Data Modernization

The client’s operations relied on disconnected legacy systems across labs, plants, and supply chain. This created silos, slowed collaboration, and made it difficult to anticipate problems or respond quickly. Four issues stood out:

Executives and operations leaders had no single view connecting lab readiness, plant efficiency, and supply chain performance. Decisions were made in silos, slowing response to disruptions and preventing data-driven planning.

SCADA logs were stored locally and reviewed manually, often only after failures had occurred. Plant supervisors lacked predictive indicators, leading to unplanned downtime on high-value equipment such as extruders and turbines.

Different ERP modules and spreadsheets managed procurement, logistics, and supplier data. This fragmentation prevented accurate demand forecasting and led to both overstocking of raw materials and costly expedited orders.

Quality control and formulation results from the Laboratory Information Management System were not automatically shared with production. Lab staff manually re-entered data into MES, creating opportunities for human error.

MES/SCADA, and ERP systems into Snowflake. Lab-to-plant recipe transfer was automated, removing the need for manual re-entry of QC and formulation data. In plants, anomaly detection models on SCADA logs introduced early warning alerts for supervisors, helping prevent costly downtime. On the supply chain side, ERP and logistics data were combined into practical demand models, improving forecasting accuracy and procurement planning. Role-based dashboards in Power BI made insights accessible to non-technical staff, while executives and managers gained cross-functional transparency for the first time, with a unified view spanning R&D, production, and logistics.