Forecast Underpinnings

Several fuel ceU types are imder development, and they have a variety of potential applications. Fuel cells are being developed to power passenger vehicles, commercial buildings, homes, and even small devices such as laptop computers. The type of fuel cell technology utilized is often dictated by the constraints of its operating environment. Thus, we have chosen the following application classification scheme stationary, portable, and mobile applications. 

As a result, stationary fuel cells are likely to be the initial applications driving commercialization and certain applications within the market since stationary fuel cells have features that lend themselves to early adopters. 

A fuel cell analyst and former Motorola executive recently told the Grove Fuel Cell Symposium in London that fuel cells could replace batteries in most applications by 2006. 

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ABSTRACT This work has been prompted by Poland s poor supply of road safety management tools which are key to developing national road safety policies. They could also be used in other developing countries. Behavioural theories and the results of literature studies have helped to develop the Country Safety Performance Function (CSPF) which describes the relationship between the normal anticipated Road Fatality Rate (RFR) in a country and the variables of its social, economic, road transport and road safety management systems. The CSPF underpins the essence of road safety measured as the national road fatality rate. It can be used to understand the current condition of road safety of a country and the forecasts. 

CPFR is in effect an extension of VMI in that it takes the idea of collaboration amongst supply chain partners a step further. Underpinning CPFR is the creation of an agreed framework for how information will be shared between partners and how decisions on replenishment will be taken. A key element of CPFR is the generation of a joint forecast which is agreed and signed off by both the supplier and the customer. 

Today s supply chains are underpinned by the exchange of information between all the entities and levels that comprise the complete end-to-end network. The volume of data that flows in all directions is immense and not always accurate and can be prone to misinterpretation. Visibility of actual demand and supply conditions can be obscured through the way that information is filtered and modified as it passes from one entity or level to another. The so-called bullwhip effect is a manifestation of the way that demand signals can be considerably distorted as a result of multiple steps in the chain. As a result of this distortion, the data that is used as input to planning and forecasting activities can be flawed and hence forecast accuracy is reduced and more costs are incurred. 

An important aspect of the development of forecasts on the basis of physical models is how to optimize models given observed data. Most physics-based forecasting models (including those underpinned by Voight s relation) take the... 

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