Forecasting
Operations Management
Forecasting in Operations and Supply Chain Management
Forecast are vital to every business organization and for every significant management decision.
Forecasting is the basis of corporate planning and control.
Forecasting in Operations and Supply Chain Management
In considering what forecasting approach to use, it is important to consider the purpose of the forecast.
Forecast | Description |
Strategic Forecast | Used to help set the strategy of how we will meet demand. Can be considered most appropriate when making decisions related to
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Tactical Forecast | Used to ensure that in the short term we are able to meet customer lead time expectations and other criteria related to the availability of our product and services. |
Forecasting in Operations and Supply Chain Management
When forecasting, a good strategy is to use two or three methods and look at then from a commonsense view.
In general, can be considered two techniques:
Qualitative. That use managerial judgment.
Quantitative. That rely on mathematical models.
Quantitative Forecasting Model
Time series analysis. A forecast in which past demand data is used to predict future demand.
Causal forecasting. Using the linear regression technique, assumes that demand is related to some underlying factor or factors in the environment.
Simulation.Allow the forecaster to run through a range of assumptions about the condition of the forecast.
Quantitative Forecasting Model
Components of Demand
Demand for products or services can be broken down into six components:
Quantitative Forecasting Model
Components of Demand
Quantitative Forecasting Model
Components of Demand
Are more difficult to determine because the time span may be unknown, or the cause of the cycle may not be considered.
Are caused by chance events. When all the known causes for demand are subtracted from total demand, what remains is the unexplained portion of demand.
Denotes the persistence of occurrence. The value expected at any point is highly correlated with its own past values.
Quantitative Forecasting Model
Trend lines are the usual starting point in developing a forecast. These trend lines are then adjusted for seasonal effects, cyclical elements, and any other expected events that may influence the final forecast.
�Common Types of Trends
Quantitative Forecasting Model
Time Series Analysis
Time series forecasting models try to predict the future based on past data.
Forecasting Method | Amount of Historical Data | Data Pattern | Forecast Horizon |
Simple moving average | 6 to 12 months; weekly data are often used | Stationary only | Short |
Weighted moving average and simple exponential smoothing | 5 to 10 observations needed to start | Stationary only | Short |
Exponential smoothing with trend | 5 to 10 observations needed to start | Stationary and trend | Short |
Linear regression | 10 to 20 observations | Stationary, trend, and seasonality. | Short to medium |
Trend and seasonal models | 2 to 3 observations per season | Stationary, trend, and seasonality. | Short to medium |
Quantitative Forecasting Model
Time Series Analysis
Time series forecasting models try to predict the future based on past data.
Forecasting Method | Description |
Simple moving average | The idea here is to simply calculate the average demand over the most recent periods. |
Weighted moving average and simple exponential smoothing | Allows any weights to be placed on each element, provided, of course, that the sum of all weights equals 1. |
Exponential smoothing with trend | A time series forecasting technique using weights that decrease exponentially (1-α) for each past period. |
Linear regression | A functional relationship between two or more correlated variables. It is used to predict one variable given the others. |
Trend and seasonal models | Considered from data behavior in the analysis. |
Qualitative Techniques in Forecasting
Qualitative forecasting techniques generally take advantage of the knowledge of experts and require much judgment.
Qualitative Forecasting | Description |
Market Research | Is used mostly for product Research in the sense of looking for new product ideas, likes and dislikes about existing products, which competitive products within a particular class are preferred, and so on. |
Panel Consensus | Are developed through open meetings with a free exchange of ideas from all levels of management and individuals. |
Historical Analogy | In trying to forecast demand for a new product, and ideal situation would be one where an existing product or generic product could be used as a model. |
Delphi Method | A statement or opinion of a higher-level person will likely be weighted more that of a lower-level person. |
Forecast Errors
Errors can be classified as bias or random.
Bias errors occur when a consistent mistake is made. Sources of bias include the failure to include the right variables; employing the wrong trend line; a mistaken shift in the seasonal demand from where it normally occurs; and the existence of some undetected secular trends.
Random errors can be defined as those that cannot be explained by the forecast model being used.