Results: An example of a practical application of the technology For example, let’s take a strategic goal for a small business entrepreneur

“Conquer the car service market segment.” It is easy to make sure that this goal is a qualitative, linear type.

Decomposition of the main goal is carried out using the Consensus-2 system, and as a result, we get sub-goals of the main goal:

1) Open an office.

2) Improve the level of service.

3) Increase the volume of service.

4) Hire qualified personnel.

5) Other factors.

The result of the decomposition of the main goal – a graph of the goal hierarchy is shown in Figure 7.2. Further decomposition of sub-goals relationships between goals forms the model structure, shown in Figure 7.3, where the screenshot of the DSS is presented with the display of the subject domain model of the goal-oriented type. In this form, the model of the subject domain is represented in the Solon-3 DSS.

Figure 7.3. Screenshot of Solon-3 DSS: a window with graphic view of a goal hierarchy structure with feedback arcs and threshold goals

On the graphic representation of the model structure, you can see the presence of feedback links (that is, arcs going from higher levels of the hierarchical structure to lower levels), as well as threshold goals (corresponding vertices are marked with a characteristic sign). Such a model can be built individually (by one knowledge engineer) using the Solon3 software development tool or downloaded from the web server built collectively in the Consensus2 system.

The DSS has a number of calculation tools necessary for forecasting and decision-making support. The result of calculations used to build a strategic plan, is a list of measures with funding allocated for them for a certain period.

Figure 7.4 shows the resource allocation calculation dialog box, where the total number and accuracy of resource allocation are entered. Besides that, the minimum and maximum required amounts of resources and percentages of project completion in each situation are entered for each of the projects.

The input of the accuracy value is programmatically controlled, and the value

is limited from below depending on the entered total number of resources in order to prevent situations of excessive growth of problem dimensionality.

The “Change algorithm parameters” button displays the dialog for adjusting the GA input parameters. The right column of the table after the calculations shows the recommended amount of resources to be allocated to each project.

Figure 7.4. Screenshot of Solon-3 DSS dialog box for entering input data and displaying the results of resource allocation calculations

The table displayed on the screen in Figure 7.4, the resource amount allocated to the project, ranges from R_min to R_max. In addition, the values proposed for the allocation of resources are multiples of the entered accuracy (resource discretization unit).

The mode of calculating the distribution of resources is initiated through the corresponding window, in which the parameters for calculations are entered, namely: the accuracy of the calculation of the goals achievement degrees, the planning period, the number of days (24 hours) during which the strategic goal must be achieved, and the calculations for each day of the planning period or for characteristic reference points on the time axis (see Figure 7.5).

Figure 7.5. Screenshot of the Solon-3 DSS with a window for entering calculation parameters

Projects for financial resources have been allocated (as a result of rational distribution calculation), and they will be included in the list of measures that are implemented for the most effective achievement of the strategic goal during the given period of time, under a fixed amount of funds reserved for this period. The same projects that did not receive funding as a result of the calculation (0.000 in the last column of the table with the “allocated” caption) will be excluded from consideration at this stage.

It is possible to perform a series of resource allocation calculations for different values of available resources and on different planning intervals, thereby determining the financial needs for the achievement of a strategic goal in the medium and long-term prospect.

5. Conclusion

The chapter proposes an original way of automating the process of group construction of weakly structured subject domain models based on the application of existing and newly developed knowledge-oriented methods for building strategies.

The main point of the chapter is two-fold:

1) It presents an original technology, allowing DMs from various weakly-structured domains to formalize and structure the decision-making and strategic planning processes, turning it into a more or less rigorous step-by-step procedure.

2) It illustrates the implementation of the technology using the respective original software tools.

Theoretical foundations and methods for the reliable acquisition and application of collective knowledge in various fields have been developed.

The availability of this theoretical basis has allowed us to come to a practical application of the newly-developed toolkit for strategic business planning in various areas. Although this chapter is largely focused on the theory and methodology of the strategic planning process, the technology has found multiple managerial applications in such spheres as:

• personnel evaluation;

• higher educational establishment evaluation;

• space industry efficiency evaluation;

• sustainable urban planning and environmental protection;

• information operations prevention;

• scenario analysis.

The key limitation of the approach is the involvement of human factors.

Experts are human beings who make mistakes during goal decomposition and evaluation. Moreover, the number of objects an average expert is able to analyze at the same time does not exceed 7±2. Finally, the robustness of the approach can be verified mostly through modeling and simulations of the expert estimation process (although some experiments and studies by the author and his colleagues used empirical data obtained through expert sessions). Further research will be aimed at ensuring the credibility of the suggested approach, its experimental studies, and its applications to new subject domains.

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Biographical note

Vitaliy Tsyganok obtained his Ph.D. degree in Computer Science and later the scientific degree of Doctor of Engineering in System Analysis and Decision Making at the Institute for Information Recording of the National Academy of Sciences of Ukraine, Kyiv, Ukraine. He is presently the head of the Department for Intelligent Technologies of Decision-Making Support at this institute. He is a professor at the Faculty of Information Technology of the Taras Shevchenko National University of Kyiv and the Institute of Special Communication and Information Protection of the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute.” His research interests include expert assessment methods, decision-making support, operations research, systems analysis, and mathematical modelling of complex weakly structured systems.

Suggested citation (APA Style 6^th ed.)

Tsyganok, V. (2023). Strategic business planning technology for weakly-structured subject domains. In A. Ujwary-Gil, A. Florek-Paszkowska, & A.

Kozioł (Eds.), Economic Policy, Business, and Management in the Post-Pandemic Perspective (pp. 149-176). Warsaw: Institute of Economics, Polish Academy of Sciences.