Patent Publication Number: US-2011060601-A1

Title: Automated feature-based analysis for cost management of direct materials

Description:
RELATED DOCUMENTS 
     This U.S. divisional patent application is related to, and claims the priority benefit of, U.S. Nonprovisional patent application Ser. No. 11/372,937, filed Mar. 9, 2006, which. is related to, and claims the priority benefit of U.S. Provisional Patent Application Ser. No. 60/659,992, filed Mar. 9, 2005. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an overview of one embodiment of the invention; 
         FIGS. 2   a - d  comprise process modeling diaqrams of the present invention.; 
         FIG. 2   e  describes the assembly of  FIGS. 2   a - d  to illustrate the process modeling diagram; 
         FIG. 3A  illustrates one embodiment of the analytics layer; 
         FIG. 3B  illustrates one method of sourcing analysis; 
         FIG. 3C  illustrates one embodiment of the system architecture; 
         FIG. 3D  illustrates the logical flow of a user&#39;s progression in the embodiment; 
         FIG. 4  illustrates the select parts by similar feature; 
         FIG. 5  illustrates the select parts by specific features; 
         FIG. 6  illustrates the cost savings opportunities summary; 
         FIG. 7  illustrates the select parts by category; 
         FIG. 8  illustrates the review parts for analysis in the analytics layer; 
         FIG. 9  illustrates the computations made during the analytics layer; 
         FIG. 10  illustrates the detailed parts analysis of a part; 
         FIG. 11  illustrates the cost drivers for a family of parts; 
         FIG. 12  illustrates a graphical representation of the cost drivers for a family of parts; 
         FIG. 13  illustrates the nearest neighbor analysis; 
         FIG. 14  illustrates the results sourcing analysis. 
     
    
    
