WO2008153612A2 - Architecture de la technologie d'informations pour un système d'entreprise - Google Patents

Architecture de la technologie d'informations pour un système d'entreprise Download PDF

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Publication number
WO2008153612A2
WO2008153612A2 PCT/US2008/002393 US2008002393W WO2008153612A2 WO 2008153612 A2 WO2008153612 A2 WO 2008153612A2 US 2008002393 W US2008002393 W US 2008002393W WO 2008153612 A2 WO2008153612 A2 WO 2008153612A2
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enterprise
model
models
strategic
data
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PCT/US2008/002393
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WO2008153612A3 (fr
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Neal Solomon
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Neal Solomon
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Definitions

  • the present invention pertains to enterprise systems.
  • the invention presents electronic methods to organize enterprise resource planning and strategic management processes applied to the performance of enterprise functions.
  • the present system involves the integration of information technology networks with enterprise operations.
  • the system develops novel enterprise modeling approaches which are applied to specific enterprise functions.
  • the system also applies to dynamic strategic management processes of enterprise operation that include development of active models to advise decision- making processes.
  • the system applies to networks of enterprises.
  • the present system provides technical mechanisms that allow enterprises to have specific strategic competitive advantages to maintain market leadership positions.
  • legacy systems being hardwired business processes with fixed business logic, have severe limitations.
  • the business enterprise structure and function is held hostage to outdated IT infrastructure and organizational logic and is thus uncompetitive relative to businesses that maintain more flexible infrastructures.
  • Simply wrapping an inflexible ERP system core with middleware to connect to Web services fails to solve the problem.
  • This solution of maintaining a software layer that intermediates client-server hardware systems has become a ubiquitous model for enterprise technology.
  • the legacy enterprise model is an expensive and rigid patchwork of software products that is neither active nor adaptive to the environment. So far, there is no enterprise system that is unifying, modular, dynamic, adaptive, flexible and efficient. Because the main factors of data, relationships, logic, process and policy change over time, these changes need to be accommodated in the IT and enterprise systems.
  • the presentation layer needs to continually be updated to accommodate the changing user interface (UI) standards of rapidly changing mobile or laptop devices.
  • UI user interface
  • the key component that links enterprise software applications to one another is the database management system (dbms).
  • the dominant dbms has been the relational database model developed originally by IBM and which has evolved into the object- relational database model. Yet the legacy hardware limits also affect the limiting functions of the relational data model.
  • Relational data models are separate from application logic.
  • the logic layer and the data layer are distinct but connected. Though data is "defined” in the data layer, they are referenced in the application logic layer.
  • Each process step in the logic layer locates data (and relational information) in the data layer. This results in modification to data definitions in the logic layer for both old and new meanings.
  • Making changes to definitions in the logic layer is complex, since changes need to occur at each step in the process in order to modify both data and relational information.
  • Object relational (OR) models map the object to the relational model and inherit the same relational model constraints, namely, that the relational model be synchronized with modifications in the object. The OR dbms simply adds a layer of complexity to the traditional relational model.
  • relational dbms requires thousands of hash tables, which are cumbersome to constantly update.
  • the data relations are flxed.
  • fixed relationships between data are inflexible. It is time-consuming to reorganize relations with new attributes.
  • hierarchical keys to the relational dbms create the need for repeated data sets in multiple locations, which leads to redundancy.
  • a single relational dbms as a legacy system, atrophies.
  • the relational model is static, with a finite set of definitions and relationships. With the need to update attributes, the problem of complexity emerges, which carries with it time constraints.
  • relational dbms An alternative to the relational dbms is the object-oriented database model.
  • an object encapsulates both data and processing logic, and thus data, logic and relationships can be modified.
  • the pure object dbms there is no mapping of the underlying database because there is no relational layer to access for translation of changing data inputs or logic processes. Definitions are created and interpreted at the point of object modeling.
  • One advantage of the object-oriented model is that, as UI layer standards change every few years, the UI layer does not affect the data or logic of the underlying system. Any application processes using the continuously evolving UI layer can be made from varied logic or data layers regardless of the source, facilitating a truly ubiquitous computing platform. In fact, because they are not dependent on the legacy system, UI layers in the object-oriented model can be replaced entirely as better presentation layers become available. Second, data meanings and relations can be modified without complex logic revisions. Process stages can be joined to other steps. Further, process steps can be separated, and the order of the steps can be changed.
  • the object-oriented model Unlike the relational model, which wraps a middleware layer around an inflexible legacy system, the object-oriented model lacks these structural constraints. Consequently, the object-oriented applications unify data and logic components, thereby providing increased agility. Applications can change with this object oriented model as the business changes, thereby unconstraining the business architecture.
  • Eisenhardt has observed that after periods of slow change we witness short bursts of rapid change in order to stimulate or accommodate fundamental breakthroughs. For instance, the flexible production models for manufacturing behavior which were developed by Toyota became a new paradigm that upset the standard Fordist mass- production model. Eisenhardt applies complexity science to the economics of organizations by observing that the survival of companies during periods of rapid change may require the ability to quickly adapt, particularly in high-velocity industries such as those in the technology sector.
  • Complexity theory also applies to systems biology fields as diverse as cellular biology, neural biology and immunology.
  • neural biology neurons are constantly rewired based on adaptation to varied inputs.
  • a learning process that incorporates environmental feedback mechanisms constantly rewires the neural system.
  • cells are the vehicles for the interaction of a complex protein network that also features a feedback mechanism for survival.
  • human immune system provides an example of a complex feedback mechanism that adapts to new pathogens.
  • Autonomic computing involves (a) self-configuration, (b) self-optimization, (c) self-healing, (d) self-protection and (e) self-management of network computing systems.
  • autonomic computing systems provide a useful advance of the state of the art.
  • the main ideas of autonomic computing rely for inspiration on the autonomic nervous system that provides reflex behaviors such as breathing or swallowing. While this system is generally useful for lower levels of computational network behavior, it is primarily limited to specific narrow functions and is reactive rather than adaptive or proactive.
  • the autonomic nervous system is part of the peripheral nervous system.
  • the peripheral nervous system contains all nerves not in the central nervous system that features the brain and spinal cord. While the somatic nervous system coordinates the body's movements and receives external stimuli, the autonomic nervous system contains the sympathetic nervous system, the parasympathetic nervous system and the enteric nervous system.
  • the sympathetic nervous system modulates heartbeat, blood pressure and adrenaline, responding to impending danger.
  • the parasympathetic nervous system modulates the dilation of blood vessels, constriction of pupils and digestive system stimulation, while the enteric nervous system modulates all aspects of digestion.
  • the monitoring of key subsystems occurs via continuous modulation of motor neuron activity and adjustment of levels of chemicals that are stimulated by sensory inputs. Taken together, these nervous system components provide reactions to specific feedback mechanisms.
  • the autonomic computing paradigm provides a useful advance in automation that makes possible the pervasive, or ubiquitous, computing model, or ambient informatics, in which distributed computer systems are constantly updated and repaired.
  • this system lacks is proactive behavior that responds to the prospects of unanticipated change.
  • complexity science deals with the idea of emergence of multiple agents in a dynamic system, we may look for inspiration in other biological processes in order to advance the autonomic computing paradigm.
  • One such bio-inspired, next-generation computing model may be found in the human adaptive immune system.
  • Game theoretic modeling of complex competitive behaviors uses advanced computational resources to articulate processes of strategic conflict. These models are generally passive descriptions of behaviors and reactions to competitor behaviors, however.
  • next-generation dynamic modeling once applied to active strategies in competitive situations, will provide more useful techniques and approaches for modern organizations.
  • the present system advances this new approach.
  • Hayes-Roth articulates a view of business organizational interaction in which competitive advantages of specific organizations are developed and maintained using market power.
  • Hayes-Roth describes these trends and observes that powerful organizations are using their resources to maintain their advantages, he does not show how these processes work. That is, though he recognizes that these mechanisms of information superiority produce, mainly through modeling processes, strategic advantages for successful companies, he stops at a delineation of how to perfo ⁇ n the steps necessary to attain market supremacy. The present system rectifies this deficiency.
  • SOA service oriented architectures
  • the present system uses active distributed databases to organize evolutionary objects and data streams in the enterprise.
  • Specific computer and communications techniques will be used to optimize the resource constraints of an organization's IT system. These techniques include load balancing, dynamic switching, queuing, redundancies for seamless operation, multithreading, continuous offloading of data to multiple databases and the use of software agents to optimize computational mechanisms.
  • load balancing By mining and analyzing data in large data banks, the present system offers insight into modeling multivariate data sets. Advanced artificial intelligence techniques are employed to produce advanced organizational models, including neural networks, genetic algorithms, Monte Carlo procedures and support vector machines.
  • each organization will employ a hybrid of these techniques in order to optimize its models to achieve its strategic management goals.
  • the present system articulates a biologically inspired theory of enterprise structure and functions.
  • the aim is to develop advanced enterprise computational, architectural and strategic mechanisms for the development, attainment and maintenance of the high performance organization.
  • the goal of the successful enterprise is market dominance; the present system advances this goal.
  • the present system is therefore a bio-inspired enterprise control system that regulates very specific sub-systems, the goal of which is to provide the overall enterprise with sustainable comparative advantages. Use of these organic mechanisms enables the enterprise to continuously restructure and constantly reroute dynamic pathways and thus never rest in equilibrium.
  • the twenty-first century dynamic enterprise system that the present invention articulates is as different in viewpoint from the static mass production nineteenth century enterprise as can be. The present system, in sum, more fully prepares organizations for the continuous reengineering processes and adaptation to the market that are essential to future success.
  • the present system has numerous advantages over prior systems.
  • the invention develops novel technical advances for the use of distributed databases and the processing of data objects as applied to enterprise systems.
  • the present invention also integrates enterprise planning modules (sales, customer-relationship management, human resources, finance, accounting, manufacturing, research and development, marketing and supply chain management) into an effective enterprise management system.
  • enterprise planning modules sales, customer-relationship management, human resources, finance, accounting, manufacturing, research and development, marketing and supply chain management
  • the seamless integration of these enterprise modules dramatically increases enterprise efficiencies and provides organizations that use the present system with key competitive advantages. While most enterprise systems end with the IT component, the present system merely begins here.
  • Enterprise technology is a key structural component of enterprise infrastructure.
  • Enterprise resource planning (ERP) modules are typically software products that are focused on automating a specific functionality of the overall enterprise system, such as CRM, HR or finance.
  • ERP enterprise resource planning
  • the problems confronting these traditional ERP software modules are the static nature of each sub-system, the inability to integrate these specialized units into the overall whole and the tendency to force the enterprise to fit the software rather than adapting the software to the customized needs of each business.
  • ERP enterprise resource planning
  • the present invention teaches methods for the collection of data, the organization and evolution of data sets (objects) in real time, the operation of distributed databases, accessibility to the evolving presentation layer and the real-time functioning of specific ERP best practices. All of these processes are aimed to optimize enterprise IT processes for the achievement of strategic goals.
  • the proactive network model borrows not from the autonomic nervous system but from the adaptive immune system.
  • processes occur that use a network of interacting cells and proteins, which react to pathogens, adapt and anticipate the same newly discovered pathogen.
  • pathways are dynamically rerouted when a barrier, bottleneck or anomaly is encountered.
  • the adaptive network continuously reroutes data pathways according to optimal patterns by tracking and anticipating trends and optimizing plasticity.
  • the present system uses neural network processes to synchronize and optimize network plasticity to avoid and adapt to network anomalies.
  • the present system uses multitasking problem-solving approaches to simultaneously reroute pathways to optimize network plasticity. As a way to efficiently maximize network plasticity and operation, the present system also uses queuing techniques to minimize the time required to solve problems.
  • Web 3.0 provides a network apparatus for active object relational databases and for adaptive object databases to interoperate in a pure node-to-node network process.
  • the next generation of the Web will also involve semantic layers of automation in which software programming layers are automatically inferred from the intentions of the programmers.
  • collectives of software agents will negotiate, make decisions about, and then execute specific functions. These advanced network computing processes will automatically adapt to network changes rather than merely automatically update data sets using languages such as XML or RDF.
  • the storage of data is the first step in data organization.
  • the present system uses the ubiquitous computing paradigm to develop a distributive computer storage system for archiving and warehousing large data sets.
  • the main problem with the evolution toward the pervasive computing paradigm is organization of massive data streams, which must be searched, accessed, ordered, filtered, analyzed, reordered and redistributed.
  • the present system uses the distributed data warehousing model to organize large data streams.
  • data objects are generated from various points in the enterprise network, these data objects have a fundamental temporal quality; this temporal aspect of data objects is used to track and organize data streams.
  • Distributed data warehouses shift data objects from one database to another using complex relational database hash table references that continually evolve. That is, the precise locations of data changes are based on the evolving key that is represented by the changing references to the randomly generated hash tables. This process of changing the locations of the data is useful for efficient storage and effective security.
  • the distributed data warehousing process is active and evolutionary.
  • Data archives are typically centralized data storage facilities that manage static data.
  • Data mining processes are used to search for, access, filter and collect data sets from among distributed data warehouses and archives.
  • Data mining is an active process of continuously sorting data sets from rivers of data objects in data warehouses.
  • Intelligent data mining is the process of sorting data sets or data objects with analytical software agents that make logical distinctions between categories and constantly re-sort data sets according to these distinctions.
  • Data mining approaches are integrated into specific software modules that update an enterprise software system periodically.
  • data analysis requires the assembly of a complete set of data objects, which is performed by accessing the data warehouse and collecting sufficient data sets from the rivers of data objects.
  • data collection can be performed on-demand by accessing a series of databases to obtain sufficient data.
  • various computational techniques are applied to analyze the data, including use of Monte Carlo, support vector machines, probabilistic, genetic algorithm and neural network techniques.
  • data objects are temporal and evolutionary, all versions of the objects are archived. In general, except in forensic analysis of the development of a data object, the most recent version of the object will be accessed and used.
  • the earlier versions of the object constitute a form of heuristic process describing the development of the final phase of the object.
  • the present system will save time and increase efficiencies in data management. This is a form of reverse queuing in which the resulting data object is the critical time-sensitive component rather than the traditional queuing process of preserving the best part of the data in the developmental process itself.
  • the present invention uses distributed databases to manage streams of objects.
  • the distributed object database has numerous advantages in the context of pervasive computing and on-demand enterprise software application modules. To understand the preferences for the object dbms, we must examine the concept of a data object.
  • a data object is a set of features that refers to a coherent entity. Examples include a spreadsheet, a document, a video clip or other data sets. In general, these data sets are updateable, particularly if they pertain to active data sources. These objects may transform over time and therefore create evolutionary versions of themselves. These evolutionary objects require tracking and organization in object databases. In this way, different features of an object can be compared to features of other objects and even to earlier versions of the same object.
  • Evolutionary objects may be stored in central (hub) or distributed (node-to-node) databases.
  • Data bits from complex data objects can also be continuously rotated among distributed object-relational databases for storage of massive rivers of data sets and retrieval from multiple locations.
  • the present system uses queuing techniques to efficiently track and use data objects because versions of the objects are modified, while the majority of data in the object is intact.
  • This queuing process is a way to store the data set while preserving the efficiency of retrieving the core part, and the most recent version, of the object. Queuing in this context is thus generally a temporal feature to limit bottlenecks and to increase efficiency.
  • Another aspect of the advantage of employing queuing techniques is that objects are continuously updated and the whole database is continuously refreshed.
  • specific variables in the data object can be automatically updated at a specific periodicity. In this sense, variable inputs can be auto-fed into the database system thereby automatically updating object versions.
  • the evolutionary (and temporal) nature of the data object is a key feature of the dynamic object database system.
  • objects are input and stored for access according to specific object features.
  • a stream of objects is continuously input and stored.
  • the dynamic object dbms ranks the priorities of the objects by using qualitative dimensions, including temporal features, and then proactively flushes the data storage system to reduce the less important data sets such as prior versions of the evolutionary object.
  • the effect of this transformational process is to continuously reorganize the dbms and to automatically filter variables and categories in objects that are stored in the dbms.
  • the transformational object database system is further modified by being structured in a distributive computational architecture.
  • the distributive model of database organization builds in network dynamics to protect data sets in the event that a single database is inaccessible.
  • adaptive processes of continuous restructuring are used to maximize overall network plasticity.
  • central hubs in the network emerge and diminish over an evolutionary process, while data objects (or even parts of objects) are continuously moved from database to database.
  • the present system uses a meta-hash table or evolutionary key hash table to keep track of the locations of the specific data entities in location-specific hash tables that refer to specific data object features and variables.
