years, i.e., an optimistic, neutral, and pessimistic scenario. It should, therefore, be underlined that adequately trained neural networks are a useful tool allowing to generate quickly the alternative forecasts variants of future events.