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Benjamin Ohene-Adu edited this page Feb 19, 2026 · 5 revisions

1. Executive Summary

This business case proposes transitioning Telleion from legacy P9 software to a modern, scalable Product Management Solution. The change is driven by operational growth, system limitations, performance bottlenecks, and the need for improved data-driven decision-making. The proposed solution will enable operational scalability, improve data visibility, and support the development of a modern data platform that delivers timely, intelligent business insights.

2. Background

The P9 system has supported core product and inventory operations for several years. However, as business operations have expanded in scale, complexity, and data volume, the system has become increasingly limited, constraining growth, efficiency, and strategic decision-making. A modern product management solution is required to support continued expansion, operational efficiency, and advanced analytics capability.

3. Business Drivers for Change

3.1 Expansion of Business Operations

The business has experienced significant growth in product lines, transaction volume, and operational complexity. The current system was not designed to support this scale, resulting in reduced agility and increased operational friction.

3.2 SKU Limitation and Reuse

The Pennine system imposes a restriction on the number of SKUs that can be created. This has forced the business to reuse SKUs over time, creating:

  • Data integrity risks
  • Historical reporting inaccuracies
  • Product traceability challenges
  • Increased operational confusion

A modern solution will support unlimited or significantly higher SKU capacity, ensuring clean product lifecycle management.

3.3 Slow Processing and Data Availability

System updates currently require overnight processing before becoming available for reporting and operational use. This delay:

  • Prevents timely decision-making
  • Reduces responsiveness to market and operational changes
  • Limits real-time operational visibility

A modern platform will provide near-real-time data to support faster, more informed decisions.

3.4 Data Volume and System Performance Constraints

Due to increasing data volume, the P9 system periodically requires inventory data purging to maintain usability and performance. This results in:

  • Loss of historical data
  • Reduced analytical capability
  • Operational disruption
  • Increased maintenance effort

It is expected that the new solution will support scalable data storage and processing without requiring data purging.

4. Strategic Benefits of the New Solution

4.1 Improved Operational Visibility

The modern Product Management Solution will provide enhanced visibility across:

  • Inventory levels
  • Product lifecycle
  • Stock movement
  • Supplier performance
  • Operational trends

This will enable proactive management rather than reactive intervention.

4.2 Foundation for a Modern Data Platform

The new system will enable the development of a robust data platform to support:

  • Smart, data-driven decision-making
  • Timely and automated reporting
  • Advanced analytics and forecasting
  • Integrated operational intelligence
  • Future AI and optimisation capabilities

This positions the business for long-term digital maturity.

4.3 Scalability and Future Readiness

The new solution will:

  • Support continued business growth
  • Remove system-imposed operational limits
  • Improve performance and reliability
  • Enable integration with modern tools and platforms

5. Risks of Not Changing

If the current system is retained:

  • SKU reuse will continue to compromise data integrity
  • Reporting delays will hinder decision-making
  • Data purging will reduce historical insight
  • Operational inefficiencies will increase
  • System constraints will limit future growth

6. Conclusion

The migration from P9 to a modern product management solution is a strategic necessity driven by operational expansion, system limitations, and the need for improved data visibility and analytics capability. The proposed change will improve scalability, operational efficiency, and decision-making, while laying the foundation for a future-ready data platform.

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