How Systems Thinking / Systems Dynamics helps to identify limits to growth to boost innovation value

News categories: the New Economy Economics, strategic enterprise management and business performance management, value based management

Empirical studies are proving, that innovation related investments yield today the highest return: between 11 and 17% compared with only 7-8% for investments into tangible assets that covers typically just the cost of capital (see my newsletter from July 26, 2001). And that means that companies in nearly all industries have to invest into systematic innovation and in the creation of knowledge assets such as through R&D. But innovation investments are associated with a lot of uncertainties and large inherent risks. You could perceive an investment in the development of a new product as a call option for the opportunity, to sell the new product. From stock options valuation we know, that the higher the risk, the higher the possible return. And the value of a stock option can be exponentially increased, if you are able to limit the downside, the inherent risk. In my report from Sept 08, 2001, I have explained how companies can limit such risks through a technique called scenario planning. As soon as this has been done, intangible assets based businesses should also consider a second important value lever: that is the timely identification of limits to growth.

Knowledge based businesses experience often significant spill over effects. A newly developed product concept may be easily copied by competitors. Patents and trademarks can limit this negative effect only to a certain extent. The only way to overcome this problem is therefore, to accelerate the commercialisation process in order to gain market leadership as soon as possible. The market leadership position will then enable the company to obtain the benefits of its investments in product development. Therefore fast growth of sales of the new product is the only way for such companies, to secure their significant investments into the built up of these intangible or knowledge assets. But to boost successfully growth through, for example, network effects, managers have to understand the entire business system of their companies. History is proving, that many managerial decisions intended to push sales have led even to bankruptcy. This is often due to the fact, that the responsible managers make their decisions according to a incomplete mental model of their business system. Then, by acting unconsciously against it, they slow down of sales instead of increasing it. But instead trying to push harder (which would make things only worse), they should focus to identify the real limits to growth. By eliminating these, they would be able to let the inherent forces of the system work for them. Systems thinking (also called sometimes systems dynamics) is a technique to identify limits to growth in a systematic way and to get the big picture of the business system in order to make the right choices for increasing the benefits of innovation activities.

The concept of System Dynamics or Systems Thinking

System dynamics is a methodology developed for studying and managing complex feedback systems, such as one finds in business and other social systems. The concept had been developed by Professor Jay W. Forrester at MIT in the early 1960s. At that time, he began applying what he had learned about systems during his work in electrical engineering to every day kinds of systems.

Traditional analysis focuses on the separating the individual pieces of what is being studied; in fact, the word "analysis" actually comes from the root meaning "to break into constituent parts." Systems thinking, in contrast, focuses on how the thing being studied interacts with the other constituents of the system—a set of elements that interact to produce behavior—of which it is a part. Therefore instead of isolating smaller and smaller parts of a system, systems thinking involves a broader view, looking at larger and larger numbers of interactions. In this way, systems thinking creates a better understanding of the big picture. This results in sometimes strikingly different conclusions than those generated by traditional forms of analysis, especially when what is being studied is dynamically complex or has a great deal of feedback from other sources, internal or external.

How the does Systems Thinking work ?

What makes using system thinking different from other approaches to studying complex systems is the use of feedback loops. According to the concept of system thinking, reality is made up of circles, but people usually see straight lines, which is a major limitation to see and understand the system and make the right decision related to that system. Peter M. Senge, Director of the Center for Organizational Learning at MIT’s Sloan School of Management in Boston/USA, described the Systems Thinking technique in his book: The Fifth Discipline (New York: Currency Doubleday, 1990). The book is an easy to understand description of the Systems Thinking approach and how this can be used to create “Learning Organizations”, making the whole organization more effective than the sum of its parts. Peter Senge uses in his book the following example from the cold war to explain the difference of a traditional way to solve a problem related to a complex system of action and interaction to the Systems Thinking approach, which recognizes the feedback loops inherent in this system:

The U.S. had a viewpoint to the arms race that essentially resembled the following:

U.S.S.R. threat to need to build
arms -------> Americans -------> U.S. arms

At the same time, the Soviet leaders have had a view of the arms race somewhat resembling this:

U.S. threat to need to build
arms -------> Soviets -------> U.S.S.R. arms

From the American viewpoint, the Soviets have been the aggressor, and U.S. expansion of nuclear arms has been a defensive response to the threats posed by the Soviets. From the Soviet viewpoint, the Untied States has been the aggressor, and Soviet expansion of nuclear arms has been a defensive response to the threat posed by the Americans. But the two straight lines form a circle. The two nations’ individual, “linear”, or nonsystemic viewpoints interact to create a “system”, a set of variables that influence each other like shown in figure 1.

