JP2016126726A - Cost management device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To improve cost estimation by highly accurately specifying factors causing a large difference between estimated and actual costs related to manufacturing of products.SOLUTION: A cost management device includes: an external storage device 2 that stores an estimated cost and an actual cost determined for each work executed for a case of accepting an order of manufacturing products; group error rate calculation means 11 that calculates a group error rate representing an error rate of an estimated/actual cost difference geared to grouped work for each month using a reception data of the case; improvement factor determination means 12 that determines an improvement factor corresponding to a designated type out of improvement factors of the estimated/actual cost difference geared to the case and work; improvement factor error rate calculation means 13 that calculates an improvement factor error rate representing an error rate of the estimated/actual cost difference geared to the work for each month and for each determined improvement factor; influence degree calculation means 14 for calculating a coefficient of correlation between the group error rate and the improvement factor error rate; improvement factor ranking means 15 for ranking the improvement factors using the coefficient of correlation; and factor combination means 16.SELECTED DRAWING: Figure 15

Description

  The present invention relates to a technique for evaluating a difference in expected performance (predicted difference) that is a difference between an estimated cost and an actual cost in manufacturing a product.

  It is extremely important in terms of management for a manufacturer or the like who performs a build-to-order manufacturing to appropriately estimate the cost related to the production of a product. Proper cost estimation means that the difference between the estimated cost calculated in advance before the product is manufactured and the actual cost calculated after the product is actually manufactured is zero. Means as close to zero as possible). There are various factors that cause a difference in the actual results, and there are generally a plurality of factors. For this reason, it is never easy to identify the factor that causes the difference in the actual results in order to estimate the cost appropriately. However, if a factor that causes a large difference in actual performance can be identified, it is expected that the cost estimate will be greatly improved by reviewing that factor, that is, the actual result difference can be brought close to zero.

  Japanese Patent Application Laid-Open No. 2004-228620 discloses using feature information of a work process when calculating a standard time (resource restraint time) based on work performance data on a production line. By applying the technology of Patent Document 1, it is possible to calculate the estimated cost instead of the standard time based on the feature information of the work process. Therefore, the feature information of the work process can be one of the factors that cause the difference in the preliminary performance. However, as described above, since there are various other factors that cause the difference in the actual results, the technique of Patent Document 1 is insufficient for appropriately estimating the cost.

Japanese Patent No. 4309146

  In view of such circumstances, it is an object of the present invention to improve cost estimation by specifying with high accuracy a factor causing a large difference in actual performance related to product manufacturing.

In order to achieve the above object, the present invention provides:
A cost management device that evaluates the difference between the estimated cost and actual cost in manufacturing a product,
A storage device for storing the estimated cost and the actual cost, which is determined for each work performed on a matter for which an order for manufacturing the product has been received;
A group error rate calculation that calculates a group error rate indicating an error rate of the preliminary performance difference for the work grouped for each predetermined period using the order date of the case with reference to the storage device Means,
Improvement factor determination means for determining the improvement factor corresponding to the specified type among the improvement factors of the preliminary performance difference for the case and the work;
An improvement factor error for calculating an improvement factor error rate indicating an error rate of the preliminary performance difference for the work for each predetermined period and for each determined improvement factor with reference to the storage device Rate calculation means;
Influence degree calculation for calculating a correlation coefficient between the calculated group error rate and the calculated improvement factor error rate as an influence degree of each of the improvement factors with respect to the preliminary performance difference generated for each predetermined period. Means,
Improvement factor ranking means for ranking the improvement factors using the calculated degree of influence;
Factor combination means for combining two or more types of improvement factors different from each other from the top predetermined improvement factors having a large influence, ranked by the improvement factor ranking means,
The improvement factor error rate calculating means is:
For a work including all of the combined improvement factors, a composite improvement factor error rate indicating an error rate of the preliminary performance difference for each predetermined period is calculated.
It is characterized by that.

  According to the present invention, it is possible to improve cost estimation by specifying with high accuracy a factor that causes a large difference in actual performance related to product manufacture.

