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
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.
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.
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.
Steve used the analogy of sailing on a ship where each crew member was operating with a different forecast. It is important to ensure that the forecasts are aligned different functions don’t have vastly different opinions on what is going to happen.
Forecasting needs to provide more benefits than it costs. The benefits of forecasting are pretty obvious, so mostly the focus here is on efficiency and cutting out the unnecessary. To maximize efficiency, do things in the right order, at the right speed, in the same way every time. Eliminate unnecessary movements, especially if they cause bias. And observe the results, making and documenting improvement.
Steps of Forecasting Process
• Data Collection
• Quality Control
• Data Assimilation
• Model Integration
• Post Processing of Model Forecasts
• Human Interpretation (sometimes)
• Product and graphics generation
No demand forecasting method is 100% accurate. Combined forecasts improve accuracy and reduce the likelihood of large errors. Reference class forecasting was developed by professor Bent Flyvbjerg, University of Oxford, to reduce error and increase accuracy in forecasting, including in demand forecasting. Daniel Kahneman, Nobel Prize winner in economics, calls Flyvbjerg's counsel to use reference class forecasting to de-bias forecasts, "the single most important piece of advice regarding how to increase accuracy in forecasting.” Other experts have shown that rule-based forecasts produce more accurate results than combined forecasts.
Methods that rely on qualitative assessment
Forecasting demand based on expert opinion. Some of the types in this method are,
▪ Unaided judgment
▪ Prediction market
Prediction markets (also known as predictive markets, information markets, decision markets) are speculative markets created for the purpose of making predictions. The current market prices can then be interpreted as predictions of the probability of the event or the...