SYSTEM AND METHOD FOR REDUCING EXCESSIVE RESOURCE UTILIZATION - Patent application

Abstract

A system, method, and computer readable medium having instructions stored thereon for characterizing a recipe for a production system to produce a product includes receiving (a) specification limits for product parameters of the product, (b) resource allocation input information, and (c) historical product information, generating a model that models a relationship between each of the respective proposed target values ​​and both the (a) resource allocation impact information, and (b) process capability impact information, and displaying or storing the model. In some aspects, displaying the model includes displaying a relationship between each of the respective proposed target values ​​and both the (a) resource allocation impact information, and (b) process capability impact information. Further aspects include operating the production system using the selected target values.

Description

[Technical field]

[0001] This application relates generally to characterizing recipes for production systems (e.g., in pharmaceutical filling systems) for producing products such as pharmaceuticals and more efficient use of resources in the production systems. [Background technology]

[0002] A production system may produce a product with specification limits that the product must comply with, such as an upper and / or lower limit. The specification limit may relate to a measurable quantity of a product parameter. A product parameter is one or more characteristics such as mass, temperature, time, current, luminous intensity, amount of light intensity of a substance, length, height, width, thickness, weight, volume, area, circumference, diameter, perimeter, density, voltage, resistivity, pH, viscosity, etc. A product whose product parameter does not comply with a specification limit may be less desirable or possibly unusable, in which case the product (and possibly the entire batch if the non-compliant product was produced in a batch) may have to be discarded. If there is only one specification limit, such as only a lower specification limit (LSL) or only an upper specification limit (USL), the production system may produce according to a recipe with a target value that is sufficiently far from the one specification limit. For example, if a product has only an LSL, the production system may produce the product according to a recipe with a target value of the product parameter that is sufficiently greater than the LSL. The amount by which the product parameter value is greater than the LSL is referred to herein as the "required excess resource (NER) usage."

[0003] A production system may be used to produce products through unit production, batch production, mass production, or continuous production. A production system may be used for commercial production (e.g., production of parts of a commodity or an entire commodity), scientific production (e.g., production of resources or equipment for scientific research), or other types of production.

[0004] An example of a production system is a product filling system. The product filling system may be used to fill containers with solid, liquid and / or gas products. The product filling system may be manual (e.g., operated by a hand lever used to pump the product through a tip), semi-automatic (e.g., operated by a pump controlled by an operator), or automatic (e.g., operated by a pump controlled by a computing device). The product filling system may also be used across a variety of fields and industries including, for example, life science / engineering, chemical science / engineering, medical science / engineering, mechanical science / engineering, food science / engineering, beverage science / engineering, and manufacturing and assembly corresponding to the above fields and industries.

[0005] Product filling systems are often used in drug discovery, formulation testing and clinical trials, and drug production. A drug liquid filling system is a type of product filling system. Pharmaceutical liquid filling systems with different operational scales, from small to large scale, are used by many pharmaceutical companies. These drug liquid filling systems exist in many forms, from small bench-top to large scale machines, and can accommodate many product characteristics, such as liquid viscosity.

[0006] In pharmaceutical production, specification limits are often used. Specification limits for pharmaceutical production systems may be imposed by regulatory bodies, such as government agencies (e.g., the U.S. Food and Drug Administration). In some pharmaceutical production applications, a single product unit is a container (e.g., a vial, syringe, cartridge, tube, beaker, cup, or other suitable holding structure) filled with a fill volume of a liquid pharmaceutical composition, after which a specified volume of the liquid pharmaceutical composition is removed from the container and administered to a patient. In some instances, removing a specified volume of liquid pharmaceutical from a container includes transferring a specified volume of liquid pharmaceutical to another container (e.g., a syringe) prior to administration, and in other instances, a specified volume of liquid pharmaceutical may be removed from the container directly to a patient (e.g., via an injection fluid if the container is a syringe). Thus, the fill volume must be equal to or greater than the specified volume (also called the "label volume"). In some pharmaceutical production examples, the specification limits may include either (i) only the LSL, or (ii) the USL and the LSL, where for both examples, the LSL is equal to the sum of (i) the volume specified on the product label of the pharmaceutical product (hereinafter also referred to as the label volume) and (ii) the hold-up volume. In practice, the fill volume and the LSL are not equal (i.e., the fill volume is greater than the LSL). Instead, a target value is selected for the product parameter of the fill volume to ensure that the fill volume is greater than the LSL by the NER usage. Specifically, since the target value is related to volume, the NER usage in this case may be referred to as the "necessary excess volume" (NEV) amount. The NEV ensures that there is a sufficient amount of drug in the container to allow the specified volume of drug to be administered to the patient, despite the natural variability present in pharmaceutical production systems for filling containers with liquid drug compositions. Once the specified volume of drug has been administered (e.g., via a syringe), the NEV of drug remaining in the container may be discarded. In this example, a larger fill volume target would result in a larger NEV (and more drug wasted) and fewer units of drug produced outside the LSL due to natural fluctuations in the production system.

[0007] Traditionally, in many different types of production systems, the target value of a product parameter of a recipe may be determined based on the historical actual values ​​of the product parameter, specifically using the average and standard deviation of the historical actual values ​​of the product parameter. However, these traditional methods can determine only one target value, and therefore do not provide an operator of the production system with insight into whether the target value is too conservative or not conservative enough with respect to the rate at which the production system produces units outside the specification limits. Furthermore, the operator is not provided with any insight into how changing the target value will affect resource allocation and / or process capability. With these traditional methods, production systems are routinely set to operate with target values ​​that are overly conservative relative to the specification limits, resulting in corresponding inefficiencies in resource allocation. Summary of the Invention [Means for solving the problem]

[0008] One aspect of the present disclosure provides a method for characterizing a recipe for a production system for producing a product, the method including: (a) receiving, by one or more processors, one or more specification limits for a product parameter of the product; (b) receiving, by the one or more processors, resource allocation input information; (c) receiving, by the one or more processors, historical product information for a number of batches, the historical product information including (i) a plurality of historical actual values ​​for the product parameters and (ii) one or more historical target values ​​for the product parameters; (d) applying, by the one or more processors, the specification limits, the resource allocation input information, and the historical product information, to generate, for each of a plurality of proposed target values ​​for the product parameters, a model that models a relationship between the respective proposed target value and both (i) the resource allocation impact information, and (ii) the process capability impact information; and (e) displaying and / or storing, by the one or more processors, the model.

[0009] In some aspects, displaying the model of the preceding aspects includes displaying, by one or more processors, for each proposed target value, a relationship between the respective proposed target value and both (i) resource allocation impact information and (ii) process capability impact information.

