Abstract: One embodiment of the present invention provides managing component driver for an energy-management system that manages energy within a predominantly closed power system. The component driver includes a receiving mechanism configured to receive current status information for a component associated with the component driver, a model-construction mechanism configured to construct a performance model for the component, and a transmitting mechanism configured to transmit the performance model to an optimization and control module.

Abstract: Disclosed are various systems and methods for assessing and improving the capability of a machine tool. The disclosure applies to machine tools having at least one slide configured to move along a motion axis. Various patterns of dynamic excitation commands are employed to drive the one or more slides, typically involving repetitive short distance displacements. A quantification of a measurable merit of machine tool response to the one or more patterns of dynamic excitation commands is typically derived for the machine tool. Examples of measurable merits of machine tool performance include workpiece surface finish, and the ability to generate chips of the desired length.

Abstract: The disclosure relates to a system and a method for planning the operation of, monitoring processes in, simulating, and/or optimizing a technical installation comprising several units that can be combined with each other. Said system comprises at least one process planning module, at least one process simulation module, and at least one process optimization module. Components for modeling, simulating, and optimizing the technical installation are stored in said modules. The interrelated modules cooperate with a data management layer via at least one interface, said data management layer making available actual measured and/or historical process data for determining parameters and/or operational data for the modules in order to plan operations as well as simulate and optimize processes. The parameters and/or operational data determined in the modules can be fed to the data management layer for further processing by taking into account the stored components.

Abstract: The preferred embodiments described herein relate to enterprise energy automation. In one preferred embodiment, a method for enterprise energy automation is provided. In another preferred embodiment, a method for delivering ancillary services to an energy supply and delivery system by a group of buildings is provided. In yet another preferred embodiment, a method for integrating an energy automation system is provided. Other preferred embodiments are also provided.

Abstract: A computer comprising a recordable medium on which is stored instructions for at least one model-based, run-to-run controller routine is provided. The computer includes instructions to receive a first dataset regarding a first wafer after a first process, to determine a process parameter for the first process for a second wafer using the first dataset; and to determine a second process parameter for a second process for the first wafer using the first dataset. In an embodiment, the first process is an etch process. In an embodiment, the second process is a planarization process.

Abstract: In accordance with an embodiment, a plant control apparatus includes a deviation calculation unit, a velocity-type PID calculation unit, a plurality of integral calculation units, a plurality of overwrite units, and an automatic balance unit. The deviation calculation unit calculates a deviation between a process value from a plant with operation terminals and a set value corresponding to a control object, and generates a deviation signal. The velocity-type PID calculation unit generates a velocity-type operation amount command signal corresponding to the deviation. The integral calculation units generate position command signals as defined operation terminal position command signals. The overwrite units generate additional position command signals to perform overwrite processing and newly define operation terminal position command signals.

Abstract: The invention relates to the automatic rendering of a lighting scene with a lighting system, particularly the control of the rendering. A basic idea of the invention is to improve rendering of a lighting scene by automatically compensating interference, such as an alien light source or a dynamic perturbing event of a rendered lighting scene. An embodiment of the invention provides a light control system (10) for automatically rendering a lighting scene with a lighting system, wherein the light control (10) system is adapted for monitoring the rendered lighting scene for the occurrence of interference (14, 20, 22, 24) and automatically reconfiguring the lighting system such that a monitored occurrence of an interference is compensated (16, 18, 12). As result, the invention allows to prevent dynamic disturbances or unforeseen events, for example caused by faulty or alien light sources, from distorting the rendering of an intended lighting scene.

Abstract: Described are computer-based methods and apparatuses, including computer program products, for automated predictive design space estimation. A design space of input factors and output responses is estimated for a physical process. Data is received for one or more input factors for a physical process, one or more output responses for the process, and criteria. For each of the one or more input factors, a calculated range of input values within the corresponding experimented range of input values is calculated. A modified range of input values is calculated for each of the one or more input factors. A design space estimate is predicted based at least on the modified ranges of input values, wherein the modified ranges of input values each comprise a largest region of variability for one or more of the input factors where the criteria are fulfilled.

