Demand forecasting

Demand forecasting, also known as demand planning and sales forecasting (DP&SF), involves the prediction of the quantity of goods and services that will be demanded by consumers or business customers at a future point in time, conditional on a specified forecast horizon and information set. More specifically, the methods of demand forecasting entail using predictive analytics to estimate customer demand in consideration of key economic conditions. This is an important tool in optimizing business profitability through efficient supply chain management. Demand forecasting methods are divided into two major categories, qualitative and quantitative methods: *Qualitative methods are based on expert opinion and information gathered from the field. This method is mostly used in situations when there is minimal data available for analysis, such as when a business or product has recently been introduced to the market. *Quantitative methods use available data and analytical tools in order to produce predictions. Demand forecasting may be used in resource allocation, inventory management, assessing future capacity requirements, or making decisions on whether to enter a new market. Customer demand planning (CDP) is a business planning process that allows sales teams to develop demand forecasts.

Demand forecasting, also known as demand planning and sales forecasting (DP&SF), involves the prediction of the quantity of goods and services that will be demanded by consumers or business customers at a future point in time, conditional on a specified forecast horizon and information set. More specifically, the methods of demand forecasting entail using predictive analytics to estimate customer demand in consideration of key economic conditions. This is an important tool in optimizing business profitability through efficient supply chain management. Demand forecasting methods are divided into two major categories, qualitative and quantitative methods: Qualitative methods are based on expert opinion and information gathered from the field. This method is mostly used in situations when there is minimal data available for analysis, such as when a business or product has recently been introduced to the market. Quantitative methods use available data and analytical tools in order to produce predictions. Demand forecasting may be used in resource allocation, inventory management, assessing future capacity requirements, or making decisions on whether to enter a new market. Customer demand planning (CDP) is a business planning process that allows sales teams to develop demand forecasts. Y_i=\beta_0+\beta_1X_i+e Where Y_i is the dependent variable, \beta_0 is the intercept, \beta_1 is the slope coefficient, X_i is the independent variable and is the error term. M. Roodman's demand forecasting model is based on linear regression and is described as: \lambda_q=\{t \mid t=1,\dots,n \text{ and } t \bmod Q = q\} \qquad q=1,\dots,Q \lambda_q is defined as the set of all  - indices for quarter . The process that generates the data for all periods  that fall in quarter  is given by: Y_t=(\beta+\tau\times t)\times \sigma_q +e Y_t = the datum for period = base demand at the beginning of the time series horizon = the linear trend per quarter \sigma_q = the multiplicative seasonal factor for the quarter = a disturbance term Y_i=\beta_0+\beta_1X_i+e The first step is to find the line that minimizes the sum of the squares of the difference between the observed values of the dependent variable and the fitted values from the line. This is expressed as \hat{Y}_i=\beta_0+\beta_1{X_i} which minimizes \Sigma(Y_i-\hat{Y_i})^2 and \hat{Y_i}=\beta_0 , the fitted value from the regression line. \beta_0 and \beta_1 also need to be represented to find the intercept and slope of the line. The method of determining \beta_0 and \beta_1 is to use partial differentiation with respect to both \beta_0 and \beta_1 by setting both expressions equal to zero and solving them simultaneously. The method for omitting these variables is described below: \begin{align} \beta_1 &= { \frac{n\Sigma X Y- \Sigma X \Sigma y}{n\Sigma X ^2- (\Sigma X )^2} } \\ \beta_0 &= { \frac{\Sigma Y}{n} } - { \frac{\beta_1\Sigma X}{n} } \end{align} \text{MAPE} = \frac{\sum(w\cdot|A-F|)}{\sum(w\cdot A)} . The advantage of this measure is that can weight errors. The only problem is that for seasonal products you will create an undefined result when sales = 0 and that is not symmetrical. This means that you can be much more inaccurate if sales are higher than if they are lower than the forecast. So sMAPE also known as symmetric Mean Absolute Percentage Error, is used to correct this. Finally, for intermittent demand patterns, none of the above are particularly useful. In this situation, a business may consider MASE (Mean Absolute Scaled Error) as a key performance indicator to use. However, the use of this calculation is challenging as it is not as intuitive as the above-mentioned. Another metric to consider, especially when there are intermittent or lumpy demand patterns at hand, is SPEC (Stock-keeping-oriented Prediction Error Costs). The idea behind this metric is to compare predicted demand and actual demand by computing theoretical incurred costs over the forecast horizon. It assumes, that predicted demand higher than actual demand results in stock-keeping costs, whereas predicted demand lower than actual demand results in opportunity costs. SPEC takes into account temporal shifts (prediction before or after actual demand) or cost-related aspects and allows comparisons between demand forecasts based on business aspects as well. \text{CFE} = (C_{under} \times \text{Error}_{under}) + (C_{over} \times \text{Error}_{over}) Where: C_{under} is the cost per unit of under-forecasting (e.g., lost gross margin). C_{over} is the cost per unit of over-forecasting (e.g., inventory holding cost). By optimizing for minimum CFE rather than minimum statistical error, organizations can account for the "Symmetry Trap"—a situation where a statistically accurate forecast leads to sub-optimal financial outcomes. ==== Others==== Others include:

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