Manufacturing systems are intricate and multifaceted entities that encompass a wide array of processes, machines, tools, materials, and human factors. For centuries, industries have endeavored to perfect their manufacturing processes to reduce costs, improve quality, and increase output. With the advent of the digital era, manufacturing systems have grown increasingly complex, necessitating robust tools and techniques to oversee and fine-tune these processes.
Historically, trial and error methods were predominantly used to optimize manufacturing processes. Such methods, although useful, often led to increased costs, wasted resources, and extended timeframes before achieving the desired efficiency. As manufacturing systems became more intricate, the need for methods to predict, analyze, and improve system performance became evident.
In recent decades, various simulation tools and methodologies have emerged to model manufacturing processes. Computer-based simulations, in particular, have provided manufacturers with a means to create virtual representations of their systems, allowing them to test and optimize different scenarios without implementing physical changes. Such tools include discrete event simulation, finite element analysis, and Monte Carlo simulations, among others.
Example embodiments include a computer-implemented method of evaluating operation of a manufacturing system. A resource graph may be obtained, defining 1) a plurality of resource nodes each representing a physical resource of a plurality of physical resources of the manufacturing system, and 2) a plurality of resource links each representing a causal dependency between at least two of the resource nodes. A process graph may be obtained, defining 1) a plurality of service nodes each representing a service of a plurality of services performed by the manufacturing system, and 2) a plurality of service links each representing a causal dependency between at least two of the service nodes. A mapping may also be obtained, defining a map between the service links and the resource nodes, the mapping representing use of the physical resources by the services. Performance metrics of the manufacturing system may be modeled by simulating performance of the plurality of services as a function of the resource graph and the process graph under plural sets of operational parameters. The modeling may include 1) determining a change in status of the resource nodes over a runtime as a function of operation of the service nodes, and 2) determining a change in the operation of the service nodes over the runtime as a function of the change in status of the resource nodes.
A risk may be identified based on the modeled performance metrics, the risk indicating a change in the performance metrics that exceeds a predetermined threshold. At least one of the plural sets of operational parameters associated with an outcome absent the risk may then be identified. A modification to the manufacturing system may be determined based on the operational parameters associated with the outcome absent the risk. The resource graph and/or the process graph may then be updated based on the modification.
The plurality of services may include generating a product, processing a product, and transporting a product. The plurality of resource links may represent a relationship between an operational capacity of two or more of the resource nodes. The plurality of service links may represent a relationship between an input and an output of two or more of the service nodes. The mapping may include 1) an association between one resource node and multiple service nodes, and 2) an association between one service node and multiple resource nodes. The plural sets of operational parameters may be distinct from one another by defining at least one of: failure of a resource node, a delay of a service, a modification to the service links indicating a different sequence of operations, and a modification to the service links indicating an alternative mode of operation.
The plurality of resource nodes may each include a respective emission parameter, the emission parameter indicating a rate of pollutant emission caused by the respective physical resource, and an environmental impact may be identified based on the modeled performance metrics and the respective emission parameters. At least one of the plural sets of operational parameters associated with a reduced environmental impact may be identified, and a modification to the manufacturing system based on the operational parameters associated with the reduced environmental impact.
The plurality of service nodes may each define a subset of the runtime in which the respective service is active and a subset of the runtime in which the respective service is inactive. The plurality of resource nodes may each define a capacity to perform at least one of the plurality of services during a given time period.
Variation of the risk under the plural sets of operational parameters may be identified, and a function relating the performance metrics and the risk based on the variation may be determined. The performance metrics may include a sustainability metric indicating repeatability of a manufacturing process over time, as well as an energy metric indicating an availability of energy to perform the plurality of services in excess of energy consumed by the manufacturing system. The manufacturing system may be updated to incorporate the modification.
The foregoing will be apparent from the following more particular description of example embodiments, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating embodiments.
A description of example embodiments follows.
Modern businesses are primarily defined by their translation into systems, whether it be a product, supply chain, production line, or any other set of interacting or interdependent components. These systems include millions upon millions of components, dynamic interactions and interdependencies that combine to achieve the overall objectives set forth by business stakeholders. Due to interdependencies, time constraints, and feedback loops, any changes may disrupt manufacturing processes in unanticipated ways. For example, a pandemic can cause supply chain disruptions or labor shortages, while economic turmoil can change a company's financial position overnight.
Avoiding the overall breakdown of production processes, which can be caused by strains on any interconnected internal or external systems, is an ongoing challenge for manufacturers. At the same time, manufacturing design, engineering, assembly, and services can quickly become obsolete due to innovations, changing regulations, or shifts in consumer demand.
To thrive in this new environment, manufacturers must constantly respond to change and optimize their approach across all levels. In many applications, it is no longer feasible to reactively manage the dynamics of Industry 4.0. The now predominant crisis management mode of operations should be replaced with more adaptable business practices that support a higher standard of excellence. The ability to anticipate problems and proactively seize opportunities needs to be woven into the fabric of business. Example embodiments, described below, provide manufacturers with the metrics and insights they need to deal with modern business dynamics, which have become too complex to manage using current state-of-the-art decision support tools.
Once collected, the attributes 110, 112, 114 of the manufacturing system 105 may be processed to generate the components of the system model 120. The system model 120 may be constructed according to the process described below with reference to
Similarly, the process graph 140 may define a plurality of service nodes each representing a respective service performed by the manufacturing system 105. For example, services represented by the nodes may include generating a product, processing a product, and transporting a product. The service nodes may also define a subset of the manufacturing system runtime in which the respective service is active and a subset of the runtime in which the respective service is inactive. Such a configuration may be in correspondence with the resource nodes, which may each define a capacity to perform one or more of the services during a given time period. The service nodes may be linked by service links each representing a causal dependency between two or more of the service nodes. In particular, the service links may represent a relationship between an input and an output of two or more of the service nodes, such as the relation between successive stations of a manufacturing assembly line.
The resource-process mapping 150 may define a map between the service links and the resource nodes, representing use of the physical resources by the services. The mapping may represent one-to-many and many-to-many relations between services and resources. For example, the mapping may define an association between one resource node and multiple service nodes, and/or an association between one service node and multiple resource nodes.
Thus, the manufacturing system model 120 may represent the manufacturing system 105 by incorporating the resource attributes 110, resource use attributes 112, and process attributes 114 into the interconnected resource graph 130, resource-process mapping 150, and process graph 140, respectively. The manufacturing system model 120 may be configured to represent the manufacturing system 105 at a given point in time (e.g., when the attributes 110, 112, 114 are collected), and may be updated as newer attributes are collected from the manufacturing system 105.
With reference to
Once the manufacturing system model 120 is complete, it may be modeled (simulated) under plural sets of parameters. The plural sets of parameters may be previously generated as detailed with reference to the example embodiments described below (220), and each of the plural sets of parameters may indicate respective variables having a causal relation to the performance metrics. For example, the plural sets of operational parameters may be distinct from one another by defining a failure of a resource node, a delay of a service, a modification to the service links indicating a different sequence of operations, and/or a modification to the service links indicating an alternative mode of operation.
