Man-hour estimation device, man-hour estimation system, man-hour estimation method, and program

Abstract

A man-hour estimation device (3) comprises: a first storage unit that stores an estimation master representing estimation formulas for estimating man-hours for the respective steps of a manufacturing method for a product, and a reference destination master representing reference destinations, from among type data and physical amount data included in a database, in which use data to be used for computation using the estimation formulas is stored; and a first computation unit that identifies, for each of the estimation formulas specific to the respective steps, use data on the basis of the reference destination master to acquire use data from the database, computes man-hours for the respective steps by applying the acquired use data to the estimation formulas, and adds up the man-hours computed for the respective steps to compute total man-hours necessary to manufacture the product.

Description

Man-hour estimation apparatus, man-hour estimation system, man-hour estimation method and program

[0001] The present disclosure relates to a man-hour estimation apparatus, a man-hour estimation system, a man-hour estimation method and a program.

[0002] Some man-hour estimation apparatuses estimate the man-hour of a product by using design data of the product from a three-dimensional CAD (computer-aided design) apparatus.

[0003] For example, Patent Document 1 discloses a man-hour estimation apparatus including: an estimation element extraction unit that extracts an estimation element from attribute information added to product design data of a three-dimensional CAD apparatus; a process setting unit that sets a process from the estimation element extracted by the estimation element extraction unit; and a process estimation unit that estimates man-hours from an estimation formula for the process set by the process setting unit.

[0004] Japanese Unexamined Patent Application Publication No. 2002-157282

[0005] In the man-hour estimation apparatus described in Patent Document 1, the estimation formula of the process estimation unit cannot be changed, so when it is desired to change the estimation formula to make the estimation content more accurate, changing the estimation formula is difficult.

[0006] The present disclosure has been made to solve the above problem, and an object of the present disclosure is to provide a man-hour estimation apparatus, a man-hour estimation system, a man-hour estimation method, and a program that can easily change a man-hour estimation formula.

[0007] To achieve the above objectives, the man-hour estimation device according to this disclosure comprises a database generation unit, a first storage unit, and a first calculation unit. The database generation unit generates a database from the product's design data that represents a plurality of shape elements of a product, and includes type data that identifies the type of each shape element, and physical quantity data for each shape element associated with each type of type data. The first storage unit stores an estimation master representing an estimation formula for estimating man-hours for each process of the product's manufacturing method, and a reference master representing a reference location where usage data used in calculations using the estimation formula is stored from the type data and physical quantity data included in the database. The first calculation unit identifies the usage data based on the reference master for each process's estimation formula, retrieves the usage data from the database, and then applies the retrieved usage data to the estimation formula to calculate the man-hours for each process. Furthermore, it calculates the total man-hours required to manufacture the product by summing the calculated man-hours for each process.

[0008] According to the configuration of this disclosure, the calculation formula can be easily changed by changing the estimation master and the reference master used by the first calculation unit for calculating man-hours.

[0009] Hardware configuration diagram of the effort estimation system according to Embodiment 1 of the present disclosure Block diagram of the effort estimation device included in the effort estimation system according to Embodiment 1 of the present disclosure Diagram showing an example of shape element data generated by the shape element data acquisition unit included in the effort estimation device according to Embodiment 1 of the present disclosure Diagram showing an example of acquisition rule data used by the shape element data acquisition unit included in the effort estimation device according to Embodiment 1 of the present disclosure Diagram showing an example of a shape element database generated by the shape element data acquisition unit included in the effort estimation device according to Embodiment 1 of the present disclosure Diagram showing an example of an estimate master stored in the estimate master storage unit provided in the storage device included in the effort estimation device according to Embodiment 1 of the present disclosure Diagram showing an example of a constant master stored in the constant master storage unit provided in the storage device included in the effort estimation device according to Embodiment 1 of the present disclosure Diagram showing an example of input information data stored in the input information storage unit provided in the storage device included in the effort estimation device according to Embodiment 1 of the present disclosure Diagram showing an example of a reference master stored in the reference master storage unit provided in the storage device included in the effort estimation device according to Embodiment 1 of the present disclosure A diagram showing an example of a unit price master stored in the estimation master storage unit provided in the storage device. A block diagram of the storage device provided in the man-hour estimation device according to Embodiment 2 of the disclosure. A diagram showing an example of a material cost database stored in the material cost database storage unit provided in the storage device provided in the storage device provided in Embodiment 2 of the disclosure. A diagram showing an example of an equipment depreciation database stored in the equipment depreciation database storage unit provided in the storage device provided in the storage device provided in Embodiment 2 of the disclosure. A diagram showing an example of an indirect cost rate master stored in the indirect cost rate master storage unit provided in the storage device provided in the storage device provided in Embodiment 2 of the disclosure. A diagram showing an example of a product specification master stored in the product specification master storage unit provided in the storage device provided in Embodiment 2 of the disclosure. A block diagram of the man-hour estimation device according to Embodiment 3 of the disclosure. A diagram showing an example of a performance database, which is the source data for the learning data used in machine learning of the trained model of the correction unit provided in the man-hour estimation device according to Embodiment 3 of the disclosure. A diagram showing an example of the learning data used in machine learning of the trained model of the correction unit provided in the man-hour estimation device according to Embodiment 3 of the disclosure.

[0010] Hereinafter, the man-hour estimation device, man-hour estimation system, man-hour estimation method, and program according to the embodiments of this disclosure will be described in detail with reference to the drawings. In the drawings, the same or equivalent parts are denoted by the same reference numerals.

[0011] (Embodiment 1) The man-hour estimation device according to Embodiment 1 is a device that estimates the man-hours of the processes involved in the manufacturing method of a product using product design data from a 3D CAD device. The man-hour estimation device and man-hour estimation system will be described with reference to Figures 1 and 2, using the case where the product targeted by the man-hour estimation device is manufactured by sheet metal processing as an example.

[0012] Figure 1 is a hardware configuration diagram of the man-hour estimation system 1 according to Embodiment 1. Figure 2 is a block diagram of the man-hour estimation device 3 included in the man-hour estimation system 1. In Figure 2, the device included in the memory 5 shown in Figure 1 is referred to as the storage device 50.

[0013] As shown in Figure 1, the man-hour estimation system 1 comprises a CAD device 2 and a man-hour estimation device 3 that estimates man-hours from the output data of the CAD device 2.

[0014] CAD device 2 is a computer-aided design device that assists users in designing their products. CAD device 2 comprises a processor 21, memory 22, and a network interface 23. The processor 21, memory 22, and network interface 23 are connected by a bus 24 for data exchange.

[0015] The processor 21 and memory 22 constitute the computer. The memory 22 stores a CAD program for 3D CAD processing. The CAD device 2 performs 3D CAD processing by having the processor 21 read and execute the CAD program stored in the memory 22. The user uses the CAD device 2 to design a 3D product, that is, a sheet metal product manufactured by sheet metal processing.

[0016] Meanwhile, the network interface 23 connects the processor 21 and memory 22 to an external device, such as the labor cost estimation device 3, via the network 100. This enables the network interface 23 to communicate with the labor cost estimation device 3. The network interface 23 transmits the three-dimensional design data of the sheet metal processed product designed by the user to the labor cost estimation device 3, based on a command from the processor 21.