     SUMMARY OF THE INVENTION 
     A cost management system and method using an automated features-based system and process for analyzing costs of direct, made-to-order parts is described herein. More particularly, the system utilizes a software process that employs proprietary algorithms to analyze features of the target parts including their material, shape, as well as other characteristics and estimate what parts should cost to produce. By comparing the “should costs” with vendors&#39; prices the system identifies cost saving opportunities. 
     The present embodiment utilizes information in CAD files and other drawings, analyzes key features and manufacturing characteristics of the selected components, and identifies cost relationships. It then uses these relationships to identify outliers such as, parts that appear to be unusually expensive compared with what the model predicts that they should cost. Such parts are further analyzed to determine if they are candidates for cost reduction. 
     As part of its analytical models, one embodiment performs four primary calculations. First, based on part features, materials, manufacturing processes, and purchasing demand volumes, the embodiment calculates a “should cost” price for each part. It identifies outliers by comparing the “should cost” with the vendor&#39;s quoted price. Unusually expensive parts are candidates to be bid on by purchasing professionals, and thereby reduce costs. Second, it identifies key factors called “cost drivers,” which contribute to part costs. These key factors can be used by the engineering staff to minimize cost in the design process. Third, an embodiment of the system identifies similar parts called “nearest neighbors.” Last, it analyzes the capabilities of the suppliers to identify their core capabilities and thereby determines which parts are most efficiently sourced by each respective supplier. 
     The embodiment uses a top-down approach that can analyze an enterprise-wide set of data on purchased direct materials, quickly identify “sweet spots” that have the most cost reduction potential, and provide direction on how to attain cost savings. An embodiment of this invention can be used to funnel large amounts of data through a tool that will accurately pinpoint the specific opportunities that will give the most impact and efficiency in reducing costs. As such, the invention serves as the next generation of cost management tools that work in conjunction with existing cost management methods to accurately identify specific parts that are candidates for cost reduction and to steer the process used to obtain cost savings. 
     DETAILED DESCRIPTION 
     This detailed description is presented in terms of programs, data structures or procedures executed on a computer or network of computers. The software programs implemented by the system may be written in languages such as Java, HTML, Python, or the R statistical language. However, one of skill in the art will appreciate that other languages may be used instead, or in combination with the foregoing. 
     For purposes of illustration, the invention relates to a system and software product directed to an analytical methodology for cost management of highly engineered made-to-order parts. In one embodiment, the system takes data from computer assisted drawings (CAD) files, engineering specifications files, demand data from Enterprise Resource Planning (ERP) systems, cost data from financial systems, and/or other electronic files and utilizes data mining algorithms to analyze part features, usage patterns, and engineering specifications to construct “should cost” curves across individual families of parts. Based on the should cost curves, the embodiment determine the significant cost drivers that affect the cost of the one or more target parts. 
     As best seen in  FIG. 1 , in one embodiment the system architecture consists of three distinct layers: the data management layer  120 , the analytics layer  125 , and the cost management layer  130 . The data management layer  120  in the system architecture loads and manages customer data. The middle layer in the architecture is the analytics layer  130 , which hosts various analysis algorithms that are required for invention models. The cost management layer  130  of the system architecture presents results in easy to understand and act-upon Cost Management Tools. In one embodiment, the cost management tools are presented to the user in a browser interface. 
     I. System Data Management Layer 
     In one embodiment of the system, the data management layer  120  consists of five parts. First, the system implements integration points that enable it to assimilate purchasing, financial, and part features information from the customer&#39;s internal systems. Within the integration points are data loading rules  175  the system uses as part of its data assimilation process. The reason for the data loading rules  175  is that each customer stores its parts purchasing and financial data using different formats. The data loading rules  175  aggregate data various customers and thereby enable the system to employ a business intelligence “should cost” database  165  that is reusable across customers. 
     The part features extraction process involves two types of information. The first type includes engineering specifications  115  that describe physical characteristics of the part. By processing these files the system can extract a set of physical features that describe the part. Examples of these features include material, e.g., which metal, height, width, and depth of the part, physical volume, number of cores, and characteristics of the drill holes. The second type of information involves machining specifications such as tolerances, smoothness, drill holes, drill hole volume, and parting line perimeter. There is a set of engineering specifications associated with each part. As a component of the feature extraction process, the system processes each specification and extracts relevant information for cost modeling. 
     Second, using the data loading rules  175 , the system data loading tools transform, normalize and validate parts data as it is stored in the database  165 . In one embodiment, the data loading rules  175  are written in the R statistical language. 
     Third, the system employs exception reports  160  that highlight unusual and suspect information. The reports, for example, identify unusually expensive parts or cheap parts, parts with missing weights, parts with no demand, suppliers, and many other characteristics of the data. 
     Fourth, the system analyzes 2D parts drawings and 3D engineering models of the parts and extracts features that are predictive of costs. In one embodiment, cost predictive features variables include financial information, purchasing information, and feature information. As best seen in TABLE 1, the features may involve part characteristics such as the volume of the part, which along with the density of the material, is used to calculate the part&#39;s weight, number of holes drilled into the part, type of drill used, number of cores, number of risers, surfaces, machine setups, and the like. One of ordinary skill in the art will appreciate that this table does not provide an exhaustive list, but is merely illustrative. The features characteristics are the primary drivers that enable the system&#39;s predictive models to achieve high accuracy. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Cost Predictive Features Variables 
               
            
           
           
               
               
               
            
               
                 Financial 
                 Purchasing 
                   
               
               
                 Information 
                 Information 
                 Feature Information 
               
               
                   
               
               
                 Part Number 
                 Segment 
                 Material 
               
               
                 Part Name 
                 Family 
                 Aluminum 
               
               
                 Engineering Change 
                 Class 
                 Brass 
               
               
                 Number 
               
               
                 Forecasted Annual 
                 Supplier 
                 Ductile Iron 
               
               
                 Demand 
               
               
                 Demand Past 12 
                 Buyer 
                 Gray Iron 
               
               
                 Months 
               
               
                 Base Part Price 
                 Finishes Status 
                 Malleable Iron 
               
               
                   
                 (Rough, Semi, 
               
               
                   
                 Finished) 
               
               
                 Additional Charges 
                 Part Weight 
                 Steel 
               
               
                 Packaging 
                 Quoted Annual 
                 Casting Cost 
               
               
                   
                 Demand 
               
               
                 Painting 
                 Quote Date 
                 Part Dimensions 
               
               
                 (Prime/Finish) 
               
               
                 Other 
                   
                 Height 
               
               
                 Material 
                   
                 Width 
               
               
                 Surcharge 
               
               
                 Export Charges 
                   
                 Depth 
               
               
                 Storage/Warehousing 
                   
                 Surface Area 
               
               
                 Tooling 
                   
                 Part Volume 
               
               
                 Premium Charge 
                   
                 Box Volume 
               
               
                   
                   
                 Finished Weight 
               
               
                   
                   
                 Part Features 
               
               
                   
                   
                 Cores 
               
               
                   
                   
                 Core Volume 
               
               
                   
