Patent Publication Number: US-2019171654-A1

Title: Correlation-based design method, system and device

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a non-provisional of, and claims priority to and the benefit of U.S. Provisional Patent Application No. 62/546,318, filed Aug. 16, 2017. The entire contents of such application are hereby incorporated herein by reference. 
    
    
     COPYRIGHT NOTICE 
     A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. 
     BACKGROUND 
     Capturing, sharing, and reusing knowledge, such as the ability to accurately and consistently predict outcomes given a specific set of input factors, is a major challenge in science and engineering. Human brains are excellent at developing knowledge, but they are poor containers of knowledge because: (a) of the vagaries associated with memory, (b) knowledge is insufficiently indexed and organized so it cannot be sought, shared and reused, and, (c) as a result, it is largely inaccessible to others and remains isolated in individuals. Organizations pay for the same insights and, usually unknowingly, more than once miss the opportunity to identify and examine inconsistencies across studies (evidence), among subject matter experts (SMEs). In general, SMEs without access to prior knowledge, will unknowingly regenerate prior knowledge at additional expense. In addition to the cost of the re-acquisition of prior knowledge, there is also the cost of delaying a solution to the problem or a desired discovery. Between what SMEs think and what the evidence supports, organizations are much less efficient and effective in developing their products and processes to achieve objectives related to quality, productivity, compliance, and costs. 
     For example, a chemical engineer may design a product prototype over six months of research and development (R&amp;D) work. During the R&amp;D, the engineer may use various segregated documentation tools and resources, such as spreadsheets, word processing software and physical lab notebooks. Because of a change in focus, the employer-manufacturer may place the project on hold for a year. By the time the project resumes, the engineer may have ended the employment with the manufacturer. One and a half years later, the manufacturer may assign a new chemical engineer to resume the project. Referring to the departed engineer&#39;s various notes, documents and files, the new engineer must try to rediscover the learnings, data and insights of the departed engineer. This rediscovery effort (sometimes referred to as reinventing) is inefficient and can result in a waste of valuable human resources and time slowing innovation and the benefits that can be derived from these efforts. Also, this rediscovery process does not reliably or accurately enable the full recovery of prior learnings and information. 
     The foregoing background describes some, but not necessarily all, of the problems, disadvantages and shortcomings related to the known research, development and design tools and methods. 
     SUMMARY 
     In an embodiment, the method includes receiving one or more access requests corresponding to a plurality of targeted outcomes of an improved version of a prior operation for an offering. The offering includes one of a product and a service. In response to the one or more access requests, the method includes accessing a first pool of historical control factors, accessing a second pool of historical outcome factors, and generating a first graphical correlation representation of the historical control factors, the historical outcome factors, and the targeted outcome factors. Each of the historical control factors has been previously implemented in the prior operation. Each of the historical outcome factors resulted from one of the historical control factors. The first graphical correlation representation indicates a first comparison of the historical outcome factors to the targeted outcome factors. The method also includes receiving a plurality of change requests. Each of the change requests is associated with a different design scenario. In response to each of the change requests, the method includes changing at least one of the historical control factors, and updating at least one of the historical outcome factors. The at least one changed historical control factor and the at least one updated historical outcome factor include one of the different design scenarios. The method includes generating a second graphical correlation representation of a plurality of the design scenarios and the targeted outcome factors. The second graphical correlation representation indicates a second comparison among the updated historical outcome factors of one of the design scenarios, the updated historical outcome factors of another one of the design scenarios, and the targeted outcome factors. The method includes receiving a selection request corresponding to a selection of one of the design scenarios, and designating the selected design scenario for implementation in the improved version of the prior operation. 
     In an embodiment, the method includes receiving one or more access requests corresponding to a plurality of targeted outcomes of an improved version of a prior operation for an offering. The offering includes one of a product and a service. In response to the one or more access requests, the method includes accessing a first pool of historical control factors, accessing a second pool of historical outcome factors, and generating a first graphical correlation representation of the historical control factors, the historical outcome factors, and the targeted outcome factors. Each of the historical control factors has been previously implemented in the prior operation. Each of the historical outcome factors resulted from one of the historical control factors. The first graphical correlation representation indicates a first comparison of the historical outcome factors to the targeted outcome factors. 
     In an embodiment, one or more data storage devices include instructions that, when executed by a processor, perform a plurality of steps of a method. The method includes receiving one or more access requests corresponding to a plurality of targeted outcomes of an improved version of a prior operation for an offering. The offering includes one of a product and a service. In response to the one or more access requests, the method includes accessing a first pool of historical control factors, accessing a second pool of historical outcome factors, and generating a first graphical correlation representation of the historical control factors, the historical outcome factors, and the targeted outcome factors. Each of the historical control factors has been previously implemented in the prior operation. Each of the historical outcome factors resulted from one of the historical control factors. The first graphical correlation representation indicates a first comparison of the historical outcome factors to the targeted outcome factors. 
     An advantage that may be realized in the practice of some disclosed embodiments of the method, system or device is that historical data of different operation implementations may be correlated to determine new input control factors for an enhanced design scenario that will allow the production or operation process to proceed with an enhanced level of measured outcome factors that are critical to the quality of the operation. 
     Additional features and advantages of the present disclosure are described in, and will be apparent from, the following Brief Description of the Drawings and Detailed Description. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic, block diagram illustrating an embodiment of a system for implementing a operation. 
         FIG. 2  is a schematic, block diagram illustrating an embodiment of a system for implementing a correlation-based design method. 
         FIGS. 3A-3B  are flowcharts illustrating an embodiment of a correlation-based design method. 
