System, method and computer program product for optimizing the number of product variants of a product

Abstract

Method for optimizing the number of product variants (P1, P2, ..., Pn) of a product (10), in particular variants of a motor vehicle model, wherein the product variants (P1, P2, ..., Pn) differ from one another with respect to their technical configuration and the configurations of the product variants (P1, P2, ..., Pn) are determined by boundary conditions (R1, R2, ..., Rn), wherein the configuration of a product variant (Pj) is defined by specific assemblies (B1(j), B2(j), ..., Bn(j)) and each of the assemblies (B1(j), B2(j), ..., Bn(j)) of a product variant (Pj) is assigned at least one assembly bill of materials (ST1(j), ST2(j), ..., STn(j)), wherein an assembly bill of materials (Bj(j)) is a list of one or more components (T1, T2, ..., Tn), one or more variables (V1, V2, ..., Vn) and one or more includes properties (E1, E2, ..., En) and components (T1, T2, ..., Tn), variables (V1, V2, ..., Vn) and properties (E1, E2, ..., En) of which at least one assembly bill of materials (ST1(j), ST2(j),..., STn(j)) is determined according to the respective configuration of the assigned product variant (Pj), wherein the boundary conditions (R1, R2, ..., Rn) are a safety standard and / or country-specific requirements and / or marketing requirements, wherein the components (T1, T2, ..., Tn) are mechanical components such as a bearing, a seal and / or a screw, electromechanical components such as an actuator, hardware components such as a processor and / or a memory element, and / or software programs such as firmware and / or application software, wherein the variables (V1, V2, ..., Vn) are numerical values ​​and / or size specifications, and wherein the properties (E1, E2, ..., En) concerning numerical, temporal, spatial, acoustic, electrical, optical, chemical and / or other characteristics such as the driving force of a drive system, the weight of a component, the number and arrangement of components and / or a software version, with the following procedure steps: . - Generating (S10) initial data (250) for the components (T1, T2, ..., Tn), variables (V1, V2, ..., Vn) and properties (E1, E2, ..., En) of the assembly bills of materials (ST1(1), ST2(1), ..., STn(1); ST1(2), ST2(2), ..., STn(2), ...., ST1(n), ST2(n), ..., STn(n)) of a plurality of product variants (P1, P2, ..., Pn) using sensors (20) and / or a database (220) from an input module (200); - Generating (S20) second data (270) for the boundary conditions (R1, R2, ..., Rn); - Transmitting (S30) the first data (250) and the second data (270) from the input module (200) to a transformation module (300), wherein the transformation module (300) comprises at least an encoder (350) and a graph module (370); - Transforming (S40) the first data (250) and second data (270) into coded data (355) by the encoder (350) so that the coded data (355) can be processed by the graph module (370); - Generating (S50) a set (Z) of graphs (375) from the coded data (355), wherein each graph (375) represents a possible assignment of components (T1, T2, ..., Tn), variables (V1, V2, ..., Vn) and properties (E1, E2, ..., En) to at least one assembly bill of materials (ST1(j), ST2(j), ..., STn(j)) for a plurality of product variants (P1, P2, ..., Pj), wherein the components (T1, T2, ..., Tn), variables (V1, V2, ..., Vn) and properties (E1, E2, ..., En) of the at least one assembly bill of materials ((ST1(j), ST2(j), ..., STn(j)) are determined such that the configuration of the product variants (P1, P2, ..., Pj) is feasible; - Passing (S60) the set (Z) of graphs (375) to a quantum test planning module (400), wherein the quantum test planning module (400) comprises a quantum processor (420).; - Calculating (S70) at least one optimal target graph (475) from the set (Z) of graphs (375) using a quantum algorithm (440), where the target graph (475) represents an optimal minimum with respect to the number of required product variants (P1, P2, ..., Pn) to satisfy the boundary conditions (R1, R2, ..., Rn).

Description

[0001] The invention relates to a system, a method and a computer program product for optimizing the number of product variants of a product, in particular a motor vehicle model.

[0002] A product, such as a motor vehicle, consists of various assemblies and components. Examples of assemblies in a motor vehicle include the drive system, the braking system, the lighting system, the interior, the body, etc. A parts list lists the components of an assembly. The simplest arrangement is a continuous list of all the required components of the product or an assembly of the product, but lists arranged according to other criteria can also be used. In an assembly parts list, for example, the components are arranged according to an assembly sequence. In a spare parts list, only the replaceable components are listed. In addition, the parts lists for a product can vary for different sales markets and the associated different legal requirements.Depending on the type of arrangement, a distinction is often made between a quantity overview bill of materials, a structural bill of materials, a modular bill of materials, a variant bill of materials and a complex bill of materials.

[0003] The information documented in a bill of materials represents important data structures in a manufacturing company and is required for production control and parts procurement to ensure the correct components are installed in a product during production. Bills of materials are also required for quality control, product liability, process costing, and other planning and control processes.

[0004] With a large number of model variants of a product, such as different versions of a vehicle model that serve different customer requirements and must also be adapted to country-specific differences and specifications, the individual assemblies, components, variables, and properties sometimes differ significantly from one another in terms of their characteristics. This means that a correspondingly large number of bills of materials are required for the production of the various model variants of a product. This is accompanied by increasing product complexity and correspondingly rising costs.

[0005] One way to reduce product complexity and control effort in production is to reduce the number of product variants offered, particularly in the case of a motor vehicle model. However, it is not easy to decide which product variants are absolutely necessary and which can be dispensed with. Simply sorting out certain product variants can result in the entire spectrum of required or market-demanded product variants not being covered. Instead, it is necessary to determine an optimized number of product variants with which this spectrum can be served. To do this, it is necessary to adapt the underlying bills of materials for the assemblies in such a way that they can be used to produce the optimized number of product variants.

