Engineering topics
Publications and source records attributed to Remer, D. S..
This article provides a model for estimating the cost required to do a cost estimate. Overruns may lead to concellation of a project. In 1991, we completed a study on the cost of doing cost estimates for the class of projects normally encountered in the development and implementation of equipment at the network of tracking stations operated by the Jet Propulsion Laboratory (JPL) for NASA.
This article provides a model for estimating the cost required to do a cost estimate...Our earlier work provided data for high technology projects. This article adds data from the construction industry which validates the model over a wider range of technology.
There is an ever-increasing need to achieve greater efficiency in the operation of the Deep Space Network (DSN), i.e., increased productivity at reduced cost. One of the tools used in the course of a planning workshop on this subject was a methodology for budget allocation applicable to long-range planning. This article presents a model for analysis of the TDA budget allocation. For the 1994 through 1999 period, the percentage of the total TDA budget allocated to capacity and capability is being cut in half, whereas the percentage spent on efficiency of delivery will be increasing.
A model for estimating the cost required to do a cost estimate for Deep Space Network (DSN) projects that range from $0.1 to $100 million is presented. The cost of the cost estimate in thousands of dollars, C(sub E), is found to be approximately given by C(sub E) = K((C(sub p))(sup 0.35)) where C(sub p) is the cost of the project being estimated in millions of dollars and K is a constant depending on the accuracy of the estimate. For an order-of-magnitude estimate, K = 24; for a budget estimate, K = 60; and for a definitive estimate, K = 115. That is, for a specific project, the cost of doing a budget estimate is about 2.5 times as much as that for an order-of-magnitude estimate, and a definitive estimate costs about twice as much as a budget estimate. Use of this model should help provide the level of resources required for doing cost estimates and, as a result, provide insights towards more accurate estimates with less potential for cost overruns.
A model is suggested for making long range planning cost estimates for Deep Space Network (DSN) support of future space missions. The model is a function of major mission cost drivers, such as maintenance and operations, downlink frequency upgrade, uplink frequency upgrade, telemetry upgrade, antenna gain/noise temperature, radiometric accuracy upgrade, radio science upgrade, and very long baseline interferometry. The model is derived from actual cost data from three space missions: Voyager (Uranus), Voyager (Neptune), and Magellan. The model allows one to estimate the total cost and the cost over time of a similar future space mission. The model was back tested against the three projects and gave cost estimates that range from 17 pct. below to 19 pct. above actual mission preparation costs. The model was also compared with two other independent projects: Mariner Jupiter/Saturn (MJS later became Voyager) and Viking. The model gave total preparation cost estimates that range from 15 pct. above to 4 pct. below actual total preparation costs for MJS and Viking, respectively.
A simple model is suggested to do long-range planning cost estimates for Deep Space Network (DSP) support of future space missions. The model estimates total DSN preparation costs and the annual distribution of these costs for long-range budgetary planning. The cost model is based on actual DSN preparation costs from four space missions: Galileo, Voyager (Uranus), Voyager (Neptune), and Magellan. The model was tested against the four projects and gave cost estimates that range from 18 percent above the actual total preparation costs of the projects to 25 percent below. The model was also compared to two other independent projects: Viking and Mariner Jupiter/Saturn (MJS later became Voyager). The model gave cost estimates that range from 2 percent (for Viking) to 10 percent (for MJS) below the actual total preparation costs of these missions.
Historical cost curves were developed for hydrogen masers and cesium beam standards used for frequency and timing calibration in the Deep Space Network. These curves may be used to calculate the cost of future hydrogen masers or cesium beam standards in either future or current dollars. The cesium beam standards are decreasing in cost by about 2.3% per year since 1966, and hydrogen masers are decreasing by about 0.8% per year since 1978 relative to the National Aeronautics and Space Administration inflation index.
Simple method for life-cycle cost (LCC) estimation avoids pitfalls inherent in formulations requiring separate estimates of inflation and interest rates. Method depends for validity observation that interest and inflation rates closely track each other.
The data base collected during a six month demonstration of an automated Deep Space Station (DSS 13) run unattended and remotely controlled is summarized. During this period, DSS 13 received spacecraft telemetry data from Voyager, Pioneers 10 and 11, and Helios projects. Corrective and preventive maintenance are reported by subsystem including the traditional subsystems and those subsystems added for the automation demonstration. Operations and maintenance data for a comparable manned Deep Space Station (DSS 11) are also presented for comparison. The data suggests that unattended operations may reduce maintenance manhours in addition to reducing operator manhours. Corrective maintenance for the unmanned station was about one third of the manned station, and preventive maintenance was about one half.
A model is formulated to describe the effect of the time interval chosen for preventive maintenance upon the frequency of failure and frequency of total maintenance. Trade-offs between these two frequencies are determined by computation of an optimal interval in the case where the failure distribution is known. For unknown distributions, an adaptive statistical technique is developed that converges to an optimal preventive maintenance interval. A numerical illustration is given.
The maintenance data base collected for 15 weeks of recent unattended and automated operation of DSS 13 is summarized. During this period, DSS 13 was receiving spacecraft telemetry while being controlled remotely from JPL in Pasadena. Corrective and preventive maintenance manhours are reported by subsystem for DSS 13 including the equipment added for the automation demonstration. The corrective and preventive maintenance weekly manhours at DSS 13 averaged 22 and 40, respectively. The antenna hydraulic and electronic systems accounted for about half of the preventive and corrective maintenance manhours for a comparable attended DSN station, DSS 11.
A summary is given of the data base collected for nine weeks of Deep Space Station II. Life cycle cost parameters on efficiency and productivity ratios, costs, and telemetry were calculated from this data base.
The goals and data collection requirements to be used for the economic and performance evaluation indexes and life cycle cost parameters for the upcoming operations demonstration of an automated Deep Space Station (DSS) run unattended and controlled remotely from JPL are presented. These evaluation indexes compare the remote operation of telemetry at DSS 13 with the cost and performance of a comparable manned operation at DSS 11. A description is presented of the data that needs to be collected, how the data will be analyzed, and what can and cannot be learned from this operations demonstration.
The effect of projected interest and inflation rates on life cycle cost calculations is discussed and a method is proposed for making such calculations which replaces these rates by a single parameter. Besides simplifying the analysis, the method clarifies the roles of these rates. An analysis of historical interest and inflation rates from 1950 to 1976 shows that the proposed method can be expected to yield very good projections of life cycle cost even if the rates themselves fluctuate considerably.
A mathematical model is developed for calculating the life cycle costs for a project where the operating costs increase or decrease in a linear manner with time. The life cycle cost is shown to be a function of the investment costs, initial operating costs, operating cost gradient, project life time, interest rate for capital and salvage value. The results show that the life cycle cost for a project can be grossly underestimated (or overestimated) if the operating costs increase (or decrease) uniformly over time rather than being constant as is often assumed in project economic evaluations. The following range of variables is examined: (1) project life from 2 to 30 years; (2) interest rate from 0 to 15 percent per year; and (3) operating cost gradient from 5 to 90 percent of the initial operating costs. A numerical example plus tables and graphs is given to help calculate project life cycle costs over a wide range of variables.
The described mathematical model calculates life-cycle costs for projects with operating costs increasing or decreasing linearly with time. The cost factors involved in the life-cycle cost are considered, and the errors resulting from the assumption of constant rather than uniformly varying operating costs are examined. Parameters in the study range from 2 to 30 years, for project life; 0 to 15% per year, for interest rate; and 5 to 90% of the initial operating cost, for the operating cost gradient. A numerical example is presented.