Presentation on theme: "SYSTEMS THINKING to SYSTEM SIMULATION Exploring the Building Blocks To Developing Insightful Descriptions and Understandings of Real Business Systems."— Presentation transcript:
SYSTEMS THINKING to SYSTEM SIMULATION Exploring the Building Blocks To Developing Insightful Descriptions and Understandings of Real Business Systems
About the general business model This shows a business with two outcomes— returns to investors and market share — and driven by four levers representing staff policy, branding policy, product policy, and asset policy. The number of outcomes is usually significantly fewer than the number of levers. In general, a single growth engine is controlled by many different levers. Each of the associated balancing loops has been shown as a single symbol and the key interactions from each balancing loop to the central reinforcing loop have been summarized as a single aggregate cost and a growth driver, this being an abbreviation for the effect of the given lever on attracting and retaining customers. Central to the growth engine is the investment allocation representing the decision on how best to allocate the funds for investment, generated by the business engine, across the various levers.
The Reality of Business…….. Not all of the balancing loops actively act as constraints simultaneously; rather, they usually kick in one after the other, and as soon as you have relieved one you come up against the next. Management is, thus, a continuing stoking of the fire of the business-driving reinforcing loop, while struggling to break through the successive layers of constraints, giving—if you get it right—a period of growth….. but sooner interrupted by periods of stability (at best!) as successive constraints bite which (hopefully!!!) are eventually overcome…. however, most have a bumpier ride, if not bankrupt!!!
The Realistic Business Growth Pattern Probably the more common, example shows periods of growth, during which the reinforcing loop is dominant, interspersed with periods of decline, as different balancing loops not only arrest the growth but begin to flip the reinforcing loop from virtuous to vicious. While this is going on, managers battle against the constraints, but are able to relieve them only just before the business goes bust. The general trend is growth, although it’s a struggle.
Systems Thinking to System Simulation Even quite simple causal loop diagrams can result in very complex dynamic behaviors, which are hard to understand in retrospect and largely impossible to predict with any certainty. The job of management, however, is to understand the past, to take decisions now, in order to influence the future as much as we possibly can to meet our overall goals and objectives. Managing the dynamic behavior of our business and organizational systems is our primary objective. Although causal loop diagrams are enormously helpful in understanding the underlying cause-and-effect relationships, very few of us can envisage how the dynamic behaviors of key variables—the customer base and market share, staff morale and the staff loss rate, shareholder value and reputation etc.—are likely to evolve over time, in a highly complex environment of competitors, governments, and demanding consumers. This is where computer-based simulation modeling can really add value, because the computer model can act as “laboratory of the future,” enabling us to test the consequences of different policies and decisions before we must commit.
System Dynamics The result of the simulation is a set of graphs, with time as the horizontal axis and the variable of interest—customers, profits, reputation, or whatever—as the vertical axis, so that we can see how these variables are likely to evolve over time according to the logic implied by the causal loop diagrams. If we change any of the underlying parameters—such as the annual spend on advertising, which, with a time lag, will attract new customers, so increasing profits—the model will simulate the consequences and show you that, after a nice and expected rise in new customers and sales, a short while later sales fall again because customers become disaffected as a result of poor service, driven by the failure to recruit and train enough good staff to cope with the increase in demand caused by the advertising. There are a number of specialized software packages such as, ithink, Powersim, and Vensim that enable us to draw causal loop diagrams, or take existing diagrams, and transform them into computer models enabling the time behaviour of the system to be simulated. As with all software tools, the effective use of ithink, Powersim, and Vensim requires knowledge of a lot of detail. The use of computer models to support systems thinking has its own name, system dynamics. Let us see some illustrations as to how computer simulation models can be built from causal loop diagrams.
System dynamics modelling All variables can be expressed either as stocks, which accumulate over time, or flows, which increase or decrease the corresponding stocks. Most business objectives—and certainly all the most important ones—can be expressed as the optimization of a portfolio of stocks. The only actions that a manager can take are to readjust the flows. Real systems are complex, interconnected networks of stocks and flows, as represented on a plumbing diagram. Plumbing diagrams are always consistent with the corresponding causal loop diagrams, but usually show more detail and use more specific language. The ithink modeling software operates at three levels. The main one is the diagram level, which displays the plumbing diagram of the system of interest. “Behind” the diagram is the equation level, which specifies all the calculation rules required to run the model. “Above” the diagram is the control panel, which enables you to define and change the values of input variables by deploying easy-to-use features such as knobs and sliding levers. A further very useful feature is the ability to define variables in terms of graphs, so allowing you to capture, for example, how you believe a particular input variable behaves over time. Most real problems involve a number of variables that do change over time, but you don’t know the underlying algebraic equations. No matter. In your mind you have a feel about what the behavior might be—the variable goes up or it goes down; it rises steeply or slowly; it flattens out or continues to rise or fall. This pattern is a mental graph, a mental model, and your opinion of how such patterns behave underpins many of your decisions and actions. System dynamics modeling enables you to capture these “fuzzy variables” explicitly and to explore the consequences of alternative actions. This helps the process of determining wise policies and the agreement of wise actions.
Once the diagram has been drawn, the next step is to specify the numerical values of some variables and all necessary relationships between the variables. The variables that require numerical values are: 1. The initial values of all stocks. 2. The values of all input dangles. All other variables will be expressed in terms of algebraic relationships, as determined by the connections defined in the plumbing diagram. Since ithink “knows” what these connections are, it is in fact very easy to specify the required relationships.
In fact, the marketing mix diagram fits alongside the fundamental business engine diagram very neatly: It takes as an input, the funds available for investment this month, and produces an output, the base level of new customers this month. The business engine diagram now looks as follows:
Simulation Outputs under Investment allocation policy of advertising: promotions for various ratios ( and Investment Ratio = 75%)
Simulation Output under Investment allocation policy of advertising: promotions in ratio of 80:20 and investment ratio = 50%, superposed on the previous output
Tackling real problems using systems thinking and system dynamics Complexity tamed..... Wisdom gained This run of the model (under investment ratio of 50%) shows a slightly lower sales volume this month and market share as compared to the results of the model with the investment ratio set at 75 percent, as we would expect since there is significantly less money being invested. Nevertheless the reduction is small. When the investment ratio was 75 percent a large sum of money was being thrown away each month, vainly trying to fuel the reinforcing loop of the growth engine with advertising and promotions, when the balancing loop of market saturation was pushing back harder. Spending much less money has almost no measurable impact on sales, but look what it does to the retained earnings this month! They have doubled every month. When the investment ratio was 75 percent, 25 percent of the net profit this month was channelled into retained earnings this month. Now, with the investment ratio set at 50 percent, the other 50 percent ends up in the returns to investors. As we can see, in this much more realistic case, the issue was not really about the allocation of funds between advertising and promotions—it was about being wise in deciding how much to spend in the first place. It is very tempting to pedal increasingly more vigorously to drive the growth engine. But when the reinforcing loop is being constrained, this can be a very exhausting— and impoverishing—thing to do!!! Wisdom, as an innate characteristic, is rare. But everyone can learn how to draw causal loop diagrams, to trace and understand causality, to explore the consequences of alternative actions or policies, to help make decisions that truly stand the test of time. We can indeed all become wiser.