The manufacturing data problem
Operational systems capture the events. Management reporting often reconnects them manually.
- ERP systems
- Manufacturing execution / production systems
- Quality systems
- Maintenance / CMMS platforms
- Warehouse and inventory systems
- Procurement and supplier data
- Finance and costing systems
- Machine or sensor exports where available
- Spreadsheets and manually maintained production files
When production, quality, maintenance, inventory, and finance are reported separately, it becomes difficult to explain why output changed or where operational performance is being lost.
Typical manufacturing analytics challenges
- Production reporting is manually consolidated from multiple sources
- Downtime categories are inconsistent or incomplete
- Quality and production data are not connected
- Inventory and production consumption do not reconcile cleanly
- Maintenance history is difficult to compare with production loss
- Plant or line performance cannot be compared consistently
- Supplier issues are disconnected from quality or availability reporting
- Operational KPIs are calculated differently by different teams
- Historical reporting is difficult to analyse
- Management receives information after the opportunity to act has passed
What Datazeb can deliver
Production Performance Analytics
Track output, throughput, production volume, plan vs actual, line / plant performance, and operational trends.
Downtime & Availability Reporting
Analyse downtime, stoppages, duration, categories, frequency, and related operational impact where source data allows.
Quality Analytics
Track defects, rejects, yield, rework, inspection outcomes, and quality trends using agreed definitions.
Inventory & Material Analytics
Connect stock, material movement, production consumption, ageing, shortages, and availability.
Maintenance Analytics
Analyse work orders, failure patterns, planned vs unplanned maintenance, equipment history, and downtime relationships.
Supplier & Procurement Analytics
Track supplier performance, purchasing, delivery, lead time, quality, and material availability indicators.
Manufacturing Data Integration
Connect ERP, production, quality, maintenance, files, APIs, and databases into a trusted analytical layer.
Automated Reporting & Alerts
Reduce manual plant reporting and notify users when defined operational exceptions occur.
From plant systems to one trusted operating view
- ERP / Production
- Quality / Maintenance
- Transform & Reconcile
- Trusted Model
- Power BI / Alerts
The critical work is often the mapping layer: product codes, work centres, machines, shifts, sites, production orders, downtime reasons, quality codes, and time periods need to align before a KPI becomes trustworthy.
Production performance analytics
See output and throughput in the context of plan, time and capacity
- Production volume
- Plan vs actual
- Throughput
- Units per hour / shift where appropriate
- Work-centre / line performance
- Plant comparison
- Shift trends
- Product / SKU mix
- Backlog or production status where available
The exact metrics should follow the manufacturer’s operating model rather than a generic template.
Downtime and availability
Understand where productive time is being lost
- Planned vs unplanned downtime
- Downtime duration
- Downtime frequency
- Reason / category
- Machine / line / site comparison
- Time between recurring events
- Production impact
- Trend analysis
Datazeb can help standardise downtime reason codes and create reporting that makes recurring patterns easier to identify.
Quality and yield analytics
Connect output with quality, not just volume
- Yield
- Rejects
- Scrap
- Rework
- Defect categories
- Inspection results
- Product / batch quality trends
- Line / shift comparison
- Supplier-linked quality issues where data exists
Inventory and materials
Production visibility is incomplete without material availability
- Raw-material inventory
- Work-in-progress
- Finished goods
- Material consumption
- Shortages
- Stock ageing
- Movement history
- Warehouse / site comparison
- Production-linked inventory exceptions
Where planning data exists, reporting can also support stock cover, availability, and replenishment decisions.
Maintenance analytics
Bring maintenance history into the same operational conversation
- Work-order volume
- Open / closed maintenance
- Planned vs unplanned maintenance
- Equipment history
- Failure categories
- Repair duration
- Recurring faults
- Maintenance-linked downtime
- Site / asset comparison
The goal is not to replace specialist maintenance systems, but to make maintenance performance easier to connect with production outcomes.