[0017] The effort estimation device 3 also includes a processor 4, memory 5, and network interface 6. Furthermore, the effort estimation device 3 includes an input device 7 consisting of a keyboard, mouse, etc., and an output device 8 consisting of a display. These components, the processor 4, memory 5, network interface 6, input device 7, and output device 8, are connected by a bus 9 to exchange data with each other.

[0018] Similar to network interface 23, network interface 6 also connects the processor 4 and memory 5 to an external device, such as a CAD device 2, via the network 100. As a result, network interface 6 enables the processor 4 to receive the three-dimensional design data of the sheet metal product described above from the CAD device 2.

[0019] The processor 4 and memory 5 constitute a computer. The man-hour estimation device 3 performs man-hour estimation processing to estimate the man-hours of the processes involved in the manufacturing method of a sheet metal product using the design data of the sheet metal product, and memory 5 includes various storage units used for this man-hour estimation processing. Specifically, memory 5 includes a storage device 50 having an acquisition rule storage unit 51, a shape element database storage unit 52, an estimation master storage unit 53, a reference master storage unit 54, a constant master storage unit 55, an input information storage unit 56, and a unit price master storage unit 57, as shown in Figure 2. Furthermore, memory 5 stores a man-hour estimation program for performing man-hour estimation processing.

[0020] The effort estimation device 3 performs the effort estimation process described above by having the processor 4 read and execute the effort estimation program stored in the memory 5. To perform this effort estimation process, the effort estimation device 3 includes a functional block configured as software as shown in Figure 2. In detail, the effort estimation device 3 includes a design data acquisition unit 41, a shape element data acquisition unit 42, an effort estimation calculation unit 43, an information acquisition unit 44, a cost estimation calculation unit 45, and an output unit 46.

[0021] The design data acquisition unit 41 acquires 3D design data of the sheet metal product designed by the user with the assistance of the CAD device 2. If the 3D design data includes shape element data, which will be described later, the design data acquisition unit 41 may acquire the shape element data in addition to the 3D design data. The design data acquisition unit 41 transmits the acquired design data to the shape element data acquisition unit 42.

[0022] The shape element data acquisition unit 42 analyzes the received three-dimensional design data and extracts the shape elements of the sheet metal product represented by the design data. The extracted shape element data 421 is shown in Figure 3.

[0023] Figure 3 shows an example of shape element data 421 generated by the shape element data acquisition unit 42.

[0024] The shape element data acquisition unit 42 extracts shape elements of the sheet metal product, such as the sheet metal thickness represented by the design data shown in Figure 3, the short and long sides assuming the sheet metal product is rectangular, and round holes, square holes, bends, etc., formed in the sheet metal product. The strings shown in Figure 3 indicate the names of these shape elements. Furthermore, the shape element data acquisition unit 42 identifies the physical quantities of each of these shape elements, such as length, diameter, and number. The numerical values ​​shown in Figure 3 are these physical quantities. The shape element data acquisition unit 42 extracts shape elements for each component that makes up the sheet metal product.

[0025] Furthermore, the shape element data acquisition unit 42 acquires estimation shape element data necessary for estimating man-hours from the extracted shape element data 421 and generates a shape element database. When the shape element data acquisition unit 42 acquires estimation shape element data necessary for estimating man-hours from the extracted shape element data 421, it reads acquisition rule data from the acquisition rule storage unit 51 in the storage device 50 shown in Figure 2, and acquires estimation shape element data based on the read acquisition rule data to generate a shape element database. The acquisition rule data 511 is shown in Figure 4. The generated shape element database is shown in Figure 5.

[0026] Figure 4 shows an example of acquisition rule data 511 used by the shape element data acquisition unit 42. Figure 5 shows an example of shape element database 521 generated by the shape element data acquisition unit 42. In Figures 4 and 5, specific names are shown in the shape element name and physical quantity name columns for ease of understanding, but in actual information processing, shape element names and physical quantity names are represented by codes, such as alphanumeric characters, to simplify the information processing. Also, in Figure 4, the specific contents of the acquisition rules are omitted.

[0027] As shown in Figure 4, in the acquisition rule data 511, acquisition rules are associated with combinations of shape elements and physical quantities. For example, in the first row of the acquisition rule data 511, a specific acquisition rule is associated with the combination of the shape element "plate thickness" and the physical quantity "lot size". Although Figure 4 does not show the specific details of the acquisition rules, each acquisition rule consists of (1) the storage location in the generated shape element database and (2) the method for obtaining the variables to be stored in that storage location from the shape element data 421. To illustrate the method for (2), the method for (2) is as follows: a) Search the first column of the shape element data 421 for a name that identifies the shape element specified in the method for (2), and if a matching name is found, use the value of the specified column number as the variable. Or, b) Use the specified value as the variable. Or, c) Use the value obtained by calculating with the specified calculation formula as the variable.

[0028] The shape element data acquisition unit 42 uses the acquisition rule data 511 to acquire shape element data for estimation and generates the shape element database 521 shown in Figure 5. That is, it generates a shape element database 521 in which a physical quantity name and the value of that physical quantity are associated with each shape element. The shape element data acquisition unit 42 then stores the shape element database 521 in the shape element database storage unit 52 located in the storage device 50.

[0029] On the other hand, as explained with reference to Figure 1, the man-hour estimation device 3 is equipped with an input device 7 for inputting information used to estimate man-hours, such as the names of equipment and coefficients, hereinafter simply referred to as "information". Before the man-hour estimation device 3 performs the man-hour estimation process, the user inputs the information in advance using the input device 7. Returning to Figure 2, the information acquisition unit 44 acquires the information input from the input device 7 and stores the acquired information as input information data in the input information storage unit 56 in the storage device 50.

[0030] The man-hour estimation calculation unit 43 estimates the man-hours for all processes involved in manufacturing the sheet metal product based on the shape element database 521 described above. Specifically, in order to perform the estimation, the man-hour estimation calculation unit 43 reads the estimation master from the estimation master storage unit 53 in the storage device 50. The man-hour estimation calculation unit 43 also reads the constant master and input information data from the constant master storage unit 55 and the input information storage unit 56 described above for use with the estimation master. Furthermore, in order to interpret the read estimation master 531, the man-hour estimation calculation unit 43 reads the reference master from the reference master storage unit 54. The estimation master 531, constant master 551, input information data 561, and reference master 541 are shown in Figures 6-9.

[0031] Figure 6 shows an example of an estimate master 531 stored in an estimate master storage unit 53 provided in the storage device 50. Figure 7 shows an example of a constant master 551 stored in a constant master storage unit 55 provided in the same storage device 50. Figure 8 shows an example of input information data 561 stored in an input information storage unit 56 provided in the same storage device 50. Figure 9 shows an example of a reference master 541 stored in a reference master storage unit 54 provided in the same storage device 50.

[0032] Note that in Figures 6-8, specific names are shown in the columns for process name, machine name, type name, etc., for ease of understanding. However, in actual information processing, these are represented by codes, such as alphanumeric characters, to simplify the information processing. Also, in Figure 6, since the explanation is based on an embodiment, the specific details of the conditions and estimation formula are omitted.

