The Balanced Scorecard (BSC) is being regarded as a new measure of performance for any organization, and is also being used in strategic planning, encompassing operational logistics and market presence of that particular organization. A company model referred here, as a value chain model, simply follows business rules, to make a decision about investment. The different permutations and combinations concerning any product-based company yield a complex combinational domain. In this context, this paper proposes, an innovative agent-based modeling for a strategy-oriented balanced scorecard, where agents are bio-inspired. Typically, pheromone concentration module, along with predefined rules using rough sets, would like to predict the most suitable strategy for any company. The introduction of Ant Colony Optimization (ACO) metaheuristics, functionally establishes a proper interface between management sciences and adaptive intelligence.
The Financial Metrics measure the economic consequences of actions already taken in terms of Return on Investment (ROI)/Economic Value Added (EVA). Customer Metrics define the market share of the organization and the perception it enjoys from the stakeholder point of view. It includes several generic measures of successful outcomes from a well-formulated and dexterously implemented strategy. Internal Business Metrics deal with the organizational excellence achieved by the company through the process of new product/technology development/process improvements (Banerjee et al., 2005).
Lastly, Learning and Growth Metrics evaluate the organization in terms of Innovative Research, Intellectual Capital Growth through Knowledge Management/Knowledge Sharing and IPRs.
Considering the multiobjective scenario of balanced scorecard as a strategy planner, several efforts have been initialized focusing on Artificial Intelligence (AI) and Genetic Programming Tools. The randomness and optimization in different data sets makes the strategy-making part difficult and complex, and thus, more and more emphasis has been given to normalize the result. Recently, agent-based modeling, metaheuristics and colony optimization techniques, have collectively solved the complex problem of multiobjective searching domain. It has also been successfully deployed for different Non-Polynomial (NP) Hard problems. In this present work/research, the tendency of a particular consumer element (investment decision) has been studied and the investment process behind that selection has also been discussed. |