[0010] In some aspects, the product of the previous aspects is a pharmaceutical product and the production system of the previous aspects is a filling system.

[0011] In some aspects, the above aspects further include operating, by the one or more processors, a production system using the selected target values.

[0012] Another aspect of the present disclosure provides a computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform the method of any one of the preceding aspects.

[0013] Another aspect of the present disclosure provides a system that includes: (a) one or more processors; and (b) one or more non-transitory computer-readable media that store instructions that, when executed by the one or more processors, cause the one or more processors to perform a method of any one of the preceding aspects.

[0014] Those skilled in the art will appreciate that the figures described herein are included for illustrative purposes and are not intended to limit the disclosure. The figures are not necessarily to scale, with emphasis instead being placed on illustrating the principles of the present disclosure. It should be understood that in some instances, various aspects of the described implementations may be shown exaggerated or enlarged to facilitate understanding of the described implementations. In the figures, like key characters across the various figures generally refer to functionally similar and / or structurally similar components. [Brief description of the drawings]

[0015] [Figure 1]FIG. 1 is a simplified block diagram of an example system for characterizing a recipe for a production system for producing a product. [Diagram 2] 2 illustrates an exemplary graphical display generated by the user interface unit of FIG. 1; [Diagram 3] 1 illustrates an exemplary process for determining annual savings for proposed targets. [Figure 4] 1 illustrates an exemplary output table relating proposed target values ​​to both resource allocation impact information and process capability impact information. [Diagram 5] 1 shows a comparative example of product output between a conventional method and the disclosed technology when applied to a product filling system. [Figure 6] FIG. 1 is a flow diagram illustrating an example of a method for characterizing a recipe for a production system for producing a product. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] The present disclosure aims to reduce problems associated with the prior art (e.g., those described in the Background section) by providing techniques for characterizing a recipe for a production system for producing a product. The present technique may include generating, for each of a plurality of proposed target values ​​for a product parameter, a model that models a relationship between the respective proposed target value and both (i) resource allocation impact information and (ii) process capability impact information. By generating, displaying and / or storing the model, the technique aims to characterize the recipe for the production system and provide an operator of the production system with insight into the plurality of proposed target values, thereby avoiding many of the shortcomings associated with the prior art.

[0017] When an operator of a production system makes decisions regarding setting target values ​​for the production system, it is advantageous for the operator to have specific insight into the impact of a number of different proposed target values ​​on resource allocation (or the probabilities associated with one or more possible resource allocation impacts) and on process capability (or the probabilities associated with one or more possible process capability impacts). The operator can then use these insights generated by the present technique to, for example, improve the performance and / or efficiency of the production system.

[0018] Advantageously, the technique of the present invention can reduce the traditional practice of selecting target values ​​that are too far from specification limits by providing improved insight. For example, when applied to applications having at least LSL, NER usage can be reduced. Reducing NER usage provides many advantages. One advantage of reducing NER usage is that fewer resources (e.g., pharmaceuticals) are used to produce each unit of product. Thus, less resource waste is achieved, resource efficiency is improved, and the sustainability of the production system is improved. Making the production system more sustainable in terms of resource usage can also improve the energy efficiency of the production system, since less energy is required to produce each unit of product. Furthermore, making the production process more sustainable in terms of resource usage can also reduce the financial and / or economic costs of producing each unit of product. Another advantage of reducing NER usage is that production throughput can be increased, since more product units are produced in a given time. Another advantage, particularly relevant for computational and / or computer-related applications, is that reducing NER can reduce computational processing resources (time, power, memory, etc.).

[0019] Another benefit of reducing NER usage is that it may prevent or reduce the frequency of product shortages, since fewer resources (which may be scarce) are used to produce each unit of product. This may be particularly important in pharmaceutical production systems, where drug shortages can have serious health consequences for patients who are unable to obtain medically necessary drugs and must instead substitute less effective drugs, or cannot use the drug at all. Thus, by reducing the occurrence of drug shortages, the technology of the present invention may alter the drugs administered to patients, the order in which the drugs are administered, and / or the timing at which the drugs are administered, thereby improving patient care and outcomes.

[0020] Another benefit of reducing NER usage, particularly relevant to pharmaceutical production systems, is the reduction of fraudulent drug pooling. Drug pooling can occur as a result of an entity administering a drug combining the NER of multiple units of a drug to form additional units of the drug. In some instances, this practice may violate regulations and / or laws, thereby resulting in fraud. Reducing the NER usage per unit of drug makes it more difficult to pool drugs. Thus, certain fraudulent activities can be reduced by the technology of the present invention.

[0021] It should also be noted that the technology of the present invention may reduce the usage of NER, which may result in a higher frequency of product units being produced with product parameters outside of specification limits (out-of-specification (OOS) product), but this does not necessarily mean that there is any impact on the product that the customer gets. OOS product may be flagged and discarded by the production system. For example, in a pharmaceutical production system, units of a drug below the LSL (label volume plus hold-up volume) may be removed to ensure that patients do not receive units of the drug that are OOS.

[0022] Further advantages of the techniques of the present invention over conventional approaches to characterizing recipes for production systems to produce products will be appreciated by those skilled in the art throughout this disclosure. The various concepts introduced above and discussed in more detail below may be implemented in any of a number of ways, and the concepts described are not limited to any particular implementation. Example implementations are provided below for illustrative purposes.

[0023] Exemplary System 1 is a simplified block diagram of an exemplary system 100 for characterizing a recipe for a production system to produce a product. In some embodiments, the system 100 may include a standalone device, while in other examples, the system 100 may be integrated into other devices. At a high level, the system 100 includes a client computing device 110, one or more production systems 140, one or more sources of historical product information 150, one or more sources of specification limits 160, and one or more sources of resource allocation input 170, at least a portion of which may be communicatively coupled via a network 180, which may be a dedicated network, a secure public Internet, a virtual private network, or other type of network, such as a dedicated access line, a regular telephone line, a satellite link, a cellular data network, a combination of these, or the like. When the network 180 includes the Internet, data communications may occur over the network 180 via Internet communication protocols. In some aspects, system 100 may include more or fewer instances of the various components of system 100 (e.g., one instance of computing device 110, five instances of production system 140, ten instances of historical product information sources 150, etc.).

