Simulation of Supply Chain Disruptions

Daniel Dumke — Wed, 22 Sep 2010 13:06:00 +0000

Still too many cooperations do not analyze their supply networks using consistent and scientifically proven methods. Some already do. One case of a company (ABC) is described below.

Goals and Methods

ABC company wanted to know more about their exposure to supply chain disruptions originating from their own plants but also the connected transportation links, suppliers and customers. Specifically, the goals were:

Assess the current level of supply chain disruption risk in the system
Test different mitigation strategies
Provide a tool that can be used in the case of a disruption to validate recovery steps before putting them into action
Identify redundancy in the system that can be removed without affecting the risk level

The authors therefore designed a supply chain model for two major products (one high, one low volume product) using discrete event simulation in Arena, one of the most often used simulation environments. To obtain distributions for the resulting performance measures the authors take advantage of Monte Carlo simulation using an excel tool called @Risk.

Simulation

The demand and risk patterns were evaluated using available data and expert interviews. In the second step mathematical distribution functions were adjusted to fit the empirical observations / forecast to input those into the model.

The company was very concerned with the effects of the disruptions on their customer service, so the Fill Rate was chosen as the relevant performance measure.

Results

The model was analyzed in two ways: First in an steady state analysis the current risk levels in the model were analyzed. In a second step the model was adjusted, by implementing one of the following strategies: implementation of inventory control and improvement of sourcing.

Even though only one simple measure was used, the ABC company was able to gain valuable insights into their supply chain.

Status Quo: All runs of the risk model experienced at least one week where 0% of the incoming orders were satisfied for the available inventory levels (two to six weeks)
Mitigation Strategies: The output clearly showed that reducing response times or increasing capacity of back-up methods were able to reduce the impact of a disruption and speed recovery.
ABC realized that they lack a database with historic distribution information for disruptions
There were no formal strategic-level mitigation procedures
Risk exposure is very dependent on the current state of the models parameters (eg. if a disruption occurs in a low inventory state, recovery lasts much longer)

The most important result is that the process of implementing such a simulation model can help the company think about supply chain disruptions more intensively and such a project can be used as a starting point for systematically documenting the risks and contingency plans in case of a disruption.

Reference:

Schmitt, Amanda J., & Singh Mahender (2009). Quantifying Supply Chain Disruption Risk Using Monte Carlo and Discrete-Event Simulation Proceedings of the 2009 Winter Simulation Conference, 1237-1248

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Tags:

case study

demand uncertainty

network design

disruptions

Amanda J. Schmitt

Simulation of Supply Chain Disruptions

Goals and Methods

Simulation

Results