         FIGS. 4A-4I  are schematic, block diagrams illustrating embodiments of graphical user interfaces, e.g., for depicting graphical correlation representations. 
         FIGS. 5-25  are schematic, block diagrams illustrating embodiments of graphical user interfaces, e.g., for managing and administrating system for implementing a operation. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure relates to, in one aspect, techniques that facilitate generating an optimized or improved version of a prior operation. Examples of an operation include: an operation for producing or performing an offering, such as a product or service; a manufacturing process or procedure for manufacturing an offering, such as a product or service; a production process; logistics; R&amp;D; design; and any other process or technological procedure that includes a number of steps that are performed to produce or cause an outcome. Such an operation may be carried out in factories, manufacturing plants, R&amp;D centers, laboratories, warehouses, shipping depots, airports, and other facilities and applications in which numerous computer-based devices are used to implement the steps required to produce or perform an offering, such as a product or service offering. Advantageously, the techniques disclosed herein make use of enhanced database, correlation-based architectures, and/or machine learning to enable improvements in computing technologies and the application of computing technologies to the field of operation optimization for offering design. 
     Various challenges in the operation optimization field have been identified in the background above. For instance, a first challenge is in obtaining, holding and exploiting a comprehensive understanding of the factors that impact a process, and learning how those factors may interact with each other and affect the results of the process. These results can include both the immediate responses of a particular step and the across-production-process responses. For example, conventional techniques fail to fully account for the interaction between different factors. 
     In addition, another challenge is to incorporate and learn from the relationships that are generated across different formal and informal experiments and observations across multiple people over time. Further, information from different experiments may conflict, and those conflicts are not readily resolved, leading to a lack of understanding. For example, conventional techniques are limited to relationships that are present in a single experiment, and fail to correlate information across numerous experiments. These conflicts usually go unnoticed but, if identified and eventually understood, could lead to exploitable insights instead of confusion. 
     In one example of the disclosed system, the combination of previous observations (e.g., the atomic unit of knowledge), factor information, statistical significance and the prediction models from past experiments are used as prior knowledge which is fed into a method, system or device having, e.g., machine learning systems that perform a correlation-based technique to identify which factors at which levels are most promising in maximizing the results that are critical to quality (CTQ). The outputs of the correlation-based technique may then be fed into an operation, resulting in an improvement to the process for creating the product or service offering. 
     One advantage of the disclosed system is that if a problem arises that is related to a particular CTQ or a particular step in production, the system can allow a lookup of prior research into that problem, so that the prior research can be considered. Because the disclosed system provides availability and access to prior knowledge generated by others, even if they are not available or no longer with the entity, engineers and scientists may use this historical prior knowledge to compare their thinking (e.g., their theories) against those of others, and against prior collected evidence, or collect new evidence to challenge their thinking, leading to improved efficiency and effectiveness. Such a system can be useful for engineers and scientists who tend to work alone and often start only with their own thinking and experience. This system expands the range of experience and thinking across their peers, past and present and allows them to work collaboratively over time, comparing their experience with those of others and generating new experience (e.g., collecting evidence) where none exists or to verify/validate previously collected evidence and the relationships they support. This expansion provides a fast and comprehensive start and ongoing input into research and problem solving leading to faster and better solutions and discoveries. 
       FIG. 1  is a schematic, block diagram illustrating an embodiment of a system for implementing an operation involving a production process associated with a product offering  15 . It should be appreciated, however, that the operation can involve a service process associated with a service offering  15 . As depicted in the embodiment of  FIG. 1 , a process optimization system  10  may be coupled to a process management system  11 . In one example, the process management system  11  is used to control an operations process that is implemented using various production devices, such as production devices  12 - 1 - 12 - 4 , referred to collectively as production devices  12 , and the process management system  11  may implement the improved process design generated by the process optimization system  10 . During, before, or after operations of the production devices  12 , one or more measurement devices  13  may be used to measure performance results or performance outputs of the process. A plurality of key performance indicators (KPI), critical to quality metrics (CTQ) and historical outcomes include or are based on these measured results or outputs of the process. In another embodiment, instead of dedicated measurement devices  13 , the product devices  12  can perform any needed measurements. 
     By way of explanation, during a run of an operation or production process, each of the production devices  12  may be controlled by one or more control factors, such as control factors X 1 -X 5 . In the embodiment of  FIG. 1 , production device  12 - 1  has control factor X 1 , production device  12 - 2  has control factor X 2 , production device  12 - 3  has control factor X 3 , and production device  12 - 4  has control factors X 4 , X 5 . Conceptually, these control factors are analogous to knobs that can control the inputs to the production process, and a goal of the disclosed system is to determine the settings that lead to improvements of the process. These inputs could be process parameters such as pressure, temperature, volume, concentration, recipe (e.g., chemical components), size, destination, weight, etc. 
     The process optimization system  10 , described in further detail below with respect to  FIG. 2 , may be indirectly or directly connected to the process management system  11 . An example of an indirect connection is that output of the process optimization system  10  is entered by into the process management system  11  on an ad hoc basis. An example of a direct connection is that output of the process optimization system  10  and input therefrom automatically flow between the systems over a computer network, advantageously facilitating, for example, machine learning or artificial intelligence operations of the production process using the techniques set forth herein. 