It is a functional lineblock diagram of a cost control device of a 1st embodiment. It is a data structure figure of matter data. It is a data structure figure of work data. It is a data structure figure of improvement factor data. It is explanatory drawing of a group error rate calculation means. It is explanatory drawing of an improvement factor determination means. It is a flowchart which shows the process of an improvement factor determination means. It is explanatory drawing regarding calculation of the improvement factor error rate regarding a process. It is explanatory drawing regarding calculation of the improvement factor error rate regarding a worker. It is explanatory drawing regarding calculation of the improvement factor error rate regarding a customer. It is explanatory drawing regarding calculation of the improvement factor error rate regarding a person in charge of estimation. It is a flowchart which shows the process which calculates an improvement factor error rate. It is explanatory drawing for calculating the correlation coefficient (influence degree) of a group error rate and an improvement factor error rate. It is a figure explaining ranking of influence degree. It is a functional block diagram of the cost management apparatus of 2nd Embodiment. It is a figure explaining the combination of an improvement factor. It is a flowchart which shows the process which calculates a composite improvement factor error rate.

  Next, modes for carrying out the present invention (hereinafter referred to as “embodiments”) will be described with reference to the drawings as appropriate. For convenience of explanation, a case will be described in which a manufacturer that manufactures orders from a plurality of customers uses the cost management apparatus of the present embodiment.

<< First Embodiment >>
As shown in FIG. 1, the cost management apparatus of this embodiment includes an input unit (input device 4), an output unit (display device 5), a control unit (computer 1), and a storage unit (external storage device 2 and memory (described later). It is a computer including hardware such as an error rate information storage unit 3)). The input unit includes, for example, an input interface and includes means operated by the user such as a keyboard, a pointing device, and a touch panel. The output unit includes, for example, an output interface and includes a display unit such as a display. The control unit includes, for example, a CPU (Central Processing Unit) and a dedicated circuit. The storage unit includes, for example, a storage medium such as a random access memory (RAM), a read only memory (ROM), a hard disk drive (HDD), and a flash (registered trademark) memory. When the control unit is composed of a CPU, information processing by a computer including the control unit is realized by a program execution process by the CPU. Further, the storage unit included in the computer stores a program (including a cost management program) for realizing the function of the computer instructed by the CPU. This realizes cooperation between software and hardware.

  The present embodiment can be realized by a program for executing the information processing, and the program can be provided by being stored in a computer-readable recording medium (eg, CD-ROM). The program can also be provided through a network such as the Internet.

  Referring to FIG. 1, the computer 1 includes a group error rate calculation unit 11, an improvement factor determination unit 12, an improvement factor error rate calculation unit 13, an influence degree calculation unit 14, and an improvement factor ranking unit 15. . The external storage device 2 includes item data 21, work data 22, and improvement factor data 23. For example, the external storage device 2 may be implemented as an external database server, or may be implemented as a storage device provided in the computer 1. The project data 21, the work data 22, and the improvement factor data 23 can take a data structure in a database format, or can take a data structure in another format. The error rate information storage unit 3 includes a group error rate storage unit 31 and an improvement factor error rate storage unit 32.

(Item data 21)
The case data 21 is data for specifying a case indicating a manufacturing request from a customer regarding a predetermined product. The case data 21 is created by, for example, input from the input device 4.
As shown in FIG. 2, the case data 21 includes elements such as a case ID (Identifier), a customer ID, an order date, a delivery date, and an estimate person ID.

The case ID is an identifier for identifying the case.
The customer ID is an identifier for identifying a customer who is an ordering source for product manufacture.
The order date is the date when a manufacturing request is received from a customer.
The delivery date is the date when the product is delivered to the customer.
The estimate person ID is an identifier for identifying an estimate person who estimates a case. The person in charge of estimation calculates an estimated cost for each operation described later.

(Work data 22)
The work data 22 is data for specifying work performed on a case for which a product manufacturing order has been received. The work data 22 is created by, for example, input from the input device 4. Generally, a plurality of operations are performed for one item.
As shown in FIG. 3, the work data 22 includes elements such as a work ID, a project ID, a process ID, an estimated cost, an actual cost, and a worker ID.