Abstract: A quality regulating apparatus and method is disclosed which operates on a product stream having an incoming product stream quality level so that an outgoing product stream quality level provided by the apparatus follows a target quality level so that it is better than the target quality level providing a maximum yield. The apparatus includes a conveying device, a product stream imager, an inspection engine, a routing engine, and an object diverting station. In operation, the inspection engine and routing engine cooperate in a manner so that objects classified as defective are intentionally passed to the output product stream when their inclusion maintains the quality level of the first output product stream at a level that is better than or equal to the target quality level.

Abstract: A method of model identification for a process with unknown initial conditions in an industrial plant, the method comprising collecting a set of manipulated variables and corresponding set of process variables from the process; obtaining a plurality of manipulated variables from the collected set of manipulated variables; for each of the plurality of manipulated variables, obtaining optimal model parameters of a model transfer function and computing a model fitting index for optimized simulated process variables generated by the model transfer function using the optimal model parameters; identifying a best model fitting index among the model fitting indices computed; selecting a manipulated variable associated with the best model fitting index as an initial steady state condition for the model transfer function; and selecting the optimal model parameters corresponding with the best model fitting index as the best model parameters of the model transfer function to tune the controller.

Abstract: A method for optimizing operational settings for a plurality of energy devices includes representing each of the plurality of energy devices in terms of a set of decision variables and operational parameters. The decision variables and operational parameters are constrained based on operational conditions and interrelationship within the plurality of energy devices. A two-tiered model of the plurality of energy devices is generated wherein a top tier of the model represents interaction of various sub-models and a bottom tier of the model includes a set of the sub-models that form the top tier, each sub-model representing detailed operation of the plurality of energy devices. The two-tiered model is optimized to provide either a schedule of operation for the plurality of energy devices or real-time control for the plurality of energy devices.

Abstract: Systems and methods that include selecting a physical model and a procedural model associated with an industrial plant, projecting a cross-product of the physical model and the procedural model into a unit-operation association set using a projection matrix, the unit-operation association set associating units representing equipment in the industrial plant and operations representing processes performed in the industrial plant, and establishing internal and external adjacency sets within structures, the internal adjacency set identifying connections between inputs and outputs of individual units and operations, the external adjacency set identifying connections between outputs of some units and operation and inputs of other units and operations. These systems and methods may also include generating a hyperstructure representing the industrial plant using the association and adjacency sets.

Abstract: A method includes identifying one of multiple regions in a range where an output (OP) value used to implement a manipulated variable is located. The manipulated variable is associated with an industrial process, and the OP value represents an output of a downstream controller. The method also includes calculating an achievable manipulated variable (MV) limit for the manipulated variable based on the region in which the OP value is located. For example, when the OP value is located in one region, the achievable MV limit could match a user-specified limit or be based on a gain between the OP value and a value of a process variable. When the OP value is located in another region, the achievable MV limit could track the value of the process variable with a gap.

Abstract: Method of reducing performance variability of membrane scaling devices. Scaling device performance uncertainty is reduced, thereby reducing the scaling safety factor, by specifying a narrow range or subset of the set of all qualified manufactured membranes for installation into scaling devices. In certain embodiments, the scalability factor is reduced by determining where within the performance distribution a particular membrane lies, and adjusting the scaling factor accordingly.

Abstract: An action analysis apparatus includes an acquiring unit that acquires moving image data including a series of frame image data obtained by imaging a human body and environmental information in a period when the moving image data is imaged, a unit that detects at least one image area in which a predetermined portion of the imaged human body is imaged in the frame image data and generates and stores information to identify the detected image area, a unit that (i) generates feature quantity information, (ii) generates information to identify frames of the moving image data imaged at a timing at which the feature quantity information satisfies a predetermined condition, (iii) obtains a length of continuous timings at which a length of the feature quantity information exceeds the predetermined condition based on to generated information, and (iv) stores the information.

Abstract: A method and a device for robust rejection of the periodic disturbances in a motor-position control structure of the RST type, wherein the assembly including the motor, the position sensor, the element for computing the functions 1/So(z?1) and Ro(z?1), in a loop, is called central controller. Within the framework of the invention, to the central controller are added two additional computing elements, the first one being an estimator block E(z?1) and the second one a modifier block Q(z?1), as well as an additional loop, the estimator block E(z?1) allowing a load disturbance estimation signal {circumflex over (v)}(t) to be computed, the modifier block Q(z?1) receiving the load disturbance estimation signal {circumflex over (v)}(t) as an input, to produce a modified signal, which is applied to a negative input of an additional adder arranged upstream from the element for computing the function 1/So(z?1) and then forming the additional loop, the modifier block having a variable transfer function.

