Demand forecasting
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Demand forecasting

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Demand forecasting Demand forecasting Presentation Transcript

  • Demand Forecasting
  • Forecasting  When managers plan, they determine in present what courses of action they will take in future.  First step in planning is FORECASTING i.e. estimating the future demand for products and services and the resources necessary to produce these outputs.  Estimates of the future demand for products and services are commonly called as sales forecasts.
  • Categories of Forecasts  Long-range forecasts  Medium-range forecasts  Short-term forecasts
  •  Long-range forecasts used to make strategic decisions about products, processes and facilities.  Short-range forecasts needed to assist in decision making about operations issues that span only the next few days or weeks.  Medium range forecasts group products into product families.
  • Why forecasting is essential in OM?  New facility planning  Production planning  Workforce scheduling
  • Forecasting methods  Qualitative Forecasting Methods  Quantitative Forecasting Methods
  • Qualitative Forecasting Methods  Based on the judgements about the causal factors that underlie the sales of particular products or services and on opinions about the relative likelihood of those causal factors being present in the future.  These methods may involve several levels of sophistication.
  •  Educated guess-when one person uses his or her intution and experience to estimate a forecast.  Executive committee consensus and Delphi method describe procedures for assimilating information within a committee for the purpose of generating a sales forecast and are useful for either existing or new products and services.
  •  Survey of sales force and survey of customers are methods primarily used for existing products and services.  Historical analogy and market surveys describe procedures that are useful for new products and services.  Forecasting method that is appropriate depends on product’s life cycle stage.
  • Quantitative Forecasting Models  Mathematical models based on historical data.  Such models assume that past data are relevant to the future.  These models can be used with time series.  Time series is a set of observed values measured over successive time periods such as monthly sales for the last two years.
  • Linear Regression  A model uses least-square method to identify the relationship between a dependent variable and one or more independent variables that are present in a set of historical observations.  In simple regression there is only one independent variable.  In multiple regression there is more than one independent variable.  If the historical data set is a time series, the independent variable is the time period and the dependent variable in sales forecasting is sales.
  •  A regression model does not have to be based on a time series.  In that case, the knowledge of future values of the independent variable known as causal variables is used to predict future values of the independent variable.  Linear regression is ordinarily used in long range forecasting.
  • Moving average  Short range time series type of forecasting model that forecasts sales for the next time period.  In this model the arithmetic average of the actual sales for a specific number of most recent past time periods is the forecast for the next time period.
  • Exponential smoothing  Short range forecasting model that forecasts sales for the next time period.  Forecast sales for the last period is modified by information about the forecast error of the last period.  This modification of the last period’s forecast is the forecast for the next time period.
  • Exponential smoothing with trend  The exponential smoothing model but modified to accommodate data with a trend pattern.  Such patterns can be present in medium range data.  Also called double exponential smoothing
  • Forecast accuracy  How close forecasts come to actual data.  Because forecasts are made before the actual data become known, the accuracy of forecasts can be determined only after the passage of time.  If forecasts are very close to the actual data we ssay that they have high accuracy and forecast error is low.
  • Long range forecasts  Estimating future conditions over time spans that       are usually greater than one year. Historical data is made of several components. These components are cycles,trends,seasonality and random fluctuations or noise. Long range trends are upward or downward sloping line. Cycle is a data pattern that may cover several years before it repeats itself again. Noise is a pattern resulting from random variation. Seasonality is a pattern that repeats itself after a period of time usually one year.
  • Short range forecasts  Estimates future conditions over time spans that range from a few days to several weeks.  These forecasts may span such short periods of time that cycles,seasonality and trend patterns have little effect.  The main data pattern affecting these forecasts is random fluctuations.  These forecasts provide information to make such decisions as :  How much inventory of particular product be carried next month?  How much of each product should be scheduled for production next week?
  • Evaluating forecasting model performance  Impulse response:-forecats that respond very fast to changes in historical data have high impulse response.  When forecasts reflect few of the changes in historical data, these forecasts are said to have low impulse response.  Noise Dampening Ability:-forecasts that reflect every little happenstance fluctuation in the past data are said to include random variation or noise.  Such forecasts are unpredictable from period to period.
  •  It is usually desirable to have short range forecasts that have both high impulse response and high noise dampening ability but this is not possible.  A forecasting system that responds very fast to changes in the data has a great deal of random fluctuations.  Forecasters must choose which characteristic has more value.