Yara, a leader in the fertilizer sector, faced significant challenges with its order allocation process. The company’s manual and decentralized approach, spread across multiple channels including spreadsheets, emails, and messaging apps, was creating inefficiencies. Without a centralized tool to manage dates, status updates, and order balances, the Customer Service team spent valuable time on manual tasks while lacking real-time visibility of allocated volumes against forecasts.
The challenge was multifaceted. Order allocation requests were being managed through various channels including Excel spreadsheets, emails, phone calls, and messaging apps. This fragmented approach led to time-consuming manual processes, increased risk of errors, and limited visibility into real-time data. The Customer Service team spent significant time on manual SAP interventions, while the lack of automated validation rules created operational bottlenecks.
To address these challenges, we developed AloC, an innovative platform designed to centralize and automate order allocation processes. Built using modern technologies including React for the frontend and C# .NET 8 for the backend, the system follows Clean Architecture principles to ensure maintainability and scalability.
The platform revolutionizes order management by automating validation rules and providing real-time forecast visualization. It offers tailored interfaces for different roles – from Customer Service representatives to managers – while maintaining seamless SAP integration. The system manages allocation windows for different business units and includes comprehensive security controls, enabling users to work autonomously within their defined permissions.
AloC has become more than just an order allocation tool—it’s a comprehensive solution that has transformed how Yara manages its distribution operations. By centralizing processes, automating validations, and providing real-time visibility, the platform has enabled more strategic use of resources while significantly improving operational efficiency.