Safety stock is often calculated to accommodate a given service level (SL), where service is defined in some manner by the user. In this regard, the two favorite definitions of service are the percent of good cycles and the fraction of demand satisfied by off‐the‐shelf inventory. Judging from its increased appearance in the literature, especially in textbooks, the latter definition apparently is gaining in popularity. Unfortunately, this definition requires a little more effort to use. A procedure originally worked out by Brown (Brown's Method) shows how the calculations are to proceed. The partial expectation, E(z), is first calculated as a function of the expected lead time demand (μ), the standard deviation (σ) of lead time demand, and the required service level. Once E(z) is found, a table is consulted (Brown's Table) to determine z. Finally, the safety stock is calculated from the formula, zσ. There are several problems with using Brown's Table. First, z is given as the dependent variable rather than the independent variable, so the user has to search a bit to find the nearest pair of entries that straddle the required value of E(z). Then the user must perform an awkward interpolation, something many business students find very confusing. An additional problem with the table is that it is both sparse and inaccurate for high values of z. In fact, there are no entries at all for z 4.50, and there is only one significant digit of accuracy for z 3.93. Finally, Brown does not supply a computer program that calculates z directly from E(z). While this lack of a program is not important to the casual user of the tables, it can be important when one wishes to set safety stock levels automatically and internally by the computer. This article presents a new table, in which E(z) is the dependent variable rather than the independent variable. The computer program used to evaluate the entries yields solutions that are accurate to about fourteen decimal places. With this kind of precision the authors were able to extend Brown's Table to high values of z, while at the same time providing for virtually any number of significant digits. Incidentally, the entry density in this table is nonuniform (somewhat logarithmic) to accommodate usage over various orders of magnitude of z and E(z). The article also includes a simpler version of our computer program (single precision), which yields about four decimal place accuracy. The double precision program actually used is available on request, but it is somewhat complicated and depends on the computer used (the authors used a TRS‐80).

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Improved criteria are necessary to aid in determining awards of federal funds for metropolitan transit projects. Commuting is the main use for public transit. Thus a primary objective of an urban transit system should be to provide a flexible and balanced set of options to the workers in the metropolitan area for their journey to work. This paper discusses various facets of an appropriate balance among the three modes: rapid rail, bus, and automobile. Three cities are selected for further analysis: Baltimore, Kansas City, and Phoenix. These cities represent different stages in economic-transportation development, and also present different spatial patterns of residence and employment. The applicability of rapid rail transit to each city is examined in view of central city worker concentration and recent trends.

We were recently forced to spend some time on a committee re-evaluating course content of our department's graduate Production Management course. As is usual in such meetings, we quickly reached a point of diminishing returns, with the discussion going round in circles. However, since we were Management Scientists, we put the situation to good use by constructing a mathematical model of our problem, which will be presented below. This is a neat theoretical application which is quite weak on data, since the value curves depend on the individual professor's subjective assessments. However, it proved to be a useful way of organizing our thinking about a controversial situation.