Patent Publication Number: US-2013238379-A1

Title: Multi-dimensional life cycle project execution system

Description:
This application claims the benefit of priority to U.S. provisional application having Ser. No. 61/609,458 filed Mar. 12, 2012. 
    
    
     FIELD OF THE INVENTION 
     The field of the invention is life cycle management technologies. 
     BACKGROUND 
     Currently, project execution tools supporting the engineering and construction industry are primarily focused on 3D design where each of the three spatial dimensions are used to define the location, size and orientation of various project elements. More recent tools comprising a class collectively known as Building Information Models (BIM) provide additional information to project models. This added information includes time based information relative to construction sequencing and certain sets of attribute information primarily related to the performance and material characteristics of select elements. BIM systems may be linked to certain external tools that support generation of materials lists, first cost estimates and other outputs traditionally linked to initial facility delivery. 
     Although known project execution tools provide for three spatial dimensions and to some extent a time dimension, the tools fail to take into account additional dimensions of relevance related to a project that can impact the project&#39;s cost, facility life cycle cost, efficiency, operation, management, or other factors. For example, U.S. Pat. No. 7,502,714 to Nakadate et al. titled “Device, Method, and Program for Optimization Analysis”, filed Dec. 22, 2004, merely considers three-dimensional information to determine cost calculations. Further, U.S. patent application publication 2012/0203806 to Panushev titled “Building Information Management System”, filed Feb. 7, 2011, describes a project management system capable of storing files representing three-dimensional models that are accessible by project constituents (e.g., owners, builders, engineers, etc.). However, Nakadate and Panushev fail to appreciate fully that other dimensions of relevance could also be of import when determining an impact of a project. 
     Some effort has also been directed toward multi-dimensional project frameworks. U.S. patent application publication 2008/0040364 to Li titled “Extensible Multi-Dimensional Framework”, filed May 29, 2007, describes a management system for managing enterprise information technology systems, where the management system leverages a multi-dimensional framework and a three dimensional iterative unified process to generate key deliverables of adaptive-to-change quality focuses architecture, optimistic agile iterations, and market-centric business-driven risk-mitigation process. Such techniques are most likely useful for enterprise IT systems. However, Li lacks insight into using multiple dimensions of relevance for large construction projects having extensive life cycles or determining the impacts of such construction projects over their life cycle. 
     Additional effort directed to construction project management has focused on specific aspects of construction project management. U.S. patent application publication 2004/0205519 to Chapel et al. titled “Method and System for Automatically Generating Construction Documents”, filed Jan. 10, 2002, describes creating a project model representing an interrelated design-construction planning environment. The project model information is exported to expert systems that could calculate an estimated construction period. U.S. Pat. No. 5,893,082 to McCormick titled “System for Processing and Presenting Cost Estimates in the Construction Industry”, filed Dec. 23, 1996, discloses estimating a cost for construction materials based on measurements. Still, further U.S. Pat. No. 8,370,192 to Deo et al. titled “Method and System for Dynamic Project Management and Capacity Management”, describes a project management system that seeks to identify correlations between a projected time or costs and actual time or actual costs. Another example includes U.S. patent application publication 2011/0307281 to Creveling et al. titled “Model Inventory Manager”, filed Jun. 11, 2010, which discusses using a database storing element specifications and instance data related to versions of building modeling datasets. Engineering cost analyses can be made reflecting changes in cost due to changes in the version of the building modeling datasets. Although useful with respect to estimating costs or times for construction projects, a better solution would incorporate multiple impact factors. 
     In addition to cost, others have focused their efforts on other factors including environmental impacts. U.S. patent application publication 2011/0246155 to Fitch et al. titled “Computer-Readable Medium and Systems for Applying Multiple Impact Factors”, filed Sep. 30, 2010, discusses storing a plurality of impact factors that include normalized percentage change of an estimated building parameter (e.g., energy consumption, ROI, cash flow, etc.) attributed to a design choice. Similarly U.S. patent application publication 2011/0246381 to Fitch et al. titled “Systems and Methods of Modeling Energy Consumption of Buildings”, filed Mar. 30, 2010, focuses producing a baseline energy usage module related to a proposed building. Another example includes U.S. patent application publication 2011/0313808 to Kavanaugh et al. “Titled Built Environment Management Systems”, filed Jun. 18, 2010, which describes a system that uses project plan items to determine which plan items best meet an organizations objectives for a built environment (e.g., buildings, assets, etc.) rather than natural environment. Similarly, U.S. patent application publication 2012/0215574 to Driessnack et al. titled “System, Method, and Computer Product for Enhanced Performance Management”, filed Jan. 18, 2011, also describes a project management system that estimates to complete a project and can indicate progress toward a goal or objective. Interestingly, within the construction industry there is no known effort toward assessing a construction project&#39;s impact on less tangible factors, especially a social impact, which could include direct and indirect impacts on a range of communities from those locally affected or in a tiered way up to the entire human ecosystem. Further, the above examples fail to address a holistic view or approach, which would encompass many different dimensions in an integrated methodology where interactions of the dimensions would be observable. 
