Demand Forcasting
Demand Forecasting
Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase. Demand forecasting involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data from test markets. Demand forecasting may be used in making pricing decisions, in assessing future capacity requirements, or in making decisions on whether to enter a new market.
Demand forecasting is the area of predictive analytics dedicated to understanding consumer demand for goods or services. That understanding is harnessed and used to forecast consumer demand. Knowledge of how demand will fluctuate enables the supplier to keep the right amount of stock on hand. If demand is underestimated, sales can be lost due to the lack of supply of goods. If demand is overestimated, the supplier is left with a surplus that can also be a financial drain.
Elements of good forecasting
Timely
A forecast needs to be prepared far enough out ahead to make the changes that can be made. After the initial forecast is made, the forecast should be updated on an interval tight enough to use it for the major decision points that happen after the original forecast is created.
In order to be timely, the forecast pretty much needs to be a rolling process that is independent of nominal dating restrictions and traditional budgeting cycles.
Actionable
Forecast should be built around the changes that can potentially be made, and should give enough information to take/not take certain actions that will affect the final outcome.
Reliability
There are two sources of error in a forecast: variation, which is the natural error, and bias, which is the systematic and avoidable error. It is impossible to get the variation out, so the focus should be on avoiding and minimizing the bias.
Alignment
Steve used the analogy of sailing on a ship
Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase. Demand forecasting involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data from test markets. Demand forecasting may be used in making pricing decisions, in assessing future capacity requirements, or in making decisions on whether to enter a new market.
Demand forecasting is the area of predictive analytics dedicated to understanding consumer demand for goods or services. That understanding is harnessed and used to forecast consumer demand. Knowledge of how demand will fluctuate enables the supplier to keep the right amount of stock on hand. If demand is underestimated, sales can be lost due to the lack of supply of goods. If demand is overestimated, the supplier is left with a surplus that can also be a financial drain.
Elements of good forecasting
Timely
A forecast needs to be prepared far enough out ahead to make the changes that can be made. After the initial forecast is made, the forecast should be updated on an interval tight enough to use it for the major decision points that happen after the original forecast is created.
In order to be timely, the forecast pretty much needs to be a rolling process that is independent of nominal dating restrictions and traditional budgeting cycles.
Actionable
Forecast should be built around the changes that can potentially be made, and should give enough information to take/not take certain actions that will affect the final outcome.
Reliability
There are two sources of error in a forecast: variation, which is the natural error, and bias, which is the systematic and avoidable error. It is impossible to get the variation out, so the focus should be on avoiding and minimizing the bias.
Alignment
Steve used the analogy of sailing on a ship