In recent years it has become accepted that Logistics and Supply Chain systems
are susceptible to uncertainty by the generation of deterministic chaos
[Wilding, 1998a; Levy, 1994; Mosekilde & Larsen, 1988]. In this paper an
explanation of a methodology for detecting and quantifying deterministic chaos
within measured supply chain data is discussed. The paper describes the use of
Lyapunov exponents [Peitgen, Jurgens, & Saupe, 1992; Wolf, 1986] and how these
can be used to determine the average predictability horizon of a chaotic system
[Wilding, 1997b]. This can then be used as a method of quantifying the amount of
uncertainty from chaos within a system. The magnitude of the Lyapunov exponent
gives a reflection of the time scale over which the dynamics of the system are
predictable, so the exponent can be used to approximate the average prediction
horizon of a system [Wolf et al., 1985; Shaw, 1981]. After this prediction
horizon has been reached the future dynamics of the system become
unforecastable. This occurs because any cause and effect relationship between
current data and previous data becomes increasingly blurred and is eventually
lost
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