USINDOPACOM requires the capability to determine and assess current logistics situations and develop logistics
forecasts that anticipate requirements up to and in excess of 30 days anywhere within the USINDOPACOM area of
responsibility. While the current logistic systems and tools in the USINDOPACOM theater rely on external information
to report various supply inventories, there is minimal forecasting available to anticipate potential supply chain
issues, distribution shortfalls, and/or other unseen and unexpected logistical hurdles that can impede mission
success. Such a capability must be able to properly align resources in the face of many predictable and
unpredictable factors to rapidly and efficiently fulfill demand requests and to accurately predict demand requests
to aid in the process of pre-positioning supplies and assets to rapidly meet the demand as it appears.

Cybernet is leveraging various artificial intelligence techniques, including Deep Learning, Expert Systems, and other
machine learning techniques, along with team expertise on past, current, and future logistic concepts to develop an
Intelligent Logistics Planning System (ILPS) that enhances the capability to anticipate potential supply chain
issues, distribution shortfalls, and/or other unseen/unexpected logistic hurdles that might impede mission success.
The resulting system will help to create an agile, just-in-time logistics chain that can quickly respond to both
planned and unplanned events, and also demand pulls. This will ensure that the logistics chain provides the required
support for forces in the operational arena. The intent is to properly align the various actors required to fulfill
demand requests and eventually to predict potential logistics shortfalls to enable vastly improved Joint logistics
enterprise solutions.