Manufacturing line management methods

Abstract

To provide a manufacturing line management method that is able to efficiently process the lots.SOLUTION: There is provided a manufacturing line management method according to one embodiment. The manufacturing line management method includes determining one or more variation characteristics. The one or more variation characteristics include at least one of a lot arrival variation characteristic at a process area in a manufacturing line, a capacity variation characteristic of the process area, and a lot stay variation characteristic in the process area. The manufacturing line is arranged with a plurality of process areas. Each of the plurality of process areas includes a plurality of resources. The manufacturing line management method includes determining inventory information according to the one or more variation characteristics. The inventory information is information on inventories to be provided respectively in the process areas.SELECTED DRAWING: Figure 7

Description

[Technical Field]

[0001] This embodiment relates to a method for managing a manufacturing line. [Background technology]

[0002] In a manufacturing line, when a lot is introduced into a process area containing multiple resources, those resources become operational to process the lot. Efficient lot processing is desirable in a manufacturing line. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Special Publication No. 2022-531919 [Patent Document 2] Japanese Patent Publication No. 2013-33466 [Patent Document 3] Japanese Patent Publication No. 2021-51342 [Overview of the project] [Problems that the invention aims to solve]

[0004] One embodiment aims to provide a method for managing a manufacturing line that can efficiently process lots. [Means for solving the problem]

[0005] According to one embodiment, a method for managing a manufacturing line is provided. The method for managing a manufacturing line is: Based on the characteristics of lot arrival variability at process areas in the manufacturing line and the characteristics of process area capacity variability, This includes determining the variation characteristics of the lot's stay within the process area. Made A manufacturing line consists of multiple process areas. Each of these process areas contains multiple resources. The method for managing the manufacturing line is: stay This includes obtaining inventory information according to fluctuation characteristics. Inventory information refers to information about the inventory that should be maintained within the process area. [Brief explanation of the drawing]

[0006] [Figure 1] A diagram showing the configuration of the manufacturing line in the first embodiment. [Figure 2] A diagram showing the fluctuation in inventory levels in response to variations in lot arrival at resources and variations in resource capacity in the first embodiment. [Figure 3] This figure shows the relationship between inventory capacity shortage, throughput, TAT (Turn Around Time), and variability in the first embodiment. [Figure 4] A diagram showing the functional configuration of the management system in the first embodiment. [Figure 5] A diagram showing the probability of operational loss occurring in the first embodiment. [Figure 6] A diagram showing the hardware configuration of the management system in the first embodiment. [Figure 7] A flowchart illustrating the general operation of the management system in the first embodiment. [Figure 8] A flowchart showing the process for determining the variation characteristics in the first embodiment. [Figure 9] A flowchart illustrating the process for obtaining inventory information in the first embodiment. [Figure 10] A diagram showing the configuration of the manufacturing line in the second embodiment. [Figure 11] A diagram showing the functional configuration of the management system in the second embodiment. [Figure 12] A flowchart showing the process for determining the variation characteristics in the second embodiment. [Figure 13] A flowchart illustrating the process for obtaining inventory information in the second embodiment. [Figure 14] A diagram showing the configuration of the manufacturing line in the third embodiment. [Figure 15] A diagram showing the functional configuration of the management system in the third embodiment. [Figure 16] A diagram illustrating circular references in mathematical formulas in the third embodiment. [Figure 17]A flowchart showing the process for determining the variation characteristics in the third embodiment. [Figure 18] A flowchart illustrating the process for obtaining inventory information in the third embodiment. [Figure 19] A diagram showing the configuration of the manufacturing line in the third embodiment. [Figure 20] A diagram showing the functional configuration of the management system in the fourth embodiment. [Figure 21] A diagram showing inventory fluctuations in two adjacent process areas in the fourth embodiment. [Figure 22] A flowchart showing the process for determining the variation characteristics in the fourth embodiment. [Figure 23] A flowchart illustrating the process for obtaining inventory information in the fourth embodiment. [Modes for carrying out the invention]

[0007] The manufacturing line control method according to the embodiments will be described in detail below with reference to the attached drawings. Note that the present invention is not limited by these embodiments. In the following description, identical or similar components will be denoted by the same reference numeral, and components that have already been described will be omitted from the description as appropriate. Furthermore, for components with reference numerals ending in letters for differentiation, if there is no need to distinguish between such components in the description, the reference numeral ending in letters may be omitted.

[0008] (First embodiment) The manufacturing line management method according to the first embodiment manages a manufacturing line having multiple process areas. Each process area includes multiple resources. In the manufacturing line, when a lot is placed into a process area, the resources become active and process the lot. The manufacturing line management method incorporates measures to ensure that lot processing is carried out efficiently on the manufacturing line.

[0009] Figure 1 shows the configuration of a manufacturing line in the first embodiment. A manufacturing plant for producing the products to be manufactured has multiple manufacturing lines P1 and P2. Multiple manufacturing lines P1 and P2 correspond to multiple products to be manufactured OB1 and OB2. Product OB1 is manufactured on manufacturing line P1, and product OB2 is manufactured on manufacturing line P2. Figure 1 shows two manufacturing lines P1 and P2 as an example, but a manufacturing plant may have three or more manufacturing lines.

