Patent Application
20070288212
The present invention relates generally to a computer system for modeling and simulating complex business processes having multiple discrete tasks, each of which may be performed by one or more available resource. More particularly, the invention relates to a computer system which includes a modeling interface to a generic simulation and optimization database that allows a user to easily define and modify models representative of the discrete tasks and the available resources and attributes associated with the tasks and resources to represent any business process.
The following paragraphs in this section are intended to introduce the reader to various aspects of art that may be related to various aspects of the present invention that are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present invention. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
Complex business processes, such as sales processing and patient scheduling and processing, generally involve many discrete tasks that can be performed by many different resources having different availability. Not only can the discrete tasks be performed by different resources, but the tasks may also be performed according to various different task flows and dependencies. However, the multitude of different tasks, variability in processing times, task arrangements, and available resources introduce numerous variables, variable dependencies, and combinations of variables, making it difficult to design an optimal process or system for a given anticipated demand level and pattern. These levels of interdependence and randomness preclude the use of flow charting, simple spreadsheets and manual analysis.
The planning and design of such complex processes and systems have typically been approached in two ways. Under the first approach, the process or system is simply designed and implemented. The system or process is then actually performed in an experimental environment to verify its operation and efficiency. This approach, however, is costly because of the consumption of valuable resources (e.g., skilled workers, materials, capital, etc.) required to purchase, install, and verify the process or system, as well as the resources required to correct inadvertent errors in the design and planning that may have occurred and were not discovered until after implementation. In many instances the trial and error would destroy the operation.
Under the second approach, simulation software is used to model the process or system and then to optimize operation of the process or system. This approach is advantageous as it provides the opportunity to think through, test and verify the design before investing in the actual implementation. Further, once developed, the model can be used to play out “what-if” scenarios to evaluate alternative implementations, thus facilitating optimization of the final design. In operation, the model itself can be deployed as a decision support engine which compares actual system operation with the desired state or entitlement and then clearly provides the decision support for introduction.
Although the simulation approach seemingly offers a practical and efficient solution to designing complex processes and systems, existing simulation software traditionally is expensive and difficult to use for those not highly skilled in the art. Development of simulation models must be performed by software programmers having expertise in the simulation language and simulation programming techniques. Such programmers are a significant incremental cost and often may not have special domain knowledge regarding the particular process or system that the modeler/programmer is modeling. Further, once developed, the underlying model can be changed only by interacting with the simulation software code, thus requiring the continued participation of the programming or simulation expert.
Accordingly, existing simulation systems are not particularly flexible initially, in changes and as decisioning engines. Moreover, such existing systems are difficult to use without the continued assistance of a programming expert. Still further, the costs associated with the acquisition and use of a simulation and decisioning system often are prohibitive.
Accordingly, it would be desirable to provide a simulation system that could be easily used by non-software experts, particularly by users having special knowledge with respect to the process or system being simulated. Also, it would be desirable to provide a simulation system in which simulation models can be easily created, modified, and stored so that iterative or alternative design processes may be carried out and the same simulation system could be used to simulate numerous different types of processes. Further, it would be advantageous if such a system could be designed with a modeling interface that could be used by many users concurrently, thus reducing costs associated with modeling and simulating processes. In addition, the system could be designed such that the simulation could be performed over a network (e.g., an intranet, the Internet, etc.) thus allowing the user, such as a design consultant, to work from a remote location (e.g., a customer's facility). And finally that the resultant models are deployable in decisioning environments providing decision support with manual and/or automatic data feeds or as an automated decisioning platform not requiring human intervention.
Certain aspects commensurate in scope with various embodiments of the invention are set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of certain forms the invention might take and that these aspects are not intended to limit the scope of the invention. Indeed, the invention may encompass a variety of aspects that may not be set forth below.
In accordance with one aspect, the invention provides a system to simulate a process of discrete events or tasks having a plurality of available resources associated therewith. The system comprising a database to store a plurality of models, each model including a plurality of one or more entity, task, and resource parameter. The system further comprising a model application in communication with the database and configured to receive commands from a user, to retrieve one of the plurality of models and the corresponding plurality of one or more entity, task, and resource parameter in response to a user command, to receive input data corresponding to attributes of one or more entity, task, and resource parameter from a business database system, and to generate a simulation model based on the selected business database system and the input data; and an optimizing application in communication with the model application and configured to receive commands from a user, to select at least one entity, task, and resource parameter of the simulation model with respect to an objective function, to define bounds of at least one of the entity, task, and resource parameter selected, and to generate values for the objective function based on the at least one of the entity, task, and resource parameter selected. The system also comprises a server to perform a simulation of the process by processing the simulation model and to generate an output data file containing output data representative thereof.