                   
                 Pressure test - Air 
               
               
                   
                   
                 Pressure test - Fuel 
               
               
                   
                   
                 Pressure test - Oil 
               
               
                   
                   
                 Pressure test - Water 
               
               
                   
                   
                 Machining Cost 
               
               
                   
                   
                 Direct 
               
               
                   
                   
                 Ports 
               
               
                   
                   
                 Port Volume 
               
               
                   
                   
                 Drill Holes 
               
               
                   
                   
                 Drill Hole Volume 
               
               
                   
                   
                 Heat Treat 
               
               
                   
                   
                 Parting Line Perimeter 
               
               
                   
                   
                 Grinding 
               
               
                   
                   
                 Machine Setups 
               
               
                   
                   
                 Riser Removal 
               
               
                   
                   
                 Surface Area Flatness 
               
               
                   
                   
                 Indirect 
               
               
                   
                   
                 Forecasted Annual Demand 
               
               
                   
                   
                 Log Annual Demand 
               
               
                   
                   
                 Assembly Cost 
               
               
                   
                   
                 Direct 
               
               
                   
                   
                 Bearings 
               
               
                   
                   
                 Fasteners 
               
               
                   
                   
                 Seals 
               
               
                   
               
            
           
         
       
     
     The fifth part of the system&#39;s data management layer is the database  165 . In one embodiment, the system organizes parts data using snowflake schema data warehouse model with fact tables for parts and suppliers. An embodiment of the snowflake database schema is shown in  FIG. 2   a - 2   e . One of ordinary skill in the art will appreciate the snowflake schema is but one architecture of a data warehouse, and other schemas, including but not limited to a star schema, may be used. 
     It should be appreciated that part of this invention relates to choices of variables which may be loaded and data loading rules  175  used to process the data. There are many possible features that can be extracted from CAD data and many possible purchasing and demand variables. One aspect of the invention is the selection of variables and modeling techniques that are predictive of cost. 
     1. Data Management Architecture 
     At the architectural level, one embodiment of the system performs data management functions using a four-step process, as best seen in  FIG. 3A , In this embodiment, the data management process is performed as follows: 
     First, in one embodiment, the system extracts the data from the customer delivered formats and loads the files into memory. Next, the system aggregates, categorizes and filters the data based on customer defined rules. At this point, the system performs extreme value elimination by applying the data loading rules  175  and looking for extreme statistical values. The parts associated with the extreme values are eliminated from the data set under consideration. The system then takes the data from step 2 and loads it into database  165  for analysis. If a part is excluded from loading, the system will generate exception reports  160  which provide the user with information on any data load failures or exceptions. Once the data is properly loaded into the database  165 , the analytics layer  120  performs model fitting algorithm analysis. 
     II. Analytics Layer 
     The second layer of the system&#39;s architecture is the analytics layer  125 . This analytics layer  125  consists of a series of statistical routines that, in one embodiment, are implemented using the R Statistical Language. Further, this analytics layer  125  in the disclosed embodiment comprises two parts: the analytics module and analytics architecture. 
     A. Analytic Modules 
     As part of its analytical layer  125 , an embodiment of the system performs four primary calculations. First, based on part features, material, manufacturing processes, and purchasing demand volumes, the should cost  300  module of the analytics layer  120  calculates a “should cost” price for each part. For purposes of illustration, “should cost” refers to the amount of money a part should reasonably cost. In this embodiment, the system identifies outliers by comparing the “should cost” with the vendor&#39;s quoted price. Outliers refers to parts which seem to be unusually expensive compared with what the model predicts that they should cost. Second, the cost drivers  350  module of the analytic layer  125  identifies key factors called “cost drivers,” which contribute to part costs. These key factors can be used by the engineering staff to minimize costs in the design process. Third, the nearest neighbor  375  module identifies similar parts called “nearest neighbors.” Last, the sourcing analysis  325  module of the analytics layer  125  analyzes the capabilities of the suppliers to identify their core capabilities and thereby determines which parts are most efficiently sourced which each respective supplier. 
     1. Should Cost—Predicting What Each Part Should Reasonably Cost 
     The should cost  300  module models the costs of parts by predicting the price/kg for each part using generalized linear models. 
     a. Linear Combination Algorithm—Predicting the Price/kg 
     This algorithm predicts the log of the cost per kilogram of a part using a linear combination of features and categories.
         log(costperkg)˜transform(dmd)+finwt.kg*material+boxvol +height +width +depth +risers*material +drillholeComp*material+surfarea*material+partingLinePerim*material+factor(hasCores)+nCores+factor(nCores)+coreVol+sqrt(coreVol)+sqrt(nCores)+factor(nCores)+heatTreat+sqrt(pressTestAir)+sqrt(pressTestOil)+sqrt(pressTestWater)+sqrt(pressTestFuel)+sqrt(drillholes)*material+nPorts+factor(rsf)+class.desc+nBearings+nSeal+NFasteners)+factor(material)       

