Modeling a balanced scorecard of an enterprise: The scenario approach
Evgeniy N. Efimov
Professor, Department of Information Technologies and Information Protection Rostov State Economic University
Address: 69, Bolshaya Sadovaya Street, Rostov-on-Don, 344002, Russian Federation E-mail: [email protected]
Abstract
Implementation of a balanced scorecard in an enterprise requires a significant investment of time and resources. Modeling parameters substantially improves their design process and allows us to specify a situation and to track changes adjusting the strategy in parallel. It is possible to identify and, if necessary, to correct causal relationships of a complex of strategic goals, as well as to pre-define actions, resources, timelines and responsibility necessary to implement the defined goals. In this case, the analysis of scenarios obtained when modeling allows us to choose an optimal trajectory for developing the enterprise over a certain period.
Using of the method of cognitive modeling opens the possibility to create a simple and intuitive algorithm to achieve this goal. This is a safe way to form an image of its future, to see the possibilities and consider the risks before beginning active operations.
This method of modeling facilitates combination of elements of the enterprise's internal and external economic environment into a single system, as well as analysis of the system as a whole and of its separate components without losing the relationships between them and taking into account both quantitative and qualitative characteristics of the processes.
Key words: balanced scorecard, enterprise, cognitive modeling, development scenario.
Citation: Efimov E.N. (2016) Modeling a balanced scorecard of an enterprise: The scenario approach. Business Informatics, no. 2 (36), pp. 41-47. DOI: 10.17323/1998-0663.2016.2.41.47.
Introduction
The Balanced Scorecard (BSC) methodology was proposed by D.P. Norton and R.S. Kaplan in 1992 to supplement the limited representation of the organization's effectiveness only on the basis of measuring its financial performance. It is proposed to measure in BSC the performance of a company in three additional areas, outside the sphere of finance (customers, internal business processes, training and development) to get a more balanced view of development of the organization [1].
Quality measurement of work in these areas expands the factor space to evaluate enterprise performance. BSC complements the financial parameters system as already accomplished, and also indicates the origin of revenue growth, which clients provide it and why, what key business processes the company should focus on for improve-
ment in order to convey its unique proposition to the client. BSC helps to direct investments and orient work with personnel in this direction, as well as development of internal systems, corporate culture and climate [2].
BSC development entails formulating strategy in several outlooks, setting strategic goals and measuring the achievement of these goals by using indicators. BSC is projected onto the entire organization through development of individual goals within the framework of the already developed corporate strategies and stimulates the understanding by employees of their place in the company's strategy.
By using BSC, it is possible to implement the strategy with regular activities of all units managed through planning, accounting, control and analysis of the balanced scorecard, and motivating staff to achieve them.
When designing BSC, developers actually model situations of implementing possible strategic goals on strategy maps describing scenarios as a set of strategic goals and causal relationships between them. However, the vast majority of tools that implement the concept of Business Process Management (BPM), cannot model BSC for assessment of possible development of processes in the enterprise that would enable us to choose the effective scenario and most precisely match the strategic goals [3, 4].
The situation described exists in the framework of the methodology of "soft" system analysis [5]. Formalization of "soft" systems is not based on accurate quantitative measurements but on qualitative, fuzzy and hypothetical concepts about the system in the form of expert assessments and on heuristic reasoning [6]. The task of management decision-making support in the "soft" dynamic situation is defined as development of a strategy to transfer a situation from its current state to the target on the basis of a subjective model of the situation. This model includes expert measured values of factors of the situation and a functional structure which describes the laws known to the analyst and regularities of the situation observed. This subjective model is recorded as a directed signed graph — cognitive map [7].
1. Proposed methods and approaches
Due to the fact that in BSC there are both quantitative and qualitative characteristics, the estimation of efficiency of the enterprise by modeling becomes an extraordinary task. Analysis of the modeling methods shows that this task can be solved only by cognitive models, which allow us to combine elements of internal and external economic environment of the enterprise into a single system, as well as to analyze the system as a whole and in its separate components without losing the relationships between them. In addition, the model can incorporate both quantitative and qualitative characteristics of processes [8].
When doing cognitive modeling, the following sequence of actions is usually applied [9, 10]:
♦ determination of initial conditions, trends characterizing development of the situation at this stage, which is necessary to ensure the appropriateness of the model scenario to the real situation;
♦ setting desired target directions (increase, decrease) and strength of process trend changes (weak, strong);
♦ selection of a set of measures (combination of control factors), determination of their possible or desired strength and direction of impact on the situation;
♦ selection of a complex of possible influences on the situation (activities, factors) the strength and direction of which must be defined;
♦ selection of observable factors (indicators) that characterize development of the situation is carried out depending on the purposes of the analysis and user's desires.