  • the object dbms active and dynamic, it is also anticipatory. By analyzing data objects and relations between objects in complex patterns and trends, the present system uses techniques to anticipate changes. These changes are then factored into its organizational categories, and new data objects that emerge are placed into the context of the new categories. Because the dynamic distributed object dbms is transformational and anticipatory, the most recent tasks and the most used tasks are provided with high priorities in the pattern analysis. This process of efficiently organizing the distributed data objects in a dynamic system creates crucial shortcuts to maximize efficiency of database operation. The anticipatory functions of the dynamic distributed dbms optimize the efficiency of the object organizational process.
  • One advantage of the present system is the use of intelligent mobile software agents to assist in performing the functions of data organization. Collectives of software agents are organized to work together to solve problems and to automate functions. Software agents perform these automated functions by negotiating with each other to make decisions by using various artificial intelligence techniques.
  • presentation layer provides an on-demand user interface with a dynamic object database model for data organization.
  • the user can be virtually anywhere and use any device or system and still access the data source.
  • SOA service oriented architecture
  • SAAS software as a service
  • SOA combines software as a service with an event-driven component of the enterprise application.
  • goals of the second generation SOA are to reduce cost, provide constant updateability, unify software systems, increase environmental responsiveness, promote agility, decrease computer resources required to carry out specific tasks and, in general, simplify the system.
  • IBM has employed the service-oriented modeling and architecture (SOMA) approach for several months as a competitive advantage.
  • SOA and SOMA models require a business to use multiple expensive, cumbersome and proprietary enterprise application modules that may not be easily integrated, (b) are ad hoc and may not be suited to all businesses, (c) are event oriented rather than business process oriented, (d) may require training to implement and (e) may require the business to change to the IT architecture rather than have the IT customize to the evolving enterprise architecture.
  • the present system in contrast, allows organizations to develop their own systems without depending on outside vendors, thereby freeing them from the shackles of service providers.
  • the present invention seeks to customize and modify the IT system to the needs of the enterprise, not the other way around.
  • This system has the advantages of the open source movement while allowing each business to maintain the independence and integrity of its own system.
  • the maintenance of the present system is the only service that is required from outside vendors, but even this function is vastly reduced because of the automation features.
  • the invention thus allows businesses to bring in- house superior enterprise business architecture functions that they would otherwise farm out to large IT companies at great expense.
  • ERP II offers the best practices of SOA and SAAS.
  • SOA and SAAS SOA and SAAS.
  • SOA and SAAS SOA and SAAS.
  • these systems are ad hoc, haphazard, inflexibly implemented, not integrated, not cost-effective and not customized to the needs of disparate business customers.
  • What is needed is a software portfolio of specific functional enterprise modules that are scalable, constantly improving and upgradeable, available as an easily accessible utility, customizable, specialized, easily integrated, responsive and cost effective.
  • Intelligent mobile software agents bridge the specific functionality of enterprise modules with the data layer and the open-opportunity presentation layer.
  • Software agents organize the specific data sets connected to the enterprise modules and automate the process of intermediation between enterprise modules.
  • the software agents also intermediate the process between enterprise modules and the object database management system on which the modules rely for data.
  • the enterprise modules are divided into categories of (a) external (customer relationship management (CRM), sales, marketing, revenue management, supply chain management (SCM) and procurement), (b) functional (manufacturing, R&D and innovation management) and (c) internal (administrative, finance (accounting, payroll, accounts receivable and accounts payable) and human resources (HR)) modules. These three main functional enterprise application categories are discussed here.
  • External enterprise modules face outwards to the market.
  • the traditional modules of CRM fit into this class of module. With these, customers are tracked and analyzed.
  • Follow-up transactions are offered, and collaborative filtering mechanisms are integrated to offer related products or services to existing customers.
  • the CRM module is integrated with general sales modules that look at potential customers as well. These systems are linked to a general marketing module that tracks and analyzes general market trends. Customers need to be lured with quality products and services before the CRM module is effective at tracking them.
  • the enterprise Prior to the customer contact, the enterprise must identify and seek to satisfy customer needs, which is a prerequisite of sales and marketing approaches. Nevertheless, external enterprise modules need to be customized to each industry type in order to maximize its utility.
  • the marketing module also needs to provide adequate market analysis and pricing analysis prior to CRM functional activation. Revenue management approaches need to be integrated into a CRM module and pricing analysis module in order to maximize revenue and profit. But the link between the CRM, sales and marketing modules is a key enterprise function that must constantly analyze and seek to deliver quality products at an excellent value along with great service. The external enterprise modules are merely an efficient means of providing and improving upon this important customer-centric business process.
  • the entire supply chain requires calibration and optimization by use of an SCM module.
  • the just-in-time (JIT) nature of the global supply chain requires precise tracking of both upstream parts for assembly and downstream finished products. Therefore, the SCM module must be integrated with the other external modules.
  • the R&D and product development process is also a necessary component to manufacturing systems.
  • the product is at the core of the development and production process. Therefore, product design is integrated with production. Since products are localized for each region and product generations evolve rapidly in a global economy, the product development process is tightly linked with increasingly flexible production mechanisms. If R&D is at the core of technology development, general scientific research and innovation management is at the heart of the technology development process.
  • the enterprise modules that are required within the main functional mode of manufacturing, product development and R&D aim to organize the process of production from the birth of the idea to the production of the item to its shipment.
  • the logistics of supplier management, inventory management, scheduling, order management and supply chain planning are integrated into the functional processes that have production at its core.
  • the main functional enterprise components are also connected to the external components of CRM and sales because the market analysis and customer analysis processes are involved with the market creation and because the product's downstream supply chain is integrated with the product delivery process and product enjoyment by the customer. Groups of customers then provide feedback on the product and the product is continually improved.
  • the functional mode of enterprise production is then seen as integrated networks of technology research, product creation, development, manufacturing and delivery and customer use and feedback.
  • the allocation of financial capital is one of the main challenges facing any organization.
  • software modules are implemented that account for the tracking of production of a product or service, calculate the assets and liabilities of the business, account for the capital used by the business, measure the metrics of financial organization and structure financial planning and schedules for the use of capital budgets.
  • These finance and accounting processes generally go beyond the accounts payable and accounts receivable functions that provide the tracking of financial capital from accounting to reporting.
  • These important processes also involve the procurement of capital, the valuation of business components, the negotiation and structuring of transactions, the management of investments and the allocation of financial resources to projects prioritized in the business strategy.
  • the HR module is also integrated with the accounting module for the organization of payroll, health and retirement benefits and other employee services.
  • the HR module continually monitors business competitors in order to assess the most recent employee market conditions.
  • the HR module is also integrated with training programs to continually teach managers and employees the latest skills. In particular, it is important to identify, track, incentivize and cultivate the peak performers in any organization. During periods of intense demand, recruiting efforts are coordinated, while in periods of excess capacity, it is necessary to downsize. Since human capital is an important commodity, it is important to organize and optimize these processes.
  • the internal enterprise functional modules are also integrated with the external and functional enterprise modules.
  • the finance component is integrated with the manufacturing, R&D and SCM functions, while the HR function is integrated with all aspects of the organization.
  • CRM is integrated with the product development and manufacturing components.
  • SCM is integrated with both marketing as well as manufacturing aspects of the enterprise. The integration of these components reveals the critical need to coordinate multiple functions of the enterprise, to customize combinations of enterprise operations to suit each organization, and to design a system for the whole organization rather than a narrow specialization.
  • a middleware system intermediates between object databases in networked computers and specific enterprise applications.
  • object request broker ERP
  • applications send objects and request services from the central system, but in the present distributed enterprise system, the broker works in a node-to-node network without any centralized computer.
  • Data objects in the object database network are requested by specific applications just-in- time in order to solve a particular problem.
  • the objects and the applications may be in any node in the network, thereby providing maximum flexibility.
  • a middleware system intermediates between object databases in specific enterprise applications in client-server systems.
  • object request broker (ERB)
  • applications send objects and request services from the central system.
  • ERP object request broker
  • the intermediation process works in a node-to-node network without any centralized computer.
  • Data objects in the object database network are requested by specific applications just-in-time in order to solve a particular problem.
  • the objects and the applications may be in any node in the network, thereby providing maximum accessibility.
  • Fig. 2 is a schematic drawing of a computer network with distributed databases.
  • Fig. 3 is a set of schematic drawings showing the plasticity behavior of a computer network.
  • Fig. 4 is a flow chart describing the automated generation and application of solutions to network anomalies.
  • Fig. 5 is a schematic drawing of a central computer linked to a computer network.
  • Fig. 6 is a set of tables illustrating the random evolution of positions of data in distributed databases using hash tables.
  • Fig. 7 is a flow chart describing the behavior of intelligent mobile software agents to analyze and sort data objects in databases.
  • Fig. 8 is a flow chart describing the process of searching data objects in databases.
  • Fig. 9 is a schematic drawing of a set of objects being input into a database.
  • Fig. 10 is a schematic drawing of an intelligent mobile software agent that interacts with several databases.
  • Fig. 1 1 is a schematic drawing of several versions of several objects, with data from the most recent version entering a database.
  • Fig. 12 is a schematic drawing of a database hash table with special reference to the most recent versions of specific data objects.
  • Fig. 13 is a flow chart illustrating the process of updating data objects in a database.
  • Fig. 14 is a schematic drawing of the process wherein a set of objects is input into a network of databases.
  • Fig. 15 is a schematic drawing of a distributed database network with data objects input into specific databases.
  • Fig. 16 is a schematic drawing showing the most recent data objects input into distributed databases.
  • Fig. 17 is a flow chart describing the process of inputting data objects into databases.
  • Fig. 18 is a schematic drawing of a network of distributed databases showing the changing positions of the data objects.
  • Fig. 19 is a schematic drawing of a set of recent versions of data objects being stored in a distributed network.
  • Fig. 20 is a schematic drawing of multiple versions of data objects being stored at different times into multiple databases and being accessed by different devices through a common interface.
  • Fig. 21 is a schematic drawing of a multiple versions of data objects being stored by databases, which are then accessed by software agents by automated enterprise software modules.
  • the information technology architecture of the present system incorporates a number of system layers, which are described in fig. 1. At layer one is the user interface. Web applications represent layer two. Specific applications are specified at layer three. These include internal, external and functional enterprise applications. The object database management system is represented at the fourth layer.
  • the distributed network of computers and databases are represented by layer five.
  • Intelligent mobile software agents IMSAs
  • Modeling systems are represented by layer seven.
  • Economic forecasting and enterprise expectations that generate from modeling scenarios are specified at layer eight.
  • Layer nine represents the strategic management of specific operations, while active strategic optionality, i.e., the selection of specific strategies on-demand, represents layer ten.
  • Networks of enterprises in an industrial ecosystem represent the eleventh layer, while network collaboration between and within enterprises represents the twelfth layer.
  • Fig. 2 illustrates a network of computers with databases.
  • the databases 210, 230, 260 and 280
  • computers 200, 220, 250 and 270
  • An external computer 240 is connected to the network.
  • Fig. 3 shows the plasticity effects of a transforming enterprise computer network.
  • a network of computers 300, 305, 310, 315 and 320
  • the computer at position 310 in the first phase and position 335 in phase two
  • the addition of a computer at 370
  • the addition of a computer at 370
  • these plasticity processes are continuous and indefinite.
  • Fig. 4 is a flow chart describing the automated generation and application of solutions to network anomalies.
  • a custom solution is identified and applied to the problem (410). This is performed by employing genetic algorithms and other metaheuristic techniques.
  • the solution is stored in a database (420).
  • the database is accessed for solutions to a specific problem (440).
  • the solution is activated to solve the problem (450) and future anomalies are identified as they emerge (460).
  • the solution is applied to the problem as it continues to emerge (470) as a rapid reaction to similar problems.
  • Fig. 5 illustrates a computer network with a central computer (540) connected to a distributed computing grid (500, 510, 520 and 530).
  • the central computer is a useful conduit to specific enterprise database and enterprise modeling processes, which will then connect to the network for on-demand solutions.
  • Fig. 6 shows several phases of generation of multiple hash tables.
  • Each successive hash table refers to the positions of data in databases in the distributed computer network.
  • the hash table (600) refers to a sequential reference of data objects to specific database locations, much as a key to locate specific data positions.
  • a randomizer is applied to the second generation of hash table (610) at phase two. In this generation, the data sets are randomized, with the data moved to different positions in the databases.
  • the randomizer randomizes the data sets by randomizing the hash table (620). While this process continues indefinitely, one clear benefit of the relocation process is that the data sets are protected as long as the most recent hash table keys are available. This process continually protects rivers of data sets, such as evolutionary data objects, and thereby maintains security in large distributed computer networks.
  • Fig. 7 is a flow chart describing the behavior of intelligent mobile software agents (IMSAs) to analyze and sort data objects in databases.
  • IMSAs intelligent mobile software agents
  • IMSAs identify and analyze specific variables in the data objects (710).
  • IMSAs sort objects according to specific variables (720).
  • the objects are directed to new database locations corresponding to the variables (730).
  • the IMSAs resort data objects according to new variables (740) as they arise, such as from the emergence of new categories.
  • the data objects are then stored in databases and the process continues.
  • Fig. 9 illustrates several data objects 1, 2, 3 and 4 (at 900, 910, 920 and 930, respectively) being input into a database (940).
  • Fig. 10 shows an IMSA sequentially accessing several databases 1, 2, 3 and 4 (at 1010, 1020, 1030 and 1040, respectively). After first searching db 1 (1010) and receiving information from db 1, the IMSA proceeds to access db 2 and so on in order.
  • Fig. 1 1 shows several sets of objects, "A", “B” and “C”, with multiple versions of each, being input into a database (1175).
  • Multiple versions of object A are represented at Al (1100), A2 (1105), A3 (1110), A4 (1115) and A5 (1120) in sequential order, though the object versions may continue for "n" periods with multiple versions. All versions of the objects are input into the database and recorded as specific versions of the same object by delineating the time differences and the distinctions between the variables of each object.
  • the table (1200) shows a set of objects referenced in a grid. Each object is specified by the number of versions of the object; object A has three versions, with A3 as the most recent version, as an example. The circles highlight the most recent version of each object that is available to the dbms.
  • Fig. 13 shows how objects are stored and searched in databases. Once data objects are updated with new versions (1300), all versions of the objects are stored in a database (1310) and the most recent version of the object is given priority for access (1320). A search agent accesses the most recent version of the object (1330) and the older versions of the objects are stored in lower priority of the database (1340).
  • Fig. 14 illustrates how different data objects are stored in multiple databases.
  • Object A and object B have several versions (Al to A5 and Bl to B5). Each version of each object is stored in multiple databases. Al (1400) and A2 (1405) are stored in db 1 (1425), while A3 (1410) is stored in db 2 (1430), A4 (1415) is stored in db 3 (1435) and A5 (1420) is stored in db 4 (1440).
  • Object B versions are similarly stored in several additional databases.
  • Bl (1445) and B2 (1450) are stored in db 5 (1470), B3 (1455) is stored in db 6 (1475), B4 (1460) is stored in db 7 (1480) and B5 (1465) is stored in db 8 (1485).
  • Fig. 15 shows several versions (Al - A4) of object A initially input into several databases (db 2, db5, db 9 and db 11) in a computer network.
  • the use of a distributed computer network with data objects reveals a complex and effective data storage and management system.
  • Fig. 16 shows multiple data objects (A to E) input into multiple databases (db 1 to db 4).
  • objects B and C are input into db 2.
  • all versions of the object are input into the same database. As shown, there are not limits to the number of versions of each data object.
  • Fig. 17 is a flow chart describing the process of inputting data objects into databases.
  • Objects are continually updated (1700) and a stream of objects are input into databases (1710).
  • the dynamic object database management system ranks priorities of objects (1720). Objects are ranked according to specific variables and compared to system priorities. The lower ranked objects (and object versions) are placed in low priority storage (1730). Also, the most recent information is given a high priority (1740). In addition, the most used tasks are given a high priority (1750). Once ranked and sorted, the database reorganizes objects as new objects are input (1760). Search agents then access the highest priority objects in the dbms (1770).
  • Fig. 18 shows a set of six databases (db 1 to db 6) in a distributed computer network in which data sets are organized in different orders.
  • db 1 (1800)
  • the data sets are sequential.
  • Data sets are moved from db 1 to db 4 (1830) or db 2 (1810), where the data positions have been randomly moved to different locations.
  • Data from db 2 are then sent to db 6 (1850) or to db 5 (1840) and further randomly reordered.
  • This process continues to move data sets from db 5 to db 1 or to db 3 (1820).
  • Data from db 4 is moved to either db 2 or to db 5.
  • the effect of this process is a scrambling of data sets for security. It is critical to maintain the most recent hash table to track the data for collection into a specific object.