     Social performance has been used as a success factor as part of project management activities. For example, U.S. patent application publication 2009/0099887 to Sklar et al. titled “Method of Undertaking and Implementing a Project Using at Least One Concept, Method or Tool Which Integrates Lean Six Sigma and Sustainability Concepts”, filed Oct. 12, 2007, indicates that a triple bottom line for measuring success of a project can include taking into account environmental, financial, and social performance. In addition, U.S. patent application publication 2012/0116837 to Dunlop et al. titled “Social Risk Management System and Method”, filed Nov. 4, 2010, describes managing social risk for an economic venture. Although Sklar and Dunlop discuss social aspects of projects within the scope of projects per se, Sklar and Dunlop lack any insight into mapping fine grained aspects of a construction project, especially construction assemblies, to their impact on society. 
     Thus there is a need for project execution and life cycle management systems capable of taking into account many additional dimensions of relevance beyond three spatial dimensions as well as capable of providing an indication of possible impacts that the project could have on bottom lines. 
     These and all other extrinsic materials discussed herein are incorporated by reference in their entirety. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply. 
     Unless the context dictates the contrary, all ranges set forth herein should be interpreted as being inclusive of their endpoints, and open-ended ranges should be interpreted to include commercially practical values. Similarly, all lists of values should be considered as inclusive of intermediate values unless the context indicates the contrary. 
     SUMMARY OF THE INVENTION 
     The inventive subject matter provides apparatus, systems and methods in which one can use a project execution and life cycle management system to model a facility across its life cycle according to many different dimensions of relevance. One aspect of the inventive subject matter includes a project execution and life cycle management system comprising a construction assembly database and a project modeling engine. The construction assembly database stores many construction assembly objects representative of various components, elements, items, or other modules that might be used during construction of a facility, a nuclear power plant for example. Each assembly can be considered a generic representation of a best practice or “know-how” associated with the module that the assembly models. Preferably the assembly objects comprises multiple dimensions of attribute sets where the attributes values in the sets describe the nature of the assembly with respect to the corresponding dimension. Example attributes sets include spatial dimension attribute sets, time dimension attribute sets, first delivery attribute sets, life cycle attribute sets, or performance attribute sets. For example, an assembly could have five, six, seven, or more dimensions of attribute sets. The modeling engine is preferably configured to construct a project model based on a collection of assembly objects, possibly in response to instructions received via a project management interface. The project model can be defined to model a facility across multiple stages or phases, or throughout the life cycle of the facility. Thus, a project manager or other user can submit changes to the module and see how a model might change through its life cycle. The modeling engine preferably is further configured to generate one or more bottom line impacts, a social bottom line impact for example, as a function of project-specific attribute values across the multiple dimensions of attributes sets. Example bottom line impacts could include financial impacts, social impacts, environmental impacts, or other types of impacts. 
     Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING 
         FIG. 1  is a schematic of project management ecosystem. 
         FIG. 2  is an example of an assembly object having multiple dimensions of attributes. 
         FIG. 3  is an example of attributes across a life cycle of a project. 
         FIG. 4  is an overview of generating a social bottom line impact from a project model. 
     
    
    
     DETAILED DESCRIPTION 
     It should be noted that while the following description is drawn to a computer/server based project execution and life cycle management system, various alternative configurations are also deemed suitable and may employ various physical computing devices including servers, interfaces, systems, databases, agents, peers, engines, controllers, or other types of computing devices operating individually or collectively. One should appreciate the computing devices comprise a processor configured to execute software instructions stored on a tangible, non-transitory computer readable storage medium (e.g., hard drive, solid state drive, RAM, flash, ROM, etc.). The software instructions preferably configure the computing device to provide the roles, responsibilities, or other functionality as discussed below with respect to the disclosed apparatus. In especially preferred embodiments, the various servers, systems, databases, or interfaces exchange data using standardized protocols or algorithms, possibly based on HTTP, HTTPS, AES, public-private key exchanges, web service APIs, known financial transaction protocols, or other electronic information exchanging methods. Data exchanges preferably are conducted over a packet-switched network, the Internet, LAN, WAN, VPN, or other type of packet switched network. Within he context of computing devices in this document, the term “configured to” is used euphuistically to include the concept of programming a computing device. 
     One should appreciate that the disclosed techniques provide many advantageous technical effects including generating signals that configure an output device to present one or more bottom line impacts for a modeled facility. 
     The following discussion provides many example embodiments of the inventive subject matter. Although each embodiment represents a single combination of inventive elements, the inventive subject matter is considered to include all possible combinations of the disclosed elements. Thus if one embodiment comprises elements A, B, and C, and a second embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly disclosed. 
     As used herein, and unless the context dictates otherwise, the term “coupled to” is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms “coupled to” and “coupled with” are used synonymously. Within the scope of this document “coupled to” and “coupled with” are used to mean “communicatively coupled with” over a network, possibly via one or more intermediary networking devices. 