[0033] As shown in Figure 6, in the estimation master 531, estimation formulas are associated with process names, machine names, work names, conditions, and type names. Here, the machine name is the name of the equipment used in that process. The work name is the work performed in that process, also called an elemental work. The condition is the condition for whether or not the work with the work name in that row occurs, and if that condition is met, the man-hours of the estimation formula occur. The type name is the name of the type that identifies whether it is the main work for processing in that process or a preparatory work for preparing for processing in that process. The estimation formula is a formula for calculating the man-hours incurred for a specific work in a process identified by the process name, machine name, work name, etc. In short, the estimation formula is a formula for calculating the man-hours in each process of manufacturing sheet metal products. Thus, the estimation master 531 stores data such as estimation formulas and conditions for estimating the man-hours for each process of the manufacturing method assumed to be used for sheet metal products.

[0034] Furthermore, as shown in Figure 7, the constant master 551 stores multiple constant names and the corresponding constant values ​​in association with each constant name. Here, a constant name refers to the name of the constant used in the estimation formula of the estimation master 531.

[0035] Furthermore, as shown in Figure 8, the input information data 561 stores multiple input information names and the corresponding input information values. Here, the input information name refers to the name of the information input from the input device 7. For example, it could be the name of a correction value or coefficient used to correct the estimation formula.

[0036] As described above, in the estimation master 531 shown in Figure 6, the estimation formula is written in a specific notation style to indicate the reference location of the parameters of the estimation formula, that is, to indicate the reference location within the shape element database 521, constant master 551, and input information data 561. The reference destination master 541 shown in Figure 9 stores data that identifies that specific notation style. In other words, the reference destination master 541 stores information that associates a specific notation style with how the parameters are referenced in the shape element database 521, constant master 551, and input information data 561 in the case of that notation style.

[0037] More specifically, the reference master 541 indicates that: (1) when the notation method for identifying the parameters of the estimation formula is [1, P], the parameters are identified by the referencing operation of "referencing the constant master. Obtain the constant with the code specified by P in the constant master"; (2) when the notation method for identifying the parameters of the estimation formula is [2, Q], the parameters are identified by the referencing operation of "referencing the input information table. Obtain the information with the code specified by Q in the input information table"; and (3) when the notation method for identifying the parameters of the estimation formula is [3, X, Y], the parameters are identified by the referencing operation of "referencing the shape element database. Obtain the physical quantity specified by Y for the shape element specified by X in the shape element database". Thus, the reference master 541 stores data representing the references that store the data used in calculations using the estimation formula of the estimation master 531.

[0038] Note that P, Q, X, and Y above represent code names. In this specification, we will explain using specific names rather than code names. In terms of specific names, P and Q are the names of constants and input information, respectively. X and Y are the names of shape elements and physical quantities, respectively.

[0039] The man-hour estimation calculation unit 43 reads the estimation master 531, constant master 551, input information data 561, and reference master 541. The man-hour estimation calculation unit 43 also reads the shape element database 521. Then, the man-hour estimation calculation unit 43 calculates the man-hours using an estimation formula for each process specified in each row of the estimation master 531 shown in Figure 6, which it has read. When the man-hour estimation calculation unit 43 calculates the man-hours using the estimation formula for each process specified in each row of the estimation master 531, it first (1) identifies the data to be used to apply to the parameters of the estimation formula from the above description method that identifies the parameters of the estimation formula based on the reference master 541 that it has read. Subsequently, (2) the man-hour estimation calculation unit 43 obtains the data to be used from the constant master 551, input information data 561, and shape element database 521. Furthermore, (3) the man-hour estimation calculation unit 43 applies the acquired usage data to the estimation formula and estimates the man-hours for the process in that formula. The man-hour estimation calculation unit 43 performs these steps (1) to (3) when calculating man-hours using the estimation formula for each process. In this way, the man-hour estimation calculation unit 43 estimates the man-hours for each process.

[0040] The man-hour estimation calculation unit 43 estimates man-hours for all rows in the estimation master 531. The man-hour estimation calculation unit 43 then estimates the man-hours for each process name in the estimation master 531 by summing the estimated man-hours for that process name. The man-hour estimation calculation unit 43 also calculates the man-hours required to manufacture the sheet metal product, i.e., the finished product, by summing the man-hours for all processes. The man-hour estimation calculation unit 43 may also estimate man-hours separately for each type name in the estimation master 531, i.e., for each type of work such as main work and preparation work.

[0041] Returning to Figure 2, the man-hour estimation calculation unit 43 transmits the man-hour estimation result data to the cost estimation calculation unit 45 and the output unit 46.

[0042] When the cost estimation calculation unit 45 receives the man-hour estimation result data from the man-hour estimation calculation unit 43, it reads the unit price master stored in the unit price master storage section 57 of the storage device 50, and calculates the cost based on the unit price master. Said unit price master is shown in FIG. 10.

[0043] FIG. 10 is a diagram showing an example of the unit price master 571 stored in the unit price master storage section 57 provided in the storage device 50.

[0044] As shown in FIG. 10, in the unit price master 571, each process name is associated with the unit price of the corresponding process. Said unit price refers to the cost per man-hour.

[0045] The cost estimation calculation unit 45 calculates the cost for each process by multiplying the man-hour estimation result for each process calculated by the man-hour estimation calculation unit 43 by the unit price of the corresponding process stored in the unit price master 571. Thereby, the cost estimation calculation unit 45 calculates the cost related to the man-hours of each process. In addition, the cost estimation calculation unit 45 sums up the costs of each process to calculate the cost related to the man-hours required for manufacturing the sheet metal processed product, i.e., the product. The cost estimation calculation unit 45 transmits the calculated cost estimation result data to the output unit 46 shown in FIG. 2.

[0046] Although not shown in the drawings, a material cost database may be stored in the storage device 50. In this case, the cost estimation calculation unit 45 may calculate the total cost required for manufacturing the product based on the material cost database.

[0047] The output unit 46 receives the man-hour estimation result data from the man-hour estimation calculation unit 43, and further receives the cost estimation result data from the cost estimation calculation unit 45, then outputs these data to the output device 8 shown in FIG. 1. When the output device 8 is a liquid crystal display device, the output device 8 displays the man-hour and cost estimation results on its display section.

[0048] Furthermore, the output unit 46 may cause the storage device 50 to store the estimation results of man-hours and costs. Each time the output unit 46 receives estimation result data from the man-hour estimation calculation unit 43 and the cost estimation calculation unit 45, the output unit 46 causes the output device 8 to output the current man-hour and cost estimation results together with the previous man-hour and cost estimation results. For example, the previous estimation results and the current estimation results may be displayed in a comparable manner on a display unit of a liquid crystal display device. Accordingly, the output unit 46 presents information that enables cost analysis to a user.