  • Fig. 19 shows the dbms (1900) with the most recent versions of several objects, A3 (1910), B4 (1920), C2 (1930) and D5 (1940). These objects are then sent to different databases at multiple locations (1950, 1960, 1970 and 1980) for long term storage.
  • Fig. 20 shows an object A, with versions Al to A5, entered into databases dbl to db 4.
  • the databases interface with a central computer (2045) or Internet portal.
  • the central computer or Internet portal are then accessed by different devices (2050 to 2065).
  • Fig. 21 shows multiple objects (A and B) input into databases (db 1 and db 2).
  • the databases are then each accessed by a set of IMSAs (2120, 2125, 2145 and 2150), which interface with enterprise modules (1, 2 and 3).
  • the enterprise modules such as internal enterprise module, external enterprise module and functional enterprise module, will access databases on-demand by using IMSAs to collect data sets from multiple versions of different data objects.
  • the present invention pertains to enterprise systems.
  • the invention presents electronic methods to organize enterprise resource planning and strategic management processes applied to the performance of enterprise functions.
  • the present system involves the integration of information technology networks with enterprise operations.
  • the system develops novel enterprise modeling approaches which are applied to specific enterprise functions.
  • the system also applies to dynamic strategic management processes of enterprise operation that include development of active models to advise decision- making processes.
  • the system applies to networks of enterprises.
  • the present system provides technical mechanisms that allow enterprises to have specific strategic competitive advantages to maintain market leadership positions.
  • legacy systems being hardwired business processes with fixed business logic, have severe limitations.
  • the business enterprise structure and function is held hostage to outdated IT infrastructure and organizational logic and is thus uncompetitive relative to businesses that maintain more flexible infrastructures.
  • Simply wrapping an inflexible ERP system core with middleware to connect to Web services fails to solve the problem.
  • This solution of maintaining a software layer that intermediates client-server hardware systems has become a ubiquitous model for enterprise technology.
  • the legacy enterprise model is an expensive and rigid patchwork of software products that is neither active nor adaptive to the environment. So far, there is no enterprise system that is unifying, modular, dynamic, adaptive, flexible and efficient. Because the main factors of data, relationships, logic, process and policy change over time, these changes need to be accommodated in the IT and enterprise systems.
  • the presentation layer needs to continually be updated to accommodate the changing user interface (UI) standards of rapidly changing mobile or laptop devices.
  • UI user interface
  • the key component that links enterprise software applications to one another is the database management system (dbms).
  • the dominant dbms has been the relational database model developed originally by IBM and which has evolved into the object- relational database model. Yet the legacy hardware limits also affect the limiting functions of the relational data model.
  • Relational data models are separate from application logic.
  • the logic layer and the data layer are distinct but connected. Though data is "defined” in the data layer, they are referenced in the application logic layer.
  • Each process step in the logic layer locates data (and relational information) in the data layer. This results in modification to data definitions in the logic layer for both old and new meanings.
  • Making changes to definitions in the logic layer is complex, since changes need to occur at each step in the process in order to modify both data and relational information.
  • Object relational (OR) models map the object to the relational model and inherit the same relational model constraints, namely, that the relational model be synchronized with modifications in the object. The OR dbms simply adds a layer of complexity to the traditional relational model.
  • relational dbms requires thousands of hash tables, which are cumbersome to constantly update.
  • data relations are fixed.
  • fixed relationships between data are inflexible. It is time-consuming to reorganize relations with new attributes.
  • hierarchical keys to the relational dbms create the need for repeated data sets in multiple locations, which leads to redundancy.
  • a single relational dbms as a legacy system, atrophies.
  • the relational model is static, with a finite set of definitions and relationships. With the need to update attributes, the problem of complexity emerges, which carries with it time constraints.
  • relational dbms In order to overcome some of the limitations of the relational model, it is possible to structure the relational dbms with updated definitions at regular intervals so that the database constantly restructures.
  • relational dbms An alternative to the relational dbms is the object-oriented database model.
  • an object encapsulates both data and processing logic, and thus data, logic and relationships can be modified.
  • the pure object dbms there is no mapping of the underlying database because there is no relational layer to access for translation of changing data inputs or logic processes. Definitions are created and interpreted at the point of object modeling.
  • One advantage of the object-oriented model is that, as UI layer standards change every few years, the UI layer does not affect the data or logic of the underlying system. Any application processes using the continuously evolving UI layer can be made from varied logic or data layers regardless of the source, facilitating a truly ubiquitous computing platform. In fact, because they are not dependent on the legacy system, UI layers in the object-oriented model can be replaced entirely as better presentation layers become available. Second, data meanings and relations can be modified without complex logic revisions. Process stages can be joined to other steps. Further, process steps can be separated, and the order of the steps can be changed.
  • the object-oriented model Unlike the relational model, which wraps a middleware layer around an inflexible legacy system, the object-oriented model lacks these structural constraints. Consequently, the object-oriented applications unify data and logic components, thereby providing increased agility. Applications can change with this object oriented model as the business changes, thereby unconstraining the business architecture.
  • Eisenhardt has observed that after periods of slow change we witness short bursts of rapid change in order to stimulate or accommodate fundamental breakthroughs. For instance, the flexible production models for manufacturing behavior which were developed by Toyota became a new paradigm that upset the standard Fordist mass- production model. Eisenhardt applies complexity science to the economics of organizations by observing that the survival of companies during periods of rapid change
  • Complexity theory also applies to systems biology fields as diverse as cellular biology, neural biology and immunology.
  • neural biology neurons are constantly rewired based on adaptation to varied inputs.
  • a learning process that incorporates environmental feedback mechanisms constantly rewires the neural system.
  • cells are the vehicles for the interaction of a complex protein network that also features a feedback mechanism for survival.
  • human immune system provides an example of a complex feedback mechanism that adapts to new pathogens.
  • Autonomic computing involves (a) self-configuration, (b) self-optimization, (c) self-healing, (d) self-protection and (e) self-management of network computing systems.
  • autonomic computing systems provide a useful advance of the state of the art.
  • the main ideas of autonomic computing rely for inspiration on the autonomic nervous system that provides reflex behaviors such as breathing or swallowing. While this system is generally useful for lower levels of computational network behavior, it is primarily limited to specific narrow functions and is reactive rather than adaptive or proactive.
  • the autonomic computing paradigm does not feature higher levels of behavior that require a more complex computational system for organization.
  • the autonomic nervous system along with the somatic nervous system, is part of the peripheral nervous system.
  • the peripheral nervous system contains all nerves not in the central nervous system that features the brain and spinal cord. While the somatic nervous system coordinates the body's movements and receives external stimuli, the autonomic nervous system contains the sympathetic nervous system, the parasympathetic nervous system
  • the sympathetic nervous system modulates heartbeat, blood pressure and adrenaline, responding to impending danger.
  • the parasympathetic nervous system modulates the dilation of blood vessels, constriction of pupils and digestive system stimulation, while the enteric nervous system modulates all aspects of digestion.
  • the monitoring of key subsystems occurs via continuous modulation of motor neuron activity and adjustment of levels of chemicals that are stimulated by sensory inputs. Taken together, these nervous system components provide reactions to specific feedback mechanisms.
  • the autonomic computing paradigm provides a useful advance in automation that makes possible the pervasive, or ubiquitous, computing model, or ambient informatics, in which distributed computer systems are constantly updated and repaired.
  • this system lacks is proactive behavior that responds to the prospects of unanticipated change.
  • complexity science deals with the idea of emergence of multiple agents in a dynamic system, we may look for inspiration in other biological processes in order to advance the autonomic computing paradigm.
  • One such bio-inspired, next-generation computing model may be found in the human adaptive immune system.
  • Game theoretic modeling of complex competitive behaviors uses advanced computational resources to articulate processes of strategic conflict. These models are generally passive descriptions of behaviors and reactions to competitor behaviors, however.
  • next-generation dynamic modeling once applied to active strategies in competitive situations, will provide more useful techniques and approaches for modern organizations.
  • the present system advances this new approach.
  • Hayes-Roth articulates a view of business organizational interaction in which competitive advantages of specific organizations are developed and maintained using market power.
  • Hayes-Roth describes these trends and observes that powerful organizations are using their resources to maintain their advantages, he does not show how these processes work. That is, though he recognizes that these mechanisms of information superiority produce, mainly through modeling processes, strategic advantages for successful companies, he stops at a delineation of how to perform the steps necessary to attain market supremacy. The present system rectifies this deficiency.
  • modeling systems are essential for effective management of modern enterprises. Since numerous variables are involved in the modeling process, organizations require efficient modeling systems for the processing of complex data sets. Both the quality of the data and the specific modeling techniques employed present problems for the efficient achieving of an enterprise's goals. Generally, sophisticated models require the generation of scenarios for the selection of solution options and simulations for the testing of these options before strategic action is advanced. Only after multivariate models are built and tested can predictions be advanced that would inform strategic action.
  • each organization will employ a hybrid of these techniques in order to optimize its models to achieve its strategic management goals.
  • the present system uses advanced computer techniques to create adaptive models for forecasting, prediction, scenario development and simulation testing.
  • the goal of modeling processes is to deliver customized solution options to complex enterprise problems in real time.
  • Advanced modeling will involve complex game theoretic representations of industry behaviors.
  • the present system employs software agent processes to achieve and optimize these modeling goals.
  • the present system articulates a biologically inspired theory of enterprise structure and functions.
  • the aim is to develop advanced enterprise computational, architectural and strategic mechanisms for the development, attainment and maintenance of the high performance organization.
  • the goal of the successful enterprise is market dominance; the present system advances this goal.
  • a successful enterprise requires mechanisms of adaptation at the heart of its strategic management capabilities. In order to effectively integrate environmental adaptation into the strategic functions, it is necessary to continually leam from disparate operating units. The current system establishes original ways to develop and implement adaptive enterprise strategies.
  • the present system is therefore a bio-inspired enterprise control system that regulates very specific sub-systems, the goal of which is to provide the overall enterprise with sustainable comparative advantages. Use of these organic mechanisms enables the enterprise to continuously restructure and constantly reroute dynamic pathways and thus never rest in equilibrium.
  • the twenty-first century dynamic enterprise system that the present invention articulates is as different in viewpoint from the static mass production nineteenth century enterprise as can be. The present system, in sum, more fully prepares organizations for the continuous reengineering processes and adaptation to the market that are essential to future success.
  • the present system has numerous advantages over prior systems.
  • the present invention develops novel computer modeling components that are critical for effective operation of enterprise systems.
  • the analysis and modeling of customer and competitor behaviors in the present system are achieved via development of novel modeling processes that employ advanced modeling techniques. Rapid construction of these complex models is made possible by using intelligent software agents.
  • the modeling of strategic interactions of industry relations is particularly complex and useful in order for an effective enterprise system to maintain a competitive edge.
  • Advanced dynamic modeling techniques that incorporate updateable and transformable software agent models allow for statistical forecasting, multivariate scenario development and simulation testing in a time-sensitive environment. Without these advanced modeling techniques for strategic development, the enterprise is operating in the absence of crucial information.
  • the present system provides superior modeling processes that lead to superior enterprise performance.
  • the present invention uses dynamic active modeling of enterprise behavior and planning and industry behavior and interactions.
  • the dynamics of enterprise behaviors derive from a process-view of a theory of action. Since the environment in which all enterprises operate are dynamic, with multiple enterprise interactions, the modeling process needs to accurately reflect this transformative nature of the market. The goal is to develop and optimize "living" models that describe past trends and the current situation, that are constantly updated by new information and that have predictive and prescriptive capacities.
  • Enterprise modeling systems are generated and continuously developed by using software agents and by accessing and analyzing database systems.
  • Enterprise model building is useful for forecasting.
  • the present system develops novel methods to forecast customer demand and customer behaviors.
  • the system also develops multiple enterprise forecasting scenarios, based on risk probabilities generated from the models.
  • the system is designed to generate multiple simulations in order to
  • the present system also applies modeling to industry interactions via the use of game theoretic approaches.
  • the mapping of competitors' strategies is critical to understanding an industry's competitive dynamics. Modeling the strategic interaction between an industry and specific industry players with the use of evolving multilateral and multivariate tools enables an enterprise to attain strategic advantages.
  • Data mining techniques are used to analyze patterns in data sets from large databanks. For example, the trends of an industry product or of customer behaviors are analyzed using quantitative techniques that assess variables such as the average consumer age of adoption of a product. While a model may use analyses about a market, in general, conventional analytics merely inform the model.
  • the present system uses on-demand data collection to inform and build dynamic pro-active models.
  • the present system uses distributed databases to continuously stream, or push, data in order to continually update the model building process.
  • These active model building processes are also used to build interactive models of the behaviors of multiple enterprise strategies.
  • the active modeling used here integrates feedback and adaptive behavior not only to improve the model but to use the model in action as well.
  • the model building process begins when an enterprise describes its current situation in terms of quantitative variables.
  • the operational review reveals specific
  • modeling systems in the present invention employ intelligent mobile software agents for model generation, development and restructuring processes.
  • Software agents are mobile software code that use artificial intelligence techniques to accomplish specific tasks or solve specific problems.
  • Agents are used to direct the data mining activities, identify and acquire external data, orchestrate the analytical process of reorganizing the model and reporting the results of the most recent model outputs. Agents use various techniques to search for, compare, calculate, negotiate, update, synchronize and orchestrate collective processes in model development.
  • Software agents use hybrid AI techniques including genetic algorithms, Monte Carlo approaches, support vector machines, probabilistic techniques and artificial neural networks. For instance, genetic algorithms will train artificial neural networks, which are then used to model relationships between inputs and outputs. Neural networks are also useful for identifying patterns in data sets. In general, neural nets are excellent non-linear modeling tools for making time-series models and predictions, sequential decisions and sequential recognition. Monte Carlo techniques and support vector machines are used to test random variable samples in the model and to test correlated variables. Support vector machines generate multiple vectors with multivariate assumptions to create scenarios.
  • the present modeling system is dynamic and evolutionary, accommodating new data sets to constantly update the core model, the invention thus avoids the problem of model shift, in which a static model is never kept in sync with new information; in
  • customer behavior is modeled using a multitude of variables.
  • the variables are analyzed for predictive value using probabilistic techniques.
  • Anomalies of customer demand (such as seasonal fads that cause spiking demand) are identified. Searches for customer data sources are performed, and new data sets on customer demand are input into the model.
  • the simplest form of a model is a spreadsheet. Variables are input into the spreadsheet, and relationships between the variables are indicated as fo ⁇ nulae. The variables change across the time series while the formulae remain consistent, producing constant outputs; as the variables and inputs change, the outputs correspondingly change.
  • Several scenarios are created in the spreadsheet with different variables in order to
  • the present system uses a more advanced meta-analysis process to automatically refocus the modeling. Trends are continuously monitored, and variables are updated from the most recent information available.
  • the meta-analysis approach to modeling involves the use of machine learning techniques that generate predictive forecasts from the limited data sets. Specific variables are isolated and tested by using simulations.
  • the variables constantly change. Statistical methods are used to test and refine the variables, but since the variable inputs are changing, the output scenario options are also constantly changing.
  • the model is active, not static, and continually modifies its predictive forecasts based on the stream of new data sets. Though the active model emphasizes the dynamic effects of the temporal element, because the weights of the variables are constantly changing, the outputs and predictions are changing also. Weighting probabilities to scenarios is a key part of the dynamic modeling process. This dynamic model continuously reorients itself by using the most recent and highest quality information.
  • Simulation predictions generated from a model are used by management to execute a strategic plan. Generating scenarios of possible enterprise action is critical for recommending the best strategic management path. In the finance industry, for example, simulations and scenarios may predict the direction and timing of interest rate changes and thus assist management in making decisions about lending strategy and policy.
  • Oligopoly modeling resembles a multi-player multi-phasal game.
  • a single competitor will launch a product and another will respond by launching products or adjusting prices.
  • any competitor may launch a price war, but the profit margins of the entire industry will be restricted, which is a disincentive to aggressive action.
  • Modeling oligopolous industry behavior is performed in the present system by using game theoretic simulations in which the interactive behaviors of the players are modeled in the context of the effects of the prior period behavioral results.
  • the simulation of behaviors of specific players in the oligopoly is determined by the market position inhabited by each business.
  • the market leader will have a different position than a new market entrant.
  • Companies with a second- and third-ranked market share will engage in different strategies than the market leader.
  • the leader with the most chips may use this market power to exploit the inferior positions of the less competitive firms.
