Modeling Techniques

The use of modeling techniques is important to companies who are deciding upon their new logistics network. The various modeling techniques can allow companies to look at a comparison of the functioning, cost efficiency, and customer service efficiency of the various logistics networks that have been proposed. Companies can look at the various modeling techniques and decide which one offers them the best insight into their network options.

Optimization Modeling

The optimization model is derived from the precise mathematical procedures that offer the best or optimum solution based on the mathematical formula used. This model is based on mathematical formula only.

This means that there is no subjective input to the model, only assumptions and data. The optimization model looks at data such as the level of customer service to be obtained, the number and location of distribution centers, the number of manufacturing plants, the number of distribution centers assigned to a manufacturing plant, and the inventories that must be maintained.

One optimization model that has been used for logistics networks is the model using linear programming, sometimes referred to as LP. This is particularly useful for linking supply and demand limitations of manufacturing plants, distribution centers, and market areas.

Given the goal of minimizing costs, linear programming can define the optimum facility distribution pattern, based on the constraints identified.

However, as this uses mathematical formulas, there is no allowance for any subjective input.

Simulation Models

A simulation model is defined as creating a model that is based on the real world. When the model has been created, you can perform experiments on the model to see how changes made to the model can affect the overall cost of the logistics network.

For example, by changing the constraints on the network, it is possible using a simulation model to see how this affects the cost-effectiveness of the overall network.

For a simulation model to be effective, you need to collect significant amounts of data on transportation, warehousing, labor costs, material handling, and inventory levels, so that when you make changes to the constraints, the model accurately reflect the changes. However, the changes to the simulation model will not produce the optimum logistics network, as produced by the optimization model; it will just evaluate the changes that were made to the model. This type of model is very useful when companies have made general decisions on the network and want to see what the overall effect of any changes will be.

Heuristic Model

Similar to simulation models, heuristic models do not generate an optimum solution for a logistics network.

A heuristic model is used to reduce a large problem to a more manageable size. It has to be understood that heuristic models do not guarantee a solution and that a number of heuristic models may contradict or give different answers to the same question and still be useful to the overall creation of a logistics network.

Heuristic models are often referred to a “rule of thumb” which can be useful in creating a logistics network.

For example, a heuristic model can be used to consider the best site for a distribution center that is at least ten miles from the market area, fifty miles from a major airport, and more than three hundred miles from the next closest distribution center. A heuristic model will look at all areas that fit within the parameters defined, and finds the areas best suited.