Manufacturers
Unify finance, supply chain, production and quality data without replacing the systems that run the business.
Turn scattered operational data into one decision system.
PACK BI unifies ERP, MES, CRM, databases and spreadsheets into governed metrics, interactive analytics, structured reports and controlled data collection — deployed in your own environment.
Data sources
ERPMESCRMExcel
Governed metric layer
- Standardized definitions
- Business rules
- Data access
- Traceable lineage
Finance
Sales
Inventory
Production
Quality
Built for the hard middle between ERP and decisions.
Multi-entity groups
Standardize KPI definitions and reporting across entities, business units and source systems.
ERP & solution partners
Extend existing ERP projects with governed analytics, complex reporting and controlled data collection.
One platform. Five connected jobs.
Move from source data to governed decisions without stitching together a separate warehouse tool, dashboard tool, reporting engine and spreadsheet workflow.
PackEDW
Governed metrics & dimensional models
Configure dimensional models, reusable measures, refresh logic and lineage for a consistent analytical foundation.
PackBI
Interactive analytics across screens
Build and publish interactive analysis for web, mobile and large-screen operating views.
P-Report
Complex operational reporting
Design structured, cross-tab and free-form reports when management needs detail as well as visualization.
P-Form
Controlled data collection
Bring spreadsheet-like manual inputs into governed forms, formulas, permissions and approval flows.
Data Scheduling
Repeatable ETL operations
Organize refresh strategies, incremental extraction, job schedules and execution logs in one place.
Start with the decision flow that matters most.
PACK BI is strongest where operational questions cross systems, teams and reporting formats.
Manufacturing operations
Finance · Supply chain · Production · Quality
Connect financial results to orders, inventory, production progress and quality signals across plants and business systems.
Discuss this use caseGroup performance
Multi-entity reporting · Governed KPIs · Drill-through
Create shared definitions across entities while preserving the ability to trace a group result back to its business origin.
Discuss this use caseSupply chain control
Procurement · Inventory · Sales · Fulfillment
Follow materials and orders across purchasing, stock, sales and delivery instead of reconciling disconnected extracts.
Discuss this use caseYour data stays on your terms.
PACK BI is designed for organizations that need analytical control without moving core operational data into an unfamiliar public environment.
Private deployment
Run PACK BI in your own controlled environment.
Broad data connectivity
Connect Oracle, MySQL, SQL Server, PostgreSQL, Hive, Vertica, ClickHouse, files and APIs.
Granular access control
Manage access by role, module, report, page, field, row and column.
Source systems
OracleMySQLSQL ServerPostgreSQLHiveVerticaClickHouseFiles & APIs
PACK BIPACK BI · your environment
Data access layerDimensional modelsGoverned metricsReports & analysis
Business users
ExecutivesManagersAnalystsOperational users
From first question to governed insight.
A practical engagement starts with a real decision or reporting bottleneck, then works backward to the data and operating model it requires.
Connect
Map the systems, files, owners and refresh windows behind the decision.
Model
Define reusable dimensions, measures, rules, permissions and lineage.
Build
Deliver dashboards, operational reports and data-collection workflows.
Enable
Publish to the right roles and transfer the knowledge to maintain it.
Project cases grounded in delivery material.
Three anonymized manufacturing projects show how ERP, production, quality, spreadsheet, and reporting data were organized into governed decision flows.
U8 · MES · OA · CRM · controlled inputs
Multi-plant metal products: one operating view across U8, MES, OA, and CRM
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.
- Production receipts, output-versus-sales, and scheduling evidence
- Raw-material inventory, turnover, stock limits, and shortage signals
U9C · production and quality data · controlled forms
Lithium battery manufacturing: finance, supply chain, production, and quality in one model
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.
- Incoming inspection batches, defects, defect rates, and reason analysis
- Reusable finance, supply-chain, production, and manual-input metrics
NC65 · controlled forms · sales collaboration data
Paper products: connect order-to-cash with inventory availability and age
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.
- Sales execution and customer receivables analysis
- Current stock, warehouse mix, material detail, and inventory age
Organizations across our delivery ecosystem.
Selected customer and partner marks from existing corporate materials, spanning manufacturing, energy, infrastructure, healthcare and group enterprises.
Logos are displayed for organizational identification; trademarks remain the property of their respective owners.
Client voices, kept in their original form.
Two appreciation letters from completed BI engagements. Open either document to inspect the original Chinese wording instead of relying on rewritten performance claims.
Credentials behind the work.
Software rights, partner authorizations, management certification and industry memberships supplied in the company knowledge materials.
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Scroll horizontally to review all credentials.
Practical guide
How should manufacturers unify ERP, MES, and spreadsheet metrics?
Start with shared business keys, a consistent data grain, and one governed definition for every operational KPI.
Bring us one hard reporting problem.
Tell us what is fragmented today. We’ll map the data, decision and deployment path for your team.
Questions before a first conversation
Straight answers based on the current PACK BI product knowledge base.
01Can PACK BI run in our private environment?
Yes. Current product materials document private deployment for single organizations and multi-entity groups. The final architecture and capacity plan should be assessed against your data volume, concurrency, database and availability requirements.
02What data sources can PACK BI connect to?
Documented sources include Oracle, MySQL, SQL Server, PostgreSQL, Hive, Vertica, ClickHouse, text files and third-party APIs. Existing solution materials also cover integration patterns around ERP, MES, OA and manually collected data.
03Can we keep our existing ERP and reports?
Usually, yes. PACK BI is positioned as an analytical layer around existing operational systems. It can centralize models and KPI logic while supporting interactive dashboards, structured reports and controlled data collection. Fit still depends on the specific ERP version and report design.
04Who is PACK BI best suited for?
The strongest fit is a manufacturer, multi-entity group or ERP/solution partner facing cross-system reporting, inconsistent KPI definitions, complex operational reports or spreadsheet-based data collection — especially when private deployment matters.