  • an industrial model maps the strategic behaviors of each organization. This industrial model will accommodate the various market positions of the main players and assign strategic options to each player. From this main industrial model, specific simulations will be generated to ascertain and test the scenario options of each player, much as a chess player will calculate a range of options several future moves into the contest. While the entire industry may be volatile, the degree of uncertainty about the horizon of future behavior is a key determinant of prospective strategic success. In particular, models and simulations are useful for determining key turning points in an industry that all firms must address,
  • One of the advantages of industrial modeling is that it models not only the interaction of firms but also the collective behavior of firms in an industry. This game theoretic behavior of industrial modeling is critical to understanding the social aspects of an industry beyond the limits of a single firm. For example, such modeling can analyze the market segmentation of an industry and predict key opportunities as they evolve. Such a model also may predict competitive confrontations beyond a specific competitor as well as the intensity of competition from multiple players.
  • the present system uses Monte Carlo simulations, support vector machines, machine learning, artificial neural networks, fuzzy logic and other probabilistic techniques to generate highly accurate and useful industrial models for use in strategic management.
  • Animations provide a way to understand and map the interactive characteristics of an industry and, particularly, highlight the key temporal aspect of the model. While Rockwell International' s Arena simulations are an example of animation simulations, their outdated approach is relatively static.
  • the present system develops an evolutionary model of industrial change that uses continuous data updates to generate multiple dynamic scenario forecasts of risks and behavior. Unlike earlier systems, the present system is interactive and adaptive to market change because it is intended to use the dynamic model to affect strategic enterprise change.
  • economic models emulate the entire economy. These highly complex models use hundreds of variables to mimic the behavior of many industries over a long horizon. While these models are useful for forecasting economic behavior, they are only used as background analysis for specific enterprise strategic action. Most industrial models will locate some of their assumptions about the business cycle, for example, in a more general economic model.
  • the present system uses multivariate, multilateral and multiphasal techniques to build evolving enterprise and industrial models that are useful to enterprise managers.
  • model simulations are periodically forwarded to a specific modeling application in the distributed network upon request. For example, automated updates of objects, or versions of objects, are provided to the model at regular phases.
  • model simulations will request specific objects just-in-time in order to solve a particular modeling problem.
  • This "push-pull" approach to brokering modeling and object database data collection processes is performed by software agents.
  • the software agents request a specific object, or set of objects, from multiple network locations in order to complete a model in some cases.
  • the software agents push data objects from different databases in the network to specific models periodically. For example, when new data is supplied to construct new versions of data objects, the new versions of data objects are forwarded to the model to update the model data.
  • Fig. 1 is a schematic drawing showing a model generated from a set of databases, with a set of scenarios generated from the model.
  • Fig. 2 is a schematic drawing showing the evolution of an enterprise model.
  • Fig. 3 is a schematic drawing of an enterprise model and the generation of scenarios.
  • Fig. 4 is a schematic drawing of data objects input into multiple models.
  • Fig. 5 is a schematic drawing illustrating the relationships of an enterprise.
  • Fig. 6 is a schematic drawing of a computer network in an enterprise.
  • Fig. 7 is a schematic drawing showing the continuous updating of an enterprise model from the changes of an enterprise environment.
  • Fig. 8 is a schematic drawing of a software agent that collects data from the environment and updates the enterprise model.
  • Fig. 9 is a schematic drawing of multiple enterprise networks in which strong connections between nodes shift over time.
  • Fig. 10 is a chart illustrating multiple thresholds of performance as enterprise behavior is mapped onto a graph.
  • Fig. 11 is a flow chart showing the process of updating a spreadsheet with changing assumptions.
  • Fig. 12 is a pair of charts showing the changed thresholds of enterprise performance.
  • Fig. 13 is a flow chart describing how enterprise models generate scenarios with changed assumptions.
  • Fig. 14 is a flow chart describing how the enterprise models are updated.
  • Fig. 15 is a chart showing the clustering behaviors of two industries across the product life cycle.
  • Fig. 16 is a schematic drawing showing multiple phases of industry configuration evolution.
  • Fig. 17 is a flow chart showing the development process of an enterprise model over several phases.
  • Fig. 18 is a flow chart showing the adjustment of model variables.
  • Fig. 19 is a schematic drawing showing the development of a model from multiple data object inputs.
  • Fig. 20 is a schematic drawing showing the input of data into a model and the generation of scenarios from the model.
  • Fig. 21 is a schematic drawing showing the transformation of probabilistic scenarios into specific strategic enterprise options.
  • Fig. 22 is a flow chart illustrating the construction of an enterprise model.
  • Fig. 23 is a flow chart showing the development and application of a meta-model.
  • Fig. 1 illustrates three databases (dbl, db2 and db3) in a computer network (100, 110 and 120).
  • a model (130) is generated from data in the databases. From the model, three scenarios (135, 140 and 145) are created.
  • Fig. 2 shows an enterprise meta-model (210) created from, and constantly updated by, macroeconomic analysis, environmental analysis, industry analysis and competitor analysis. The enterprise meta-model then creates an enterprise model (200).
  • Fig. 3 shows how past data and analytical techniques (300) are input into a model (310), which evolves, and produces predictions that are generated as scenarios (320 and 320) with specific probabilities of success.
  • Fig. 4 shows the generation of several versions of a model A.
  • the initial data set (400) is input into the initial version of the model (410). More data (420) is input into the model to create an updated version of the model (430). This process continues with the addition of more data (440) into a most recent version of the model (450). Though there are three stages specified in this drawing, the process may continue indefinitely.
  • Fig. 5 shows several vendors (500, 505 and 510) supplying goods or services to an enterprise (520).
  • the enterprise competes with competitor 1 (515) and competitor 2 (525) over customers (530, 535 and 540).
  • This framework of a network of suppliers, competitors and customers provides a structural ecosystem for the interaction of stakeholders in the enterprise process.
  • Fig. 6 shows a distributed computer network connecting computers in an enterprise system. This drawing illustrates how models and data are shared in computer networks without a central computer.
  • Fig. 7 shows the evolution of an enterprise's environment from El (700) to E2 (710) to E3 (720) and then to E4 (730). At each stage, a new model is organized. At El, model Ml (740) is constructed, while at stage E2 the model M2 (750) is updated with new information and so on with M3 (760) and M4 (770).
  • an intelligent mobile software agent (830) is used to select data from several different environments (800, 810 and 820) in order to construct a model (840).
  • Fig. 9 shows three phases in the evolution of a network.
  • phase A the links between positions 910 and 915, and 915 and 920, are strengthened though higher use patterns
  • phase B positions 930 to 935, and 930 to 955, are highlighted with
  • Fig. 10 shows the evolution of change over time of an enterprise (1000). As the threshold for competition decreases, the chances of success increase. As shown here, the enterprise maintains a competitive advantage above the line at 1010, while as the threshold increases at 1020, the burden of performance increases further. Since the threshold increases at 1030 with more competition, it takes longer for the business to meet its goals.
  • Fig. 11 is a flow chart showing the process of updating a spreadsheet with changing assumptions. After spreadsheet scenarios are constructed with a set of assumptions (1100), the assumptions are tested with Monte Carlo techniques (1110). The assumptions are refined by selecting the average of a cluster of assumptions (1120). This average assumption is applied to the spreadsheet scenarios (1130) and new spreadsheet scenario results are obtained with the refined assumptions (1140). The process continues with new information obtained to supplement the assumptions (1150) with these assumptions tested, refined and applied to the model.
  • Example of a spreadsheet modeling include: (1) market analysis in which industry trends, size and growth scenarios are modeled; (2) competitor analysis in which products and innovation are modeled; (3) possible customer leads, sales contacts and relationships are modeled and; (4) organizational interactions with competitors, vendors and customers are modeled. In such dynamic situations, continuous adjustments are made over time which are reflected in changed data sets and in the dynamics of the models.
  • Fig. 12 shows two graphs illustrating clusters of data sets.
  • phase A the clusters shown relative to the reduced growth stage of the industry.
  • the second phase the growth phase has accelerated with a steeper slope. While the businesses represented by the cluster have not changed relative position, the position on the right side of the line shows that their performance has not been maintained.
  • the industry is profitable, while in the second phase, increased competition has made the industry much less profitable.
  • Fig. 13 is a flow chart describing how enterprise models generate scenarios with changed assumptions. After enterprise models use data as assumptions (1300), new models are generated based on the new data (1310). Data variables are analyzed and data sets refined (1320). New data in the model assumptions provides new scenario options (1330) and the model outputs change with the new assumptions (1340). The scenarios of models are weighted with specific probabilities (1350). The models are dynamic because they continuously reorient with the latest and the highest quality data (1360).
  • Fig. 14 is a flow chart describing how the enterprise models are updated. Once models generate simulations of strategic plans based on main scenarios (1400), an enterprise strategy is selected based on an optional simulation (1410). Data sets in the enterprise environment change (1420) and new data sets update the model (1430). Enterprise managers select the best scenario based on an updated model (1440) and select the new strategy path based on the best model scenario (1450). The enterprise activates the strategy, obtains environmental feedback (1460) and continues to generate models.
  • Fig. 15 is a chart showing the clustering behaviors of two industries across the product life cycle.
  • the industry emerges with an innovative company; over time, another company competes to create an industry duopoly configuration.
  • an oligopoly configuration forms, this grows to a larger set of competitors (1530).
  • a shake- out progresses and competitors depart, leaving a smaller oligopoly (1540).
  • the industry continues to decline until only a single competitor (1555) is left.
  • the evolution of industry configurations is similar but the industry is relatively accelerated in a high velocity market. The industry gains competitors faster, and declines faster as well, because of market forces.
  • Fig. 16 is a schematic drawing showing multiple phases of industry configuration evolution.
  • the incumbent (1600) has 80% of market share with a new entrant (1605) left with 20% of market share.
  • the incumbent 1 (1610) maintains 70% market share and the new entrant from phase one becomes an incumbent (1615) with 20% market share.
  • a new entrant (1620) obtains ten percent market share.
  • the first two incumbents (1625 and 1630) maintain 60% and 15% market share, respectively, while the third incumbent (1635) has 15% and the
  • phase four the new market entrant from phase three has been acquired by incumbent two and the three main incumbents (1645, 1650 and 1655) maintain 50%, 30% and 20% market shares respectively.
  • phase five a mature industry configuration provides the incumbents (1660, 1665 and 1670) with 40%, 30% and 20% market share while a new market entrant (1675) has 10% market share.
  • Game theoretic modeling of industry organization and function are performed by plotting the changing industrial player behaviors and analyzing the competitive dynamics of the industry.
  • game theoretical dynamics of industrial competitors are plotted. In a sense, this is similar to the case of tracking multiple players in a poker game in which multiple hands present multilateral competition but the overall process leads to a single winner.
  • Fig. 17 is a flow chart showing the development process of an enterprise model over several phases. Once a main model is constructed with information from multiple sources (1700), the model is supplemented with data inputs in multiple categories (1710). The multiple model components are constructed separately (1720) and assembled from numerous data inputs (1730). A macro-model is constructed from a portfolio of micro models (1740).
  • Fig. 18 is a flow chart showing the adjustment of model variables.
  • a model's variables are tested (1800) and adjusted using hybrid genetic algorithms (1810).
  • the model receives feedback from the market (1820) and variables are modified using hybrid genetic algorithms (1830). Parameters of the model are bounded in order to maximize efficiency of producing the model (1840); narrowing the parameters accelerates the model (1850).
  • the models are then constructed within time constraints (1860) and scenarios are generated from the modified variables (1870).
  • Enterprise multi-objective optimization problems are modeled by presenting scenario options to solve specific problems given the organization's preferred goals.
  • Metaheuristics including search, genetic algorithms, swarm intelligence and immunocomputing approaches, are used to solve optimization problems within constraints.
  • Fig. 19 shows several data objects (A, B and C) being input into a database (1945).
  • the objects have multiple versions.
  • Object A has versions Al (1900), A2 (1905) and A3 (1910).
  • Object B has versions Bl (1915), B2 (1920) and B3 (1925).
  • Object C has versions Cl (1930), C2 (1935) and C3 (1940). These versions of the data objects are input into the database, which then continuously reorganizes the data sets in order to most rapidly access the most useful information.
  • Fig. 21 shows three main scenarios A, B and C generating multiple enterprise strategies. Scenario A (2100), with a 25% chance of success, generates three strategies (2130, 2135 and 2140). Scenario B (2105), with a 35% chance of success, generates two strategies (2145 and 2150). Finally, scenario C (21 10), with a 40% chance of success, generates three strategies (2155, 2160 and 2165).
  • Fig. 22 is a flow chart illustrating the construction of an enterprise model. After a request for assembly of a model (2200), the system accesses data sources from multiple distributed locations (2210). Once data is blocked (2220), the system assembles an initial model with limited data (2230) and the system seeks more complete data by accessing multiple sources (2240). The system builds a model with all available data (2250) and the model data is stored in multiple distributed locations (2260).
  • Fig. 23 is a flow chart showing the development and application of a meta-model. A model is created from the perspective of each competitor in the market environment
  • the competitive configuration of the market environment is modeled (2310).
  • Asymmetric competitive configurations are modeled (2320) and a meta-model is created to factor in multiple firms' strategic options (2330).
  • the meta-model overlays multiple firms' models (2340).
  • the enterprise builds a model of a portfolio of business units by assembling multiple industry behaviors (2350).
  • the meta-model is used to inform enterprise strategy (2360).
  • the present invention pertains to enterprise systems.
  • the invention presents electronic methods to organize enterprise resource planning and strategic management processes applied to the performance of enterprise functions.
  • the present system involves the integration of information technology networks with enterprise operations.
  • the system develops novel enterprise modeling approaches which are applied to specific enterprise functions.
  • the system also applies to dynamic strategic management processes of enterprise operation that include development of active models to advise decision- making processes.
  • the system applies to networks of enterprises.
  • the present system provides technical mechanisms that allow enterprises to have specific strategic competitive advantages to maintain market leadership positions.
  • legacy systems being hardwired business processes with fixed business logic, have severe limitations.
  • the business enterprise structure and function is held hostage to outdated IT infrastructure and organizational logic and is thus uncompetitive relative to businesses that maintain more flexible infrastructures.
  • Simply wrapping an inflexible ERP system core with middleware to connect to Web services fails to solve the problem.
  • This solution of maintaining a software layer that intermediates client-server hardware systems has become a ubiquitous model for enterprise technology.
  • the legacy enterprise model is an expensive and rigid patchwork of software products that is neither active nor adaptive to the environment. So far, there is no enterprise system that is unifying, modular, dynamic, adaptive, flexible and efficient. Because the main factors of data, relationships, logic, process and policy change over time, these changes need to be accommodated in the IT and enterprise systems.
  • the presentation layer needs to continually be updated to accommodate the changing user interface (UI) standards of rapidly changing mobile or laptop devices.
  • UI user interface
  • the key component that links enterprise software applications to one another is the database management system (dbms).
  • the dominant dbms has been the relational database model developed originally by IBM and which has evolved into the object- relational database model. Yet the legacy hardware limits also affect the limiting functions of the relational data model.
  • Relational data models are separate from application logic.
  • the logic layer and the data layer are distinct but connected. Though data is "defined” in the data layer, they are referenced in the application logic layer.
  • Each process step in the logic layer locates data (and relational information) in the data layer. This results in modification to data definitions in the logic layer for both old and new meanings.
  • Making changes to definitions in the logic layer is complex, since changes need to occur at each step in the process in order to modify both data and relational information.
  • Object relational (OR) models map the object to the relational model and inherit the same relational model constraints, namely, that the relational model be synchronized with modifications in the object. The OR dbms simply adds a layer of complexity to the traditional relational model.
  • relational dbms requires thousands of hash tables, which are cumbersome to constantly update.
  • data relations are flxed.
  • fixed relationships between data are inflexible. It is time-consuming to reorganize relations with new attributes.
  • hierarchical keys to the relational dbms create the need for repeated data sets in multiple locations, which leads to redundancy.
  • a single relational dbms as a legacy system, atrophies.
  • the relational model is static, with a finite set of definitions and relationships. With the need to update attributes, the problem of complexity emerges, which carries with it time constraints.
  • relational dbms In order to overcome some of the limitations of the relational model, it is possible to structure the relational dbms with updated definitions at regular intervals so that the database constantly restructures.
  • relational dbms An alternative to the relational dbms is the object-oriented database model.
  • an object encapsulates both data and processing logic, and thus data, logic and relationships can be modified.
  • the pure object dbms there is no mapping of the underlying database because there is no relational layer to access for translation of changing data inputs or logic processes. Definitions are created and interpreted at the point of object modeling.
  • One advantage of the object-oriented model is that, as UI layer standards change every few years, the UI layer does not affect the data or logic of the underlying system. Any application processes using the continuously evolving UI layer can be made from varied logic or data layers regardless of the source, facilitating a truly ubiquitous computing platform. In fact, because they are not dependent on the legacy system, UI layers in the object-oriented model can be replaced entirely as better presentation layers become available. Second, data meanings and relations can be modified without complex logic revisions. Process stages can be joined to other steps. Further, process steps can be separated, and the order of the steps can be changed.