Abstract: A control of a multi-variable process throughout a plurality of successive, distinct operational phases of the process involves accumulation of sets of values of the process and quality variables (a-j) from previous multiple operations of the process through all its phases, as respective datasets of a historical record. Each dataset includes an identifier (p) of the phase to which it relates. The current values of the applicable process variables (Qa-Qh) during each phase and the phase identifier (p) are indicated on respective axes (Xp,Xa-Xh) of a multi-dimensional display in relation to an operational envelope which defines bounds (UL,LL) for the values of the individual process and quality variables (a-j) relevant to that phase and which is derived from the datasets of the historical record identified with that phase. Changes in the current process variables (Qa-Qh) result in changes of the bounds (UL,LL), and alarm is given when a current value departs from within them.

Abstract: Exemplary embodiments relate to a method and system for updating a model in a model predictive controller. The system executing a method that includes assessing the deviation of the operating performance level from the desired performance level of the process plant, and diagnosing the model predictive control for the model plant mismatch by updating the model in a model predictive controller. The step of diagnosing the model predictive controller includes determining the model prediction error in relation to model plant mismatch, quantifying the model plant mismatch, and updating the model in the model predictive controller.

Abstract: A model predictive controller (MPC) for controlling physical processes includes a non-linear control section that includes a memory that stores a non-linear (NL) model that is coupled to a linearizer that provides at least one linearized model, and a linear control section that includes a memory that stores a linear model. A controller engine is coupled to receive both the linearized model and linear model. The MPC includes a switch that in one position causes the controller engine to operate in a linear mode utilizing the linear model to implement linear process control and in another position causes the controller engine to operate in a NL mode utilizing the linearized model to implement NL process control. The switch can be an automatic switch configured for automatically switching between linear process control and NL process control.

Abstract: Systems and methods are disclosed to improve energy efficiency of a farm with livestock wastes by generating a cooling, heating, and power (CCHP) microgrid model; performing on a computer a multi-objective optimization to improve system efficiency of energy utilization and reduce environmental problems caused by animal wastes; and displaying results of the optimization for review.

Abstract: Information from a real sensor and a virtual sensor are fused to form a hybrid sensor. Control signals (and/or the absolute value of the control signals) applied to an actuator are accumulated and converted to a position the actuator should be in based on the accumulated control signals to form the virtual sensor. The actuator position from the virtual sensor is fused with an actuator position from a real sensor to form the hybrid sensor. Small periodic corrections can be made to the accumulating control signals to maintain or achieve, if possible, correlation between the virtual sensor and the real sensor over time. The corrections slowly decrement errors in the actuator position indicated by the virtual sensor. Accumulating numerical errors in the accumulating control signals are reduced and the significance of long past events is de-emphasized by a forgetting factor (kff) used at defined periods of time (tff).

Abstract: Diagnostic systems and methods are presented for determining the current condition of a production plant and the resources thereof, in which successively more complex diagnostic abstractions are used to determine the plant condition, with a more complex abstraction being selected when the most recently selected diagnostic abstraction is logically inconsistent with the current fault status indications.

Abstract: A power model that relates the settings of resource actuators to power consumption levels of the resource actuators and a condition model that relates the settings of the resource actuators to an environmental condition at the location of the at least one entity and a power consumption level of the at least one entity are developed. A constraint optimization problem having an objective function and at least one constraint is formulated, where the objective function computes at least a proportional quantity of a total power consumption level of the resource actuators and the at least one constraint comprises a setpoint environmental condition at a location of the at least entity. A solution to the constraint optimization problem is determined, where the solution provides optimal values for the resource actuator settings.

Abstract: A method and system for estimating context offsets for run-to-run control in a semiconductor fabrication facility is described. In one embodiment, contexts associated with a process are identified. The process has one or more threads, and each thread involves one or more contexts. A set of input-output equations describing the process is defined. Each input-output equation corresponds to a thread and includes a thread offset expressed as a summation of individual context offsets. A state-space model is created that describes an evolution of the process using the set of input-output equations. The state-space model allows to estimate individual context offsets.