     What should be appreciated is that our model does not predict “should cost” directly. Instead, for each family of parts, the algorithm predicts the log of cost per kilogram as a linear function of the log of the annual demand for parts, physical features of the part, machining costs, and engineering specifications. The type of material, which the model includes as a variable, is also important. The predicted “should cost” price is then the exponential of the predicted log cost per kilogram of the part. 
     In one embodiment of the system, models of this form are developed for all of the parts together and then again for each family of parts (e.g., Bonnets, Brackets, Covers, Housings, Elbows, and Supports). After the full model is fit, the embodiment refines its models using R&#39;s step procedure. In this embodiment, step applies the stepAlC algorithm. In this embodiment, the algorithm refines the model, adds and removes variables, and iterates until it finds the best fit. It will be appreciated by one skilled in the art that other refinement procedures may be used and that the above described embodiment is not exclusive but merely illustrative. 
     2. Cost Drivers 
     In one embodiment, the cost driver  350  module identifies outliers by comparing the “should cost” with the vendor&#39;s quoted price. After outliers are eliminated, in a similar calculation to “should cost,” the cost drivers for a family of parts are predicted using a linear combination of features and categories. The system models the cost per kilogram of each part as:
         costperkg˜finwt.kg(alum, duct, brass, iron, gray, steel)+boxvol+height+width+depth+risers+drillholes+drillHoleComp+surfarea+partingLinePerim+nCores+coreVol+heatTreat+factor(pressTestAir)+factor(pressTestWater)+factor(pressTestfuel)+factor(pressTestOil)+nBearings+nSeals+nFasteners+nPorts,+portVol,+flatness+log(demand)  2  John M. Chambers and Trevor J. Hastie (1992). Statistical Models in S, Wadsworth &amp; Brooks/Cole Cole Computer Science Series, Pacific Grove, Calif.       

     What should be appreciated is that our model does not predict “cost drivers” directly. Instead, for each family of parts it predicts the cost per kilogram as a linear function of the log of the annual demand for parts, features that describe the part, machining costs, and engineering specifications. The type of material, which the model includes as an interaction term, is also important. The predicted “cost driver” price is then the exponential of the predicted log cost per kilogram of the part. In one embodiment, models of this form are developed for all of the parts together and then again for each family of parts (e.g., Bonnets, Brackets, Covers, Housings, Elbows, and Supports). 
     In one embodiment of the system, most predictive factors (cost drivers) and their relative effects are easy to interpret.  FIG. 9  shows sample output from the system&#39;s Prediction Model. For the example illustrated in  FIG. 9 , certain key variables in the Model are marked with symbols, such as “***”, “**”, or “*”, to indicate their level of significance in the cost driver significance  900  column. In an embodiment of this particular model (model of a direct materials part analysis), the key variables for predicting costs include log (annual demand), box volume, part volume, drill holes, part type, material, and type of pressure test. 
     The relative effects of cost drivers for this example are shown in Table 2. The units in the table are incremental costs measured in cents per unit change in the cost driver. Thus, for example, on average a 10× increase in demand (logdmd) (1× in log scale) decreases the cost per kilogram of a part by $1.99. 
                     TABLE 2                  Cost Drivers and their relative effects in cents.                                 Incremental costs           Cost Drivers (CD)   (¢/unit change in CD)                                         Logdmd   −199.87           Boxvol   1.08           Height   −.69           Width   −.91           Depth   −.50           Partvol   −7.56e−5           Drillholes   9.80           CoreVol   7.54           factor(class.desc)BONNETS   −24.20           factor(class.desc)BRACKETS   −217.95           factor(class.desc)COVERS   −333.12           factor(class.desc)ELBOWS A   229.05           factor(class.desc)HOUSINGS   297.75           factor(class.desc)SUPPORTS-   −121.31           ENGINE           factor(heatTreat)Yes   −824.10           factor(pressTestVal)Air   129.85           factor(pressTestVal)Fuel   1767.42           factor(pressTestVal)Oil   332.38           factor(pressTestVal)Unknown   −320.61           Factor(pressTestVal)Water   −24.93           factor(material.coarse)DUCT   −1233.37           factor(material.coarse)GRAY   −1366.98           factor(material.coarse)IRON   −1090.80           factor(material.coarse)STLCAST   −359.44                        
It should be appreciated from linear regression theory that the parameters in Table 2 are the cost drivers that are displayed in the system&#39;s Cost Management Analysis (CMA) user interface. These parameters estimate the incremental costs for each of the features included in the model. In one embodiment of the system, these features are validated by applying the business rules (are these the data loading business rules?). It is sometimes the case that randomness in the statistical models results in aberrant estimates. The business rules flag suspect values and provide explanations such as insufficient data in the case of extreme randomness.
 
