Details
Expert Selection
Expert selection allows the Forecasting Module to select an appropriate univariate forecasting technique automatically. Expert selection operates as follows. If the data set is very short, Forecasting Module defaults to simple moving average. Otherwise Forecasting Module examines the data for the applicability of the intermittent or discrete forecast models. Although the forecasts produced from such models are just straight horizontal lines, they often provide forecasts superior to those from exponential smoothing for low- volume, messy data. If neither of these models are applicable to the data, the choice is now narrowed down to different forms of exponential smoothing and Box-Jenkins models. Forecasting Module next runs a series of tests on the data and applies a rule-based logic that may lead to a model selection based on data characteristics. If the rule-based logic does not lead to a definitive answer, Forecasting Module performs an out- of-sample test to choose between an exponential smoothing model and a Box-Jenkins model.
Simple Methods
Simple moving average is the simplest of all forecast methods, it averages recent sales and can work well for items that are in consistent demand, however it doesn’t pick up trends or seasonality from the data.
If the data set is very short or has fewer than 10 points the Forecasting Module defaults to simple moving average.
Exponential Smoothing
Exponential smoothing works as its name suggests. It extracts the level, trend and seasonal indexes by constructing smoothed estimates of these features, weighting recent data more heavily. It adapts to changing structure but minimizes the effects of outliers and noise.
The ‘wait-and-see’ attitude to changes around them is the intuitive way people employ exponential smoothing in their daily living.
Keep in mind that although exponential smoothing can take the following factors into consideration when projecting a forecast; the trend, level, seasonal effects, event effects, random events and noise. They do not and cannot include the effects of future random events or noise, so the forecast is much smoother than the actual future will turn out to be.
The method is a family of twelve exponential smoothing models; see the full Demand Forecaster User Manual for further details.
Discrete Distribution
These models apply to data consisting of small whole numbers, including some zeroes. The forecasts are non-trended and non-seasonal. Discrete distributions are for use on data that might consist entirely of zeroes and small integers. Infrequently used spare parts is an example of items that often fall into this class.
Although the forecasts produced are just straight horizontal lines, they often provide forecasts superior to those from exponential smoothing for low-volume, messy data.
Croston’s Intermittent Demand Model
The Croston’s model is designed for data with numerous zeroes, like orders for a slow- moving part that is usually ordered to replenish stock. The non-zero data points are normally or log-normally distributed. The forecasts are non-trended and non-seasonal.
The time series consists of much sales data, especially for lower volume items with irregular demand. For many periods there is no demand at all. This might be the case for items that are usually ordered in batches to replenish downstream inventories.
The forecasts produced will show straight horizontal lines.
Curve Fitting
Curve fitting identifies the general form of the curve that the data is following and is used to model the global trend of the historic sales data. Curve fitting is quite useful for short time series data, where the suggested minimum length is 10 data points. The curve fitting supports four types of curves; a straight line, quadratic, exponential, and growth. Keep in mind that the curve does not accommodate seasonal patterns.
Box-Jenkins
Box-Jenkins works well for stable data sets and can capture and forecast both trend and seasonality. The data must consist of a minimum of 40 data points. The method is, quite simply, the richest family of statistical models that can be practically applied in the real world. Ideally, a forecaster would switch between Box-Jenkins and exponential smoothing models, depending on the properties of the data, which is precisely what the Forecasting Module automatic selection is designed to do.
Safety Stocks
Safety stock is kept to meet volatile demand during lead time (the time between placing an order and placing the units in stock), the Forecasting Module calculates the appropriate safety stock quantity based on the defined service level of a given product. Inventory control analysis requires the manager to balance the trade-off between inventory holding cost and the cost of a lost sale. Keep in mind that items with steady demand will have lower levels of safety stock than items with volatile demand.
The trade-off between holding cost and stock out cost can be seen in the following image.
Weekly and Daily Distribution
The Forecast Module uses monthly sales data to generate the initial forecast. The monthly forecast values are then distributed down to weekly and daily levels. The module assigns each week a specific sales amount based on the number of days the week has within the month. Then the weighted proportional sale for each weekday within each week is calculated, based on the last 30 weeks. This is for a general week, for a predefined number of weeks, the module calculates a special day distribution based on the historical distribution for that distinctive week such as (Easter week, week before Christmas, week before New Year’s etc.).
Keep in mind the following two rules:
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If there are less than five data points, then the Forecasting Module switches to a simple moving average model.
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If there is less than two years’ worth of data, then the Forecasting Module does not consider seasonal models.
For more information about Demand Forecasts, Safety Stock Calculation, and/or Order Calculation, see Detailed Order Logic Information.