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Forecast accuracy of individual analysts

a nine-industry study

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Published by Administrator in Sloan School of Management, Massachusetts Institute of Technology

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    • Sloan School of Management, Massachusetts Institute of Technology

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        StatementSloan School of Management, Massachusetts Institute of Technology
        PublishersSloan School of Management, Massachusetts Institute of Technology
        LC Classifications1987
        The Physical Object
        Paginationxvi, 69 p. :
        Number of Pages68
        ID Numbers
        ISBN 10nodata
        2Working paper (Sloan School of Management) -- 1940-87.
        3Working paper / Alfred P. Sloan School of Management -- WP 1940-87

        nodata File Size: 5MB.

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Forecast accuracy of individual analysts by Sloan School of Management, Massachusetts Institute of Technology Download PDF EPUB FB2

Simply addressing exceptions by manually correcting erroneous forecasts will not help you in the long run as it does nothing to improve the forecasting process. We need to keep in mind that a forecast is relevant only in its capacity of enabling us to achieve other goals, such as improved on-shelf availability, reduced food waste, or more effective assortments.

Yet, in practice even a perfect forecast would not have any impact on the business results; the on-shelf availability is already perfect and the stock levels are determined by the presentation stock requirements and batch size of this product see Figure 4.

Primarily measure what you need to achieve, such as efficiency or profitability.

Individual differences and analyst forecast accuracy

in forecasting, or could you improve forecast accuracy through more sophisticated forecasting? Furthermore, there would be no positive impact on store replenishment. We will have a closer look at these next. Also, due to the considerable sales volume and frequent deliveries, the forecast is truly driving store replenishment and making sure the store is stocked up nicely just before the demand peaks Figure 5.

If these planned changes are not reflected in your forecast, you need to fix your planning process before you can start addressing forecast accuracy. This is one of the reasons why it is so difficult to do forecast accuracy comparisons between companies or even between products within the same company.

Table 4: The same example data presented on a day-level, including day and product level MAPE. The final or earlier versions of the forecast: As discussed earlier, the longer into the future one forecasts, the less accurate the forecast is going to be.

It is often more important to understand in which situations and for which products forecasts can be expected to be good or bad, rather than to pour vast resources into perfecting forecasts that are by their nature unreliable.

In any case, setting your operations up so that final decisions on where to position stock are made as late as possible allow for collecting more information and improving forecast accuracy. Critically review assortments, batch sizes and promotional activities that do not drive business performance. Table 5: Volume-weighted MAPE Forecast accuracy of individual analysts per product calculated from daily sales data and for the group of products. Figure 6: In our second example, the forecast has a clear impact on store replenishment, with deliveries arriving nicely before the demand peaks, securing perfect availability and attractive shelves, with a reasonable amount of stock.

Machine learning algorithms are able to consider hundreds of potentially demand-influencing factors when forecasting retail sales, something that a human demand planner could never do.

Measuring Forecast Accuracy: The Complete Guide

On the other hand, if we are managing replenishment of ice-cream to grocery stores, we can make use of short-term weather forecasts when planning how much ice-cream to ship to each store. On the other hand, it is also obvious that demand forecasts will always be inaccurate to some degree and that the planning process must accommodate this.

To be able to effectively identify relevant exceptions, it usually makes sense to classify products based on their importance and predictability. 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.