[0006] Mathematical methods using classic algorithms are therefore increasingly being used to create optimized parts lists that can cover an optimized number of possible configurations of a product, particularly a motor vehicle. However, due to the large number of categories, variables, and boundary conditions, the results are not always very reliable and are often too imprecise to be implemented in practice. This is further exacerbated by the long computing times required to calculate the parts lists. Furthermore, complex systems often result in solution spaces with a large number of local secondary minima, making it difficult to determine which of these minima might be suitable for solving the problem. This makes planning inflexible, making it virtually impossible to react quickly to current events, for example due to changes in national regulations.

[0007] US 2008 228 450 A1 discloses a computer-implemented method for generating construction instructions for a construction model, wherein the construction model contains a plurality of building elements and the construction instructions specify a sequence of construction steps for constructing the construction model. Graph coloring is used to optimize the construction instructions with regard to the buildability of the construction model and the smallest possible number of construction steps.

[0008] JP 2018-100166 A discloses an optimization method for operating a storage area for steel plates with regard to logistical inputs and outputs using graph coloring.

[0009] US 2019 / 0 019 103 A1 discloses a method for generating software instructions at a quantum-capable solution level, also using graph coloring.

[0010] US 2010 / 0 106 280 A1 discloses a method for identifying variable and common module variants of a product family. A product is broken down into systems, which in turn are divided into modules. For a product, each module is implemented by a specific module variant from a set of possible module variants.

[0011] US 2007 / 0 033 204 A1 discloses a method for generating a model of interrelated tree structures, for example, for aircraft production planning. A plurality of product trees and a plurality of configuration trees are provided, each configuration tree being linked to one of the plurality of product trees.

[0012] The paper "CHAPUIS, Guillaume [et al.]: Finding Maximum Cliques on the D-Wave Quantum Annealer. In: Journal of Signal Processing Systems, Vol. 91, 2019, pp. 363–377. DOI: 10.1007 / s11265-018-1357-8" describes the evaluation of the performance of the D-Wave 2X (DW) quantum annealer in finding a maximum clique in a graph, one of the most fundamental and important NP-hard problems. For smaller graphs, formulations of the maximum clustering problem as a quadratic unconstrained binary optimization (QUBO) problem are provided. Furthermore, several quantum implementations are compared with current classical algorithms such as simulated annealing and clique-finding heuristics.

[0013] US 2020 / 0 401 650 A1 discloses a method for solving an optimization problem, wherein the optimization problem may be a graph coloring problem.

[0014] WO 2022 / 087535 A1 discloses a computer method and system for optimizing the distribution of supply items from a plurality of stock locations to a plurality of demand locations, using a quantum computer to solve the optimization problem.

[0015] An object of the present invention is therefore to create possibilities for optimizing the number of product variants of a product, in particular of a motor vehicle model, in order to thereby achieve a reduction in the number of product variants of the product as well as an optimized use of resources and thus an improved cost structure.

[0016] This object is achieved according to the invention with respect to a method by the features of patent claim 1, with respect to a system by the features of patent claim 7, and with respect to a computer program product by the features of patent claim 12. The further claims relate to preferred embodiments of the invention.

[0017] According to a first aspect, the invention provides a method for optimizing the number of product variants of a product, in particular variants of a motor vehicle model. The product variants differ from one another with regard to their technical configuration, and the configurations of the product variants are determined by boundary conditions. The configuration of a product variant is defined by specific assemblies, and at least one assembly parts list is assigned to each of the assemblies of a product variant. An assembly parts list comprises a list of one or more components, one or more variables, and one or more properties, and the components, variables, and properties of the at least one assembly parts list are determined according to the respective configuration of the assigned product variant.The boundary conditions are a safety standard and / or country-specific requirements and / or technical standards and / or marketing requirements. The components are mechanical components such as a bearing, a seal and / or a screw, electromechanical components such as an actuator, hardware components such as a processor and / or a memory element, and / or software programs such as firmware and / or application software. The variables are numerical values ​​and / or size specifications. The properties are numerical, temporal, spatial, acoustic, electrical optical, chemical and / or other characteristics such as the driving force of a drive system, the weight of a component, the number and arrangement of components and / or a software version. The method comprises the following method steps: - Generating initial data for the components, variables and properties of assembly parts lists of a plurality of product variants using sensors and / or a database from an input module; - Generating second data for the boundary conditions for creating the assembly parts lists of the product variants; - transmitting the first data and the second data from the input module to a transformation module, wherein the transformation module comprises at least one encoder and a graph module; - transforming the first data and second data into coded data by the encoder so that the coded data is processable by the graph module; - generating a set of graphs from the coded data, wherein each graph represents a possible assignment of components, variables and properties to at least one assembly parts list for a plurality of product variants, wherein the components, variables and properties of the at least one assembly parts list are determined in such a way that the configuration of the product variants defined by the boundary conditions can be realized; - passing the set of graphs to a quantum test planning module, wherein the quantum test planning module comprises a quantum processor; - Calculating at least one optimal target graph from the set of graphs using a quantum algorithm, wherein the target graph represents an optimal minimum with regard to the number of product variants required to satisfy the boundary conditions.

[0018] In a further development, it is planned that the target graph is passed on to an output module, which transforms the target graph into output data, whereby the output data includes the number of selected product variants with their respective configuration and the corresponding assembly parts lists, in particular in the form of graphics, text messages, holograms, key figures, diagrams, and / or PowerPoint presentations, etc.

[0019] In an advantageous embodiment, the quantum algorithm is designed as quantum annealing.

[0020] In a further embodiment, it is provided that graph coloring and / or clique partitioning is / are used to find a target graph.

[0021] Advantageously, the output module comprises at least one decoder for transforming and decoding the target graph into output data. In a further embodiment, it is provided that the input module and / or the transformation module and / or the quantum test planning module and / or the output module has / have one or more technical interfaces and protocols for accessing a cloud computing infrastructure or is / are integrated into the cloud computing infrastructure.

