NISAC has developed a range of capabilities for analyzing the consequences of disruptions to the chemical manufacturing industry. Each capability provides a different but complementary perspective on the questions of interest—questions like Given an event, will the entire chemical sector be impacted or just parts? Which chemicals, plants, and complexes could be impacted? In which regions of the country? How long will these impacts last?

Impacts to the chemical manufacturing sector can come from changes in regulatory policy or from physical threats like natural disasters. A natural disaster, accident, or intentional attack can damage chemical plants, ports, pipelines, and rail and road transportation routes, impacting the ability of chemical facilities to produce and deliver chemicals.

The chemical industry and supporting government agencies need to understand chemical supply chain relationships, dynamics, and cascading effects to improve the sector’s resilience to these disruptive events. This includes the sector’s ability to prepare for, respond to, and recover from disruptions.

The U.S. Chemical Sector converts raw materials into countless products used throughout all aspects of life. Consequently, events that change the functional dynamics of the chemical manufacturing industry can have significant impacts on the economy and national security. NISAC’s chemical supply chain analysis capability includes the following tools, each providing complimentary degrees of detail depending on the depth of analysis required and time allotted:

Chemical Data Model

NISAC’s supply chain analysis requires robust and up-to-date access to data on chemical manufacturing, economic statistics, and chemical reactions.  As such, Sandia has acquired and modified disparate sets of commercial databases over several years to create an in-house suite of application-ready data.  This Chemical Data Model (CDM) also includes critical expertise developed within Sandia National Laboratories to support chemical supply chain analyses.

Geospatial Analysis with FASTMap

Leveraging the Chemical Data Model, FASTMap rapidly identifies which facilities will likely be impacted based on the disruptive scenario.  This includes their location, what chemical they produce, and their production capacities.

Network-based Analysis with Loki

Built upon the Chemical Data Model (CDM), the Loki network model rapidly estimates indirect effects to the chemical sector from a disruptive event.  The Loki model uses the stoichiometry of the chemical production reactions in the CDM to identify other facilities and chemicals outside the geographic area physically disrupted that will be impacted through the supply chain.

Agent-based economic model with N-ABLE™

N-ABLE™ is an agent-based chemical supply chain model composed of:

(1) Chemical plants with autonomous operations such as purchasing, production scheduling, and inventories

(2) Merchant chemical markets

(3) Multi-modal chemical transport networks. Large-scale simulations of chemical-plant activities and supply chain interactions are conducted to estimate the scope and duration of disruptive-event impacts, including the extent to which chemical plants adapt by modifying their internal operations versus their external operations.