The work ID is an identifier for identifying a work.
The case ID is an identifier for identifying a case relating to work, and is associated with the case ID of the case data 21.
The process ID is an identifier for identifying a process required for manufacturing a product. In general, a plurality of processes are performed to manufacture one product. Since the work to which the work ID is attached is determined for each process, it can be said that the process corresponds to the work. Since predetermined equipment is used to perform the process, for example, the process ID can be used as an equipment identifier.

The estimated cost is an amount that is considered necessary in order to carry out the work. The estimated cost of the project can be calculated, for example, by totaling the estimated costs for each operation.
The actual cost is the amount actually spent when the work is performed. The actual cost of the project can be calculated, for example, by adding up the actual costs for each operation.
In addition, since the calculation method of estimated cost and actual cost is known, description is abbreviate | omitted. In addition, assuming that there is a predetermined conversion rate between the amount of money for the work and time, it is possible to calculate the estimated cost value and the actual cost value as time.

  The work person ID is an identifier for identifying the work person who performed the work.

(Improvement factor data 23)
The improvement factor data 23 is data that specifies the type of factor that causes a difference in the actual performance, which is the difference between the estimated cost and the actual cost. The type of factor specified by the improvement factor data 23 is an improvement factor expected to be able to appropriately estimate the cost. The improvement factor data 23 is created by, for example, input from the input device 4.
As shown in FIG. 4, the improvement factor data 23 includes elements such as a factor ID, target data, and error rate calculation items.

The factor ID is an ID for identifying the type of improvement factor.
The target data is data that is a target for selecting the type of improvement factor. Specifically, the target data is either the project data 21 or the work data 22.
The error rate calculation item is a specific type of improvement factor to be used when calculating the error rate of the preliminary performance difference described later.

  In the present embodiment, two items of a customer ID and an estimate person in charge ID are set as error rate calculation items for the case data 21. For the work data 22, two process IDs and work person IDs are set as error rate calculation items. That is, four types of customer, estimate person, process, and worker are handled as improvement factor types. Note that in order to evaluate the difference in the actual results, the contents of the case data 21 and the work data 22 can be changed as appropriate, and the contents and the number of error rate calculation items in the improvement factor data 23 can be changed as appropriate (3 Set up improvement factors of less than 5 types or more).

(Group error rate calculation means 11)
The group error rate calculation means 11 calculates a group error rate indicating an error rate of the difference between the actual results for the work in the work data 22 grouped for each predetermined period using the order date of the case data 21. . In the present embodiment, the “predetermined period” is one month. The group error rate calculation means 11 groups the work in the work data 22 by month (for example, for October, November, December,...). Then, the group error rate calculation means 11 calculates the group error rate using the following formula 1 for each group.

ここで、Ｎは、グループの属する作業の集合、ｎは集合Ｎの元（つまり、作業）、Ｅｎは、作業の見積原価、Ａｎは、作業の実際原価である。

Here, N is a set of work to which the group belongs, n is an element of the set N (that is, work), En is an estimated cost of the work, and An is an actual cost of the work.

  As shown in FIG. 5A, the work identified by W_001 to W_007 in the work data 22 is grouped by month using the order date in the project data 21. The group error rates for October, November, and December calculated using Equation 1 are calculated as shown on the right side of FIG.

  As shown in FIG. 5B, the calculated group error rate for each month is stored in the group error rate storage unit 31 included in the error rate information storage unit 3. The error rate information storage unit 3 can be mounted on the memory of the cost management apparatus.

(Improvement factor determination means 12)
The improvement factor determination means 12 determines the improvement factor corresponding to the arbitrarily designated type among the various types of improvement factors for the case data 21 and the work data 22. Specifically, the improvement factor determination means 12 designates one of the error rate calculation items (types of improvement factors) in the improvement factor data 23, and determines the improvement factor corresponding to the designated error rate calculation item as the project data. 21 and the work data 22 and the obtained improvement factors are listed. An example of a method for designating the type of improvement factor will be described later in the description of the processing shown in FIG.

  As shown in FIG. 6 (a), when the improvement factor determination means 12 designates a customer ID as an error rate calculation item in the improvement factor data 23 (see thick frame), as shown in FIG. 6 (b) One customer ID (C_001) included in the data 21 is acquired. Thereafter, as shown in FIG. 6C, the improvement factor determination means 12 creates an improvement factor list 322 that lists the acquired customer IDs. All of the remaining customer IDs in the case data 21, all estimate person IDs in the case data 21, all process IDs in the work data 22, and all work person IDs in the work data 22 are created in the same manner.

