Case Study 6: Automated Pricing Orchestration for a Leading Retail Chain
Challenge:
A leading retail chain has demanded an automated pricing orchestration engine to dynamically process complex pricing rules for multiple product categories. The existing system required all of the changes associated with price adjustments to be done manually, leading to delays, errors, and inefficiencies in price adjustments. The company needed a real-time, scalable solution to automate pricing updates based on market conditions, demand fluctuations, and promotional strategies.
Solution:
OKRUTI developed an automated pricing orchestration engine with real-time computation model:
– Java & Spring BOOT for robust scalable microservices architecture.
– Redis for caching; instant price data retrieval for fast computation.
– Kafka for event-driven processing-real time updates across multiple retail locations.
– Rule based automation-dynamic price adjustment by crossing logical business rules.
The new system ensured seamless execution in price updates without any manual intervention and in turn better accuracy of the prices.
Results achieved:
✔ Automatic price adjustments to market conditions.
✔ Improved price accuracy & transparency.
✔ Reduced manual intervention in pricing processes.
✔ Enhanced scalability to dynamic pricing of thousands of SKUs.
With the real-time computation and event-driven processing, OKRUTI ‘s solution enabled the retail chain to realize greater efficiency in pricing, faster response to the market, and optimised revenue strategies.
Feedback from a customer:
“According to OKRUTI, the pricing engine has brought efficiency and accuracy into the retail operations by allowing them to optimize pricing on a live basis.”