Mitigating Supply Chain Disruptions by using Option Contracts

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Mitigation strategies for supply chain risks have have been discussed in the literature for a long time now. Khan and Burnes (2007) mention several strategies like:

  • Multiple sources of supply vs single sourcing
  • Buffers
  • Risk sharing/knowledge transfer
  • Proactive supply management

But it seems difficult to assign a value and maximum cost for these strategies. Xu and Nizick (2009) are filling this gap.

Case: Supply Chain Model

The supply chain consists of multiple suppliers for two plants. The suppliers are scattered around the world. There are two types of suppliers: Base suppliers and Option suppliers.
Base suppliers have lower per unit costs than option suppliers and deliver the ordered parts. Disruptions can lead to a partial or full loss of the suppliers capacity. Option suppliers have to fill the gap if one or more of the base suppliers fail to deliver the orders due to disruptions (like huricanes, fires, earthquakes).
Option suppliers have to be paid a fixed amount to establish the relationship.

Focussing on disruption risk they calculate an optimal solution for this supply chain network design problem.

The stage one questions are:

  • Who should be the base supplier?
  • Who should be the option suppliers (if any)?

Stage two:

  • How much should be acquired in a specific period?
  • How should the parts be transported to the plans (train, ship, truck, plane)


It is obvious that it might be useful to have an “option” supplier at hand, to bridge a gap in supply due to an unforeseen event (eg. high cost of lost demand, low cost of establishing a relationship). So this, of course is also the solution by the authors.

The additional benefit from using this model is the calculation of how much these “options” are worth for the company and therefore how much can be paid for such an option to be established.


Xu, N., & Nozick, L. (2009). Modeling supplier selection and the use of option contracts for global supply chain design Computers & Operations Research, 36 (10), 2786-2800 DOI: 10.1016/j.cor.2008.12.013

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