  The improvement factor list 322 is created for each improvement factor by associating the improvement factor list ID, the factor name, the item name, the target data, the next improvement factor list ID, and the error rate information. The created improvement factor list 322 is stored in the improvement factor error rate storage unit 32 included in the error rate information storage unit 3.

The improvement factor list ID is an identifier for identifying improvement factors to be listed. The ID is assigned to each improvement factor acquired from the case data 21 or the work data 22.
The factor name is the name of the improvement factor to be listed.
The item name is an error rate calculation item in the improvement factor data 23.
The target data is target data in the improvement factor data 23.
The next improvement factor list ID and error rate information will be described later, but the initial value is null.

  As shown in FIG. 6C, the improvement factor determination means 12 creates a list head array 321 in which only the improvement factor list IDs of the improvement factor list 322 are collected. The created list head array 321 is stored in the improvement factor error rate storage unit 32 included in the error rate information storage unit 3.

  As shown in FIG. 7, the procedure by which the improvement factor determining means 12 generates the improvement factor list 322 and the list head array 321 from the improvement factor data 23 is as follows.

  First, in step S01, the improvement factor determination means 12 substitutes the number of rows of the improvement factor data 23 into the variable len1. In the present embodiment, since four types of improvement factors such as a customer, a person in charge of quotation, a process, and a person in charge of work are handled, 4 is substituted into the variable len1. Thereafter, from step S02 to step S08, loop processing is performed for each i of 1 ≦ i ≦ variable len1.

  Next, in step S02, the improvement factor determination means 12 substitutes the entire i-th line of the improvement factor data 23 into the variable fac. After step S02, the process proceeds to step S03.

  Next, in step S03, the improvement factor determination means 12 assigns the value of the error rate calculation item in the variable fac to the variable eitem and also assigns the value of the target data in the variable fac to the variable target. In the present embodiment, the value of the target data that is substituted into the variable target is the case data 21 or the work data 22. After step S03, the process proceeds to step S04.

  Next, in step S04, the improvement factor determination means 12 substitutes the number of rows of the variable target into the variable len2. That is, the number of lines of the matter data 21 or the number of lines of the work data 22 is substituted for the variable len2. Thereafter, a loop process is performed for each j of 1 ≦ j ≦ variable len2 from step S05 to step S08.

  In step S05, the improvement factor determination means 12 is assigned to the variable eitem among the values (improvement factors) included in the jth line of the data (that is, the case data 21 or the work data 22) assigned to the variable target. The improvement factor corresponding to the error rate calculation item (type of improvement factor) is assigned to the variable item1. The value assigned to the variable item1 becomes the factor name in the improvement factor list 322 (FIG. 6C). After step S05, the process proceeds to step S06.

  Next, in step S06, the improvement factor determination means 12 determines whether or not the value assigned to the variable item1 has already been added as a factor name in the improvement factor list 322. If it has been added (Yes in step S06), the same determination is made for the other i and j. If all i and j have been added, the processing in FIG. 7 is terminated. On the other hand, if it has not been added (No in step S06), the process proceeds to step S07.

  Next, in step S07, the improvement factor determination means 12 newly adds a line corresponding to the improvement factor assigned to the variable item1 to the improvement factor list 322. After step S07, the process proceeds to step S08.

  Next, in step S08, the improvement factor determination means 12 newly adds the improvement factor list ID of the newly added row to the list head array 321. If all i and j are newly added to the improvement factor list 322 and newly added to the list head array 321, the processing in FIG. 7 ends.

(Improvement factor error rate calculation means 13)
The improvement factor error rate calculation means 13 targets the work for each improvement factor determined by the improvement factor determination means 12 by a predetermined period (in this embodiment, by month) using the order date of the project data 21. Then, the improvement factor error rate indicating the error rate of the preliminary performance difference is calculated. The improvement factor error rate calculation means 13 categorizes the work in the work data 22 by month (for example, for October, November, December,...), And further by type of improvement factor (by customer, by estimate). Group by person in charge, process, and person in charge of work). And the improvement factor error rate calculation means 13 calculates a group error rate using the following formula 2 for each group.