[0022] According to a second aspect, the invention provides a system for creating an optimized number of product variants of a product, in particular variants of a motor vehicle model. The product variants differ from one another with regard to their technical configuration, and the selected configurations of the product variants are determined by boundary conditions. The configuration of a product variant is defined by specific assemblies, and at least one assembly parts list is assigned to each of the assemblies of a product variant. An assembly parts list comprises a list of one or more components, one or more variables, and one or more properties, and the components, variables, and properties of the at least one assembly parts list are determined according to the respective configuration of the assigned product variant.The boundary conditions are a safety standard and / or country-specific requirements and / or technical standards and / or marketing requirements. The components are mechanical components such as a bearing, a seal and / or a screw, electromechanical components such as an actuator, hardware components such as a processor and / or a memory element, and / or software programs such as firmware and / or application software. The variables are numerical values ​​and / or size specifications. The properties are numerical, temporal, spatial, acoustic, electrical optical, chemical and / or other characteristics such as the driving force of a drive system, the weight of a component, the number and arrangement of components and / or a software version.The system comprises an input module, a transformation module, and a quantum test planning module, wherein the quantum test planning module comprises a quantum processor. The input module is configured to generate first data for the components, variables, and properties of assembly parts lists of a plurality of product variants using sensors and / or a database, and second data for the boundary conditions for creating the assembly parts lists of the product variants, and to transmit them to a transmission module. The transformation module comprises at least one encoder and a graph module, wherein the encoder is configured to transform the first data and second data into coded data such that the coded data can be processed by the graph module.The graph module is configured to generate a set ℤ of graphs from the encoded data and to forward the set ℤ of graphs to the quantum test planning module, wherein each graph represents a possible assignment of components, variables, and properties to one or more assembly bills of materials for a plurality of product variants, wherein the components, variables, and properties of the at least one assembly bill of materials are determined such that the configuration of the product variants defined by the constraints is feasible. The quantum test planning module is configured to calculate at least one optimal target graph from the set ℤ of graphs using a quantum algorithm, wherein the target graph represents an optimal minimum with regard to the number of product variants required to fulfill the constraints.

[0023] In a further development, it is provided that the quantum test planning module is designed to forward the target graph to an output module, wherein the output module is designed to transform the target graph into output data, and wherein the output data comprise the number of selected product variants with their respective configuration and the corresponding assembly parts lists, in particular in the form of graphics, text messages, holograms, key figures, diagrams, and / or PowerPoint presentations, etc.

[0024] In an advantageous embodiment, the quantum algorithm is designed as quantum annealing.

[0025] In a further embodiment, it is provided that graph coloring and / or clique partitioning is / are used to find a target graph.

[0026] Advantageously, the output module comprises at least one decoder for transforming and decoding the target graph into output data.

[0027] According to a third aspect, the invention relates to a computer program product comprising an executable program code which is configured to carry out the method according to the first aspect when executed.

[0028] The invention is explained in more detail below using an embodiment shown in the drawing.

[0029] It shows: Fig. 1 a schematic representation of a system according to the invention for optimizing the number of product variants of a product; Fig. 2 a schematic overview of the assembly parts lists of a product; Fig. 3 a schematic representation of a graph; Fig. 4 a flow chart to explain the individual method steps of a method according to the invention; Fig. 5 a computer program product according to an embodiment of the third aspect of the invention.

[0030] Additional features, aspects and advantages of the invention or its embodiments will become apparent from the detailed description taken in conjunction with the claims.

[0031] Fig. 1 shows a system 100 for optimizing the number of product variants of a product 10. The product 10 can be, for example, a motor vehicle, an autonomously driving vehicle, an agricultural vehicle such as a combine harvester, a robot in production or service facilities, a watercraft, a flying object such as an airplane or a drone, as well as their subcomponents and subassemblies. Furthermore, the product 10 can also be designed as a scientific analysis instrument, a medical device for diagnosis, therapy, and / or support of bodily functions, a household appliance such as a washing machine, a machine tool, a test bench, a mechatronic component, a drive system, an electric motor, etc. In particular, the product 10 is a new model or model series of a motor vehicle.

[0032] A product 10, such as a motor vehicle, consists of various assemblies B1, B2, ..., Bn. Assemblies B1, B2, ..., Bn of a motor vehicle include, for example, the drive system with an electric motor and / or combustion engine, the braking system, the lighting system, the interior, the body, but also various software modules, such as the software for highly automated driving functions, etc. For a new model of a product 10, such as a new motor vehicle model, various product variants P1, P2, ..., Pn are developed. These differ from one another with regard to the technical configuration of the individual assemblies, relating to different components, properties, and equipment features. The various configurations of the product variants P1, P2, ..., Pn result from different boundary conditions R1, R2, ..., Rn for example due to country-specific differences and specifications, different customer wishes, marketing requirements or safety requirements, to which the design and equipment of a new motor vehicle model must be adapted accordingly.

[0033] An assembly BOM ST lists the components as well as other variables and properties of an assembly B of a product 10. The simplest arrangement is a continuous list of all required components of product 10, but others can also be provided according to specific criteria. The information documented in an assembly BOM ST represents important data structures for the manufacture of product 10 in a manufacturing company and is required for production control and parts procurement to ensure that the correct components are installed in product 10 during production.

[0034] With a large number of product variants P1, P2, ..., Pn of a product 10, such as different model variants of a motor vehicle that serve different customer requirements and must also be adapted to country-specific differences and specifications, the individual assemblies, components, variables, and properties sometimes differ significantly from one another in terms of their characteristics. This means that a correspondingly large number of assembly bills of materials (ST) are required for the production of the various model variants of a product 10 in order to cover this large number of product variants P1, P2, ..., Pn of a product 10. However, this leads to increasing product complexity and correspondingly rising costs.

[0035] To reduce product complexity and production control effort, it is useful to determine an optimized number of product variants P1, P2, ..., Pn offered for a product 10, such as, in particular, a motor vehicle model. Since this is a highly complex optimization problem, quantum algorithms are used to solve the problem.