The aforementioned stages of cognitive analysis are basic and are successfully implemented by using existing software systems, such as "Situation", "Compass", "KIT" (V.A. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences), systems of decisionmaking support based on cognitive modeling "Igla" [11], the Software System of Cognitive Modeling (SS CM CogMap TTI Southern Federal University) [12] and many others [13]. In the present study we used for our modeling processes SS CM CogMap.
The simplest cognitive model is a sign directed graph — cognitive map [10]:
G = <V, E>, where
Vis a vertex set, wherein vertices V. e V(i = 1, 2, ..., k) are elements of the system under investigation — strategic goals;
Eis an arc set, wherein arcs e..e E(i, j = 1, 2, ..., N) reflect the relationship between vertices V. and V. (positive relationship: when increasing the value of one factor, the value of the other increases; negative relationship: when increasing the value of one factor, the value of the other decreases and vice versa).
The cognitive map is a result of cognitive-targeted structuring of knowledge about strategic goals of the enterprise and external environment that allow us to identify and systematize internal and external factors that have quantitative and qualitative certainty (vertices V. ), as well as setting causal relationships (arcs e ) between them.
The constructed directed graph reflects the influence of factors taking into account arc weights w (for example, from interval —10 to +10) established by experts. The arc weights can be determined on the basis of functional dependence (if any), as well as a coefficient b.. of linear regressive dependence of factors like y. = a + b..x. [12].
Thus, the direct (quantitative) and indirect (qualitative) influence of factors on the indicators selected for effectiveness evaluation are taken into account. This makes it possible to provide a substantially more complete description of the problem area and resultant effect of implementing certain activities or projects.
2. Example of BSC modeling of a company.
Development scenarios
In the beginning of BSC design, a base scenario was determined which includes certain concepts of the BSC model of the enterprise established by expertise and the
relationships between them (tables 1 and 2) [14, 15]. The base scenario includes what the developer understands to be a full understanding of the set of the strategic goals of enterprise development.
Table 1.
Concepts of a model
Contents of the Vertex
Finance V1 Profit
V2 Costs
Clients V3 Number of clients
V4 Client satisfaction
Processes V5 Control of construction and installation works (CIW)
V6 Purchase of quality materials, components, tools (MV)
V7 Timely shipping of MV
V8 Optimization of MV inventory
V9 Quality of project works
V10 Accurate project planning
V11 Introduction of process management
Training and development V12 Qualified employees
V13 Creation of a corporate training center
V14 Development of corporate information systems (CIS)
Figure 1 shows a cognitive BSC model of the enterprise for the base scenario.
The next step is modeling the possible impulse processes in vertexes V of the built card when introducing perturbations (pulses q.= ±1) and the variations of the arcs conversion functionality of the cognitive map f(xp Xj, et).
The impulse process on the graph in this software system is described by the formula proposed by F.S. Roberts [16]:
where x(n), x.( n+1) — value of parameter x. at vertex v. in the simulation moments n and n+1 respectively; f(x.,x., e.) — arcs conversion functionality of the cognitive map (in the particular case it can be function f.. or weight coefficient w„), where x. — parameters of vertices v. , x. — parameters of vertices v. , vertex vtje V, i, j = 1, 2, ..., k, e.. — arc reflecting the relationship
between vertices v. and v., rcs e.. e E, i, j = 1, 2, N;
i j' i] , , , ,
P — value of impulse at vertex Vj ;
Q. (ri) — perturbations coming to vertices v .
Implementation of this phase is carried out by defining a list of possible control actions on internal sources
Client satisfaction
CIW Control
Quality MV purchase
Timely shipping of MV
Creation of a corporate training center
Introduction of process management
Fig. 1. Cognitive BSC model of the enterprise
Number of clients
Quality of project works
Accurate project planning
Table 2
Cognitive map of a model
V1 V2 V3 V4 V5 V6 V7 V9 V10 V11 V12 V13 V14
v1
V2 -
V3 + +
V4 + +
V5 + +
V6 +
V7 + +
V8 + -
V9 + + +
V10 +
V11 + + + + + + +
V12 + + + +
V13 + +
V14 + + + + + + +
of factors and further analysis of scenarios of possible development of situations under the influence of simulated perturbations.