ここで、Ｍは、グループの属する作業の集合（Ｎ（式１参照）の部分集合）、ｍは集合Ｍの元（つまり、作業）、Ｅｍは、作業の見積原価、Ａｍは、作業の実際原価である。

Here, M is a set of work to which the group belongs (a subset of N (see Equation 1)), m is an element of the set M (that is, work), Em is the estimated cost of the work, Am is the actual work Cost.

  FIG. 8 shows how the improvement factor error rate calculation means 13 calculates the improvement factor error rate when the improvement factor is a process. As shown in FIG. 8A, the work identified by W_001 to W_007 in the work data 22 is grouped by month using the order date in the case data 21. When a process is designated as the improvement factor type (thick frame in FIG. 8A), the work identified by W_001 to W_007 in the work data 22 is P_001 to P_003 determined by the improvement factor determination means 12. It is further grouped using the identified steps. The improvement factor error rates for October, November, December, and P_001 to P_003 calculated using Equation 2 are calculated as shown on the right side of FIG. For example, the improvement factor error rate of P_001 for October is named “October P_001”.

  As shown in FIG. 8B, the calculated improvement factor error rates for each month and each process are stored in the improvement factor error rate storage unit 32 included in the error rate information storage unit 3. Specifically, the improvement factor error rate for each month (for each order month) is added to the column of error rate information in the improvement factor list 322 created so that the improvement factor determination means 12 lists each process.

  9, 10, and 11 show how the improvement factor error rate calculation means 13 calculates the improvement factor error rate when the improvement factor is the worker, customer, and estimate person. Yes. Since the calculation method of these improvement factor error rates is the same as the method shown in FIG. 8, the description thereof is omitted.

  As shown in FIG. 12, the procedure by which the improvement factor error rate calculation means 13 calculates the improvement factor error rate for each of the improvement factors is as follows.

  First, in step S11, the improvement factor error rate calculation means 13 refers to the list head array 321 in the improvement factor error rate storage unit 32 and substitutes the number of rows of the list head array 321 into the variable len3. Thereafter, a loop process is performed for each k of 1 ≦ k ≦ variable len3 from step S12 to step S17.

  In step S12, the improvement factor error rate calculation means 13 initializes the array cond []. cond [] is an extraction condition for extracting work satisfying a specific improvement factor from the work data 22. The initialization of the array cond [] is to make the work extraction condition unconditional. After step S12, the process proceeds to step S13.

  Next, in step S13, the improvement factor error rate calculation means 13 substitutes the k for the improvement factor list ID of the list head array 321 into the variable facId. After step S13, the process proceeds to step S14.

  Next, in step S14, the improvement factor error rate calculation means 13 assigns the row of the improvement factor list 322 having the improvement factor list ID assigned to the variable facId to the variable item2. After step S14, the process proceeds to step S15.

  Next, in step S15, the improvement factor error rate calculation means 13 uses the factor name, item name, and target data included in the line of the improvement factor list 322 assigned to the variable item2 as the work extraction condition cond []. Assign to. Note that the value assigned to cond [] may be only the factor name. After step S15, the process proceeds to step S16.

  Next, in step S16, the improvement factor error rate calculation means 13 extracts a work satisfying the extraction condition substituted for cond [] from the work data 22, and substitutes the extracted work for the array works []. The array works [] is a temporary storage area for work extracted from the work data 22. After step S16, the process proceeds to step S17.

  Next, in step S17, the improvement factor error rate calculation means 13 calculates the improvement factor error rate for the work assigned to the array works [], and the calculation result is used as error rate information in the improvement factor list 322. It adds (refer FIGS. 8-11). When the improvement factor error rate is added to the error rate information of the improvement factor list 322 for all k, the processing in FIG. 12 ends.

