2022-2: A propagation model to quantify business interruption losses in supply chain networks


Today’s supply chains are global, highly interconnected, and increasingly digital. These three attributes of supply chains compound the effects of disruptions in production. For a company comprised of many factories, a disruption in production at one site can impact production at other locations as well as production at other companies linked through the supply chain. Quantifying the financial impact of business interruption, such as production loss at a factory caused by a natural catastrophe (NatCat) such as an earthquake or hurricane, is challenging. The difficulty in quantification is due to complex risk propagation dynamics and complications related to the allocation of business profit to specific sites of production. Complex risk propagation dynamics reflect product and supplier dependencies and the inter-connectivity of related risks.

The aim of this research is to estimate production losses at company locations to enable the quantification of exposed business interruption values (i.e. potential gross profit/earnings losses) taking into account interdependencies among the company and the supplying partners within its supply chain network. This approach can provide insurers and reinsurers with the required financial metrics to better address these risks. In this paper, after defining the adapted stochastic fully decomposed supply chain network (FDSN), we propose a new methodology to model the production rate potential at each site of production as a stochastic process via a recursive procedure. Finally, we consider the HAZUS Earthquake Model (HAZUS-EM) to estimate downtime and to quantify the impact of business interruption. Business interruption is propagated through the FDSN given an interruption in production in the supply chain network.

KEYWORDS: supply chain, business interruption (BI), network model, risk propagation, contingent
business interruption (CBI)

Nariman Maddah
Reyhaneh Mohammadi
Elena Pesce
Alicia Montoya
Katherine Dalis
Publication date: 
December 5, 2022
Publication type: 
Working Papers