[0024] As mentioned above, computing device 110 may be included in system 100. Computing device 110 may include a single computing device or multiple computing devices that are co-located or remote from one another. Computing device 110 is generally configured to: (a) receive one or more specification limits for a product parameter of a product; (b) receive resource allocation input information; (c) receive historical product information for a number of batches, the historical product information including (i) a plurality of historical actual values ​​for the product parameters and (ii) one or more historical target values ​​for the product parameters; (d) apply the specification limits, the resource allocation input information, and the historical product information to generate, for each of a plurality of proposed target values ​​for the product parameters, a model that models a relationship between the respective proposed target value and both (i) the resource allocation impact information, and (ii) the process capability impact information; and (e) display and / or store the model.

[0025] The components of computing device 110 may be interconnected via an address / data bus or other means. Components included in computing device 110 may include a processing unit 120, a network interface 122, a display 124, a user input device 126, and a memory 128, which are described in further detail below.

[0026] Production system 140 may include a single production system or multiple production systems that are co-located or remote from one another. Production system 140 may generally include physical devices configured for use in producing (e.g., manufacturing) a product. In embodiments where production system 140 is a product filling system, production system 140 may be used, for example, for pharmaceutical filling, chemical filling, or biological material filling. In other embodiments, production system 140 includes equipment used in processes unrelated to pharmaceutical development or production (e.g., a food or beverage production system, an oil production system, etc.).

[0027] Production system 140 may include one or more sensors capable of providing sensor data regarding the operation of production system 140. Such sensor data may be provided to computing device 110 via network 180. Production system 140 may be configured to be controllable by manual or automatic input. In some embodiments, production system 140 may be configured to receive such control input locally, such as via a user input device local to production system 140. In some embodiments, production system 140 is configured to receive control input remotely, such as from computing device 110 via network 180. The control input may include operational instructions, such as one or more target values ​​according to which production system 140 should operate.

[0028] As described above, the exemplary system 100 includes one or more historical product information sources 150, one or more specification limit sources 160, and one or more resource allocation input sources 170. Each of one or more of the sources 150-170 may be a single source or may include multiple sources that are co-located or remote from one another. One or more of the sources 150-170 may provide information to the computing device 110 over a network 180. The provided information may be data, such as nominal data, ordinal data, discrete data, and / or continuous data. The provided information may be in the form of a suitable data structure and stored in a suitable format, such as JSON, XML, CSV, etc. One or more of the sources 150-170 may provide information to the computing device 110 automatically and / or upon request. For example, a user of the computing device 110 may desire to generate a model for each of a plurality of proposed target values ​​of a product parameter that models a relationship between each proposed target value and both (i) resource allocation impact information and (ii) process capability impact information. In response, one or more of sources 150-170 may transmit the information to computing device 110 over network 180. One or more of sources 150-170 may themselves be databases of information and / or may be configured to receive information via user input, etc.

[0029] Historical product information source 150 typically includes historical product information that may correspond to one or more production batches of one or more products having one or more product parameters by one or more production systems (e.g., production system 140). The historical product information may include, for each of the one or more batches, (i) a plurality of historical actual values ​​of the product parameters, and (ii) one or more historical target values ​​of the product parameters. The historical actual values ​​of the product parameters may include historical actual values ​​of the product parameters that the production system generated, and the historical target values ​​of the product parameters may be corresponding target values ​​at which the production system was instructed to operate. For example, for an exemplary liquid drug filling system that manufactures a drug, the drug may have a product parameter of weight. The historical target value of the liquid drug filling system may be, for example, 5.00 grams, and the liquid drug filling system may have produced 10 batches of the drug at the historical target value. The historical actual values ​​may include, for example, for each of 10 batches of a drug manufactured, the average weight of all drugs in the batch ({4.98 grams, 5.00 grams, 4.92 grams, 4.94 grams, 5.02 grams, 5.08 grams, 5.08 grams, 5.06 grams, 4.95 grams, 5.01 grams}, etc.).

[0030] The specification limit source 160 may typically provide one or more specification limits for one or more product parameters for one or more products. The specification limits may include an upper specification limit and / or a lower specification limit. The specification limits may include one or more values ​​for the product parameters. The specification limits may be applied at a per unit, per batch level, and / or per production system level. For example, returning to the exemplary liquid drug filling system described above, an LSL of average weight of 4.95 grams may be applied at the per batch level. Based on the historical actual values ​​shown in the example above, batches with average weights of 4.98 grams, 5.00 grams, 5.02 grams, 5.08 grams, 5.06 grams, 4.95 grams, and 5.01 grams are within the specification limits (which may be referred to as "in specification"), while batches with average weights of 4.92 grams and 4.94 grams are outside the specification limits (which may be referred to as "OOS").

[0031] Resource allocation input sources 170 may typically provide resource allocation input information that may be useful in determining the resource allocation impact information of various proposed target values. The resource allocation input information may include financial input information (e.g., what are the financial costs associated with producing a product), material input information (e.g., the amount of raw materials used to produce a product), energy input information (e.g., the amount of energy used to produce a product), labor input information (e.g., the amount of labor used to produce a product), and / or other scarce / finite resource input information.

[0032] In some embodiments, system 100 may omit one or more of sources 150-170 and instead receive information / data locally, such as via user input. Techniques for receiving information / data corresponding to sources 150-170 without using sources 150-170 are further described and illustrated in, for example, FIG.

[0033] Referring again to computing device 110, processing unit 120 includes one or more processors, each of which may be a programmable microprocessor that executes software instructions stored in memory 128 to perform some or all of the functions of computing device 110 described herein. Alternatively, one or more of the processors in processing unit 120 may be other types of processors (e.g., application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), etc.).

[0034] Network interface 122 may include any suitable hardware (e.g., front-end transmitter and receiver hardware), firmware, and / or software configured to communicate with external devices and / or systems (e.g., production system 140, historical product information source 150, specification limit source 160, resource allocation input source 170, etc.) using one or more communications protocols. For example, network interface 122 may be or include an Ethernet interface. Computing device 110 may communicate with any device that provides an interface between computing device 110 over a single communications network or over one or more types of multiple communications networks (e.g., one or more wired and / or wireless local area networks (LANs) and / or one or more wired and / or wireless wide area networks (WANs), such as the Internet or an intranet).

[0035] The display 124 may use any suitable display technology (e.g., LED, OLED, LCD, etc.) to present information to a user, and the user input device 126 may be a keyboard or other suitable input device. In some aspects, the display 124 and the user input device 126 are integrated into a single device (e.g., a touch screen display). Typically, the display 124 and the user input device 126 may be combined to allow a user to interact with a graphical user interface (GUI) or other (e.g., text) user interface provided by the computing device 110, for purposes such as displaying one or more flow profiles, displaying parameters, recommending changes to one or more parameters, notifying the user of equipment malfunctions or other deficiencies, etc.