       FIG. 2  is a schematic, block diagram illustrating an embodiment of the process optimization system  10  for implementing a correlation-based design method. In the embodiment of  FIG. 2 , the process optimization system may include one or more processors  22 , a memory  23 , a storage  24 , a database  25 , one or more programs  26 , an input/output  27 , and a display  28 . The process optimization system may communicate using a network  29 . The process optimization system  10  may include one or more co-located or distributed computing systems having the one or more processors  22 , such as computer servers, which may be locally hosted or hosted on a cloud computing platform. The memory  23  and storage  24  may be shared and/or local. The memory  23  may include random access memory for running software programs to implement the methods set forth herein. The storage  24  may be used to store executable programs having instructions for implementing the methods set forth herein. The database  25  may be a relational database, and may be accessible by all the one or more processors  22 , and may be used to store pools of historical information derived from the production processes described above with respect to  FIG. 1 . 
     The one or more programs  26  may execute on some or all of the one or more processors  22 , and may have access to the memory  23 , storage  24 , database  25 , input/output  27 , display  28  and network  29 . For example, the one or more programs  26  may facilitate the process optimization system  10  communicating with the process operations system  11  ( FIG. 1 ). 
       FIGS. 3A-3B  are flowcharts illustrating an embodiment of a correlation-based design method  30 , e.g., for improving the results of a process. In one embodiment, the method  30  is executed by the one or more programs  26  of the process optimization system  10  ( FIG. 2 ). 
     As depicted in the embodiment of  FIG. 3A , the method  30  at block  31  receives one or more access requests corresponding to a plurality of targeted outcomes of an improved version of a prior operation or prior production process for an offering. In such a case, the offering may include a product offering, a service offering, or some combination thereof. 
     In an embodiment, in response to the one or more access requests, the method  30  at block  32  accesses a first pool of historical control factors. For example, the one or more programs  26  ( FIG. 2 ) executing the method  30  at block  32  may access the first pool of historical control factors from the database  25  or the storage  24  ( FIG. 2 ). In one embodiment, each of the historical control factors has been previously implemented in the prior production process managed by process management system  11  ( FIG. 1 ), and may include, for example, the control factors X 1 -X 5  related to the production devices  12 - 1 - 12 - 4  ( FIG. 1 ). Advantageously, the historical control factors are used to index the various formal and informal experiments, and may be correlated by comparing the different control factors used in each experiment and determining which control factors are common across each of the experiments. For example, a first historical experiment could include historical data such as temperature and pressure of a specific production device  12 , and a second historical experiment could include historical data such as temperature and recipe that the specific production device  12  was operated at. In such a case, a comparison of the two experiments would reveal that a common control factor of temperature applied to both of these experiments, allowing the method to directly correlate and compare these experiments. In one example, an analysis of the production process may be performed in order to determine what control factors are most relevant, e.g., to the goal of yielding improved outcome factors. In another example, a plurality of control factors may be simply collected, and analyzed later to determine which are most relevant to yield an improvement to the process. 
     Continuing, in one embodiment, the method  30  at block  33  accesses a second pool of historical outcome factors. Similar to the first pool, the one or more programs  26  ( FIG. 2 ) executing the method  30  at block  33  may access the second pool from the database  25  or the storage  24  ( FIG. 2 ). In one embodiment, each of the historical outcome factors resulted from one of the historical control factors, e.g., an implementation of the prior operation or prior production managed by process management system  11  ( FIG. 1 ) and using the control factors X 1 -X 5  related to the production devices  12 - 1 - 12 - 4  ( FIG. 1 ), may be measured by the measurement device  13  ( FIG. 1 ) to have historical outcome factors such as CTQ 1  and/or CTQ 2 . One advantage of separating the control factors from the outcome factors is that different experiments can be compared and correlated by reference to common control and outcome factors, so that experiments with improvements in control factors can be determined. 
     In the embodiment of  FIG. 3A , the method  30  at block  34  may generate a first graphical correlation representation of the historical control factors, the historical outcome factors, and the targeted outcome factors. For instance, the first graphical correlation representation may be displayed in a graphical user interface  40  (see  FIG. 4A ). Advantageously, the first graphical correlation representation indicates a first comparison of the historical outcome factors to the targeted outcome factors. For example, the impact that a particular control factor has on a particular outcome factor is graphically correlated and displayed so that the system benefits from knowledge of the prior runs of an operation or production process, e.g., by displaying which control factors should be changed to gain the most improvements in the outcome factors. 
     Further, the method  30  at block  35  may receive a plurality of change requests. For instance, a change request for outcome factors  500 A ( FIG. 4G ) or a change request for outcome factors  500 B ( FIG. 4H ) may be received. Each of the change requests may be associated with a different design scenario that is considered in order to optimize the production process. In operation, the system may receive many change requests, such as hundreds or thousands of change requests, allowing the system to evaluate the results of each such request to succeed at finding a scenario with superior results. 
     Continuing with the embodiment of  FIG. 3B , in response to each of the change requests, the method  30  at block  36  may change at least one of the historical control factors, and update at least one of the historical outcome factors. For example, such changes and updates are depicted in  FIGS. 4G-4H . The at least one changed historical control factor and the at least one updated historical outcome factor may include one of the different design scenarios. One benefit of correlating changes in control factors to updated outcome factors is that the different design scenarios can be evaluated against each other, e.g., to determine which offers the most improvement over a plan of record. 
     Next, the method  30  at block  37  may generate a second graphical correlation representation of a plurality of the design scenarios and the targeted outcome factors. For instance, the second graphical correlation representation may be displayed in a graphical user interface  41  (see  FIG. 4E ). The second graphical correlation representation can indicate a second comparison among the updated historical outcome factors of one of the design scenarios, the updated historical outcome factors of another one of the design scenarios, and the targeted outcome factors. The second graphical correlation representation advantageously builds upon the historical data to produce and visualize new design scenarios that can have different outcomes than any of the historical scenarios. For example, when a new combination of control factors is selected that is different than any historical data, such a scenario can be evaluated to predictively determine if the outcomes are at or near a target outcome. A machine learning technique, such as iteratively trying numerous combinations of previous control factors and then narrowing in on combinations that show improved outcomes may be employed to determine ideal control factors to reach a target. 