  • the object-oriented model Unlike the relational model, which wraps a middleware layer around an inflexible legacy system, the object-oriented model lacks these structural constraints. Consequently, the object-oriented applications unify data and logic components, thereby providing increased agility. Applications can change with this object oriented model as the business changes, thereby unconstraining the business architecture.
  • Complexity theory also applies to systems biology fields as diverse as cellular biology, neural biology and immunology.
  • neural biology neurons are constantly rewired based on adaptation to varied inputs.
  • a learning process that incorporates environmental feedback mechanisms constantly rewires the neural system.
  • cells are the vehicles for the interaction of a complex protein network that also features a feedback mechanism for survival.
  • human immune system provides an example of a complex feedback mechanism that adapts to new pathogens.
  • Autonomic computing involves (a) self-configuration, (b) self-optimization, (c) self-healing, (d) self-protection and (e) self-management of network computing systems.
  • autonomic computing systems provide a useful advance of the state of the art.
  • the main ideas of autonomic computing rely for inspiration on the autonomic nervous system that provides reflex behaviors such as breathing or swallowing. While this system is generally useful for lower levels of computational network behavior, it is primarily limited to specific narrow functions and is reactive rather than adaptive or proactive.
  • the autonomic computing paradigm does not feature higher levels of behavior that require a more complex computational system for organization.
  • the autonomic nervous system along with the somatic nervous system, is part of the peripheral nervous system.
  • the peripheral nervous system contains all nerves not in the central nervous
  • the autonomic nervous system contains the sympathetic nervous system, the parasympathetic nervous system and the enteric nervous system.
  • the sympathetic nervous system modulates heartbeat, blood pressure and adrenaline, responding to impending danger.
  • the parasympathetic nervous system modulates the dilation of blood vessels, constriction of pupils and digestive system stimulation, while the enteric nervous system modulates all aspects of digestion.
  • the monitoring of key subsystems occurs via continuous modulation of motor neuron activity and adjustment of levels of chemicals that are stimulated by sensory inputs. Taken together, these nervous system components provide reactions to specific feedback mechanisms.
  • the autonomic computing paradigm provides a useful advance in automation that makes possible the pervasive, or ubiquitous, computing model, or ambient informatics, in which distributed computer systems are constantly updated and repaired.
  • this system lacks is proactive behavior that responds to the prospects of unanticipated change.
  • complexity science deals with the idea of emergence of multiple agents in a dynamic system, we may look for inspiration in other biological processes in order to advance the autonomic computing paradigm.
  • One such bio-inspired, next-generation computing model may be found in the human adaptive immune system.
  • Game theoretic modeling of complex competitive behaviors uses advanced computational resources to articulate processes of strategic conflict. These models are generally passive descriptions of behaviors and reactions to competitor behaviors, however.
  • next-generation dynamic modeling once applied to active strategies in competitive situations, will provide more useful techniques and approaches for modern organizations.
  • the present system advances this new approach.
  • Hayes-Roth articulates a view of business organizational interaction in which competitive advantages of specific organizations are developed and maintained using market power.
  • Hayes-Roth describes these trends and observes that powerful organizations are using their resources to maintain their advantages, he does not show how these processes work. That is, though he recognizes that these mechanisms of information superiority produce, mainly through modeling processes, strategic advantages for successful companies, he stops at a delineation of how to perform the steps necessary to attain market supremacy. The present system rectifies this deficiency.
  • the challenge for strategic management is an overall optimization of the enterprise portfolio, achieved by organizing the sum of organizational resources within specific time constraints. This challenge is particularly prominent when considering the competitive environment in which unexpected breakthroughs by rivals require rapid changes in strategic innovation and execution.
  • the goal of strategic agility is achieved by the implementation of systems that coordinate the competitive processes.
  • the goal of effective enterprise and industry modeling is to inform active strategic management decisions.
  • environmental feedback is crucial to effective strategic management and because the industrial and macro-economic environment is constantly changing, such modeling and active strategies require constant adjustments and application of learning mechanisms to be integrated into the decision process.
  • the present system employs computational mechanisms to emulate the complex processes of the human immune system. These processes ultimately optimize the strategic agility of an enterprise and markedly increase the probability of success.
  • the present system articulates a biologically inspired theory of enterprise structure and functions.
  • the aim is to develop advanced enterprise computational, architectural and strategic mechanisms for the development, attainment and maintenance of the high performance organization.
  • the goal of the successful enterprise is market dominance; the present system advances this goal.
  • a successful enterprise requires mechanisms of adaptation at the heart of its strategic management capabilities. In order to effectively integrate environmental adaptation into the strategic functions, it is necessary to continually leam from disparate operating units. The current system establishes original ways to develop and implement adaptive enterprise strategies.
  • Dynamic specialization can be achieved by either switching specialized aspects of production, process or product-design or by sequentially ordering a series of time resource constraints according to a varied set of priorities that match the market demand and the organizational goals.
  • the ultimate achievement of strategic agility is the ability to actively apply the IT infrastructure mechanisms and the advanced modeling processes in order to continually adapt active strategies.
  • the present system develops active models of real options and rational expectations that are useful for planning, learning and decision making.
  • the present system is therefore a bio-inspired enterprise control system that regulates very specific sub-systems, the goal of which is to provide the overall enterprise with sustainable comparative advantages. Use of these organic mechanisms enables the enterprise to continuously restructure and constantly reroute dynamic pathways and thus never rest in equilibrium.
  • the twenty-first century dynamic enterprise system that the present invention articulates is as different in viewpoint from the static mass production nineteenth century enterprise as can be. The present system, in sum, more fully prepares organizations for the continuous reengineering processes and adaptation to the market that are essential to future success.
  • the present system has numerous advantages over prior systems.
  • the present system applies insights from rational expectations economic theory to solve problems involving strategic planning with limited information.
  • the present system establishes mechanisms for problem solving by applying novel learning processes for the adaptation of the enterprise to uncertain competitive environments.
  • Each enterprise applies its operational competence and vision for customer satisfaction with specific benchmark performance goals. Once the goals are in place, the firm will develop a strategic plan to achieve the goals. Modeling approaches, strategic planning methods and refinement of the firm's operating core are all applied to meeting the enterprise goals. Strategic management has developed into a science that applies modeling and strategic planning methodologies to enterprise achievement. The chief constraints of strategic management science applied to the enterprise are the limits of resources (capital, human, customers, technology and commodities) and the competitive behavior of rivals.
  • ROA real options analysis
  • game theory are critical tools to apply to the problem of active enterprise strategic planning design, which requires constant refinement from the realities of market feedback.
  • Real options theory is useful for managers when making strategic decisions about performance initiatives from financial valuation of specific operational scenarios.
  • ROA applies in calculating an enterprise's optimal path in a sequence of choices with resource constraints.
  • Rational expectations theory applies to enterprise problem solving in the context of anticipated behaviors within a narrow horizon of general economic expectations.
  • Game theory models the strategic interactions of multiple competitors in an industry.
  • the three main theories are integrated into the active strategic management processes of an enterprise to accomplish true strategic agility.
  • Our goal is to create an enterprise system that automatically provides strategic optimization. No system has effectively developed these processes for superior enterprise performance.
  • the first step in strategic management is establishment of an operational review of the organization.
  • the firm's operating model will form the core values of the organization from which its goals and strategies will emanate.
  • a situational analysis of the firm will reveal elements about the firm's mission, challenges and strengths. Specifically, a situational analysis will identify a firm's market position in the context of multiple competitors and articulate the firm's products, growth prospects and customer relationships. Particularly in the context of the global economy, the analysis of each firm's unique market position will involve understanding specific regional markets and the details of specific operating units. Since most firms offer multiple products or services, it is important to analyze and focus a firm's portfolio of offerings across its markets.
  • Enterprise strategies are critical to align the firm's goals with its resources. In effect, strategic planning becomes a resource allocation problem, with budgeting central to financial optimization and scheduling central to the temporal optimization of strategies.
  • a firm's overall strategy seeks to address the best way to solve problems of risks in the market (pertinent to competitors and customers), technology, human resources and financial capital. In this sense, an enterprise strategy is a form of risk- adjustment within a firm's performance.
  • Real options analysis is a financial theory that analyzes a firm's risk scenarios and selects the best path from among various option scenarios across a time span to optimize a firm's performance.
  • ROA is used in valuation analyses as well as in mapping a firm's opportunities over time by developing a flexible strategy that weighs and compares risks thereby allowing a firm to hedge its bets as it interacts with its market. Because a firm's plans will change while it is interacting with rivals and must be flexible to adjust to changing customer demand, the feedback that it obtains from the market provides specific inputs of strategic interaction.
  • ROA and game theoretic modeling help to simulate the firm's strategic interactions.
  • the software models assist in a firm's strategic planning.
  • plans will need to change within a specified time frame; f hence the strategy needs to be flexible in order to respond to unexpected changes.
  • the - models get updated and the strategies reformulated to meet the firm's goals.
  • the strategic planning process requires continuous reformulations at different points over time.
  • the reformulations are required because of new inputs from environmental sources that change the original assumptions on which the initial strategic plan and the initial model were based.
  • the newest environmental feedback information then updates the model and the plan (which relies on the model for guidance).
  • This process allows a firm to plan yet is limited to reliable planning only a few phases into the future. After a few phases, the forecasting accuracy diminishes and the scenarios begin to diverge with varying degrees of probabilistic certainty.
  • the firm's strategies are implemented from the top down.
  • One model of such a hierarchical structure is the military.
  • the firm's strategies are implemented in a hub-like structure in which middle managers are responsible for their unit's performance. These latter organizations are more likely to be able to respond rapidly to environmental feedback. Projects are ranked and re-ranked according to the latest performance information.
  • the challenge lies in developing an enterprise architecture that combines the benefits from a centralized synergy of multiple business units that employ a continuous restructuring portfolio of strategies with the responsiveness advantage of the decentralization model.
  • Strategic planning processes therefore need to constantly recalibrate the portfolio of strategies by using feedback mechanisms from environmental change.
  • the scheduling of events in the strategic plan is a fo ⁇ n of temporal optimization task in which priorities are constantly recalibrated.
  • the budgeting process is a financial optimization problem in which limited resources must be efficiently allocated. The strategic planning process thus becomes one of strategic optimization of resource constraints.
  • the specific enterprise strategic processes are targeted to specific operational and functional categories, such as marketing, customer- relationships, human resources, production, finance, R&D, technology and knowledge management.
  • Marketing strategies require market analysis, product analysis and pricing analysis in order to identify the best ways to solve customer problems.
  • Production strategies require a method of selecting the most efficient way to solve resource constraint problems, particularly resource bottlenecks, in order to optimally match customer demand with production in real time.
  • Human resource strategies require
  • Real options analysis presents a business with an option to pursue a particular strategic agenda.
  • a real option gives the enterprise a right, but not the obligation, to select a particular choice of strategic direction, mainly with an emphasis on investment in a specific initiative.
  • ROA provides analytical tools to ascertain the value of specific enterprise strategic optionality, such as the expansion of a factory, the opening of a mine, the drilling of an oil field or the development of a new drug. If, for example, during the eight years of its expected period of development a drug becomes too costly to develop relative to its prospective success, the option to invest more capital resources into its development may be abandoned. Similarly, if the market for the price of oil diminished, a firm may choose to defer an investment into developing an oil field until prices increase to a higher marginal level.
  • the valuation of real options typically begins with the calculation of a net present value (NPV) of an asset using the discounted cash flow (DCF) methodology.
  • NDV net present value
  • DCF discounted cash flow
  • Cash flows are mapped out in a spreadsheet over the term of the asset's utility but are discounted every year by an amount that reflects the use of capital and the relative risk of investing in the opportunity.
  • the DCF approach is supplemented by (a) a reduced discount rate during the period in which the asset is not exposed to the market and (b) an analysis of volatility rates. The more volatile a market, the more risk there is of capturing a higher upside over time as well as the potential of encountering a lower downside.
  • Option pricing relies upon observation of the time duration in which an asset is exposed to risk. These option pricing models are demonstrated by the use of decision trees which demarcate specific option pathways over a series of time periods.
  • ROA pricing establishes a new net present value (NPV*) by calculating the optionality factors.
  • Strategic management theory must use ROA to calculate NPV* and to actively manage the asset using corporate strategic planning and active control approaches.
  • active strategic management approaches are used to allocate limited resources to the most effective projects in order to maximize the overall performance of the product portfolio in the short run.
  • ROA is applied to collections of projects to optimize overall corporate performance in the long run. More specifically, ROA is useful in valuing projects and advising on the active management of the projects for which investment of capital is most likely to provide a reasonable return with limited risk.
  • the combination of ROA with strategic planning and modeling processes provides a powerful set of tools for the development of active strategic management of an enterprise product portfolio.
  • the enterprise maintains maximum flexibility without committing to any given project the full resources that otherwise could be spent on other projects that will yield a higher return.
  • the enterprise may obtain a windfall of profits such as those available to communications, energy and pharmaceutical investment opportunities that are associated with monopolistic market dominance.
  • ROA is an important methodology for capital budgeting and production scheduling, particularly for resource allocation under constraints.
  • the ROA approach provides the tools for active management of complex strategic market contingencies, principally for critical long-term investment opportunities that typically provide the greatest investment rewards.
  • ROA is combined with game theory to more realistically represent market behavior.
  • the dynamics of game theoretic analysis of interacting firm behaviors are represented with ROA investment decisions.
  • the feedback from competitor behavior will inform other firms to either withhold or increase investment into a set of projects.
  • Competitive reaction may not be factored into a firm's initial strategic decisions but generally will be factored into updated strategic models.
  • the use of ROA approaches is optimal in competitive environments because adjustments in strategic projects can be actively managed by making changes over various time periods contingent on specific feedback from rivals.
  • rivals may disguise their behavior in order to limit information about their own strategic plans that would allow other businesses to update their models and prepare to invest in competitive products.
  • the present system uses approaches that build effective models based on limited information about competitor behavior.
  • Rational expectations is an economic theory used to model how businesses and individuals forecast future events to plan their actions. Rational expectations theory uses available information to permit economic agents to make and optimize decisions about future behavior. Rational expectations theory may also be coupled with aspects of adaptive expectations theory, which continually updates the most recent information in the forecasting model, to create a hybrid synthesis. Our reference to rational expectations refers to this hybrid synthesis. The application of rational expectations theory to enterprise strategic management processes is a logical advance in enterprise systems theory.
  • the ultimate goal of rational expectation is to identify the best solution at a particular time given available information.
  • the challenge is to identify and solve problems as they emerge in real time.
  • One way to solve complex problems is to develop a process of experimentation that rejects false solutions while focusing on the most promising prospective solutions. These prospective solutions are tested, feedback is obtained and those solutions that solve the problems most completely are used.
  • This process of experimentation to search for solutions to problems, particularly during periods of high uncertainty, is associated with learning processes. Multiple variables are tested in order to search for those variables that most clearly solve the problem, with the best solutions retained for similar future problem solving.
  • Bayes theorem maintains feedback mechanisms over time to constantly update (in a feedback loop) correct solutions to problems and to create an adaptation process. Specific action is recommended based on the high probability of success of the tested solution. Strategic plans are continuously updated with solutions to problems as they evolve. The effects of the application of the learning process on strategy constitute strategic adaptation.
  • Rational expectations theory uses the best available information to allow managers to make plans using forecasts about the horizon of future uncertainties. For example, the growth of the inflation rate will influence interest rates, and the expected probable changes in the growth rate will help a business plan its financial strategy. Similarly, expectations about the direction and amount of change of the business cycle will help managers plan hiring decisions.
  • Rational expectations theory in particular provides insight into the specific range of future temporal horizons. These possible horizons are as structured as scenarios, with a best-case and worst-case scenarios contrasting with the average-case scenarios. Specific probabilities are attached to the scenarios, with increased chances of events occurring in the short run with maximum information and decreased chances of events occurring in the long run with limited info ⁇ nation. As the horizon elongates and uncertainty increases, volatility about the possible outcomes increases. Risk is quantified in the context of various presented models with different probabilistic outcomes over a specific time frame. In general, there is a correlation between increased uncertainty and volatility and between increased volatility and risk. The ancient view of an "oracle" receiving and analyzing data to predict the future is replaced by a more rational view of model building and scientific management.
  • Strategic plans will satisfy contingency thresholds based on past experiences. For instance, when a specific benchmark of action is activated, then a strategy may be implemented. In this sense, a business may "pre-react" by setting up benchmark contingencies that must be met before strategic action will be triggered.