[0010] As shown in Figure 1, the manufacturing line P has multiple process areas S. Figure 1 shows manufacturing line P1 with six process areas S1 to S6 arranged in order. 11 ~S 16 A manufacturing line P2 in which these are arranged in order is given as an example. The number of process areas arranged within each manufacturing line P may be 5 or less, or 7 or more.

[0011] Multiple process areas S1-S6 in manufacturing line P1 correspond to multiple processes in the manufacturing method of the product OB. Similarly, multiple process areas S in manufacturing line P2 11 ~S 16 This corresponds to multiple processes in the manufacturing method of the product to be manufactured. If the product to be manufactured OB is a semiconductor device, the multiple processes include processes such as coating, exposure, development, etching, cleaning, impurity introduction, film formation, and heat treatment of the semiconductor substrate.

[0012] Each process area S is allocated one or more resources E from resource group M. Resource group M includes multiple resources E. When the product to be manufactured OB is a semiconductor device, each resource E is a semiconductor manufacturing device that performs the processing for that process. If the process is a coating process, resource E includes a coating device. If the process is an exposure process, resource E includes an exposure device. If the process is a developing process, resource E includes a developing device. If the process is an etching process, resource E includes an etching device. If the process is a cleaning process, resource E includes a cleaning device. If the process is an impurity introduction process, resource E includes an ion implanter. If the process is a film deposition process, resource E includes a film deposition device. If the process is a heat treatment process, resource E includes a heat treatment device.

[0013] The objects processed by resource E to manufacture the target product OB will be referred to as a lot. A lot includes one or more substrates mounted on a hoop (not shown) and can be transported between resources E within the manufacturing line P by a transport device (not shown) or the like while mounted on the hoop. If resource E is single-wafer type, substrates are processed one by one in resource E; if resource E is batch type, substrates are processed in lot units in resource E. However, for simplification, in the following, we will assume that lots are processed in resource E.

[0014] If the product to be manufactured OB is a semiconductor device, the manufacturing line P may include multiple reentrant process areas S. For example, the multiple process areas S1 to S6 in manufacturing line P1 may include multiple reentrant process areas S2 to S5, as shown enclosed by dotted lines in Figure 1. Multiple process areas S in manufacturing line P2 11 ~S 16 As shown by the dotted lines in Figure 1, there are multiple reentrant process areas S. 12 ~S 15 It may include.

[0015] When the manufacturing target OB is a semiconductor device, the plurality of process areas S may include a process area S that is compatible among the plurality of manufacturing lines P. For example, the process area S2 of the manufacturing line P1 and the process area S of the manufacturing line P2 11 are similar processes with similar processing contents, as indicated by the dashed-dotted line enclosure in FIG. 1, and different lots corresponding to different manufacturing targets OB may be processable by the resource E according to a common recipe.

[0016] An inventory B may be provided in the manufacturing line P. The inventory B, also referred to as a buffer stocker, functions as a buffer that absorbs differences and fluctuations in the capacity of the resource E in the manufacturing line P. In FIG. 1, in the manufacturing line P1, inventories B2, B4, B5 are provided in the process areas S2, S4, S5, and in the manufacturing line P2, the process area S 12 , S 14 , S 15 is provided with inventory B 12 , B 14 , B 15 , and such a configuration is exemplified.

[0017] The number of lots that can be accommodated in the inventory B is referred to as the inventory capacity, and the number of lots actually accommodated in the inventory B is referred to as the inventory quantity. In FIG. 1, the inventories B2, B4, B5, B 12 , B 14 , B 15 have capacities of 3 lots, 5 lots, 2 lots, 1 lot, 3 lots, and 1 lot respectively, and such a configuration is exemplified. In FIG. 1, the inventories B2, B4, B5, B 12 , B 14 , B 15 have inventory quantities of 1 lot, 2 lots, 1 lot, 0 lots, 1 lot, and 0 lots respectively, and such a state is exemplified.

[0018] The following description focuses on one manufacturing line P, but the same applies to other manufacturing lines P.

[0019] In manufacturing line P, the inflow of lots into resource E and the capacity of resource E fluctuate. The inflow of lots corresponds to the lot arrival rate and indicates the number of lots (work-in-progress lots) input to resource E per unit time. If inventory B is set up in resource E, the number of lots input to resource E per unit time represents the number of lots input to inventory B per unit time. If inventory B is not set up in resource E, the number of lots input to resource E per unit time represents the number of lots directly input to resource E per unit time. The capacity of resource E corresponds to the lot processing rate by resource E and indicates the number of lots that resource E can process per unit time. The unit time may be one day. The reciprocal of the processing rate represents the lot processing time by resource E. The throughput of manufacturing line P indicates the number of lots (processed lots) that manufacturing line P outputs per unit time.

