Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 61
cost vis-à-vis changes in the operating environment. They are able to evaluate huge numbers of scenarios, allowing corporations to determine the ideal number, size, and location for distribution centers and cross-dock facilities. However, while these models are precise and can allow for the manipulation of huge amounts of data, they are limited in that they can't accurately represent on-the-ground local details such as traffic congestion, inefficient highway interchanges, or delay related to transfer points between modes. Additionally, these models are largely static and cannot easily incorporate future changes to the network or its capacity. As an example, a one-hour drivetime analysis for a site on the outskirts of a major metropolitan area will usually show that a truck can travel just as far into and through the city as outward from the city. Anyone who uses this same roadway network during the morning or evening commute might suggest that travel will be easier in one direction and considerably more difficult in the other. While computer models are powerful, useful, and increasing in sensitivity, they are not yet (nor are they likely to be) a practical substitute for local knowledge of actual conditions. Nevertheless, they are widely applied and tend to govern decisions in the initial planning stage, meaning that the large scale design of supply chains is determined by the factors they consider or omit, as well as the methods they employ. Transportation network congestion Network congestion for all modes impacts freight facility location decisions. Most modes have at least one identified trouble point. For example, containerized ocean shippers may view Southern California ports as an area of concern. Rail freight experiences difficulty in major urban areas, at the interface point between Class I railroads, or between Class I railroads and short line carriers. Truck carriers experience difficulty in any number of urban markets. Freight Facility Location Selection: A Guide for Public Officials 61