     Overview 
     The project execution and life cycle management system which is the subject of this disclosure significantly expands the attribute set associated with first delivery and adds two additional dimensions related to life cycle analysis and performance encompassing all three dimensions of a triple bottom line perspective as well as higher level system performance attributes related to relative uncertainty levels, susceptibility to Black Swan type events and associated assessment of inherent resiliency. One should appreciate that although the following discussion relates to seven dimensions of attribute sets, the concepts can be expanded readily to any number of dimensions. The project execution and life cycle management system further integrates supply chain and operations and maintenance chains as part of the execution system. 
     The proposed execution system represents an improvement over current 3D and BIM execution methodologies in several ways. These are enumerated as they relate to each of the seven dimensions considered in this new project execution ontology. 
     Spatial Dimensions 
     Current systems provide absolute and relational spatial information for various components that comprise the facility. The 7D SM  project execution model described here incorporates these capabilities but expands system modeling by allowing of assemblies to range from partial pre-fabrication through assemblies to more comprehensive and fully integrated modules. The 7D SM  incorporates spatial and other attribute information not only at the component and element level but more importantly at a so called assembly level. Assembly level information can include fixed spatial relationships (e.g., absolute relationships, relative positions, requirements, options, etc.) between components such that a selected assembly has known system level attributes in all dimensions including what other assemblies are required to complete a higher level functionality. Further, the attributes include levels of information required for later in the life cycle but not necessarily of interest during other phases (e.g., design phase) except to the extent that it results in sub optimization of the overall life cycle. Such features are referenced as “optionering”. Incorporation of assembly level attributes and spatial information facilitates design re-use; better supports pre-fabrication and modularization; and allows for tight integration of vendor design information into the final facility data base and deeper tracking of supply chain performance by providing a view thru capability into supplier supply chain performance. 
     The 3D attributes in the 7D SM  project execution model also incorporate real time GPS or other tracking linkages such that major components, assembly and other project components and elements in the supply chain can be physically tracked relative to the final design model. This provides spatial capabilities linked right to the design allowing for an automated quality check on installed work progress. 
     Time Dimension 
     The 7D SM  project execution model facilitates process status tracking or predictive linkages that support re-sequencing of construction for late delivery of equipment, materials and importantly the means of construction. Supply chain GPS or other spatially linked tracking supports assessment of enabling linkages such as documentation; logistics; payments. 
     The 7D SM  project execution model provides an expanded workface capability built on a deeper understanding of spatial and sequence of construction attributes. Labor or tool requirements are assembly and sequence linked and required training including lead times for such training are driven off a more robust project execution model. Expanded time related information ensures that required workface plans are initiated in appropriate timeframes and are on track for adequate review and approval. Overall safety is improved through model based assessment of evolving hazards and expanded identification of time based safety interfaces. The expanded time information facilitates not only strengthened project planning but also assessment and selection of preferred strategies and options for project execution. In one configuration it allows real time progress measurement based on not only completed work but also its impact on a future work sequence. 
     Pre-commissioning and startup activity schedules and necessary pre-commissioning activities are tracked integrally with the balance of a dynamic project model allowing significantly earlier identification of emerging issues or constraints, or evaluation of alternative strategies and development of a range of contingency approaches beyond what is possible today. 
     Expanded First Delivery Attributes 
     The 7D SM  project execution model provides for an attribute set that goes well beyond current attribute sets associated with BIM project execution and life cycle management systems. This expanded first delivery attribute set specifically differs from current execution models in at least three significant ways. 
     First, the expanded set of component attributes can relate to second (environmental) and third (social) bottom lines such as energy, water, greenhouse gases, waste, labor, various social metrics, other. 
     Second, assembly level attributes can augment/replace component level attributes as primary attribute set used for modeling. This supports design reuse; pre-fabrication; modularization and tighter supply chain integration up to and including driving reconfiguration of the supporting industrial supply chain. 
     Third, system level attributes can support conceptual design and estimates and facilitate life cycle optimization. Other levels of optimization are also possible if required to explicitly support shorter term needs such as the stabilization or restoration of a damaged facility such as a nuclear power plant or infrastructure system; or staged openings to facilitate earlier production of a valuable or critical component such as a vaccine or weapons system. 
     Balance of Life Cycle Attributes 
     Current project execution and life cycle management systems such as the various 3D, 4D and BIM systems do not address life cycle considerations other than providing a physical database for use post completion of initial facility delivery. Various facility management systems build on these BIM models but current systems lack full consideration of the life cycle attributes necessary to undertake early multi-variate analysis and optimization such as that required under a full triple bottom line environment. The 7D SM  project execution model is Triple Bottom Line Life Cycle Analysis focused and considers new parameters and relationships along each of the three bottom line impacts including the following:
         Financial through integration of revenue models that support life cycle analysis not just life cycle costing. This is accomplished through linkage to vendor integrated reliability data, RAM analysis and expanded cost sets that include LCC, Indirect Asset Costs, Externalities as well as a facility revenue model incorporating facility performance characteristics and market linked forecasts. Additionally, financing models may be incorporated to optimize life cycle configurations or operating regimes with available or structured financing regimes including consideration of periodic refinancing and risk postures and tolerances.   Environmental through integration of facility energy, water, Green House Gasses (GHG), waste models through the balance of the life cycle post completion of the project.   Social through consideration of the human rights, labor practices, the environment, fair operating practices, local or national job creation, community involvement &amp; development, educational impact, community impact, regulation impact, jurisdictional impact, or legislative impact attributes inherent in the project design and selected operating and maintenance methodology.       

