Dynamic Network Discovery
Because TransVoyant is watching and learning from every shipment, across every mode of transportation, from N-Tier supplier to customer door, including every supply chain node in between—port, manufacturing facility, warehouse, store—it dynamically self discovers a company’s supply chain network. This eliminates the need for costly and drawn out, network mapping exercises that result in hard-coded, static network models. TransVoyant is constantly watching, analyzing and updating a company’s supply chain network, adding new nodes, lanes and routes as soon as they materialize.
Live Event Monitoring
TransVoyant collects, analyzes and learns from massive amounts of real-time big data gathered from IoT devices around the world every day. TransVoyant displays these risk events in a visual control tower which makes it easy for users to see current and future risks unfolding around their supply chain nodes, routes and lanes.
TransVoyant has established living behavior models for carriers, lanes, routes, ports, airports, countries and other supply chain nodes under varying conditions. These behavior models are just one, albeit important, input into TransVoyant’s advanced machine learning algorithms that produce future risk predictions and calculate their impacts.
Relating Risks to Assets
An external risk (i.e., earthquake, strike) is irrelevant if it has no current impact, or predicted impact, on your inventory or assets. TransVoyant relates current risks to the current locations of your inventory and assets, current risks to the predicted future locations of your inventory and assets, and predicted future risks to the predicted future locations of your inventory and assets.
TransVoyant scores and ranks current and predicted risks based on risk probabilities and severity of impact. Impact severity is a function of revenue impact, cost impact, and customer service impact, the weightings for each are all configurable by the user. TransVoyant’s risk rankings continuously evolve as events unfold and circumstances change.
TransVoyant visually displays the upstream and downstream impacts from current and predicted risks. Users can drill down into any upstream or downstream node to see more details associated with the impact, such as affected manufacturing runs or customer orders.
Because TransVoyant has been watching carriers, lanes, routes and nodes for years, when current or predicted disruptions arise, the system is aware of and can make prescriptive recommendations for remediation.
Collaborative Issue Resolution
TransVoyant enables collaborative discussions and task assignments tied to each risk. The solution stores and analyzes all resolution paths. This searchable repository serves as a knowledge base for organizations, as well as a data input to TransVoyant’s machine learning algorithms. As similar risks arise, TransVoyant can make better, more granular, prescriptive recommendations around resolution.