  • the advantage of this approach is that the strategic action is rapid and time-sensitive in order to attain a competitive advantage.
  • a firm may, for example, organize a strategy to wait for a threshold to be met in the market before activating a pre-set plan. This model of preparing to activate a strategy if contingency thresholds are satisfied is a form of anticipating change.
  • Anticipatory planning uses forecasts for guidance as well as learning mechanisms for expected adaptive change, but actually activates a strategy after the satisfaction of a specific pre-established contingency.
  • a firm may plan to experiment with various strategies, factoring in feedback and then implementing a strategy that anticipates eventualities that it has established from prior campaigns.
  • Strategic agility refers to a process of strategic action in which a firm's own actions and responsiveness change the market.
  • the idea of agility refers to the ability of firms to make decisions rapidly. Companies leverage their core competencies to generate pro-active behavior as they employ tactics that embody strategic agility.
  • a process of adaptation comes into play as a firm promotes strategic agility in order to interact with, integrate into, and lead its market.
  • Strategic optimality refers to the enhanced speed of adopting strategically agile processes. As models are tested in the market through experimentation processes, the models are updated by feedback and optimized. As the models continue to develop and refine and as the company learns from experience, their application to strategic management processes enhance the speed of strategic adoption and increase their market effectiveness. The feedback process between strategic implementation by various firms and the market continuously updates the firms' strategies. The firms that can compete more robustly are those for which strategic optimality is applied. Strategic optimality is achieved more fully when firms apply models and anticipate change using the most recent information and analysis of past experience.
  • An evolving meta- hash table represents a key for the identification of data locations in network databases and acts as a proxy for a central computer.
  • the problem is how to create custom application solutions in a distributed computer network in which customers request from any location multiple applications on demand and in which the data objects on which the modeling and strategic management approaches are continuously evolving. In this case, the model updates are collected, updated and stored until requested.
  • Core application programs that are continuously operational from any node in the distributed enterprise network are ubiquitous in shared networks, while peripheral applications are requested by specific locations on-demand. Specific applications are requested from multiple databases in the network.
  • the databases receive continuous updates of multiple versions of data objects.
  • Once the applications request data objects from the databases a novel set of application program code is collected by software agents to create a custom application. In effect, these peripheral applications are "pulled” from time to time to solve novel problems.
  • the core applications are "pushed” from the databases as they are constantly updated. This push model also presents customized application processes as the continuously updated objects present new program code features as they become available.
  • any computer in the computer network may create a combination of custom applications on demand.
  • Any computer acts as a virtual router in which custom applications are developed on demand by configuring each machine just- in-time as a user requests a specific solution to a problem for which the system generates a customized application program.
  • This model presents a temporary portal from a specific computer that collects data from a combination of object databases in the network, solves a problem with a customized application program, and then proceeds to the next computer to configure a specific custom application program to solve another problem, and so on. This process facilitates rapid solution generation.
  • hash table represents a key for the identification of data locations in network databases and acts as a proxy for a central computer.
  • the problem is how to create custom application solutions in a distributed computer network in which customers request from any location multiple applications on demand and in which the data objects feeding modeling and strategic management approaches are continuously evolving. In this case, the model updates are collected, updated and stored until requested.
  • Core application programs that are continuously operational from any node in the distributed enterprise network are ubiquitous in shared networks, while peripheral applications are requested by specific locations on-demand. Specific applications are requested from multiple databases in the network.
  • the databases receive continuous updates of multiple versions of data objects.
  • a novel set of application programs are collected by software agents to create a custom application. In effect, these peripheral applications are "pulled” from time to time to solve novel problems.
  • the core applications are "pushed" from the databases as they are constantly updated. This push model also presents customized application processes as the continuously updated objects present new program code features when they become available.
  • any computer in the computer network may create a combination of custom applications on demand.
  • Any computer acts as a virtual router in which custom applications are developed on demand by configuring each machine just- in-time as a user requests a specific solution to a problem for which the system generates a customized application program.
  • This model presents a temporary portal from a specific computer that collects data from a combination of object databases in the network, solves a problem with a customized application program, and then proceeds to the next computer to configure a specific custom application program to solve another problem, and so on. This process facilitates rapid solution generation.
  • Fig. 1 is a schematic drawing of a set of enterprise contingencies with probabilistic scenarios.
  • Fig. 2 is a schematic drawing of enterprise management system categories.
  • Fig. 3 is a schematic drawing of enterprise functions, including enterprise modeling and enterprise resource management.
  • Fig. 4 is a schematic drawing showing enterprise relationships.
  • Fig. 5 is a chart showing several enterprise functions.
  • Fig. 6 is a schematic drawing showing enterprise feedback mechanisms.
  • Fig. 7 is a chart showing two main enterprise valuation methodologies.
  • Fig. 8 is a chart showing analogies of economic and biological systems.
  • Fig. 9 is a schematic drawing showing the elements of strategic management.
  • Fig. 10 is a chart showing cumulative volatility over five years in various scenarios.
  • Fig. 11 is a chart illustrating aggregate enterprise valuation including volatility over five years.
  • Fig. 12 is a flow chart showing a sequence of enterprise investment consequences.
  • Fig. 13 is a schematic drawing showing specific enterprise business units competing with multiple organizations in different industries.
  • Fig. 14 is a flow chart describing solution options for enterprise strategies in uncertain markets.
  • Fig. 15 is a chart showing comparative projects among two competing enterprises.
  • Fig. 16 is a schematic chart showing investment options of a project over three phases.
  • Fig. 17 is a schematic chart showing three sets of scenarios for different strategic options.
  • 98 Fig. 18 is a schematic drawing showing relative investment into a set of projects over ten years.
  • Fig. 19 is a flow chart indicating the activation of a strategic contingency.
  • Fig. 20 is a flow chart showing the process of environmental feedback to an evolving enterprise strategy.
  • Fig. 21 is a flow chart showing the process of optimization of strategic agility.
  • Fig. 22 is a flow chart showing the application of enterprise models to enterprise strategic management.
  • Fig. 23 is a flow chart showing the evolution of a model as it is applied to enterprise strategy.
  • Fig. 24 is a flow chart showing the co-evolution of enterprise modeling and strategic management as the enterprise responds to market feedback.
  • Fig. 25 is a flow chart showing the process of modulation of portfolios of enterprise strategies.
  • Fig. 26 is a flow chart showing enterprise solution option selection in response to enterprise constraints.
  • Fig. 1 is a schematic drawing of a set of enterprise contingencies with probabilistic scenarios.
  • the figure covers three projects over several years, with Project 1 covering year one to year four (175), Project 2 covering year two to year five (180) and Project 3 covering year three to year five (185).
  • the projects receive initial funding of $10M, $45M and $100M, respectively.
  • the projects have a 40% to 80% chance of success, with 60% as an average outcome. This average 60% chance of success is manifest in project contingency A (130).
  • the projects have a 50% chance of competing in an uncompetitive industry (137) and a 50% chance of competing in a competitive industry (142).
  • Fig. 2 is a schematic drawing of enterprise management system categories.
  • the two main categories are service oriented enterprise (SOE) (200) for heterogeneous collaboration and enterprise management architecture (EMA) (210).
  • SOE has two main sub-categories consisting of enterprise networks (203) and service oriented architecture (SOA) (208).
  • Enterprise networks contain peer to peer network infrastructure (205).
  • the EMA has seven main categories, each with sub-categories that reflect organizational structure: (1) knowledge management (212), (2) enterprise risk
  • ERP enterprise resource planning
  • SEM enterprise resource planning
  • ERP enterprise resource planning
  • ERP enterprise resource planning
  • ERM operational risk management
  • SEM strategic planning
  • SCM strategic information system
  • SCM e-sourcing (242), total quality management (244) and just-in-time management (248).
  • ERP consists of human resource management (252), financial management (254) and resource management (256), including revenue management (258), customer relationship management (259) and marketing management (260).
  • IT system management consists of IT portfolio management (264) and IT infrastructure management (266).
  • Factory automation consists of "pull” based manufacturing (272), mass-customization (274) and on-demand manufacturing (276).
  • the HR management functions consist of hiring, payroll, training and benefits sections, while the financial management functions consist of accounts payable and accounts receivable functions. Flexibly automating these enterprise functions is a challenge to any enterprise system.
  • Fig. 3 shows how an enterprise model, whether a market (300) model or a product/service (310) model, is integrated into active resources of financial capital (320) and human capital (330).
  • a market interaction between enterprises provides feedback for an enterprise's strategy, which then updates the model and adjusts resources (340) for optimal results. If more resources are needed (350), the model informs the enterprise strategy to increase resources, while if less resources are needed (360), the model informs the strategy to decrease resources.
  • Fig. 4 shows the enterprise ecosystem.
  • the enterprise (440) is linked to vendors (400, 410 and 420) as it competes against competitors (430 and 450). The competitors share some of the same suppliers. Similarly, the enterprise has links to customers (460, 470 and 480) that will overlap with competitors. From time to time the enterprise will cooperate with a competitor (430) in a strategic alliance.
  • Fig. 5 shows a chart illustrating the integration of enterprise structures and functions. Each enterprise (510) is organized into separate divisions, synchronizing the
  • the environment (500) has constant changes that are generated by the behaviors of various industry players in a typology of industry dynamics. In particular, there are rapid changes at specific points in an industry evolution, precipitated by new innovations or intense competition, which characterizes high velocity markets.
  • the enterprise is constantly realigning its goals and plans with the environment as the enterprise and industry co-evolve. In order to maximize its outcomes with limited resources, each company develops an enterprise strategy (520). To do so, the enterprise will develop competitive market models and future scenario models. The strategies are constantly restructured according to the enterprise priorities and environmental interactions.
  • Enterprise software systems (530) are integrated into computer network databases. These software systems are organized for small, medium, large and global companies.
  • Fig. 6 illustrates the feedback mechanisms of strategic plans (600) as models continuously adapt to feedback from environmental change. As real situations unfold (630), contingencies on environmental data (650) and investment strategies (640) are adapted. Strategic plans (660) also adapt as scenarios evolve and contingent options are selected.
  • Fig. 7 is a chart that contrasts two main valuation models of discounted cash flow (DCF) (700) and real options analysis (ROA) (740).
  • DCF is a traditional valuation analysis of enterprises that obtains net present value (NPV) (710) by adjusting scenarios of the discount rate (720) to reflect relative risk.
  • the discount rate is typically connected to interest rates across the business cycle (730) to reflect a reasonable rate of return after inflation.
  • the goal of DCF is to value a business at the present time (735), not a time in the future as would be appropriate for an emerging business.
  • Complex spreadsheets will use main valuation assumptions of a business' s assets and liabilities over time to determine NPV.
  • ROA seeks to value the NPVq (745) of a company typically by adjusting the assumptions of the DCF model. For example, the discount rate is generally lower in the ROA model, which will yield a nominally higher NPVq; this is justified in research projects that are not yet exposed to the market.
  • ROA model will have a variable discount rate across product, industrial or business cycles.
  • a main feature of ROA is to factor in volatility (760) into the valuation model, particularly over a multi-phasal time horizon.
  • volatility is input as a range of possible percentage changes in each phase, from 10% to 200%.
  • the volatility rate can be extremely high, leading to unusually large swings of valuation.
  • ROA valuations are applied to the next several years to provide a range of value prospects (770).
  • the range of valuations over time is tightly linked to the control by enterprise management to select specific projects into which to invest resources, based on positive market feedback, to enhance a successful project or to cut the losses of an unsuccessful project.
  • Fig. 8 is a chart that shows the similarities between economic (and business) systems and biological systems.
  • the organization is analogized to a body and an industry is analogized to a population.
  • An individual agent is similar to a biological cell, while an organizational department is like a tissue.
  • An organization's security system is similar to the immune system.
  • the production process (such as manufacturing) is similar to the muscular system, while the senior executives of an organization perform the function of the brain.
  • the organization's marketing process is similar to a language and information networks are analogous to a nervous system.
  • Fig. 9 shows the elements of strategic management (930) as consisting of strategic planning (900), game theory (mainly for competitive analysis) (920), collective temporal logic (915) for strategic scheduling and real options analysis (910) for scenario analysis.
  • the chart in fig. 10 is used to analyze the NPV-ROA range over best-, worst- and average-case scenarios as depicted in fig. 11 in order to determine the maximum aggregate valuation of an enterprise over five years.
  • 103 fig. 10 is multiplied by the valuation determined in best-, worst- and average-case scenarios for each respective volatility rate (20%, 30%, 35%, 40%, 50%, 60% and 80%).
  • the resulting numbers reveal the maximum valuation options of an enterprise in five years. For example, if Y is one billion dollars, then the maximum average expected value of the business in five years, with a forty percent volatility rate, will be $5.38B.
  • Fig. 12 is a flow chart describing investment optionality.
  • An enterprise invests in project A (1200), which achieves benchmark results in phase I (1210).
  • the enterprise then invests in project B (1220), which does not achieve expected results in phase I (1230).
  • the enterprise decides to invest more in project A for phase II (1240) and not to invest more in project B (1250).
  • This scenario is applied to drug testing, in which a pharmaceutical company will chose not to invest more money in a drug that fails to pass a key test, but actively invests more in a drug that does meet its goals.
  • Fig. 13 illustrates how a central organization (1300) with several business units (1310, 1320, 1330, 1340 and 1350) competes with different businesses in different markets.
  • Business unit 1 (1310) competes with three competitors (1355)
  • business unit 2 (1320) competes with four competitors (1360)
  • business unit 3 (1330) competes with five competitors (1365)
  • business unit 4 (1340) competes with three competitors (1370)
  • business unit 5 (1350) competes with six competitors (1375).
  • this multi-lateral market competitive configuration is typical.
  • Fig. 14 is a flow chart describing solution options for enterprise strategies in uncertain markets.
  • An initial forecast is made about a market based on available information (1400).
  • the market situation changes unexpectedly and a problem with strategy emerges (1410).
  • New strategy options are formulated as solutions to the problem (1420) and tested in the market (1430).
  • Market feedback is received and projects are reformulated (1440).
  • the most fit solutions are retained and integrated into the strategy (1450).
  • Fig. 15 is a chart showing comparative projects among two competing enterprises.
  • business A invests in projects A and B (1500).
  • business B invests in projects C and D (1505). Project A is implemented and project B is
  • Projects C and D are implemented by business B (1515). Project A competes with Project C (1520). Project A loses ground to project C and more capital is provided by business A to Project A (1525). Project C has an advantage over project A and more capital is provided to project C by business B (1530). Project B is implemented and succeeds (1535). Project D fails and is cancelled by business B (1540). Project A is restructured but is ultimately withdrawn by business A (1545), while project C prevails (1550). Ultimately, project B from business A and project C from business B succeed. These successes are based on feedback from the market and successive investment of resources into the projects.
  • Fig. 16 shows the optionality of investments into project A (1600).
  • the business will either invest X (1605) into project A or not invest (1610). If it invests X into project A, in a successive phase, it will either chose to invest Y (1615), invest Z (1620), keep options open (1625) to make other choices (such as selling the project) or chose not to invest more (1630), for example, if the project does yield positive feedback. If the business chooses to invest more capital resources into project A in phase II, it will have options to invest R (1640), invest T (1645), keep an option open (1650) (for instance to sell the project) or not to invest (1635) in phase III.
  • Fig. 17 is a schematic chart showing three sets of scenarios for different strategic options.
  • strategy A (1700) which is a traditional model of valuing a project with discounted cash flow
  • the enterprise invests in project I in the first phase (1702), while the enterprise has the option to exit the investment (1705).
  • the enterprise has the option to invest in the second phase and to initiate phase one of project II (1707) or to exit (1709).
  • the enterprise has the option to either invest in phase two of project two and phase three of project one (1711) or to exit (1714).
  • Phase four continues this optionality matrix.
  • the initial option is either to invest in project one in phase one (1722) or to exit the project (1724), while in phase two, an option exists to initiate project two (1726) or to exit (1728).
  • the option is available to invest in phase two of project one (1730) or to exit (1732).
  • the option is provided to invest in phase three of project one and phase two of project two (1734) or to exit (1736).
  • the initial option is either to invest in project one in phase one (1742) or to exit the project (1744).
  • phase two an option is available to continue project one investment (1746) or to exit (1748).
  • phase three the option is available to continue the project one investment (1750) or to exit.
  • phase four the option exists to continue the project one investment (1758) or to exit.
  • Fig. 18 is a schematic drawing showing relative investment into a set of projects over ten years.
  • the enterprise has the option to continue to invest in these ten projects over time.
  • the enterprise has limited resources and must chose which investments have the best prospects of rewards.
  • all ten projects obtain an initial investment.
  • the initial benchmark investment may be $100M.