     One should appreciate that the disclosed system can also analyze a facility model with respect to other types of bottom line impacts. Additional types of bottom line impacts could include broader impacts such as degradation or enhancement of national scale response, preparedness capabilities such as in the case of certain infrastructure systems, or other types of impacts. 
     System Performance Attributes 
     Current BIM models incorporate limited system information and are not structured to support system level performance modeling in several dimensions. Specifically, the 7D SM  project execution model provides for an expanded risk view as it relates to first delivery by augmenting the traditional first delivery risk models where quantitative and event uncertainties for so called “known knowns”, “known unknowns” and “unknown knowns” are considered with the added considerations of complexity, white space risk identification and evaluation and a failure range of additional risk assessment methodologies such as mode effects analysis (FMEA), which aid in understanding or addressing potential “unknown unknowns”. Specifically, design and construction complexity are assessed and driving factors identified. Additionally, weak interface definition or documentation is analyzed for potential white space risks. Low probability, high consequence risks are provided enhanced visibility through FMEA or other risk assessment methodologies. 
     Life cycle risk assessment is supported by the 7D SM  project execution model by facilitating both traditional life cycle risk analysis that considers inherent system attributes captured in the expanded project model as well as non traditional risks associated with classes of risks that might be best characterized as Black Swans. This later consideration results in an overall resiliency assessment which is increasingly important for critical infrastructure and mission critical facilities. Also considered in this resiliency assessment are the flexibility inherent in design and operating methodology; the agility of the selected supply chain; the state of good repair as captured throughout the facilities life cycle and benchmarked against assembly and system level specifications; and externally provided assessments related to factors such as organizational agility. Additionally, the level of remaining “optionality” can be assessed to support financial risk assessments such as “real options” analysis. 
     One should appreciate that the disclosed techniques can be applied to other areas including a comparable level treatment for major programs, portfolios of projects, existing asset portfolios both to support management as well as a tool in asset financial planning; prioritization of investment programs; scenario planning for extreme as well as routine events; governmental and critical infrastructure owner resiliency assessment. Further, the following discussion presents the inventive subject matter with respect to generating a social bottom line impact with respect to life cycles of one or more construction projects. For example, the social bottom line impact can be generated for significant capital projects (e.g., nuclear power plants, bridges, highway systems, dams, etc.). One should also appreciate that the disclosed seven dimensions, or other higher order dimensionality, can be used to determine the impacts of a yet to-be-built facility on its environments, which allows the disclosed inventive subject matter to be used as a business tool even beyond a project management tool. 
       FIG. 1  presents an overview of project management ecosystem  100  that includes a program modeling engine  120  capable of providing insight into how construction project  110  could give rise to one or more bottom line impacts  170  based on project model  140 . Contemplated project management systems include a construction assembly database  125  and a project modeling engine  120 . One should appreciate that project modeling engine  120  is considered to be a hardware computing device (e.g., server, computer, etc.) configured or programmed to operate according the roles or responsibilities disclosed herein. Further, construction assembly database  125  should also be construed to comprise a hardware computing device configured or programmed to exhibit the characteristics of the disclosed database. 
     Construction assembly database  125  is configured to store assembly objects  150 A through  150 N, collectively referred to as assembly objects  150 , representative of construction modules. Each of assembly objects  150  comprises multiple sets of attributes, where each set of attributes can correspond a dimension of project  110 . Further, each of assembly objects  150  can be considered modules representing features or aspects of historical projects. In some sense, assembly objects  150  could be considered templates for portions of a project. For example, assembly objects  150  could include physical devices; pumps, pipes, values, control units, cable trays, HVAC units, or other devices. Such assembly objects can be obtained from assembly database  125  and instantiated according to the specific requirements for modeling construction project  110 . Assembly objects  150  can cover a broad spectrum of assemblies including mechanical assemblies, module assemblies (e.g., modular processing units, HVAC systems, etc.), know-how, assembled data sets, training materials or documents, maintenance documentation, forms or checklists, or other types of assemblies that can be leveraged over the life cycle of construction project  110 . Assembly objects  150  can also include parametric assemblies that provide for instantiating conceptual assemblies that are generated specifically for a specific construction project  110 , which allows for many different discrete designs by simply adjusted parameters of assembly objects  150 . Suitable techniques for providing an assembly database includes those disclosed by co-owned U.S. patent application 2012/0072386 to Willems et al. titled “Intelligent Plant Development Library Environment”, filed Sep. 15, 2011, co-owned U.S. patent application publication 2012/0130914 to Kinghorn et al. titled “Jurisdiction Compliance Engines”, filed Nov. 2, 2011, or co-owned U.S. patent application publication 2012/0158370 to Logatoc titled “Automated Cabling Layout Systems and Methods”, filed Dec. 7, 2011. 
