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Optimizing Supply Chain Analytics with SAP Datasphere

Client Overview

A major Retailer & Digital Production firm sought to enhance its Supply Chain Analytics by integrating SAP and non-SAP systems. The key objectives of this initiative were:

  • Optimizing Supply Chain Processes: The company aimed to streamline complex analytics and improve decision-making by leveraging SAP Datasphere.
  • Overcoming Manual Bottlenecks: The firm’s operations were largely manual, leading to inefficiencies in tracking inbound and outbound material.
  • Developing Comprehensive Reports: The initiative required detailed analytics on inbound and outbound material tracking, outstanding equipment status, and cost analysis for equipment replacement.

Landscape

To achieve these goals, the company implemented a centralized data architecture that integrated various systems:

  • SAP S/4HANA and Non-SAP Data Sources (Oracle, SQL Server, and Flat Files) were connected to SAP Datasphere via SAP BTP (Business Technology Platform).
  • A Live Connection was established between SAP Datasphere and SAP Analytics Cloud (SAC), enabling real-time reporting and visualization.

Solution

The team implemented several strategic improvements:

  1. Shell Conversion for Migration: Since data migration was not part of the strategy, Metadata Collection & Migration was fully controlled, allowing model optimization during migration.
  2. BOBJ to SAC Migration: BusinessObjects (BOBJ) reports were migrated to SAP Analytics Cloud (SAC) for advanced analytical insights.
  3. BW Bridge Utilization: Existing BW objects were reused in SAP Datasphere, reducing rework and accelerating the migration process.
  4. Cross-functional Expertise: The project was driven by a team comprising SAP Datasphere experts, SAC specialists, and customer IT teams.
  5. Optimized Migration Timeline: The overall migration was successfully completed within 18 weeks.

Challenges

During the implementation, several challenges were encountered:

  • Custom Code Compatibility: Certain custom codes were not compatible with the cloud-based solution, requiring modifications.
  • BEx Query Limitations: BEx queries were not supported in the new system, necessitating alternative solutions.
  • OLAP Engine Restrictions: The lack of OLAP engine support affected functionalities like analysis authorization, requiring process adjustments.

Key Reports & KPIs Generated

As part of the optimization, various reports were developed to enhance operational efficiency:

1. Inbound Material Tracking
  • Helps warehouse users track product returns.
  • KPIs: Return Quantity, Return Rate, Defect %, Lead Time, and Time Interval SLA.
2. Outbound Material Overview
  • Provides insights into shipping efficiency, accuracy, and delivery timelines.
  • KPIs: Shipping Delay Ratio, Inventory Turnover, Fill Rate, and Time Interval SLA.
3. Outstanding Equipment Status
  • Tracks equipment that is yet to be received or shipped.
  • KPIs: Shipping Delay Interval, Bin Time Ratio, Out of Stock, and Average Turnaround Time.
4. Replacement Cost Analysis
  • Assists warehouse teams in making repair vs. replacement
  • KPIs: Replacement Cost, % of Items Replaced, % of Repair, Pilferage %, and Defect %.

Impact & Business Benefits

By implementing SAP Datasphere, the retailer achieved:

  • Centralized Data Integration: Harmonized data across SAP and non-SAP sources.
  • Informed Cost Decisions: Equipment costs were extracted from SAP, improving decision-making on repairs vs. replacements.
  • 35% Faster Equipment Movements: Automated processes led to a significant acceleration in inbound and outbound material handling.
  • Increased Operational Efficiency: Manual decision-making was reduced through automation.
  • Strategic Dashboards for Decision-Making: Bird’s-eye view dashboards allowed management to track key metrics for better decision-making.

Conclusion

By leveraging SAP Datasphere and SAP Analytics Cloud, the retailer successfully optimized its Supply Chain Analytics, significantly reducing inefficiencies, improving reporting accuracy, and driving data-driven decision-making. This transformation resulted in a 35% improvement in equipment movement efficiency and better cost management through predictive analytics.