[0036] The memory 128 may include one or more physical memory devices or units, including volatile and / or non-volatile memory, and may or may not include memory located in various computing devices of the computing device 110. Any suitable memory type or types may be used, such as a read-only memory (ROM), a solid-state drive (SSD), a hard disk drive (HDD), etc. The memory 128 stores instructions for one or more software applications that may be executed by the processing unit 120, including a product system characterization (PSC) application 130. In the exemplary system 100, the PSC application 130 includes a data collection unit 132, a model generation unit 134, a user interface unit 136, and a production system operation unit 138. The units 132-138 may be separate software components or modules of the PSC application 130, or may simply represent functions of the PSC application 130 that are not necessarily divided among different components / modules. For example, in some embodiments, the data collection unit 132 and the user interface unit 136 are included in a single software module. Further, in some embodiments, units 132-138 are distributed among multiple copies of PSC application 130 (e.g., running on different components in computing device 110) or among different types of applications stored and running on one or more of computing device 110.

[0037] The data collection unit 132 is typically configured to receive (e.g., via sources 150-170, user input received via user interface unit 136, or other suitable means) one or more specification limits for the product parameters, resource allocation input information, and / or historical product information, the historical product information including (i) a plurality of historical actual values ​​for the product parameters, and (ii) one or more historical target values ​​for the product parameters. The model generation unit 134 is typically configured to generate, for each of a plurality of proposed target values ​​for the product parameters, a model that models a relationship between the respective proposed target value and both (i) resource allocation impact information, and (ii) process capability impact information, by applying the specification limits, resource allocation input information, and product history information collected / received by the data collection unit 132. The user interface unit 136 is typically configured to receive a selected target value of the proposed target values, and / or display the model generated by the model generation unit 134. The production system operation unit 138 is typically configured to operate the production system using the selected target value (e.g., user selection received via user interface unit 136). In other embodiments, unit 138 is omitted (e.g., the production system is manually configured instead using the selected target values). The operation of each of units 132-138 is described in further detail below with reference to the operation of system 100.

[0038] Example Graphic Display 2 illustrates an exemplary graphical display 200 of a user interface for receiving input and displaying an output model. Graphical display 200 includes an input interface 210 and an output interface 250. Graphical display 200 may be displayed on a display, such as display 124 of computing device 110 (e.g., when generated by user interface unit 136). In other embodiments, the information displayed in graphical display 200 may instead be split into two or more graphical displays (e.g., windows displayed on two or more monitors of two or more co-located or remote devices) and may include additional and / or different information as compared to the example of FIG. 2.

[0039] One or both of the input interface 210 or the output interface 250 may be configured to facilitate receiving inputs, such as from the computing device 110, possibly via the network 180. In some aspects, the inputs may be user inputs received, for example, via the user input device 126, via the user interface unit 136 of the computing device 110. In some aspects, the user inputs include inputs (e.g., specification limits, resource allocation input information, or historical product information) used in generating the model. The model models, for each of a plurality of proposed target values ​​of the product parameters, a relationship between each proposed target value and both (i) resource allocation impact information, and (ii) process capability impact information. In some aspects, the user inputs may include inputs that affect the display of the model (e.g., zooming in or out of the model, filtering particular data points, or otherwise performing other formatting operations on the model). In some aspects, the inputs may be non-user inputs, such as inputs from sources, such as sources 150-170. If the input is a non-user input, the input interface 210 may facilitate receiving the input using the network interface 122 via the data collection unit 132 of the computing device 110. In some aspects, the non-user input may include input for generating and / or displaying a model, as well as user input.

[0040] One or both of the input interface 210 or the output interface 250 may be configured to facilitate providing output, where possible, to components of the system 100, such as via the network 180. The output may be initially received (e.g., as an input) by one or both of the input interface 210 or the output interface 250. For example, the input interface 210 may first receive user input, including input for generating a model. The input interface 210 then facilitates providing the input as output that is received by the model generation unit 134, enabling the model to be generated.

[0041] The input interface 210 may include any number of inputs for receiving (a) one or more specification limits for a product parameter of the product, (b) resource allocation input information, and (c) historical product information for a number of batches. As shown, the example input interface 210 includes a proposed target reduction maximum input 220, a proposed target reduction number input 222, a historical actual value median input 230, a specification limit lower input 232, a historical actual value 0.135 percentile input 234, a single unit cost input 240, a units per batch input 242, an annual planned batches input 244, and a batch value input 246.

[0042] Inputs 220, 222 indicate which proposed targets are included in the model. The proposed targets may be measured in any suitable quantity (grams, liters, volts, inches, degrees, etc.) related to the product parameters. As shown, inputs 220-222 relate to proposed target reductions, which correspond to reducing the baseline target by various amounts depending on the proposed target reduction. The baseline target may be a historical target or other target. Specifically, input 220 may be a proposed target reduction maximum measured in grams (entered as {0.05} as shown), and input 222 may be a proposed target reduction number (entered as {15} as shown). Thus, the model includes 15 proposed targets, with the proposed target reduction range being 0 grams to 0.05 grams, as shown.

[0043] Input interface 210 is shown to include a maximum proposed target value reduction input 220 and a number of proposed target value reductions input 222, although other inputs may be used instead or in addition to determine how many and which proposed target values ​​to include in the model. In one example, the proposed target values ​​may be a list of individual proposed target values, e.g., the list may be {1, 4, 6, 7, 10, 22}, indicating that each value should be a proposed target value. In another example, the target values ​​may include one or more exclusive / inclusive ranges of proposed target values, including either the number of proposed target values ​​or the step size of the proposed target values; for example, the range may be {20, 30}, indicating that the proposed target values ​​should fall in the range of 20 to 30, the number of proposed target values ​​may be {5}, indicating that four proposed target values ​​should fall within a range of equal step sizes (e.g., {20.0, 22.5, 25.0, 27.5, 30.0}), or the step size may be {2}, indicating that proposed target values ​​spanning a range of step size 2 (e.g., {20, 22, 24, 26, 28, 30}) are included.