     Continuing with the embodiment of  FIG. 3B , the method  30  at block  38  may receive a selection request corresponding to a selection of one of the design scenarios. Then, the method  30  at block  39  may designate the selected design scenario for implementation in the improved version of the prior production process. For example, the scenario may be selected for implementation by process management system  11  ( FIG. 1 ) using the control factors that are part of the selected scenario by inputting those control factors into the production devices  12 - 1 - 12 - 4  ( FIG. 1 ), in order to achieve the targeted outcome factors. In such a case, whether or not the targeted outcome factors are achieved may be measured by the measurement device  13  ( FIG. 1 ). Advantageously, after implementation of the production process, all data from the implemented production run is then added to the pool of historical data, allowing the historical pool to grow. In one usage model, dozens or hundreds of small experiments can be performed iteratively using the foregoing method  30 . As each production run completes, the next iteration of the method  30  will have access to an increased pool of historical data that can then be used yield an improved set of control factors for the next run. 
     In another example, the method  30  could be performed across a series of production domains controlled by different entities. For example, numerous entities may own a set of production devices that perform similar tasks, such as, for example, the production of pharmaceutical products, such as tablets and capsules, or the fabrication of semiconductor devices. Each of these example processes include numerous production devices, and learnings from one entity can be captured into a historical pool that is shared with the other entities. In such a way, global knowledge of the performance of the production machines and how that relates to the interplay between various control factors leading to outcome factors can be maintained to the benefit of all entities. In one example, data analytics techniques, such as big data analytics, machine learning, artificial intelligence, and the like, may be employed to determine correlations between the historical pools of data to yield graphical correlation representations, e.g., to zero in on the factors that can be tuned to best improve the process. 
       FIGS. 4A-4J  are schematic, block diagrams illustrating embodiments of graphical user interfaces, e.g., for depicting graphical correlation representations. 
       FIG. 4A  depicts a graphical user interface  40  for depicting graphical correlation representations. As depicted in  FIG. 4A , the graphical user interface  40  includes a visualization element  100 , control factor element  200 , selection filter element  300 , data filter element  400 , outcome factor element  500 , and file element  600 . Given a historical pool of data, the filter elements allow for only a subset of the data to be used in a given run of the method  30 . For example, the data may be filtered by the date the experiment was run, the production process type, etc., allowing an easy way to start broadly and then narrow in on more likely correlations that could yield insights into how to achieve a superior and improved production process. In another embodiment, manual filters are replaced with automatic filtering that automatically presents the results of the correlation-based method  30  for different models based on different automatically set filtering levels. 
       FIG. 4B  depicts an exemplary legend to succinctly illustrate the potential use of different lines in a correlation visualization, e.g., the correlation visualization element  100  ( FIG. 4A ). The following line types are depicted in  FIG. 4B : 
     A strong relationship line  120 . 
     A weak relationship line  122 . 
     A very inconsistent line  124 . 
     A somewhat inconsistent line  126 . 
     A strong theory line  130 . 
     A weak theory line  132 . 
     An interaction line  140 . 
       FIG. 4C  depicts an enlarged view of the graphical user interface  40  of  FIG. 4A . As depicted in  FIG. 4C , historical control factors X 1 -X 14  are displayed in the control factor element  200 , and historical outcome factors CTQ 1 -CTQ 7  and Y 1 -Y 5  are displayed in the outcome factor element  500 . The outcome factors CTQ 1 -CTQ 7  are final process outcome factors, and the outcome factors Y 1 -Y 6  are intermediate outcome factors, outcomes for particular steps within the process. The visualization element  100  has at the bottom a step indicator element  105 , which indicates which step each factor is related to. In the embodiment of  FIG. 4A , on the left hand side, a selection filter allows selection of steps S 1 -S 5 . In another embodiment, the step labels may be highlighted responsive to a mouse hovering over any region of visualization element  100  related to that step. 
     The visualization element  100  depicts a series of dots  110  and lines (e.g., lines  120 ,  122 ,  124 ,  126 ,  130 ,  132 ,  140  as described in the legend of  FIG. 4B ) that extend from the dots. The dots  110  indicate which of the control factors X 1 -X 7  displayed in the control factor element  200  has significance at each of the steps S 1 -S 5 . Specifically, vertical positions of the dots  110  aligns with the labels in the control factor element  200  to indicate the control factor, e.g., control factors X 1 -X 7 , and the horizontal position of the dots  110  aligns with the step indicator element  105  to indicate the step, e.g., steps S 1 -S 5 . 
     The lines (e.g., line  120 ) of the visualization element  100  indicate a relationship between the control factors X 1 -X 14  and the outcome factors CTQ 1 -CTQ 7  and indirect outcome factors Y 1 -Y 6 . A line (e.g., line  120 ) extending from a specific dot  110  corresponding to a specific control factor to another specific dot  110  that is aligned with a specific indirect outcome factor (either at the same or at a different step, because there may be interactions across steps) indicates that the specific control factor has been found to historically impact the specific indirect outcome factor at that step. In turn, a line (e.g., line  120 ) may extend from the other specific dot corresponding to the specific indirect outcome factor to a direct outcome factor, indicating a relationship between the indirect and direct outcome factor, e.g., indicating which factors to focus on to achieve improvements in a process. 
     In a similar manner, a line (e.g., line  120 ) extending from a specific dot  110  corresponding to a specific control factor to a specific direct outcome factor indicates a relationship between those factors at that step. 