     3. Ne rest Neighbor Algorithm—Identifying Similar Parts 
     The second class of system algorithms involves searching feature space to identify similar parts or nearest neighbors. In one embodiment, calculation of data structures subsequently applied to produce predictions and used in the nearest neighbor analysis is performed at data loading time or whenever new data is added to the system&#39;s database. The system uses pre-determined variables as feature vector and defines these vectors as a point in feature space: 
       v i =(v 1 , v 2 , v n ) 
     where v i  is the value of feature i for the particular part under consideration. Table 3 shows a list of variables used in one embodiment of the nearest neighbor analysis. It should be obvious to one of ordinary skill in the art that the table is meant to be only illustrative and not exclusive. The system then normalizes each of the numeric features using the standard normal transform and in one embodiment calculates the Euclidean distance (d) between the points representing the different parts in feature space. One of skill in the art will appreciate that other distance metrics, besides the Euclidean, may be used. 
         d ( v   part1   , v   part2 )=|| v   part1   −v   part2 || 
     where || || is the standard Euclidean distance function. When the user selects a target part, pre-selected feature variables of that part become reference points and the system then provides the distance between those target variables and all other parts. The nearest neighbor algorithm constrains the match so that certain attributes of the parts must match exactly, e.g., the parts must be made of the same material and be the same part type. Within this restricted class it enumerates all distances and returns the n candidates to the user interface. 
     
       
         
           
               
             
               
                 TABLE 3 
               
             
            
               
                   
               
               
                 Variables for Nearest Neighbor analysis 
               
            
           
           
               
               
               
            
               
                   
                 Comparables Analysis 
                 Comparables Analysis 
               
               
                   
                 Variable 
                 Variable Definition 
               
               
                   
                   
               
               
                   
                 Finwt 
                 finished weight 
               
               
                   
                 height 
                 height dimension 
               
               
                   
                 Width 
                 width dimension 
               
               
                   
                 Depth 
                 depth dimension 
               
               
                   
                 partvol 
                 part volume dimensions 
               
               
                   
                 Surfacea 
                 surface area dimension 
               
               
                   
                 partingLinePerim 
                 parting line perimeter 
               
               
                   
                   
                 grinding 
               
               
                   
                 Risers 
                 risers (removal) 
               
               
                   
                 Drillholes 
                 number of drill holes 
               
               
                   
                 Nports 
                 number of ports 
               
               
                   
                 HeatTreat 
                 heat treat of part 
               
               
                   
                 PressTestAir 
                 pressure test air 
               
               
                   
                 PressTestFuel 
                 pressure test fuel 
               
               
                   
                 PressTestOil 
                 pressure test oil 
               
               
                   
                 PressTestWater 
                 pressure test water 
               
               
                   
                 NCores 
                 number of cores 
               
               
                   
                   
               
            
           
         
       
     
     4. Sourcing Analysis—Evaluating the Suppliers 
     One possible reason for an over priced part maybe because it is sourced with a supplier who cannot produce it efficiently. For each part the system rates each supplier on an Overall Sourcing Fit Rating  1400  (See  FIG. 14 ). An Overall Sourcing Fit Rating  1400  is calculated for each supplier by determining how far the target part is away from the range of efficiency for each supplier for each of the different part source variable categories, including but not limited to the variables listed in TABLE 4. One of ordinary skill in the art will appreciate that the table is meant to be only illustrative, and not exclusive. If the overall sourcing fit rating  1400  is low, it suggests that perhaps another source might be more appropriate for this part. 
     
