A.A. Sukhobokov – Ph.D.(Eng.), Associate Professor, Department «Information Processing and Control Systems», Bauman Moscow State Technical University; Head of Department of Upstream Oil&Gas, SAP DBS CIS (Moscow)
R.Z. Galimov – Undergraduate, Department «Information Processing and Control Systems», Bauman Moscow State Technical University
A.A. Zolotov – Undergraduate, Department «Information Processing and Control Systems», Bauman Moscow State Technical University
The methods and systems of strategic management for companies and organizations are considered in the article. In economic theory, a number of methods for the strategic management of companies and organizations have been developed. Among them, one of the most widespread is the method of strategic management developed by D. Norton and R. Kaplan using the system of balanced scorecards. Systems of strategic management based on balanced scorecard systems have passed several generations in their development. The article suggests an approach to solving two problems that are typical for the current level of these systems: insufficient synchronization of strategic management’s systems with operational management’s systems and insufficient adaptability of strategic management’s systems to changes in the external environment.
To increase the synchronization (alignment) of strategic management's systems and operational management systems, it is proposed to form perspectives in the balanced scorecard system in such a way that their number and composition necessarily correspond to the number and composition of the main categories of objects and the main control loops in the management system of the company or organization: materials, financials, human capital, assets, strategy, business processes. An important aspect of strategic management is the need to take into account changes in the external environment that may affect the company's performance. These external changes in the environment in which the company operates, may have an even greater impact on performance and outcome indicators. The authors of the article, by analogy with the internal perspectives in which the company's strategy is presented, propose to form completely symmetric external perspectives for the environment in which this company operates.
Another problem that does not currently have a satisfactory solution in the existing systems of strategic management is their static nature – the strategy is developed for some future period of time, but if the external conditions change, the strategy does not adapt, everything has to be done anew. This shortcoming can be overcome through scenario planning. The problem of adaptive strategy’s change due to changes in the values of external and internal indicators during the planning period can be considered as the problem of Reinforcement Learning, when the enterprise (organization) orients in the external environment, applies some decisions that work at this stage, then, at the end of the stage, the results are analyzed, and new decisions are developed that are relevant for the next stage. The problem of strategic management is formulated as the problem of Systemic Learning because several various perspectives are used simultaneously. To solve the formulated problem, it is proposed a combined active-passive algorithm of Systemic Learning. The presence of the passive component is due to the fact that there are a lot of time between the individual steps of the algorithm (quarter, half year, year), during this time complex calculations can be performed. For a set of scenarios with a relatively high probability, all states and possible transitions between them will be calculated for the entire depth of the forecasting period. To ensure high adaptation to changing external conditions, the developed algorithm besides the passive has an active component. After each step, information on trends is updated, target values of indicators and probability of scenarios are updated, sets of initiatives may be changed, etc. In order to most easily implement the proposed approach, the architecture of the system’s prototype is developed. This architecture contains two parallel running On-Premise instances of the strategic management’s solution as well as additional modules for the Systemic Learning algorithm execution and the scenario network’s maintenance.
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