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A Kennedy Space Center Parametric Facilities Cost Model (PFCM) is presented as a simple, fast method with high accuracy, leveraging normalized historical data in an Excel 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estimates can be created in less thana minute by applying powerful techniques andalgorithms to create an Excel-based parametriccost model.Infive easy steps you will learn how tonormalize your company's historical cost data tothe new project parameters.This paper provides acomplete,easy-to-understand,step by step how-toguide.Such a guide does not seem to currentlyexist.  \nOver 2,000 hours ofresearch,data collection,and trial and error,and thousands of lines ofExcel Visual Basic Application (VBA)code wereinvested in developing these methods.While VBAis not required to use this information,it increasesthe power and aesthetics of the model.Implementing all of the steps described,while notrequired,will increase the accuracy of the results.  \nPaper  \nKennedy Space Center (KSC)is agovernmental entity that imparts unique budgetaryrequirements.Project budgets must be requestedyears before design has been completed or truerequirements are known.To provide projectbudget information,the Parametric Facilities CostModel(PFCM)was created.Its operation issimple and fast,and its results are very accurate—the documented average is typically within 5percent of the low bidder,which is within theexpected accuracy range of a Class 1 or DetailedEstimate.Figure 1 shows the back-checked  \nestimate accuracy of 44 concrete office-type  \nbuildings.The total average accuracy of the PFCMestimate is 3.04 percent.  \nDeaFromLow Bid%  \nProject SF  \nFigure 1-Back-Checked Accuracy of PFCM for 44 ConcreteOffice Buildings  \nPFCM works by applying algorithms tonormalize historical project information that hasbeen entered into an Excel spreadsheet.KSC'srequirements,some of which are unique,drove themodel design concept.Therefore,other companiesmay need to adjust the techniques described,especially the economies-of-scale algorithm.  \nThe only requirements for applying thesetechniques are a basic knowledge of Excel andsome historical data of a project type that iscomparable enough to enable interpolation  \nKnowledge of VBA and statistics would beextremely helpful,but is not a prerequisite forsuccess.The formulas are presented in an Excelformat,where  \n●*=multiplication,  \n● /=division,and  \n●^=exponent.  \n# Step 1-Adjust for the Number ofBidders\n\nOne critical but frequently overlooked facet ofdata normalization is the Number of BiddersConcept developed by Dr.Martin Skitmore,whohas documented this occurrence statistically innumerous high-level papers.The basic theory isthat when times are good and there is plenty ofwork,fewer contractors are willing to spend thetime and money required to prepare bids.Conversely,when times are bad and there are notmany projects,contractors will spend a great dealof time preparing bids and cut their margins in aneffort to stay in business.  \nAlthough the concept is well documented,theauthor could not locate any published data tablesthat indicate the specific percentage adjustmentthat should be applied for bid cost adjustment.However,by applying the Number of BiddersConcept to historical bid abstract data for over  \n1,000 KSC projects,the author developed thefollowing algorithm:  \nY=0.74*the number of bidders ^0.14  \nwhere Y=the percentage adjustment requiredfor that particular project.  \nIt should be noted that in instances of one bidresponse,the correction required can besubstantially higher when that bid is received as asolicitation by a 8(a)Set Aside,or HUBZoneproject;however,it is the best fit that can bestatistically derived.Figure 2 demonstrates theadjustment generated by the algorithm.Thisconcept will be applied twice in our estimatepreparation,once to normalize historical cost data  \nand once to adjust the final estimate for anticipatedmarket conditions at the time of bid.  \nNumber of Bidders  \nFigure 2-Number ofActual Bidders Algor","cbCaitkVDQWUSEo2","https://ap.wps.com/l/cbCaitkVDQWUSEo2","pdf",17406966,9,46,"English","# Abstract\n# PFCM Background and Accuracy\n# Step 1 - Adjust for the Number of Bidders\n## Number of Bidders Concept Algorithm\n# Step 2 - Normalize the Project Costs\n## Economies-of-Scale Adjustment","[{\"question\":\"How does the Excel-based parametric cost model generate estimates quickly?\",\"answer\":\"It applies algorithms that normalize historical project information entered into an Excel spreadsheet, producing estimates rapidly using predefined formulas.\"},{\"question\":\"What is the purpose of Step 1 in the estimation process?\",\"answer\":\"Step 1 adjusts normalized costs based on the Number of Bidders concept, applying a percentage adjustment derived from historical bid abstract data.\"},{\"question\":\"What does Step 2 focus on when normalizing project unit costs?\",\"answer\":\"Step 2 applies an economies-of-scale adjustment (capacity-based normalization), using a nonlinear relationship between project size and costs to reflect expected cost changes.\"}]","Fast Conceptual Cost Estimating Aerospace Projects Using Historical Information - page 1 | PDF",1789185876]