[0049] As described above, in the man-hour estimation device 3 according to the first embodiment, the storage device 50 stores: an estimation master 531 representing an estimation formula for estimating man-hours for each process of a manufacturing method for a sheet metal processed product that is a product; and a reference destination master 541 representing a reference destination where usage data to be used for calculation using the estimation formula is stored, the usage data being selected from shape element name data and physical quantity name data included in a shape element database 521. Then, for each estimation formula for each process, the man-hour estimation calculation unit 43 specifies the usage data based on the reference destination master 541, acquires the usage data from the shape element database 521, and further applies the acquired usage data to the estimation formula, thereby calculating man-hours for each process. Furthermore, the man-hour estimation calculation unit 43 integrates the calculated man-hours for each of the processes to calculate the total man-hours required for manufacturing the product.

[0050] As described above, in the man-hour estimation device 3, the reference destination master 541 necessary for calculation is stored in the storage device 50. Therefore, by changing the reference destination master 541, the man-hour estimation device 3 can easily change the reference destination, and thus can substantially change the calculation formula. As a result, the man-hour estimation device 3 can quickly change the calculation formula when correction is required.

[0051] Similarly, in the man-hour estimation device 3, the estimation master 531 necessary for generating a calculation formula is stored in the storage device 50. Therefore, by changing the estimation master 531, the man-hour estimation device 3 can easily change the calculation formula. As a result, the man-hour estimation device 3 can quickly change the calculation formula when correction is required.

[0052] In the estimation master 531, the parameters of the estimation formula are associated with reference data indicating the reference destination in the reference destination master 541. The man-hour estimation calculation unit 43 then identifies the data to be used in calculating the estimation formula based on the reference data, applies the data to the parameters, and calculates the man-hours for each process. In the man-hour estimation device 3, the estimation formula can be changed simply by modifying the association between the parameters of the estimation formula and the reference data. For this reason, the man-hour estimation device 3 is highly versatile.

[0053] The sheet metal processed product described in Embodiment 1 above is an example of a product as defined in this disclosure. The shape element data acquisition unit 42 is an example of a database generation unit as defined in this disclosure. Furthermore, the shape element database 521 is an example of a database as defined in this disclosure. The shape element name data and physical quantity name data of the shape element database 521 are examples of database type data and physical quantity data as defined in this disclosure. The man-hour estimation calculation unit 43 is an example of a first calculation unit as defined in this disclosure. The storage device 50 is an example of a first storage unit as defined in this disclosure.

[0054] Furthermore, as explained earlier, the reference master 541 associates a specific description method with information on how parameters are referenced in the shape element database 521, constant master 551, and input information data 561 in the case of that description method. The information in the reference master 541 on how parameters are referenced in the shape element database 521, constant master 551, and input information data 561 may also be called reference data. This reference data is an example of the first reference data as referred to in this disclosure. The information in the reference master 541 on how parameters are referenced in the constant master 551 is an example of the second reference data as referred to in this disclosure. The information in the reference master 541 on how parameters are referenced in the input information data 561 is an example of the third reference data as referred to in this disclosure.

[0055] (Embodiment 2) In Embodiment 1, the cost estimation calculation unit 45 uses a unit price master 571, which stores the unit price for each process, to calculate the cost of the man-hours required to manufacture the product. However, the cost estimation calculation unit 45 may use a database other than the unit price master 571 to calculate the cost of manufacturing the product. Alternatively, the cost estimation calculation unit 45 may use a database other than the unit price master 571 to calculate the cost of manufacturing the product.

[0056] In the man-hour estimation device 3 according to Embodiment 2, the cost estimation calculation unit 45 calculates the cost of manufacturing the product using other databases in addition to the unit price master 571. The man-hour estimation device 3 according to Embodiment 2 will be described below with reference to Figures 11 to 15. Embodiment 2 will be described in a manner different from Embodiment 1.

[0057] Figure 11 is a block diagram of the storage device 50 provided in the man-hour estimation device 3 according to Embodiment 2. For ease of understanding, Figure 11 only shows the connection relationship between the storage device 50 and the cost estimation calculation unit 45, and omits the illustration of the connection relationships with other components of the man-hour estimation device 3.

[0058] As shown in Figure 11, the storage device 50 includes, in addition to the unit price master storage unit 57 described in Embodiment 1, a material cost database storage unit 58, an equipment depreciation cost database storage unit 59, an indirect cost rate master storage unit 60, and a product specification master storage unit 61. The material cost database storage unit 58, the equipment depreciation cost database storage unit 59, the indirect cost rate master storage unit 60, and the product specification master storage unit 61 each store the material cost database, the equipment depreciation cost database, the indirect cost rate master, and the product specification master, respectively. Furthermore, a cost estimation calculation unit 45 is connected to the material cost database storage unit 58, the equipment depreciation cost database storage unit 59, the indirect cost rate master storage unit 60, and the product specification master storage unit 61, making them available for use.

[0059] The cost estimation calculation unit 45 uses the unit price master 571 described in Embodiment 1, as well as data from the material cost database storage unit 58, the equipment depreciation cost database storage unit 59, the indirect cost rate master storage unit 60, and the product specification master storage unit 61 to calculate the cost for each process and the cost required to manufacture the sheet metal processed product, i.e., the product. Hereinafter in Embodiment 2, sheet metal processed products will simply be referred to as products.

[0060] In detail, the cost estimation calculation unit 45 first calculates the amount of material used from the data of shape element names, physical quantity names, and physical quantity values ​​contained in the shape element database 521 shown in Figure 5, or from the design data of the CAD device 2. Next, the cost estimation calculation unit 45 reads the material cost database stored in the material cost database storage unit 58 and calculates the material cost using that material cost database. That material cost database is shown in Figure 12.

[0061] Figure 12 shows an example of a material cost database 581 stored in a material cost database storage unit 58 provided in a storage device 50 of the man-hour estimation device 3.

[0062] As shown in Figure 12, the material cost database 581 associates the unit price and yield rate with the material name, dimensions, and quantity. Here, the quantity refers to the inventory quantity. The yield rate is the percentage of the material that is actually used as a product when the material specified by the material name in the material cost database 581 is used in the manufacture of a product. For example, it is the ratio of finished products to the material obtained after removing the scraps separated by cutting and the parts treated as defects during processing. To give an example from the material cost database 581 shown in Figure 12, the yield rate is 95% for a steel plate with dimensions of 1 x 2 m, and the yield rate is 90% for an aluminum plate with dimensions of 1 x 1 m. Although not shown in Figure 12, the above yield rates are set for each assumed process.

[0063] The CAD device 2 described in Embodiment 1 holds product design data along with data on the materials to be used in the product. In Embodiment 2, the design data acquisition unit 41 shown in Figure 2 acquires the material data along with the product design data and transmits the acquired material data to the cost estimation calculation unit 45. As a result, the cost estimation calculation unit 45 identifies the materials to be used in the manufacture of the product. Alternatively, the material names corresponding to the product design data may be input from the input device 7, and the cost estimation calculation unit 45 may identify the materials to be used in the manufacture of the product from the input material names.

[0064] Returning to Figure 11, the cost estimation calculation unit 45 reads the material cost database 581 and applies the unit price and yield rate corresponding to the specified material in the read material cost database 581 to the amount of material used calculated using the shape element database 521 to calculate the material cost. This calculation is performed for each process to calculate the material cost for each process.