In accordance with a further aspect, the invention provides a method to simulate a process of discrete events or tasks having a plurality of available resources associated therewith. The method comprising storing a plurality of models at a database, each model including a plurality of one or more entity, task, and resource parameter. The method further comprising communicating with a model application by a user, the model application in communication with the database and configured to receive commands from a user, to retrieve one of the plurality of models and the corresponding plurality of one or more entity, task, and resource parameter in response to a user command, to receive input data corresponding to attributes of one or more entity, task, and resource parameter from a business database system, and to generate a simulation model based on the selected business database system and the input data; and communicating with an optimization application by a user, the optimizing application in communication with the model application and configured to receive commands from a user, to select at least one entity, task, and resource parameter of the simulation model with respect to an objective function, to define bounds of at least one of the entity, task, and resource parameter selected, and to generate values for the objective function based on the at least one of the entity, task, and resource parameter selected. The method also comprising performing a simulation of the process by processing the simulation model and generating an output data file containing output data representative of the simulation.
In accordance with a further aspect, the invention provides a storage medium encoded with machine-readable program code for simulating a process of discrete events or tasks having a plurality of available resources associated therewith. The program code including instructions for causing a computer to implement a method. The method comprising retrieving one of a plurality of models and corresponding plurality of one or more entity, task, and resource parameter in response to a user command, receiving input data corresponding to attributes of one or more entity, task, and resource parameter from a business database system, and generating a simulation model based on the selected business database system and the input data. The method further comprising receiving a selection of at least one entity, task, and resource parameter of the simulation model with respect to an objective function, receiving a definition of bounds of at least one of the entity, task, and resource parameter selected, and executing a simulation engine to generate values for the objective function based on the at least one of the entity, task, and resource parameter selected. The method comprising executing a simulation engine to generate values for the objective function based on at least one of the entity, and resource parameter selected. The method also comprising performing a simulation of the process by processing the simulation model.
The historical models characterizing processes, stored in the database, are themselves model objects. These model objects contain all of the modeling data handling. Algorithms and I/O of standalone models or models deployed in decisioning. Multiple model objects can be combined to instantiate new models for deeper analysis of the existing system or to describe other systems.
In accordance with a further aspect, the invention provides an apparatus for simulating a process of discrete events or tasks having a plurality of available resources associated therewith. The apparatus comprising means for storing a plurality of models at a database, each model including a plurality of one or more entity, task, and resource parameter. The apparatus further comprising means for communicating with a model application by a user, the model application in communication with the database and configured to receive commands from a user, to retrieve one of the plurality of models and the corresponding plurality of one or more entity, task, and resource parameter in response to a user command, to receive input data corresponding to attributes of one or more entity, task, and resource parameter from a business database system, and to generate a simulation model based on the selected business database system and the input data; and means for communicating with an optimization application by a user, the optimizing application in communication with the model application and configured to receive commands from a user, to select at least one entity, task, and resource parameter of the simulation model with respect to an objective function, to define bounds of at least one of the entity, task, and resource parameter selected, and to generate values for the objective function based on the at least one of the entity, task, and resource parameter selected. The apparatus also comprising means for performing a simulation of the process by processing the simulation model and means for generating an output data file containing output data representative of the simulation.
In accordance with a further aspect of the invention, the objective function comprising a combination of system financial performance measures (e.g., revenue, costs, and operation margin) and process performance measures (e.g., cycle time, throughput, and utilization).
A technical contribution for the disclosed invention is a system and method for optimizing simulation of a discrete event process using business system data.
The present invention can be more fully understood by reading the following detailed description together with the accompanying drawing, in which like reference indicators are used to designate like elements, and in which:
Various embodiments of the present invention will be described below. In an effort to provide a concise description of these embodiments, not all features of an actual implementation are described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.
The foregoing description of various products, methods, or apparatus and their attendant disadvantages described in the in the “Background of the Invention” is in no way intended to limit the scope of the invention, or to imply that the invention does not include some or all of the various elements of known products, methods, and/or apparatus in one form or another. Indeed, various embodiments of the invention may be capable of overcoming some of the disadvantages noted in the “Background of the Invention,” while still retaining some or all of various elements of known products, methods, and apparatus in one form or another.
As used herein, any term in the singular may be interpreted to be in the plural, and alternatively, any term in the plural may be interpreted to be in the singular.