     For example, the visualization element  100  of  FIG. 4C  indicates that at step S 1 : there is a theory of a strong relationship between control factor X 4  and outcome factor CTQ 4 ; there is no known relationships regarding control factor X 6 , which is relevant at step S 1 ; there is a strong relationship between control factor X 7  and outcome factor Y 2 ; there is a strong relationship between control factor X 8  and outcome factor CTQ 7 ; and there is a theory of a strong relationship between control factor X 8  and outcome factor CTQ 3 . 
     In addition, the visualization element  100  of  FIG. 4C  indicates that at step S 2 : there is a strong interaction relationship involving factors X 4  and X 6  affecting outcome factor Y 1 . 
     Next, the visualization element  100  of  FIG. 4C  indicates that at step S 3 : there is a strong relationship between control factor X 1  and outcome factor CTQ 1 ; there is a weak relationship between control factor X 2  and outcome factor CTQ 2 ; and there is a strong relationship between control factor X 3  and outcome factor CTQ 2 . 
     Next, the visualization element  100  of  FIG. 4C  indicates that at step S 4 : there is no known relationships regarding control factor X 5 , which is relevant at step S 3 ; there is a weak relationship between control factor X 9  and outcome factor CTQ 7 ; and there is no known relationships regarding control factor X 14 , which is relevant at step S 3 , and there are no theories relating X 5 , X 9  or X 14  with any of the outcome factors. 
     And finally, the visualization element  100  of  FIG. 4C  indicates that at step S 5 : there is no known relationships regarding control factor X 10 , which is relevant at step S 5 ; there is a weak relationship between control factor X 11  and outcome factor CTQ 7 ; there is an inconsistent theorized relation, meaning that different SMEs hold different theories of this relation, between control factor X 11  and outcome factor CTQ 3 ; there is a strong relationship between control factor X 12  and outcome factor CTQ 7 ; there is a somewhat inconsistent (note the less dashed line  126  as described in the legend of  FIG. 4B ) theorized relationship between control factor X 12  and outcome factor CTQ 6 ; and there is a weak relationship between control factor X 13  and outcome factor CTQ 7 . 
     The visualization element  100  will display different relationships of the historical pools of data based on how the filtering selections are set. Although  FIG. 4C  focuses on a graphical view, the same underlying data techniques can be used to automatically cycle through different subsets of the pools of historical data to reveal different patterns and combinations of the factors. 
       FIG. 4D  depicts an enlarged view of the control factor element  200 . The control factors X 1 -X 5 , X 7 -X 8  and X 10 -X 14  are indicated by a continuous factor indicator  206 , that may take on values in a continues range, e.g., between 5 and 50. These continuous factor indicator  206  include normalized factor range indicators  201 , which show a horizontal range bounded by vertical lines, within the range from left to right of the control factor element  200 . In such a case, each of the factors may be normalized to fit in a normalized range indicator  204 , e.g., range of 0-100%. In another case, rather than a range, a fixed level factor indicator  205  indicates that a factor has a fixed value, graphically positioned in a normalized range across the ranges of all input factors depicted in the illustration. On the other hand, control factors X 6  and X 9  are categorical factor indicators  202  with width proportional to the number of occurrences. Categorical factor indicators  202  are used when a factor can have one of a few values, rather than a continuous range. In one example, a factor group indicator  203  may be clicked to highlight and select related groups. In a further example, the historical data loaded may be filtered to only show the process of record (PoR) by using the PoR range selector  208 . 
       FIG. 4E  depicts a graphical user interface  41 , after a specific range R 1  of control factor X 13  has been selected on the graphical user interface  40  of  FIG. 4C . In such a case, historical data that does not include production processes in which control factor X 13  is in range R 1  is excluded, and all remaining data is correlated for display. Ranges of other factors that are not associated with the same operations represented by R 1  are grey, rather than black. Those that correspond to the operations selected by R 1  are highlighted in black. A range indicator element  207  then depicts the ranges in the (low, high) format to the left of the control factor element  200 A. For instance, range R 1  corresponds to control factor X 13  have a value between  10  and  20 , in whatever units are applicable to that control factor. 
     Notably, visualization element  100 A depicts only those relationships that include the control factor X 13  within range R 1 . The relationships depicted are as follows: at step S 1  there is a strong relationship between control factor X 8  and outcome factor CTQ 7 ; at step S 1  there is a theory of a strong relationship between control factor X 8  and outcome factor CTQ 3 ; at step S 5  there is a weak relationship between control factor X 11  and outcome factor CTQ 7 ; at step S 5  there is an inconsistent theorized relation between control factor X 11  and outcome factor CTQ 3 ; and at step S 5  there is a weak relationship between control factor X 13  and outcome factor CTQ 7 . In one example, a user clicking on the range R 1  will cause the method  30  to select the relevant pools of historical data that include control factor X 13  in range R 1 , correlated the data, and display the correlated data. In one example, all the matching historical data that has control factor X 13  in range R 1  will be displayed in the visualization element  100 A. One further enhancement of the present technique is that in addition to displaying all the matching data, the matching data can be correlated through the use of averaging or statistical analysis and the averages can be displayed. Advantageously, the method  30  can define a metric that focuses on critical outcome factors, and a weighted average of the matching historical data can be performed using this metric, to reveal another correlated view of the impact of various control factors to outcome factors, and this view can be displayed. 