The impulse modeling when introducing perturbations q. at vertex of the cognitive map (v.) allows us to obtain a sufficient number of realizations of random processes. Existence of such realizations allows us to set up and solve the task of optimum nominal proposed by D.V. Svecharnik for analysis of economic situations selected as the "best" impulse process. These can later be adopted as the desired development strategy of the investigated object [17, 18].
If there is a sufficient amount of statistical or expert data on possible quantitative values of effects (e.g., the volume of investment, volume of production), then when interpreting the results it is possible to speak about a specific quantitative change in the parameters of the vertex of the graph. In this case, a scenario calculation is performed to analyze possible dynamics of development of the process being studied. The arc weights of the graph when qualitative simulation of development scenarios of the object are not set, and impulses are assumed to be equal to fixed val-ues1. In this case, the results of impulse modeling reflect only possible trends of the development process.
Thus, under the influence of various disturbances, the values of variables at vertices of the graph may change and the signal received at one of the vertices apply the chain on the rest, amplifying or fading.
As a result of impulse modeling (10 stages), the following process development scenario is received (figure 2a).
The graph shows that in this scenario, at first stages all modeling parameters selected for display have a tendency to increase, and at later stages they stabilize.
Other scenarios of processes developing in the enterprise were obtained by successive exclusion of certain strategic goals from the cognitive map and, respectively, from the model:
♦ scenario Si — vertex v13 "Creation of a corporate training center" is excluded from the base scenario;
♦ scenario S2 — vertex v14 "CIS Development" is excluded from scenario S1, during which we assumed the implementation process, automation of such business processes as CIW control, quality MV purchase and timely shipping control, optimization of MV inventory, quality control of project works, implementation of programs supporting process management.
The results of the impulse modeling (10 stages) of process development scenarios Sl and S2 are shown in the graphs (figures 2b and 2c, respectively).
Visual analysis of the graphs allows us to build a chain of priorities resulting in scenarios according to their effectiveness S0 > S1 > S2 . Later, the results of development processes can be used for in-depth analysis on the optimum for the selected criteria.
1 In our example, to reduce the scale of the display indicators, it is taken that: q. = ±0,1
Fig. 2. Dynamics of indicators of BSC modeling of the enterprise for different scenarios
Conclusion
By itself, developing the strategy of an enterprise is a very important stage of management, and starting this process tells us about its maturity and relatively high achievements. Once they have developed a strategy and realized it, enterprises are faced with the need to assess the success of their efforts; particularly as the development process of strategy is cyclical. At this point, the problem of specific change evaluation appears, whether positive or negative, as well as what it should be compared with. In the absence of appropriate data, these questions remain unanswered.
In this case, BSC modeling of the enterprise enables us to:
specify a situation and to track changes in parallel adjusting the strategy;
determine and, if necessary, correct causal relationships of the combination of strategic objectives;
pre-define activities, resources, timing and responsibilities required to implement the established goals.
BSC implementation is a process that requires a significant investment of time and resources. BSC modeling of an enterprise enables us significantly to improve their design process in time and labor. Cognitive modeling is the ability to create a simple and intuitive algorithm to achieve this goal, a safe way to form an image of its future, to see the possibilities and consider risks before beginning active operations.
It has been shown that the BSC cognitive modeling allows us to combine elements of the internal and external economic environment of the enterprise into a single system, as well as to analyze the system as a whole and in its separate components without losing the relationships between them, taking into account both quantitative and qualitative characteristics of the processes. ■
References
1. Norton D., Kaplan R. (1996) The Balanced Scorecard: Translating strategy into action. Harvard Business Press.
2. Kaljanov G.N. (2006) Modelirovanie, analiz, reorganizacija i avtomatizacija biznes-processov [Modeling, analysis, reorganization and automation of business processes] Moscow: Finance and Statistics (in Russian).
3. Efimov E.N., Lapickaja G.M. (2013) Ocenka effektivnosti IT-proektov v ramkah Balanced Scorecard [Evaluation of IT projects effectiveness using Balanced Scorecard]. Information Systems, Economics, Labor and Manufacturing Management. Proceedings of the Rostov State Economic University, no. 15, pp. 59—65 (in Russian).
4. Efimov E.N. (2015) Effektivnost' IT-proektov v sisteme sbalansirovannyh pokazatelei [Efficiency of IT projects in the Balanced Scorecard system]. Aspirant, no. 2, pp. 8—10 (in Russian).
