Background
The pharmacy retailer had accumulated large volumes of sales, inventory, pricing, promotion, and product data across its location network. These datasets contained useful demand and commercial signals, but they were analyzed through separate tools and manual reporting routines. Inventory teams tracked stock levels and replenishment needs, while commercial teams reviewed sales performance, pricing effects, and promotion outcomes. Because these workflows were disconnected, teams reacted to demand shifts later than the business required.
The retailer faced two connected problems. Some products remained overstocked long enough to create write-off risk, while others went out of stock before replenishment decisions caught up with demand. Commercial teams could see performance changes, but identifying the specific drivers behind those changes required significant manual effort.
Leadership needed a system that could connect demand forecasting with inventory risk, sales-performance analysis, and commercial action planning.
Approach to solution
Computools started by mapping how demand, stock, pricing, promotions, and sales performance were managed across pharmacy locations. The team defined the data sources required for product- and location-level forecasting: historical sales, product movement, stock levels, expiration exposure, pricing changes, promotion calendars, and seasonal patterns.
The first delivery focus was a unified data foundation. Inventory, sales, pricing, promotion, and product data had to use consistent definitions for product, location, time period, and category before forecasting and commercial recommendations could work reliably.
The next layer introduced demand forecasting and inventory-risk detection. Forecasts identified likely product demand by location, while inventory logic highlighted overstock, shortage, and expiration-risk scenarios.
Computools then added commercial intelligence workflows. Teams could investigate changes in demand or sales through natural-language queries and receive explanations based on connected business signals.
Computools role
Computools acted as the product and technology partner responsible for:
- analyzing pharmacy retail demand, inventory, and commercial workflows;
- mapping sales, inventory, pricing, promotion, product, and location data;
- defining the forecasting model, KPI framework, and phased delivery roadmap;
- designing dashboards, natural-language query flows, and recommendation views;
- building the data integration and normalization layer;
- developing demand forecasting, inventory-risk, and commercial-driver analysis logic;
- implementing decision-support workflows for pricing, promotion, assortment, and inventory actions;
- supporting QA, rollout validation, data-quality monitoring, and post-launch optimization.
The team worked with stakeholders responsible for pharmacy operations, supply chain, category management, pricing, marketing, analytics, finance, and executive reporting.
Key decisions and outcomes
The first key decision was to connect inventory and commercial data before expanding analytical functionality. This gave teams a single view of product movement, stock risk, pricing changes, promotional activity, and demand patterns.
The second decision was to generate explanations alongside forecasts. Commercial teams needed to understand the drivers of changes in demand before approving actions related to inventory, pricing, promotion, or assortment.
The third decision was to connect insights with recommended actions. Pharm Balance evaluated commercial options and showed expected business impact, helping teams prioritize changes with measurable ROI.
The completed platform moved the retailer from fragmented, manual analysis to continuous support for demand, inventory, and commercial decision-making.