     In the example shown, project modeling engine  120  is configured or programmed to construct project module  140  based on a collection of assembly objects  150  (e.g., assembly object  150 A through  150 N) where project model  140  models construction project  110  across multiple phases of a project life cycle of construction project  110 . The collection of assembly objects  150  can be aggregated to form project model  140  according to one or more techniques. In some embodiments, a design engineer, possibly at output device  180 , can select assembly objects  150  from assembly database  125  and connect the assembly objects  150  together using logical connectors. In scenarios where assembly objects  150  represent assembly templates, the engineer can populate required fields manually or automatically based on a priori established definitions of construction project  110 . Perhaps the design engineer creates or obtains an XML file that includes information about the construction object, possibly including location, jurisdiction, relevant regulations, personnel files, or other information. Such information can be combined with the assembly templates to give rise to or instantiate assembly objects  150  as project model  140 . 
     One should appreciate that project model  140  can represent that nature of construction project  110  across many different phases the project&#39;s life cycle. Phases can include engineering, design, procurement, construction, inspection(s), delivery, hand over, maintenance, possible sale, updating, decommissioning, end-of-life (EOL), or other phase. In view that construction project  110  could represent significant capital projects, one should appreciate that a life cycle of project  110  could extend to years, decades, or even centuries. For example, a bridge might be constructed to last 100 years, or a highway system might be constructed to last several hundred years. Thus, project model  140  aggregates information from assembly objects  150 , possibly along with other additional modeling or project data  130 , to form a simulation of project  110  that spans years, decades, or even centuries. 
     In some embodiments project module  140  represents a proposed future project. In other embodiments project model  140  represents a current snap shot of construction project  110  with possible future projections. Further, as illustrated project model  140  can also include a real-time representation of construction project  110  based on project data  130  obtained in real-time. For example, project data  130  could include real-time sensor data obtained from the project site possibly over network  115 , manager data (e.g., Gantt charts, spreadsheets, etc.), news reports relating to the project, or other types of data. In response to obtaining project data  130 , modeling engine  120  can refine or update project model  140  accordingly. 
     Project modeling engine  120  can be further configured to or programmed to establish map  160  according to project module  140  where map  160  maps attribute values of attributes sets of assembly objects  150  to at least a social bottom line. Map  160  euphemistically represents a module capable of operating on project model  140  to simulate or model construction project  110  to arrive at a social bottom line. For example, map  160  could simply be project model  140  where the attributes values within the attributes sets of assembly objects  150  could be indicative of an impact on one or more facets of a social bottom line (e.g., good will, human rights, labor practices, fair operating practices, job creation, family or local economic impacts, community involvement or development, cultural impact, religious implications, etc.). Map  160  could then be a sum of the values for each facet across each dimension of assembly objects. Another example of map  160  could include a simulation where map  160  applies time-based rules across project model  140 . In such an embodiment, construction project  110  could be simulated over its life cycle in a virtual environment. Such an approach is considered advantageous because stakeholders (e.g., owners, vendors, clients, partners, etc.) can play out different scenarios that could alter the social bottom line (e.g., natural disasters, protests, alternative supply chain strategies, project execution strategies, etc.). By running a simulation that simulates the life cycle of construction project  110 , the direct or indirect affect of each assembly&#39;s contribution to a social bottom line can be aggregated to generate a a social bottom line impact for the simulation. Yet another example of map  160  could include a Monte Carlo simulation where construct project  110  is modeled many times to build statistics with respect to the social bottom line impact. In such embodiments, attributes of assembly objects  150  can include multi-valued attribute values where the values provide an indication of how the corresponding assembly objects  150  should behave during the Monte Carlo simulation. Example values could include average and variance local jobs created or local economic impacts, possibly MTBF for mechanical assemblies where the MTBF could impact employees or local community, personnel hiring requirements, probability of protests related to the assembly, impact on other societal activities associated with plant construction or operation such as the impacts that might be created from associated truck traffic (distinguished from more traditional environmental impact analyses which focus on emissions and would include impacts on things such as school bus routing, economic impacts on community businesses, obsolesce of portions of existing community infrastructure), or other values that could be simulated. Thus, map  160  could comprise multiple Monte Carlo runs of project model  140 , which can in turn generate one or more statistical values of social bottom line impacts. Example techniques that can be leveraged for use with the inventive subject matter for construction and modeling of project model  140  includes those disclosed by U.S. Pat. No. 5,987,242 to Bentley et al. titled “Object-Oriented Computerized Modeling System”, filed Nov. 10, 1997. 
