Softensity enhances capacity management for major pharmacy chain

A leading pharmacy chain sought to enhance its capacity management for AWS services. This enhancement was crucial for improving and integrating inventory management, optimizing logistics and supply chain management, and supporting machine learning (ML) forecasting. The pharmacy chain partnered with Softensity to achieve these goals.  

 The challenge  

 The primary challenge was to enhance capacity management for AWS services to eliminate compute bottlenecks and support better store-level allocations and ML forecasting. The pharmacy chain needed to rearchitect its data stores to achieve these improvements, enabling more effective inventory and supply chain management. 

The solution 

 Softensity’s expert team tackled this challenge by rearchitecting the data stores based on a partitioning strategy. This approach eliminated compute bottlenecks and improved system performance. The new architecture enabled the integration of advanced inventory management, optimized logistics, and better store-level allocations. It also supported machine learning forecasting, allowing the pharmacy chain to predict demand more accurately and allocate inventory more effectively.  

 Achievements  

 The successful rearchitecture and enhancement of capacity management led to significant measures of success for the pharmacy chain:

Demand Planning and Forecasting:

Before the implementation of AI, the pharmacy chain relied on traditional methods like weekly print advertising supplements in newspapers. As consumer behavior shifted towards digital channels, the chain turned to AI to enhance demand planning and forecasting. This shift allowed the chain to better serve an evolving customer base, with a focus on personalized and proactive engagement.  

Customer outreach and satisfaction:  

The integration of AI enabled the pharmacy chain to better predict customer demands, ensuring more effective inventory allocation. This shift not only improved customer satisfaction but also marked a successful transition from traditional print marketing to a more advanced, AI-driven approach.  

Data utilization:  

By leveraging voluminous sets of localized, granular-level data, the pharmacy chain was able to tune its demand around customers’ daily lives. This approach considered factors like local weather forecasts, social media trends, and local events, significantly impacting product demands at different store locations.  

Operational Efficiency:  

The use of AI tools allowed the pharmacy chain to simplify complex data sources into effective demand forecasts and inventory planning across its stores. This has been crucial in managing a vast network of 9,000 locations and handling a 300 million time series forecast.  

Role in Healthcare Services:  

The AI-powered tools reinforced the pharmacy chain’s position as a provider of customer solutions, such as vaccinations and other health services. This has been especially important during times of supply chain volatility, such as the shortage of baby formula, where AI tools helped in anticipating product demands.  

Recognition and Awards:  

The success of the pharmacy chain’s AI-powered demand planning initiative has been acknowledged in the retail sector, with the company’s leadership receiving recognition for its innovative approach.  

Overall, the pharmacy chain’s implementation of AI has been a pivotal factor in its transformation, enabling it to adapt to technological changes and enhance its service to millions of customers. The company’s journey with AI sets a benchmark for other retailers looking to harness the potential of AI in retail.