(Influence calculation means 14)
The influence degree calculation means 14 sets the correlation coefficient between the group error rate calculated by the group error rate calculation means 11 and the improvement factor error rate calculated by the improvement factor error rate calculation means 13 for each predetermined period (monthly). It is calculated as the degree of influence of each improvement factor on the generated difference in the actual performance. As shown in FIG. 13, when the difference in the forecast results for fiscal 2014 is targeted, 12 group (monthly) error rates from January to December and each improvement factor (P_001 is attached in FIG. 13). The correlation coefficient can be calculated using the twelve improvement factor error rates for Since the calculation method of the correlation coefficient is well known, the description is omitted.

(Improvement factor ranking means 15)
The improvement factor ranking means 15 ranks the improvement factors using the influence degree calculated by the influence degree calculation means 14. As shown in FIG. 14, the factor name, the item name, the target data, and the influence factor calculated by the influence degree calculation means 14 are set in the descending order of influence degree (thickness in FIG. 14). The improvement factors are ranked in (see the frame). The monthly improvement factor error rate caused by an improvement factor having a high ranking, that is, a large influence degree, shows a similar tendency to the monthly group error rate. Therefore, the higher the ranking of improvement factors, the more likely that the improvement factor is the main factor of the group error rate that occurred during the corresponding period. According to FIG. 14, it can be estimated that the process to which P_001 was attached is suspicious with respect to the cause of the preliminary performance difference indicated by the target group error rate. Therefore, it can be expected that the investigator will investigate the process of P_001 in the future and find the cause to effectively improve the difference in the actual results.

  According to the first embodiment, the group error rate for the work grouped by a predetermined period (monthly) using the order date of the case and the improvement by the predetermined period (monthly) The correlation coefficient with the improvement factor error rate for the work by factor, that is, the degree of influence is calculated. Thus, by estimating the improvement factor having a large influence level as the main factor causing the difference in the predicted performance of the group error rate, it is possible to identify the factor causing the large difference in the predicted performance related to the manufacture of the product with high accuracy. Therefore, it is possible to improve the cost estimate by specifying a factor that causes a large difference in the actual performance of manufacturing the product with high accuracy.

  Also, as the types of improvement factors, we take the customer who orders the production of the product, the person in charge of estimating the project, the process corresponding to the work, the person in charge of performing the work, and the difference in the predicted results of the group error rate Use at least one of these to identify improvement factors. As described above, since various improvement factors are used as compared with the conventional case, an improvement point of the difference in the actual results can be surely found.

<< Second Embodiment >>
When the second embodiment is described, when the description of the first embodiment overlaps with the first embodiment, the description is omitted and the difference is mainly described. As shown in FIG. 15, the computer 1 of the cost management apparatus of the present embodiment further includes factor combination means 16.

(Factor combination means 16)
The factor combination means 16 combines two or more improvement factors of different types from the top predetermined number of improvement factors having a large influence degree, ranked by the improvement factor ranking means 15. For example, in FIG. 16A, as the improvement factors to be combined, the top three (n = 3) having the greatest influence can be set (thick frame in FIG. 16A). The number of improvement factors to be combined can be designated from the input device 4, for example.

  As shown in FIG. 16 (b), the factor combination means 16 selects two of the top three improvement factors and creates a total of three types of improvement factor sets (factor 1, factor 2). Here, a group of improvement factors of the same type is excluded from the viewpoint of obtaining a difference in the actual results. For example, as shown in FIG. 16B, the set of the improvement factor of P_001 and the improvement factor of P_002 is excluded to exclude an erroneous idea of using two facilities for one work. As a result, a total of two sets of improvement factors (P_001, E_003) and (P_002, E_003) are created.

  The improvement factor determination unit 12 creates an improvement factor list 322 and a list head array 321 for the improvement factor group created by the factor combination unit 16 and stores them in the improvement factor error rate storage unit 32. As shown in FIG. 16C, the combined improvement factors (improvement factors with improvement factor list IDs X_1011, X_1012) are combined in the next improvement factor list ID column of the improvement factor list 322. The improvement factor list ID (X_2001, X_2001) of the improvement factor to be the partner is added. Furthermore, a new line having the added improvement factor list ID is added. In the list head array 321, improvement factor list IDs X_1011, X_1012, X_2001, and X_2001 added when the factor combination unit 16 combines improvement factors are added. In this way, by adding the improvement factor list ID as the combination partner to the next improvement factor list ID column of the improvement factor list 322, the combination of improvement factors combined by the factor combination means 16 is managed. Thereafter, the improvement factor error rate calculation means 13 calculates the improvement factor error rate for the improvement factor having the next improvement factor list ID.

