1 edition of Forecast accuracy of individual analysts found in the catalog.
Published 1987 by Administrator in Sloan School of Management, Massachusetts Institute of Technology
|Statement||Sloan School of Management, Massachusetts Institute of Technology|
|Publishers||Sloan School of Management, Massachusetts Institute of Technology|
|The Physical Object|
|Pagination||xvi, 106 p. :|
|Number of Pages||55|
|2||Working paper (Sloan School of Management) -- 1940-87.|
|3||Working paper / Alfred P. Sloan School of Management -- WP 1940-87|
nodata File Size: 6MB.
It can easily disguise very large errors. Some forecasting systems on the market look like black boxes to the users: data goes in, forecasts come out. Chapter 2: What Factors Affect the Attainable Forecast Accuracy There are several factors that have an impact on what level of forecast accuracy can realistically be attained.
If a supplier delivers from the Far East with a lead time of 12 weeks, what matters is what your forecast quality was when the order was created, not what the forecast was when the products arrived.
in forecasting, or could you improve forecast accuracy through more sophisticated forecasting? To be able to adjust forecasts that do not meet your business requirements, you need to understand where the forecast errors come from. All the while our customers are enjoying the benefits of increased forecast accuracy with our machine learning algorithms, we still strongly feel that there is a need to discuss the role of forecasting in the bigger picture.
To efficiently debug forecasts, you need to be able to separate the different forecast components.
In the example see Table 3we have a group of three products, their sales and forecasts from a single week as well as their respective MAPEs. Essentially, this means that all vendors get the same data from the retailers, which Forecast accuracy of individual analysts will then insert into their planning tools to show what kind of forecast accuracy they can provide.
In some circumstances demand forecasting is, however, easier than in others. Chapter 1: The Role of Demand Forecasting in Attaining Business Results If you are not in the business of predicting weather, the value of a forecast comes from applying it as part of a planning process.
There may be seasonality, such as demand for tea increasing in the winter time, or trends, such as an ongoing increase in demand of organic food, that can be detected by examining past sales data. For example, when testing different variants of machine learning on promotion data, we discarded one approach that was on average slightly more accurate than some others, but significantly less robust and more difficult for the average demand planner to understand.
Basically, it tells you by how many percentage points your forecasts are off, on average. This can be resolved by weighting the forecast error by sales, as we have done for the MAPE metric in Table 5 below.
In the short-term, weather forecasts can be used to drive replenishment to stores you can read more about. There are usually many types of variation in demand that are somewhat systematic.