  • project "C” information obtained from initial feedback indicates that there is no further investment in phase 2.
  • project "B” there is no further investment in phase 3.
  • Projects "E” and "H” investment is stopped after phase 3.
  • Projects "A” and “J” also have no further investments after phase 4.
  • Project “G” does not have further investment after phase 5.
  • Project “D” does not have investment after phase six and project “F” does not have investment after phase eight. Only project “I” obtains investment over ten years.
  • ROA is a very useful tool to not only evaluate price of a product but also to determine the long term investment options and enterprise strategies. These strategies are interactive with the market since they rely on environmental feedback and on achieving specific goals for the project development.
  • the present system uses as an analogy the process of solving new problems that have evolved in the human immune system.
  • the immune system has two main layers, the innate immune system and the adaptive immune system. Antigens that are already known to the innate immune system are destroyed when they are encountered by using
  • enterprise strategies rely on enterprise models to solve problems.
  • the enterprise models require constant updating, which rely on past data; yet problems are encountered that require customized solutions.
  • problems are encountered that require customized solutions.
  • This analogy is similar to updating the memory of the innate immune system in order to provide immunity to problems (antigens).
  • the combination of these elements which are described in figs. 19 through 26, provide rapid solutions to complex enterprise problems which allow the enterprise to optimize enterprise strategic agility and enterprise leadership.
  • Fig. 19 is a flow chart indicating the activation of a strategic contingency.
  • Benchmark contingencies are established by an enterprise, to be triggered by a specific event or set of events (1900). A benchmark of activity is then achieved (1910). The satisfaction of a specific constraint activates a strategy contingency (1920) and the enterprise reacts to the benchmark achievement (1930). These contingency planning strategy development and implementation approaches allow the enterprise to maximize agility. The strategic contingency is then accelerated by pre-reaction (1940).
  • Fig. 20 is a flow chart showing the process of environmental feedback to an evolving enterprise strategy. Once an enterprise develops a strategy based on models of its experience with the environment (2000), it activates the strategy (2010). The enterprise encounters an unexpected event (2020), however, and develops options to solve the problem (2030). The enterprise tests these options in the market and obtains feedback on success (2040). Once a solution receives positive feedback, the solution is
  • Fig. 21 is a flow chart showing the process of optimization of strategic agility.
  • An enterprise strategic agenda is organized based on analytic modeling (2100) and the strategy is activated (2110).
  • the enterprise risks are identified as the enterprise interacts with its environment (2120), new risks are modeled and strategies are updated and activated (2130). It is important to realize that the analytic and active components of strategic management co-evolve (2140) in this dynamic interactive process.
  • Strategic agility is maximized in a proactive enterprise (2150).
  • Fig. 22 is a flow chart showing the application of enterprise models to enterprise strategic management.
  • an enterprise strategy is developed and activated (2200).
  • the market environment is accelerated by introduction of new technologies (2210) and the enterprise models the environmental dynamics and velocity (2220).
  • the enterprise develops a new model to accommodate market changes (2230) and restructures the organization by decentralizing the business units to maximize the environmental flexibility (2240).
  • the enterprise then activates the new strategy based on a new model (2250). When the enterprise encounters further feedback to the new strategy, it adapts to the feedback (2260). This process then continues as the enterprise adapts its structure to the changing environment in order to maximize the response to the feedback in the market.
  • Fig. 23 is a flow chart showing the evolution of a model as it is applied to enterprise strategy.
  • the enterprise system requests specific types of models just-in-time (2300).
  • the system requests specific data from specific distributed sources (2310), such as databases in different geographic locations, in real time.
  • the system then develops a model (2320) and analyzes the model (2330).
  • the system uses the model data to inform the enterprise strategy (2340).
  • the model is automatically updated with data from distributed sources (2350).
  • the automatic updates of models inform an active strategy (2360) and the enterprise activates the strategy (2370).
  • Fig. 24 is a flow chart showing the co-evolution of enterprise modeling and strategic management as the enterprise responds to market feedback.
  • the enterprise system generates a model by requesting data on-demand (2400). From the model,
  • 108 simulated scenarios are generated with probable outcomes (2410).
  • the enterprise devises a strategy by accessing the model (2420) and activates the strategy (2430).
  • the enterprise strategy receives feedback in the market (2440) and updates the model from data of market feedback (2450). While the model will then generate more scenarios from simulations, the just-in- time strategic management approach promotes rapid action (2460) and strategic agility.
  • Fig. 25 is a flow chart showing the process of modulation of portfolios of enterprise strategies.
  • the system accesses strategic management models (2510) and analyzes the models (2520).
  • the system modulates the portfolio of strategies by prioritizing key factors (2530).
  • the system executes the strategies (2540) and accumulates data on enterprise performance (2550).
  • the system modulates a portfolio of strategies to maximize performance (2560).
  • Fig. 26 is a flow chart showing enterprise solution option selection in response to enterprise constraints. Because it has limited resources, the enterprise must choose between a set of options (2600). Two or more enterprise constraints are identified (2610) and the enterprise priorities are ranked and re-ranked (2620) according to the relative priorities. The strategic solution options are developed to solve multi-objective constraints (2630) and a solution option is selected to solve the multi-objective problem (2640). The strategic solution is embedded in an enterprise strategy (2650) and the enterprise strategy is activated (2660).
  • the present invention pertains to enterprise systems.
  • the invention presents electronic methods to organize enterprise resource planning and strategic management processes applied to the performance of enterprise functions.
  • the present system involves the integration of information technology networks with enterprise operations.
  • the system develops novel enterprise modeling approaches which are applied to specific enterprise functions.
  • the system also applies to dynamic strategic management processes of enterprise operation that include development of active models to advise decision- making processes.
  • the system applies to networks of enterprises.
  • the present system provides technical mechanisms that allow enterprises to have specific strategic competitive advantages to maintain market leadership positions.
  • legacy systems being hardwired business processes with fixed business logic, have severe limitations.
  • the business enterprise structure and function is held hostage to outdated IT infrastructure and organizational logic and is thus uncompetitive relative to businesses that maintain more flexible infrastructures.
  • Simply wrapping an inflexible ERP system core with middleware to connect to Web services fails to solve the problem.
  • This solution of maintaining a software layer that intermediates client-server hardware systems has become a ubiquitous model for enterprise technology.
  • the legacy enterprise model is an expensive and rigid patchwork of software products that is neither active nor adaptive to the environment. So far, there is no enterprise system that is unifying, modular, dynamic, adaptive, flexible and efficient. Because the main factors of data, relationships, logic, process and policy change over time, these changes need to be accommodated in the IT and enterprise systems.
  • the presentation layer needs to continually be updated to accommodate the changing user interface (UI) standards of rapidly changing mobile or laptop devices.
  • UI user interface
  • the key component that links enterprise software applications to one another is the database management system (dbms).
  • the dominant dbms has been the relational database model developed originally by IBM and which has evolved into the object- relational database model. Yet the legacy hardware limits also affect the limiting functions of the relational data model.
  • Relational data models are separate from application logic.
  • the logic layer and the data layer are distinct but connected. Though data is "defined” in the data layer, they are referenced in the application logic layer.
  • Each process step in the logic layer locates data (and relational information) in the data layer. This results in modification to data definitions in the logic layer for both old and new meanings.
  • Making changes to definitions in the logic layer is complex, since changes need to occur at each step in the process in order to modify both data and relational information.
  • Object relational (OR) models map the object to the relational model and inherit the same relational model constraints, namely, that the relational model be synchronized with modifications in the object. The OR dbms simply adds a layer of complexity to the traditional relational model.
  • relational dbms requires thousands of hash tables, which are cumbersome Xo constantly update.
  • data relations are fixed.
  • fixed relationships between data are inflexible. It is time-consuming to reorganize relations with new attributes.
  • hierarchical keys to the relational dbms create the need for repeated data sets in multiple locations, which leads to redundancy.
  • a single relational dbms as a legacy system, atrophies.
  • the relational model is static, with a finite set of definitions and relationships. With the need to update attributes, the problem of complexity emerges, which carries with it time constraints.
  • relational dbms In order to overcome some of the limitations of the relational model, it is possible to structure the relational dbms with updated definitions at regular intervals so that the database constantly restructures.
  • One advantage of the object-oriented model is that, as UI layer standards change every few years, the UI layer does not affect the data or logic of the underlying system. Any application processes using the continuously evolving UI layer can be made from varied logic or data layers regardless of the source, facilitating a truly ubiquitous computing platform. In fact, because they are not dependent on the legacy system, UI layers in the object-oriented model can be replaced entirely as better presentation layers become available. Second, data meanings and relations can be modified without complex logic revisions. Process stages can be joined to other steps. Further, process steps can be separated, and the order of the steps can be changed.
  • the object-oriented model Unlike the relational model, which wraps a middleware layer around an inflexible legacy system, the object-oriented model lacks these structural constraints. Consequently, the object-oriented applications unify data and logic components, thereby providing increased agility. Applications can change with this object oriented model as the business changes, thereby unconstraining the business architecture.
  • the economics literature provides an additional bio-inspired theory of business networks that comprise a dynamic economic ecosystem. Further, complex game theoretical models are applied to economic behaviors by economists to show the interaction dynamics of inter-firm rivalry. Additionally, the computer science literature provides examples of database networks for enterprise systems and ubiquitous and pervasive network computing architectures for enterprise operation. These biological, economic and computer science literatures are discussed here.
  • Eisenhardt has observed that after periods of slow change we witness short bursts of rapid change in order to stimulate or accommodate fundamental breakthroughs. For instance, the flexible production models for manufacturing behavior which were developed by Toyota became a new paradigm that upset the standard Fordist mass- production model. Eisenhardt applies complexity science to the economics of organizations by observing that the survival of companies during periods of rapid change
  • 121 may require the ability to quickly adapt, particularly in high-velocity industries such as those in the technology sector.
  • Complexity theory also applies to systems biology fields as diverse as cellular biology, neural biology and immunology.
  • neural biology neurons are constantly rewired based on adaptation to varied inputs.
  • a learning process that incorporates environmental feedback mechanisms constantly rewires the neural system.
  • cells are the vehicles for the interaction of a complex protein network that also features a feedback mechanism for survival.
  • human immune system provides an example of a complex feedback mechanism that adapts to new pathogens.
  • Autonomic computing involves (a) self-configuration, (b) self-optimization, (c) self-healing, (d) self-protection and (e) self-management of network computing systems.
  • autonomic computing systems provide a useful advance of the state of the art.
  • the main ideas of autonomic computing rely for inspiration on the autonomic nervous system that provides reflex behaviors such as . breathing or swallowing. While this system is generally useful for lower levels of. computational network behavior, it is primarily limited to specific narrow functions and is reactive rather than adaptive or proactive.
  • the autonomic computing paradigm does not feature higher levels of behavior that require a more complex computational system for organization.
  • the autonomic nervous system along with the somatic nervous system, is part of the peripheral nervous system.
  • the peripheral nervous system contains all nerves not in the central nervous system that features the brain and spinal cord. While the somatic nervous system coordinates the body's movements and receives external stimuli, the autonomic nervous system contains the sympathetic nervous system, the parasympathetic nervous system
  • the sympathetic nervous system modulates heartbeat, blood pressure and adrenaline, responding to impending danger.
  • the parasympathetic nervous system modulates the dilation of blood vessels, constriction of pupils and digestive system stimulation, while the enteric nervous system modulates all aspects of digestion.
  • the monitoring of key subsystems occurs via continuous modulation of motor neuron activity and adjustment of levels of chemicals that are stimulated by sensory inputs. Taken together, these nervous system components provide reactions to specific feedback mechanisms.
  • the autonomic computing paradigm provides a useful advance in automation that makes possible the pervasive, or ubiquitous, computing model, or ambient informatics, in which distributed computer systems are constantly updated and repaired.
  • this system lacks is proactive behavior that responds to the prospects of unanticipated change.
  • complexity science deals with the idea of emergence of multiple agents in a dynamic system, we may look for inspiration in other biological processes in order to advance the autonomic computing paradigm.
  • One such bio-inspired, next-generation computing model may be found in the human adaptive immune system.
  • Game theoretic modeling of complex competitive behaviors uses advanced computational resources to articulate processes of strategic conflict. These models are generally passive descriptions of behaviors and reactions to competitor behaviors, however.
  • next-generation dynamic modeling once applied to active strategies in competitive situations, will provide more useful techniques and approaches for modem organizations. The present system advances this new approach.
  • Hayes-Roth articulates a view of business organizational interaction in which competitive advantages of specific organizations are developed and maintained using market power.
  • Hayes-Roth describes these trends and observes that powerful organizations are using their resources to maintain their advantages, he does not show how these processes work. That is, though he recognizes that these mechanisms of information superiority produce, mainly through modeling processes, strategic advantages for successful companies, he stops at a delineation of how to perform the steps necessary to attain market supremacy. The present system rectifies this deficiency.
  • ROA real options analysis
  • Rational expectations is an economic theory that provides analysis for strategic action in uncertain environments.
  • Novel approaches to ROA and rational expectations are explicated in the enterprise system invention.
  • Recent advances in economic modeling have increasingly enlisted the advisory capacity of these two theories for optimizing resource constraints and improving decision making, in order to more clearly meet strategic goals.
  • the present invention is an original extension of these main complex theoretical endeavors.
  • the present system articulates a biologically inspired theoiy of enterprise structure and functions.
  • the aim is to develop advanced enterprise computational, architectural and strategic mechanisms for the development, attainment and maintenance of the high performance organization.
  • the goal of the successful enterprise is market dominance; the present system advances this goal.
  • our goal is to provide specific automated mechanisms to organize enterprise systems using biologically inspired processes.
  • the present system is therefore a bio-inspired enterprise control system that regulates very specific sub-systems, the goal
  • the twenty-first century dynamic enterprise system that the present invention articulates is as different in viewpoint from the static mass production nineteenth century enterprise as can be.
  • the present system in sum, more fully prepares organizations for the continuous reengineering processes and adaptation to the market that are essential to future success.
  • the present system has numerous advantages over prior systems.
  • the present invention advances the enterprise system in the context of global communications networks.
  • networks may consist of aggregations of multiple business units from multiple enterprises working in tandem to achieve common goals on a range of projects in various stages of development.
  • the key problem with integrating these networks is to develop solutions for collaboration between various agents.
  • the development of effective collaboration processes for networks of aggregating and reaggregating agents, including coordinating specialized processes, is critical to the optimization of network strategic processes. Mastering these strategic processes lead to dominance of an industry.
  • the present invention therefore, develops a system for strategic optimality and effective enterprise leadership of an industry.
  • collaboration becomes a form of temporary simultaneous communication in which collectives of individuals are combined for each distinct project, disaggregated, retrained and reaggregated as market demand ebbs and flows.
  • the ultimate goal of firm success is enterprise leadership of an organization's industry.
  • One way to achieve this superior position is through strategic optimality in which all strategic, active and analytical elements are combined to meet the goal of enterprise success.
  • the agency model of the firm views the enterprise as an intermediary engaged in various sorts of arbitrage.
  • Many firms use spatial arbitrage in which they buy a product in one place, for example China, at a reduced rate because of nineteenth century labor costs, and sell it in industrial nations.
  • Other firms use temporal arbitrage in which they buy (or obligate themselves to buy in a futures contract that locks in a favorable rate) a product now and sell it in the future in an inflationary market.
  • Some firms use information arbitrage in which they have superior information on which to act, or a timeliness of action that is possible because of their superior information capabilities. All of these forms of arbitrage assume asymmetries between the buy and the sell which will provide the agents the advantage of exploiting imbalances. Arbitrage opportunities exploit inefficiencies in the market.
  • Keiretsu firms represent a system, or set of companies like a cartel, that have interlocking relationships. Several main Keiretsu will generally compete with one another. Rather than allowing specific industries to emerge as competitors, the Keiretsu firms compete between themselves across the entire economy. Each Keiretsu will include a major bank, an automobile manufacturer, and various suppliers. In effect, the Japanese economy emerged as a self-organizing system that benefits their specific communitarian style of civil. The tendency is for companies within each Keiretsu to cooperate with each other but to compete with other companies in other Keiretsu.
  • Autonomic processes will increasingly use advanced information technology to continuously create, manage, optimize and reorganize networks among firms.
  • Autonomic processes are becoming increasingly ubiquitous in controlling the background IT enterprise functions automatically.
  • These autonomic computational processes control the self-configuration, self-healing, self-optimizing, self-protection and self-management of network systems.
  • these processes have been entirely reactive.
  • autonomic computational processes for enterprise functions have been organized to automatically process already established tasks. These processes have as an analogy the network processes of the human brain, the city and ecosystems in which collective behaviors produce emergent and self-organizing behaviors. But as they appear in the context of enterprise networks, autonomic systems have not evolved beyond a simple automation of pre-established control systems.