[0044] Inputs 230-234 represent (a) one or more specification limits for a product parameter of a product, and (b) historical product information for a number of batches, where the historical product information includes (i) a number of historical actual values ​​for the product parameter, and (ii) one or more historical target values ​​for the product parameter. The historical actual value information and specification limits, and thus inputs 230-234, may be used to determine process capability information. The specification limits and historical product information may be measured in any suitable units (e.g., grams, liters, volts, inches, degrees, etc.) corresponding to the associated product parameter. As shown, inputs 230-234 relate to historical product information and specification limits. Specifically, input 230 may be for the median of the historical actual values ​​measured in grams (as shown, entered as {3.64}), input 232 may be for the lower specification limit measured in grams (as shown, entered as {3.50}), and input 234 may be for the 0.135th percentile of the historical actual values ​​measured in grams (as shown, entered as {3.57}). Thus, as shown, the historical value information includes that the median for the historical actual values ​​is 3.64 grams, and the 0.135th percentile historical actual value is 3.57 grams. Further, the LSL becomes 3.50 grams as shown.

[0045] Although input interface 210 is shown to include a historical actual value median input 230, a lower specification limit input 232, and a historical actual value 0.135 percentile input 234, other inputs may additionally or alternatively be used to provide specification limit and / or historical actual value information. In one example, there may be an input for a list of the raw historical actual values ​​themselves at a per unit, per batch, and / or per production system level. In another example, the average historical actual value may be an input. In another example, a percentile other than 0.135 may be an input. In another example, a USL may be an input. In another example, the standard deviation of the historical actual values ​​may be an input. Any number of other inputs may be inputs related to specification limits and / or historical actual value information useful in determining process capability information.

[0046] Turning now to inputs 240-246, inputs 240-246 depict resource allocation impact information for various proposed target values. As shown, inputs 240-246 relate to financial information and therefore may be referred to as examples of "financial input information" used in determining resource allocation impact information associated with the financial impact information accordingly. Specifically, input 240 may be a single unit cost measured in dollars (entered as {10} as shown), input 242 may be units per batch (entered as {100000} as shown), input 244 may be annual planned batches (entered as {150} as shown), and input 246 may be a batch value measured in dollars (entered as {500000} as shown). Thus, as shown, assuming that each batch contains 100,000 units, production of 150 batches is planned per year, and the value / profit per batch produced is $500,000, the production cost per unit could be $10.

[0047] Although input interface 210 is shown to include a single unit cost input 240, a units per batch input 242, an annual planned batches input 244, and a batch value input 246, other suitable financial input information may additionally or alternatively be used to determine the financial impact information. For example, rather than a single unit cost input, a cost per unit of material (e.g., cost per gram ($ / g) or cost per inch ($ / in), etc.) may be used. In another example, rather than an annual planned batches, batches planned over a different period of time may be used.

[0048] Additionally, input interface 210 includes inputs 240-246 directed to financial input information, but may additionally or alternatively use other suitable resource allocation input information to determine resource allocation impact information for various proposed target values ​​that do not necessarily include financial impact information. In some aspects, the resource allocation impact information includes material impact information (e.g., the amount of raw materials used in production), energy impact information (e.g., the amount of energy used in production), labor impact information (e.g., the amount of labor resources used in production), or the use of other scarce / finite resources, based on material input information, energy input information, labor input information, or other scarce / finite resource input information, respectively. It should also be noted that in some aspects, the resource allocation impact information may further include throughput impact information (e.g., the amount of units produced in a specified time), based on throughput input information.

[0049] The output interface 250 may include any number of outputs for displaying, for each of a plurality of proposed target values ​​for the product parameter, a model (or a representation of the model) that models a relationship between each proposed target value and both (i) the resource allocation impact information and (ii) the process capability impact information. As shown, the output interface 250 includes a process capability impact information output 260 and a resource allocation impact information output 270. Each of the outputs 260, 270 includes a graph, although any suitable data visualization technique may be used, such as one or more of a chart, table, plot, graph, map, diagram, histogram, etc. More specific examples of suitable data visualization techniques may include one or more of bar graphs, pie charts, doughnut charts, half doughnut charts, multi-layer pie charts, line graphs, scatter plots, cone graphs, pyramid graphs, funnel graphs, radar triangles, radar polygons, area graphs, tree graphs, flow charts, tables, maps, icon arrays, percentage bars, gauges, radial wheels, concentric circles, Gantt charts, circuit diagrams, timelines, Venn diagrams, histograms, mind maps, dichotomous keys, PERT charts, choropleth maps, cartesian graphs, box plots, hexagon plots, heat maps, pair plots, KDE charts, time series charts, correlation charts, violin plots, rain cloud plots, stem and leaf plots, bubble charts, pictogram graphs, or other suitable data visualization techniques. As previously mentioned, the outputs 260, 270 of the output interface 250 may be reformatted or manipulated automatically or on demand. For example, any graph included in the outputs 260, 270 may be configured to grow or shrink in response to feedback from the user.

[0050] The process capability impact information output 260 may display a model of the relationship between each of the multiple proposed target values ​​and the process capability impact information. The output 260 may display a process capability index (P pk) is plotted as a function of the proposed target reduction (measured in grams) for each proposed target reduction point. Each of the proposed target reduction points may be based on input from input interface 210. The proposed target reduction points plotted in output 260 are based on inputs 320 and 322 as shown, and thus the proposed target reduction points include 15 proposed target reduction points increasing in 15 equal increments up to 0.05 grams. As shown, a baseline target point of 0 grams target reduction (corresponding to the historical target as shown) is also included in output 260.

[0051] For each proposed target reduction point in output 260, output 260 is pk Includes: pk is an example of information that may be included in process capability impact information. pk is an index that measures the overall process capability of a production system in terms of the process meeting its specification limits; more specifically, it is a ratio that compares (1) the distance from the process mean to the nearest specification limit and (2) the one-sided spread of the process based on its overall variability (3-σ variation). Thus, P pk can be calculated according to the general formula:

number

number

number

number

number

[0052] As the target reduction increases, P pk As the baseline target approaches the LSL, the likelihood of individual batches being produced OOS increases, decreasing process capability. As shown, the baseline target is at its highest. pk P pk ≒2. P pk corresponds to a 6σ level of 99.9999998% process yield or 0.002 ppm process fallout. pk It is understood that P = 2 corresponds to a production system having a relatively high process capacity. Conversely, as shown, a target reduction point of 0.05 grams corresponds to P pk ≈1.3, which corresponds to a process yield of about 99.99% or a process fallout of about 63 ppm, as shown. Thus, as shown, a reduction in the target of 0.05 results in a process capability that is worse than the baseline target.

[0053] P pkis shown in output 260, other suitable information for indicating the relationship between the proposed target values ​​and the process capability impact information may additionally or alternatively be included. In some embodiments, the process capability impact information may include other process capability index quantities, such as P p , C pk Or C p or any quantity approximating any process capability index quantity. In some embodiments, the process capability impact information may include any one or more of percent out of specification, percent in specification, sigma level, area under a probability density function, process yield, process fallout, or any other suitable quantity for indicating / measuring the overall process capability of a production system.

