Business, Legal & Accounting Glossary
Demand forecasting is an activity a company does internally when it sets its sales budget. The demand forecast influences all upstream commitments and decisions. Forecasting is important and fundamental to any business. It is the act of looking ahead and anticipating the future.
Forecasting provides lead time to do the following:
Long-term forecasts are for strategic management decisions such as those concerning new product introduction, large investments, acquisitions, entry into new regions or markets, and more.
Medium-term forecasts relate to tactical, yearly, decisions. These include inventory planning, master production planning, subcontracting policies, hiring, setting staff/sales targets and bonuses, and more.
Short-term forecasts are for daily and weekly scheduling.
An overestimation of demand can result in:
An underestimation of demand can result in various opportunity costs including:
The qualitative method of demand forecasting can use various models. They are:
The Delphi method is an anonymous group forecasting exercise. None of the participants knows who the others are. The exercise is managed by a Delphi coordinator.
By being anonymous, bias is removed from members that would otherwise skew the data. However, the Delphi method may require many sessions before a consensus is reached.
This method is usually employed for new products, technologies, and industry forecasts.
Time series forecasting uses historical figures to predict future results. For example, a restaurant may use last month’s sales figures to predict how much food it will sell the next month.
One of the drawbacks of time series forecasting is that it assumes the future will be the same (or similar) to the past. It does not address any other variables.
A time series has four major elements:
In the quantitative time series method of demand forecasting, the x-axis (horizontal) = time. Various models include:
In the quantitative causal regression method of demand forecasting, the independent variables = causal variables. The independent variables are those that the firm can manipulate.
There are many large-scale forecasts which can be useful in looking forward to future operations. They include:
It is important to be able to measure the accuracy of forecasts. If a company’s forecasts have been accurate in past periods, they should remain so. If they have not been accurate, it is worth understanding how they have been wrong and where to correct them.
In general, the accuracy can be determined by subtracting the findings from the actual results:
Error = Actual - Forecast
For example, if a company forecast it would sell $10 million, and it only sold $8 million, we can see that the error is $-2 million.
Or, an absolute error can be determined:
Absolute Error = | Actual - Forecast |
This type of measure shows an absolute figure (not positive or negative).
Ft represents the forecast in period t.
At represents the demand in period t.
Sigma means the sum of.
Mean absolute deviation (MAD) is a measure of a model’s forecast error. It shows how accurate the model is. It is the sum of the absolute values of each forecast error, divided by the number of time periods (n).
Mean Absolute Deviation (MAD) = Mean Absolute Error = (Sigma| At - Ft|)/n MAD is around 0.8 times std. deviation
Mean Absolute % Deviation (MAPD) = ( MAD / Avg. Sales) * 100%
Mean squared error is another way of calculating the overall accuracy of a forecast model. It is the average of the squared differences of the forecast and actual values.
The formula is as follows:
Mean Squared Error (MSE) = Sigma(At - Ft)squared/n
Because of the squaring involved, MSE can often make large errors show up to a significant degree.
This type of measure shows the positive or negative figure (keeps the sign of error).
A tracking signal is the rolling sum of forecast errors / MAD. The ideal value is zero. A value of zero means that the demand forecasting is right on target.
A high positive tracking signal = consistent underestimation
A high negative tracking signal = consistent overestimation
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This glossary post was last updated: 28th March, 2020 | 18 Views.