The operation of the manufacturing system model 120 is then modeled under the plural sets of parameters to generate respective performance metrics (e.g., sustainability metrics, environmental impact metrics) (225). Performance metrics of the manufacturing system 105 may be modeled by simulating performance of the plurality of services as a function of the resource graph 130 and the process graph 140 under plural sets of operational parameters. The modeling may include, for example, determining a change in status of the resource nodes over a runtime as a function of operation of the service nodes, and determining a change in the operation of the service nodes over the runtime as a function of the change in status of the resource nodes. The performance metrics may include a sustainability metric indicating repeatability of a manufacturing process over time, as well as an energy metric representing an availability of energy to perform the plurality of services in excess of energy consumed by the manufacturing system 105.
From an analysis of the plural sets of parameters and the resulting performance metrics, occurrence probabilities for each of the sets of parameters can be determined to identify risks to the system (230). The occurrence probabilities may indicate predicted probabilities of the manufacturing system model transitioning from an initial state to each of a plurality of successive states, each of the successive states corresponding to a respective one of the plural sets of parameters. Some of those successive states may be identified to relate to an adverse outcome, such as a failure state representing the manufacturing system 105 being unable to continue operations, or an outcome wherein the manufacturing system's performance metrics decrease below a given threshold. Accordingly, one or more risks may also be identified, wherein the risks indicate a likelihood of the manufacturing system 105 (represented by the model 120) transitioning from the initial state (corresponding to the initial state at which the attributes were collected) to an adverse outcome such as a system failure. The risk may indicate a change in the performance metrics that exceeds a predetermined threshold of change, or a predetermined threshold rate of change, over time. Further, variation of the risk under the plural sets of operational parameters may be identified, and a function relating the performance metrics and the risk based on the variation may be determined.
Further, the analysis of the plural sets of parameters and the resulting performance metrics can identify parameters that result in outcomes that are absent the risk. Those sets of parameters can include, for example, a difference in resources and/or processes compared to the initial system state. Thus, by modifying the system in the manner indicated by those identified parameters may result in avoiding a given risk. Accordingly, one or more modifications to the manufacturing system may be determined based on the operational parameters associated with the outcome absent the risk (235). Such analysis can also assess an environmental impact of the manufacturing system 105. For example, one or more of the plural sets of operational parameters associated with a reduced environmental impact may be identified, and a modification to the manufacturing system based on the operational parameters associated with the reduced environmental impact.
Based on the findings of risk and risk avoidance, a report may be generated for the manufacturing system 105 (240). The report may indicate the identified risks, and may also provide a diagnosis of the manufacturing system 105 and one or more modifications that are predicted, based on the plural sets of parameters and resulting performance metrics, to avoid or prevent an adverse outcome. Example processes for determine such diagnoses and remedies are described in further detail below. The manufacturing system 105 can then be updated to incorporate one or more of the identified modifications, thereby improving the system 105 in accordance with the identified goals, such as sustainable operation over time and/or minimal environmental impact. The resource graph 130 and/or the process graph 140 may also be updated based on the modification.
Example embodiments, as described herein, provide for emulating a manufacturing system model 120 through a number of differing scenarios, where the results of such emulations can be analyzed to identify operational concerns and potential remedies and optimizations for the manufacturing system 105. One aspect of this emulation, as described above, is to generate plural sets of parameters (220). In order to generate those sets of parameters, a set of input parameters may be permutated, by altering one or more values, to generate one or more additional scenarios for emulation. Such selection of differing parameters is described herein, and in particular with reference to
In an example embodiment, a first of the sets of parameters may correspond to an initial state of the manufacturing system 105 (e.g., corresponding to measured attributes at an initial point in time), or may correspond to a hypothetical or predicted state of the manufacturing system 105. Further, additional instances of the sets of parameters may correspond to a range of permutations of the first set of parameters, which may correspond to deviations from the initial state of the manufacturing system. Such deviations can include the permutations described above.
With the sets of parameters defined, the model may then be simulated under each of the sets of parameters to generate corresponding sets of performance metrics (225). The sets of performance metrics may also include a dimension of time (referred to, for example, as time “T1”), indicating that the results correspond to the first set of input parameters upon simulation for a given length of (simulated) time. Following obtaining resulting performance metrics, those metrics may be analyzed, as described above with reference to
Given the identified adverse outcome(s), a map can be generated to relate the adverse outcome(s) to corresponding instances of the plural sets of parameters. An example map is described below with reference to
The parameters 311A-N may correspond to each of the scenarios modeled as described above, and the outcomes 320A-N may include corresponding performance metrics resulting from the modeling. Further, if an adverse outcome (e.g., system failure 110A) is identified from modeling the manufacturing system model under a given set of parameters, the adverse outcome is associated with the outcome (e.g., outcome 320A), thereby “flagging” the outcome. Over a range of different (e.g., permutated) parameters 310A-N, some of the corresponding outcomes 320A-N may be associated with adverse outcomes 110A-B, while others may not. In an alternative embodiment, a map may be generated to include only outcomes that are associated with adverse outcomes.
From the map 300, one or more risks to the manufacturing system model can be determined. A risk, as described above, may indicate a probability that the manufacturing system model will encounter an outcome that includes an adverse outcome such as a system failure. Such risks can be calculated through a number of means and may be expressed in a number of different ways, and examples of such analysis and presentation are provided in further detail below. In one example, an occurrence probability may be assigned to each of the successive states 310A-310N, where the occurrence probability indicates a likelihood that the manufacturing system model will move from the initial state 305 to a state having the given parameters. For example, the state 310A is assigned an occurrence probability of 10%, indicating that the manufacturing system model has a 10% chance of transitioning to the state 310A, under which the manufacturing system model is simulated under the corresponding parameters 311A. Such an occurrence probability may be determined based on historical data about the manufacturing system model, industry data, historical simulation data, data about comparable manufacturing system models, the manufacturing system's performance attributes, and/or other sources. Based on the occurrence probability of each of the states 310A-N, one or more risks (e.g., the probability of an outcome including one or more of the adverse outcomes 110A-B) can be determined. The risks may be reported to a user, including details of the predicted adverse outcomes and the likelihood of each. The risks may also be further processed, for example, to generate a lookup table, an example of which is described below with reference to
As shown, the map 300 may depict two points in time: the initial manufacturing system state 305 representing the manufacturing system at an initial point in time (T1), and the successive manufacturing system states 310A-N (and interventions 312A-B) representing the potential outcomes for a manufacturing system at a later point in time (T2). Although the successive states 310A-N may represent different potential outcomes at a common point in time, they may instead represent different points in time. For example, state 310A may represent at an earlier simulated point in time than state 310B because the simulation reveals that the state 310A results in the adverse outcome 110A, and as such, the modeling of the state 310A may be terminated earlier, while state 310B may be simulated for longer to ensure that the outcome 320B does not include an adverse outcome.