[0065] Next, the cost estimation calculation unit 45 calculates the equipment costs using the equipment depreciation cost database stored in the equipment depreciation cost database storage unit 59. The equipment depreciation cost database is shown in Figure 13.

[0066] Figure 13 shows an example of an equipment depreciation expense database 591 stored in the equipment depreciation expense database storage unit 59 provided in the storage device 50.

[0067] As shown in Figure 13, the equipment depreciation cost database 591 associates the process name and the name of the equipment used in the process specified by that process name with the monthly depreciation cost, monthly operating hours, and hourly rate of that equipment. Here, the hourly rate is the unit price of equipment depreciation cost per hour for the equipment used in the process, and is obtained by dividing the monthly depreciation cost of that equipment by the monthly operating hours. To explain using the example shown in the equipment depreciation cost database 591 in Figure 13, the monthly depreciation cost of the laser cutting machine used in the cutting process is 1,000,000 yen, and the operating hours are 200 hours. As a result, the hourly rate is 5,000 yen.

[0068] In Figure 13, "laser" refers to a laser cutting machine, and "plasma" refers to a plasma cutting machine. Also, "Press 1" and "Press 2" refer to press machine No. 1 and press machine No. 2. In the example shown in Figure 13, monthly depreciation expenses and monthly operating hours are included in the equipment depreciation expense database 591, but these data may be omitted, and instead, only the hourly rate may be associated with the process name and equipment name.

[0069] The cost estimation calculation unit 45 reads the equipment depreciation cost database 591 and calculates the equipment cost by multiplying the hourly rate for each process in the equipment depreciation cost database 591 by the man-hour estimation calculation unit 43 that calculates the man-hours for each process.

[0070] The cost estimation calculation unit 45 further calculates the indirect costs using the indirect cost rate master stored in the indirect cost rate master storage unit 60 and the product specification master stored in the product specification master storage unit 61. These indirect cost rate master and product specification master are shown in Figures 14 and 15.

[0071] Figure 14 shows an example of an indirect cost rate master 601 stored in the indirect cost rate master storage unit 60 provided in the storage device 50. Figure 15 shows an example of a product specification master 611 stored in the product specification master storage unit 61 provided in the storage device 50.

[0072] As shown in Figure 14, the indirect cost rate master 601 associates the allocation basis and the allocation rate with the indirect cost items that should be allocated to the manufacturing cost of the product. Here, the indirect cost items are, for example, administrative expenses, sales expenses, utilities, building rent, quality control expenses, insurance premiums, and depreciation expenses. The allocation basis is, for example, the direct cost basis which determines the amount to be allocated based on direct costs, the labor hour basis which determines the amount to be allocated based on labor hours, and the area basis which determines the amount to be allocated based on the area of ​​the building used in the process. The allocation rates shown in Figure 14 specify the rates corresponding to each allocation basis. For example, in the example shown in Figure 14, if the indirect cost item is administrative expenses, the allocation rate is 15% of direct costs. If the indirect cost item is utilities, the allocation rate is 500 yen per hour.

[0073] Direct costs refer to the costs directly involved in the manufacturing of the product, such as the material costs, equipment costs, and labor costs for each process, as mentioned above. Here, labor costs are calculated by the cost estimation calculation unit 45 multiplying the hourly rate stored in the unit price master 571 described in Embodiment 1 by the man-hours for each process.

[0074] In contrast, in the product specification master 611, as shown in Figure 15, the product ID set for each product is associated with the area of ​​the building used for manufacturing the product, the monthly production quantity which is the planned number of products to be produced each month, the number of products per lot, and the material.

[0075] The cost estimation calculation unit 45 reads the indirect cost rate master 601 and the product specification master 611, and calculates the indirect costs to be allocated to the manufacturing cost of the product from the read indirect cost rate master 601 and product specification master 611. For example, the cost estimation calculation unit 45 calculates the indirect costs according to (1)-(3) below.

[0076] (1) The cost estimation calculation unit 45 determines the amount to be allocated based on direct costs and sets that amount as indirect costs. Specifically, for the administrative expenses, sales expenses, quality control expenses, and insurance premiums in the indirect cost items of the indirect cost rate master 601 shown in Figure 14, the allocation rate specified in the allocation rate column of the indirect cost rate master 601 is multiplied by the direct costs. In this way, the cost estimation calculation unit 45 calculates the indirect costs for each indirect cost item.

[0077] (2) The cost estimation calculation unit 45 determines the amount to be allocated based on man-hours and sets that amount as indirect costs. Specifically, for utilities and depreciation expenses in the indirect cost items of the indirect cost rate master 601 shown in Figure 14, the total man-hours required to manufacture the product calculated by the man-hour estimation calculation unit 43 are multiplied by the hourly cost specified in the allocation rate column of the indirect cost rate master 601. In this way, the cost estimation calculation unit 45 calculates the indirect costs for each indirect cost item.

[0078] (3) The cost estimation calculation unit 45 determines the amount to be allocated based on area and sets that amount as indirect costs. Specifically, in the case of building rent, which is an indirect cost item in the indirect cost rate master 601 shown in Figure 14, the indirect cost, which is the building usage cost per product, is calculated using the building area specified in the usage area column of the product specification master 611 and the planned monthly production quantity of products specified in the monthly production quantity column.

[0079] The cost estimation calculation unit 45 calculates the indirect costs for all indirect cost items in the indirect cost rate master 601 according to (1)-(3) above. Then, the cost estimation calculation unit 45 sums up all the indirect costs for the indirect cost items to obtain the total indirect cost.

[0080] The cost estimation calculation unit 45 calculates the manufacturing cost of the product by summing up the calculated material costs, equipment costs, labor costs, and total indirect costs. The cost estimation calculation unit 45 calculates the selling price of the product by applying the target profit margin to the calculated manufacturing cost of the product. The cost estimation calculation unit 45 transmits the calculated manufacturing cost and selling price data of the product to the output unit 46 shown in Figure 11.

[0081] The output unit 46 outputs the received data on the manufacturing cost and selling price of the product to the output device 8 shown in Figure 1. For example, if the output device 8 is a liquid crystal display, it displays the calculated manufacturing cost and selling price of the product on its display unit. In this way, the output unit 46 informs the user of the manufacturing cost and selling price of the product.

[0082] Furthermore, the output unit 46 may obtain data on material costs, equipment costs, labor costs, and overall indirect costs from the cost estimation calculation unit 45 and output this cost data to the output device 8. In this way, the output unit 46 informs the user of the amounts of material costs, equipment costs, labor costs, and overall indirect costs incurred in the manufacture of the product, providing the user with the information necessary to analyze the breakdown of the product's manufacturing costs and the manufacturing costs themselves.