The systems view of the arms race shows a perpetual cycle of aggression. The United States responds to a perceived threat to Americans by increasing U.S. arms, which increases the threat to the Soviets, which leads to more Soviet arms, which increases the threat to the United States, which leads to more U.S. arms, which increases the threat to the Soviets, which … and so on.

Figure 1: The arms race was a dynamic system with variables that influence each other

From their individual viewpoints, each side achieves its short-term goal. Both sides respond to a perceived threat. But their actions end up creating the opposite outcome, increased threat, in the long run. The long-term result of each side’s efforts to be more secure is heightened insecurity for all, with a combined nuclear stockpile of ten thousand times the total firepower of World War II.

The same problem occurs in the business world. Conventional forecasting, planning, and analysis methods are not equipped to deal with dynamic complexity. When the same action has dramatically different effects in the short and the long run, there is dynamic complexity. When an action has one set of consequences locally and a very different set of consequences in another part of the system, then there is dynamic complexity. And the concept of systems dynamic is a way to master this complexity.

In order to do that, for the object of decision (a business system, strategy, or scenario) first its feedbacks will be identified. Then it will be further analyzed in order to identify reinforcing feedbacks, balancing feedbacks, and delays – the building blocks of systems dynamics.

Reinforcing feedbacks are the engines of growth. Whenever you are in a situation where things are growing, you can be sure that reinforcing feedback is at work. Reinforcing feedback can also generate accelerating decline – a pattern of decline where small drops amplify themselves into larger and larger drops, such as the decline in bank assets when there is a financial panic. In a reinforcing process, a small change builds on itself. Whatever movement occurs is amplified, producing more movement in the same direction. Figure 2 depicts a typical reinforcing feedback or loop.

Figure 2: A reinforcing feedback

Balancing feedback operates whenever there is a goal-oriented behavior. If the goal is to be not moving, then balancing feedback will act the way the brakes in a car do. If the goal is to be moving at hundred kilometers per hour, then balancing feedback will cause you to accelerate to hundred but no faster. The “goal” can be an explicit target, as when a firm seeks a desired market share, or it can be implicit, such as bad habit, which despite disavowing, we stick to nevertheless. In a balancing system, there is a self-correction that attempts to maintain some goal or target. Filling a glass of water is a balancing process with the goal of a full glass. Hiring new employees is a balancing process with the goal of having a target workforce size or rate of growth. Balancing feedback processes underlie all goal-oriented behavior (see figure 3). What makes balancing processes so difficult in management is that the goals are often implicit, and no one recognizes that the balancing process exists at all. And often this has something to do with corporate culture. But identifying these balancing processes is crucial for system dynamics modeling.

Figure 3: A balancing feedback

Many feedback processes contain “delays”, interruptions in the flow of influence which make the consequences of actions occur gradually. Delays are interruptions between actions and their consequences. Delays can make you badly overshoot your mark, or they can have a positive effect if you recognize them and work with them. Delays exist everywhere in business systems. We invest now, to reap a benefit in the distant future; we hire a person today but it may be months before he or she is fully productive. But delays are often unappreciated and can lead to instability or even breakdown, especially when they are long. Adjusting the shower temperature, for instance, is far more difficult when there is a ten-second delay before the water temperature adjusts, then when the delay takes only a second or two. During the ten seconds after you turn up the heat, the water remains cold. You receive no response to your action; so you perceive that your act has had no effect. You respond by continuing to turn up the heat. When the hot water finally arrives, it is too hot and you turn back; and after another delay, it’s frigid again. Each cycle of adjustments in the balancing loop compensates somewhat for the cycle before (see figure 4).

According to system dynamics, you can model a complete dynamic system by combining these different elements, like reinforcing feedbacks, balancing feedbacks, and delays. For example a model could be built, to analyze a business system with limits to growth. An reinforcing process is set in motion to produce a desired result, It creates a spiral of success but also crates inadvertent secondary effects, manifested in the balancing process, which eventually slow down the success.

An example of limits to growth occurs when a professional organization, such as a law firm or consultancy, grows very rapidly when it is small, providing outstanding promotion opportunities.

Figure 4: A balancing feedback including a delay and its consequences

Morale grows and talented junior members are highly motivated, expecting to become partners within ten years. But as the firm gets larger, its growth slows. Perhaps it starts to saturate its market niche. Or it might reach a size where the founding partners are no longer interested in sustaining rapid growth. However the growth rates slows, this means less promotion opportunities, more in-fighting among junior members, and an overall decline in morale. This can be modeled like in figure 5.