This invention addresses the problems discussed above, as well as others. The invention provides an easy to use and accurate model building system. The invention seamlessly utilizes information from operational business systems to automatically build base line simulation models. Mappings are maintained between the operational systems and the modeling system to allow future updates of the model. Simulation models may be created by users unfamiliar with programming techniques. With the invention, users can create new versions of models and test a variety of alternative system configurations.
Turning now to the drawings, and referring first to
The system 10 also allows a user to view output generated as a result of running a simulation using the defined model. The output can be any of a variety of types of outputs, such as a graph or table displayed on a graphical user interface, a report printed using an output device, or a data file transmitted via a network to remote locations for viewing or storage. Provision of a feature that allows customization of outputs allows data to be provided and formatted in a manner that is most beneficial to the particular user for viewing analyzing the results of the simulation. Further, the output feature provides a mechanism that allows the user to direct the simulation output data to another type of software application (e.g., a financial analysis program, a decision engine, “a digital cockpit”/flight simulator”, an engineering model, a control system) for performing other types of analyses (e.g., performance of a cost/benefit analysis, process decisioning, asset management, engineering design tradeoff) based on the simulation. The other software application can then provide output data that can be formatted as specified by the user.
Various elements advantageously used for accomplishing the features discussed above include a graphical user interface (GUI) 12, a modeling and output module 14, a database 16, a simulation engine 18, and an optimization engine 502. The GUI 12 includes a display 20 (e.g., a CRT or LCD monitor display or interactive display), and various input devices, such as a mouse 22 and an alphanumeric keyboard 24. The GUI 12 provides for user interaction with system 10 via a variety of graphically displayed screens including images, such as icons, windows, menus and dialog boxes, which appear on display 20. A user of the system 10 can provide commands and input data to the system 10 by using input devices 22 and 24 to select, manipulate, input text and otherwise interact with the displayed graphical images.
As illustrated in
The relationship between parameter tables associated with a particular model template stored in the database 16 is illustrated in
As shown in
To create a simulation model of the medical imaging process following the flow illustrated in
After step 100, as shown in
In step 180, the user is then provided with an opportunity to adjust the input data. If the user does indeed wish to adjust the input data, then the user might again go through a selection model process as shown in step 182. After step 182, the process again returns to step 114, and proceeds as described both. Alternatively, the user may not wish to adjust input data. As a result, the process passes from step 180 to step 184, in which the process ends.
As shown in
It should be understood that the allocation algorithm illustrated in
In general, once the simulation model has been defined; it is optimized by specifying decision variables (i.e., a tasks or resource parameters) of the simulation model, defining an objective function (e.g., utilization rate, which includes system throughput, inventory, investment, operating expenses, and fulfillment), and applying stochastic optimization. The optimized simulation model is then used to calculate performance/risk metrics, which are utilized in the decision process.
A flow chart of the steps for optimization is shown in
As shown in
As shown in
The optimization of the present invention improves model performance to allow for more informed financial decisions. It should be understood that optimization can be applied to any decision variables of the model and that the foregoing is merely exemplary.
With further reference to
The modular structure of the system 10 is particularly advantageous for allowing the user to access various components of the system 10 from a GUI 12 that is disposed at a location remote from the other components. For example, the user of the system 10 may be a consultant who offers process or system planning services to clients. The database 16, the modeling and output module 14, the simulation application 18, and the optimization application 502 may be located on a server at the user's place of business, while the GUI 12 may be located at the client's place of business. The user can access the remote server via a network connection initiated using the GUI 12 and the appropriate network communication software and network communication hardware. Such a remote access system is illustrated in
The Network 186 in
Accordingly, a simulation system and method has been described above in which simulation models may be created by users unfamiliar with programming techniques. The simulation models can be executed by any suitable, and advantageously, generic simulation software application that can read the data files representing the models. Further, the system provides a structure for allocating multiple available resources with different work schedules to the various discrete tasks of the modeled process. Moreover, the system is structured such that the user can create and run simulations from a remote location, such as a client's facility. When configured as a decisioning system or an engine within a decisioning system, the algorithm which is the model or objective function, may be invoked locally or remotely.
In accordance with one aspect of the invention described above, a user selects parameters and attributes and inputs data corresponding to the attributes as appropriate to describe thoroughly the tasks that must be performed, the sequence in which the tasks should be performed, the resources available for performing the tasks, and the occurrence of any other discrete events, such as scheduled arrivals, for example, that have an affect on the process. The data is stored in the database 16 in data records or files associated with the model. However, it should be appreciated that the invention is not limited to relying on such input by a user. Rather, the system of the invention may utilize any of a variety of business database systems so as to obtain information for the modeling process.