       FIG. 4F  depicts the outcome factor element  500  of  FIG. 4C . In the embodiment of  FIG. 4F , the historical outcome factor CTQ 7  is a factor that requires a value higher than a certain value as indicated by the medium weight right pointing arrow. The certain value that must be achieved is indicated by a specification limit indicator  501 . Next, the historical outcome factor CTQ 6  is a factor that requires a value lower than a certain value as indicated by the medium weight left pointing arrow. Further, the historical outcome factor CTQ 5  is a very important factor that requires a value higher than a certain value as indicated by the heavy weight right pointing arrow. In addition, the historical outcome factor CTQ 4  is a less important factor that requires a value higher than a certain value as indicated by the light weight right pointing arrow. Next, the historical outcome factor CTQ 3  is a factor that require a response within a range, as indicated by the inward pointing pair of outer arrows and the two specification limit indicators. Next, the historical outcome factor CTQ 2  is a factor that requires a value lower than a certain value as indicated by the medium weight left pointing arrow. And, finally, the historical outcome factor CTQ 1  is a factor that requires a response within a range, as indicated by the inward pointing pair of outer arrows and the two specification limit indicators, but is less important, as indicated by the light weight lines and arrows. 
       FIG. 4G  depicts the selection of targeted outcome factors in the output factor element  500 A of graphical user interface  41  ( FIG. 4E ). As depicted in  FIG. 4G , a user may draft the left or right side of dark rectangles in the output factor element  500 A for each of the outcome factors, to select the desired targets. Upon selection of the desired targets, the required input factors will be correlated as depicted in  FIG. 4E  using the method  30  of  FIG. 3A . Upon the selection of the target ranges, the method  30  performs a correlation of the historical data to find the matching historical data that is within the targeted outcome ranges. This data is then correlated. In one example, a user selecting a range will cause the method  30  to select the relevant pools of historical data that include those outcome ranges, correlate the data, and display the correlated data. In another example, all the matching historical data that has the desired target outcome will be displayed in the visualization element  100 A. The matching data can then be correlated through the use of averaging or statistical analysis and the averages can be displayed. Advantageously, the method  30  can define a metric that focuses on critical control factors, and a weighted average of the matching historical data can be performed using this metric, to reveal another correlated view of the impact of various outcome factors to control factors, and this view can be displayed. 
       FIG. 4H  illustrates another selection of targeted outcome factors in an output factor element  500 B. Upon such a selection, a different set of required input factors will be determined by the method  30  of  FIG. 3A . For example, the targeted outcome factors may be selected based on quality control metrics, to target an improvement in yield, quality, strength, performance, or any other such metric. 
       FIG. 4I  illustrates a graphical user interface  42  for correlating from a historical outcome control factor CTQ 7  to show the factors and steps that have backwards dependencies to that control factor. In this example, the historical pool of data is filtered to only include data that has results that measure CTQ 7 , and all other results are not displayed. The resulting matching data can then be further correlated as described above. In one example, all the matching historical data that has CTQ 7  will be displayed in the visualization element  100 A. The matching data can then be correlated through the use of averaging or statistical analysis and the averages can be displayed. Advantageously, the method  30  can define a metric that focuses on critical control factors, and a weighted average of the matching historical data can be performed using this metric, to reveal another correlated view of the impact on CTQ 7  of various control factors, and this vicw can be displayed. 
       FIG. 5-25  are schematic, block diagrams illustrating embodiments of graphical user interfaces, e.g., for managing and administrating system for implementing an operation. These embodiments may be used to manage the pools of historical data, and manage the results of various experiments, in order to present a set of improved design scenarios that can be implemented or stored for future use. 
       FIG. 5  depicts a graphical user interface  500  of a home screen. The home button is found throughout the application and returns the user to this screen. Inputs are in the form of data and other sorts of files, as well as theories, where users generate a visual representation of expected relationships. This information is saved in The system&#39;s database (currently supported on the Microsoft SQL Server platform). Various reports can be found via the CPP and Search/Reporting (under Query/Analyze/Download) functions which can be navigated to from the home GUI. The Recipe Similarity function, which is discussed below, is also available. Various administration functions are under the Admin tab. Files can be organized by projects. Managing nomenclature (which is described below) is accessible from this screen, as well as various other settings. 
       FIG. 6  depicts a graphical user interface  600  for uploading files. Users can choose to upload individual files or an entire directory (folder) of files. Here we demonstrate loading individual files. 
       FIG. 7  depicts a graphical user interface  700 . In Windows Explorer or the Macintosh Finder, select the file(s) to be uploaded and choose “Choose.” Any file (XLSX, PNG, PDF, JPG, etc.) can be uploaded but, in an embodiment, it is easiest to accompany non-JMP files with JMP files that have the parameter (factor, response) details. JMP® is a suite of computer programs for statistical analysis that is commercially available through SAS Institute. In an embodiment, the one or more programs  26  ( FIG. 2 ) include or are programmed to interface with part or all of the code, logic, configuration, methodologies, specifications, data structures or other features of such JMP® suite of computer programs, all of which are hereby incorporated herein by reference. Any other suitable analytical software program package may be used to implement these statistical features, including, but not limited to Minitab or Tableau. 
       FIG. 8  depicts a graphical user interface  800 . The system looks at the files to be loaded and compares the associated factor and response names to the current set in its database. Parameters known to The system will automatically be assigned a domain. 
       FIG. 9  depicts a graphical user interface  900 . Right-click on the unassigned parameters to “Change Domain.” In an embodiment, domain refers to a process Step (such as S 1 , S 2 , etc.). In another embodiment, e.g., in the case of a product, domain refers to a particular sub-assembly of components (e.g., in an ink jet printer, a sub-assembly for the paper transport, a sub-assembly for the ink dispensing, a sub-assembly for power management). Sub-assemblies may be made up of component sub-assemblies and individual components. 
       FIG. 10  depicts a graphical user interface  1000 . A dialogue (appears and the domain (a production step or a product sub-assembly) can be chosen. Click “OK.” 