Further, any of the simulated scenarios involving the successive states 310A-N (and/or interventions 312A-B) may be extended beyond those successive states to predict a state and outcome of the manufacturing system model at a still later point in time (T3). For example, as shown in
As described above, the operating the manufacturing system model through plural sets of parameters may yield a set of results, including performance metrics, positive and negative outcomes, risks of adverse outcomes, and, potentially, changes to the performance attributes of the manufacturing system model in subsequent scenarios. Those results may be used to generate a map 300 as shown in
Following diagnosis or as a separate process, one or more remedies can be determined. The remedies may correspond to one or more modifications to the manufacturing system model (e.g., performance attributes) that have a causal relation to one or more positive changes to the manufacturing system model, such as changes that reduce or avoid a risk of an adverse outcome (e.g., a system failure), or a positive change in the manufacturing system model's performance metrics or performance attributes in subsequent modeling. The process of identifying such remedies may implement features of the process 900 described below, except that the targets to be identified are performance attributes and/or sets of parameters (e.g., positive interventions) that are responsible for a positive change in the manufacturing system model and/or performance metrics (420). For example, a modeled scenario under a given set of parameters (e.g., an intervention) may result in positive performance metrics for the manufacturing system model. Through multiple simulations and analysis, one or more parameters of the set of parameters may be identified as having a causal relation with the positive performance metrics. Accordingly, a remedy can be determined as a change to the manufacturing system model in accordance with the one or more parameters, and that remedy may then be reported to a user (425). In a further example, a subset of the performance metrics that are negatively correlated with an adverse outcome (e.g., system failure) is identified, and a remedy can be identified as one or more of the respective variables (e.g., of a set of parameters) that are associated with the subset of performance metrics. In a still further example, determining the remedy may include 1) generating an additional set of parameters, the additional set of parameters indicating a performance intervention; 2) modeling the performance metrics of the manufacturing system model under the additional set of parameters to generate a performance metric result; and 3) identifying the remedy based on the performance metrics associated with the performance intervention.
Optionally, the remedy may be incorporated into a reference table such as the table 500 described below with reference to
The lookup table 500 may be accessed using information on a given state of the information manufacturing system model. For example, for diagnostic applications, the state of the manufacturing system model may be analyzed and then compared to entries in the lookup table to determine the risk inherent in the manufacturing system model. The remedies 530, including remedies and/or suggested actions (e.g., modifications to the manufacturing system model) to avoid the risk(s), can also be reported, such that they may be implemented by the manufacturing system model itself.
The stages, as shown in
As an initial stage of preparing a mathematical model of a subject manufacturing system model and environment, information is collected regarding each of the components of the subject manufacturing system model, as well as the operational connections between those components. The information to be collected is sufficient for drawing an accurate mathematical model as described above with reference to
In order to achieve an accurate static deconstruction of the subject manufacturing system model, the following actions may be taken:
In order to characterize a model of the manufacturing system model beyond its static description, additional information about the subject manufacturing system model and its components is collected and incorporated into the model as a definition of the dynamic complexity of the manufacturing system model. Inputs of this stage include the static complexity definition produced in stage (605), as well as information regarding how the static complexity changes over time. This information can be obtained through analysis of historical data about the manufacturing system model, epidemiological data (e.g., data derived from a given population that relates performance attributes and incidences of various changes exhibited by the population), historical simulation data, data about comparable manufacturing system models, the manufacturing system's performance attributes, and/or other sources. The output of this stage (610) is a definition of the dynamic complexity base model of the manufacturing system model. In order to achieve an accurate dynamic deconstruction of a manufacturing system model, the following actions may be taken:
Given the static and dynamic definitions of the subject manufacturing system model (605, 610), a mathematical model of the subject manufacturing system model is then constructed for emulation (615). The mathematical model may be constructed as described above with reference to
The mathematics of the emulator may include the following definitions:
U.S. Pat. No. 7,389,211 establishes the basis for a mathematical predictive solution that analytically predict system performance (in general terms). According to one embodiment such solution can be conceptually expressed in the form:
X=X
0+ΣM(d)XM+ΣN(s)XN (1)
Convolution theorem allows a solution of a combined mathematical expression of two function-domains:
with the combined solution using Laplace Transform :
Complexity Function h(σ)=∫X(τ)·σ(t−τ)dτ (2)
Let us denote the vector σ=σ(k) that represent the set of metrics that define a domain The system of equations that represents the variations is:
From (1) and (2) the impact of complexity on the domain metrics and using Laplace transform, is:
d and s denote the 2 types of complexities and,
are computed by the method proposed in NA(3) where (σ′(d),σ″(d)) and ((σ′(s),σ″(s)) are representing σ through different coordinates and σi,s or d represent the complexity (i order) derivative, expressed in exponential form
σ′(i)=Σk(i)Σn(i)Cn,k exzt (5)
where z is a complex variable that represent the two complexities z=σ(s)+iσ(d) where i=√{square root over (−1)}, σ(s)) and σ(d)) the static and dynamic complexity respectively
The set of equations 3, 4 and 5 allow the computation of all domain metrics as a function of varying the two portions of complexity representation.
We propose an aggregative concept, let us call it Complexial that represents the aggregated impact produced in each domain X0 of the vector X0 where X0 (1) is performance, X0 (2) denotes cost, X0 (3) means quality of service and X0 (4) represents availability etc.
From the above:
Complexial=ξ=Πn(X0(n)+X′(n)+X″(n)+. . . ) where Xi are the complexity contribution of higher order perturbations (direct and indirect) of domain metrics n.
Once the mathematical model of the subject manufacturing system model or environment has been defined, the model is then emulated. The mathematical model may be constructed as described above, and may implement approaches as described in U.S. Pat. No. 6,311,144 (herein incorporated by reference). Inputs of this stage (620) include the mathematical model (emulator) from the previous stage (615), as well as one or more sets of operational scenarios that will be the actions that drive the emulation of the subject environment or manufacturing system model. Outputs of this stage (620) include a set of reactions of the emulated manufacturing system model that shows its behavior under a set of varying scenarios and how its complexity changes, as well as conditions and points in time when the behavior of the environment or manufacturing system model becomes singular or encounters another adverse or unacceptable outcome.
The outputs of this stage (620) allow for discovery and identity of when the behavior of the emulated environment or manufacturing system model becomes ‘unexpected’ due to a sudden change. This may comprise running a number of starting positions and controlling the emulator to run for a number of different time lines under different initial conditions.
In short, to establish a “manufacturing system model limit” due to complexity, two results in particular are identified. First, the manufacturing system model limit due to static complexity (the “ceiling”) is what may be deemed to be the predictable limit that we understand from simple extrapolations, statistical trending and actual experiences. The “ceiling” is what is normally understood as the operational limits of a manufacturing system model. Second, the manufacturing system model limit due to dynamic complexity (a singularity), which is unpredictable by conventional methods (e.g. statistical trending) is identified. A singularity may occur at any point in time, predictable and governable through the mathematical methods that emulate interactions, feedback and interferences provided in example embodiments.
