CHAPTER 6

• Forecasting Methods

  A) various

  B) no method

  C) combining various methods

  
• There are two general approaches to forecasting

  qualitative

  quantitative
• Qualitative methods - consist mainly of subjective inputs, which often defy precise numerical description.
• Quantitative methods - involve either the projection of historical data or the development of associative models that attempt to utilize causal (explanatory) variables to make a forecast.
• Qualitative techniques - permit inclusion of soft information (e.g., human factors, personal opinions, hunches) in the forecasting process.
• Quantitative techniques - consist mainly of analyzing objective, or hard data. They usually avoid personal biases that sometimes contaminate qualitative methods.
• 2 types of forecast

  judgmental

  time-series
• Forecasts Based on Judgement and Opinion

  executive opinions

  salesforce opinions

  consumer surveys
• Executive Opinions - A small group of upper-level managers (e.g., in marketing, operations, and finance) may meet and collectively develop a forecast.
• Executive Opinions - This approach is often used as a part of long-range planning and new product development. It has the advantage of bringing together the considerable knowledge and talents of various managers. However, there is the risk that the view of one person will prevail, and the possibility that diffusing responsibility for the forecast over the entire group may result in less pressure to produce a good forecast.
• Salesforce Opinions - Members of the sales staff or the customer service staff are often good sources of information because of their direct contact with consumers. They are often aware of any plans the customers may be considering for the future.
• Consumer Surveys - Because it is the consumers who ultimately determine demand, it seems natural to solicit input from them. In some instances, every customer or potential customer can be contacted. However, usually there are too many customers or there is no way to identify all potential customers.
• consumer surveys - is that they can tap information that might not be available elsewhere.
• Surveys - can be expensive and time-consuming. In addition, even under the best conditions, __ of the general public must contend with the possibility of irrational behavior patterns.
• time series - is a time-ordered sequence of observations taken at regular intervals (e.g., hourly, daily, weekly, monthly, quarterly, annually)
• time-series data - are made on the assumption that future values of the series can be estimated from past values.
• time-series data - requires the analyst to identify the underlying behavior of the series. This can often be accomplished by merely plotting the data and visually examining the plot.
• Trend - refers to a long-term upward or downward movement in the data.
• Seasonality - refers to short-term, fairly regular variations generally related to factors such as the calendar or time of day.
• Cycles - are wavelike variations of more than one year’s duration. These are often related to a variety of economic, political, and even agricultural conditions.
• Irregular variations - are due to unusual circumstances such as severe weather conditions, strikes, or a major change in a product or service.
• Random variations - are residual variations that remain after all other behaviors have been accounted for.
• naive forecast - uses a single previous value of a time series as the basis of a forecast.
• naive approach - can be used with a stable series (variations around an average), with seasonal variations, or with trend.
• naive approach - may appear too simplistic, it is nonetheless a legitimate forecasting tool.
• The main objection to this naive method is its inability to provide highly accurate forecasts.
• positive and negative errors - on periods are important to analyze because they potentially provides with information about seasonality trends. These trends can helps to understand the performance of a product during particular times of the year.
• naïve method - is the simplest form of business forecasting, but once the company have some real data and multiple products, move to something more robust.
• Averaging techniques - smooth variations in the data.
• Ideally, it would be desirable to completely remove any randomness from the data and leave only “real” variations, such as changes in the demand.
• As a practical matter, however, it is usually impossible to distinguish between these two kinds of variations, so the best one can hope for is that the small variations are random and the large variations are “real.”
• Averaging techniques - smooth fluctuations in a time series because the individual highs and lows in the data offset each other when they are combined into an average.
• Moving Average - One weakness of the naive method is that the forecast just traces the actual data, with a lag of one period; it does not smooth at all. But by expanding the amount of historical data a forecast is based on, this difficulty can be overcome.
• moving average - forecast uses a number of the most recent actual data values in generating a forecast.
• Exponential smoothing - is a sophisticated weighted averaging method that is still relatively easy to use and understand. Each new forecast is based on the previous forecast plus a percentage of the difference between that forecast and the actual value of the series at that point.
• two most important factors are

  cost

  accuracy
• manager - can take a reactive or a proactive approach to a forecast.
• manager - can take a reactive or a proactive approach to a forecast.
• reactive approach - views forecasts as probable future demand, and a manager reacts to meet that demand (e.g., adjusts production rates, inventories, the workforce).
• proactive approach - seeks to actively influence demand (e.g., by means of advertising, pricing, or product/service changes).