Hereinafter, aspects will be described in accordance with further embodiments of the invention. In addition to the various features described above, the invention provides the capability to integrate a generic business system dynamic modeling capability, such as is described above, with digitized business processes. As a result, the generic business modeling system provides an analysis and control capability that leverages existing information maintained by such digitized business systems. Such business systems might include Workflow, ERP, MRP, Factory Control, CMMS, Tracking systems, Asset Management, or others, for example.
Accordingly, the description below provides an additional or alternative approach to input data used in the modeling process. However, it should be appreciated that the embodiments described below may be used in part or in whole with any of the above-described embodiments and/or any of the embodiments or features described in U.S. patent application Ser. No. 09/481,252, which is related to the present application. U.S. patent application Ser. No. 09/481,252 filed Jan. 11, 2000 (Attorney Docket No. GERD:0003) is incorporated herein by reference in its entirety.
For example, an illustrative system might obtain data from a business database system, according to the below disclosure, and report a modeling of that data using reporting techniques described above.
By accessing digitized business process data, the invention provides fast and efficient model development and “what if” analysis capability. The invention can be integrated with any of a variety of digitized system data repositories. Further, in accordance with some embodiments, the invention maintains information mappings to the information source system, automates process time and arrival rate distribution generation, and maintains a model repository for easy comparison of process alternatives. These and other features will be described below. Further, it should be again noted that the system and method of the invention are not restricted to users with model programming expertise. Rather, such expertise is not needed in order to perform the dynamic system “What If” analysis, in accordance with various embodiments of the invention.
In accordance with one embodiment of the invention, a web based generic business process modeling capability is integrated with digitized business systems via intelligent data interrogation methods. The process interrogation uncovers the actual business process behavior as exhibited by the digitized business system and constructs a simulation model of the process. The base system elements, which include for example tasks, resources, and entities, are identified as well as the relationships between these elements (resource groups, job assignments, and process sequences). The system then utilizes an automated curve-fitting component to generate entity type specific arrival rates and processing times based on the historical digital system data. Further, models can be subsequently updated with new arrival and processing times utilizing the curve-fitting capabilities. The model can then be altered to perform “what if” analysis on the business processes. As a result, a user can maintain a library of process configuration alternatives to test a wide range of business strategies.
In accordance with one embodiment of the invention, the model portion 230 is in the form of a web server 230. However, the model portion may take on other forms as well. That is, for example, the model portion 230 might directly interface with a user and might be provided with business system data, i.e., in such a manner that communication over the Internet or another network is not needed.
In accordance with one embodiment of the invention, the model system 200 performs a system interrogation of the business database system 250. That is, the model system 200 extracts process history from the business database system 250 and builds a model based on that history. The building of the model may use a variety of parameters including resources that are available, tasks that are performed, workflow processing times, and/or a mixture of job start times and arrival rates, for example.
The model system 200 links the generated model to the workflow system from which data is retrieved, i.e., model system 200 links the generated model to the business database system 250, for example. Such links allow for future updating of the model once the parameters in the business database system 250 have changed. Further, the model system 200 auto-generates model distributions, in accordance with one embodiment of the invention.
In accordance with one embodiment of the invention, the model portion 230 in the model system 200 provides a modeling interface. For example, this modeling interface might utilize JSP (JavaServer Page) technology. The model portion 230 interrogates the business database system 250 to retrieve data from the business database system 250. This data is then used in generation of a desired model of a business process. In accordance with one embodiment of the invention, the model portion 230 uses a curve fitter 232, as shown in
As shown in
The model system 200 also includes the model server 210. The model server 210 performs various operations in conjunction with the model portion 230. The model server 210 monitors the database 220 for simulation requests, extracts model data from the database 220 and creates model definition files. Further, the model server 210 runs “Generic Simulation Models” and places the results in the database 220.
The web server 230 interrogates the business database system 250 for data used in generation of a model, i.e., at the request of a user. Once this data is input, the user can then adjust any of a wide variety of parameters using the techniques described herein. These adjustable parameters might be characterized as “system Xs”. On the other hand, the system Xs are used by the model system 200 to generate “system Ys”. The system Ys are generated parameters and are not generally adjustable by a user.
Illustratively, the system Xs might include resource levels, resource assignments, demand profiles, task times, process steps, or new workflows. The user may save different parameter sets by storing alternative models. This allows the user to compare the various system Xs and system Ys so as to understand system variability, and the manner in which the system varies based on different system Xs and the impact they have on the different system level Ys (Cycle time, throughput, inventory levels, for example).
