Automated and continuous process manufacturing typically involves complex sequences of steps and operations. As raw materials move through each step, final product quality will be determined by the sometimes complex interactions between upstream and downstream processing, raw materials, media, and instrumentation. In order to perform meaningful reporting, process monitoring, root cause analyses, and process optimization based on data collected at each step, it is necessary to manage views across processing steps, and across time.
Measurements collected at different steps must be related to key process parameters. Interactions between process parameters at different stages, and their effect on final product quality, need to be assessed. Clusters and batches of materials that shared common processing steps, tools and equipment need to be identified. Process data at every stage in the process need to be aggregated. Moreover, personnel responsible for monitoring these processes need to be able to link together diverse data sources and data historians, and integrate data from a variety of sources.
High quality manufacturing processes require that the quality assurance professionals and managers have access to the Hierarchical Process Cube that contains all information about how the material moved through the sequence of steps, so that meaningful monitoring, qc-charting, reporting, and root cause and other analyses can be performed. The ability to have a complete view of such data is a required prerequisite for modern PAT and QbD initiatives and approaches. Also, maintaining the records describing the complete manufacturing process that accurately reflects the process itself is mandated by regulatory reporting requirements in many industries.
StatSoft’s Product Traceability solution for the manufacturing industry empowers engineers and analysts to select the relevant process and easily gain access to the necessary data, and to analyze the movement of materials and batches through the production process.
Statistica presents the data in the most relevant and meaningful way, handling the connections to the existing data historians, LIMS, and other systems, and aggregating and aligning discrete and continuous data to prepare it for review and analyses. Engineers simply interact with Statistica in a natural way, following exactly the sequence of steps defining the complete process.