Stock Demand Trends and Forecast
The tool to calculate stock demand trends and make prediction for future demand statistically
Buying now you save 10.0% of the standard price. The offer is valid till October, 15
The tool is compatible with both Odoo Enterprise and Odoo Community
The tool price includes all necessary dependencies
If you knew stock demand trends per warehouses, you would have a clue to decrease keeping costs and to have a flawless supply chain. Regretfully, you can't know the future. However, you can predict it with a certain reliability. This is the tool for that goal. The app let you construct stock demand per periods and forecast further demand.
It is the simplest but still widely used statistical method for time series forecast. Using the method you consider stock demand trends being linear without seasonal effects, without a purely defined trend, and without smoothing abnormal observation.
Moving Average (MA) and Autoregressive Moving Average (ARMA)
The moving average method takes into account 'errors' in previous observations, and in comparison to the AR method smooths abnormal data.
The autoregressive moving average method is a combination of both AR and MA methods. To apply the ARMA method use the MA method with auto regression coefficient (P coefficient) as 2
Autoregressive Integrated Moving Average (ARIMA)
The method which also combines the methods AR and MA, but also tries to make data stationary. It is appropriate to use for historical data with pure trend but without seasonal changes.
Seasonal Autoregressive Integrated Moving-Average (SARIMA)
The SARIMA method enriches the ARIMA method with considering seasonal changes. It is one of the most complex and wide spread methods utilized for forecasting time series now
Simple Exponential Smoothing (SES)
The SES model usage is similar to the AR method, but instead of relying upon linear function, it exploits exponential one
Holt Winter’s Exponential Smoothing (HWES)
The HWES method enriches the SES method to work with time series trends and seasonal effects.
When this tool should be used
- You have enough historical stock demand data (per location or company), since it is senseless to make forecast based on last 5 days of operations
- Stock demand is regular and is not chaotic, meaning that your decisions do not have 100% impact and there is at least some correlation between market demand and your WMS operations
- You have some seasonal and from period to period trends, which you noticed but can't fully analyse
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To guarantee tool correct work you would need a number of Python libraries: pandas, numpy, statsmodels, scipy, xlsxwriter. To install those packages execute the command:
pip install pandas numpy statsmodels scipy xlsxwriter