ForecastingDiscuss the different types of forecasts to include tim.pdf
- 1. Forecasting Discuss the different types of forecasts to include time-series, causal, and qualitative models. When might a researcher or project manager utilize exponential smoothing? What benefit does a Delphi technique provide when working with qualitative-based decision making? Solution Forecasting is basically the process of estimating or predicting the future trend, based on the trend and information of the past and the present.Forecasting is a calculated assumption of how the trend is going to be in a future date based on what we saw in the past and what we are observing in the present scenario. Time series methods: These methods use historical data to assume future trends. There are various time series methods such as, 1)Simple Moving Average Method: it is commonly used in technical analysis of financial data such as stock prices,trading volumes or returns.Among the most popular technical indicators, moving averages are used to gauge the direction of the current trend.It is calculated by averaging a number of past data points. Once determined, the resulting average is then plotted onto a chart in order to allow traders to look at smoothed data rather than focusing on the day-to-day price fluctuations that are inherent in all financial markets. As new values become available, the oldest data points must be dropped from the set and new data points must come in to replace them. Thus, the data set is constantly "moving" to account for new data as it becomes available. This method of calculation ensures that only the current information is being accounted for. for example, to calculate a basic 10-day moving average you would add up the closing prices from the past 10 days and then divide the result by 10. The average thus obtained is plotted on a chart. As the time progresses, we replace the first variable with the latest variable available ie. latest closing price of 11th day, therefore getting a new avaerage. We plot this one too in the chart. The chart thus formed gives a trend which is used for forecasting future movements. 2)Exponentially smoothed moving average: Over the years, technicians have found two problems with the simple moving average. The first problem lies in the time frame of the moving average (MA). Most technical analysts believe that price action, the opening or closing stock price, is not enough on which to depend for properly predicting buy or sell signals of the MA's crossover action. To solve this problem, analysts now
- 2. assign more weight to the most recent price data by using the exponentially smoothed moving average (EMA).It is a type of infinite impulse response filter that applies weighting factors which decrease exponentially. The weighting for each older datum decreases exponentially, never reaching zero. The exponentially smoothed moving average addresses both of the problems associated with the simple moving average. First, the exponentially smoothed average assigns a greater weight to the more recent data. Therefore, it is a weighted moving average. But while it assigns lesser importance to past price data, it does include in its calculation all the data in the life of the instrument. In addition, the user is able to adjust the weighting to give greater or lesser weight to the most recent day's price, which is added to a percentage of the previous day's value. The sum of both percentage values adds up to 100. 3)Auto regressive moving average model : It uses time series data to predict future trends. It is a form of regression analysis that seeks to predict future movements along the seemingly random walk taken by stocks and the financial market by examining the differences between values in the series instead of using the actual data values. Lags of the differenced series are referred to as "autoregressive" and lags within forecasted data are referred to as "moving average." B) Causal models: Estimating techniques based on the assumption that the variable to be forecast (dependent variable) has cause-and-effect relationship with one or more other (independent) variables. For example, taking into consideration , the improving infrastructure of a developing country or its rising per capita income, we can arrive at a better forecast of car sales in that country. c) Qualitative methods: 1. Executive Opinions The subjective views of executives or experts from sales, production, finance, purchasing, and administration are averaged to generate a forecast about future sales. Usually this method is used in conjunction with some quantitative method, such as trend extrapolation. The management team modifies the resulting forecast, based on their expectations. The advantage of this approach: The forecasting is done quickly and easily, without need of elaborate statistics. Also, the jury of executive opinions may be the only means of forecasting feasible in the absence of adequate data. The disadvantage: This, however, is that of group-think. This is a set of problems inherent to those who meet as a group. Foremost among these are high cohesiveness, strong leadership, and insulation of the group. With high cohesiveness, the group becomes increasingly conforming through group pressure that helps stifle dissension and critical thought. Strong leadership fosters group pressure for unanimous opinion. Insulation of the group tends to separate the group from
- 3. outside opinions, if given. 2. Delphi Method This is a group technique in which a panel of experts is questioned individually about their perceptions of future events. The experts do not meet as a group, in order to reduce the possibility that consensus is reached because of dominant personality factors. Instead, the forecasts and accompanying arguments are summarized by an outside party and returned to the experts along with further questions. This continues until a consensus is reached. Advantages: This type of method is useful and quite effective for long-range forecasting. The technique is done by questionnaire format and eliminates the disadvantages of group think. There is no committee or debate. The experts are not influenced by peer pressure to forecast a certain way, as the answer is not intended to be reached by consensus or unanimity. Disadvantages: Low reliability is cited as the main disadvantage of the Delphi method, as well as lack of consensus from the returns. 3. Sales Force Polling Some companies use as a forecast source salespeople who have continual contacts with customers. They believe that the salespeople who are closest to the ultimate customers may have significant insights regarding the state of the future market. Forecasts based on sales force polling may be averaged to develop a future forecast. Or they may be used to modify other quantitative and/or qualitative forecasts that have been generated internally in the company. The advantages of this forecast are: It is simple to use and understand. It uses the specialized knowledge of those closest to the action. It can place responsibility for attaining the forecast in the hands of those who most affect the actual results. The information can be broken down easily by territory, product, customer, or salesperson. The disadvantages include: salespeople’s being overly optimistic or pessimistic regarding their predictions and inaccuracies due to broader economic events that are largely beyond their control. 4. Consumer Surveys Some companies conduct their own market surveys regarding specific consumer purchases. Surveys may consist of telephone contacts, personal interviews, or questionnaires as a means of obtaining data. Extensive statistical analysis usually is applied to survey results in order to test hypotheses regarding consumer behaviour. When to use exponential smoothing:
- 4. Exponential smoothing methods are particularly attractive for production and operations applications that involve forecasting for a large number of items. These methods work best under the following conditions: 1) The forecasting horizon is relatively short; for example, a daily, weekly, or monthly demand needs to be forecasted 2) There is little outside information available about cause and effect relationships between the demand of an item and independent factors that influence it. 3) Small effort in forecasting is desired. Effort is measured by both a method’s ease of application and by the computational requirements (time, storage) needed to implement it 4) Updating of the forecast as new data become available is easy and can be accomplished by simply inputting the new data. 5) It is desired that the forecast is adjusted for randomness (fluctuation in demand are smoothed) and tracks trends and seasonality. There are many advantages of the Delphi Technique. More participants can be involved than a face to face method allows. The time and cost of participants travelling to meetings is saved, while still enabling their participation. The anonymity of participants is preserved. This can avoid self-censorship, and give participants the flexibility to modify their views as they learn from others, without the social pressure that exists in face to face meetings The remote process also avoids negative group influences such as dominating members and political lobbying. Provides a structured way for a group of people to make decisions in a political or emotional environment about complex problems. The problem does not lend itself to precise analytical techniques but can benefit from subjective judgments on a collective basis. The individuals needed to contribute to the examination of a broad or complex problem have no history of adequate communication and may represent diverse backgrounds with respect to experience or expertise. The heterogeneity of the participants must be preserved to assure validity of the results, i.e., avoidance of domination by quantity or by strength of personality ("bandwagon effect").