  As shown in FIG. 17, the procedure for calculating the composite improvement factor error rate, which is the improvement factor error rate for the improvement factor set, is as follows. Of the procedure shown in FIG. 17, the description of the same procedure as that shown in FIG. 12 is omitted. The difference from the procedure shown in FIG. 12 is that steps S18, S19, and S20 are mainly added. After step S15 in FIG. 12, the process proceeds to step S18.

  In step S18, the improvement factor error rate calculation means 13 determines whether or not the next improvement factor list ID included in the row of the improvement factor list 322 assigned to the variable item2 is null. If the next improvement factor list ID is Null (Yes in step S18), the process proceeds to step S19. On the other hand, when the next improvement factor list ID is not Null (No in step S18), the improvement factor corresponding to the line of the improvement factor list 322 assigned to the variable item2 is the combined improvement factor error rate by the combination by the factor combination means 16. (In FIG. 16C, the improvement factor with the improvement factor list ID: X_1011, X_1012 in FIG. 16C), the process proceeds to step S20.

  In step S19, the improvement factor error rate calculation means 13 determines whether or not the improvement factor corresponding to the line of the improvement factor list 322 assigned to the variable item2 is an improvement factor to be combined by the factor combination means 16. Determine. If it is not an improvement factor to be combined (No in step S19), it means that this improvement factor is an improvement factor that is a calculation target of a single improvement factor error rate (an improvement factor error rate calculated in FIG. 12). In step S16, the process already described with reference to FIG. 12 is performed. On the other hand, if it is an improvement factor to be combined (Yes in step S19), this improvement factor (corresponding to the improvement factor with the improvement factor list ID: X_2001, X_2002 in FIG. 16C) Information processing is performed in order to obtain a composite improvement factor error rate that is a target of combination by the factor combination means 16 and that is an improvement factor (an improvement factor having the next improvement factor list ID) that is the combination partner in step S18. It exists only for convenience. Therefore, it is not particularly necessary to perform the calculation by the improvement factor error rate calculation means 13 with respect to this improvement factor, so the row (kth) processing for this improvement factor is completed, and the next row (k + 1) is processed. Continue.

  In step S20, the improvement factor error rate calculation means 13 adds the factor name, item name, and target data of the improvement factor corresponding to the next improvement factor list ID to the cond [] as a work extraction condition. As a result, the extraction condition substituted for cond [] is an AND condition of two factor names (P_001 and E_003 in FIG. 16C). After step S20, the process proceeds to step S16.

  When the processes of steps S16 and S17 are executed via step S20, the improvement factor error rate calculation means 13 targets the work including all of the combined improvement factors (P_001 and E_003 in FIG. 16C). Thus, the composite improvement factor error rate indicating the error rate of the difference in the preliminary performance for each predetermined period (monthly) is calculated. The calculated composite improvement factor error rate is added to the error rate information in the improvement factor list 322 in a predetermined format.

  If the combined improvement factor error rate calculated by the improvement factor error rate calculation means 13 exceeds a predetermined value (an arbitrary value input from the input device 4), the cause of the difference in the preliminary performance indicated by the calculated group error rate It can be estimated that the improvement factor (E_003) added to the next improvement factor list ID is further suspicious with respect to the improvement factor (P_001) originally estimated to be suspicious. In other words, for the process identified in P_001, the person in charge of E_003 who performed the process compared with the customer who ordered the product manufactured by the process or the person in charge of the estimate who calculated the estimated cost of the corresponding operation. Can be speculated, especially suspicious.

  According to the second embodiment, a combined improvement factor error rate is calculated from a combination of improvement factors by combining the top predetermined improvement factors having a large influence. As a result, with respect to the main factor estimated to cause a difference in the actual results of the group error rate, the improvement factor having a predetermined relationship with the main factor is identified with high accuracy as a sub-factor causing the difference in the actual results of the group error rate. can do. Therefore, it is possible to narrow down improvement factors that improve cost estimation.