5. Checkland P.B. (1981) Systems thinking, systems practice. New York: Wiley.
6. Kosko B. (1986) Fuzzy cognitive maps. Man-Machine Studies, no. 24, pp. 65—75.
7. Axelrod R. (1976) The structure of decision: Cognitive maps of political elites. Princeton: Princeton University Press.
8. Efimov E.N., Efimova E.V. (2014) Modeli i tehnologii setevyh elektronnyh biznes-otnoshenii [Models and technologies of network electronic business relationships]. Rostov-on-Don: Rostov State Economic University (in Russian).
9. Efimov E.N., Efimova E.V. (2011) Kognitivnoe modelirovanie ocenki effektivnosti jelektronnyh biznes-otnoshenij predprijatija [Cognitive modeling evaluating the effectiveness of e-business relations between enterprises]. Proceedings of the VI scientific-practical Internet-conference "Problems of Information Security", 20 May — 20 June 2011. Rostov-on-Don: Rostov State Economic University, pp. 9—13 (in Russian).
10. Denisov M.Yu., Dolzhenko A.I., Efimov E.N. (2012) Kognitivnoe modelirovanie ocenki effektivnosti elektronnyh biznes-otnoshenij predprijatija [Cognitive modeling of evaluation of effectiveness of e-business relations between enterprises]. Bulletin of the Rostov State Economic University, no. 1 (37), pp. 83—90 (in Russian).
11. Gulakov V.K., Podvesovsky A.G., Lagerev D.G. (2007) Sistema podderzhki prinjatija reshenij na osnove kognitivnogo modelirovanija "Igla" [Decision support system based on cognitive modeling "Igla"]. Software Systems and Products, no. 3, p. 50 (in Russian).
12. Gorelova G.V., Zaharova E.N. Ginis L.A. (2005) Kognitivnyj analiz i modelirovanie ustojchivogo razvitija social'no-ekonomicheskih system [Cognitive analysis and modeling of socio-economic systems sustainable development]. Rostov-on-Don: Rostov University Publishing House (in Russian).
13. Kulinich A.A. (2010) Komp'uternye sistemy modelirovanija kognitivnyh kart: podhody i metody [Computer systems for cognitive maps modeling: approaches and methods]. Control Sciences, no. 3, pp. 2—16 (in Russian).
14. Razrabotka sbalansirovannoj sistemy pokazatelej (sistema biznes-modelirovanija "Business Studio") [Development of Balanced Scorecard ("Business Studio" business modeling system)]. Available at: http://businessstudio.ru/wiki/docs/current/doku.php/ru/bsc/bsc (accessed 14.01.2016) (in Russian).
15. Strategija i BSC-KPI / Tipovye celi i strategicheskie karty BSC (sistema biznes-modelirovanija "Biznes-inzhener") [Strategy and BSC-KPI / Model goals and BSC strategy map ("Business engineer" business modeling system)]. Available at: //http://www.betec.ru/secure/index. php?id=10&sid=12&tid=02 (accessed 14.01.2016) (in Russian).
16. Roberts F.S. (1986) Diskretnye matematicheskie modeli s prilozhenijami k social'nym, biologicheskim i jekologicheskim zadacham [Discrete mathematical models with applications to social, biological and environmental objectives]. Moscow: Nauka (in Russian).
17. Svecharnik D.V. (1957) Zadacha ob optimume nominala [The problem of the optimum value]. Trudy Instituta mashinovedenija. Moscow: USSR Academy of Sciences, no. 10, pp. 78—94 (in Russian).
18. Gorelova G.V., Zdor V.V., Svecharnik D.V. (1970) Metod optimuma nominala i ego primenenija [Optimum of face value method and its application]. Moscow: Energy (in Russian).
Моделирование сбалансированной системы показателей предприятия: Сценарный подход
Е.Н. Ефимов
доктор экономических наук, профессор кафедры информационных технологий и защиты информации Ростовский государственный экономический университет (РИНХ) Адрес: 344002, г. Ростов-на-Дону, ул. Большая Садовая, д. 69 E-mail: [email protected]
Аннотация
Внедрение сбалансированной системы показателей на предприятии требует значительных затрат времени и ресурсов. Моделирование показателей существенно улучшает процесс их проектирования и позволяет конкретизировать ситуацию и отслеживать изменения, параллельно корректируя стратегию. Появляется возможность определить и, при необходимости, откорректировать причинно-следственные связи совокупности стратегических целей, а также предварительно определить мероприятия, ресурсы, сроки и ответственность, необходимые для реализации установленных целей. При этом анализ получаемых при моделировании сценариев позволяет выбрать оптимальную траекторию развития предприятия на определенный период времени.
