Proactive Out-of-StockProactive Out-of-Stock
The Challenge
The FMCG industry loses enormous revenue to stockouts because traditional inventory management is reactive and based on historical averages that miss seasonality, promotions, and demand surges. Out-of-stock events can drive double-digit sales declines for affected categories, and siloed data blocks coordinated response.
The Solution
Coditas built a proactive monitoring system over data sampled from 1M+ stores globally. Simulated Annealing optimized visit planning for 15,000+ field auditors. An ensemble of regression models, including Multiple Linear, Decision Tree, Random Forest, and Support Vector, forecasted demand using internal and external signals, generating real-time out-of-stock alerts based on inventory thresholds.
The Outcome
Stores and brands could pre-empt stockouts instead of reacting after the impact. The solution improved product availability, lifted sales, optimized inventory levels, and created richer insight into market trends and consumer behavior.
Engineered With: Python · R · Java Microservice · Ensemble ML · MLR · DTR · RFR · SVR · SQL and NoSQL
At our scale, the challenge was not collecting more inventory data. It was knowing where intervention would have the greatest commercial impact. Coditas gave us a more intelligent way to prioritize field activity, anticipate availability gaps, and direct attention to the stores and products that needed it most.
Build an AI Strategy Your Teams Can Act On
Ashutosh Zatke
Director - Growth Strategy
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