To explain further, the workflow system of
The business database system 250 can be one or a combination of a variety of systems. For example, the business database system 250 might use a process control system, financial system, a CRM system, a sales system, an accounts receivable and/or an ERP system. In accordance with one embodiment of the invention, the business database system 250 preferably utilizes a processing protocol by which a job, upon entry into the workflow engine 252, for example, is assigned a “job number.” This “job number” identifies the job throughout its life in the workflow engine 252. Accordingly, all tasks that are performed for that job and all resources that were used to process that job, for example, are associated with the particular job number. This allows the business database system 250 to monitor discrete events in the life of that job. These discrete events are then obtained and used by the modeling in accordance with one embodiment of the invention.
Accordingly, in further explanation of one embodiment of the invention, the model system 200 automatically extracts system data, in the workflow or business database system 250 so as to integrate the digitized business system with the analysis and decision support technology provided by the invention. This process includes an automated system model build, as well as typically updating. Further, the database 220 may process the data obtained from the business database system 250 using an automated distribution curve fitting process, described further below. Further, the results of the modeling may be integrated with business and/or economic forecasting systems.
With further reference to
Further, the business modeling system 200 includes and uses a variety of features. These features include building and maintaining process capability and an analysis knowledge repository, as well as to provide analyze and control capability, i.e., which might include “what-ifs scenarios” and strategy comparisons, for example. The model system 200 may further incorporate business analytics, forecasting, and planning. Further, it should be appreciated that the model system 200 maintains digital system links to the business database system 250. These links provide for accurate historical demand patterns and processing times, over a period of time, such as weeks or years in the future.
After step 320, the process passes to step 330. In step 330, a user may edit the various parameters of the model. For example, the user might edit the tasks, times, flow of tasks, arrivals, and/or create new versions, for example. This editing may be performed using the techniques described above.
After the model is edited in step 330, the process passes to step 340. In step 340, the model is run and the results are reviewed and analyzed by the user. For example, the user might compare the output with another version of the model, which used different parameters. After step 340, the process ends in step 350. This high level flow would be repeated as the user performs analysis of the system output and makes adjustments to the model parameters to improve the modeled system's performance.
Distributions are generated for each entity type (i.e., which have a distinct sequencing of tasks that work is completed in) to represent the processing time required at each task. Distributions will also be generated for each entity type to represent the arrival pattern for that particular type of work into the business system. These distributions are placed into the newly generated model as well as being placed in a distribution history table so that changes in task times and arrival patterns can be monitored over time as the model is updated with new distribution utilizing the most recent history from the digitized business systems. Other data elements may be present in the business system data such as job attributes (value, size, customer identification, for example) that can also help segment or distinguish between types of work being processed. Each business system may require slight changes in the interrogation queries and algorithms and may provide different levels of completeness with respect to auto generation of the simulation model requirements for a particular business system. However, it should be appreciated that generally the underlying generic simulation data structure and generic model engine will require no changes to effectively model the business system.
As shown in
In response, in step 334, the web server retrieves the model data from the database. The model data is then made available for viewing and editing by the user.
After step 334, the process passes to step 336. In step 336, the user interfaces with the web server to edit the model. This is done via a model information screen that provides links to the various model elements or parameters that can be added, edited or deleted. Such an illustrative user interface is shown in
After step 336, the process passes to step 338. In step 338, the process returns to step 340 of
As described above, in step 336, the user interfaces with the web server to edit the model. This interfacing may be done using a variety of interface screens. Illustrative screens are shown in
In response, in step 346, the model server, which monitors the database for requests, retrieves the needed data from the database, creates the required model input files and runs the model. Then, the model server returns the results to the database.
After step 346, the process passes to step 347. In step 347, the user via the web server retrieves the result reports from the database. For example, the user might view the results using a suitable browser. It should be appreciated that various models may be compared, as desired.
Various illustrative user interface screens are described herein and shown in the drawings. It should be appreciated that such screens are representative samples of possible user interface screens. However, changes can be made to the screens to target industry or user specific requests and/or to simplify interaction with the modeling system. Further, these changes would not require any changes to the underlying generic database structures or the generic model engine. Simply put the interface can be tailored to specific requirements of a specific installation and use of the technology.
As shown in
Similar to
It should be appreciated that a well-designed model may be used to support a wide variety of business systems. In other words, a well-designed model may accommodate or be effectively mapped onto any of a variety of business systems. Illustratively, as shown in
The mapping from the workflow objects 254 to the model elements 224 may be done in any of a variety of ways, as is desired, so as to effectively capture the operation of the real life business process in the model.