Once the mathematical model has been emulated through one or more test scenarios as described above, the results of the emulation can be analyzed to identify the root causes of the various detected results, including adverse outcomes (e.g., a system failure), and changes to the manufacturing system model (e.g., remedies) to avoid such adverse outcomes. Inputs at this stage (625) include the calculated results of emulation from the previous stage (620), as well as measurements and observations of the actual manufacturing system model to condition and verify the outputs of the previous stage (620). Outputs of this stage (625) include remedies, which are suggested changes to the manufacturing system model.
Operations at this stage (625) include various methods of analyzing the emulation results, including discovering changes to performance metrics, discovering risks of adverse outcomes, and building and computing further scenarios for emulation. Further, the results of the previous stage (620) may be quantified and qualified in a number of ways, including assessing the result for each scenario; combining scenarios to determine interventions. A method of determining whether an adverse outcome has occurred is described below with reference to
In response to recommended changes to the manufacturing system model provided in the previous stage (625), those changes are incorporated into a revised model of the manufacturing system model, and the revised model may be emulated to determine the specific benefits incurred by those changes. Inputs of this stage (630) include the ouputs of the previous stage (625), as well as defined improvement scenarios. Such improvement scenarios may include changes to the manufacturing system model intended improve performance attributes, lower the occurrence probability of adverse or higher-risk manufacturing system states, improve performance metrics and/or lower the risk of one or more adverse outcomes, such as a system or subsystem failure. Such improvements may be suggested as a result of a process as described above with reference to
Operations at this stage (630) include use of the reference predictive emulator to compute the improvement scenarios and define the plan. Further, the emulator may driven to provide ongoing monitoring of complexity (e.g., over long-term simulated scenarios) to identify degradation due to increase in complexity, determining the impact of such degradation, define actions to address the degradation, and determine the frequency of complexity monitoring and analysis (e.g., continuous, daily, weekly).
As a result of the previous stages, once implemented to identify and take preventive action against adverse outcomes resulting from dynamic complexity within an emulated manufacturing system model, the dynamic complexity of the manufacturing system model can be deemed to be controlled and predictable within an acceptable tolerance. An adverse outcome may be identified based on a rate of change in performance metrics or other characteristics, where one or more of those metrics exceed a threshold rate of change. A singularity may be an example of such an adverse outcome, as well as other rapid changes to the performance or characteristics of a manufacturing system model. Thus, the results, and particularly the proposed changes to the manufacturing system model, can be exported from the model as recommendations to modify and improve the real-world manufacturing system model corresponding to the model.
Inputs of this stage include the outputs, knowledge and experiences of all previous stages, a change management plan, and information on the identified problems and challenges underlying the manufacturing system model. The outputs and ongoing states of this stage include a proposal regarding reporting structure, destination, frequencies, and content; the operations of a control function to implement the plan; and ongoing maturity improvements.
Initially, a mathematical model is obtained for emulation (705). The mathematical model may be constructed according to the process described above with reference to
Embodiments of the invention, as described above, provide for emulating a model manufacturing system model through a number of differing scenarios, where the results of such emulations can be analyzed to identify problems and potential solutions for the manufacturing system model. One method of this emulation is to permutate a set of input parameters, by altering one or more values, to generate one or more additional scenarios for emulation. Such selection of differing parameters is described herein with reference to
Following obtaining the results of the first and second performance metrics, those metrics may be compared (730) and reported to a user (730) to determine the effect of the different input parameters on the performance of the manufacturing system model. The performance metrics can be analyzed further, as described below with reference to 9, to identify the cause or causes of the modeled results (735). The steps of permutation, simulation and analysis (715-735) may be repeated to determine performance and identify adverse outcomes under a range of scenarios corresponding to different input parameters.
At an initial stage, changes to a set of input parameters are identified (805) and incorporated into a new set of parameters (810) for emulation. These steps may correspond to step 715 described above with reference to
Due to the dynamic complexity of a manufacturing system model, an adverse outcome may only develop after an extended length of operating time, and may develop despite the failure to predict such an adverse outcome over a shorter length of simulated time. Thus, by extending the simulation through time T2, a model manufacturing system model can be tested more thoroughly to determine whether adverse outcomes result over greater lengths of time. If the resulting performance metrics after time T2 exceed an acceptable threshold (845), then an adverse outcome is reported (860). Otherwise, an acceptable outcome can be reported (850), indicating that the model manufacturing system model exhibits positive performance metrics under the given set of input parameters.
Next, a component is identified that is most proximate to the adverse outcome (910). For example, a manufacturing system model may exhibit a failure of a single assembly station, which in turn lowers a performance metric (e.g., quantity of products delivered) below an acceptable threshold and raises the risk of a larger, system-wide failure of resources and/or services. Once the initial and proximate causes are identified, a path may then be traced between them (915), where the path encompasses all operations and factors connecting the initial causes to the proximate causes. From this path, a series of nodes and dependencies can be identified in the path, each of which can be considered to have contributed to the causal chain leading to the adverse outcome (920). Each of these components can then be evaluated individually for failures, degradation, and other changes in the manufacturing system model that may have contributed to the adverse outcome (930). With reference to the example above, it may be determined that a suboptimal organization of resources of the manufacturing system, such as an absence of a redundant assembly robot within an assembly line, lowered the manufacturing system's sustainability metric, thereby increasing susceptibility to failure of the assembly line, which in turn increased the risk of failure of a transportation system that transports the products output by the assembly line. In addition, recognizing that other causally-linked factors (outside of this path) may also contribute to an adverse outcome, those other causes may be evaluated in the same manner. With the causes contributing to the adverse outcome identified, those components, as well as the specific problems inherent in each, may be reported for further analysis and remedies (940).
Further description of deconstruction of dynamic complexity and prediction of adverse outcomes, including example applications, is provided in U.S. Pub. No. 2012/0197686, the entirety of which is incorporated herein by reference.
Applying the modeling techniques described above, a range of manufacturing system models can be simulated as a multi-layer mathematical model having layers corresponding to performance attributes, performance metrics rules, and other aspects of the system model. In some embodiments, one or more such layers may be partially or wholly merged, or otherwise reconfigured to accommodate the particular manufacturing system model being modeled. For example, in a relatively simple manufacturing system model, where processes can be described easily with direct relation to the physical components, the process and implementation layers may be implemented in a common layer. Similarly, the implementation and physical layers may be implemented in a common layer.
The possibility of an adverse outcome, as described above, presents an apparent risk to the operation of a manufacturing system model, or even to the integrity of the manufacturing system model itself. An adverse outcome may be identified based on a rate of change in performance metrics or other characteristics, where one or more of those metrics exceed a threshold rate of change. A singularity, as described above, may be an example of such an adverse outcome, as well as other rapid changes to the performance or characteristics of a manufacturing system model. By identifying outcomes including adverse outcomes and their causes, as described above, embodiments of the invention can enable a manufacturing system model to be reconfigured to avoid such adverse outcomes.