[0054] Resource allocation impact information output 270 may display a model of the relationship between each of a plurality of proposed target values ​​and the resource allocation impact information. In output 270, annual savings (measured in dollars) are plotted as a function of proposed target value reduction (measured in grams) for each proposed target value reduction point. The proposed target value reduction points included in output 270 may be the same as the proposed target value reduction points included in output 260.

[0055] Each proposed target reduction point at output 270 includes an annualized savings amount, as shown. The annualized savings amount is an example of information that may be included in the resource allocation impact information. The annualized savings amount may be determined in a variety of suitable manners for each proposed target reduction point. As shown, the annualized savings amount is determined based on inputs 240-246. Further discussion and description of example methods for determining the annualized savings amount is included in FIG. 3.

[0056] As shown in output 270, as the target reduction increases, the annual savings increase, and as the target approaches the LSL of the production system, the costs associated with operating the production system decrease. As shown, the baseline target has no annual savings (because, by definition, all other annual savings are compared to the annual savings of the baseline target). Conversely, as shown, reducing the target by 0.05 grams equates to annual savings of over $3 million.

[0057] Although annual savings are shown in output 270, other suitable information may additionally or alternatively be included to indicate the relationship between each of the proposed targets and the resource allocation impact information. In some embodiments, the resource allocation impact information may include one or more of raw material usage, energy usage, financial resource usage, labor usage, or other scarce / finite resource usage.

[0058] The output interface 250 may be presented to an operator of the production system to provide insight to assist the operator in selecting a target value from the proposed target values ​​that corresponds to a desired balance of resource allocation impact information and process capacity impact information. In some embodiments, when applied to an application having at least an LSL, the selected target value may be less than the historical target value, thereby reducing NER usage and providing one or more benefits of reducing NER usage as described herein.

[0059] Exemplary Process for Determining Annual Savings Against Proposed Targets 3 illustrates an exemplary process 300 for determining annual savings for a proposed target value. In general, the process 300 included in FIG. 3 is directed to generating a relationship between each proposed target value (each of a plurality of proposed target values) and resource allocation impact information. As previously discussed, annual savings may be described as being included in the resource allocation impact information (specifically as an example of financial impact information), although other suitable resource allocation impact information (e.g., material impact information, energy impact information, labor impact information, throughput impact information, etc.) may additionally or instead be included.

[0060] Process 300 may be implemented using system 100 (e.g., by model generation unit 134 of PSC application 130). Process 300 may use resource allocation input information as input (e.g., inputs 240-246 as shown). Output of process 300 may be included in and / or represented by a model that models, for each of a plurality of proposed target values ​​for a particular product parameter, a relationship between the respective proposed target value and both (i) resource allocation impact information and (ii) process capability impact information. The output / model may be displayed using components of computing device 110 and may also be displayed using an output interface the same as or similar to output interface 250. In some aspects, the output / model may be displayed in the same or similar manner as output 270.

[0061] In the process 300, each of the items 310-334 includes example values. At least some of the example values ​​may correspond to example values ​​included in FIG.

[0062] The input items are indicated by hatching in process 300. The input items include single unit cost 320, units per batch item 324, annual planned batch item 330, and batch value item 334, which may correspond to single unit cost input 240, units per batch input 242, annual planned batch input 244, and batch value input 246, respectively.

[0063] Yield improvement item 328 may correspond to one proposed target reduction of the fifteen proposed target reductions included in FIG.

number

[0064] The PSC application 130 may perform the steps of the process 300. Typically, as shown, the PSC application multiplies the yield improvement item 328 by the annual plan batches item 330 to obtain an additional production batches item 326, (1) multiplies the additional production batches item 326 by the units per batch item 324 to obtain an additional production units item 322, (2) multiplies the additional production units item 322 by the single unit cost item 320 to obtain a unit cost savings item 312, (3) rounds down the additional production batches item 326 to the nearest integer to obtain a number of batches saved item 332, (4) multiplies the number of batches saved item 332 by the batch value item 334 to obtain a unit slot savings item 314, and (5) adds the unit cost savings item 312 to the unit slot savings item 314 to obtain a total savings item 310. Total savings item 310 may correspond to output 270, and specifically corresponds to the y values ​​for each of the proposed target reduction points of output 270.

[0065] Example information on the influence of the proposed target value selection 4 illustrates an example of an output table 400 relating proposed target values ​​to resource allocation impact information and process capability impact information. Typically, output table 400 may correspond to a model that models, for each of a plurality of proposed target values ​​for a product parameter, a relationship between the respective proposed target value and both (i) the resource allocation impact information and (ii) the process capability impact information. In some aspects, output table 400 itself may be an example of a model. In other aspects, output table 400 may correspond to a model, such as by being a representation of the model.

[0066] The output table 400 may be generated using the system 100, and in particular may be generated using, for example, the model generation unit 134 of the PSC application 130 stored in the memory 128 of the computing device 110. The output table 400 may be displayed using a graphical display, such as the display 124 of the computing device. In some aspects, the output may be displayed in the same or similar manner as the output 270 and / or may be displayed alongside the output 270. In some aspects, the output table 400 may serve as a representation of a model, and the model may serve as an input to the output table 400.

[0067] Output table 400 includes example values. At least some of the example values ​​may correspond to example values ​​included in FIG. 2 in either or both of input interface 210 or output interface 250. As shown, output table 400 may correspond to data / information included in output interface 250. Specifically, as shown, output table 400 may include process capability impact information (e.g., fill weight P pk (whole batch), filling weight P pk(worst batch) and deliverable volume out-of-spec rate (ppm)) and resource allocation impact information (e.g., 5-year savings ($) and 9-year savings ($)) corresponding to the baseline target value of FIG. 2 and three proposed target values ​​of the 15 proposed target values ​​of FIG. 2 (e.g., Proposed Target I, Proposed Target II, and Proposed Target III).

[0068] As shown, output table 400 includes notes that may be generated by user input, such as keyboard and / or voice commands. In other aspects, the notes in output table 400 may be generated by artificial intelligence and / or data analysis algorithms that may apply qualitative labels to the data included in output table 400. As shown, out-of-specification rates are included in output table 400. Note that the production of OOS products does not necessarily impact the product received by the customer. OOS products may be flagged and discarded by the production system.