Abstract: A method of dynamic model selection for hybrid linear/non-linear process control includes developing a plurality of process models including at least one linear process model and at least one non-linear process model from inputs including dynamic process data from a processing system that runs a physical process. At least two of the plurality of process models are selected based on a performance comparison based on at least one metric, wherein the selected process models number less than a number of the plurality of process models received. A multi-model controller is generated that includes the selected process models. The physical process is simulated using the multi-model controller by applying the selected process models to obtain closed loop performance test data for each of the selected models. The performance test data is compared. A selected process model is then selected.

Abstract: A method for advanced condition monitoring of an asset system includes sensing actual values of an operating condition for an operating regime of the asset system using at least one sensor; estimating sensed values of the operating condition by using an auto-associative neural network; determining a residual vector between the estimated sensed values and the actual values; and performing a fault diagnostic on the residual vector. In another method, an operating space of the asset system is segmented into operating regimes; the auto-associative neural network determines estimates of actual measured values; a residual vector is determined from the auto-associative neural network; a fault diagnostic is performed on the residual vector; and a change of the operation of the asset system is determined by analysis of the residual vector. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.

Abstract: A method for feedback-based optimization of a measurement data life cycle in joining processes during production, comprising the following steps: 1) analysing and simulating production on the basis of assumptions, in particular on the basis of production data, in order to prepare an initial production strategy and/or inspection strategy, 2) preparing and/or adapting a production and/or inspection order, 3) checking the production and/or inspection order for consistency, 4) exporting and storing the production and/or inspection order, 5) aligning and/or joining a component made of at least two sub-assemblies in an assembly zone, 6) analysing and simulating production on the basis of actual measurement results and feedback of an optimized production and/or inspection order into method step 2), and 7) at least one repetition of method steps 2) to 6). The invention also relates to a device for carrying out the method.

Abstract: The identification of Hammerstein models relates to a computerized method for identifying Hammerstein models in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN), and in which a particle swarm optimization (PSO) algorithm is used to estimate the neural network parameters and a numerical algorithm for subspace state-space system identification (N4SID) is used for estimation of parameters of the linear part.

Abstract: The method for Hammerstein modeling of a steam generator plant includes modeling of the linear dynamic part of a Hammerstein model with a state-space model, and modeling the nonlinear part of the Hammerstein model with a radial basis function neural network (RBFNN). Particle swarm optimization (PSO), typically known for its heuristic search capabilities, is used for estimating the parameters of the RBFNN. Parameters of the linear part are estimated using a numerical algorithm for subspace state-space system identification (N4SID).

Abstract: The method for the identifying of multiple input, multiple output (MIMO) Hammerstein models that includes modeling of the linear dynamic part of a Hammerstein model with a state-space model, and modeling the nonlinear part of the Hammerstein model with a radial basis function neural network (RBFNN).

Abstract: A method includes receiving data associated with operation of an industrial process system. The method also includes identifying a model defining a behavior of the industrial process system using the data, an orthonormal bases function, and an ordinal spline bases function. The orthonormal bases function can be generated using estimated poles of the industrial process system. The ordinal spline bases function can be generated using a specified set of cubic splines. The ordinal spline bases function can also be generated using a distribution of knots and multiple ordinal spline functions associated with the knots. More knots can be associated with a more nonlinear portion of the industrial process system, and fewer knots can be associated with a less nonlinear portion or a linear portion of the industrial process system.

Abstract: A method and system for automatically generating simulations for a distributed control system is disclosed herein. A programmed process model generator automatically incorporates a variety of process model data from pre-defined model libraries into descriptions of process equipment including control devices to render simulation models of various degrees of fidelity.

Abstract: Real-time condition-based analysis is performed on a machine for providing diagnostic and prognostic outputs indicative of machine status includes a signal processor for receiving signals from sensors adapted for measuring machine performance parameters. The signal processor conditions and shapes at least some of the received signals into an input form for a neural network. A fuzzy adaptive resonance theory neural network receives at least some of the conditioned and shaped signals, and detects and classifies a state of the machine based upon the received conditioned and shaped signals, and upon a predetermined ontology of machine states, diagnostics, and prognostics. The neural network can also determine from the machine state a health status thereof, which can comprise an anomaly, and output a signal representative of the determined health status. A Bayesian intelligence network receives the machine state from the neural network and determines a fault probability at a future time.

