If you are specialist retailer looking for an AI solution Island Pacific Forecast Manager can help
Key Features & Benefits
Provides simple user interface so users can easily determine:
- What to forecast
- What period / horizon
- Which history to use
Lean on ‘user thought process’. Island Pacific Forecast Manager is designed to be lean on user thought process. By feeding data into the system which can leverage established retail statistical methods and more modern data mining capabilities via analytics.
Enables accuracy of forecasting and comparisons. At the end of the process, we collate results from different models and forecasting approaches, calculating the divergence from actual results in ‘holdout period’ so that we use the most accurate forecast going forward.
Easily integrated. Forecast results can then be fed into all elements of retail operations, from allocation and replenishment to strategic planning and everything in between (i.e. assortments, line cards, WSSIs, KPI dashboard etc.)
How Do Retailers Use Island Pacific Forecast Manager Work?
Retail users can use the following step by step process within Island Pacific Forecast Manager
Step 1 Determine the Forecast Horizon Step one would be to determine the forecast horizon. i.e. the time period which you wish to review. It would also need to include a ‘Holdout period’ which essentially is a period that has already happened. The “Holdout period” will work as a comparison to test the forecast accuracy.
Step 2 Select & Run Forecast Model Training Analysis. This is where the user selects a model / algorithm based on the forecast type that is required, parameters can be tweaked based on the criteria they are reviewing. Users can look at historical data to find patterns, using standard models like the following
- Exponential smoothing
Users also have the option to input different parameters for each model
Step 3 Forecast Hold out period. Using the holdout period as a reference tool, users can then determine the different model’s historical accuracy.
Step 4 Compare Forecast Values with Actual Values. The aim here, is to compare forecast values with actual values to detect how different the models are and create error messages.
- Mean Absolute Error (MAE)
- Mean Absolute Percent Error (MAPE)
- Root Mean Squared Error (RMSE)
Step 5 Review Model & Select Lowest Error Model. Review and select the lowest error model.
Step 6 Use Selected Model & Apply to All Data. Use the selected model and apply to all data, including the holdout period to generate forecast for the horizon.
Step 7 Run ‘What if Scenario’s” Apply the “what-if” scenarios you wish to run with the data.
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