Supplier and procurement analytics
- Purchase volume and value
- Supplier lead time
- Delivery reliability
- Material shortages
- Supplier-linked quality issues
- Price trends
- Order / receipt variance
- Supplier concentration
Definitions should be aligned with procurement and finance before supplier scorecards are used for management decisions.
Plant, line and site comparison
Create one KPI model across operations without losing local detail
- Common KPI definitions
- Plant / line comparison
- Shift-level views
- Central management dashboard
- Local drill-down
- Role-based access
- Standardised downtime / quality categories
- Shared reporting calendar
OEE and composite manufacturing KPIs
Use composite KPIs carefully
Metrics such as Overall Equipment Effectiveness (OEE) can be useful when availability, performance, and quality are captured consistently.
Datazeb can help structure the data and reporting needed to calculate composite measures, but the component definitions should be agreed with operations and engineering teams first.
Cost and management reporting
Connect operational performance with financial context where appropriate
- Production cost trends
- Material cost
- Labour or operating cost categories
- Scrap / rework cost where available
- Budget vs actual
- Plant / product cost views
- Margin or contribution measures where finance data supports them
Cost definitions should be validated with finance before they are used in operational performance reporting.
Automation opportunities
Reduce repetitive plant reporting and exception checks
- Scheduled production reports
- Daily shift summaries
- Downtime exception alerts
- Failed data-load notifications
- Quality exception workflows
- Inventory shortage alerts
- Maintenance status notifications
- Automated management pack preparation
AI opportunities
Apply AI around knowledge, documents and operational support
- Maintenance / SOP knowledge assistants
- Natural-language questions over approved operational data
- Quality or maintenance report summarisation
- Document classification
- AI-assisted exception summaries
- Internal troubleshooting knowledge search
AI should sit on top of trusted operational data and approved knowledge, not replace engineering judgement or safety-critical procedures.
Data quality and reconciliation
- Missing production orders
- Unmapped machines or work centres
- Inconsistent downtime reason codes
- Incorrect or missing timestamps
- Duplicate records
- Quality records without matching production context
- Inventory movements that do not reconcile
- Late source feeds
- Invalid product or site mappings
Automated validation can help surface these issues before they distort operational reporting.
Why Datazeb for manufacturing analytics
- Strong BI and data-engineering capability in one delivery model
- Experience with SQL, Python, APIs, files, databases, and Power BI
- Focus on reconciliation and master-data alignment before visualisation
- Ability to combine reporting, alerts, automation, and managed support
- Practical architecture matched to the real production environment
- Flexible project and retainer models
- Global remote delivery
Engagement options
- Manufacturing reporting assessment
- Production / plant dashboard project
- ERP / production data integration
- Downtime and quality analytics
- Inventory / material reporting
- Maintenance analytics
- Automated operational reporting
- Managed analytics support
How we work
- Understand
Clarify the manufacturing process, decisions, plants, products, users, reporting requirements, and current pain points. - Map
Document ERP, production, quality, maintenance, inventory, finance, and the identifiers that connect them. - Reconcile
Align products, machines, work centres, shifts, sites, timestamps, statuses, and KPI definitions. - Build
Develop integrations, trusted models, Power BI reporting, validation, alerts, and automation. - Validate & Improve
Reconcile results with operational stakeholders, document the model, and refine it as processes change.
FAQ
Can you connect our ERP with production and quality data?
Often, yes. The integration method depends on the APIs, databases, files, exports, and access available in each system.
Can you build production dashboards in Power BI?
Yes. Datazeb can build management and operational Power BI reporting where the required production data is available.
Can you report downtime and OEE?
Yes, where the necessary event, availability, performance, and quality data is captured consistently and the KPI definitions are agreed.
Can you support multiple plants or production lines?
Yes. Datazeb can create a standardised reporting model with consolidated management views and controlled plant- or line-level detail.
Can you integrate maintenance data?
Yes. Maintenance data can be connected to production reporting where system access and identifiers allow the relationship to be modelled reliably.
Do you provide ongoing support?
Yes. Managed analytics support can cover new reports, source changes, troubleshooting, data-quality issues, integrations, and ongoing enhancements.