Abstract: A computer implemented method of conducting closed-loop control of a physical system comprising the steps of carrying out an initialization of the physical system to commencing closed-loop control, evaluating the optimal constrained control move using the system error and the initial normalized matrix using a control move solver; calculating a first control action by the sum of delta u(0) and the initial control action; and implementing the result to the physical system by converting the control action to an output control signal to effect a change in at least one operating variable.

Abstract: A machine vision system for controlling the alignment of an arm in a robotic handling system. The machine vision system includes an optical imager aligned to simultaneously capture an image that contains a view of the side of an object, such as a test tube, along with a view of the top of the object provided by a mirror appropriately positioned on the robotic arm. The machine vision system further includes a microcontroller or similar device for interpreting both portions of the image. For example, the microcontroller may be programmed to determine the location of the object in the reflected portion of the image and transpose that information into the location of the object relative to the robotic arm. The microcontroller may also be programmed to decode information positioned on the object by interpreting visual information contained in the other portion of the captured image.

Abstract: Embodiments of the present disclosure include systems and a method. In one embodiment, a system is provided. The system includes a risk calculation system configured to calculate a risk based on a static input and a dynamic input, and a decision support system configured to use the risk to derive a decision. The system also includes a plant control system configured to update operations of a plant based on the decision, wherein the decision predicts future plant conditions.

Abstract: One method includes obtaining a preliminary model associated with a system to be controlled and constructing a weighted model using one or more weighting factors. The method also includes identifying a final model of the system using the preliminary and weighted models, where the final model has a stability margin that is greater than an uncertainty associated with the final model. The method further includes controlling the system using a controller designed based on the final model. Another method includes identifying a first model associated with a system to be controlled, performing model order reduction to identify a second model, and controlling the system using a controller designed based on the second model. Performing the model order reduction includes reducing a weighted coprime factor model uncertainty between the first and second models.

Abstract: A method for ascertaining functional parameters for a control unit and to a control unit in which the provided method is carried out. The control unit is provided for controlling a technical system wherein, in the method, at least one target variable on a system response is specified and a variation of the functional parameters is carried out, from a response received to the functional parameters, a valuation being carried out of the set functional parameters while taking into account the at least one specified target variable.

Abstract: A method for controlling behavioral intervention of a submodule, where a device model is used to model and operate an automation system, where the device model comprises an input/output device, a first module including at least one submodule and a first controller, a supervisory controller and a control module including at least one control submodule. The submodule is provided with a behavior identification code and behavior information in addition to interconnection information. A respective control submodule having a specific behavior identification code is addressed by the supervisory controller to control the intervention in the submodule, where the addressed control submodule thereupon issues a control command containing the specific behavior identification code to all other submodules, and where the other submodules having the specific behavior identification code are induced to adopt a behavioral change based on the behavior information.

Abstract: A system and a method are used for maintaining and analyzing manufacturing equipment. The system includes an embedded forecast device (EFD) configured in the manufacturing equipment and a server in communication with the EFD. The EFD is built in with a feature extraction (FE) algorithm and a forecast model (FM), so as to obtain a real time data of the manufacturing equipment and carry out forecasts on the manufacturing equipment to generate a forecast result. The server has various types of FE algorithms and modeling algorithms, which are selected and combined by a user for training and building a required FM and setting related parameters. The embedded forecast device also has a novelty detection model (NDM), which is capable of informing the server to retrain the models when the manufacturing equipment fails and then to update the FM and the NDM of the embedded forecast device.

Abstract: An in-plane compressive strength evaluation device (10) provided with a memory unit (21) into which, when an in-plane compressive strength evaluation is actually performed using a test piece, an evaluation result is stored as first information; a first analysis unit (22) that obtains second information that is an evaluation result related to the load and a displacement of the structural model; an adjustment unit (23) that adjusts an elemental factor of the structural model that influences the in-plane compressive strength such that the second information approaches the first information; and a second analysis unit (24) that, in a state where the elemental factor used when the second information approaches the first information most closely expands the structural model into the structural form of the actual tube support plate, and that performs the in-plane compressive strength evaluation using the expanded structural model.

Abstract: A scheduling optimizer system, method and program product that analyzes a device for sensitivities, such as ESD sensitivities, and allows for modification of a floor schedule of the assembly unit of the device based on the sensitivity of the device while improving the overall performance of the assembly unit are disclosed. The scheduling optimizer analyzes sensitivity data for a device during operation of the assembly unit on the floor schedule. The floor schedule is then optimized based on the analyzed sensitivity data.

