Difference Between Simulation and Optimization

Topics: Supply chain management, Normal distribution, Probability theory Pages: 6 (1556 words) Published: August 7, 2009
Objectives Paragraph

Uncertainty in the business environment is a major threat at each and every level of the supply chain. Every day new challenges and opportunities arise – rising cost of fue, implications of an organization’s carbon footprint, outsourcing regulations, tax incentives, and political fluctuation. Proactively monitoring the implications of such events at frequent intervals is crucial for an organization. By using a variety of Supply Chain modeling and mathematical tools, an organization is able to develop an understanding of the implications of such factors. However, the vast array of tools also creates a dilemma about the best modeling approach. In the field of supply chain modeling, one dilemma that a corporation faces today is whether optimization, simulation, or a hybrid model (combination of optimization and simulation) is a better option to pursue.

In this paper, we fundamentally distinguish the two modeling approaches – Supply Chain Optimization vs. Supply Chain Simulation, and the scenarios where the each option should be employed.


Optimization focuses on finding the optimal solution from millions of possible alternatives while meeting the given constraints of the supply chain. Optimization utilizes mixed integer programming (MIP) or linear programming (LP) to obtain the optimal solution. Optimization models are used for network optimization, allocation management (refinery and terminals), route optimization (retail logistics) and vendor-managed inventory (retail network management).

Simulation identifies the impact of different variables on an organization’s entire supply chain. It answers the fundamental question – what will happen to the cost and service levels associated with a Supply Chain if an ‘X’ factor is manipulated The tool however does not drive to an optimal solution. Simulation also enables a user to visualize real world behavior of an “optimal solution” derived from the optimization. Simulation models are best suited for decision analysis, diagnostic evaluation, and project planning.

Supply Chain Optimization VS. Simulation

The biggest challenge when conducting Supply Chain Optimization is the overall complexity of a model, which increases as more variables are introduced. Running an Optimization model requires a user to make a large number of assumptions. Additionally, modeling tools do not account for variability such as non-linear and stochastic properties. As such, this approach does not fully capture the true complexity of an Organization’s Supply Chain. This is where simulation modeling becomes more attractive. It mimics more accurately the true Supply Chain, including necessary nonlinear and stochastic properties. Simulation takes into account the elaborate and complex relationships between supply chain components

Optimization is ideal for environments with low uncertainty, where supply chain efficiency is the primary focus. This approach is better suited when looking at a long-term horizon, and where stakes are high (i.e. strategic and high level tactical planning). However, when the uncertainty is very low, optimization can even be extended down to low level tactical planning like inventory optimization.

Simulation is ideal for rapidly changing environments where firms focus mostly on their supply chain responsiveness. Hence, it is primarily used for short planning horizons (i.e. operational planning). Simulation enables planners to perform what-if analysis in a rapidly changing environment.

Modeling approaches are different for simulation and optimization. For instance during optimization of a network planning model, a base model is initially created. This model is then optimized based on the given demand. Different network scenarios are used to change the base model. The scenario that offers a combination of the lowest network cost and highest service level is taken as the optimum network solution.

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