[0069] The output table 400 can be presented to an operator of the production system to provide insight to assist the operator in selecting a target value from the proposed target values ​​that corresponds to a desired balance of resource allocation impact information and process capacity impact information. In some embodiments, when applied to an application having at least an LSL, the selected target value can be less than the historical target value, with a corresponding reduction in NEV and providing many of the benefits of reducing NER usage described herein. As illustrated, proposed target value III corresponds to the greatest NEV reduction of the four target values ​​shown. Thus, proposed target value III provides the greatest savings in both 5 and 9 years and the lowest P for both all batches and the worst batch. pk Proposed target I corresponds to less NEV reduction, less savings, and a higher P than proposed target III. pk An operator of a production system can use the output table 400 to help consider risk and reward, with increasing risk corresponding to P pkAs indicated in the notes of output table 400, the operator may select proposed target value II because it has "better savings and acceptable performance," while proposed target value I has only "some savings" and proposed target value III has "below acceptable performance." The choice of target values ​​selected may depend largely on what the operator values ​​and / or considers important, and the numbers included in FIGS. 2-4 are merely exemplary for illustrative purposes and do not necessarily correspond to any values ​​of resource allocation impact information and / or process capability impact information that may be desired.

[0070] Example Output of a Product Fill System 5 shows a comparison diagram 500 between the output of a product filling system operating at a historical target value and a selected target value having a lower NEV than the historical target value. The historical target value may be the same as or similar to the historical target value included in output table 400, and the selected target value may be the same as or similar to the proposed target value II included in output table 400.

[0071] In some aspects, reducing the historical target value to the selected target value may be in response to receiving the selected target value via user input (e.g., via the user interface unit 136 and / or the user input device 126 of the computing device 110) and / or automatic selection (e.g., via an artificial intelligence or data analysis algorithm). In some aspects, reducing the historical target value to the selected target value may include displaying (e.g., via the display 124 of the computing device 110) a relationship between the selected target value and both (i) resource allocation impact information and (ii) process capability impact information of the selected target value. In some aspects, reducing the historical target value to the selected target value may include operating a production system (e.g., the production system 140 of the system 100) using the selected target value (e.g., via the production system operation unit 138 of the computing device 110).

[0072] As shown, diagram 500 shows a product that is a container 510 filled with a liquid medication by a pharmaceutical production system, which is later drawn into a syringe 540. The medication is composed of three portions: a label volume 530, a hold-up volume 532, and a historical / selected NEV 534A / B. The label volume 530 is the amount of medication that is administered to a patient, and the label volume 530 may be set, for example, by a regulatory agency (e.g., the U.S. Food and Drug Administration).

[0073] Hold-up volume 532 may be the amount of drug that remains in syringe 540 when drug is drawn from container 510 into syringe 540 after the syringe is fully expelled and is therefore not recoverable. Although hold-up volume 532 is shown as being entirely within syringe 540, it should be noted that in some examples not all of hold-up volume 532 is within syringe 540 as some of the drug may remain as a residue within container 510 when drug is drawn into syringe 540. Thus, in some examples a first portion of hold-up volume 532 may remain within container 510 and a second portion of hold-up volume 532 may be drawn into syringe 540.

[0074] Line 520 is above label volume 530 and hold-up volume 532 and may correspond to a lower specification limit. Line 522A is above label volume 530, hold-up volume 532 and historical NEV 534A. Line 522A corresponds to the amount of drug filled into container 510 to produce product according to the historical target value. Line 522B is above label volume 530, hold-up volume 532 and selected NEV 534B. Line 522B corresponds to the amount of drug filled into container 510 to produce product according to the selected target value. Measurement 524 is a measurement of the difference in levels between lines 522A and 522B. Measurement 524 corresponds to the amount of drug saved per unit of product produced according to the historical target value and the selected target value (this amount is also shown as NEV reduction 536).

[0075] As shown, by reducing the historical target value to the selected target value, the historical NEV 534A is reduced to the selected NEV 534B by the NEV reduction amount 536. The NEV reduction amount 536 corresponds to the amount of drug saved per unit of product produced, which corresponds to a number of benefits as described herein.

[0076] Exemplary Flow Diagram 6 is a flow diagram illustrating an example method 600 for characterizing a recipe for a production system to produce a product. The example method 600 includes the steps of: (1) receiving specification limits for product parameters (block 602), (2) receiving resource allocation input information (block 604), (3) receiving resource allocation input information (block 606), (4) generating a model that models the relationship between each of the individual proposed target values ​​and (i) resource allocation impact information, and (ii) process capability impact information (block 608), and (4) displaying or storing the model (block 610).

[0077] When receiving specification limits for a product parameter (block 602), one or more specification limit sources, such as specification limit source 160 of FIG. 1 and / or input interface 210 of FIG. 2, and possibly data collection unit 132 of FIG. 1, may be used. The specification limits may include upper and / or lower specification limits for the product parameter to which the product must conform. The specification limits may relate to a measurable quantity of the product parameter. The product parameter may be one or more characteristics such as length, mass, temperature, time, current, photometric quantity of a substance, etc.

[0078] Receiving resource allocation input information (block 604) may use one or more of resource allocation input sources 170 of Figure 1 and / or input interface 210 of Figure 2 and possibly data collection unit 132 of Figure 1. The resource allocation input information may include financial input information (e.g., what are the financial costs associated with producing a product), material input information (e.g., the amount of raw materials used to produce a product), energy input information (e.g., the amount of energy used to produce a product), labor input information (e.g., the amount of labor used to produce a product), or other scarce / finite resource input information.

[0079] Receiving historical product information (block 606) may use one or more sources of historical product information, such as historical product information source 150 of Figure 1 and / or input interface 210 of Figure 2, and possibly data collection unit 132 of Figure 1. The historical product information may include (i) a number of historical actual values ​​for the product parameters, and (ii) one or more historical target values ​​for the product parameters.

[0080] A computing device, such as computing device 110 of FIG. 1, may be used to generate (block 608) a model that models the relationship between each of the proposed target values ​​and both (i) the resource allocation impact information and (ii) the process capability impact information. In particular, an application, such as PSC application 130 with model generation unit 134, may be used within the computing device. The generated model may be the same as or similar to (or may be expressed in the same or similar manner as) outputs 260-270. The computing device may, for example, perform steps the same as or similar to those corresponding to process 300 of FIG. 3 to determine the relationship between each of the respective proposed target values ​​and the resource allocation impact information.

