This is an illustrative example scenario, based on common challenges we see in manufacturing environments. It is not a case study from a specific client, but it reflects how an engagement would typically run.
Background: A Mid-Sized Manufacturing Plant in Gauteng
Imagine a mid-sized manufacturing plant in Gauteng with:
- Two production lines running PLCs from different vendors
- An existing ERP system (for this example, we’ll assume Sage, but it could be SAP, Oracle, or another ERP)
- A small internal IT team and no dedicated data engineering function
On the shop floor, the team relies on PLCs to track:
- Machine uptime and downtime
- Production counts by line and shift
- Scrap and rework
- Basic quality metrics
In the ERP, the finance and operations team needs:
- Accurate WIP and finished goods balances
- Reliable job costing and margin analysis
- Trustworthy inventory movement and scrap reporting
- Timely data for month-end close and management reporting
Today, those two worlds are poorly connected. Production data lives in PLCs and spreadsheets. Financial data lives in ERP. Every month, the plant manager and finance controller spend days reconciling the two.
The Problem: Blind Spots Between PLCs and ERP
The plant has already invested in automation on the production side, but there is no consistent, trusted flow of data into ERP. The symptoms look like this:
- Inventory variances at month-end that are hard to explain
- Manual spreadsheet reconciliations between production counts and ERP quantities
- Delayed job costing because production data arrives late or incomplete
- Disagreements between production and finance on “what really happened” on the shop floor
From a data perspective, there are four core issues:
- Protocols and formats don’t match
- PLCs expose data via industrial protocols (e.g. OPC UA, Modbus).
- ERP expects structured records in database tables or REST APIs.
- Business context is missing
- PLC tags know counts, speeds, and states—but not work orders, part numbers, or cost centers.
- Data quality is inconsistent
- Some lines have detailed tags; others are minimal.
- Naming conventions differ between lines and shifts.
- No agreed integration architecture
- Ad-hoc scripts and one-off exports rather than a designed, monitored pipeline.
Our Approach: Apply the Data Product Lifecycle
We treat this as a data product, not a one-off integration project. The same lifecycle we use for finance automation on invoices and statements applies here too.
Phase 1: Source — Data Audit & Strategy
We start with a structured data audit:
- Catalogue PLC tags on each line: what they measure, how often they update, and how they map to production events.
- Map those tags to ERP concepts: work orders, materials, BOMs, cost centers, and GL accounts.
- Review current reports and spreadsheets used by the plant manager and finance team.
The output of Phase 1 includes:
- A data map from PLC tags to ERP fields (what exists, what’s missing, what needs deriving).
- A view of data gaps (e.g. missing scrap tags, no direct link between counts and work orders).
- A proposed integration architecture: industrial gateway, buffering/aggregation layer, and ERP integration endpoints.
This aligns directly with the “Phase 1: Source” work described on our How It Works page.
Phase 2: Integration — Building the Data Pipeline
Once we understand sources and requirements, we design and build the data pipeline that connects PLCs and ERP.
Key elements:
- Industrial connectivity
- Connect to PLCs via OPC UA / Modbus / vendor-specific drivers using an industrial gateway.
- Buffering and aggregation
- Collect high-frequency PLC data in real-time.
- Aggregate it into ERP-ready summaries: production counts per line, per product, per shift or work order.
- Enrichment and validation
- Add business context from ERP master data (materials, work orders, routing).
- Validate that counts, scrap, and status changes make sense before posting to ERP.
- Controlled posting to ERP
- Use APIs or batch interfaces to update ERP with production quantities, scrap, and status.
- Ensure idempotency and duplicate protection, so the same run doesn’t post twice.
At this stage, we also implement monitoring and logging: if a PLC goes offline or an ERP call fails, the right people know immediately.
Phase 3: Implementation — Automation, KPIs, and Governance
With pipelines in place, we focus on making the data usable for finance and operations:
- Automated production posting
- Shift-level or order-level production quantities automatically flow into ERP.
- Scrap and rework are captured and posted consistently.
- Dashboards for plant and finance
- Views showing output by line, scrap rates, and downtime—aligned with ERP postings.
- KPIs such as OEE, yield, and cost per unit that both operations and finance trust.
- Data governance and lineage
- Clear ownership: who owns PLC tag definitions, who owns ERP mappings.
- Lineage that shows: this inventory movement in ERP came from these PLC tags at these times.
This corresponds to the “Phase 3: Implementation” stage of our How It Works lifecycle.
Expected Outcomes (Targets, Not Claims)
Because this is an example scenario, we don’t claim specific client results. Instead, we define targets we would design for in a real engagement:
- Inventory accuracy
- Significant reduction in stock variances caused by mismatched production and ERP data.
- Faster month-end close
- Fewer manual reconciliations between plant spreadsheets and ERP, freeing finance time.
- Better cost visibility
- More reliable job costing and margin analysis by product, line, or customer.
- Shared truth between plant and finance
- A single, trusted set of numbers for production output, scrap, and downtime.
In practice, we’d work with your team to set concrete targets (for example, X% reduction in variances or Y days faster close) as part of the engagement.
When This Scenario Sounds Like You
This example scenario is relevant if:
- You run one or more plants with PLCs on your lines.
- Your ERP is the system of record for finance, but it doesn’t see full, timely production data.
- You rely heavily on spreadsheets and manual checks to reconcile production and inventory.
If that sounds familiar, we can apply the same data product lifecycle we use for finance automation to your manufacturing environment.
You can:
