Demand Forecasting. Other Resource ï¿ Causal Forecasting. ⤠Causal forecasting assumes that demand is related to some underlying factor for factors in the environment. Causal forecasting methods develop forecasts after establishing and measuring an association between the dependent variable and one or more independent variables. The more commonly used methods of demand forecasting are discussed below: The various methods of demand forecasting can be summarised in the form of a chart as shown in Table 1. Opinion Polling Method: In this method, the opinion of the buyers, sales force and experts could be gathered to determine the emerging trend in the market. In an informal way, forecasting is an integral part of all human activity, but from the business point of view increasing attention is being given to formal forecasting systems which are continually being refined. Some forecasting systems involve very advanced statistical techniques beyond the scope of this book, so are not included. Thus, we can say that the techniques of demand forecasting are divided into survey methods and statistical methods. The survey method is generally for short-term forecasting, whereas statistical methods are used to forecast demand in the long run.
Reviewed by: Michelle Seidel, B.Sc., LL.B., MBA
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Reviewed by: Michelle Seidel, B.Sc., LL.B., MBA
Forecasts serve as decision support tools that allow leaders to plan for the future by performing âwhat-ifâ analyses to determine how changes in inputs affects outcomes. For example, forecasts help a business identify appropriate responses to changes in demand levels, price-cutting by the competition, economic ups and downs and more. To receive the greatest benefit from forecasts, leaders must understand the finer details of the different types of forecasting methods, recognize what a particular forecasting method type can and cannot do, and know what forecast type is best suited to a particular need.
Naive Forecasting Methods
The naïve forecasting methods base a projection for a future period on data recorded for a past period. For example, a naïve forecast might be equal to a prior periodâs actuals, or the average of the actuals for certain prior periods. Naïve forecasting makes no adjustments to past periods for seasonal variations or cyclical trends to best estimate a future periodâs forecast. The user of any naïve forecasting method is not concerned with causal factors, those factors that result in a change in actuals. For this reason, the naive forecasting method is typically used to create a forecast to check the results of more sophisticated forecasting methods.
Qualitative and Quantitative Forecasting Methods
Whereas personal opinions are the basis of qualitative forecasting methods, quantitative methods rely on past numerical data to predict the future. The Delphi method, informed opinions and the historical life-cycle analogy are qualitative forecasting methods. In turn, the simple exponential smoothing, multiplicative seasonal indexes, simple and weighted moving averages are quantitative forecasting methods.
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Casual Forecasting Methods
Regression analysis and autoregressive moving average with exogenous inputs are causal forecasting methods that predict a variable using underlying factors. These methods assume that a mathematical function using known current variables can be used to forecast the future value of a variable. For example, using the factor of ticket sales, you might predict the variable sale of movie-related action figures, or you might use the factor number of football games won by a university team to predict the variable sale of team-related merchandise.
Supply Forecasting MethodsJudgmental Forecasting Methods
The Delphi method, scenario building, statistical surveys and composite forecasts each are judgmental forecasting methods based on intuition and subjective estimates. The methods produce a prediction based on a collection of opinions made by managers and panels of experts or represented in a survey.
Time Series Forecasting Methods
The time series type of forecasting methods, such as exponential smoothing, moving average and trend analysis, employ historical data to estimate future outcomes. A time series is a group of data thatâs recorded over a specified period, such as a companyâs sales by quarter since the year 2000 or the annual production of Coca Cola since 1975. Because past patterns often repeat in the future, you can use a time series to make a long-term forecast for 5, 10 or 20 years. Long term projections are used for a number of purposes, such as allowing a companyâs purchasing, manufacturing, sales and finance departments to plan for new plants, new products or new production lines.
Demand forecasting is a field of predictive analytics[1] which tries to understand and predict customer demand to optimize supply decisions by corporate supply chain and business management. Demand forecasting involves quantitative methods such as the use of data, and especially historical sales data, as well as statistical techniques from test markets. Demand forecasting may be used in production planning, inventory management, and at times in assessing future capacity requirements, or in making decisions on whether to enter a new market.
Methods[edit]Qualitative assessment[edit]
Forecasting demand based on expert opinion. Some of the types in this method are,
Quantitative assessment[edit]
Others are as follows[edit]
a) time series projection methodsthis includes:
b) causal methodsthis includes:
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Calculating demand forecast accuracy[edit]
Calculating demand forecast accuracy is the process of determining the accuracy of forecasts made regarding customer demand for a product.[2][3] Understanding and predicting customer demand is vital to manufacturers and distributors to avoid stock-outs and maintain adequate inventory levels. While forecasts are never perfect, they are necessary to prepare for actual demand. In order to maintain an optimized inventory and effective supply chain, accurate demand forecasts are imperative.
Calculating the accuracy of supply chain forecasts[edit]
Forecast accuracy in the supply chain is typically measured using the Mean Absolute Percent Error or MAPE. Statistically MAPE is defined as the average of percentage errors.
Most practitioners, however, define and use the MAPE as the Mean Absolute Deviation divided by Average Sales, which is just a volume weighted MAPE, also referred to as the MAD/Mean ratio. This is the same as dividing the sum of the absolute deviations by the total sales of all products. This calculation â(|AâF|)âA{displaystyle sum {(|A-F|)} over sum {A}}, where A{displaystyle A} is the actual value and F{displaystyle F} the forecast, is also known as WAPE, Weighted Absolute Percent Error.
Another interesting option is the weighted MAPE=â(wâ
|AâF|)â(wâ
A){displaystyle MAPE={frac {sum (wcdot |A-F|)}{sum (wcdot A)}}}. The advantage of this measure is that could weight errors, so you can define how to weight for your relevant business, ex gross profit or ABC. The only problem is that for seasonal products you will create an undefined result when sales = 0 and that is not symmetrical, that means that you can be much more inaccurate if sales are higher than if they are lower than the forecast. So sMAPE is also used to correct this, it is known as symmetric Mean Absolute Percentage Error.
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Last but not least, for intermittent demand patterns none of the above are really useful. So you can consider MASE (Mean Absolute Scaled Error) as a good KPI to use in those situations, the problem is that is not as intuitive as the ones mentioned before. You can find an interesting discussion here: http://datascienceassn.org/sites/default/files/Another%20Look%20at%20Measures%20of%20Forecast%20Accuracy.pdf
Calculating forecast error[edit]
The forecast error needs to be calculated using actual sales as a base. There are several forms of forecast error calculation methods used, namely Mean Percent Error, Root Mean Squared Error, Tracking Signal and Forecast Bias.
Types Of Forecasting MethodsSee also[edit]References[edit]
Bibliography[edit]
Demand Forecasting Models
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