[0036] As in the Fig. 2, it is assumed within the scope of the invention that the configuration of a product variant Pj is defined by selected assemblies B1(j), B2(j), ..., Bn(j) and that each of the assemblies B1(j), B2(j), ..., Bn(j) of a product variant Pj is specified by an assembly parts list ST1(j), ST2(j), ..., STn(j). An assembly parts list Bj(j) comprises a list of one or more components T1, T2, ..., Tn, one or more variables V1, V2, ..., Vn and / or one or more properties E1, E2, ..., En. Here, the respective components T1, T2, ..., Tn, variables V1, V2, ..., Vn and / or properties E1, E2, ..., En of the respective assembly parts lists ST1(1), ST2(1), ..., STn(1); ST1(2), ST2(2), ..., STn(2), ...., ST1(n), ST2(n), ..., STn(n) are determined in such a way that the different configurations of the product variants P1, P2, ..., Pn can be realized.

[0037] The components T1, T2, ..., Tn can be, for example, mechanical components such as a bearing, a seal and / or a screw, electromechanical components such as an actuator, hardware components such as a processor and / or a memory element, but also software programs such as firmware and / or application software. The variables V1, V2, ..., Vn can be numerical values ​​and / or size specifications. Properties E1, E2, ..., En can include numerical, temporal, spatial, acoustic, electrical, optical, chemical and / or other characteristics such as the drive force of a drive system, the weight of a component, the number and arrangement of components and / or a software version. It can be provided that the properties E1, E2, ..., En are measured with sensors 20.The sensors 20 can be, for example, clocks, counters, pressure sensors, piezo sensors, speed sensors, capacitive sensors, inductive sensors, temperature sensors, and / or image-capturing sensors such as cameras in the visible, ultraviolet, and / or infrared ranges. Within the scope of the invention, further subdivisions and assignments of characteristic values, key figures, and features to the various variables V1, V2, ..., Vn as well as the properties E1, E2, ..., En are also possible. The characteristic values, key figures, and features can be required, for example, to fulfill the boundary conditions R1, R2, ..., Rn.

[0038] For cost reasons, it is desirable to limit both the assembly parts lists ST1(1), ST2(1), ..., STn(1); ST1(2), ST2(2), ..., STn(2), ...., ST1(n), ST2(n), ..., STn(n) as well as the product variants P1, P2, ..., Pn. Thus, the object of the invention is an optimization problem in which the assembly parts lists ST1(1), ST2(1), ..., STn(1); ST1(2), ST2(2), ..., STn(2), ...., ST1(n), ST2(n), ..., STn(n) are determined by selecting suitable components T1, T2, ..., Tn, variables V1, V2, ..., Vn and / or properties E1, E2, ..., En in such a way that the number of product variants P1, P2, ..., Pn can be reduced and at the same time the respective boundary conditions R1, R2, ..., Rn can be met. The aim is therefore to configure at least some of the assembly parts lists ST1, ST2, ..., STn in such a way that the assemblies B1, B2, ..., Bn produced with them can be used for two or more product variants P1, P2, ..., Pn.The challenge therefore arises from a suitable assignment and allocation of the components T1, T2, ..., Tn, variables V1, V2, ..., Vn and / or properties E1, E2, ..., En to assembly parts lists ST1, ST2, .... STn in order to be able to determine those product variants P1, P2, ..., Pn that can share one or more assembly parts lists ST1, ST2, .... STn.

[0039] As the number of components T1, T2, ..., Tn, variables V1, V2, ..., Vn and / or properties E1, E2, ..., En required for the various product variants P1, P2, ..., Pn increases, the complexity of planning the assembly BOMs ST1, ST2, ..., STn increases and thus the difficulty of optimization. Planning the configuration of the required assembly BOMs ST1, ST2, ..., STn therefore represents a highly complex combinatorial optimization problem. Such highly complex problems often require a significant amount of computing time to solve, so that resource consumption increases accordingly. It is known to use optimization algorithms such as genetic and evolutionary algorithms to solve an optimization problem. However, due to the large number of components, variables and properties, the results are not always very reliable and are often too inaccurate to be implemented in practice.This is further exacerbated by the long computing times required to calculate the optimized assembly parts lists ST1, ST2, ..., STn. Furthermore, complex systems often result in solution spaces with a multitude of local secondary minima, so it is often unclear which of these minima might be suitable for solving the problem.

[0040] According to the invention, quantum computer algorithms and quantum processors are therefore used to solve the described problem, as they can significantly increase computational speed. Quantum algorithms and quantum computers enable certain nondeterministic operations that lead to an increase in computational speed and can thus solve problems that cannot be solved with classical computers in a reasonable amount of time.

[0041] According to the invention, the system 100 comprises an input module 200, a transformation module 300, a quantum test planning module 400, and an output module 500. The input module 200, the transformation module 300, and the output module 400 are preferably designed as conventional modules and are each provided with a conventional processor and / or a memory unit. In particular, the transformation module 300 comprises a conventional processor 320 and a memory unit 340.

[0042] In the context of the invention, a classic "processor" can be understood, for example, as a machine or an electronic circuit. A processor can be, in particular, a central processing unit (CPU), a microprocessor, or a microcontroller, for example, an application-specific integrated circuit or a digital signal processor, possibly in combination with a memory unit for storing program instructions, etc. A processor can also be understood as a virtualized processor, a virtual machine, or a soft CPU.For example, it may also be a programmable processor that is equipped with configuration steps for executing the aforementioned method according to the invention or is configured with configuration steps such that the programmable processor implements the inventive features of the method, the component, the modules, or other aspects and / or sub-aspects of the invention. In particular, the processor may contain highly parallel computing units and powerful graphics modules.

[0043] In the context of the invention, a "storage unit" or "storage module" and the like can be understood to mean, for example, a volatile memory in the form of random-access memory (RAM), a permanent memory such as a hard drive or a data storage device, or, for example, a removable storage module. However, the storage module can also be a cloud-based storage solution.