In accordance with one embodiment of the invention, the system of the invention automatically generates simulation model elements based on the workflow system history, i.e., the workflow objects 254. Further, the invention maintains the mapping for future model updates, as described above. As a result, a base model is established to perform analysis and planning in an effective and accurate manner.
The data in the digital system 410 may include a variety of workflow objects or data types, such as event data, demand rates and/or process times, for example. The desired data in the digital system 410 is retrieved by a web server, as described above, and output to the curve-fitting component 420 in a suitable manner. That is, the data may be output to the curve-fitting component 420 in suitable files or in some other organized manner such that the curve-fitting component 420 can determine the relationship between the data. The web server may retrieve the data from the digital system 410 using SQL database techniques or by any other suitable processing technique. The data, i.e., a data sample, may be application specific based on the particular needs of the model that is requested.
To further explain, in accordance with one embodiment of the invention, the curve-fitting component 420, inputs the data from the digital system 410 as a stream of real numbers, i.e., a data set, for example. However, other methods may be used to output the data to the curve-fitting component 420. The curve-fitting component 420 then performs a “goodness of fit” test of the data against a set of distributions. The set of distributions, which may be utilized, include for example, normal, lognormal, exponential, uniform, triangular, as well as Weibull or Poisson, for example. Other known distributions used in known “goodness of fit” techniques may also be used, as is desired.
As a result, an output 430 is generated as shown in
A variety of sets of data may be processed by the curve-fitting component 420. As a result of the processing, each data set is associated with a particular “best fit distribution.” This distribution is then associated with the data and stored. The data set and the distribution that is associated with the data set may then be used for reporting purposes and for model usage.
To explain with reference to
For example, as described above, jobs 1 and 5 are shown in
This approach to operation of the curve fitter portion 232 improves the accuracy of the model, as well as helps segment flow by job types. Further, the approach illustrated by
In accordance with one embodiment of the invention, each Entity type will have associated with it as part of the model output a cycle time (the total time it takes to process), distribution and throughput (total quantity processed) distribution. The entity type associated with jobs 1 and 5 in table 412, as shown in
In summary, the various embodiments of the invention provide various features and functionality to effectively use digitized business data in the generation of models. The invention provides a web based generic process simulation engine and a database construct for defining any business process for simulation modeling. A server-based method simulates the data construct with a pre-developed simulation model. Further, a web-based interface allows for building alternative process configuration models, submitting models for analysis, and reporting capabilities for analyzing process changes.
The invention provides methods for intelligent interrogation of digitized business systems. This interrogation is performed by a set of queries and algorithms that extract the business system behavior, and create an instance of a generic simulation model. An automated curve fitting mechanism is used in accordance with some embodiments of the invention. This system component is integrated with the intelligent system interrogation to generate processing times and arrival rates based on data samples extracted from the digitized system.
Accordingly, various advantages are provided by the invention. The invention provides automated business system simulation model development and allows for easy comparison of system alternatives. The models used in the invention are highly accurate because actual digital system data is used to generate processing times and arrival rates, for example. The system of the invention allows for 6-sigma process design. Also, the invention provides analysis and control via the web browser that is integrated with the operational digitized business systems. Of note, the practice of the invention by a user, as described above, requires no programming knowledge and requires only a web browser to access the system, in accordance with one embodiment of the invention.
As discussed further below, it should be appreciated that the method in accordance with one embodiment of the invention may be implemented on any of a wide variety of computer mediums. That is, a computer readable medium may be used to simulate a process of discrete tasks having a plurality of available resources associated therewith, as described above. In accordance with one embodiment of the invention, the computer readable medium includes a first portion that stores a plurality of models in a database, each model including a plurality of task and resource parameters. Further, a second portion may be provided that communicates with a user, the second portion in communication with the first portion and configured to receive commands from the user, to retrieve one of the plurality of models and corresponding task and resource parameters in response to a user command, to receive input data corresponding to attributes of one or more task and resource parameters from a business database system, and to generate a simulation model based on the selected business system and the input data. Also, the computer readable medium may include a third portion that performs a simulation of the process by processing the simulation model, and that generates an output data file containing output data representative of the simulation.
As described above, various embodiments of the system of the invention are set forth. Further,
As noted above, the processing machine executes the instructions that are stored in the memory or memories to process data. This processing of data may be in response to commands by a user or users of the processing machine, in response to previous processing, in response to a request by another processing machine and/or any other input, for example.
