Abstract: Feature engineering can be performed on time series data making the data easy to manipulate and accessible to business users for analysis according to existing best practices. A computer system can, after receiving time series data related to a device, contextualize the time series data based on business data related to the device from, for example, an enterprise resource planning database. The contextualized data can be windowed by a selected feature based on execution data related to the device from, for example, a manufacturing execution system database. The windowed data can be transformed into summary data using a time series transformation. The summary data can be easily manipulated by, for example, generating genetic maps of the segmented and transformed data for clustering or searching for anomalies and patterns in response to user requests or automatically.

Abstract: Feature engineering can be performed on time series data making the data easy to manipulate and accessible to business users for analysis according to existing best practices. A computer system can, after receiving time series data related to a device, contextualize the time series data based on business data related to the device from, for example, an enterprise resource planning database. The contextualized data can be windowed by a selected feature based on execution data related to the device from, for example, a manufacturing execution system database. The windowed data can be transformed into summary data using a time series transformation. The summary data can be easily manipulated by, for example, generating genetic maps of the segmented and transformed data for clustering or searching for anomalies and patterns in response to user requests or automatically.

Abstract: Changes in the supply chain of one product may affect the price and availability of many other products. Likewise, a product may be composed of a number of smaller parts. Changes in the cost or availability of any of the parts that comprise a product may result in changes in cost or availability of the product as well as many other related products. A supply chain analysis engine is used to model a supply chain. The supply chain analysis engine may identify dependencies between products, sub products, and supply chains. The dependencies may be used to analyze the impact of a change in the supply chain on all relevant products.

Abstract: Feature engineering can be performed on time series data making the data easy to manipulate and accessible to business users for analysis according to existing best practices. A computer system can, after receiving time series data related to a device, contextualize the time series data based on business data related to the device from, for example, an enterprise resource planning database. The contextualized data can be windowed by a selected feature based on execution data related to the device from, for example, a manufacturing execution system database. The windowed data can be transformed into summary data using a time series transformation. The summary data can be easily manipulated by, for example, generating genetic maps of the segmented and transformed data for clustering or searching for anomalies and patterns in response to user requests or automatically.

Abstract: Changes in the supply chain of one product may affect the price and availability of many other products. Likewise, a product may be composed of a number of smaller parts. Changes in the cost or availability of any of the parts that comprise a product may result in changes in cost or availability of the product as well as many other related products. A supply chain analysis engine is used to model a supply chain. The supply chain analysis engine may identify dependencies between products, sub products, and supply chains. The dependencies may be used to analyze the impact of a change in the supply chain on all relevant products.

Abstract: Various arrangements are presented for performing comparisons of indented cost BOM (bill of material) structures of two or more items in a supply chain management system. By understanding the hierarchical nature of the indented cost BOM structure of each item and various nested relationships, it may be possible to perform complex comparisons that involve multi-level structures.