[0044] In the context of the invention, a "module" can be understood, for example, as a processor and / or a memory unit for storing program instructions. For example, the processor is specifically configured to execute the program instructions in such a way that the processor and / or the control unit performs functions to implement or realize the method according to the invention or a step of the method according to the invention.

[0045] In addition, the input module 200 can be connected to the sensors 20, which measure and transmit data from variables V1, V2, ..., Vn and / or properties E1, E2, ..., En. The data acquired by the sensors 20 can be images and / or data about physical, chemical, or electronic processes and states.

[0046] Furthermore, the input module 200 can be connected to at least one database 220 in which historical data is stored in the form of tables, images, graphics, time series, parameters, etc. In particular, digital twins of the components T1, T2, ..., Tn can be stored in the database 220. A digital twin represents a digital model in the virtual world and contains simulations and algorithms that describe the characteristics and behavior of the components T1, T2, ..., Tn. Furthermore, boundary conditions R1, R2, ..., Rn, such as target variables and target values, are stored in the database 220, which define a safety standard and / or country-specific requirements and / or marketing requirements. The term "database" refers to both a storage algorithm and the hardware in the form of a storage unit. In particular, the database 220 can be embodied as a cloud computing infrastructure 700.

[0047] In the context of the invention, “data” is understood to mean both raw data and already processed data from the measurement results of the sensors 20 and / or data stored in the database 220 and / or a cloud computing infrastructure 700 and / or manually entered data.

[0048] The input module 200 is designed, in particular, as a user interface for inputting and generating data 250, 270 in the form of text messages and / or voice messages and / or images and graphics. For this purpose, a keyboard, a microphone, a camera, and / or a display configured as a touchscreen are provided.

[0049] The data 250, 270 generated by the input module 200 are forwarded to the transformation module 300 via communication connections such as a CAN bus system (Controller Area Network). However, wireless connections may also be provided. A wireless communication connection is particularly suitable as a mobile radio connection and / or a near-field communication connection such as Bluetooth. ® , Ethernet, NFC (near field communication) or Wi-Fi®.

[0050] The output module 500 is configured to output output data 550, such as a configuration list of the selected product variants P1, P2, ..., Pn with the respective associated assemblies B1(j), B2(j), ..., Bn(j) or assembly parts lists ST1(j), ST2(j), ..., STn(j). The input module 200 and the output module 500 can be integrated into a hardware device such as a computer, a tablet, a smartphone, etc.

[0051] The transformation module 300 and / or the output module 500 can be implemented as standalone computing units or as a cloud-based solution. The database 220 can also be integrated into the cloud computing infrastructure 700.

[0052] It can be provided that, in particular, the input module 200 and / or the output module 500 are equipped with mobile radio modules of the 5G and / or 6G standards. 5G is the fifth-generation mobile radio standard and, compared to the 4G mobile radio standard, is characterized by higher data rates of up to 10 Gbit / sec, the use of higher frequency ranges such as 2100, 2600, or 3600 megahertz, increased frequency capacity and thus increased data throughput, and real-time data transmission, since up to one million devices per square kilometer can be addressed simultaneously. Latency times range from a few milliseconds to less than 1 ms, enabling real-time transmission of data and calculation results. The data 250, 270 input into the input module 200 can be sent in real time to the transformation module 300 integrated in the cloud computing infrastructure 700.

[0053] This speed of data transmission is necessary if cloud-based solutions are to be used for data processing.

[0054] Cloud-based solutions offer the advantage of high and therefore fast computing power. To protect the connection to the cloud computing infrastructure 700 via a mobile network connection, cryptographic encryption methods are provided.

[0055] The data for the various components T1, T2, ..., Tn, variables V1, V2, ..., Vn and / or properties E1, E2, ..., En of assembly parts lists ST1(j), ST2(j), ..., STn(j) are transmitted to the input module 200, from which the input module 200 generates initial data 250. The initial data 250 can be generated by entering data in the form of text messages, voice messages, graphics, and images, which can be, for example, as follows: a) Assembly parts list 1 contains the components T1, T2 and T5 with the variables V1 and V3 and the specified properties E1, E5 and E7. b) Assembly parts list 2 contains the components T4, T5 and T7 with the variables V1 and V4 and the specified properties E3 and E5. c) Assembly parts list 3 contains the components T3, T7 and T9 with the variables V3, V5 and V7 and the specified properties E7 and E9. d) etc.

[0056] In addition, data on the boundary conditions R1, R2, ..., Rn are transmitted, which can look like this: a) Boundary condition R1 concerns the charging time of an electric motor. b) Boundary condition R2 concerns exhaust emission limits for an internal combustion engine. c) etc.

[0057] The first data 250 and second data 270 generated by the input module 200 are passed to the transformation module 300. Since the generated first data 250 and second data 270 are categorical data in particular, they are converted in the transformation module 300 by an encoder 350 such that they can be used by machine learning algorithms. In particular, the encoder 350 is designed as a one-hot encoder. The basic idea of ​​one-hot encoding is to create new variables that take the values ​​0 and 1 and represent the original categorical values ​​of the first and second data 250, 270. For example, the categorical data "electric motor" or "combustion engine" are mapped into corresponding numerical integers as variables.

[0058] The encoder 350 typically uses classical algorithms for coding. However, it can also be intended that artificial intelligence algorithms, particularly neural networks, are used. A neural network consists of neurons arranged in multiple layers and connected to each other in various ways. A neuron is capable of receiving information at its input from outside or from another neuron, evaluating the information in a specific way, and forwarding it in a modified form to another neuron at the neuron output or outputting it as the final result. Hidden neurons are arranged between the input neurons and output neurons. Depending on the network type, several layers of hidden neurons can be present. They ensure the forwarding and processing of the information. Output neurons ultimately provide a result and transmit it to the outside world.The arrangement and connection of neurons creates different types of neural networks, such as feedforward networks, recurrent networks, or convolutional neural networks. These networks can be trained using unsupervised or supervised learning.

