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Building a New Paradigm of Financial Intelligence in Wind Power Manufacturing

Shanghai Electric Wind Power Group's SAP Deep Integration RPA Solution

S t r a t e g y

The Work

Production

Design. Build. Experience.
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As a global leader in wind power equipment manufacturing, the company has implemented RPA systems in core production scenarios, achieving automated processing of over 50,000 cost data entries monthly. Through deep integration with SAP ERP, it has established three capability matrices - intelligent cost allocation, precision month-end closing, and variance analysis - enhancing financial processing efficiency by 85%.

Business Pain Points

Manual allocation of over 200 monthly material variances exceeds 40 labor hours.

Monthly cost closing involves repetitive execution across 15 SAP transaction codes.

Cross-border project cost revaluation experiences an 18% error rate.

Business Highlights

Intelligent Allocation Accuracy Rate Achieves 99.6%

100% Automation Coverage Rate for Month-end Closing Process

Real-time Variance Alert for Work-in-Progress (WIP).

Solution

Building a Digital Hub for Wind Turbine Manufacturing:

  • Achieve deep integration of SAP-MM/FICO modules
  • Intelligent Allocation Engine embedded with wind energy-specific BOM (Bill of Materials) rules
  • Three-tier Early Warning System for anomaly discrepancies.

Application Scenarios

Intelligent Cost Control Hub

Delivering:

  • Automated variance calculation between semi-finished and finished goods;
  • Intelligent matching of multi-dimensional allocation rules;
  • Automatic exchange rate revaluation for cross-border projects.

Achieving a 75% efficiency gain by compressing processing time from 4 hours to 20 minutes per allocation cycle.

Month-End Closing Smart Factory

Delivers:

  • Automated execution of 15 SAP transaction codes
  • Real-time monitoring of work-in-process (WIP) costs
  • Automated generation of variance analysis reports

Achieving a 70% reduction in the month-end closing cycle.

Wind Power Cost Intelligence Platform

Construction:

  • Developed 100,000+ blade BOM cost models
  • Implemented full lifecycle cost tracking for projects
  • Established supply chain cost fluctuation alerts

Achieved a 40% improvement in cost prediction accuracy

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