Manufacturing BI Project Cases: ERP, MES, and KPI Governance
These anonymized project notes show how manufacturers moved from separate ERP accounts, production systems, and recurring spreadsheets toward governed metrics for finance, inventory, production, quality, and delivery risk.
What makes a manufacturing BI case credible?
Direct answer: A credible case names the operational problem, source systems, shared business dimensions, KPI rules, delivered outputs, and evidence boundary. It separates implementation facts from customer endorsements and does not turn demonstration data or unverified outcome figures into claims.
The cases are summarized from PackData project and solution materials in the audited internal knowledge base. Customer identities and unverified outcome figures are intentionally omitted; these are implementation notes, not customer testimonials.
Multi-plant metal products: one operating view across U8, MES, OA, and CRM
Situation
Three independently accounted plants used separate U8 account sets, while historical and current data, OA, CRM, and shop-floor information sat in different places. Managers relied on repeated spreadsheet collection for cross-plant decisions.
Implementation approach
The project scope organized plant, customer, material, order, warehouse, and time dimensions across sources, then reused governed definitions for finance, sales, procurement, inventory, production, scheduling, and risk alerts.
Delivered analysis
- Production receipts, output-versus-sales, and scheduling evidence
- Raw-material inventory, turnover, stock limits, and shortage signals
- Receivables, overdue contracts, collections, and other operating alerts
Lithium battery manufacturing: finance, supply chain, production, and quality in one model
Situation
Operational history accumulated in U9C, but key management data still had to be collected from multiple places. Functional teams repeatedly prepared Excel files, and market or shop-floor information was not reported consistently enough for management review.
Implementation approach
The project architecture extracted finance, supply-chain, production, quality, and controlled manual data into a standard warehouse, built shared multidimensional models, applied role-based access, and published PC, mobile, and structured report outputs.
Delivered analysis
- Incoming inspection batches, defects, defect rates, and reason analysis
- Reusable finance, supply-chain, production, and manual-input metrics
- A governed path from source extraction to model, screen, and report
Paper products: connect order-to-cash with inventory availability and age
Situation
Sales, receivables, and inventory questions crossed company, customer, salesperson, warehouse, and material dimensions. The team needed detailed operational reporting as well as management analysis, without putting analytical load or write-back risk on the ERP.
Implementation approach
The project modeled the order-to-cash chain from order, shipment, outbound, and invoice through receivable and collection, then connected it to current stock quantity and inventory-age analysis using shared business dimensions.
Delivered analysis
- Sales execution and customer receivables analysis
- Current stock, warehouse mix, material detail, and inventory age
- Consistent PC and mobile views backed by the same model
The KPI contract behind all three projects
The technology stack changes by plant, but the governance questions remain stable. Before a metric is published, the project team should be able to answer each item below.
Frequently asked questions
01Are these named customer testimonials?
No. They are anonymized implementation summaries based on internal project and solution materials. Customer names and unverified outcome claims are omitted, so the page documents problem patterns, source systems, modeling work, and delivered analysis rather than presenting a customer endorsement.
02What was common across the three manufacturing BI projects?
Each project started with operational questions that crossed systems and reporting formats. The common work was to map stable business keys, set a consistent data grain, define KPI ownership and formulas, preserve lineage, control manual inputs, and publish dashboards or reports from the same governed model.
03Can this project pattern work for overseas factories?
Yes. The same pattern can connect headquarters and overseas factories while preserving local systems. The overseas design also needs explicit rules for group codes, local and reporting currencies, language labels, time zones, units, network constraints, access boundaries, and data-location requirements.
04How should a manufacturer choose its first BI project scope?
Choose one recurring decision that currently requires manual reconciliation, such as delivery risk, inventory availability, production attainment, quality loss, or factory profit. Define the users, action, source records, KPI contract, refresh deadline, and acceptance test before expanding to more plants or topics.
Evidence boundary and related guidance
The cases are summarized from PackData project and solution materials in the audited internal knowledge base. Customer identities and unverified outcome figures are intentionally omitted; these are implementation notes, not customer testimonials.
Start with one decision chain, not a wall of dashboards.
We will map the source records, shared dimensions, KPI contract, report outputs, permissions, refresh rules, and acceptance checks for a practical first scope.
Discuss your first project scope