[0059] The data 355 encoded in this way are passed to a graph module 350, which generates a set ℤ of graphs 375 from the encoded data 355. As in the Fig. As shown in Figure 3, a graph 375 is an abstract structure that represents a number of objects along with the connections between these objects. The objects themselves are called nodes of the graph, and the connections between two nodes are called edges. The edges can be undirected or have a direction. Multiple edges can also exist between two nodes.

[0060] The nodes KG1, KG2, ..., KGn of the graph 375 represent the various possible components B1, B2, ..., Bn, variables V1, V2, ..., Vn and / or properties E1, E2, ..., En. The boundary conditions R1, R2, ..., Rn are assigned to the connections between the nodes KG1, KG2, ..., KGn.

[0061] The set ℤ of graphs 357 now represents a multitude of possibilities with regard to the number of product variants P1, P2, ..., Pn and the configuration of assembly parts lists ST1(j), ST2(j), ..., STn(j). The individual graphs 357 thus differ with regard to the number of required product variants P1, P2, ..., Pn as well as the associated assembly parts lists ST1(j), ST2(j), ..., STn(j).

[0062] To find an optimized graph in the set ℤ of graphs 357, an algorithm such as graph coloring and / or clique partitioning is used. During graph coloring, the nodes KG1, KG2, ...; KGn are assigned colors from which the different product variants P1, P2, ..., Pn can be calculated. However, it is difficult to solve the graph coloring problem using classical methods. Therefore, according to the invention, a quantum algorithm 440 is used to solve the graph problem.

[0063] To find the optimized graph in the set ℤ of graphs 357, the set ℤ of graphs 357 is transmitted to the quantum test planning module 400. The classical transformation module 300 and the quantum planning module 400 are connected to each other via a communication link. In particular, it is envisaged that the quantum test planning module 400 can be embedded into an existing hardware and software architecture. This means that no or only minor changes need to be made to the classical modules of the system 100 to integrate the quantum test planning module 400.

[0064] The quantum test planning module 400 has at least one quantum processor 420 configured to execute a quantum algorithm 440. The quantum algorithm 440 is configured to find at least one optimal minimum with respect to the number of required product variants P1, P2, ..., Pn over the set ℤ of graphs 357. This optimized graph determined by the quantum algorithm 440 is referred to as the target graph 475.

[0065] A quantum algorithm 440 is an algorithm that requires a quantum computer for execution. Since in quantum mechanics the state of a system cannot be precisely predicted as in classical mechanics and therefore only allows a probability to be assigned to a possible measured value, quantum algorithms 440 are probabilistic algorithms that can only specify a probability for a result. However, by repeating computational operations with the quantum algorithm 440, the error probability can be made arbitrarily small. For the calculation, different, simultaneously existing quantum mechanical states of subsystems are superimposed (superposition principle). The algorithm variables are stored in quantum bits (qubits).

[0066] In particular, a quantum annealing algorithm is used as the quantum algorithm 440 for finding at least one optimal minimum over the set ℤ of graphs 357. Quantum annealing uses quantum physics to determine the minimum energy state of an objective function. It is thus a meta-method based on quantum fluctuations to find at least one optimal minimum within a potentially very large, but finite set of possible solutions, which, in particular, exhibit numerous local minima. Quantum annealing assumes a quantum-mechanical superposition of all possible states with equal weights. The system then evolves according to the time-dependent Schrödinger equation.

[0067] The target graph 475 can be output directly by the output module 500. However, it can also be provided that the output module 500 has an output transformation module that translates the target graph 475 found by the quantum test planning module 400 into a user-accessible data format in the form of output data 550. The output data 550 can, for example, be a representation of the configuration of the relevant product variants P1, P2, ..., Pn and associated assembly parts lists ST1(j), ST2(j), ..., ST(n) in the form of text, images or graphics, diagrams, mathematical formulas, PowerPoint presentations, etc. For this purpose, the output transformation module 520 can, in particular, have a decoder 570. In particular, it can be provided that the calculation results are stored in the form of prototype configurations and test plans in the cloud computing infrastructure 700 and are each available via the Internet.

[0068] The use of the quantum test planning module 400 with a quantum algorithm 440 enables an acceleration of the computing time for optimizing the required number of product variants P1, P2, ..., Pn. The quantum test planning module 400 enables complex optimization problems to be solved in a short computing time. This allows the number of required product variants P1, P2, ..., Pn to be limited, thus reducing planning and production costs. Furthermore, the planning of the required assembly parts lists can be optimized, making it more efficient. In a further development of the invention, provision can be made for newly emerging components and boundary conditions to be entered as data 270 based on new specifications and environmental conditions, so that a correspondingly modified plan for the product variants can then be developed.Since the computing time is significantly reduced due to the use of quantum algorithms, changes can be reacted to quickly and a new number of product variants P1, P2, ..., Pn can be created.

[0069] As in Fig. 4, a method for optimizing the number of product variants P1, P2, ..., Pn of a product 10 comprises the following method steps: In a step S10, first data 250 for the components T1, T2, ..., Tn, variables V1, V2, ..., Vn and / or properties E1, E2, ..., En of the assembly parts lists ST1(1), ST2(1), ..., STn(1); ST1(2), ST2(2), ..., STn(2); ....; ST1(n), ST2(n), ..., STn(n) of a plurality of product variants P1, P2, ..., Pn are generated by an input module 200 by means of sensors 20 and / or a database 220.

[0070] In a step S20, second data 270 are generated for the boundary conditions R1, R2, ..., Rn.

[0071] In a step S30, the first data 250 and the second data 270 are transmitted from the input module 200 to a transformation module 300, wherein the transformation module 300 comprises at least one encoder 350 and a graph module 370.

[0072] In a step S40, the first data 250 and the second data 270 are transformed into coded data 355 in the encoder 350 so that the coded data 355 can be processed by the graph module 370.