  • the present system appends to the existing idea of the enterprise network autonomic model a critical supplemental insight of anticipation.
  • inputs from endogenous or exogenous sources may create stimuli that fundamentally alter the configuration of the system.
  • these inputs are not predictable in the long-run.
  • the probability of specific variables affecting the overall network performance can be measured and tracked.
  • the input of these variables can be expected to some degree and factored into the strategic plans of each enterprise as contingency plans.
  • all firms have contingency plans in the event of a disaster, for example. Lists of possible problems that may emerge are calculable and predictable in a systematic planning process.
  • Anticipatory autonomic information technology systems allow the enterprise to maintain strategic agility in order to respond accurately and rapidly to any external inputs and to exploit markets through innovation techniques as they arise. These anticipatory systems require companies to maintain processes for continuous improvement in order to gain competitive advantages. For example, in the context of the supply chain, a disruption would be devastating and have long-lasting effects. Yet the anticipation of possible types of disruptions and rapid redirection of resources across the network develop and optimize plasticity processes, which provide tremendous advantages to successful firms. Ultimately, the firm's reputation is preserved by limiting supply disruptions to customers. The firm may withstand minor problems because of the effective execution of highly organized business processes.
  • collaboration processes have been embedded in distributed systems in such a way that they are limited to file sharing processes.
  • the right file is requested at a specific location at a specific time. For example, in the case of a wiki, multiple individuals may access, and co-edit, a common document concurrently since collaboration is a form of information sharing in a distributed network.
  • the main advantage of a collaborative system is problem-solving in real time by utilizing the best practices of a combination of specialists.
  • the best way to achieve collaborative processes is to create a network computing system that uses intelligent mobile software agents.
  • the software agents are involved in
  • the software agents will analyze a problem and present alternatives. Parts of the problem that require detailed solutions are forwarded to the best available specialists.
  • the present system goes further by providing an anticipatory function that integrates solutions from past problem-solving and develops expectations for solution-option prospects in the future.
  • a new kind of advanced wiki emerges in which the document is an evolving model that seeks out the best available specialist on the team in real-time to solve a key part of the problem. Meanwhile, other team members are busy resolving other problems in the order of priority.
  • An analogy can be seen in a busy hospital operating room, in which multiple surgeries are processed by multiple specialists according to the level of urgency.
  • the present model embodies an advanced management consulting firm, which is constantly evolving its performance with multiple projects simultaneously, but which also integrates the traditional organizational model with the addition of fundamentally new information technology systems that transform the enterprise into a dynamic functioning organization.
  • Collaboration processes are an important part of the dynamic enterprise. Collaboration processes take advantage of the temporary aggregation of human capital for the engagement of specific projects in real time. Collaboration may include teams both internal to an organization and external, such as specialist consultants. Further, collaboration may occur both upstream and downstream in the supply chain. In effect, collaboration processes provide a mechanism for pooling resources for just-in-time problem solving.
  • collaboration is also a form of learning process.
  • collaboration has a leader and follower, and elements of sharing, as well as evolving, knowledge.
  • the teaching process is a collaborative one in which a Socratic teaching method is used to attain knowledge.
  • the accumulation of learning is a key factor that allows individuals to achieve a competitive advantage in mastering information in real-time, such as in order to pass certification tests.
  • collaboration processes may accelerate the learning process.
  • an accumulation of knowledge occurs when one achieves proficiency over time.
  • the learning mechanisms for collectives are more efficient.
  • the enterprise architecture will evolve from the founding team to a multinational corporation.
  • the successful firm evolves with its industry and sustains superior performance relative to its peers by utilizing mechanisms to achieve strategic agility and strategic optimality.
  • a great company will innovate at every phase of its evolution.
  • Fig. 1 is a schematic drawing of a distributed network.
  • Fig. 2 is a schematic drawing of industry evolution configurations.
  • Fig. 3 is a schematic drawing of competitive configurations over several phases.
  • Fig. 4 is a flow chart showing the enterprise strategic management process.
  • Fig. 5 is a schematic drawing showing the transformation of an enterprise network.
  • Fig. 6 is a schematic drawing showing the changing position of a network with the subtraction of a node.
  • Fig. 7 is a schematic drawing of transforming teams of workers in several projects over two phases.
  • Fig. 8 is a flow chart showing the problem solving process of a team of specialists.
  • Fig. 9 is a flow chart showing the use of software agents to solve enterprise problems.
  • Fig. 10 is a flow chart showing the use of software agents to assist a team of specialists to solve problems on demand.
  • Fig. 11 is a schematic drawing showing the repositioning of specialists over several phases to solve enterprise problems.
  • Fig. 12 is a flow chart describing a method of enterprise problem solving.
  • Fig. 13 is a flow chart showing the process of specialists solving problems.
  • Fig. 14 is a flow chart illustrating the process of multi-functional employees solving enterprise problems.
  • Fig. 15 is a flow chart delineating the process used by the human immune system to solve problems of new antigens.
  • Fig. 16 is a flow chart showing the process of solving enterprise network problems.
  • Fig. 17 is a schematic diagram of the transformation of a reorganizing network.
  • Fig. 18 is a flow chart illustrating the plasticity process of an enterprise network.
  • the first group of computers consisting of 100, 110 and 120, is linked to both the second group of computers (130, 140 and 150) and to the third group of computers (160, 170 and 180).
  • Network computing in an enterprise allows the technology system to generate inputs from any source and relay the data to the other computers in the system. This distributed network configuration is important to the enterprise collaboration process.
  • Fig. 2 tracks the evolution of an industry across the product cycle.
  • a monopoly and duopoly emerges (200).
  • an emergent duopoly or oligopoly emerges (210).
  • the oligopoly grows (220) in the third phase of the industry evolution.
  • the industry is consolidated and the oligopoly of firms is restructured (230).
  • the oligopoly shrinks to only a few players (240).
  • the oligopoly of the mature phase gives way to a duopoly or a monopoly of firm(s) (250). Tracking the timing and configuration of industry evolution is critical for an enterprise to plan its strategies.
  • Fig. 3 shows the competitive configurations of several organizations over time.
  • products are launched by two companies (1 and 2) at 300 and 305, which directly compete with each other.
  • a third company (325) launches a product to compete with the second company, in addition to the competition between the first and second companies.
  • the third company launches a product (340) to compete with the first company (330) as well.
  • the second company launches a product (350) to compete with the third company (355).
  • products are launched in which all of the companies (360, 365 and 370) compete with each other.
  • This schema illustrates how enterprises experience multilateral competition over time.
  • Fig. 4 is a flow chart showing the enterprise strategic management process. Once an enterprise develops a strategic plan with multiple options (400), it executes the strategy (410). The enterprise then encounters changes in the environment (420) and
  • Fig. 5 illustrates the process of restructuring networks of suppliers and customers.
  • suppliers 500, 505 and 510) supply goods and services to a wholesale layer in the industry (515, 520, 525 and 530), which in turn supplies these goods and services to customers (535, 540 and 545).
  • vendors 500, 505 and 510) supply goods and services to a wholesale layer in the industry (515, 520, 525 and 530), which in turn supplies these goods and services to customers (535, 540 and 545).
  • vendors 500, 505 and 510
  • vendors there are only two vendors (550 and 555).
  • the wholesale layer rather than four wholesalers, there are only three (560, 565 and 570).
  • there is a reduction in the number of customers from three in the first phase, to two (575 and 580) in the second phase.
  • This restructuring of the industrial ecosystem will continue across the business cycle, with increases in the number of suppliers, wholesalers and customers as well as decreases.
  • the net effect of the reduction of vendors, wholesalers and customers is
  • Fig. 6 is a schematic drawing showing the changing position of a network with the subtraction of a node.
  • the first phase there are six nodes connected in a distributed network (600, 605, 610, 615, 620 and 625).
  • the node formally at 625 is removed. Additional nodes will be added and the network will reconfigure. This reconfiguration process with the loss and addition of nodes provides a flexible growth opportunity for enterprises.
  • Fig. 8 is a flow chart showing the problem solving process of a team of specialists. After the enterprise management encounters a problem (800), the managers assemble a team of specialists to find a solution (810) to the problem. The specialists on
  • Fig. 9 is a flow chart showing the use of software agents to solve enterprise problems.
  • intelligent mobile software agents IMSAs
  • IMSAs intelligent mobile software agents
  • a computer network divide the problem into parts (910) and connect specific parts of the problem to specific specialists at different locations (920).
  • Multiple specialists work on parts of the problem in real time (930) and the IMSAs coordinate the generation of a solution (940).
  • the enterprise managers apply the solution (950) and the solution is saved in a database (960).
  • Fig. 10 is a flow chart showing the use of software agents to assist a team of specialists to solve problems on demand.
  • An enterprise problem is analyzed by IMSAs (1000), which search a database for past solutions to similar problems (1010). Parts of an enterprise problem are then forwarded to specialists by IMSAs (1020) and the IMSAs assemble solution options to solve the problem (1030).
  • the IMSAs anticipate new problems (1040) by analyzing trends in the enterprise model and assemble on-demand solutions by combining past solution options and reassembling specialist solutions (1050).
  • the evolving model uses IMSAs to seek out the best available specialist on the team to solve a key part of the problem (1060).
  • Fig. 1 1 is a schematic drawing showing the repositioning of specialists over several phases to solve enterprise problems.
  • Five different main specializations (1, 2, 3, 4 and 5) are demarcated; multiple individual specialists possess the specific specializations.
  • a and B (1100) have specialization 1 ;
  • C, D and E (11 10) have specialization 2;
  • F and G (1120) have specialization 3;
  • H, I and J (1130) have specialization 4 and;
  • K, L and M (1140) have specialization 5.
  • phase one of fig. 1 1 A, F and I, which have specializations 1 , 3 and 4, are combined on a specific project (1050).
  • phase two (1160) individuals B, C, H and K have specializations 1, 2, 4 and 5 and are combined on a project.
  • phase three (1270)
  • D, G, J and L have specializations 2, 3, 4 and 5 and are combined on a project.
  • phase four (1180), E and M have specializations 2 and 5 and are combined on a project.
  • the enterprise's management divides the problem into parts (1210) in order to seek a solution.
  • Each part of the problem is forwarded to specific specialists to seek solutions (1220).
  • the specialists solve the problems in parts that have been encountered by accessing a database of prior solutions (1230).
  • Parts of the new problems are then analyzed by the specialists and solution options proposed (1240).
  • the solution options to new problems are tested (1250), successful solutions to new problems are applied and the database is updated (1260).
  • Fig. 13 is a flow chart showing the process of specialists solving problems. Once enterprise management request that new problems are solved (1300), requests are compared to available specialists and the requests are ranked by priority (1310). The problem is matched to the expert specialist that can solve the problem most efficiently (1320). Specialists negotiate over a distributed network to solve the problem (1330) and the enterprise managers with the problem collaborate with expert specialists to solve it (1340). A specialist solves the problem and stores the solution in a database for future, reference (1350).
  • a problem is encountered and referred to a specialist to solve (1410), but a specialist is not available to solve the problem (1420).
  • Enterprise managers request, solutions from multi-functional employees (1430), who analyze the problem and access a database for solutions to similar problems (1440).
  • the multi-functional employees produce, rank, test and select solution options (1450).
  • the solution options are applied to solve the problem (1460).
  • Multi-functional employees share solutions with other multifunctional employees and store solutions in the database (1470). This process then repeats.
  • Fig. 15 is a flow chart delineating the process used by the human immune system to solve problems of new antigens.
  • the innate immune system applies a targeted solution to a problem (antigen) that has been previously discovered and whose solution has already been developed (1500). However, a new anomaly is discovered (1510). The innate immune system accesses its memory and cannot solve the new problem (1520).
  • the adaptive immune system identifies the anomaly (1530) and develops a complement mold to the new anomaly (1540), which solves the problem (1550).
  • the adaptive immune system passes the solution to the innate immune system (1560) as a form of memory so that the next time the innate immune system encounters the problem, it may access its memory and apply the solution. The process repeats.
  • the enterprise borrows concepts from this problem solving mechanism in the present invention.
  • Fig. 16 is a flow chart showing the process of solving enterprise network problems.
  • the exogenous inputs affect the enterprise network (1605).
  • the specific variables affecting the enterprise network are measured and tracked (1610), and input into the enterprise strategy (1615).
  • the enterprise then develops a strategic plan (1620) and develops contingency plans from the strategic plan (1625).
  • the possible problems are predicted in a systematic planning process (1630).
  • the enterprise anticipates possible actions and prepares strategies for contingencies (1635).
  • the enterprise encounters unexpected behavior (1640) and activates the contingency plan (1645).
  • the enterprise receives feedback from the environment to the contingency plan and updates the strategy (1650). The process repeats.
  • Fig. 17 is a schematic diagram of the transformation of a reorganizing network.
  • phase one there are three tiers (A, B and C) of nodes, including nodes 1702, 1704 and 1706 in tier A, nodes 1708 and 1710 in tier B and nodes 1712, 1714 and 1716 in tier C.
  • the nodes are connected by pathways that link nodes between layers.
  • the node formally at location 1706 in tier A and the node formerly at location 1712 in tier C are removed.
  • the pathways between the nodes that connect the nodes between layers are maintained, but the pathways between the removed nodes are removed.
  • the system uses the experience of prior periods to anticipate the conditions of node and pathway changes. These experiences are analyzed to produce anticipatory expectations for next generations of phases; when the conditions for change present themselves, the system will more rapidly change its positions by adding or subtracting nodes and pathways based on the prior experiences.
  • a new tier of nodes is added to the network to connect nodes between tiers C and D.
  • tier A is removed, including
  • the system uses metaheuristics that anticipate process flows based on analysis of network functional experience.
  • Fig. 18 is a flow chart illustrating the plasticity process of an enterprise network.
  • an external event removes a node in the network (1810).
  • the network structure changes to remove the pathways of the removed node (1820).
  • a node is added to the network and pathways are added to the new node (1830).
  • the system anticipates network node changes based on tracking old node performance (1840).
  • the system then builds a model of possible network node and pathway changes and generates scenarios (1850).
  • the model is updated (1860).
  • the enterprise rapidly redirects resources to imminent network change when the network node or pathway factor changes (1870).
  • the enterprise network activity and plasticity is optimized (1880).

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Abstract

La présente invention concerne une architecture de technologie d'informations ébauchée pour un système d'entreprise. L'architecture se compose d'objets de données évolutifs dans des bases de données d'objet. Lorsque les versions des objets de données sont mises à jour, les objets sont stockés dans celles-ci, et accessibles par les bases de données d'objet en ordre inverse. Les bases de données d'objet réorganisent continuellement les données objet fixées pendant que les nouvelles versions des objets sont entrées, avec les versions objet de haute priorité étant les objets de données les plus récents. Les applications personnalisées sont assemblées sur demande pour résoudre les nouveaux problèmes dans le système d'entreprise en accédant aux objets de données dans le réseau de base de données d'objet.
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US20090182596A1 (en) * 2008-01-15 2009-07-16 International Business Machines Corporation Method and system of analyzing choices in a value network
US20140279675A1 (en) * 2012-09-28 2014-09-18 Rex Wiig System and method of a requirement, compliance and resource management methodology
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US10761952B2 (en) 2018-04-13 2020-09-01 International Business Machines Corporation Intelligent failover migration across multiple high availability stacks based on quality of prior failover migrations
CN110751452B (zh) * 2019-09-18 2023-11-10 九江明阳电路科技有限公司 一种工作流程管理系统、方法及存储介质
CN110751452A (zh) * 2019-09-18 2020-02-04 九江明阳电路科技有限公司 一种工作流程管理系统、方法及存储介质
CN111882049A (zh) * 2020-07-02 2020-11-03 清华大学 基于企业智能神经网络物质流监控及分析系统
CN111882049B (zh) * 2020-07-02 2022-11-11 清华大学 基于企业智能神经网络物质流监控及分析系统
CN111784199A (zh) * 2020-07-23 2020-10-16 中国人民解放军国防科技大学 业务与信息与技术相互演化的系统、方法及存储介质
WO2022266362A1 (fr) * 2021-06-18 2022-12-22 Jabil Inc. Systèmes et procédés de traitement de risque sur des données d'un système de gestion de chaîne logistique
US12026644B2 (en) 2021-08-02 2024-07-02 Toyota Motor Engineering & Manufacturing North America, Inc. Machine-learning-based adaptive threads orchestrator design in the MFG-based data offloading mechanism
CN116611710A (zh) * 2023-06-21 2023-08-18 深圳传世智慧科技有限公司 一种可视化动态展示方法及系统

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