     Regardless of the nature of map  160 , project modeling engine  120  is further configured to or programmed to generate social bottom line impact  170  as a function of the attributes values across the multiple dimensions of attribute sets by modeling (e.g., model, simulate, aggregate, etc.) the attribute values for the construction project according the rules or policies associated with project model  140 . As briefly mentioned above, bottom line impact  170  can be generated by summing constituent values across assembly objects as appropriate according to the modeled point within the life cycle, generating a value based on a simulation, or generating multiple or statistical values across multiple Monte Carlo runs. Once a social bottom line impact is generated, project modeling engine  120  can configured one or more of output device  180  (e.g., computer, browser, workstation, server, tablet, phablet, cell phone, etc.) to present the social bottom line impact possibly over network  115 . Although the above discussion is presented with respect to a social bottom line impact, the disclosed techniques can be adapted for use with other bottom lines including financial bottom lines, environmental bottom lines, risk bottom lines, or other values. However, social bottom lines are considered most difficult to generate due to their subjective nature. 
     Ecosystem  100  can further include one or more project management interface  127  coupled with the project modeling engine  120 . The project management interface  127  provides a communication portal among stakeholders (e.g., vendors, construct firm, owners, trainers, contractors, etc.) that are authorized to engage with project modeling engine  120 . For example, a project manager can configure project modeling engine  120  to model construction project  110 . As the project manager submits changes to project model  140 , project modeling engine  120  can generate updates to a social bottom line impact, or other bottom line impact, possibly in real-time. Thus, as new data becomes available, project data  130  or other type of data, to project modeling engine  120 , stakeholders can be updated or notified of the affects on the bottom line impacts. 
       FIG. 2  illustrates an example assembly object  250  that comprises multiple dimensions of attribute sets  251 - 257 . One should appreciate that assembly object  250  represents many different possible types of assembly objects including construction modules (e.g., modular processing unit, pipe, valve, etc.), a best practice, training material, inspections, construction equipment, logistical elements, or other features of a construction project. Regardless of the nature of assembly object  250 , assembly object  250  preferably includes multiple attribute sets where each attribute set corresponds to a different project dimension of relevance. 
     In the example shown, assembly object  250  comprises seven dimensions of relevance with respect to the life cycle of a construction project. In some embodiments, the dimensions of relevance can adhere to a hierarchical structure. For example, several dimensions can be grouped together as a class of dimensions as indicated by attribute sets  251 - 253 , which collectively represent a class of spatial dimensions. Although the spatial dimensions can be considered orthogonal to each other, they can still be grouped together. Additional dimensions of relevance can include a time dimension as represented by attribute set  254 , a life cycle dimension as represented by attribute set  255 , a system performance dimensions as represented by attribute set  256 , or a first delivery dimension as represented by attribute set  257 . Although seven dimensions are presented within assembly object  250 , one should appreciate that the attribute sets could include at least four dimensions of attributes sets, five dimensions of attributes, six dimensions of attributes, seven dimensions of attributes, or more dimensions of attribute sets. 
     Each of attribute sets  251 - 257  includes one or more attributes that include an attribute identifier (e.g., a name, GUID, UUID, etc.) and a corresponding value. For example, a spatial X dimension could correspond to a length of a construction component or module having a value measured in a linear unit of measure (e.g., mils, inches, feet, millimeters, centimeters, meters, etc.). Further, each attribute can also include one or more aspects (e.g., properties, characteristics, etc.) that directly or indirectly contribute to one or more bottom lines; a social bottom line impact for example. 
       FIG. 3  provides a more detailed view of an attribute set  351  with respect to attributes and their corresponding values. In the example shown, attribute set  351  comprises attributes  358  arranged as a matrix for illustrative purposes. However, attributes  358  could comprise different forms of representations including tag clouds, N-Tuples, ontologies, vectors, or other arrangements. To continue with the present example, attributes  358  have several characteristics of note. First, each of attributes  358  includes a name or identifier (e.g., label, GUID, etc.) corresponding to feature within attribute set  351 . For example, in scenarios where attribute sets  351  corresponds to a spatial attribute dimension, attributes  358  could include “location”, “size”, or other attribute name. In more preferred embodiments, each of attributes  358  can include values that correspond to phases or stages of a project life cycle as illustrated. Thus, in some embodiments, attributes  358  can include a vector of attribute values where each member of the vector corresponds to a specific stage. Example phases or stages include planning, design, engineering, construction, procurement, delivery (e.g., hand over to a client), operation, maintenance, sale, end-of-life (EOL), or other phases. Although  FIG. 3  illustrates several phases as combined (e.g., planning/design, operations/maintenance, etc.), one should appreciate that such phases can be easily separated and are merely presented as combined for illustrative purposes. In scenarios where an attribute value does not necessarily have a corresponding value for a life cycle phase, the value can be represented as a NULL value. Alternatively, attributes  358  could include an N-Tuple that includes attributes  358  having values. If the N-Tuple lacks an attribute value for a specific project life cycle phase, then the attribute value can be assumed to be NULL or non-existent. 
