An integrated systems approach to policy planning and strategy formulation requires an understanding of non-linear, interactive, dynamic sub-systems of any organization as a system. Strategic decisions have a long-term impact on the organization; impacting almost all stakeholders; once implemented, cannot be easily undone and hence have a huge opportunity cost of going wrong. Therefore, it is of utmost importance that such decisions are made after a deep understanding of the consequences of our choices. Generally, such major strategic decisions are made at a higher level of management.
It is rather ironic that in practice such decisions are made either based on short-term myopic perception or for emotional reasons rather than rational ones. If at all rational approaches are used; it makes use of SWOT analysis; moving average method of forecasting, brainstorming, nominal grouping techniques or focused interview or survey-based approaches or simply intuition-based gut-feel driven mental models of decision-makers. Such an approach fails to incorporate the non-linear, interactive, and dynamic nature of systems and results in what Forrester; the father of system dynamics called as ‘counter-intuitive behavior’ of social systems, which have three major drawbacks:
- The real control points which can truly change the system behavior are rather few and are unlikely to get the attention of the decision-maker who generally addresses the symptoms of a problem rather than its root cause.
- Even if they are able to identify the control point, the gut feels based decisions will be the opposite of what needs to be done because the effort is made only to suppress the symptoms rather than remove the root cause. Thus the solution prescribed in the long run is worse than the original problem.
- Generally, the short-term goal is in conflict with the long-term system behavior. Hence what may be quite popular in the short-term may erode the system performance in the long run and vice versa. Hence with a myopic mindset, one tends to take a decision which in the short run looks popular but ruins the system in the long run. Thus, organizational decay can be attributed to a series of populist decisions managers make in the short term.
In contrast, a System Dynamics based modeling approach addresses the problem by going to the root cause through circular causality via causal loop diagram (CLD) which uses experienced manager’s intuitive causal links of all interactive sub-systems. Here the manager’s intuition and experience play a role and hence they are involved in the decision-making process. This is the crucial phase and after that convert CLD into Stock Flow Diagram (SFD) which has a ‘rate’ as the policy decision variable and ‘level’ as the system behavior indicator. The software such as STELLA then takes over to convert them into dynamo (dynamic modeling) equations and develops a model after testing and validation which is then ready to do ‘What-if’ kind of policy experimentation and scenario building. The output from system dynamics is always in the form of a graphical display with time on the x-axis and system behavior on the y-axis as a result of a particular policy option. System dynamics do not optimize per se but examining a number of scenarios enables one to choose an option that gives stable and desirable system behavior in the long run.
Though at MIT Sloan School the study and application of System dynamics (then industrial dynamics) started in 1961; it has remained confined to few pockets of research in engineering/management institutions. In India, it was strong at one time at IIT Kharagpur and still has some presence in some IIT’s/IIM’s, but in general, remains a technique with untapped potential in policy planning and strategy formulation.
Hence, the conclusion is that conventional approaches for policy and strategy formulation shall be suboptimal or ineffective to deal with complex issues. Therefore, it is strongly recommended, that important decision related to policy or strategy at national, regional, and organizational levels must leverage system dynamics modeling as a scientific engine to make evidence-based decisions, that have sustained contextual relevance. This shall help our leaders and policymakers across all spheres, to effectively counter the ever-increasing complexities arising from the change dynamics of disruptive forces and unpredictable environmental factors, which we are all experiencing in this COVID 19 pandemic crisis. Such decisions would have far-reaching ramifications into the future, hence the stakes also at its highest for all of us, which must compel our leaders to rethink the approach towards the formulation of policy and strategy, along with its execution.
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