Meaning of forecasting
Forecasting is the process of projecting past sales demand into the future.
Implementing a forecasting system enables you to assess current market trends and sales quickly so that you can make informed decisions about the operations. Forecasts are used to make planning decisions about: Customer
orders. Forecasting is a technique that uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends. Benefits of forecasting in strategic management According to Palot, the potential uses of forecasting in strategic decision processes can be stated as follows:
1. Goal Setting: Strategic planning requires input and the forecasting system in a company provides the underlying input necessary for the underlying process. Strategic managers can base their plans on these inputs in determining more realistic and attainable goals. In other words, forecasts can be taken as benchmarks for determining what are (are not) possible and achievable goals in a managerial decision context.
2. Firm Performance: The information produced by the forecasting system is ready-to-use material for measuring the firm performance and whether the predetermined strategic goals are achieved. Hence, forecasting can be used as a performance evaluation and a monitoring device in assessing the success of strategic plans.
3. Strategy Formulation: Strategy formulation is one of the key processes in strategic management. Broad range of forecasting processes provides with company managers the relevant information from procedural and analytical designs so that the outcomes from various scenarios can be investigated and taken as a ground for such processes and activities. This would give managers the opportunity to make more realistic assumptions in their plans and projections and determine alternative strategies concerning different outcomes of forecast results for the scenarios being considered.
4. Strategy Implementation: Good communication between strategic managers at all levels is a prerequisite for achieving these objectives,
which requires clear, concrete, and understandable messages. Forecasts produced, in this sense, are a major part of the messages in the
communication between both different levels of strategic and operational managers, and among themselves, as well Strategic management forecasting methods
1. Qualitative Methods.
Qualitative forecasting techniques are subjective, based on the opinion and judgment of consumers, experts; appropriate when past data is not available. It is usually applied to intermediate-long range decisions.
Example of qualitative forecasting methods:
1. Informed opinion and judgment
2. Delphi method
3. Market research
4. Historical life-cycle Analogy.
2. Quantitative forecasting methods
Quantitative forecasting models are used to estimate future demands as a function of past data; appropriate when past data is available. It is usually applied to short-intermediate range decisions. Example of Quantitative forecasting methods:
1. Last period demand
2. Arithmetic Average
3. Simple Moving Average (N-Period)
4. Weighted Moving Average (N-period)
5. Simple Exponential Smoothing
6. Multiplicative Seasonal Indexes
1. Naïve Approach
Naïve forecasts are the most cost-effective and efficient objective forecasting model, and provide a benchmark against which more sophisticated models can be compared. For stable time series data, this approach says that the forecast for any period equals the previous period’s actual value.
2. Time series methods
Time series methods use historical data as the basis of estimating future outcomes.

1. Moving average
2. Weighted moving average
3. Exponential smoothing
4. Autoregressive moving average
5. Autoregressive integrated moving average
6. Extrapolation
7. Linear prediction
8. Trend estimation
9. Growth curve

3. Casual / econometric forecasting methods
Some forecasting methods use the assumption that it is possible to identify the underlying factors that might influence the variable that is being forecast. For example, including information about weather conditions might improve the ability of a model to predict umbrella sales. This is a model of seasonality which shows a regular pattern of up and down fluctuations. Casual forecasting methods are also subject to the discretion of the forecaster. There are several informal methods which do not have strict algorithms, but rather modest and unstructured guidance. One can forecast based on, for example, linear relationships. If one variable is linearly related to the other for a
long enough period of time, it may be beneficial to predict such a relationship in the future. This is quite different from the aforementioned model of seasonality whose graph would more closely resemble a sine or cosine wave. The most important factor when performing this operation is using concrete and substantiated data. Forecasting off of another forecast produces inconclusive and possibly erroneous results. Such methods include:
a) Regression analysis includes a large group of methods that can be used to predict future values of variable using information about
other variables. These methods include both parametric (linear or non-linear) and non-parametric techniques.
b) Autoregressive moving average with exogenous inputs
4. Judgmental methods
Judgmental forecasting methods inorganizational intuitive judgments, opinions and subjective probability estimates.
1. Composite forecasts
2. surveys
3. Delphi method
4. Scenario building
5. Technology forecasting
6. Forecast by analogy
7. Artificial intelligence methods
8. Artificial neural networks
9. Group method of data handling
10.Support vector machines
Other methods
1. Simulation
2. Prediction market
3. Probabilistic forecasting and ensemble forecasting
4. Reference class forecasting

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