Использование метода когнитивного моделирования открывает возможность создать простой и понятный алгоритм достижения поставленной цели, безопасный способ сформировать образ своего будущего, увидеть возможности и учесть риски, до начала активных действий.
Данный метод моделирования позволяет объединить элементы внутренней и внешней экономической среды предприятия в единую систему, а также проанализировать систему в целом и отдельные ее компоненты, не теряя взаимосвязей между ними, с учетом как количественных, так и качественных характеристик процессов.
МАТЕМАТИЧЕСКИЕ МЕТОДЫ И АЛГОРИТМЫ БИЗНЕС-ИНФОРМАТИКИ
Ключевые слова: сбалансированные показатели, система, предприятие, когнитивное моделирование, сценарий
развития.
Цитирование: Efimov E.N. Modeling a balanced scorecard of an enterprise: The scenario approach // Business Informatics. 2016. No. 2 (36). P. 41-47. DOI: 10.17323/1998-0663.2016.2.41.47.
Литература
1. Norton D., Kaplan R. The Balanced Scorecard: Translating strategy into action. Harvard Business Press, 1996.
2. Калянов Г.Н. Моделирование, анализ, реорганизация и автоматизация бизнес-процессов: Учебное пособие. М.: Финансы и статистика, 2006.
3. Ефимов Е.Н., Лапицкая Г.М. Оценка эффективности ИТ-проектов в рамках Balanced Scorecard // Информационные системы, экономика, управление трудом и производством. Ученые записки. Ростовский государственный экономический университет (РИНХ). 2013. № 15. С. 59-65.
4. Ефимов Е.Н. Эффективность ИТ-проектов в системе сбалансированных показателей // Аспирант. 2015. № 2. С. 8-10.
5. Checkland P.B. Systems thinking, systems practice. New York: Wiley, 1981.
6. Kosko B. Fuzzy cognitive maps // Man-Machine Studies. 1986. No. 24. P. 65-75.
7. Axelrod R. The structure of decision: Cognitive maps of political elites. Princeton: Princeton University Press, 1976.
8. Ефимов Е.Н., Ефимова Е.В. Модели и технологии сетевых электронных бизнес-отношений. Ростов-на-Дону: РИНХ, 2014.
9. Ефимов Е.Н., Ефимова Е.В. Когнитивное моделирование оценки эффективности электронных бизнес-отношений предприятия // Материалы VI Всероссийской научно-практической Интернет-конференции «Проблемы информационной безопасности», 20 мая - 20 июня 2011 г. Ростов-на-Дону: РИНХ, 2011. С. 9-13.
10. Денисов М.Ю., Долженко А.И., Ефимов Е.Н. Когнитивное моделирование оценки эффективности электронных бизнес-отношений предприятия // Вестник Ростовского государственного экономического университета (РИНХ). 2012. № 1 (37). С. 83-90.
11. Гулаков В.К., Подвесовский А.Г., Лагерев Д.Г. Система поддержки принятия решений на основе когнитивного моделирования «Игла» // Программные системы и продукты. 2007. № 3. С. 50.
12. Горелова Г.В., Захарова Е.Н., Гинис Л.А. Когнитивный анализ и моделирование устойчивого развития социально-экономических систем. Ростов-на-Дону: Изд. Ростовского университета, 2005.
13. Кулинич А.А. Компьютерные системы моделирования когнитивных карт: подходы и методы // Проблемы управления. 2010. № 3. С. 2-16.
14. Разработка сбалансированной системы показателей (система бизнес-моделирования Business Studio). [Электронный ресурс]: http://businessstudio.ru/wiki/docs/current/doku.php/ru/bsc/bsc (дата обращения: 14.01.2016).
15. Стратегия и BSC-KPI / Типовые цели и стратегические карты BSC (система бизнес-моделирования «Бизнес-инженер»). [Электронный ресурс]: /http://www.betec.ru/secure/index.php?id=10&sid=12&tid=02 (дата обращения: 14.01.2016).
16. Робертс Ф.С. Дискретные математические модели с приложениями к социальным, биологическим и экологическим задачам / Пер. с англ. М.: Наука, 1986.
17. Свечарник Д.В. Задача об оптимуме номинала // Труды Института машиноведения. М.: Изд. Академии наук СССР, 1957. № 10. C. 78-94.
18. Горелова Г.В., 3дор В.В., Свечарник Д.В. Метод оптимума номинала и его применения. М.: Энергия, 1970.
БИЗНЕС-ИНФОРМАТИКА № 2(36) - 2016