     Second, attributes  358  could be considered to have a temporal nature as well. In such cases, attribute values could change value as a function of time other than project phase or as new project data becomes available where the attribute values could be considered a snap shot of a historical or past state, current state, or projected or predicted future state. Further, the attribute values could be algorithmically derived as a function of time where the value is calculated according to a project model. For example, the “material” attribute could include a modeled expected rate at which concrete is consumed during construction at the project site where a corresponding attribute value might include cubic yards of concrete used-to-date calculated from the modeled expected rate. Further, the attribute values could also include an actual value reflecting the real-world consumed amount of concrete. 
     Third, attributes  358  preferably include properties that directly (i.e., a direct contribution to the bottom line impact) or indirectly (i.e., used to calculate the bottom line impact with a project model) relate to a bottom line impact. In the example show, some of attributes  358  comprises social bottom line properties. For example, the “Good will” attributes could be considered a social attribute associated with the corresponding assembly object. Such a social bottom line attribute could be considered to have direct measures; for example representing the number of local college graduates used during a design phase. Such metrics can be summed over the life cycle to arrive at a social bottom line impact representing local “good will”. In the example, the “good will” could just the total number of local graduates hired, or more likely a “good will” measure calculated from the number of local graduates hired. Such values can be determined by tracking project data mapped to social parameters or could represent subjective data. Subjective data could include compilation of survey results from surveys provided to individuals impacted by the projected where the survey a directed to the attributes. 
     The reader should appreciate that a social bottom line can be considered to represent subjective measures rather than more objective measures such as financial or environmental impacts (e.g., financial or natural environmental impact across extended time periods including across the lifecycle). Thus, binding such social subjective measures to attributes  358  is a non-trivial, difficult task. Attributes  358  can be bound with such subjective measures through analysis of historical project data, possibly through one or more crowd sourcing techniques. For example, one or more mechanical turk workers could provide human insight in the subjective measures. In some embodiments, a public crowd sourcing infrastructure (e.g., Amazon&#39;s MTurk, etc.) could be leveraged. However, in other embodiments an internal crowd sourcing infrastructure could be purpose built for the construction project where only authorized stakeholders or their agents could provide insight. The social subjective measures or values can be assigned at the assembly level as required. 
     Indirect social bottom line impacts can be derived from attribute values that might not, at first blush, be considered relevant attribute values. Consider the example of the “materials” attributes, which has attributes values associated with a “metal” metric during a construction phase, or a “dust” metric related to an EOL demolition of the project site. Such metrics lack direct impact on a social bottom line. However, the project model can leverage such information to derive a social bottom line to one or more modeled systems. Consider social bottom line impacts that reflect community involvement or development. The metal metrics or dust/debris removal metric can be used to recommend development of local industries, which increase the community involvement or development in general, not just with the project. Thus, the project model can be considered to include modules configured to or programmed to derive social bottom line impacts from the attribute values across the life cycle of the construction project. 
       FIG. 4  provides an overview of generating a social bottom line impact, or other bottom line impacts, as a function of the attributes values across the multiple dimensions of attributes set. The social bottom line impact can be generated by modeling the attribute values for the construction project according project model  440 . In the example shown, project model  440  comprises multiple assembly objects  450 A through  450 N combined together as a model. Project model  440  could represent a proposed processing plant yet to be built, a nuclear facility currently under construction, an aircraft carrier at the end of its life, or other large scale construction project. Regardless of the nature of the construction project, assembly objects  450 A through  450 N represent digital or virtual representations of the project&#39;s elements. It should be appreciated that although project model  440  could include a digital rendering of a construction project, project model  440  is also considered to include other virtual representations beyond a mere digital rendering of a 3D structure. Project model  440  can also include time-based information that reflects how various systems (e.g., physical systems, personnel systems, maintenance processes, work flow, inspection processes, logistics, etc.) behave during the entire life cycle of the project as determined by the attributes or attribute values of assembly objects  450 A through  450 N. Further project model  440  is constructed to give rise to an indication of how the construction project, even through its life cycle, will impacts the social bottom line. 
     The social bottom line impact is generated by establishing map  460  that maps or translates from the assembly objects  450 A through  450 N to the derivation of one or more values representing the social bottom line impact as illustrated in graph  490 . Map  460  can take on many different forms. In some embodiments, map  460  could simply include project model  440  itself. For example, project model  440  includes instantiated attributes values within assembly objects  450 A through  450 N. The social bottom line impact could then include a summation of the various attributes values reflecting each assembly object&#39;s social bottom line impact. In such an embodiment, the social bottom line impact could include multiple values; possibly a social bottom line impact for each dimension of attribute sets or for each phase or point in time of the project life cycle. 
     Map  460  can also include more complex mappings. In more preferred embodiments, map  460  can include a simulation of the construction project with respect to the social bottom line impact. The simulation can include a single run yielding one or more social bottom line impact values (e.g., with respect to dimensions of relevance, with respect to phases in a project life cycle, etc.). Further, map  460  can comprises multiple Monte Carlo simulation runs that can be used to aggregate statistics with respect to the social bottom line impact. Thus, based on the Monte Carol runs, the social bottom line impact can include statistical values (e.g., averages, variances, modes, distributions, etc.). Still further, each dimension of relevance, or point in time on the life cycle could include statistical values related to the social bottom line impact. 