[0081] Finally, method 600 is shown where the model is displayed and / or stored (block 610). In some aspects, the model itself may be displayed, while in other aspects, a representation of the model may be displayed. Displaying the model may use a computing device such as computing device 110 (e.g., particularly using display 124 and / or user interface unit 136). In some aspects, the model itself may be stored, while in other aspects, a representation of the model may be stored. Storage of the model may use a computing device such as computing device 110 (e.g., particularly using memory 128).

[0082] In some aspects, method 600 may be performed entirely by automation, e.g., by one or more processors (e.g., CPUs and / or GPUs) executing instructions stored in one or more non-transitory computer-readable storage media (e.g., volatile or non-volatile memory, read-only memory, random access memory, flash memory, electronically erasable programmable read-only memory, and / or one or more other types of memory), or may be performed by a partially automated and partially manual process (e.g., via a human operator). Method 600 may use any of one or more components, processes, and / or techniques of FIGS. 1-5.

[0083] Additional Considerations Some of the drawings described herein show example block diagrams having one or more functional components. It will be understood that such block diagrams are for illustrative purposes and that the devices described and shown may have more, fewer, or alternative components than those shown. Additionally, in various aspects, the components (and the functionality provided by each component) may be associated with or integrated as part of any suitable component.

[0084] Some aspects of the present disclosure relate to non-transitory computer-readable storage media having instructions / computer-readable storage media for performing various computer-implemented operations. The term "instructions / computer-readable storage media" is used herein to include any medium capable of storing or encoding a sequence of instructions or computer code for performing the operations, methods and techniques described herein. The media and computer code may be of a type known and available to those skilled in the art of computer software technology, or may be of a type specially designed and constructed for the purposes of the aspects of the present disclosure. Examples of computer-readable storage media include, but are not limited to, magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and holographic devices, magneto-optical media such as optical disks, and hardware devices specially configured to store and execute program code, such as ASICs, programmable logic devices ("PLDs"), and ROM and RAM devices.

[0085] Examples of computer code include machine code produced by a compiler and files containing high-level code executed by a computer using an interpreter or compiler. For example, an aspect of the present disclosure may be implemented using Java, C++, or other object-oriented programming languages ​​and development tools. Additional examples of computer code include encryption and compression code. Furthermore, aspects of the present disclosure may be downloaded as a computer program product and transferred from a remote computer (e.g., a server computer) to a requesting computer (e.g., a client computer or another server computer) over a transmission channel. Other aspects of the present disclosure may be implemented in hardwired circuitry in place of, or in combination with, machine-executable software instructions.

[0086] As used herein, the singular terms "a," "an," and "the" can include plural referents unless the context clearly dictates otherwise.

[0087] As used herein, the terms "approximately," "substantially," "substantial," "about," and "about" are used to describe and take into account slight variations. When used in conjunction with an event or circumstance, these terms can refer to an event or circumstance occurring exactly, as well as an event or circumstance occurring approximately. For example, when used in conjunction with a numerical value, these terms can refer to a range of variation that is within ±10% of the numerical value, such as within ±5%, ±4%, ±3%, ±2%, ±1%, ±0.5%, ±0.1%, or ±0.05%. For example, two numerical values ​​can be considered to be "substantially" the same if the difference between their values ​​is within ±10% of the mean value of the values, such as within ±5%, ±4%, ±3%, ±2%, ±1%, ±0.5%, ±0.1%, or ±0.05%.

[0088] In addition, amounts, ratios, and other numerical values ​​may be presented herein in a range format. It is understood that such range formats are used for convenience and brevity and include numerical values ​​expressly stated as the limits of the range, but should be understood flexibly to include all individual numerical values ​​or subranges subsumed within that range, as if each numerical value and subrange was expressly stated.

[0089] Although the techniques disclosed herein have been described primarily with particular operations performed in a particular order, it will be understood that these operations may be combined, divided into parts, or reordered to form equivalent techniques without departing from the teachings of the present disclosure. Thus, unless specifically indicated herein, the order and grouping of operations is not intended to be a limitation of the present disclosure.

Claims

1. A method for characterizing the recipe of a production system for producing a product, One or more processors receive one or more specification limits of the product parameters of the said product, The aforementioned one or more processors receive resource allocation input information, The receiving of historical product information for a certain number of batches by one or more processors, wherein the historical product information includes (i) a plurality of historical actual values ​​of the product parameters, and (ii) one or more historical target values ​​of the product parameters. The one or more processors apply the specification limits, resource allocation input information, and historical product information to generate a model that models the relationship between each of the proposed target values ​​of the product parameters and both (i) resource allocation impact information and (ii) process capability impact information. The one or more processors display or store the model. A method that includes this.

2. The method according to claim 1, wherein the production system is a filling system, the product is a liquid, and the product parameter is either the filling weight or the filling volume.

3. The method according to claim 2, wherein the product is a pharmaceutical drug.

4. The specification limits are provided by the regulatory body, according to the method of claim 1.

5. The method according to claim 1, wherein the specification limits include either or both of the lower specification limit and / or the upper specification limit.

6. The method according to claim 1, wherein the number of batches is at least 25.

7. The method according to claim 1, wherein the historical actual values ​​of the product parameters include, for each batch of the number of batches, one or more raw data, mean, standard deviation, minimum value, or maximum value.

8. The method according to claim 1, wherein (i) the resource allocation input information includes one or more material input information, financial input information, labor input information, energy input information, or throughput input information, and (ii) the resource allocation impact information includes one or more material impact information, financial impact information, labor impact information, energy impact information, or throughput impact information.

9. The method according to claim 1, wherein the process capability impact information includes one or more of a process capability index, an out-of-spec rate, an in-spec rate, an out-of-spec quantity, or an in-spec quantity.

10. The method according to claim 1, further comprising receiving a selected target value from the proposed target values ​​by one or more processors.

11. The method according to claim 1, further comprising automatically selecting a selected target value from among the proposed target values ​​using one or more processors.

12. The method according to claim 11, further comprising displaying the relationship between the selected target value and both (i) resource allocation impact information and (ii) process capability impact information of the selected target value using one or more processors.

13. The method according to claim 1, wherein displaying the model includes, by one or more processors, displaying the relationship between each of the proposed target values ​​and both (i) resource allocation impact information and (ii) process capability impact information.

14. The method according to claim 1, further comprising using one or more processors to operate the production system using the selected target value.

15. One or more non-temporary computer-readable media that, when executed by one or more processors, store instructions causing the one or more processors to perform the method according to any one of claims 1 to 14.

16. One or more processors, When executed by the one or more processors, one or more non-temporary computer-readable media storing instructions causing the one or more processors to perform the method according to any one of claims 1 to 14, and A system that includes this.

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