GRANULAR DATA SIGNALS are the foundation of data-driven AI planning. In additional to sales data trends, daily, weekly, and monthly seasonality, our AI engine supports unlimited causal factors such as app-promotions, store promotions, media-ads, store closures, stock-out, temperature, precipitation, web activity, etc. at SKU-Location level or any higher level.
PROBABILISTIC FORECASTING & OPTIMIZATION allows us to capture the uncertainty in demand at granular SKU-Location-day level, and sift through millions of possible revenue-inventory trade-off combinations to arrive at an optimal solution. We use segmentation to drive business results at a segment level and avoid broad brush-planning.
cognitive analytics algorithms automatically extract knowledge from raw data, and clean and store it in a knowledge graph. Cognitive analytics uses this knowledge to serve deep insights on-demand without requiring any special analytical skills. AI algorithms also continuously monitor metrics in real-time and alert planners when abnormal events are detected.
Algoshelf is ZERO-FOOTPRINT SaaS. Our REST APIs allow for high frequency, high-speed data ingestion. Our platform allows massive parallelization of all algorithms and will enable plans for hundreds of thousands of Store-SKU combinations to complete in just a few minutes. Using the serverless paradigm, we reduce costs significantly by using compute only when needed.
The enterprise software has come a long way since Jason Fried wrote Why Enterprise Software Suck in 2007. It is 2020, and enterprise software continues to be inelegant and confusing to use. Enterprise software buyers have to focus on getting the Minimum Acceptable Feature (MAF) set in place, build more as use-cases emerge.
Legacy systems and approaches that expect lots of manual hand-holding and effort, and require specialized dedicated IT resources etc. tie down your planners and lower your agility to respond to market events. Adaptive planning is the right opposite, it is autonomous.