     Once sufficient data related to the social bottom line impact has been generated, the project modeling engine responsible for project model  440  can configure an output device to present the social bottom line impact, possibly similar to graph  490  as an example. Graph  490  illustrates a quantified social bottom line impact with respect to time illustrated as project phases during the life cycle of the modeled construction project. The time axis could also represent a continuous time rather than discreet time. The social bottom line impact is presented with respect to a single dimension of relevance for illustrative purposes. However, one should appreciate that each dimension of relevance could have its own distinct, possibly orthogonal, social bottom line impact. It is contemplated that in some embodiments, the dimensions of relevance could be coupled. For example, the social bottom line impact for system performance might be inversely proportional to the social bottom line impact for community development for a specific jurisdiction. 
     The social bottom line impact is presented with several features of note. The solid line is modeled social impact  491 , which can be considered results of analysis of project model  440  over the entire life cycle of the construction project. For example, modeled social impact  491  might represent community involvement. Project model  440  can be considered to indicate that, generally the community involvement is expected to increase with time with some downward fluctuations around construction or maintenance. Graph  490  also includes a current or actual social impact  493 . Actual social impact  493  can be considered a real-world representation of the social bottom line impact for the construction project based on real-world data. Still, the real-world project data can be combined with project model  440  to result in actual social impact  493 . Actual social impact  493  reflects historical information from the project under consideration and could optionally include real-time information obtained from the project site (e.g., sensors, etc.) or from stakeholders (e.g., vendors, clients, trainers, etc.). Further, the social bottom line impact can include projected social impact  495  that represents a projection of the social bottom line impact from an up-to-date actual social impact  493  where the projection is generated via project model  440  based on the real-world project data and actual social impact  493  applied to product model  440 . Thus, projected social impact  495  could include a real-time updated or course corrected representation of the impact. 
     Although a social bottom line impact is presented as a monotonic function with respect to time, one should appreciate the social bottom line information available can be leveraged for additional purposes. One example includes leveraging the social bottom line impact as a life cycle risk assessment. The life cycle risk assessment can include an indication that the social bottom line impact satisfies a risk profile defined in terms of values associated with the social bottom line impact with respect to the dimensions of the attribute sets or over the life cycle. For example, a social bottom line impact could include local community development where the life cycle risk assessment could indicate a risk to the community development. Perhaps a corresponding risk profile could depend on educational factors in the community that are affected by the project. The modeling engine can compare the attributes values of the attributes sets that relate to the educational factors along with the corresponding the social bottom line impact to yield a score or value that indicates likelihood of the risk. In some embodiments, the project modeling system can include a risk profile database configured to store a plurality of risk profiles that represent different forms of possible life cycle risks. As the project modeling engine establishes map  460  and determines one or more social bottom line impacts from map  460 , the modeling engine can further identify one or more risk profiles as a function of the social bottom line impact that might be relevant to the project. Further, if the social bottom line impacts take on an extreme nature, yet does not satisfy a known risk profile, the modeling engine could generate an notification that the social bottom line impact appears to be a leading indicator of a black swan event (i.e., an extreme event, positive or negative, that is a priori unknown). One should appreciate that black swan events can also be associated with other bottom line impacts beyond social bottom line impacts. Thus, the black swan events could be associated with financial bottom line impacts, environmental bottom line impacts, or other impacts. 
     The dimensions of the project and bottom line impacts can be leveraged to measure as a resiliency assessment. Resiliency is a considered a measure that indicates an entity&#39;s ability to avoid a risk, respond to risks that emerge, or ability to recover from severe event (e.g., black swan events). Thus, resiliency can be considered to be bound closely with risk assessment. A more comprehensive discussion with respect to resiliency can be found in co-owned U.S. provisional application having Ser. No. 61/732,193 titled “Resiliency Assessment and Management System”, filed Nov. 30, 2012. In some embodiments, the resiliency assessment can include one or more derived values relating to the flexibility of an entity to deal with risk as a function of the social bottom line impact. For example, perhaps the construction project is exposed to the risk of one or more protests during construction. The social bottom line impact could indicate a measure of “good will” that the owner has in the community, which can then be used to determine a value indicating the ability of the owner to avoid the protest, respond to the protest, or recover from a catastrophic or damaging protest. Further, the project modeling engine can offer recommended courses of actions based on historical project data. 
     Although the above discussion relates to presenting a social bottom line impact, one should appreciate that the disclosed project modeling engine can be configured to generate a plurality of bottom line impacts beyond the social bottom line impact. Additional bottom line impacts can include financial bottom line impacts, environmental bottom line impacts, resiliency bottom line impacts, or other bottom line impacts. Thus, modeling engine can be configured to generate at least one, two, three, or more bottom line impacts. 
     It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the scope of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refers to at least one of something selected from the group consisting of A, B, C . . . and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.