Given the increasing supply chain risks, it is very important for organisations and supply chains to have the abilities, both at the supply chain design and operation stages, to be aware of and be responsive to such risks, in order to achieve supply chain robustness and resilience. On the other hand, the pressure for better supply chain performance in an ever more dynamic environment requires a supply chain manager to find the fine balance between different supply chain strategies, control mechanisms and parameters settings. Thus, there is a need for problem-solving methods in supply chain management that address these uncertainties and balances (Reiner and Trcka, 2004).

Simulation. Simulation is an important tool to analyse supply chain behaviour, typically in terms of throughput, cost, delivery reliability and variability and risk. It allows possible supply chain configurations to be tested against different future supply chain scenarios. It may also allow the dynamic performance, risks and limitations of a proposed extended enterprise to be explored rapidly.

The term “simulation” is a broad one, referring to wide range of methods.  The common factor is the intention to investigate the behaviour of some real system by experimentation with a model, rather than with the real system itself.  In most cases simulation involves the use of computers, exploiting the spectacular improvements in hardware and software over recent decades.

In simulation the model is not solved by mathematical analysis, but by computing the progress of variables over time.  It follows from this characteristic that simulation does not provide a definitive solution, but that experiments must be carried out with a variety of different input values to obtain corresponding outputs.  Further statistical analysis is then frequently required to evaluate the results obtained.  Simulation also offers the possibility of repeating the same experiment a number of times - a scientific concept not normally available to the management scientist [Pidd, 1988].

Simulation requires the preparation of a simulation model: some representation of the real world process or system that it is desired to imitate. 

Data-driven simulation. In conventional simulation practice, if the model configuration needs to be changed significantly, the whole multi-stage process must be repeated, again with involvement from a simulation expert. Such model configuration changes are particularly likely when it is desired to explore design options, for example those of a hypothetical supply chain or extended enterprise. Any supply chain is also likely to change over time: supply chain partners may come and go as different types and levels of input are required at different stages of a project. Different supply chain processes and logistic controls may become viable at different volumes of manufacture.

To enhance the value of simulation when applied to the supply chain domain, a data-driven approach to modelling and simulation is very important and useful. In a data-driven simulation, the simulation computer model is constructed (coded) automatically by a model-builder software program based on pre-existing user data. The model-builder program that codes the model from the input data therefore replaces to a great extent the expert analyst, who would perform this task in the conventional simulation approach.

A prototype model-builder program, the Supply Chain Model Builder (SCMB), has been developed at the University of Nottingham, jointly with our industrial partners Volvo Aero Corporation (VAC) and MTU Aero Engines (MTU).