[0073] In a step S50, a set ℤ of graphs 375 is generated from the coded data 355, wherein a graph 375 each depicts a possible assignment of components T1, T2, ..., Tn, variables V1, V2, ..., Vn and / or properties E1, E2, ..., En to at least one assembly parts list ST1(j), ST2(j), ..., STn(j) for a product variant Pj, wherein the components T1, T2, ..., Tn, variables V1, V2, ..., Vn and / or properties E1, E2, ..., En of the at least one assembly parts list ST1(j), ST2(j), ..., STn(j) are determined such that the configuration of the product variant Pj defined by the boundary conditions R1, R2, ..., Rn can be realized.

[0074] In a step S70, the set ℤ of graphs 375 is passed to a quantum test planning module 400).

[0075] In a step S70, at least one optimal target graph 475 is calculated from the set ℤ of graphs 375 by means of a quantum algorithm 440, wherein the target graph 475 represents an optimal minimum with regard to the number of required product variants P1, P2, ..., Pn for the fulfillment of the boundary conditions R1, R2, ..., Rn.

[0076] Through the use of quantum algorithms, particularly quantum annealing, the present invention offers the possibility of providing an optimized selection of relevant product variants. Since the complexity of the problem is reduced by a quantum mechanical optimization algorithm, an optimized number of required product variants can be calculated, which is necessary to meet specified constraints. Furthermore, the number of assembly parts lists that can cover the optimized number of required product variants can be minimized. This enables resource-efficient and cost-effective production overall. Reference symbol 10 Product 20 sensors 100 systems 200 input module 220 database 250 first data 270 second dates 300 Transformation Module 320 processor 340 storage unit 350 encoders 355 coded data 370 graphene module 375 graphs 400 Quantum Test Planning Module 420 quantum processor 440 Quantum algorithm 475 Target graph 500 output module 550 output data 570 decoders 700 cloud computing infrastructure 900 computer program product 950 program code P1, P2, ..., Pn product variants B1, B2, ..., Bn assemblies ST1, ST2,..., STn assembly parts lists R1, R2, ..., Rn boundary conditions T1, T2, ..., Tn components V1, V2, ..., Vn variables E1, E2, ..., En properties KG1, KG2, ..., KGn graph nodes ℤ Set of all graphs 375