       FIG. 11  depicts a graphical user interface  1100 . Once assigned the Parameter information is updated. Then simply click “Upload” to upload the information to The system&#39;s database. 
       FIG. 12  depicts a graphical user interface  1200 . Here various files are being uploaded including non-JMP files. 
       FIG. 13  depicts a graphical user interface  1300 . Select the parameters in the JMP file and copy them to the checked-off non-JMP files (instead of entering the parameters for non-JMP files manually). 
       FIG. 14  depicts a graphical user interface  1400 , which shows a group of files are selected to be uploaded en masse. 
       FIG. 15  depicts a graphical user interface  1500 . This function will allow analysts to enter a factor combination and get a list of observations, with CTQ values and other information. With data submitted from various individuals, it is fair to assume that there will be inconsistencies in naming parameters as shown in  FIG. 15 . 
       FIG. 16  depicts a graphical user interface  1600 , in which two factors that were uploaded from separate files with different names; one Temperature the other Temp. Imported data from different people over time is bound to have varying nomenclature. This needs to be fixed. To do so, select the factor names of interest (Temperature and Temp) then click Rename. 
       FIG. 17  depicts a graphical user interface  1700 , in which the user can select from the variant names or enter a new name altogether. Clicking OK will change the corresponding name references in the system. Note that the original names are still available, and an audit trail may be maintained within the system to track these or other changes. 
       FIG. 18  depicts a graphical user interface  1800  for renaming the List of Factors and consolidating. 
       FIG. 19  depicts a graphical user interface  1900  in which various reports can be found from the reporting button. In one embodiment, a report may answer a question such as: “who else has looked at certain CTQs or certain factors at certain steps” in order to provide a starting point for further research that takes advantage of prior knowledge, including insights available from the work of others as stored in the historical pools of information in the system  10  ( FIG. 2 ). 
       FIG. 20  depicts a graphical user interface  2000  in which analysts can select factors and responses from various production steps or product components and then click search. 
       FIG. 21  depicts a graphical user interface  2100 . The files associated with the selection are identified on the left and the statistical p-values from the models in these files are shown. Each vertical block represents a factor (Concentration, Line, Pressure, etc.) and on the X axis a corresponding CTQ (Y 1 , Y 2 , etc.) Those lines that are below the red dashed line indicate significant relationships at the 0.05% level. 
       FIG. 22  depicts a graphical user interface  2200 . Logworth (defined as the base-10 logarithm of the p-value) is easier to see and is a relatively new practice in statistical methods. Here, those lines above the dashed line are significant at the 5% level. 
       FIG. 23  depicts a graphical user interface  2300  depicting Set 01 as it was downloaded (on the left) and the original on the right. Note that now, on the left, the first column is “Temp” whereas originally it was “Temperature.” As noted in the discussion above with respect to  FIG. 16 , these different names for the same parameter can be combined to facilitate comparison of different datasets. 
       FIG. 24  depicts a graphical user interface  2400 , which helps analysts understand the history of the relationships among variables. In the top-right we have a plot of logworth values. None of the experiments explained Y 1 &#39;s variability (blue line). Analysts can look at the factors and ranges used in these studies to know not to repeat what didn&#39;t explain Y 1 &#39;s variability in the past. For Y 2 , (red line), Set 02 through Set 05 explained a lot of variability but not Set 06 and Set 07. Here analysts can download these files, compare the factors and ranges to understand which factors explain the most. These results correspond to those show with the p-values and Log-Worth values above. 
       FIG. 25  depicts a graphical user interface  2500 , which depicts yet another view comparing the Root Mean Square Error (RMSE) which is an indication of unexplained variation, for the responses. Y 2 &#39;s unexplained variation dropped noticeably beyond Set 07. This means that they have demonstrably improved their knowledge by explaining more of the operation&#39;s behavior with respect to Y 2 . For Y 1  they are all over the map. They can examine Set 03 to see what factors and ranges lead to the most explanatory power for Y 1  and use that knowledge in subsequent experimentation. 
     Turning again to  FIGS. 1-3B , depending upon the embodiment, network  29  of  FIG. 2  can include one or more of the following: a wired network, a wireless network, an LAN, an extranet, an intranet, a WAN (including, but not limited to, the Internet), a virtual private network (“VPN”), an interconnected data path across which multiple devices may communicate, a peer-to-peer network, a telephone network, portions of a telecommunications network for sending data through a variety of different communication protocols, a Bluetooth® communication network, a radio frequency (“RF”) data communication network, an infrared (“IR”) data communication network, a satellite communication network or a cellular communication network for sending and receiving data through short messaging service (“SMS”), multimedia messaging service (“MMS”), hypertext transfer protocol (“HTTP”), direct data connection, Wireless Application Protocol (“WAP”), email or any other suitable message transfer service or format. 
     In an embodiment, the one or more processors  22  can include a data processor or a central processing unit (“CPU”). Each such one or more data storage devices can include, but is not limited to, a hard drive with a spinning magnetic disk, a Solid-State Drive (“SSD”), a floppy disk, an optical disk (including, but not limited to, a CD or DVD), a Random Access Memory (“RAM”) device, a Read-Only Memory (“ROM”) device (including, but not limited to, programmable read-only memory (“PROM”), electrically erasable programmable read-only memory (“EPROM”), electrically erasable programmable read-only memory (“EEPROM”), a magnetic card, an optical card, a flash memory device (including, but not limited to, a USB key with non-volatile memory, any type of media suitable for storing electronic instructions or any other suitable type of computer-readable storage medium. 
