Demand Forecasting
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Key Benefits
- Improve forecast quality
- Reduce stock-outs with providing proper safety stock amount
- Increase forecast accuracy due to the strong structure.
Key Features
- Exponential Triple Smoothing (ETS) models
- Trend and seasonality analyzes
- Engine minimizing the forecast variance
- Data analyze and outlier identification
Functional Highlights
Most of the industrial tools rely on the residual analyze and prediction of the variance between historical data and of the applied model for same period. However, the forecast of such approach may lead to a great variance and require to hold very high amount of stock.
Arkhon's algorithm considers minimizing future variance as the primarygoal. With this direction, it works on models to specify the best trend and seasonality parameters. After calculating the forecast, it also provides the safety stock amount to cover the unexpected deviations.
In addition, the historical sales data can be analyzed by the application and outliers may be trimmed. For the products with short life cycle, the forecast can be generated for groups and delisted goods can transfer their history to new-born ones. The campaings can be highlighted in the sales data and be used as leverage for the forecast.