Further, embodiments of the invention can be applied, in a more comprehensive manner, to the identification and avoidance of a range of adverse outcomes. By modeling performance metrics of a manufacturing system model under a range of parameters, the risk of an outcome including an adverse outcome can be ascertained as a probability. The risk can be qualified by a particular adverse outcome, as well as a predefined period of time. Several such risks can be reported simultaneously when warranted.
In an example embodiment of identifying and reporting one or more risks, a multi- layer mathematical model of a manufacturing system model bay be provided as described above. Performance metrics of the multi-layer model may be modeled under plural sets of parameters, where the performance metrics may include a sustainability metric, an environmental impact metric, and/or a risk index. From these performance metrics, one or more adverse outcomes may be identified based on a change or a rate of change in the performance metrics exceeding at least one predetermined threshold. Given the identified adverse outcome(s), a map can be generated to relate the adverse outcome(s) to corresponding instances of the plural sets of parameters. Based on this map, one or more risks can be determined and reported, where the risk(s) define a probability of an outcome including the at least one adverse outcome. Example embodiments providing predictive risk assessment and management are described in further detail below.
Prior to implementing embodiments for determining risk as described above, initial risk perception 2805 (phase one) may be incomplete. Accordingly, in phase two (risk modeling) 2810, information is collected as necessary to perform the deconstruction and causal analysis based on gathered information from experience and benchmarks of similar situations. From this data, the investigation and provocative scenarios that will reveal the risk and singularities may be built. Using the mathematical formulation and the deconstructed characteristics, dependencies and content behavior, a mathematical emulator that represents the manufacturing system model dynamics and the dynamic complexity is delivered. Using this emulator, scenarios can be deployed under different patterns of initial conditions and dynamic constraints to identify the risk and the conditions under which the risk will occur, as well as the possible mitigation strategies. The emulator can be continuously updated to replicate any changes that may happen over time with impacts on the problem definition, due to the evolution of dynamic complexity, environmental changes or content dynamics. Success is enhanced by the ability to keep the emulator representative, accurate, and able to capture all risks with sound projection of predictions.
After building the emulator in phase two 2810, in phase three 2815 (risk discovery), modified scenarios are run to identify possible risks. By modifying the parameters of each scenario within the emulator, one by one, by group or by domain, to represent possible changes, one may extrapolate each time the point at which the manufacturing system model will hit a singularity and use the corresponding information to diagnose the case. The emulator supports risk categorization based on the severity of impact, the class of mitigation, and many other characteristics that support decision making such as urgency, and the complexity and/or cost of implementation of mitigating actions.
For each of scenario, the ripple effect is particularly important to results interpretation. By using perturbation theory as the analytical vehicle to represent manufacturing system model dynamics involving direct and indirect effect on components, as well as trajectories representing sequence of components, the ripple effect is exerted on tightly or loosely coupled interactions.
Other scenarios may be created during this phase 2815 to explore viable and proactive remedial options that secure an acceptable risk mitigation strategy and allow the manufacturing system model to be fixed prior to realizing negative outcomes caused by an eventual risk. This last dimension may be considered crucial in risk management, which supposes that most of the risk is discovered during this phase—including risks generated by dynamic complexity.
Mitigation is the identification, assessment, and prioritization of risks as the effect of uncertainty on objectives followed by coordinated and economical application of resources to minimize, monitor, and control the impact of unfortunate events or to maximize the realization of opportunities. Risk management's objective is to assure uncertainty does not deviate the endeavor from the manufacturing system's performance. Thus, in phase four 2820, the information derived in the previous phases is implemented to mitigate risk to the manufacturing system model. The risk is identified and diagnosed, and then remediation plans may be built ahead of time to eliminate, eventually reduce or at minimum counterbalance the impact of the risk. It is the application of the knowledge gained in the earlier phases that allows us to be ready with awareness of what may happen and plans of how to remediate the risk. Example embodiments may utilize the knowledge database to continuously monitor manufacturing system models to cover the risk of both the knowns as well as the unknowns (e.g., risks) that are caused by the evolutionary nature of dynamic complexity.
In phase five, risk monitoring 2825, the monitoring process is implemented. Using the database that contains all risk cases generated in phase three 2815 and enhanced with remedial plans in phase four 2820, the manufacturing system model may be put under surveillance using automation technologies. Similar in functionality to what is used for planes, cars, and nuclear plants, the auto piloting capabilities may observe the manufacturing system model in operations to identify eventual dynamic characteristics that may lead to a pre-identified risk situation. If a matching case is found, an alert will be generated and the pre-approved remedial actions will become active.
Each stored case may contain an identifier, a diagnosis, and one or more options for remediation. If the auto piloting manufacturing system model does not find a matching case, but has identified critical symptoms that may cause a risk, the monitoring controller sends back the characteristics to the predictive modeling phase two 2810. The corresponding scenario may be run to estimate the risk, diagnose the case, and propose remedial options, which may then be sent back to the database, enriching the knowledge base with the new case. Using this approach, the auto piloting, monitoring and control manufacturing system model may gradually become more intelligent and exhaustive, which, over time, may serve to considerably reduce the occurrence of risks of adverse outcomes.
In the example shown in
Though emulation of the multi-node complex through permutations of parameters (as described above with reference to
Example indicators or risk exhibited by a manufacturing system model may be referred to as a Dynamic Complexity Indicator (Dycom) and a Risk Index (RI). Dycom and RI may be implemented in the embodiments described above with reference to
The starting point of risk management, in example embodiments, is the analysis following the causal deconstruction of a manufacturing system model:
Such an approach may provide performance professionals a platform to control, plan and identify ideal outcomes for a manufacturing system. In short, both goals of reducing uncertainty, and proactively estimating and fixing problems. Long-term machine learning process will start by modest coverage of process proactive fixing to become over time an intelligent platform that will be able to deliver fast and comprehensive recommendations for right time fixing.
In one embodiment, the processor routines 92 and data 94 are a computer program product (generally referenced 92), including a non-transitory computer-readable medium (e.g., a removable storage medium such as one or more DVD-ROM's, CD-ROM's, diskettes, tapes, etc.) that provides at least a portion of the software instructions for the invention system. The computer program product 92 can be installed by any suitable software installation procedure, as is well known in the art. In another embodiment, at least a portion of the software instructions may also be downloaded over a cable communication and/or wireless connection.