     Referring to  FIG. 2 , any suitable input/output  27  may be used to transmit inputs to processors  22  and to receive outputs from processor  22 , including, but not limited to, a personal computer (PC) (including, but not limited to, a desktop PC, a laptop or a tablet), smart television, Internet-enabled TV, person digital assistant, smartphone, cellular phone or mobile communication device. In one embodiment, such I/O device has at least one input device (including, but not limited to, a touchscreen, a keyboard, a microphone, a sound sensor or a speech recognition device) and at least one output device (including, but not limited to, a speaker, a display screen, a monitor or an LCD). 
     In an embodiment, the method  30  includes computer-readable instructions, algorithms and logic that are implemented with any suitable programming or scripting language, including, but not limited to, C, C++, Java, COBOL, assembler, PERL, Visual Basic, SQL, JMP Scripting Language, Python, Stored Procedures or Extensible Markup Language (XML). The method  30  can be implemented with any suitable combination of data structures, objects, processes, routines or other programming elements. 
     In an embodiment, the display  28  can include GUIs structured based on any suitable programming language. Each GUI can include, in an embodiment, multiple windows, pull-down menus, buttons, scroll bars, iconic images, wizards, the mouse symbol or pointer, and other suitable graphical elements. In an embodiment, the GUIs incorporate multimedia, including, but not limited to, sound, voice, motion video and virtual reality interfaces to generate outputs of the method  30 . 
     In an embodiment, the memory devices and data storage devices described above can be non-transitory mediums that store or participate in providing instructions to a processor for execution. Such non-transitory mediums can take different forms, including, but not limited to, non-volatile media and volatile media. Non-volatile media can include, for example, optical or magnetic disks, flash drives, and any of the storage devices in any computer. Volatile media can include dynamic memory, such as main memory of a computer. Forms of non-transitory computer-readable media therefore include, for example, a floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer can read programming code and/or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution. In contrast with non-transitory mediums, transitory physical transmission media can include coaxial cables, copper wire and fiber optics, including the wires that comprise a bus within a computer system, a carrier wave transporting data or instructions, and cables or links transporting such a carrier wave. Carrier-wave transmission media can take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during RF and IR data communications. 
     In view of the foregoing, embodiments of the correlation-based design method, system and device provide: (a) enhanced process measurement and prediction techniques; (b) improvements that facilitate the design of offerings; and (c) improve the efficiency of R&amp;D activity that continues (continuously or intermittently) for relatively long periods of time, for example, over the lifetime of an organization&#39;s product or service. A technical effect is the correlation of historical data of different operation or production process instances to produce input control factors that will allow a new production process instance to proceed with an enhanced level of measured outcome factors that are critical to the quality of the operation or production process. Another technical effect is the provision of an enhanced database system for storing, visualizing, and correlating data. Yet another technical effect is the increased speed of data processing by enabling access requests that are associated with control and outcome factors of greatest relevance to the user in contrast to having to process large data sets with intertwined relevant and irrelevant data. Still another technical effect is decreasing the usage of data storage space by enabling users to easily access historical control factors, outcome factors and scenarios without having to repeat prior R&amp;D, thereby solving problems faster, reducing redundancies from rediscovering insights, identifying inconsistencies which may signal where to look for further improvement in knowledge, improving products and processes, and avoiding the storage of redundant data. 
     It should be appreciated that at least some of the subject matter disclosed herein includes or involves a plurality of steps or procedures. In an embodiment, as described, some of the steps or procedures occur automatically or autonomously as controlled by a processor or electrical controller without relying upon a human control input, and some of the steps or procedures can occur manually under the control of a human. In another embodiment, all of the steps or procedures occur automatically or autonomously as controlled by a processor or electrical controller without relying upon a human control input. In yet another embodiment, some of the steps or procedures occur semi-automatically as partially controlled by a processor or electrical controller and as partially controlled by a human. 
     It should also be appreciated that aspects of the disclosed subject matter may be embodied as a method, device, assembly, computer program product or system. Accordingly, aspects of the disclosed subject matter may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.), or an embodiment combining software and hardware aspects that may all, depending upon the embodiment, generally be referred to herein as a “service,” “circuit,” “circuitry,” “module,” “assembly” and/or “system.” Furthermore, aspects of the disclosed subject matter may take the form of a computer program product embodied in one or more computer readable mediums having computer readable program code embodied thereon. 
     Aspects of the disclosed subject matter are described herein in terms of steps and functions with reference to flowchart illustrations and block diagrams of methods, apparatuses, systems and computer program products. It should be understood that each such step, function block of the flowchart illustrations and block diagrams, and combinations thereof, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create results and output for implementing the functions described herein. 
     These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the functions described herein. 
     The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions described herein. 
     Additional embodiments include any one of the embodiments described above, where one or more of its components, functionalities or structures is interchanged with, replaced by or augmented by one or more of the components, functionalities or structures of a different embodiment described above. 
     It should be understood that various changes and modifications to the embodiments described herein will be apparent to those skilled in the art. Such changes and modifications can be made without departing from the spirit and scope of the present disclosure and without diminishing its intended advantages. It is therefore intended that such changes and modifications be covered by the appended claims. 
     Although several embodiments of the disclosure have been disclosed in the foregoing specification, it is understood by those skilled in the art that many modifications and other embodiments of the disclosure will come to mind to which the disclosure pertains, having the benefit of the teaching presented in the foregoing description and associated drawings. It is thus understood that the disclosure is not limited to the specific embodiments disclosed herein above, and that many modifications and other embodiments are intended to be included within the scope of the appended claims. Moreover, although specific terms are employed herein, as well as in the claims which follow, they are used only in a generic and descriptive sense, and not for the purposes of limiting the present disclosure, nor the claims which follow.