A leading supermarket chain sought to launch a new brand of stores, necessitating a revamped store layout and advanced automation of systems to reduce operational costs and enhance efficiency in purchase and replenishment planning. This ambitious project required updating legacy systems with new demand forecasting capabilities and integrating various retail inventory systems. For this mission, the supermarket chain partnered with Softensity. This collaboration marked a significant milestone, as Softensity’s expert team provided bespoke solutions tailored to the supermarket’s complex requirements. The synergy between the supermarket chain and Softensity resulted in innovative solutions that streamlined operations and improved overall efficiency.
The Challenge
The primary challenge was to update and integrate the supermarket’s legacy store management systems (IRMA), Infor ION POS, and LogicBlox IDE with modern technologies such as Angular and AWS compute and storage services. The goal was to implement advanced automation to reduce operational costs and improve efficiency in purchase and replenishment planning. Additionally, the supermarket needed new demand forecasting capabilities to enhance its inventory management.
The Solution
Softensity’s team of developers, specializing in custom software development, tackled this challenge head-on by developing a new Forecast and Replenishment application. This application was integrated with the client’s legacy systems, POS, and inventory management systems. The integration leveraged machine learning for demand forecasting and automated optimization for replenishment plans, leading to efficient vendor purchase planning. The solution utilized ML ensembles, including multiple regression analyses, optimization algorithms for order quantity and safety stock, and advanced ML methods such as Fast Fourier Transform for items with sparse sales. MAPE and other error checking methods were coupled with data visualization for learning and operational analyses. The integration with point of sales systems enabled purchase analysis and aggregate demand forecasting (multi-echelon). Real-time queue analysis was used to understand and smooth data arrivals on purchasing systems. Furthermore, Softensity implemented Infor Birst for data visualization and operational reporting, providing the supermarket chain with actionable insights and improved decision-making capabilities.
Achievements
The successful integration and modernization of the supermarket’s systems led to remarkable results: – Enhanced store performance with a 20% improvement in in-stock metrics. – Achieved a 30% reduction in back store inventory levels. – Labor costs were reduced by five times due to automation in purchase efficiency and replenishment planning. – Overall operational costs were considerably lowered, enabling improved margins. – The implementation of Infor Birst for data visualization and operational reporting.
Conclusion
This case study exemplifies the power of collaboration and innovation in retail management. Together, Softensity and the supermarket chain successfully harnessed advanced technologies to create a streamlined, efficient system that not only meets current demands but also sets the stage for future growth. The partnership highlights the importance of combining technical expertise with a deep understanding of industry-specific challenges to deliver impactful solutions.