Blog
6 Ways Biotech Manufacturers Can Unlock Data Value
A practical guide for biotech CMOs to unlock value from manufacturing data through process optimization, compliance, digital twins, and predictive maintenance.
By Ross KatzRead
Your plant manager checks seven systems before lunch. None of them talk to each other.
Operational data exists in your ERP, WMS, production equipment, and payroll system. We build the infrastructure that connects those sources into dashboards your operations team opens every morning.
Operations Analytics
Operations teams generate enormous amounts of data. Production logs, shift reports, equipment readings, warehouse transactions, pick counts, error rates, maintenance records. The data exists across five, seven, sometimes ten different systems. Most of it never becomes information because the infrastructure to connect it does not exist.
150+
Disconnected systems unified across client engagements
24
Hours per month saved by automating one manual reporting process
145K+
Production line items tracked in real-time dashboards
Use Cases
Engagements where this is the right work.
Production efficiency tracking
Output per operator, per shift, per department, per facility, adjusted for product mix. Real-time metrics like PPOH, WIP, cycle times, and yield, broken down by the dimensions plant managers need.
Warehouse and picker productivity
Picks per hour, error rates per thousand lines, staffing needs by day of week. We turn transactional warehouse data into productivity metrics operations managers can act on.
Multi-site benchmarking
Comparing facilities is impossible when each site tracks metrics differently. We normalize operational data across locations so leadership can benchmark performance on equal terms.
Equipment utilization and monitoring
Cycle counts, fault codes, power consumption, run hours. We build pipelines that surface equipment data alongside production metrics so you can connect machine performance to operational outcomes.
ERP reporting modernization
Running the same ERP report six times to compare branches is not analytics. We extract data from JD Edwards, SAP, Oracle, Great Plains, and legacy ERPs into a modern warehouse and build reporting your team can explore without IT tickets.
Our Process
A structured approach that delivers results at every stage.
01
Understand What Your Team Needs
The requests we hear from COOs, plant managers, and warehouse directors are straightforward. Production output per shift, per department, per facility, updated hourly. Branch performance compared on the same metrics using the same definitions. Trend analysis over weeks and months without manually stacking daily snapshots. These are basic questions. They are hard to answer because the data lives in systems designed for transactions, not analysis.
Output: Stakeholder map with named decisions and metric requests
02
Extract, Transform, and Visualize
We extract data from ERP systems, warehouse management systems, production equipment, payroll, maintenance trackers, and vendor software. We load it into a modern data warehouse and build the transformation models and dashboards that turn raw operational transactions into the metrics your team needs: production analytics, warehouse productivity, equipment monitoring, and multi-site comparison with normalized definitions across every facility.
From our podcasts: Biotech Manufacturing Quality with Stewart Fossceco, Transforming CPG with AI at TICKR, and Developing Sustainable Materials Using AI with Cambrium.
Output: Operational dashboards with drill-throughs and refresh schedules
03
Iterate and Expand
The first phase delivers core KPI dashboards — the metrics your team checks every morning. From there, we layer in additional data sources, add facilities, and refine the analytics as your team identifies new questions. A warehouse analyst who was spending 75 minutes every day extracting pick data from a database and typing it into Excel now opens a dashboard that updates continuously. A plant running on seven disconnected systems now has a unified view across two facilities. These expansions happen in phases because each round of visibility surfaces the next set of questions worth answering. We also build the documentation and training so your team can own the system independently as it matures.
Output: Quarterly review and an extension backlog tied to business outcomes
Frequently Asked
Questions
Can you extract data from our ERP system?
Yes. We have extracted data from SAP, Oracle, NetSuite, Microsoft Dynamics, JD Edwards, Epicor, Infor, Sage, and Great Plains, as well as production databases underlying them such as SQL Server or IBM i-series (AS/400). ERP extraction is often the hardest part of an operations analytics project because these systems were not designed for external reporting. We build reliable pipelines that pull data on schedule without impacting production system performance.
We have machine and IoT data. Can you work with that?
Yes. We have built dashboards on machine-generated data including equipment status, cycle counts, power consumption, and sensor readings. The challenge is usually volume and format: vendor software that does not export cleanly, high-frequency data that needs aggregation, timestamps that do not align across systems. We build the pipelines that normalize all of that into a usable analytical layer.
What operational KPIs do you typically track?
It depends on the operation. For manufacturing: PPOH, WIP, cycle times, material usage, fill rates, and on-time delivery. For warehouses: picks per hour, order accuracy, errors per thousand lines, fulfillment time, and labor allocation. For supply chains: lead times, inventory turnover, and supplier on-time rates. We build the KPI framework around what your team manages, not a generic template.
Our team is worried a new system will add more work, not less.
That is one of the most common concerns we hear, and it is valid. The goal is to eliminate the manual work your team already does: the daily spreadsheet assembly, the time spent copying data between screens, the monthly report compilation. We automate the data collection and transformation. Your team opens a dashboard instead of building a spreadsheet.
How long does an operations analytics engagement take?
ERP extraction and initial dashboard delivery typically take 8-12 weeks. Operations data is messier than most: inconsistent schemas, legacy formats, undocumented business logic in stored procedures. Most clients have core KPI dashboards in the first phase and add deeper analytics in subsequent phases.
Do you replace our ERP system?
No. We extract data from it. Your ERP stays as the system of record for transactions, inventory, and production. We build a reporting and analytics layer alongside it that gives your operations team the visibility they need without changing how they work day to day.
Can you compare performance across multiple facilities?
Yes. We have built production analytics across multiple plant facilities, tracking the same KPIs at each location. Multi-facility comparison requires normalizing data definitions, equipment differences, and product mix. We handle that in the transformation layer so the numbers are comparable.
Running operations on gut feel instead of data?
We’ll connect your operational systems and build the dashboards your team needs to spot problems before they become costly.
Book an Intro CallOur Other Services
Or, see what else we do.
Business Intelligence
Turn data into decisions with BI platforms that your team will use. CorrDyn builds dashboards, reports, and analytics workflows.
Learn more
Data Engineering
Build reliable, cost-effective data pipelines on AWS, GCP, and Azure. CorrDyn designs and implements data infrastructure that scales.
Learn more
Data Reliability & Performance
Fix failing pipelines, slow queries, and unreliable data delivery. CorrDyn stabilizes your data infrastructure and keeps it running.
Learn more
Managed Data Team
Get a full data team without the hiring timeline. CorrDyn embeds the specific skill sets you need and owns outcomes, not just hours.
Learn more