Demand Planning - Essence of Forecasting - Part II

In my earlier blog, I was explaining about forecast fundamentals and in this article we will explore more about Forecasting techniques on Quantitative and Casual with various methods along with time series components. Quantitative Technique Uses historical data. In this technique single variable (eg., Sales / Demand) is used. Time Series Methods consist of Moving Average Exponential Smoothing Holt's Model - Exponential Smoothing Adjusted for Trend Winter Model – Exponential Smoothing Adjusted for Trend and Seasonal Times series decomposition Times series extrapolation Casual Techniques Uses the relationship between demand and some other factors (income, population etc) to develop forecast. This uses several independent variables rather than the historical pattern of the time series. These independent variables contain information useful to explain the sales patterns of the dependent variable. For example increase in disposable income (independent variable) lead to increase in sales (dependent variable). There is a correlation between disposable income and sales. This information may help to build a forecasting model more accurate than dependent on sales history alone. Methods used in Casual Technique Correlation Method Regression Model Econometric Model However in some cases more than one technique is used . For example ARIMA uses casual (Auto Regression) and Quantitative (Moving Average) techniques. This method is used in advanced forecasting tool. Time Series Time Series is a set of evenly spaced or continuous numerical data which is historic in nature, where the basic demand pattern varies little between years. It assumes the factors influencing past will continue in future. It uses statistical models as forecasting tools. Components of Time Series Trend – Long term (several years) tendency of a series to rise or fall (upward or downward trend), due to population, technology etc. Seasonal – Periodic fluctuation in the time series within a certain time period (within a year). These fluctuations form a pattern that tends to repeat from one seasonal period to another. This is due to weather and customs (Diwali, Christmas) Cyclical – Generally occur over a larger time interval (2 – 10 years duration), and the length of time between time successive peaks or troughs of a cycle are not necessarily the same. Due to factors influencing the economy. Random – Eratic, unsystematic, "residual" . Random noise or error in a time series. Due to unforeseen events like strike etc. We focus our attention to Trend, Seasonal, Random components rather than cyclical as it occur over larger time interval. Times series data contains systematic and random components. Systematic is a combination of Trend, Seasonal and Level. Level is a time series data without having Trend and Seasonal components. Before using the sales / demand data in a time series one must understand the components present in the data. It is not necessary that all components like Trend, Seasonal, Cyclical, Random should be present in the data. This understanding is critical in choosing the right forecast method. Given below the table which shows the forecast method to be followed against the given components in the data. Forecast Method -----------------Applicability Moving Average --------------------No Trend or Seasonality Simple Exponential Smoothing ------No Trend or Seasonality Holt’s Model ------------------------Trend but no Seasonality Winter Method----------------------Trend and Seasonality How to choose right Forecast technique and method ? Given below the diagram which shows how the forecast technique and method to be chosen based on the available data.

Acid test for the forecast personal lies in selecting correct forecasting technique and “best fit” forecast method. Let us compare car buying process against forecast technique and method selection process. For example if a person would like to buy a car, first he will decide on segment like small size, medium, luxury, SUV and then he will choose the brand based on performance (trouble free) and other criteria. Trouble free performance can be compared against “Least Error” in statistical parlance. Hence Forecast personal will choose the right technique first and then select the “best fit” method among various methods which gives least error. Forecast Technique : At the outset it may look easy to select Time Series as one need to forecast on historical sales data. But after careful consideration, other inputs from Marketing (promotion, Pricing) and sales (Field activity, competition) need to be considered with weightage while forecasting. This observation may lead to opt for casual method due to usage of many related variables. Forecasting method : Selecting right forecast method is cumbersome process due to availability of wide variety of methods. For example in Time Series lot of methods, as explained above are available. User need to plot the data in all methods chosen and then select the "best fit" method based on result. What is “Best Fit” Forecast method ? Forecast personal plot the historical data in the various forecast methods chosen based on technique. He compares the result / output of each method and choose the method which gives least error. Basically he used to compare the MSE (Mean Square Error) & MAPE (Mean Absolute Error Percentage) of each method and then select the best fitted forecast method. This opens up a project scope in Six Sigma on Forecast method. Above discussion clearly indicate that the forecast personal should familiarize with Statistical techniques, able to understand the output and interpret the results clearly. This helps the top management to plan their Sales and Marketing strategy effectively. In my next session we will explore about CPFR (collaborative Planning Forecasting Replenishment), Consensus Forecast and analyze right forecast method during recession period.

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