Manufacturing excellence is achieved by aligning the conception and execution of manufacturing procedures and processes with key performance indicators (KPIs) that support company priorities and meet the objectives of various stakeholders. As such, KPIs provide a way to measure and communicate the status of all manufacturing decision-making and change management activities from strategy and design to operations and logistics. KPIs at both local and global levels, provide stakeholders with a way to identify an ideal state of manufacturing, compare remedial options that provide a path to achieve that ideal state and execute plans with confidence in the outcome
Example embodiments can be configured to model one or more KPIs of a manufacturing system by incorporating the KPIs as performance metrics. Example KPIs that may be tracked as performance metrics include the following:
To make good decisions, manufacturing stakeholders must be able to realistically analyze potential outcomes, benefits, and risks associated with various options. Yet, some decisions must be made without the benefit of experience or historical data. This is especially true for any new activity which may be influenced by complex or unknown dynamics. In such cases, manufacturers are looking for new methodologies and generative AI technologies that fill the knowledge gaps to support smarter decisions and proactive business practices.
Example embodiments help manufacturers deal with modern business dynamics, which have become too complex to manage using experience or data-driven decision support tools. By providing a digital experimentation platform that can be used to continuously adapt to change, identify limits, proactively find and fix any problems, and take advantage of opportunities, example embodiments helps manufacturers deal with a multitude of different, competing issues that must be intelligently managed to reduce waste, increase production profitability, improve sustainability and gain a competitive edge.
Example embodiments can calculate metrics that help manufacturers discover the root cause of any problems or predict the future impacts of any changes in the manufacturing process being studied. Workload and classes, service quality, and cost are three global dimensions that can be measured. Based on as-is or to-be scenario analysis, example embodiments can compute metrics that appear in the reporting dashboards. These metrics provide stakeholders with a quantitative as well as qualitative view of system health, risk, and opportunities for improvement of existing or proposed manufacturing activities and/or designs. Each dimension considers low-level contributions to compute their influence through the graph of interdependencies.
Our experience in manufacturing optimization spans a wide spectrum of cases that differ in criticality, complexity, and scope. In each instance, we have successfully applied unique and efficient predictability and portability for both operations and strategic decisions. Although each case varies in definition and complexity, our investigations reveal requirements that span the following categories:
Manufacturing is the production of goods from materials, parts, and components. Goods may be produced one by one, in batches, or on demand using manual or highly automated production lines composed of time-critical processes. Each manufacturing process is comprised of a given number of interdependent sub-processes, systems, assets, and components that interoperate to execute a series of steps. Each step may be implemented using logical or physical production components—for example, robots, conveyors, automation, industry wafers, assembly facilities, and monitoring/testing mechanisms. And each manufacturing process is comprised of lower-level systems, assets, components, and subcomponents that have their own characteristics and dependencies. At any time, a subcomponent malfunction will cause the malfunctioning of higher-level components, which is then induced and propagated to higher-level production lines or entire processes.
A section of the table 1400, referred to as a “node extension,” can store additional properties useful for modeling particular performance metrics, such as capacity, carbon produced, fuel consumption rate, energy usage, cost type, and cost value. In one example, node extensions may be divided into five categories of information: General, Green, Availability, Failover and Cost. Node extensions may allow additional variables for Vehicle and Transport type nodes, for example, and can be attached to any node in the implementation view. An summary of example node extensions include:
The recursive effect due to the interdependencies of resources and services creates challenges that are difficult to capture unless the system is represented and reproduced through detailed analysis of the manufacturing system, as wide and as deep in sophistication as possible. One important concepts in systems theory is the notion of interdependence between systems (or subsystems). To model a nonlinear system with sufficient accuracy and reproducibility, these interdependencies must be captured in the mathematical formulation.
When modeling a manufacturing process, it is common to be missing data necessary to explain key phenomena through which independent variables interact to produce complex and synergetic nonlinear effects. Therefore, the chosen modeling method must address this lack of a priori knowledge that explains the nonlinearities in the relations between variables. That is precisely the idea of building a mechanistic mathematical twin: to represent production line dependencies constituents as well as calling upon the interdependencies produced through the contributions of lower-level twins.
The analytical solution described herein translates system dynamics into a mathematical expression, which delivers the same metric values that would have resulted if real system measurements were taken under the same set of initial conditions. Once validated, the system of equations can be reliably used for predictive and prescriptive analysis of the system being studied without a continuous feed of new data.
To make good decisions, manufacturing stakeholders must be able to realistically analyze potential outcomes, benefits, and risks associated with various options. Yet, some decisions must be made without the benefit of experience or historical data. This is especially true for any new endeavor or system which may be influenced by complex or unknown dynamics. In such cases, mechanistic digital twin technologies can fill the knowledge gaps to support smarter decisions and proactive business practices.
Through mathematical cloning, a mechanistic digital twin provides manufacturers with a virtual representation of the fit, form, and function of a real-world as-designed, as-built, or as-maintained manufacturing process, product, or asset. A digital twin that uses partial differential equations (PDEs) as the basis of its mathematical solution is robust enough to be representative of an object, product, piece of equipment, person, process, supply chain, or even a complete business ecosystem.
Once the mechanisms of the system are captured in the digital twin, users can compute the model and determine causation between many-to-many time-dependent relationships and interdependencies for millions of connections. The twin calculates missing values and accurately predicts what would happen in the real-world environment under changing conditions. We call this algorithmic intelligence, and it is what makes the approach dependable for highly critical decisions in dynamically complex manufacturing use cases.
A digital twin can be used to understand, predict, and optimize its real-world counterpart's performance and operational risks. While virtual models are conceptual in nature, the algorithmic intelligence derived from the twin provides an accurate digital representation of real events that could happen under a given set of circumstances—regardless of whether those events have occurred. This provides an experimentation platform where smart decisions and innovation can happen in a virtual world, free from physical prototyping costs and the time limitations of a traditional approach. Adjustments can be made to the digital twin to see how the system would change in real life before making any changes to the operational system.
The advanced analytics and scenario analysis capabilities of a mechanistic digital twin can help manufacturers dramatically increase productivity and reduce downtime, maximize the use-life of machinery, detect potential problems before they occur, and take corrective action quickly.
Example embodiments can improve manufacturing operations in several ways:
Because computational challenges and missing values persist when dealing with nonlinear systems that contain multiple interacting networks, numerical solutions will become necessary at some point in the analysis. The perturbed graph solution used covers the spatiotemporal evolution of nonlinear systems by expressing and solving Euler-Lagrange partial differential equations (PDEs) through tensor factorization. This method efficiently encapsulates all characteristics, dynamic behaviors, and dependencies among system components to reproduce the nearly exact behavior and adhere to all the rules of the system being mathematically emulated. The resulting analysis enables users to explore prospective cases and have confidence in the system's representation to support highly critical decisions.
As defined herein, the twin represents the graph of connections between all manufacturing components. Each is represented as a mathematical partial derivative that depicts its characteristics and contribution to the end-to-end performance and time. Graph theory provides a mathematical nonlinear data structure capable of representing various kinds of physical structures, consisting of a group of vertices (or nodes) and a set of edges that connect the two vertices.
