Question

Consider the following scenario: You run UCIO, a British utility supplying electricity to the London metropolitan area. You need to decide how much capacity to have on line, and two conflicting goals must be resolved in order to make an appropriate decision. You obviously want to have enough capacity to meet average demand, but that is not enough, because demand is uneven throughout the year. In particular, demand skyrockets during summer heat waves -- which occur randomly -- as more and more people run their air conditioners constantly. If you don't have sufficient capacity to meet peak demand, you get bad press. On the other hand, if you have a large amount of excess capacity over most of the year, you also get bad press. What sorts of forecasts would be useful in the following decision making situations? Why? What sorts of data might you need to produce such forecasts?

this is a question for demand planning and forecasting. i don't think or have any additional information than this.

Answer 1

While choosing the right forecast measure we should consider the following parameters:

a. The objective of the forecast or the problem which we aim to solve through the forecasting method.

b. Level of accuracy required.

c. Cost of the method to do the forecast and data collection

d. Type of data available that can be used for the forecast and data quality as well as the historical period of data availability.

Based on the question in the given scenario, there is an objective to forecast the demand pattern and the trend. Therefore time series analysis method of forecasting would be most appropriate. The reason is the quantitative nature of data. Within the time series method of forecasting, we should select the X-11 techniques because there is clear seasonality in demand and these techniques give very accurate results in case of seasonal variation. This method requires at least three years of data and if longer period data is available the forecast accuracy will increase further. data on the consumption and other factors that might impact the demand should also be collected. This method is also suitable for medium term forecasting which is 3 months to 1 year.