       
         
           
               
             
               
                 TABLE 4 
               
               
                   
               
               
                 FEATURE VARIABLES FOR OVERALL SOURCE FIT RATING 
               
               
                 Feature Variables for Overall Sourcing Fit Rating 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                   
                 Cost per Kg 
               
               
                   
                 Annual Demand 
               
               
                   
                 Finwt/kg 
               
               
                   
                 Height 
               
               
                   
                 box volume 
               
               
                   
                 Surface area dimension 
               
               
                   
                 heat treated 
               
               
                   
                 Pressure Testing 
               
               
                   
                       Air 
               
               
                   
                       Fuel 
               
               
                   
                       Oil 
               
               
                   
                       Water 
               
               
                   
                 Average core volume 
               
               
                   
                 Average port volume 
               
               
                   
                 Average drill hole volume 
               
               
                   
                 Maximum flatness 
               
               
                   
                 is.assembly 
               
               
                   
                   
               
            
           
         
       
     
     The sourcing fit analysis works by analyzing the parts that each supplier produces, as shown in  FIG. 3B . The first step in the calculation is to collect all parts made by supplier for a specific material. Next the system calculates the range of values for all part source categories for each part for each supplier. The system then compares the part source categories for the target parts features to the range of the source part values of each potential supplier. The system assesses 1 point for each feature that falls within [0.5,0.95]. If the target parts does not contain the feature, the system ignores it. Further, the system penalizes one point in cases of a low volume supplier. Using this scoring rating, the system calculates fit rating as a percentage of features within the range/total features 
     The score percentage displayed in the user interface is the Score(p)/number of features checked. For each part, the algorithm checks every possible supplier, sorts them in reverse order, and displays the best suppliers. Ties for suppliers that have the same percentage are broken by sorting on pdiff, the percentage difference between should cost and the actual price. 
     B. Analytics Architecture 
     At the architectural level, one embodiment of the system performs system analysis, as best seen in  FIG. 3A . 
     Using all of the parts data in the system&#39;s populated database  165 , in an off-line process, the system runs several statistical and data mining routines that fit models. The fitting process results in sets of models and coefficients that are used in subsequent analysis. In addition, the system pre-calculates many data structures that are subsequently applied to produce predictions and used in the nearest neighbor  375  module. As part of its off-line calculations, the system stores each part in the invention database for “cost reasonableness” and flags any unusual parts for further investigation. In one embodiment, model fitting and scoring are performed at data loading time or whenever new data is added to the system&#39;s database  165 . 
     In this embodiment, as shown in  FIG. 3A , the system analysis process is performed as follows: 
     Once the data is loaded into the database  165 , as discussed above and shown in  FIG. 3A , the system sequences the model fitting algorithms to ensure the proper fitting and results. Next, the system extracts data from the database  165  and loads that data into the analytical engine. The analytical engine then performs the following model fitting algorithms analysis based on input from the sequencer: 
     First, the system calculates the “should cost” price in the should cost  300  module. Here, for each part, in one embodiment, the system applies the log(costperkg) model from step  3  to predict the cost of each part. The predicted “should cost” value is compared with the vendor&#39;s price to identify large percentage differences, which one embodiment stores in a variable called pdiff. Parts with large positive pdiff&#39;s, e.g., a part is much more expensive than predicted, are candidates for cost savings. The should cost  300  module is described at length above. 
     Next, the system calculates “Cost Drivers” from the cost drivers  350  module. Here, for each part family, in one embodiment, the system uses the R statistical language to fit linear regression that predict should cost as a generalized linear function of the part&#39;s features. As with normal statistical theory, the coefficients in this model are the relative contributions of the particular features. The “cost driver”  350  module is described at length above. 
     Next, the system performs the “Nearest Neighbor” analysis in the nearest neighbor  375  module. Here, in one embodiment, for each part the system normalizes each feature to a (−1,1) scale and calculates the Euclidean distance between every part in feature space. Using this distance the system identifies the nearest parts and labels them neighbors. The nearest neighbor  375  module is described at length above, 
     Next, the system performs a Sourcing Analysis in the sourcing analysis  325  module. In one embodiment, this analysis involves analyzing every part in the dataset that each supplier produces and calculating the [0.5, 0.95] range of each feature. Then for each part the system, in one embodiment, scores each supplier on 16 possible features and give the supplier points each time the part&#39;s feature is in the [0.5, 0.95] range of the supplier&#39;s capability. The system also subtracts points in cases of a low volume supplier. The rating of a supplier for a part is its total score/number of features evaluated. The calculation is performed by material for each supplier. The sourcing analysis  325  module is described at length above. 
     The last step involves pushing out the analytical results to a database  165 . The CMA website then accesses the database  165  to provide information to CMA users. Users access the system&#39;s analytical routines, through the system&#39;s presentation layer, which is described below. A top level view of the CMA application architecture can be seen in  FIG. 3C . For a description of the elements in the CMA application application, see LEGEND 1 below. 