As noted above, the processing machine used to implement the invention may be a general-purpose computer. However, the processing machine described above may also utilize any of a wide variety of other technologies including a special purpose computer, a computer system including a microcomputer, mini-computer or mainframe for example, a programmed microprocessor, a micro-controller, a peripheral integrated circuit element, a CSIC (Customer Specific Integrated Circuit) or ASIC (Application Specific Integrated Circuit) or other integrated circuit, a logic circuit, a digital signal processor, a programmable logic device such as a FPGA, PLD, PLA or PAL, or any other device or arrangement of devices that is capable of implementing the steps of the process of the invention.
It is appreciated that in order to practice the method of the invention as described above, it is not necessary that the processors and/or the memories of the processing machine be physically located in the same geographical place. That is, each of the processors and the memories used in the invention may be located in geographically distinct locations and connected so as to communicate in any suitable manner. Additionally, it is appreciated that each of the processor and/or the memory may be composed of different physical pieces of equipment. Accordingly, it is not necessary that the processor be one single piece of equipment in one location and that the memory be another single piece of equipment in another location. That is, it is contemplated that the processor may be two pieces of equipment in two different physical locations. The two distinct pieces of equipment may be connected in any suitable manner. Additionally, the memory may include two or more portions of memory in two or more physical locations.
To explain further, processing as described above is performed by various components and various memories. However, it is appreciated that the processing performed by two distinct components as described above may, in accordance with a further embodiment of the invention, be performed by a single component. Further, the processing performed by one distinct component as described above may be performed by two distinct components. In a similar manner, the memory storage performed by two distinct memory portions as described above may, in accordance with a further embodiment of the invention, be performed by a single memory portion. Further, the memory storage performed by one distinct memory portion as described above may be performed by two memory portions.
Further, various technologies may be used to provide communication between the various processors and/or memories, as well as to allow the processors and/or the memories of the invention to communicate with any other entity; i.e., so as to obtain further instructions or to access and use remote memory stores, for example. Such technologies used to provide such communication might include a network, the Internet, Intranet, Extranet, LAN, an Ethernet, or any client server system that provides communication, for example. Such communications technologies may use any suitable protocol such as TCP/IP, UDP, or OSI, for example.
As described above, a set of instructions is used in the processing of the invention. The set of instructions may be in the form of a program or software. The software may be in the form of system software or application software, for example. The software might also be in the form of a collection of separate programs, a program module within a larger program, or a portion of a program module, for example. The software used might also include modular programming in the form of object-oriented programming. The software tells the processing machine what to do with the data being processed.
Further, it is appreciated that the instructions or set of instructions used in the implementation and operation of the invention may be in a suitable form such that the processing machine may read the instructions. For example, the instructions that form a program may be in the form of a suitable programming language, which is converted to machine language or object code to allow the processor or processors to read the instructions. That is, written lines of programming code or source code, in a particular programming language, are converted to machine language using a compiler, assembler or interpreter. The machine language is binary coded machine instructions that are specific to a particular type of processing machine, i.e., to a particular type of computer, for example. The computer understands the machine language.
Any suitable programming language may be used in accordance with the various embodiments of the invention. Illustratively, the programming language used may include assembly language, Ada, APL, Basic, C, C++, COBOL, dBase, Forth, Fortran, Java, Modula-2, Pascal, Prolog, REXX, Visual Basic, and/or JavaScript, for example. Further, it is not necessary that a single type of instructions or single programming language be utilized in conjunction with the operation of the system and method of the invention. Rather, any number of different programming languages may be utilized as is necessary or desirable.
Also, the instructions and/or data used in the practice of the invention may utilize any compression or encryption technique or algorithm, as may be desired. An encryption module might be used to encrypt data. Further, files or other data may be decrypted using a suitable decryption module, for example.
As described above, the invention may illustratively be embodied in the form of a processing machine, including a computer or computer system, for example, that includes at least one memory. It is to be appreciated that the set of instructions, i.e., the software for example, that enables the computer operating system to perform the operations described above may be contained on any of a wide variety of media or medium, as desired. Further, the data that is processed by the set of instructions might also be contained on any of a wide variety of media or medium. That is, the particular medium, i.e., the memory in the processing machine, utilized to hold the set of instructions and/or the data used in the invention may take on any of a variety of physical forms or transmissions, for example. Illustratively, the medium may be in the form of paper, paper transparencies, a compact disk, a DVD, an integrated circuit, a hard disk, a floppy disk, an optical disk, a magnetic tape, a RAM, a ROM, a PROM, a EPROM, a wire, a cable, a fiber, communications channel, a satellite transmissions or other remote transmission, as well as any other medium or source of data that may be read by the processors of the invention.