≪Others≫
In this embodiment, when calculating the group error rate and the improvement factor error rate for work grouped by a predetermined period with reference to the order date of the manufacturing request, the month is taken as an example for the predetermined period. However, various periods such as days, weeks, quarters, and years can be used instead of months.

  When the operations are grouped by month, a group in which two or more groups for each month are combined can be created, and the group error rate and the improvement factor error rate for the created group can be calculated. That is, it is possible to group work corresponding to a period obtained by combining two or more different periods, and calculate a group error rate and an improvement factor error rate for the group.

  For example, as shown in FIG. 13, when calculating the degree of influence (correlation coefficient) from the monthly group error rate and improvement factor error rate in 2014, there are 12 samples of the group error rate and improvement factor error rate, respectively. Only exists. However, a group that combines the January group and the February group, a group that combines the January group and the March group, a group that combines the August, October, and December groups, and so on. The group error rate and the improvement factor error rate for the created groups can be used as samples for calculating the correlation coefficient. By using such a combination period, it is possible to increase the number of samples for which the correlation coefficient is used for calculation, and it is possible to increase the reliability of the calculated correlation coefficient itself.

  Moreover, in this embodiment, although the improvement factor which the factor combination means 16 combined was two, you may combine three or more improvement factors.

  In the present embodiment, the improvement factor determination means 12 uses the improvement factor data 23 to specify the improvement factor type (error rate calculation item), and then selects the improvement factor corresponding to the specified type as the project data. 21 and the work data 22, an improvement factor list 322 and a list head array 321 are created. However, the improvement factor determination means 12 does not use the improvement factor data 23, but the target data (customer ID, estimate person ID, process ID, work person ID) that can be an improvement factor from the case data 21 and the work data 22. Part or all may be extracted, and the improvement factor list 322 and the list head array 321 may be created.

In the present embodiment, components realized by hardware can be realized by software, and components realized by software can be realized by hardware.
Moreover, a new technique can be realized by appropriately combining the technical matters described in the present embodiment.
Other specific configurations of hardware, software, database, and the like can be changed as appropriate without departing from the spirit of the present invention.

1 Computer 2 External storage device (storage device)
3 Error rate information storage (memory)
DESCRIPTION OF SYMBOLS 4 Input device 5 Display apparatus 11 Group error rate calculation means 12 Improvement factor determination means 13 Improvement factor error rate calculation means 14 Influence degree calculation means 15 Improvement factor ranking means 16 Factor combination means 21 Project data 22 Work data 23 Improvement factor data 31 Group error rate storage unit 32 Improvement factor error rate storage unit

Claims (2)

A cost management device that evaluates the difference between the estimated cost and actual cost in manufacturing a product,
A storage device for storing the estimated cost and the actual cost, which is determined for each work performed on a matter for which an order for manufacturing the product has been received;
A group error rate calculation that calculates a group error rate indicating an error rate of the preliminary performance difference for the work grouped for each predetermined period using the order date of the case with reference to the storage device Means,
Improvement factor determination means for determining the improvement factor corresponding to the specified type among the improvement factors of the preliminary performance difference for the case and the work;
An improvement factor error for calculating an improvement factor error rate indicating an error rate of the preliminary performance difference for the work for each predetermined period and for each determined improvement factor with reference to the storage device Rate calculation means;
Influence degree calculation for calculating a correlation coefficient between the calculated group error rate and the calculated improvement factor error rate as an influence degree of each of the improvement factors with respect to the preliminary performance difference generated for each predetermined period. Means,
Improvement factor ranking means for ranking the improvement factors using the calculated degree of influence;
Factor combination means for combining two or more types of improvement factors different from each other from the top predetermined improvement factors having a large influence, ranked by the improvement factor ranking means,
The improvement factor error rate calculating means is:
For a work including all of the combined improvement factors, a composite improvement factor error rate indicating an error rate of the preliminary performance difference for each predetermined period is calculated.
Cost control device characterized by that. The type of the improvement factor is at least one of a customer who orders the manufacture of the product, a person in charge of estimating the matter, a step corresponding to the work, and a person in charge of performing the work. Including,
The cost management apparatus according to claim 1, wherein:

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