[0083] As described above, the storage device 50 of the man-hour estimation device 3 according to Embodiment 2 stores a material cost database 581 which stores the unit price of materials used in the manufacture of the product and the yield rate in the manufacture of the product; an equipment depreciation cost database 591 which stores the hourly cost, which is the cost per hour when the equipment used in each process is in operation; a unit price master 571 which stores the cost required per man-hour in each process; and an indirect cost rate master 601 which stores the allocation rate for calculating indirect costs. The cost estimation calculation unit 45 then uses the material cost database 581, the equipment depreciation cost database 591, the unit price master 571, and the indirect cost rate master 601 to calculate the manufacturing cost of the product. For this reason, as with the reference master 541 described in Embodiment 1, the calculation of the manufacturing cost of the product can be easily modified by changing the material cost database 581, the equipment depreciation cost database 591, the unit price master 571, and the indirect cost rate master 601. Furthermore, since the cost estimation calculation unit 45 calculates the manufacturing cost of the product, informing the user of that manufacturing cost allows the user to easily understand the manufacturing cost.

[0084] Furthermore, the cost estimation calculation unit 45 informs the user of the material costs, equipment costs, labor costs, and indirect costs that formed the basis of the product's manufacturing cost. This allows the user to understand the breakdown of the product's manufacturing cost and easily analyze the product's cost.

[0085] The indirect cost rate master 601 mentioned above is an example of an indirect cost rate database as defined in this disclosure. The unit price master 571 is also an example of a unit price database as defined in this disclosure. Furthermore, the cost estimation calculation unit 45, material cost database storage unit 58, equipment depreciation cost database storage unit 59, indirect cost rate master storage unit 60, and product specification master storage unit 61 are examples of a cost estimation device as defined in this disclosure. The cost estimation calculation unit 45 is an example of a second calculation unit as defined in this disclosure, and the storage device 50 is an example of a second storage unit as defined in this disclosure.

[0086] (Embodiment 3) In the man-hour estimation device 3 according to Embodiments 1 and 2, the man-hours calculated by the man-hour estimation calculation unit 43 are transmitted directly to the cost estimation calculation unit 45 and the output unit 46. However, the man-hour estimation device 3 is not limited to this. The man-hour estimation device 3 may also include a correction unit that corrects the man-hours calculated by the man-hour estimation calculation unit 43.

[0087] The man-hour estimation device 3 according to Embodiment 3 includes a correction unit that corrects the man-hours for each process calculated by the man-hour estimation calculation unit 43 to make the man-hours closer to the actual man-hours. The man-hour estimation device 3 according to Embodiment 3 will be described below with reference to Figures 16 to 18. Embodiment 3 will be described in a configuration that differs from Embodiments 1 and 2.

[0088] Figure 16 is a block diagram of the man-hour estimation device 3 according to Embodiment 3.

[0089] As shown in Figure 16, the man-hour estimation device 3 according to Embodiment 3 includes a correction unit 47 in addition to the configuration described in Embodiment 1.

[0090] The correction unit 47 receives the calculation result data for each process from the effort estimation calculation unit 43. Upon receiving the effort data for each process, the correction unit 47 multiplies that effort data by a correction coefficient corresponding to the process to calculate new effort. This calculation is performed for each process. The correction unit 47 transmits the calculated new effort data to the cost estimation calculation unit 45 and the output unit 46. The cost estimation calculation unit 45 and the output unit 46 handle the received new effort data in the same manner as described in Embodiments 1 and 2. Therefore, in Embodiment 3, the explanation of the operation of the cost estimation calculation unit 45 and the output unit 46 is omitted.

[0091] Such a correction unit 47 is realized by using at least one trained model that has undergone machine learning.

[0092] Figure 17 shows an example of the actual database 621, which is the source data for the training data 631 used in machine learning of the trained model of the correction unit 47 of the man-hour estimation device 3 according to Embodiment 3. Figure 18 shows an example of the same training data 631.

[0093] As shown in Figure 17, in the man-hour estimation device 3 according to Embodiment 3, the user may calculate the actual man-hours against the estimated man-hours for each product and process to create a performance database. For example, in the performance database 621 shown in Figure 17, each product ID is associated with the estimated man-hours estimated by the cost estimation calculation unit 45, the actual man-hours obtained by the user, the error rate indicating the difference between the estimated man-hours and the actual man-hours, the year and month of manufacture of the product identified by the product ID, and the manufacturing process of the product identified by the product ID. The trained model of the correction unit 47 is generated by machine learning the training data 631 shown in Figure 18, which is obtained by processing such a performance database 621.

[0094] In detail, as shown in Figure 18, the training data 631 associates the estimated man-hours estimated by the cost estimation calculation unit 45 with the manufacturing process of the product, along with an error rate indicating the difference between the estimated man-hours and the actual man-hours. The training data 631 does not contain the product ID and the year and month of manufacture of the product, which are found in the actual database 621. This is because it cannot be said that the product ID and the year and month of manufacture of the product are necessarily correlated with the error rate of man-hours.

[0095] In the correction unit 47, a learning model set for each process is generated by machine learning this learning data 631. For example, a linear regression model is used for the cutting process, and a random forest machine learning algorithm model is used for the bending process. A neural network machine learning algorithm model is used for the welding process, and a gradient boosting machine learning algorithm model is used for the combined process. These learning models are then machine-trained to output a corresponding error rate when the estimated man-hours from the learning data 631 and the product manufacturing process are input. As a result, the trained models in the correction unit 47 output an error rate for the process and the estimated man-hours for that process received from the cost estimation calculation unit 45.

[0096] The storage device 50 stores the learning models used for machine learning, namely linear regression, random forest, neural network, and gradient boosting, that is, each program, along with coefficients and parameters for executing those programs based on the results of machine learning. The processor 4 described in Embodiment 1 reads these programs, coefficients, and parameters from the storage device 50 and executes them, thereby realizing the correction unit 47, which is a functional block. Furthermore, although a learning model is set for each process, the learning models for each process may be models with different algorithms or models with the same algorithm.

[0097] The correction unit 47 uses this trained model to determine a correction coefficient for the man-hour data for each process received from the man-hour estimation calculation unit 43. Specifically, the correction unit 47 inputs the man-hour data calculated by the man-hour estimation calculation unit 43 and the data for the target process for which the man-hours were calculated into the trained model, and obtains the error rate data output by the trained model. From the obtained error rate, the correction unit 47 calculates a correction coefficient to bring the man-hours of the target process closer to the actual man-hours. Once the correction coefficient is calculated, the correction unit 47 uses this correction coefficient to correct the man-hours of the target process calculated by the man-hour estimation calculation unit 43. The correction unit 47 repeats this correction for each process. As a result, the correction unit 47 obtains a more accurate man-hour for the target process.

[0098] As described above, the man-hour estimation device 3 according to Embodiment 3 includes a correction unit 47 that corrects the man-hours for each process calculated by the man-hour estimation calculation unit 43 using a learned model. The learned model is a correction coefficient for each man-hour calculated by the man-hour estimation calculation unit 43, and is a model that has learned error correction coefficients obtained from the actual man-hours required to manufacture the actual product corresponding to each man-hour. For this reason, the estimated man-hours in the man-hour estimation device 3 according to Embodiment 3 are more accurate.

[0099] Furthermore, in the pre-trained models, each model is tailored to the specific manufacturing process of the product, and the training data 631 has been used for machine learning, resulting in more accurate calculations of the man-hours for each process.

