Forecast error

Of the total 2.2 Gt C02/year emissions covered by the EU ETS overall, the power sector currently accounts for around 60%. To illustrate the potential magnitudes involved, after allowing for forecasting errors 19... 

If this forecast is used in place of N +2 in the control scheme in equation (12), the resulting output deviation from target (e ) at time t will be given by the two-step ahead forecast error of... 

During adverse meteorological events the use of an interface module to model dispersion parameters can have the advantage to reduce forecast error effects on predicted concentrations. Anyway, further analysis showed that the discussed results are strongly dependent on the radiation scheme used by RAMS model. 

Fig. 13.1 Observations black) and model forecasts of NO2, wind speed, 2 m temperature and wind direction left), and forecast error for NO2, wind speed and 2 m temperature right) at observation site Valle Hovin. Forecasts dotted) and observations solid) of vertical temperature gradient 2-25 m bottom right) (from 0degaard et al. 2004)...

Time series analysis Mean and forecast error caracterization... 

These indexes can be extended to consider the variance ratios of the fc-step-ahead forecast error to the variance of e k). A performance index similar to CLP, CLPk is defined as [148] ... 

MSD stands for Mean Squared Deviation. MSD is always computed using the same denominator, n, regardless of the model, so we can compare MSD values across models. MSD is a more sensitive measure of an unusually large forecast error than MAD. 

A forecast error is equal to the difference between genuine or real and predicted or forecast values. As in other cases, in its value may be identified two addends correctness, i.e., closeness of the predicted value to the genuine one, and precision as a measure of random errors with equal probability of variance from some medium value. The former ones may be eliminated, the latter ones may only be decreased. 

One of the simplest models of demand is to use an estimate of the average demand. This average demand, assuming a constant rate each period, can then be used to understand the effect of production costs or transport costs on inventory levels. Such models are appropriate when we deal with products in situations with predictable demand, that is, low forecast error. In particular, we will focus on the... 

In the earlier section, we modeled demand as a constant rate. Often, however, demand is not very predictable but has a significant amount of randomness. To understand the effect of demand forecast error, we first focus on problems where decisions regarding inventory are made once for an entire period. Examples of products that might require inventory decisions that cover demand over a single period include... 

An interesting issue is the fact that in the absence of demand forecast error, lead time has no impact on costs. This is seen by the fact that while the order trigger times are affected by lead time... 

By choosing the assortment of dresses closer to the season, ASSORT faces a lower forecast error. This enables the retailer to have fewer stockouts, the manufacturer to have higher revenue, and the customer to have a higher service level. All of this is accomplished without a decrease in retailer profits. 

Victor Fung refers to the firm s capabdity as the soft 3 of the supply chain. He explains that if a product that leaves a plant costing 1 ends up at retail for 4, the 3 represents the cost of inventory, forecast error, exchange rates, retail markup, and other factors. There is a much better chance at reducing the 3 than the 1. Li Fung focuses on creating a customized value chain for each order ([82]). This represents a classic example of a pure supply chain company. 

But to make VMI economical, the manufacturer may have to pool dehveries across multiple retailers to optimize costs associated with frequent delivery. In addition, VMI may require the manufacturer to have access to detailed outbound retail shipment information in order to lower manufacturer forecast error and decrease safety stock at the retailer DC required to maintain the desired service level. 

Another approach to coordination is scanner-based promotions. Under this approach, the manufacturer announces special discounts for all units sold at the retailer during specific periods. Iyer and Ye [50] smdy manufacturer costs with and without scanner-based promotions. They show that scanner-based promotions increase the predictability of retail sales for the manufacturer. This is because it becomes profitable for the retailer to schedule retail promotions at times when the manufacturer offers these deals. The associated lift in sales happens at predictable periods determined by the manufacturer, thus improving manufacturer forecasts and decreasing retail order forecast error. This retailer coordination permits manufacturer inventories to be better synchronized with projected retail sales, which decreases costs. 

The preceding example shows that, given the buyer s inherent difficulty in classifying the product, the forecast error associated with classifications is large. For example, an accurate classification of a product as a dog would have a forecast error (standard deviation/mean) of 3.35/4.5,... 

This shows that improving lead times and enabling decisions under a lower demand forecast error may require coordination agreements between members of the apparel supply chain. Once such coordination agreements are estabhshed, the access to manufacturing capacity closer to demand enables improved competitiveness of the apparel supply chain. Notice that all four Cs played a role in improving the supply chain. 

No accuracy measure is generally applicable to all forecasting problems due to variation in forecasting objectives and data scales (De Gooijer and Hyndman, 2006 Hyndman and Koehler, 2006). Let denote the observation at time t and F, denote the forecast of Y. Then define the forecast error e=Y F. In this chapter,... 

New products will increase forecast error, inventory, and supply chain waste. 

Real-time supply visibility. As demand is shaped, forecast error increases. As a result, it is important to have flexible supply processes to translate the demand impacts to internal and external supply organizations through supply visibility with minimal signal distortion and latency. 

Make data sharing and forecast accuracy part of top-to-top meetings with trading partners. Take ownership of the forecast error in the extended value network. 

The medium-range forecasting of this case accurately predicted that the rainstorms would happen in the period of 9-10 June. But in the short-range forecasting, it predicted that the rainstorms would happen on 10 June. In fact, the heavy rain occurred at the daytime on 9 June. The error of the numerical model product of the 500 hPa weather systems led the forecasting error. 

According to Armstrong and Fildes (1995), the objective of a forecast accuracy measure is to provide an informative and clear understanding of the error distribution. Theoretically, when the forecast errors are randomly structured, the form of the forecasts is independent of the selected accuracy measure. Otherwise, it is generally accepted that there is no single best accuracy measure, and deciding on the assessment method is essentially subjective. In this study, a simple form of relative error (E) is selected as the forecast accuracy measure, since it offers a number of desirable properties ... 

Campbell P. R. 2002. Evaluating Forecast Error in State Population Projections Using Census 2000 Counts. U.S. Bureau of Census, Population Division Working Paper Series No. 57. 

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