Figure 5: A limit of growth model based on system dynamics

Typically, most people react to limits to growth situations by trying to push hard. In the early stages when you can see improvement, you want to do more of the same. When the rate of improvement slows down, you want to compensate by striving even harder. Unfortunately, the more vigorously you push the familiar levers, the more strongly the balancing process resists, and the more futile your efforts become, because you are working against the system. Sometimes, people just give up their original goals. But there is another way to deal with limits to growth situations. In each of them leverage lies in the balancing loop – not the reinforcing loop. To change the behavior of the system, you must identify and change the limiting factor.

Such system dynamic models can be used to model an entire business system, a strategy or a scenario to find out more about the dynamics in the system and to manage the upside – growth. By combining different sub models for different processes or tasks into one comprehensive model which is based on a software system that delivers historic information (from a datawarehouse) and executable models (based on formulas and algorithms), it can used to simulate the quantitative outcomes of a business system over the time dimension, of a strategy, or of a scenario under certain assumptions, taking into account the dynamic relationships between the different sub models (see figure 6). This can help managers to assess the impact over time of certain actions on selected limiting factors and consider dynamic reinforcing or accelerating decline effects. Therefore a system dynamic based simulation of a strategy or scenario can provide additional insight into that strategy and can especially help to boost the upside in intangible value creation processes, what is often critical to leverage first mover advantages and network effects.

Figure 6: Systems dynamics model used for computer based dynamic simulations in a strategic planning process (Screenshot from SAP SEM / Powersim “in place”)

An example, how systems thinking can be used to increase the benefits of innovation by seeing the big picture instead of only a part, is described in the article How Systems Thinking Can Improve the Results of Innovation Efforts by Daniel Aronson, principal at Success Systems, a consulting firm in Cambridge, Massachusetts/USA, and a member of the Systems Dynamics Group at MIT.

Summary

Both, scenario planning (see newsletter from Sept 8, 2001) and systems thinking / systems dynamics are useful concepts to support the strategic planning process – especially if they are combined. Scenario planning provides the necessary insight into the possible futures in order to allow for example a management team to create different models for alternative scenarios. Systems thinking provides the techniques to set up such models for simulating the future, to understand the dynamics in these models, and to estimate the consequences of certain actions of today on outcomes and events tomorrow. With these techniques, companies are able to both reduce risks in innovation and change management activities and to boost growth at the same time, by systematically eliminating limits to growth.

Additional resources:

Systems Dynamics Society

The MIT Systems Dynamics Group

Society for Organizational Learning

Systems Dynamics Group at London Business School

Daniel Aronson’s Systems Thinking Site

Günther Ossimitz System Dynamics / Systems Thinking Mega Link List

The classic introductory example for systems thinking is the production distribution game called “The Beer Game”. The Beer Game was developed to introduce students, managers and executives to concepts of system dynamics. The purpose of the game is to illustrate the key principle that "structure produces behavior." Players experience the pressures of playing a role in a complex system and can see long range effects during the course of the game. Each player participates as a member of a team that must meet its customers' demands. The object of the game is to minimize the total cost for your team. In the structured debriefing that follows it, the game illustrates a number of insights about management systems that generalize well beyond inventories: The Beer Game (Game Instructions for offline play); The Beer Game (Play it on the Internet !)).

Example for simulation software based on the systems dynamic approach: Powersim

How to incorporate more predictive information into the business performance management process of a company and how to get rid of the traditional performance management process that limits managers thinking to the past and present, is described in the new New Economy Analyst Report about the Beyond Budgeting concept from May 22, 2001.

I will continue in future reports to present some other methods, which complement scenario planning, such as real options valuation (to manage risks). To subscribe for my free-of-charge e-mail newsletter click here.

A comprehensive concept for a new management system for knowledge and intangible assets based businesses, that integrates strategy management (strategic innovation) and product and market development (product and market innovation) with operations management (supply chain management, customer relationship management) and resource management (finance, hr, alliances, IT) is described in detail in my forthcoming book "Intangible Assets oder die Kunst, Mehrwert zu schaffen: Erfolgreiche Unternehmensführung im Zeitalter des Intellectual Capital" ("Intangible Assets or the Art to Create Value: Successfull Enterprise Management in the Era of Intellectual Capitalism").

Additional books about Systems Thinking and more forward looking management techniques can be found in Juergen Daum’s book store, section “The learning and adaptive organization”.

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