Abstract
System dynamics is an approach for thinking about and simulating situations and organisations of all kinds and sizes by visualising how the elements fit together, interact and change over time. This chapter, written by John Morecroft, describes modern system dynamics which retains the fundamentals developed in the 1950s by Jay W. Forrester of the MIT Sloan School of Management. It looks at feedback loops and time delays that affect system behaviour in a non-linear way, and illustrates how dynamic behaviour depends upon feedback loop structures. It also recognises improvements as part of the ongoing process of managing a situation in order to achieve goals. Significantly it recognises the importance of context, and practitioner skills. Feedback systems thinking views problems and solutions as being intertwined. The main concepts and tools: feedback structure and behaviour, causal loop diagrams, dynamics, are practically illustrated in a wide variety of contexts from a hot water shower through to a symphony orchestra and the practical application of the approach is described through several real examples of its use for strategic planning and evaluation.
The chapter uses edited excerpts and selected figures from my book Strategic Modelling and Business Dynamics (1997), copyright John Wiley & Sons Limited. Reproduced with permission.
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- 1.
Here I use the term ‘feedback systems thinking’ to avoid confusion with ‘systems thinking’ often used in connection with Soft Systems Methodology (SSM), as described elsewhere in this book.
- 2.
This more sophisticated assignment of link polarity works equally well for normal straightforward causal links and for links that correspond to stock accumulation processes. The distinction will become clear when stock accumulation is introduced as a vital concept for modelling and simulating dynamical systems.
- 3.
You may be thinking this method of creating time charts is rather loose and in a sense you are right because we have very little data about the problem. But even in practice, with real clients, the information sources for modelling are always a pragmatic blend of informed opinion, anecdote, objective facts and clear reasoning. For a good example of this balanced approach in the area of drug policy, see Homer (1993) and Levin et al. (1975).
- 4.
Notice that all the terms in the diagram are nouns or so-called ‘noun-phrases’. This is an important diagramming convention because you want concepts to denote things, attributes or qualities that can, in imagination, be unambiguously increased or decreased. Then, and only then, is it possible to cleanly assign polarity to causal links and thereby deduce the loop types - balancing or reinforcing. Take for example price and drug related crime. It is easy to imagine the price of drugs going up or down and separately to imagine drug related crime increasing or decreasing. Therefore, when a causal link is drawn between these two concepts, it is meaningful to ask whether an increase in one leads to an increase or decrease in the other. This thought experiment would make no sense if one or other concept were labelled as an activity, say pricing instead of price.
- 5.
By using a cloud symbol we assume that the pool of police officers assigned to duties other than drug busting is outside the boundary of the model. If for some reason we wanted to track the number of officers in this pool, then the cloud symbol would be replaced by a stock accumulation with its own initial number of officers. It would then be apparent from the diagram that shifting more officers to drug-busting reduces the number available to work on other duties.
- 6.
We are drawing a distinction between wanting a product or service and actually buying it. The distinction is important in practice because customers often go through stages of adoption. First they become aware and interested. Then, with more time and further persuasion, they buy. The most basic feasibility check is whether the firm can generate enough interested customers to fill 12 planes.
- 7.
In some cases very low fares may deter passengers due to concerns about safety. But in this particular case easyJet was flying a fleet of brand new 737s which instilled confidence.
- 8.
Rivals are portrayed at a high level of aggregation. The purpose is to capture in broad (but dynamically accurate) terms how rival airlines respond to price competition.
- 9.
Large carriers will match low seat prices regardless of cost by providing some seats at a discount. Price cuts can be implemented very quickly through on-line yield management systems that allow dynamic pricing according to load factors. But narrowly targeted discounts are an ineffective weapon for companies like BA and KLM in the competitive fight with low-cost airlines. For example, out of 150 seats there may be only 15 cheap ones. For very popular flights there are no cheap seats at all. Only cost parity can deliver competitive prices and profitability in the long-term for large carriers catering to a growing population of price-conscious fliers.
- 10.
An empirical study of cost and productivity convergence among US airlines, conducted by Peter Belobaba from MIT’s International Centre for Air Transportation, confirms significant cost convergence between Network Legacy Carriers and Low Cost Carriers spread over several years.
- 11.
Let’s assume each aircraft carries 150 passengers and makes three round-trip flights a day. So a fully loaded plane needs 900 passengers each day (150*3*2). A fully loaded fleet of 12 planes needs 10,800 passengers a day, or 3,888,000 passengers each year, which is very nearly four million. If we make the further assumption that each potential passenger is likely to fly the available routes twice a year on round-trip flights, then the start-up airline needs to attract a pool of almost one million fliers to ensure commercially viable load factors. This rough calculation is typical of the sort of judgmental numerical data required to populate an algebraic model. Perfect accuracy is not essential and often not possible. The best estimates of informed people, specified to order-of-magnitude accuracy (or better), are adequate, drawing on the informal but powerful knowledge base derived from experience.
- 12.
I am grateful to Bernhard Kerres (1999) for this example which is based on excerpts from an article entitled ‘Orchestras in a complex world’ first published in Harmony, 8, pp. 45-58 (Forum of the Symphony Orchestra Institute). Bernhard is now Intendant and CEO of the Vienna Konzerthaus, one of the most active concert houses in the world.
- 13.
The simulations are not included in this chapter. Readers who wish to see them should refer to Chapter 3 of Strategic Modelling and Business Dynamics.
- 14.
The Romeo and Juliet simulator is described in chapter 6 of an edited book entitled Tracing Connections, Voices of Systems Thinkers (Morecroft 2010). See the list of references for a full citation.
- 15.
For more information about the model see Chapter 9 of Strategic Modelling and Business Dynamics.
References
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Kerres B.H. 1999. Orchestras in a Complex World, Harmony (Forum of the Symphony Orchestra Institute), 8 (April), 44-58.
Levin G., Roberts E.B., Hirsch G. 1975. The Persistent Poppy: A Computer-Aided Search for Heroin Policy, Cambridge MA: Ballinger.
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Morecroft, J. (2010). System Dynamics. In: Reynolds, M., Holwell, S. (eds) Systems Approaches to Managing Change: A Practical Guide. Springer, London. https://doi.org/10.1007/978-1-84882-809-4_2
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