Further, the memory or memories used in the processing machine that implements the invention may be in any of a wide variety of forms to allow the memory to hold instructions, data, or other information, as is desired. Thus, the memory might be in the form of a database to hold data. The database might use any desired arrangement of files such as a flat file arrangement or a relational database arrangement, for example.
In the system and method of the invention, a variety of “user interfaces” may be utilized to allow a user to interface with the processing machine or machines that are used to implement the invention. As used herein, a user interface includes any hardware, software, or combination of hardware and software used by the processing machine that allows a user to interact with the processing machine. A user interface may be in the form of a dialogue screen for example. A user interface may also include any of a mouse, touch screen, keyboard, voice reader, voice recognizer, dialogue screen, menu box, list, checkbox, toggle switch, a pushbutton or any other device that allows a user to receive information regarding the operation of the processing machine as it processes a set of instructions and/or provide the processing machine with information. Accordingly, the user interface is any device that provides communication between a user and a processing machine. The information provided by the user to the processing machine through the user interface may be in the form of a command, a selection of data, or some other input, for example.
As discussed above, a user interface is utilized by the processing machine that performs a set of instructions such that the processing machine processes data for a user. The user interface is typically used by the processing machine for interacting with a user either to convey information or receive information from the user. However, it should be appreciated that in accordance with some embodiments of the system and method of the invention, it is not necessary that a human user actually interact with a user interface used by the processing machine of the invention. Rather, it is contemplated that the user interface of the invention might interact, i.e., convey and receive information, with another processing machine, rather than a human user. Accordingly, the other processing machine might be characterized as a user. Further, it is contemplated that a user interface utilized in the system and method of the invention may interact partially with another processing machine or processing machines, while also interacting partially with a human user.
The invention provides a system to simulate a process of discrete tasks having a plurality of available resources associated therewith and processing a plurality of work items or entity types. The system may comprise a database to store a plurality of models, each model including a plurality of entity types, task and resource parameters. The system may further include a model portion user interface in communication with the database and configured to receive commands from a user, to retrieve one of the plurality of models and corresponding entity, task and resource parameters in response to a user command.
The invention can receive input data corresponding to attributes of one or more entity, task and resource parameters from a business database system, and can generate a simulation model automatically based on the selected business system data. The system may further record and maintain links between the database and the digitized business system database to augment future updates of the database with new data samples from the business system databases. The invention may further maintain the history of distribution generated for a business system model there by identifying changes in task performance or entity type arrival patterns. The system may further provide an ability to alter the arrangement and relationships between the various model elements (entities, tasks and resources) to define new job descriptions, resource schedules, new workflows, and completely distinct alternative business system configurations, for example.
The system may further include a model server to perform a simulation of the process by processing a “generic” simulation model utilizing the stored process description in the process database and to generate an output data file containing output data representative of the simulation. The system may further provide the ability to compare several distinctly different business system configuration model results to determine the best alternative to maximize business system performance. The system is intended to be used by business process owners/managers and does not require programming experience or simulation modeling expertise.
It will be readily understood by those persons skilled in the art that the present invention is susceptible to broad utility and application. Many embodiments and adaptations of the present invention other than those herein described, as well as many variations, modifications and equivalent arrangements, will be apparent from or reasonably suggested by the present invention and foregoing description thereof, without departing from the substance or scope of the invention.
Accordingly, while the present invention has been described here in detail in relation to its exemplary embodiments, it is to be understood that this disclosure is only illustrative and exemplary of the present invention and is made to provide an enabling disclosure of the invention. Accordingly, the foregoing disclosure is not intended to be construed or to limit the present invention or otherwise to exclude any other such embodiments, adaptations, variations, modifications or equivalent arrangements.
This application is a continuation of U.S. patent application Ser. No. 10/723,110, filed on Nov. 25, 2003, and entitled “SYSTEM AND METHOD FOR OPTIMIZING SIMULATION OF A DISCRETE EVENT PROCESS USING BUSINESS SYSTEM DATA,” which is a continuation-in-part of U.S. patent application Ser. No. 10/222,894, entitled “SYSTEM AND METHOD FOR SIMULATING A DISCRETE EVENT PROCESS USING BUSINESS SYSTEM DATA,” filed Aug. 19, 2002. The foregoing applications are herein incorporated by reference in their entirety.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 10723110 | Nov 2003 | US |
| Child | 11753868 | May 2007 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 10222894 | Aug 2002 | US |
| Child | 10723110 | Nov 2003 | US |