[0100] In Embodiment 3, the trained model learns from training data 631, in which the estimated man-hours estimated by the cost estimation calculation unit 45 and the product manufacturing process are associated with an error rate indicating the difference between the estimated man-hours and the actual man-hours. However, the training data 631 is not limited to this. The training data 631 may also include a correction coefficient that corrects the estimated man-hours estimated by the cost estimation calculation unit 45 and the product manufacturing process. This is because by learning from such training data 631, the trained model can directly output the correction coefficient. Furthermore, the correction unit 47 does not need to calculate the correction coefficient from the error rate.

[0101] Furthermore, when creating the training data 631, it is advisable to calculate a confidence interval (in statistical terms) for the error rate of the performance database 621 and check whether that confidence interval falls within a predetermined acceptable range. If the confidence interval falls within the acceptable range, then it is advisable to use the error rate of the performance database 621 for the training data 631. This method allows for the creation of more accurate training data 631.

[0102] Furthermore, the correction unit 47 described in Embodiment 3 is an example of the third calculation unit as referred to in this disclosure. The estimated man-hours estimated by the cost estimation calculation unit 45 described above are an example of the man-hours for each process determined by the first calculation unit as referred to in this disclosure.

[0103] The effort estimation device 3, effort estimation system 1, effort estimation method, and program according to the embodiments of this disclosure have been described above, but the effort estimation device 3, effort estimation system 1, effort estimation method, and program are not limited thereto.

[0104] For example, in Embodiments 1-3, the product is a sheet metal processed product, but this disclosure is not limited thereto. The object for which the man-hour estimation device 3 estimates man-hours may be, for example, one manufactured by cutting. Thus, the object for which the man-hour estimation device 3 estimates man-hours may be manufactured by methods other than sheet metal processing.

[0105] Furthermore, in the above embodiments 1-3, the CAD device 2 and the man-hour estimation device 3 are separate devices, but the CAD device 2 and the man-hour estimation device 3 may be an integrated device. The CAD device 2 and the man-hour estimation device 3 may, for example, be formed by the same personal computer.

[0106] In the above embodiments 1-3, the effort estimation device 3 is equipped with a storage device 50. This storage device 50 may be provided on a device that can connect to the effort estimation device 3 via a network 100, for example, a cloud server accessible via the Internet. In that case, the cloud server may have an access authentication function to confirm that the effort estimation device 3 has legitimate authority when accessed, and a synchronization function to synchronize data with the effort estimation device 3.

[0107] Furthermore, although there was only one man-hour estimation device 3 in the above embodiments 1-3, for example, man-hour estimation devices 3 may be installed in each of multiple business locations. In that case, it is preferable that the storage device 50 can be connected to each man-hour estimation device 3 via the network 100. This configuration allows for the sharing of a single storage device 50, thereby reducing the operating costs of each man-hour estimation device 3.

[0108] In the above embodiments 1-3, the man-hour estimation device 3 estimates man-hours using data from the shape element database storage unit 52, the input information storage unit 56, and the constant master storage unit 55. However, the man-hour estimation device 3 may estimate man-hours using data from the shape element database storage unit 52 without using data from the input information storage unit 56 and the constant master storage unit 55. In other words, the input information storage unit 56 and the constant master storage unit 55 in the man-hour estimation device 3 may have any configuration.

[0109] Furthermore, in embodiments 1-3 described above, the man-hour estimation device 3 generates a shape element database 521. However, this disclosure is not limited thereto. The man-hour estimation device 3 does not need to generate the shape element database 521. Instead, the CAD device 2 can generate the shape element database 521. In this case, the man-hour estimation device 3 does not need to have a shape element data acquisition unit 42, and the CAD device 2 can have a shape element data acquisition unit 42 and generate the shape element database 521.

[0110] In the above embodiments 1-3, the acquisition rule storage unit 51, the shape element database storage unit 52, the estimation master storage unit 53, the reference master storage unit 54, the constant master storage unit 55, the input information storage unit 56, and the unit price master storage unit 57 are provided in one storage device 50, but they may be divided and provided in two or more storage devices 50. For example, the shape element database storage unit 52 may be provided in a storage device 50 provided in the CAD device 2. Also, two or more storage devices 50 may be connected to the man-hour estimation device 3 via the network 100.

[0111] In Embodiment 1-3, the effort estimation program is stored in memory 5, but the effort estimation program may also be distributed on a computer-readable non-temporary recording medium such as a flexible disk, CD-ROM (Compact Disc Read-Only Memory), DVD (Digital Versatile Disc), or MO (Magneto-Optical Disc). In this case, the effort estimation program stored on the recording medium is installed on a computer to configure a functional block that performs effort estimation processing, specifically a design data acquisition unit 41, a shape element data acquisition unit 42, an effort estimation calculation unit 43, an information acquisition unit 44, a cost estimation calculation unit 45, and an output unit 46, etc.

[0112] Furthermore, the effort estimation program may be stored on a disk device of a server on a communication network such as the Internet, and the effort estimation program may be downloaded, for example, by being superimposed on a carrier wave. Alternatively, the effort estimation process described above may be achieved by starting and executing the effort estimation program while it is being transferred over the communication network. Moreover, the effort estimation process described above may also be achieved by having all or part of the effort estimation program run on the server device, and by having a computer execute the program while sending and receiving information related to the process over the communication network.

[0113] Furthermore, when the effort estimation process is shared among various operating systems (OS), or when it is implemented through collaboration between the OS and an application, only the non-OS components may be stored and distributed on a medium, or they may be available for download. Also, the means for implementing the functional block that performs the effort estimation process are not limited to software; some or all of it may be implemented by dedicated hardware, including circuits.

[0114] This disclosure allows for various embodiments and modifications without departing from the broad spirit and scope of this disclosure. Furthermore, the embodiments described above are for illustrative purposes only and do not limit the scope of this disclosure. In other words, the scope of this disclosure is indicated by the claims, not by the embodiments. Various modifications made within the scope of the claims and the equivalent significance of the disclosure are considered to be within the scope of this disclosure.

[0115] This application is based on Japanese Patent Application No. 2025-29582, filed on 26 February 2025. The entire specification, claims, and drawings of Japanese Patent Application No. 2025-29582 are incorporated herein by reference.

[0116] 1. Man-hour estimation system, 2. CAD device, 3. Man-hour estimation device, 4. Processor, 5. Memory, 6. Network interface, 7. Input device, 8. Output device, 9. Bus, 21. Processor, 22. Memory, 23. Network interface, 24. Bus, 41. Design data acquisition unit, 42. Shape element data acquisition unit, 43. Man-hour estimation calculation unit, 44. Information acquisition unit, 45. Cost estimation calculation unit, 46. Output unit, 47. Correction unit, 50. Storage device, 51. Acquisition rule storage unit, 52. Shape element database storage unit, 53. Estimation master storage unit, 54. Reference master storage unit, 55. Constant master storage unit, 56. Input information storage unit, 57. Unit price master storage unit, 58. Material cost database storage unit, 59. Equipment depreciation cost database storage unit, 60. Indirect cost rate master storage unit, 61. Product specification master storage unit, 100. Network, 421. Shape element data, 511. Acquisition rule data, 521 Shape element database, 531 Estimate master, 541 Reference master, 551 Constant master, 561 Input information data, 571 Unit price master, 581 Material cost database, 591 Equipment depreciation cost database, 601 Indirect cost rate master, 611 Product specification master, 621 Actual performance database, 631 Learning data.