     LEGEND 1: Elements in CMA application Architecture 
     View 
     Java Server Pages—Jave Pages for UI 
     JS Javascript 
     CSS—Cascading Style Sheets for web pages 
     Images—Images for web pages 
     Help—Third party help system 
     Business 
     Struts Controller—Part of the Apache Framework 
     Action layer—Part of the Apache Framework 
     Action Form—Unique forms for defining the actions of the action layer 
     JAAS—Java Authentication and Authorization Service 
     Value Objects—Objects used to define business rules 
     JFREE Chart—Third party charting object 
     Model Classes—Classes to interface between the action layer and the database layer 
     DB Layer—Interface layer to the database 
     III. Cost Management Layer 
     The third layer of the system architecture is the cost management layer  130 . The system&#39;s cost management layer  130  allows for the user to automatically group parts for analysis and provides a detailed analysis of cost saving opportunities. 
     A. Accessing the System 
     Users may access the system in one of three ways: (i) selecting parts by feature, (ii) selecting parts by category, or (iii) retrieving parts selected in previous analysis session. The logical flow of the cost management layer  130  is best represented by  FIG. 3D . 
     One way for the user to access the system is to search for parts by features, as best seen in  FIG. 4 . The user begins by inputting a part number  400  as a reference point. The embodiment then displays the part name  405 , the part supplier  440 , and the part annual demand  445 . The user may then optionally select the columns for display such as the part name  405 , the part weight  435 , the part annual demand  445 , the part material  410 , the part material reference  450 , the part supplier  440 , the part platform  445 , and the part envelope  460 . The system will then use the nearest neighbor algorithm to find parts with similar features in the database to analyze and display the results. As best seen in  FIG. 6 , the search results display the part set summary  600 , the part segment analysis  610 , and the nearest neighbor list  620 . The nearest neighbor list  620  set becomes the systems working set for this particular analysis. 
     In one embodiment of the system, as best seen in  FIG. 5 , the above-described search feature provides the user with the ability to refine the search criteria using several search filters including but not limited to part material  410 , part buyer  520 , part supplier  440  and part annual purchasing demand  445 . 
     The second entry point to the system provides a Category Part Selector mechanism for specifying a system database search. In one embodiment of the system, users can create search rules for category part searches. In this embodiment, system users may create rules by selecting parts segments  700 , part families  710  and part classes  720  to include in the search rules as well as filters based on part material  410 , part buyer  510 , part supplier  440  and part annual purchasing demand  445 . The search rule list  740  is displayed and the user may add a rule by engaging the add search  730  function. Optionally, the user may remove a rule by engaging the remove rule  740  function. One of ordinary skill in the art will appreciate that the categories for creating search rule listed above are not exhaustive but are merely illustrative of possible search criteria. The system will apply these rules to select parts from the system database for analysis. The Select Parts by Category mechanism is shown in  FIG. 7 . Pressing the get parts  470  function submits the working set of parts, as modified by the user, to the system&#39;s analytic engines, described above. 
     Third, users may review and “fine tune” their analysis working set using the dialogue shown in  FIG. 8 . In one embodiment, users may view their previous analysis set in a list  850  and then remove inappropriate parts or include additional parts in the analysis. Pressing the run analysis  875  function submits the working set of parts, as modified by the user, to the system&#39;s analytic engines, described above. 
     B. Cost Savings Opportunity Summary 
     Next, the system takes the results provided by the analytics layer  125  and presents the cost savings opportunities and their respective actions to the end user. For example, as can be seen in  FIG. 6  the cost management layer  130  presents a top level summary of the parts analyzed. This includes a parts segment analysis  610 , which lets the user know how the parts were segmented within the analysis and the top cost savings opportunities in order of potential savings. The analysis summary interface allows the user to access an overview of the cost drivers, and all cost savings opportunities, as well as access a detailed parts analysis for individual parts. 
     1. Detailed Part Analysis 
     The system&#39;s detailed part analysis shows the details of the analytic layer  125  applied to a single part. The system shows the user what the part should cost as well as what the current part does cost and the potential savings based on the parts demand. In addition, a summary of how each of the cost factors (pricing, sourcing and design) are applied to that part. 
       FIG. 10  shows an example report for a detailed part analysis on a single part. This report is broken into 4 quadrants, one that shows the part details including the calculated should cost, and the other three quadrants that display the cost factors related to pricing, sourcing and design. In one embodiment, the detailed parts analysis report allows the user to perform a comparables analysis, a sourcing analysis, and view the part&#39;s history. 
     2. Cost Driver Analysis: 
     The system Cost Driver Analysis provides the user with the cost model for a specific family of parts. This analysis details the costs associated with each of the parts parameters for a specific family of parts and shows graphically how the parts relate to each other. 
       FIGS. 11 and 12  shows an example report for an invention Cost Driver Analysis on a family of parts. 
     3. Comparables Analysis 
     Referring now to TABLE  5 , the nearest neighbor  375  module is used within the system to group parts based on like features (“comparables analysis”). This analysis is used when selecting parts by feature as well as when trying to find comparables to define redesign opportunities. The system nearest neighbor  375  module shows the users comparable parts as well as their characteristics. This analysis will show the user how similar parts are designed as well as provide the user with insight into design changes to the existing part that may reduce cost.  FIG. 13  represents an example report for a nearest neighbor  375  module analysis for a single part. 
     
