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DEFINITIONAL ISSUES AND POTENTIAL REVISIONS 36 BMAR Algorithm For every condition code a statistical weight is assigned based on random sampling. BMAR is equal to the sum of all equipment replacement costs multiplied by respective statistical weights. BMAR=SUMj=1,5{SUMk=1,n{(CCF)j (RC)k}} where: SUM is the summation function CCF is the condition code factor (weight) RC is the replacement cost n = the total pieces of equipment for the condition code Example Calculation of BMAR Parameters: Assume a 300 item inventory; total replacement cost = $1,000,000; 4 item statistical sample. Inventory # Repair Cost Replace Cost Repair/Replace 7 100 10000 0.10 43 500 2000 0.25 115 300 4000 0.075 267 200 3000 0.066 Total replacement cost = $1,000,000 Condition Code Factor (CCF)= (0.1+0.25+0.075+0.066)/4=0.123 BMAR = (Total Replacement Cost)(CCF)= ($1,000,000)(0.123)=$123,000 NASA Simplified BMAR Model Using Real Property Data The Dryden Flight Research Center has proposed a less complex model that does not require the use of a CMMS. Instead, it uses real property records common to all agencies. In this model, statistical sampling by facility type is used to determine the backlog of maintenance and repair. The backlog is determined by using a random sample of facilities in an agency's inventory and concentrating on a specified number of major systems, for example, structural, mechanical, and electrical. A weighted average is calculated for the net condition code, and the backlog is then assumed to be an exponential function of condition. Simplified BMAR Algorithm A statistical weight (CCF) is assigned based on random facility sampling. BMAR is equal to the sum of all facility replacement costs multiplied by the CCF. BMAR={SUM k=1,n{(CCF) (CRV)k} where: SUM is the summation function
