Academic journal article IBAR

The Role of Simulation in Solving Complex Operations Management Problems: A Review

Academic journal article IBAR

The Role of Simulation in Solving Complex Operations Management Problems: A Review

Article excerpt

This paper begins with an explanation of what is meant by modelling and shows simulation to be a sub-set of modelling. It then discusses the nature of simulation and how it may be used to solve particular types of management problems. The development, and continuing evolution, of simulation as a management tool is explored. The impact of this evolution on the interdependence of manager and management scientist is examined. The paper concludes with a look at likely future developments in simulation.

Modelling

Naert and Leeflang (1978) succinctly define a model as "a representation of the most important elements of a perceived real world system".

In certain circumstances it is practice to build physical models of systems to increase understanding and appreciation, e.g. Sir Christopher Wren built a scale model of St. Paul's cathedral. Wind tunnels are used to test the aerodynamics of prototype cars and aircraft. Engineers build model dams to experiment with water flows. These examples of iconic models, while of immense benefit to the professionals who use them, are of minimal relevance to managers.

In the early days of simulation, before computers were available, analogue models were sometimes used. These are models which imitate the process or system being studied. One such model is described by Jones (1992). Scaffolding 50 feet high was the framework on which the model was built and the vertical drop of lead shot through hoses, biscuit tins and the like was used to imitate the horizontal flow of a proposed steel plant. Three tons of lead shot (which had to be manually hauled in buckets to the top of the scaffolding) were used in the model. The approach was certainly original and the model worked, but such methods have become redundant with the advent of computers.

Conceptual models, rather than the foregoing physical models, are more likely to be of relevance to managers. Let us examine a hierarchy of such models. At the first level we have a verbal description of a system. This is a model of that system in that it attempts to capture the essence of the reality and should ignore irrelevancies. A written description is better than having no model as it helps to refine, communicate and make explicit a common understanding of a system. The second level in the hierarchy is some form of system diagram. One objective means of recording the processes in a system is to produce a flow diagram which represents, for example, the progress of work or documents through a system. More sophisticated system diagrams show cause and effect relationships. These latter models are of benefit, for example, in developing an understanding of a complex economic system. Quantification, applied to a system diagram, takes us to a higher level again. To know how many items are produced per week, what was the profit made last financial year, when did the new machine start on the production line, or, how many full time equivalent staff are in post, gives a more powerful model than one which is not quantified. Measurement enables us to gauge the performance of a system and hence to compare similar systems or examine one system over time.

Even more powerful is the establishment of mathematical relationships. In forecasting, for example, if sales can be approximated by a mathematical expression then this model may be used to predict future sales. There are many well established mathematical modelling techniques applied to management problems. For example, break-even analysis enables a manufacturer to identify at what level of production, revenue starts to exceed expenditure. Linear programming can identify a unique product mix which will maximize profit within the stated constraints of a business.

Simulation

There are occasions, however, where it is difficult to establish mathematical models for complex systems and it is here that simulation is the technique which may be of use.

Systems are prone to having queues or bottlenecks caused by an imbalance of resources, either in terms of quantity or in performance when compared with demand. …

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