In general, according to one embodiment, a data processing apparatus includes a processor including hardware. The processor acquires state data related to a state of each product and order data related to order related to manufacturing of each product. The processor calculates a first score by using the acquired state data and the acquired order data. The first score regards a change in the acquired state data and is based on a first change model. The first change model represents the change in the state data using the order data. The processor outputs the first score.

A method is provided for processing data to replicate lifecycle threads in the development of a structural product. The method includes defining a source lifecycle thread from process-related information for development of the structural product, and defining and matching a target lifecycle thread to the source lifecycle thread. The process, and source and target lifecycle thread are expressible as respectively a network and sub-networks of tasks described by a plurality of attributes. Defining and matching the target lifecycle thread includes selecting a plurality of candidate target tasks from the plurality of tasks; matching a candidate target task to a particular source task using a distance map for an attribute of the plurality of attributes, with the distance map including only unique values of the attribute and distances between the unique values; and back-chaining through the source lifecycle thread to match candidate target tasks with respective source tasks.

A production configuration is determined for controlling a product processing plant that comprises a collecting system having a conveyor and a plurality of feeders for preparing product collections from products supplied by the feeders. The production configuration defines a production of a plurality of production sequences for a plurality of delivery routes, wherein each delivery route is assigned one or more production sequences, each of which define a production of a plurality of production collections of a collection type comprising a plurality of products. For each feeder an occupancy with products, which are to be conveyed by the feeder to the conveyor for preparation for the product collections, is determined. A production optimization parameter determines whether a defined production order is to be maintained for the delivery routes or whether the production order of the delivery routes can be altered. If the production order can be altered, the production sequences of a plurality of delivery routes for the production of production collections of the same collection type are combined to a cohesive production sequence and the occupancy of the feeders is determined for the production order with combined production sequences.

A method for order prioritization includes calculating a cycle time for a product order of a plurality of product orders using an artificial neural network, determining a first order priority of the product order based on a priority index using an analytic hierarchy process, determining a second order priority of the product order based on event based simulation model, and determining a shipping date for the product order based on the second order priority. The artificial neural network calculates the cycle time based upon product order type and a plurality of component counts. The analytic hierarchy process determines a first order priority based upon a plurality of product order attributes. The simulation model determines a second order priority and completion time based upon the first order priority, product model, product type, a plurality of component counts, manufacturing capacity and inventory data, and production time data for historical product orders.