In practical applications, vertices and edges of graphs often contain specific information, such as labels or weights (such as volume and cost). Many real-life scenarios are better modeled by time-dependent graphs when the edges are activated by sequences of time-dependent elements. Horizontally, behaviors may be driven by dependencies, and vertically, behaviors may result from direct and indirect causes. Finding these causes is sometimes more important than finding the unperturbed solution itself.
The perturbation theory approach involves a dynamic system of Lagrange-like partial differential equations that represent the dynamic behavior of a cost function and a solution that will capture both direct and indirect perturbations around a base of the un-perturbed solution. Conceptually, the solution can be expressed with perturbation theory such that any metric X can be expressed in the form:
The significance has considerable importance as an unapparent statistically uncorrelated effect can play an important effect on the basic function. In different wording, a statistically unlikely risk can appear and even translates sometimes into singularity due to multiple order interactions.
In more detail, consider the following vector: σ=σ(k), where k=1, 2 . . . k and where σ is a function of time and represents the metrics that describe corporate, financial, business, and technology engineering characteristics and behavior.
Further consider that:
In general, the system of equations that represent the variations can have the form:
where X(c) represents a basic function.
Further assume that σ′ and σ″ are vectors representing σ through different coordinates, and that σ(0), σ′(0), and σ″(0) represent the unperturbed values of a metric. Then, the first order direct perturbation is:
and the first order indirect perturbation is:
This separation seems artificial from a theoretical point of view, but it is natural from a practical point of view, as the origin of perturbation on X(d) and σ(i) are different. Next,
Ck,n(i) a matrix of numerical vectors, n1*, n2*, . . . nm* are normalization constants and χ1, χ2, . . . , χm are the perturbing variables (function in time).
Therefore:
are known functions in time and can solve the two system equations (1) and (2) in the form:
where U (t) is a square matrix (K×K) and ν(t) is a known vectoral function.
The matrix is determined by:
with
Y(t0)=I (5)
where I is a unit matrix and therefore equation (3) becomes:
σ=Y(t)σ(t0)+∫t
and with X(c)=(XK(c)) U specified in the form
v(t)=))
The formula
forms the system of equations equivalent to the un-perturbed expression:
where the solution Y in equation (4) is known if the partial derivative of the unperturbed problem is computed with respect to the K integration constants such as by determining)) with the condition of equation (5).
A typical manufacturing production plant involves production stations (processes) that have a level of autonomy to deliver part of the manufacturing production activities. Components are linked together through simple or sophisticated connections. A twin represents the graph of connections between plant components, each is represented as a mathematical partial derivative that depicts its characteristics and contribution of the full picture performance and time.
The digital mechanistic twin represents the transformation of real-world manufacturing processes into the language of abstraction that represents the necessary level of details of the base (
Looking at formula (1) σ is the vector representing the metrics necessary to understand and manage the system complexity and determine the eventual risk. Dependability metric HD metric (also referred to as sustainability metric) shows how the system impacts reliability, performance, safety and cost. Energy ES score indicate the ability of the system to allow modification without negatively altering its service goal. The last metric indicates the tension on resources consumption RI. The computation of the three metrics leads to determine the eventual risk and indicate the scenarios for direction of improvements.
The vectors XCs in formula (1) represent the contributions of the manufacturing components that delivers the production process: XC the basic contribution, Xd the contributions from those elements that directly influencing the component through interdependencies of other components and finally Xi represents those contributions exerted by other factors outside the system (e.g., transportations conditions, environmental factors, congestions, or incidents).
For a simplified case, we demonstrate the vectors X's as composed of 4 elements:
The solution of the perturbed partial differential equations delivers the perturbed σ vector in the coordinates mentioned above and at different points corresponding to time and space.
An example manufacturing system may operate a 4-phase process to provide a service of producing and delivering a product: 1) Automatic Demand Sorting, 2) Production (robotic facility), 3) Assembly function, and 4) Transportation. Each of the processes relies on complex web of subprocesses that may be autonomous, depending on one another or sharing common sets of resources. All represented through a graph of lower-level interdependencies on internal multilevel components (example: ability to apply fault tolerance facilitation, efficient rules of triage and adequate right time testing) or external readiness to adapt to the impact external influencers (traffic options, readiness and availabilities, resources shortages or slowness in action).
Also, any of the processes may undergo perturbing effects in their respective domains: examples the traffic status, the weather conditions, the availability of material, the right choice of scheduling discipline, etc.
Any of the impacts and influences affects not only a component but also their perturbations extend to the full structure through direct, indirect of multiple orders which in turn will impact the contribution of each in time and space. It is practically impossible to account for such influences without a transformation to mathematical expression in perturbed graph theory that make it tractable covering the impact of influences of whatever size and criticality.
The production line is generally time sensitive and predictively should answer overarching objectives to cover:
To avoid continuous adjustments (facing production macro and micro incidents) cannot be faced and fulfilled by relying only on previous experience, and rules derived from static accounts, but can only be achieved through a full twining of the real world into full replication of mechanistic characteristics and influences into virtualized set of mathematics-based solution.
Such approach will both represents the perturbed graph but will also provide and environment to test changes that may happen at any level covered by the graph and obtain insight of building the rules that govern the status versus a host of possible scenarios.
Table 1, above, provides an example of process attributes (e.g., attributes 114 of
Other digital twin technologies replicate a simplified view of reality because the modeling method they use can't handle complexity. If for example, a manufacturer wants to create a digital twin of a process composed of 8 million variables, a data-driven digital twin will first exclude as many variables as possible, then use big data to evaluate the relationship between 2 variables at a time. Essentially, the prior-art twin pretends other variables are constant, when in fact they are not. To calibrate or train the model to represent reality, machine learning is applied to analyze thousands or even millions of data sets. This helps improve the predictive accuracy for a known scenario, but the simplification of relationships between variables has a compounding effect.
Due to rounding errors and missing values, the predictive error increases when the twin is used to evaluate scenarios that have never happened before. This makes it difficult to trust the analysis for highly critical decisions, especially for decisions that involve time dependencies or changes that lack historical data.
The number of known or suspected parameters that may influence manufacturing excellence covers multiple scales of variables that differ widely in nature, origin, evolution, and intensity. The only way to capture the contribution of such a diverse set of parameters is to unite them in one mathematical expression.
With the mechanistic approach used by example embodiments, the predictive accuracy of the model that reproduces new or unknown scenarios improves as the characteristics of the system are defined. Big data is replaced with PDEs that express a perturbed graph. PDEs allow for the inclusion of time sensitivity, environmental factors, and any other number of important parameters in the model and provide the rigor necessary to calculate future events exactly, without the involvement of randomness. Complex system dynamics and missing values make this level of model representativeness and reproducibility impossible to obtain using other methods of mathematical or statistical analysis.
By using a mathematical representation in which every variable alters according to a mathematical formula, X-ACT avoids model bias, data integrity and drift issues associated with alternative solutions. Through the use of PDE, X-ACT can calculate future events exactly. The determinism of analysis makes it possible to trust predictions about behaviors that are not represented by existing big data sets.
