Adaptive Predictive Modeling for Supply Chains

Relative to widely utilized data analytic techniques, adaptive predictive modeling of supply chain material through automated data testing and trending techniques provide an alternate approach that is less costly, more time efficient, and of greater value. This modeling approach provides the flexibility of having a more simplistic and standardized methodology for its user while the customer receives a customized solution. This provides a user the ability to derive highly accurate material requirement predictions while removing the dependency on the item manager from having personal knowledge and experience in the product being supported. Adaptive statistical modeling techniques account for factors such as the part type, sample data sets, and the varying stages of a given product's life cycle by correlating predicted reliability with realized demand of material. Additionally, artificial intelligence techniques identify trends that would otherwise be unrealized to the item manager. These techniques enable the item manager to have an accounting for otherwise unknown unique product line factors which further increases fidelity of predictions. Adaptive Predictive Modeling provides increased information and accuracy to the process while providing a more streamlined supply chain solution and its various positive impacts across the program. Adaptive Predictive Modeling aligns like product support variables into adjustable parameters and provides a common user interface that accommodates diverse product lines. Accounting for both consumable and repairable material, from piece parts to complex assemblies, this modeling enables fundamental supply chain logistics philosophy to be applied relative to the customer, industry, and process driven data sets. As an input to the Supply Chain, this modeling utilizes predicted reliability Mean Time Between Failure (MTBF) data, considered for products in the infancy of their life cycle, while simultaneously considering the products against experienced demand data allowing for identification of an ideal transition point between modeling techniques as a given product's life cycle stage progresses. Throughout the sustainment life cycle, the item manager is enabled to further scrutinize available information, increasingly detailed granularity, through greater observational functionality within the data set, pinpointing potential supply chain improvements. This methodology translates to higher fidelity information for business case driven decisions with increased accuracy of financial impact, scheduling, and contractual planning.