Abstract: Electrical distribution network (EDN) improvement method for plug-in electric vehicles receives and stores in a database EDN configuration information, demography information and load information for simulating load of the EDN assets. The method dynamically updates the EDN configuration, demography information and/or load information to provide an efficient and customizable method of simulating a PEV load impact on an EDN configuration and apply improvements to the EDN in real time.

Abstract: Techniques, systems and methods for designing, implementing, and operating model predictive controllers that can deliver perfect tracking of set points and that can reject the effect of disturbances when steady-state operation is reached are disclosed. High performance is achieved through the incorporation of set-point tracking costs, integral costs, and velocity costs, as well as the adoption of incremental model systems for prediction purposes. Embodiments can deliver offset-free performance for tracking set points with constant final values, set points of a ramp type, and set points of a parabolic form, while rejecting disturbances that have a constant final value. The approach reduces the complexity of model predictive control design, delivers improved performance, and requires modest computational power.

Abstract: A method and apparatus for optimizing the operation of a single or multiple power generating units using advanced optimization, modeling, and control techniques. In one embodiment, a plurality of component optimization systems for optimizing power generating unit components are sequentially coordinated to allow optimized values determined by a first component optimization system to be fed forward for use as an input value to a subsequent component optimization system. A unit optimization system may be provided to determine goals and constraints for the plurality of component optimization systems in accordance with economic data. In one embodiment of the invention, a multi-unit optimization system is provided to determine goals and constraints for component optimization systems of different power generating units. Both steady state and dynamic models are used for optimization.

Abstract: A method for configuring an automation device or simulator for controlling mechatronics components of an automation system, including: generating HLPN component models for each type of the mechatronic components of the automation system, creating a component instance model from an HLPN component model for each physically present mechatronic component, creating a layout configuration file, which describes relationships of the component instance models to be connected, composing the component instance models into a system model based on the layout configuration file, wherein logic ports of the component instance models are connected/linked to each other, generating configuration files based on a system model and device description files and WSDL files of the component instance models, loading the configuration files into the automation device or simulator containing the HLPN orchestration machine, and executing the configuration files by the HLPN orchestration machine of the automation device or the simulator.

Abstract: A system includes an identification component configured to identify a set of key performance indicators that fail to satisfy predetermined acceptance criteria based on acquired performance data, where the set of key performance indicators is indicative of performance of components of a process control system. The system further includes a visualization component configured to visually present the identified set of key performance indicators, the components, and the acquired performance data in a graphical user interface displayed via a monitor. The system further includes a manual override component configured to allow a user to manually override and modify the information presented by the graphical user interface based, at least in part, on the acquired performance data.

Abstract: Disclosed are computer-implemented methods of sorting particles from a particle stream in a flow cytometer. The methods include: calculating sort decision making parameters using the raw event data values from a flow cytometer and a sort logic; performing sort logic computations using the sort logic definition and the sort decision making parameters to generate sort decisions; converting the sort decisions into sort commands; and sending the one or more sort commands to the flow cytometer. Sort logic computations may include algorithmically using conditional branching logic, and may include sort logic equations having mathematical functions characterizing one or more regions of interest in multidimensional data space. Such mathematical functions may be determined based on one or more parameters provided by a user. Also disclosed are corresponding systems having a flow cytometer and a computer.

Abstract: The present invention relates to a test device for testing a control system and an associated method for operating the test device. The test device includes an addressable memory. The test device is configured to calculate at least one environment model and to execute at least one test model. The environment model is described by environment model variables whose values are filed at fixed physical addresses in memory locations of the memory. The test device further comprises an allocation unit, in which the allocation of all or a part of the environment model variable to the allocated physical addresses of the memory is filed such that the test model can read the allocation of the environment model variables to their specific memory addresses so as to modify the environment model variable values in the environment model during its execution.

Abstract: A computer readable medium comprising instructions which when executed by a computer system causes the computer to implement a method for dynamically improving a process flow is provided. For a plurality of cells, which define activities within the process flow and are characterized by a corresponding plurality of productivity data elements, the method triggers a state engine to simulate the process flow, with the state engine configured to manage all operations rules related to the process flow, and determines at least one process improvement. The method further identifies productivity data elements that correspond to the at least one process improvement, and dynamically modifies the identified productivity data elements to improve the process flow.