       
         
           
               
               
               
               
               
               
               
             
               
                 TABLE 5 
               
               
                   
               
               
                 partid 
                 2319329 
                 2260299 
                 2190628 
                 2260302 
                 2083729 
                 1534212 
               
               
                   
               
             
            
               
                 partname 
                 HOUSING- 
                 HOUSING- 
                 HOUSING 
                 HOUSING-FLY 
                 HOUSING 
                 HOUSING 
               
               
                   
                 FLYWHEEL 
                 REAR 
               
               
                 costperkg 
                 38.83553 
                 29.72777 
                 5.697382 
                 3.868642 
                 5.521958 
                 10.07332 
               
               
                 clssdesc 
                 HOUSINGS 
                 HOUSINGS 
                 HOUSINGS 
                 HOUSINGS 
                 HOUSINGS 
                 HOUSINGS 
               
               
                 material.coarse 
                 GRAY 
                 GRAY 
                 GRAY 
                 GRAY 
                 GRAY 
                 GRAY 
               
               
                 finwt.kg 
                 96.43 
                 83.57 
                 114.6 
                 145.1 
                 71.5 
                 52.78 
               
               
                 height 
                 889.8 
                 864.4 
                 836.6 
                 227.5 
                 761 
                 776.5 
               
               
                 width 
                 1253.4 
                 1055.1 
                 763.2 
                 1240.7 
                 761.4 
                 500 
               
               
                 depth 
                 203.1 
                 62.5 
                 235.5 
                 715.4 
                 293.3 
                 453.5 
               
               
                 partvol 
                 13709201 
                 9319108 
                 16235805 
                 20374896 
                 9108896 
                 7437780 
               
               
                 risers 
                 0 
                 0 
                 0 
                 0 
                 2 
                 0 
               
               
                 drillholes 
                 42 
                 62 
                 35 
                 76 
                 22 
                 39 
               
               
                 spotFaceDrillHoles 
                 0 
                 0 
                 0 
                 3 
                 0 
                 0 
               
               
                 surfarea 
                 2645594 
                 1325145 
                 2385837 
                 2479172 
                 1547739 
                 1412496 
               
               
                 partingLinePerim 
                 2143.2 
                 1919.5 
                 1599.8 
                 1956.2 
                 1522.4 
                 1276.8 
               
               
                   
               
            
           
         
       
     
     4. Sourcing Analysis: 
     The system sourcing analysis  325  module determines the capabilities of a supplier by the parts they currently make. This analysis is used to help the user determine which options are available to them to resource a specific part as well as understanding the current capabilities of their suppliers.  FIG. 14  shows an example report for an invention sourcing analysis  375  module on a single part and its current supplier. This type of analysis can also be used to evaluate suppliers other than the current supplier. 
     CONCLUSION 
     While the description above refers to particular embodiments of the present invention, it will be understood that many modifications may be made without departing from the spirit thereof. The accompanying claims are intended to cover such modifications as would fall within the true scope and-spirit of the present invention. The presently disclosed embodiments are therefore to be considered in all respects illustrative and not restrictive, the scope of the invention being indicated by the appended claims, rather than the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.