Preferred when there is limitedno historical data

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Preferred when there is limited/no historical data Sales force planning : gathering the opinions of salespeople and managers for a particular product or family of products. Frequent contact with customers often provides insight into what customers may be considering for the future and also into customer perceptions of the company and its products. e.g. surveys, analog FC (‘similar’ goods) 2. Time-series analysis: a technique that utilizes past demand data to predict future demand by examining cyclical, trend and seasonal influences. Historical data and the assumption that past patterns will continue in the future. Goal : identify underlying patterns of demand and develop a model to predict these patterns in the future e.g. average changes (FY -1) 3. Causal relationships: a technique that identifies a connection between two factors , one that precedes and causes changes in the second or effect factor. (one or more factors are related to demand and that relationship between cause and effect can be used to estimate demand) e.g. X affects Y Examples of FC methods Market research is a systematic approach to measuring customer interest in a service or product through data-gathering surveys . Market research is widely used for new products , for which we have existing similar products. Note: it has a high degree of uncertainty and must be interpreted with caution. E.g. focus groups Naïve forecast: A method of forecasting that uses the demand for the current period as the forecast for the next period
Very simple and low cost to use. Works best when demand, trend and seasonal patterns are stable and there is relatively little random variation. Casual Methods: available historical data suggest a relationship between the forecasted item and some factor Increased government grants for solar panel and greater demand for installers. Cyclone season and price of bananas Increased rain fall and demand for umbrellas. Sunshine and sales of ice-cream Causal methods employ mathematical techniques to relate one or more independent variables to the variable being forecas t. Moving Average: A technique for estimating the average of a demand series and filtering out the effects of random variation Developing a moving average involves computing the average of n previous periods of demand and then using this as the estimate for the next period of demand. The average is updated after every period to include the most recent demand data. Demand Forecasting Issues forecasting techniques depend on factors, such as: o situation at hand (judgmental forecasting is appropriate) o forecasting costs in terms of time and money (are associated with each particular forecasting technique) o accuracy of various forecasting techniques. Forecasting accuracy = relationship between actual and forecasted demand o accuracy can be affected by various considerations.

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