To support better design, engineering, assembly, and service decisions, example embodiments create a mechanistic digital twin that mathematically replicates internal and external interdependencies into a single model. The solution can then be used to experiment, identify, and evaluate options as if on a real production process, factory line, asset, or product without any limitations. This is crucial because it is the only way to discover the outcome of an event that has not yet happened, or the root cause of a phenomenon not yet identified to propose the optimal way for implementation.
Example embodiments can compute metrics and displays which details impact its value—including all dependencies at any past, present, or future point in time. The discovery process quantifies the risk, indicates how to minimize the risk, and provides options to improve products or operations using the associated algorithmic intelligence and rules. Over time the algorithmic intelligence produced by example embodiments becomes increasingly sophisticated and leads to wider coverage of known as well as the discovery of previously unknown causal relationships. The ability to discover new causal relationships is highly important as it is the only way to overcome the shortcomings of big data analysis.
Example embodiments may help manufacturers:
Using a what-if analysis, it becomes possible to develop an understanding of how a particular risk may evolve under a given set of circumstances. Developing algorithmic knowledge of various evolutionary manufacturing states makes it possible to build rules that can be preventively applied to help identify and/or take actions to avoid any set of circumstances that may lead to a critical outcome for a product or process.
Example embodiments can model all direct and indirect details relating to a manufacturing system. This allows users to discover at any moment in time, which element(s) cause a particular outcome. The approach is expandable to cover millions of connections. Any subset of connections may be the cause of certain risks or promoters of overall improvements—whether the involvement of a parameter is already proven, suspected, or currently unknown.
The dependability (sustainability) metric measures the performance of the end-to-end system against efficiency, productivity and economy benchmarks. This metric can be used to find the operational limits of the system being studied and consistently achieve the best balance between competing objectives. The dependability score of a manufacturing process that is achieving the ideal balance between efficiency, productivity and economy benchmarks is 100%. Comparisons between the current digital twin and any possible future system states proactively expose risks or opportunities for improvements.
For example, what if analysis can show how the dependability score may be impacted by a direct risk, i.e., malfunctioning assembly line components, or indirect risks, like supply chain disruptions or rising costs. Further, the analysis can help decision-makers evaluate potential enhancements. For instance, the dependability score can show whether investments in new automation technologies will achieve the desired cost-cutting and manufacturing capacity improvements.
The energy gradient measures the availability of energy to do the work for which the system was built. As both a quantitative as well as qualitative indication of a gain or loss in energy, the energy gradient is a useful target. When all of a system's energy is absorbed in productive activities, the energy gradient score is 100%. A decreasing energy gradient exposes system disorder that occurs when energy is unavailable to do work and prevents the system from performing at full capacity.
Tracking fluctuations in the energy gradient can help system stakeholders manage risk by identifying any scenarios that cause the system to underperform or improve. For example, a low energy gradient can help stakeholders identify and remove current or future bottlenecks caused by queuing problems that result from mismatched service and processing times. Further, the energy gradient can be used to quantify and plan to manage production risks posed by external factors such as a labor strike or shortages in raw materials.
The following describes an application of an example embodiment to emulate a multi-national automobile manufacturer. The approach may begin by identifying what the expected use of the emulation will be to establish the scope of the emulator. Once the scope has been identified, we begin identifying the processes used within the scope and begin deconstruction of those processes. This can be an iterative process as new processes can be identified, or processes combined to represent the company operations more accurately. As this is being done the driving factor (intensity) of work to be done by each process can be identified. This intensity represents the workload to be accomplished during an identified period, peak hour, peak day or what the expected emulation window will be.
With the processes identified, the next step is to define the process flow by creating a service process flow diagram for each process. The flow helps identify the underlying resources required for the successful emulation of the process. It additionally distributes the workload to those resources for the emulation of the process.
Once the process flows are defined and the resources to be used are identified the Implementation View can be constructed. Each resource has an action or actions that they perform in support of the process or processes. The amount of productive time and unproductive time that the resource action uses per item/unit being produced needs to be identified.
Using the identified resources, the locations and groupings can then be identified, and the locations placed on the map, groupings at each location are created and finally the resources are added to the group where they are to be identified. The action(s) identified for the resource are applied to the resource by answering the questions when creating the resource node.
Completing the implementation view requires the creation of the network. All resources and all groupings must be connected to the network so that there is a path between all resources and groups and is required for computation.
The final step is creating the connection between each activity in the processes with the resource and action of that resource. After this has been completed any messages defined in the flow can then be defined between activities and their associated actions. The emulation is now ready to compute and check for representativeness.
Examples below are provided for each step in the methodology above and how they are applied within the emulation created. We begin with establishing the scope and move on to process identification and deconstruction followed by defining process flow and actions. From there, we go to resource identification and creation of the implementation. Finishing with linking of the resource actions to the service activities and messages.
The process graph 1600 includes the following representative components:
As shown in
The process graph 1700 includes the following representative components:
In the flow defined movement is parallel on both sides of the line with (a) robots on one side of the line and (b) robots on the other side. They are passing messages to the control systems to monitor operation and progress. In the transportation preparation step logistics management is identified as well as transport from Germany to local staging, staging in France and staging in the US. This process may be replicated and modified for each model and each location giving us the ability to emulate multiple combinations or options.
As the process flow was developed resources 4 basic types were identified.
In addition to the resources, 6 locations were identified, three manufacturing locations and 3 shipping ports. Each manufacturing location has two groups defined, one for the manufacturing and the second for the staging of autos. The shipping ports have a single group defined.
The locations may be placed on the map with manufacturing and staging located in Luneburg, Germany, Tours, France and Monroe, Georgia, USA. The ports used by the manufacturer are the Port of Hamburg, Hamburg, Germany, Pot de Saint-Nazaire, Saint-Nazaire, France and Port of Charleston, Charleston, South Carolina, USA. The locations are plotted on a map, and groups may be added to each location.
The resource graph 1800 includes the following representative components:
The final steps are creating the connections between the service process graph (Flow) and the resource action (Group View). This is done by going to the activities in the flow and selecting the Add ACI button. From the lists identify the appropriate action and click add selected. This creates the link between the logical flow definition and the physical implementation.
Once this has been accomplished, if there are any Service to Service's defined in the flow it is necessary to define the message(s) that are to be passed. This consists of giving the message a name and selecting the producing activity and the consuming activity. The message is created in the list and shows the action that will be used. However, if there is more than one action defined for the activity make sure to select the correct one. This completes the emulator definition (i.e., system model) which is now ready for computation through various scenarios.
The teachings of all patents, published applications and references cited herein are incorporated by reference in their entirety.
While example embodiments have been particularly shown and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the embodiments encompassed by the appended claims.
This application claims the benefit of U.S. Provisional Application No. 63/382,809, filed on Nov. 8, 2022. The entire teachings of the above application are incorporated herein by reference.
Number | Date | Country | |
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63382809 | Nov 2022 | US |