Claims

1. A man-hour estimation device comprising: a database generation unit that generates a database from the design data of a product, the database representing a plurality of shape elements having a product, the database including type data that identifies the type of each shape element and physical quantity data for each shape element associated with each type data; a first storage unit that stores an estimation master representing an estimation formula for estimating man-hours for each process of the manufacturing method of the product, and a reference master representing a reference location that stores usage data used for calculations using the estimation formula from the type data and physical quantity data included in the database; and a first calculation unit that, for each estimation formula of each process, identifies the usage data based on the reference master, obtains the usage data from the database, and further applies the obtained usage data to the estimation formula to calculate the man-hours for each process, and further calculates the total man-hours required to manufacture the product by accumulating the calculated man-hours for each process.

2. The labor cost estimation device according to claim 1, wherein in the estimation master, a first reference data indicating the reference destination of the reference destination master is associated with the parameters of the estimation formula, and the first calculation unit identifies the usage data based on the first reference data, applies the usage data to the parameters, and calculates the labor cost for each of the processes.

3. The man-hour estimation device according to claim 1 or 2, wherein the first storage unit stores a constant master that stores a plurality of constants for use in the estimation formula, the reference master includes second reference data representing a reference within the constant master for identifying a constant from the plurality of constants in the constant master that is used in calculations using the estimation formula in each of the processes, and the first calculation unit, for each of the processes, identifies a constant to be used in calculations using the estimation formula based on the reference master, obtains the constant from the constant master, applies the obtained constant and the obtained usage data to the generated estimation formula, and calculates the man-hours for each of the processes.

4. An effort estimation device according to any one of claims 1 to 3, further comprising an input device for inputting information to be used in the estimation formula, wherein the first storage unit includes a plurality of pieces of information input from the input device, the reference master includes third reference data indicating which of the plurality of pieces of information input from the input device is to be used for calculations using the estimation formula in each of the processes, and the first calculation unit, for each of the processes, identifies the information based on the reference master and obtains the information from the first storage unit, applies the obtained information and the obtained usage data to the generated estimation formula, and calculates the effort for each of the processes.

5. The man-hour estimation device according to any one of claims 1 to 4, wherein the database generation unit comprises: a design data acquisition unit that acquires design data from a three-dimensional computer-aided design device that assists in the design of the product; a shape element data acquisition unit that analyzes the design data acquired by the design data acquisition unit to extract the shape elements of the product, acquires data of the shape elements to be used for estimating the man-hours from the extracted shape elements, and generates the database, wherein the first storage unit stores rules that define which shape elements to acquire from the shape element data of the product included in the design data; and the shape element data acquisition unit acquires data of the shape elements to be used for estimating the man-hours based on the rules.

6. The man-hour estimation device according to any one of claims 1 to 5, wherein the first storage unit is located on a cloud server that can be accessed via the Internet of the first arithmetic unit.

7. The cost estimation device further comprises a cost estimation device that calculates the cost of manufacturing the product from the man-hours of each of the processes, the cost estimation device comprising: a second storage unit that stores: a material cost database storing the unit price of materials used in the manufacture of the product and the yield rate in the manufacture of the product; an equipment depreciation cost database storing the hourly cost, which is the cost per hour when the equipment used in each of the processes is in operation; a unit price database storing the cost required per man-hour in each of the processes; and an indirect cost rate database storing an allocation rate for calculating indirect costs; and a second calculation unit that calculates the manufacturing cost of the product using the databases, the material cost database, the equipment depreciation cost database, the unit price database, and the indirect cost rate database, the second calculation unit calculates the amount of material used from the type data and physical quantity data of the database, calculates the material cost by applying the unit price of the material and the yield rate of the material in the material cost database to the calculated amount of material used, and calculates the equipment cost by multiplying the man-hours of each of the processes by the hourly cost of the equipment depreciation cost database. A labor cost estimation device according to any one of claims 1 to 6, comprising: multiplying the labor time for each of the above processes by the cost in the unit price database to calculate labor costs; calculating indirect costs based on the calculated material costs, equipment costs, and labor costs and the allocation rate in the indirect cost rate database; and further calculating the manufacturing cost of the product from the calculated material costs, equipment costs, labor costs, and indirect costs.

8. The man-hour estimation device according to any one of claims 1 to 7, further comprising a third calculation unit that corrects the man-hours for each of the processes determined by the first calculation unit using a trained model which has learned the correction coefficients obtained from the actual man-hours required to manufacture the actual product corresponding to the man-hours for each of the processes.

9. A work time estimation system comprising: a three-dimensional computer-aided design apparatus for assisting in the design of a product; a work time estimation apparatus for acquiring design data of the product created with the assistance of the three-dimensional computer-aided design apparatus from the three-dimensional computer-aided design apparatus and estimating the work time for the manufacturing method of the product based on the acquired design data of the product, wherein the work time estimation apparatus comprises: a database generation unit for generating a database from the design data of the product, the database comprising a database representing a plurality of shape elements of the product, the database including type data that identifies the type of each shape element, and physical quantity data for each shape element associated with each type data; an estimation master for storing estimation formulas for estimating work time for each process of the manufacturing method of the product; and a reference master for storing reference destinations that store usage data used in calculations using the estimation formulas from the type data and physical quantity data included in the database. A man-hour estimation system comprising: a first calculation unit that, for each of the estimation formulas of the aforementioned processes, identifies the usage data based on the reference master and obtains the usage data from the database, and further applies the obtained usage data to the estimation formula to calculate the man-hours for each of the aforementioned processes, and further calculates the total man-hours required to manufacture the product by summing the calculated man-hours for each of the aforementioned processes.

10. A computer method for estimating the man-hours of a product, comprising: a database representing multiple shape elements of a product, the database including type data that identifies the type of each shape element and physical quantity data for each shape element associated with each type data; an estimation master representing an estimation formula for estimating the man-hours for each process of the manufacturing method of the product; and a reference master representing a reference location where usage data used in calculations using the estimation formula is stored from the type data and physical quantity data included in the database, the method comprising: a step of calculating the man-hours for each process by identifying the usage data based on the reference master for the estimation formula of each process, obtaining the usage data from the database, and further applying the obtained usage data to the estimation formula; and a step of calculating the total man-hours required to manufacture the product by summing the calculated man-hours for each process.

11. A computer connected to a storage device that stores a database representing multiple shape elements of a product, the database including type data that identifies the type of each shape element and physical quantity data for each shape element associated with each type data; an estimation master representing an estimation formula for estimating the man-hours for each process of the manufacturing method of the product; and a reference master representing a reference location where usage data used for calculations using the estimation formulas is stored from the type data and physical quantity data included in the database. The computer then performs the following steps: calculate the man-hours for each process by identifying the usage data based on the reference master for each estimation formula of each process, obtaining the usage data from the database, and then applying the obtained usage data to the estimation formula; and calculate the total man-hours required to manufacture the product by accumulating the calculated man-hours for each process.

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