No single view of plant performance
Output comes from the MES, orders from the ERP and defects from quality logs, and no one view combines them.
Impact: Slow root-cause analysis and disagreement about what actually happened on the line.
Manufacturing data analytics
Connect ERP, MES and IoT Data for Real-Time Manufacturing Analytics
Production data lives in ERP, MES, quality systems and machine sensors, and it rarely agrees with itself. We build integrated manufacturing data platforms that combine these sources into dashboards for output, downtime, quality and cost, so plant and head-office teams work from the same numbers.
On the shop floor
The four data gaps that cost a plant most
OEE is calculated manually
Availability, performance and quality are estimated from shift reports, so the figures arrive late and vary by plant.
Impact: Improvement work is based on incomplete numbers.
Sensor data is not used for decisions
Machine data is stored but not joined with production orders, maintenance records or shift information.
Impact: Downtime is explained after the event rather than prevented.
Cost and scrap are reported late
Material usage, scrap and rework are closed at month-end, long after the production run.
Impact: Corrective action comes too late to protect margin.
Context
Why a line stops and nobody can say why
A plant generates a large amount of data, spread across the ERP, the MES, quality management, maintenance and machine controllers. Each system records a different part of the same event. When a line stops, the cause may be visible in the machine log, the work order and the quality record, yet the link between them is made by hand, often days later.
We join these records on common keys such as work order, machine, shift and batch, and calculate OEE, scrap and downtime once with agreed definitions. This is the foundation for predictive maintenance and for production planning with machine learning, because models are only as reliable as the shop-floor data beneath them. Plants running older lines can also review legacy system modernisation alongside the data work.
Plant data architecture
Shared keys from machine to management report
Ingestion
Batch loads from ERP and quality systems, plus streaming or frequent pulls from MES and machine data where the plant allows.
Storage and modelling
Time-aligned production models that join orders, runs, machines, shifts and defects on common keys.
Calculations
Agreed OEE, scrap and downtime definitions implemented once and reused in every dashboard.
Serving
Plant and head-office dashboards, alerts for abnormal states, and exports for planning systems.
Rollout
Site by site, with supervisors in the loop
Each phase ends with a defined output. Timelines are estimated after the assessment, once sources and scope are confirmed.
- Phase 1
Plant and system assessment
We review the ERP, MES, quality and maintenance systems at each site, and confirm what machine and PLC data can be collected.
Output: Site assessment and data availability map
- Phase 2
Common model design
We agree shared codes for runs, downtime reasons, defects and shifts, so that data from different plants can be compared.
Output: Production data model and code list
- Phase 3
Integration build
We build the batch and frequent loads, validate machine and quality data before it feeds calculations, and implement the OEE and scrap rules.
Output: Integrated pipeline with validation
- Phase 4
Dashboards and alerts
We publish line-level dashboards for plant teams and head-office views, with alerts for abnormal machine states and quality trends.
Output: Dashboards and alert rules
- Phase 5
Rollout and adoption
We roll out site by site, train supervisors and engineers, and review the dashboards with them each month.
Output: Rollout plan and training material
Plant systems
Connecting ERP, MES, quality and machine data
We confirm what each system exposes, how often it can be refreshed and who owns the data, before the design is final.
ERP
Orders, bills of materials, inventory and cost centres
MES and shop-floor systems
Production runs, cycle times and work orders
Quality management
Inspections, defects, non-conformance records
Machine sensors and PLC data
States, counts, temperatures and alarms
Maintenance system
Work orders, failures and planned stops
Plant measures
OEE, downtime and scrap on one definition
Each measure is baselined before the project starts, so improvement is shown against your own starting point.
| Measure | How it is calculated | Why it matters |
|---|---|---|
| OEE by line and shift | Availability, performance and quality, calculated from the same definition at every site | Shows where capacity is lost |
| Unplanned downtime | Hours of unplanned stops, grouped by cause and machine | Shows which failures to address first |
| Scrap and rework cost | Material and labour cost of defective output, linked to runs | Links quality problems to money |
| Reporting lag | Time between a production event and its appearance in reports | Shows how quickly teams can react |
| Cross-plant comparability | Share of plants reporting on the same definitions | Makes benchmarking between sites meaningful |
Outputs
What plant and head-office teams get
- Integrated production data model across ERP, MES and quality
- OEE and downtime dashboards by line, shift and plant
- Scrap and rework cost analysis linked to production runs
- Alerts for abnormal machine states and quality trends
- Data dictionary for shop-floor and finance teams
Illustrative scenario
OEE visibility across two plants: illustrative
This is an illustrative scenario, not a verified client result. Measured outcomes are shared only from real projects, with their baseline and method.
The challenge
A manufacturer runs two plants with different MES versions. Each plant calculates OEE differently, and head office receives the figures a week late.
The approach
- Map both MES instances to one production model with shared run and downtime codes
- Implement one OEE definition in the warehouse
- Publish daily line-level dashboards to plant managers and head office
- Link downtime events to maintenance work orders for review
What we measure: Reporting lag, agreement between plant figures, time to identify top downtime causes. Measured against a baseline from before the project.
Data quality
Validated machine and quality data only
Controls are agreed for each project, based on the sensitivity of the data and the rules that apply to your organisation.
- Access control by plant and role
- Clear ownership of each source and master data field
- Validation of machine and quality data before it feeds calculations
- Change control for OEE and cost definitions
- Retention rules for detailed machine data
Plant and IT questions
Manufacturing analytics: FAQs
Can you integrate our MES with the ERP?+
Usually yes, through the systems' exports, database access or APIs. We confirm what each system exposes, and how frequently the data can be refreshed, during the assessment.
Do you work with machine sensor data?+
Yes, where the data can be collected from PLCs, historians or gateways. The volume and frequency determine the storage and processing design.
How do you define OEE consistently across plants?+
We agree one definition with plant and finance leaders, implement it once in the data model, and document any local differences that the business decides to keep.
Can the dashboards be used on the shop floor?+
Yes. Dashboards can be designed for shop-floor screens or tablets, with the same underlying data as head-office reports.
Do we need to change our MES to use this platform?+
No. The platform reads data from the existing systems. Changes to shop-floor codes are sometimes needed for consistent reporting, and we agree these with plant teams.
How do you handle plants on different software versions?+
We map each version's data to the common model. Differences that cannot be mapped are documented, so reports show their limits clearly.
Find out where your reporting delays come from
A data platform assessment maps your systems, the reports that matter and the decisions they support, then gives you a practical first step.