Claims

[1] Method for optimising the number of product variants (P1, P2, ..., Pn) of a product (10), in particular variants of a motor vehicle model, wherein the product variants (P1, P2, ..., Pn) differ from one another with regard to their technical configuration and the configurations of the product variants (P1, P2, ..., Pn) are determined by boundary conditions (R1, R2, ..., Rn), wherein the configuration of a product variant (Pj) is defined by certain assemblies (B1(j), B2(j), ..., Bn(j)) and each of the assemblies (B1(j), B2(j), ..., Bn(j)) of a product variant (Pj) is assigned at least one assembly parts list (ST1(j), ST2(j), ..., STn(j)), wherein an assembly parts list (Bj(j)) comprises a list of one or more components (T1, T2, ..., Tn), one or more variables (V1, V2, ..., Vn) and one or more properties (E1, E2, ..., En) and comprises the components (T1, T2, ..., Tn), variables (V1, V2, ..., Vn) and properties (E1, E2, ..., En) of the at least one assembly parts list (ST1(j), ST2(j), ..., STn(j)) are determined according to the respective configuration of the assigned product variant (Pj), wherein the boundary conditions (R1, R2, ..., Rn) are a safety standard and / or country-specific requirements and / or marketing requirements, wherein the components (T1, T2, ..., Tn) are mechanical components such as a bearing, a seal and / or a screw, electromechanical components such as an actuator, hardware components such as a processor and / or a memory element, and / or software programs such as firmware and / or application software, wherein the variables (V1, V2, ..., Vn) are numerical values ​​and / or size specifications, and wherein the properties (E1, E2, ..., En) are numerical, temporal, spatial, acoustic, electrical optical, chemical and / or other characteristics such as the driving force of a drive system, the weight of a component, the number and arrangement of components and / or a software version, with the following method steps:. - generating (S10) first data (250) for the components (T1, T2, ..., Tn), variables (V1, V2, ..., Vn) and properties (E1, E2, ..., En) of the assembly parts lists (ST1(1), ST2(1), ..., STn(1); ST1(2), ST2(2), ..., STn(2), ...., ST1(n), ST2(n), ..., STn(n)) of a plurality of product variants (P1, P2, ..., Pn) by means of sensors (20) and / or a database (220) from an input module (200); - generating (S20) second data (270) for the boundary conditions (R1, R2, ..., Rn); - transmitting (S30) the first data (250) and the second data (270) from the input module (200) to a transformation module (300), wherein the transformation module (300) comprises at least one encoder (350) and a graph module (370); - transforming (S40) the first data (250) and second data (270) into coded data (355) by the encoder (350) so that the coded data (355) can be processed by the graph module (370); - generating (S50) a set (Z) of graphs (375) from the coded data (355), wherein a graph (375) depicts a possible assignment of components (T1, T2, ..., Tn), variables (V1, V2, ..., Vn) and properties (E1, E2, ..., En) to at least one assembly parts list (ST1(j), ST2(j), ..., STn(j)) for a plurality of product variants (P1, P2, ..., Pj), wherein the components (T1, T2, ..., Tn), variables (V1, V2, ..., Vn) and properties (E1, E2, ..., En) of the at least one assembly parts list (ST1(j), ST2(j), ..., STn(j)) are determined in such a way that the configuration of the product variants (P1, P2, ..., Pj) is realizable; - passing (S60) the set (Z) of graphs (375) to a quantum test planning module (400), wherein the quantum test planning module (400) comprises a quantum processor (420); - Calculating (S70) at least one optimal target graph (475) from the set (Z) of graphs (375) by means of a quantum algorithm (440), wherein the target graph (475) represents an optimal minimum with regard to the number of product variants (P1, P2, ..., Pn) required to satisfy the boundary conditions (R1, R2, ..., Rn). [2] Method according to claim 1, wherein the target graph (475) is passed on to an output module (500) which transforms the target graph (475) into output data (550), wherein the output data (550) comprise the number of selected product variants (P1, P2, ..., Pn) with their respective configuration and the corresponding assembly parts lists (ST1(1), ST2(1), ..., STn(1); ST1(2), ST2(2), ..., STn(2), ...., ST1(n), ST2(n), ..., STn(n)), in particular in the form of graphics, text messages, holograms, key figures, diagrams, and / or PowerPoint presentations, etc. [3] Method according to claim 1 or 2, wherein the quantum algorithm (440) is designed as quantum annealing. [4] Method according to one of claims 1 to 3, wherein graph coloring and / or clique partitioning is / are used to find a target graph (475). [5] Method according to one of claims 2 to 4, wherein the output module (500) comprises at least one decoder (570) for transforming and decoding the target graph (475) into output data (550). [6] Method according to one of claims 1 to 5, wherein the input module (200) and / or the transformation module (300) and / or the quantum test planning module (400) and / or the output module (500) has / have one or more technical interfaces and protocols for accessing a cloud computing infrastructure (700) or is / are integrated in the cloud computing infrastructure (700). [7] System (100) for creating an optimized number of product variants (P1, P2, ..., Pn) of a product (10), in particular variants of a motor vehicle model, wherein the product variants (P1, P2, ..., Pn) differ from one another with regard to their technical configuration and the selected configurations of the product variants (P1, P2, ..., Pn) are determined by boundary conditions (R1, R2, ..., Rn), wherein the configuration of a product variant (Pj) is defined by certain assemblies (B1(j), B2(j), ..., Bn(j)) and each of the assemblies (B1(j), B2(j), ..., Bn(j)) of a product variant (Pj) is assigned at least one assembly parts list (ST1(j), ST2(j), ..., STn(j)), wherein an assembly parts list (Bj(j)) comprises a list of one or more components (T1, T2, ..., Tn), one or more variables (V1, V2, ..., Vn) and one or more properties (E1, E2, ..., En) and the components (T1, T2, ..., Tn), variables (V1, V2, ..., Vn) and properties (E1, E2, ..., En) of the at least one assembly parts list (ST1(j), ST2(j), ..., STn(j)) are determined according to the respective configuration of the associated product variant (Pj), wherein the boundary conditions (R1, R2, ..., Rn) are a safety standard and / or country-specific requirements and / or marketing requirements, wherein the components (T1, T2, ..., Tn) are mechanical components such as a bearing, a seal and / or a screw, electromechanical components such as an actuator, hardware components such as a processor and / or a memory element, and / or software programs such as firmware and / or application software, wherein the variables (V1, V2, ..., Vn) are numerical values ​​and / or size specifications, and wherein the properties (E1, E2, ..., En) are numerical, temporal, spatial, acoustic, electrical optical, chemical and / or other features such as the driving force of a drive system, the weight of a component, the number and arrangement of components and / or a software version, comprising an input module (200), a transformation module (300) and a quantum test planning module (400), wherein the quantum test planning module (400) comprises a quantum processor (420); wherein the input module (200) is designed to input first data (250) for the components (T1, T2, ..., Tn), variables (V1, V2, ..., Vn) and properties (E1, E2, ..., En) of the assembly parts lists (ST1(1), ST2(1), ..., STn(1); ST1(2), ST2(2), ..., STn(2), ..., ST1(n), ST2(n), ..., STn(n)) of a plurality of product variants (P1, P2, ..., Pn) by means of sensors (20) and / or a database (220) and second data (270) for the boundary conditions (R1, R2, ..., Rn) and transmit it to a transmission module (300); wherein the transformation module (300) comprises at least one encoder (350) and a graph module (370), wherein the encoder (350) is designed to transform the first data (250) and second data (270) into coded data (355) such that the coded data (355) can be processed by the graph module (370), and wherein the graph module (370) is designed to generate a set (Z) of graphs (375) from the coded data (355) and to forward the set (Z) of graphs (375) to the quantum test planning module (400), wherein a graph (375) each represents a possible assignment of components (T1, T2, ..., Tn), variables (V1, V2, ..., Vn) and properties (E1, E2, ..., En) to one or more assembly parts lists (ST1(j), ST2(j), ..., STn(j)) for a large number of product variants (P1, P2, ..., Pj), and where the components (T1, T2, ..., Tn), variables (V1, V2, ..., Vn) and properties (E1, E2, ..., En) of the at least one assembly parts list ((ST1(j), ST2(j), ..., STn(j)) are determined in such a way that the configuration of the product variants (P1, P2, ..., Pj) defined by the boundary conditions (R1, R2, ..., Rn) can be realized; wherein the quantum test planning module (400) is designed to calculate at least one optimal target graph (475) from the set (Z) of graphs (375) by means of a quantum algorithm (440), wherein the target graph (475) represents an optimal minimum with regard to the number of product variants (P1, P2, ..., Pn) required to satisfy the boundary conditions (R1, R2, ..., Rn). [8] System (100) according to claim 7, wherein the quantum test planning module (400) is designed to forward the target graph (475) to an output module (500), wherein the output module (500) is designed to transform the target graph (475) into output data (550), and wherein the output data (550) comprise the number of selected product variants (P1, P2, ..., Pn) with their respective configuration and the corresponding assembly parts lists (ST1(1), ST2(1), ..., STn(1); ST1(2), ST2(2), ..., STn(2), ...., ST1(n), ST2(n), ..., STn(n)), in particular in the form of graphics, text messages, holograms, key figures, diagrams, and / or PowerPoint presentations, etc. [9] System (100) according to claim 7 or 8, wherein the quantum algorithm (440) is implemented as quantum annealing. [10] System (100) according to one of claims 7 to 9, wherein graph coloring and / or clique partitioning is / are used to find a target graph (475). [11] System (100) according to one of claims 7 to 10, wherein the output module (500) comprises at least one decoder (570) for transforming and decoding the target graph (475) into output data (550). [12] A computer program product (900) comprising an executable program code (950) configured to carry out the method according to any one of claims 1 to 6 when executed.

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