Data Modernization 

Connecting Lab,

Plant & Supply Chain Operations

CLIENT

Chemicals & advanced materials manufacturer

INDUSTRY

Manufacturing

SERVICE

Data modernization program | Cloud data warehouse | ETL pipelines | Lab-to-plant integration | Predictive maintenance analytics | Demand forecasting 

Overview

A global chemicals materials manufacturer needed to improve coordination across its R&D labs, production plants, and supply chain operations. Each function ran on separate legacy systems – LIMS in labs, MES/SCADA in plants, and ERP modules in logistics – with little integration between them. This caused delays in scaling new products from lab to production, reactive maintenance on critical machinery, and inefficiencies in procurement and inventory planning. Partnering with Sphere, the client implemented a stepwise data modernization program that connected their systems into a central warehouse, applied practical analytics, and provided role-based dashboards for lab, plant, and supply chain teams.

Challenges

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:

Limited Cross-Functional Visibility

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.

Reactive Plant Maintenance

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.

Fragmented Supply Chain Systems 

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.

Siloed Lab Data 

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.

Our Solution

Sphere worked with the client on a stepwise modernization program, focusing first on integration of existing systems, then on practical analytics and visualization that could be adopted quickly by lab staff, plant supervisors, and supply chain managers.

1. Data Foundation & Integration

  • Deployed a cloud data warehouse (Snowflake) to act as a central repository.
  • Built ETL pipelines using Fivetran and dbt to move data from:
    • Laboratory Information Management System (LIMS)
    • Plant MES (Rockwell FactoryTalk) and SCADA logs
    • ERP modules (SAP for procurement and logistics)
  • Standardized master data so material codes, batch IDs, and equipment tags matched across systems.

2. Lab-to-Plant Data Flow

  • Connected LIMS outputs (QC test results, formulation data) directly to MES, so approved lab results automatically updated production recipes.
  • Eliminated manual transcription steps between lab and plant, reducing delays and human error.

3. Plant Operations & Maintenance Visibility

  • Integrated sensor logs already collected in SCADA into the warehouse, without introducing new hardware.
  • Applied basic statistical models (regression + anomaly detection) to detect early signs of machine performance drift (e.g., rising power consumption in extruders).
  • Alerts were visualized in dashboards for plant supervisors, who could trigger maintenance checks before failure.

4. Supply Chain Synchronization

  • Pulled order history, supplier lead times, and logistics data into the central warehouse.
  • Built demand forecasting models (using Prophet for seasonality + regression for raw material correlations) to align procurement with production schedules.
  • Linked outputs back into SAP to support purchasing and inventory planning.

5. Dashboards & User Adoption

  • Developed role-based dashboards in Power BI:
    • R&D teams tracked which lab results were cleared for scale-up.
    • Plant supervisors saw OEE (Overall Equipment Effectiveness) and early warning alerts.
    • Supply chain managers viewed demand forecasts and supplier performance.
  • Training was delivered in small groups, ensuring supervisors and planners could use the system without needing data science skills.

Why We Chose This Approach

  • No rip-and-replace: All existing systems (LIMS, MES, ERP) were kept in place, with APIs and connectors used for data flow.
  • Incremental rollout: Pilot started with one production line and one material family before scaling.
  • Fair analytics: Focused on regression models and anomaly detection, not complex “black box” AI.
  • ROI came from basics: Removing manual steps, aligning lab–plant–supply chain data, and giving supervisors visibility drove most of the savings.

Key Achievements

10% faster product scale-up

from lab to plant, reducing time-to-market for new formulations.
12% reduction in unplanned downtime

by enabling supervisors to act on early warnings before equipment failures.
8% supply chain cost savings

from better demand alignment and reduced reliance on expedited freight.
Adoption within 6 months

system was piloted in one plant and scaled to three regional sites with minimal disruption to operations.

Result

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.

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Luke Suneja

Client Partner

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