[0020] For example, suppose the inflow of lots to resource E in process area S fluctuates as shown in Figure 2(a), and the capacity of resource E fluctuates as shown in Figure 2(b). Figure 2 shows the fluctuation in inventory levels in response to the variability of lot arrivals to resource E and the variability of resource E's processing capacity. Figure 2(a) shows the variability of lot arrivals to the source. Figure 2(b) shows the variability of resource capacity. The capacity of resource E shown in Figure 2(b) corresponds to the processing time of the resource per unit lot.

[0021] If the inventory capacity in the process area S is assumed to be infinite, the number of items held in inventory will fluctuate as shown in Figure 2(c). If the actual inventory capacity in the process area S is too large for the fluctuations in inventory shown in Figure 2(c), resource E can operate continuously, but the cost of that inventory B may increase excessively. If the actual inventory capacity in the process area S is too small for the fluctuations in inventory shown in Figure 2(c), resource E may lose operational capacity due to insufficient inventory B, and lots may not be processed efficiently.

[0022] For example, if the influx of lots into resource E and / or the variability in resource E's capacity increases, and inventory B becomes deficient, as shown in Figure 3(a), inventory B may not be able to absorb the influx of lots into resource E and / or the variability in resource E's capacity, potentially resulting in operational losses for resource E. When operational losses occur for resource E, resource E's throughput tends to decrease, as shown in Figure 3(b). When resource E's throughput decreases, the turn-around time (TAT) of lots increases, as shown in Figure 3(c), making efficient lot processing difficult.

[0023] Each manufacturing line P, as shown in Figures 1 to 3, can be managed by the management system 1 shown in Figure 4. Figure 4 is a diagram showing the functional configuration of the management system 1.

[0024] The management system 1 determines the staggered characteristics of lot arrivals in the process area S based on the staggered characteristics of lot arrivals in the process area S and the staggered characteristics of the capacity of the process area S. The management system 1 determines inventory information regarding the inventory that should be set up in the process area S according to the staggered characteristics of lot stays in the process area S. The management system 1 may also determine the appropriate inventory capacity that allows the resource E of the process area S to operate continuously according to the staggered characteristics of lot stays in the process area S. The management system 1 may also determine the appropriate inventory capacity that results in zero operational loss for the resource E of the process area S according to the staggered characteristics of lot stays in the process area S.

[0025] For example, the management system 1 calculates the average processing time t of resource E based on the capacity fluctuation characteristics of the process area S. m The management system 1 calculates the probability P of operational loss of resource E in process area S according to the variation characteristics of the lot's stay in process area S. The management system 1 calculates the average processing time t of resource E as shown in the following equation 1. m Depending on the probability of occurrence P, the number of lots Z, which is the critical point at which the utilization rate of resource E in process area S during period T becomes zero, may be determined.

number

[0026] Depending on the lot size Z calculated by formula 1, management system 1 may set the appropriate capacity of inventory B to "greater than or equal to lot size Z".

[0027] The management system 1 functionally comprises a control unit 6, an acquisition unit 5, a storage unit 2, an evaluation unit 3, a calculation unit 81, and a calculation unit 82.

[0028] The memory unit 2 stores the management program PG. The management program PG includes a number of processes for performing predetermined management. The predetermined management includes managing the appropriate inventory capacity so that resource operation loss is zero in each process area S.

[0029] The control unit 6 comprehensively controls each part of the management system 1 according to the management program PG. The control unit 6 can manage the period T to be processed. The control unit 6 may control each part of the management system 1 to perform processing for period T.

[0030] The acquisition unit 5 acquires parameters 2a under the control of the control unit 6. Parameters 2a include the number of resources in each process area S of the manufacturing line P, the history of WIP (Work In Progress), the history of throughput, etc. The acquisition unit 5 may acquire parameters 2a in response to input from the user. The acquisition unit 5 may acquire parameters 2a via a communication medium such as a wired communication line or a wireless communication line.

[0031] The storage unit 2 may receive the parameter 2a from the acquisition unit 5 and store it as a database under the control of the control unit 6. The database includes resource information, WIP information, throughput information, etc. Resource information is information that associates the number of resources, the identifier of the manufacturing line P, and the identifier of the process area S for multiple manufacturing lines P and multiple process areas S. WIP corresponds to the number of lots (work-in-progress lots) input to the process area S. WIP information is information that associates time information, the actual number of work-in-progress lots, the identifier of the manufacturing line P, and the identifier of the process area S for multiple manufacturing lines P and multiple process areas S. Throughput corresponds to the number of lots (processed lots) output from the process area S. Throughput information is information that associates time information, the actual number of processed lots, the identifier of the manufacturing line P, and the identifier of the process area S for multiple manufacturing lines P and multiple process areas S.

[0032] Furthermore, the storage unit 2 may store the calculation result 2b of the calculation unit 81. The calculation result 2b includes inventory information. The inventory information is information regarding the inventory that should be provided within the process area S. The inventory information includes the appropriate capacity of inventory that allows the resource E of the process area S to operate continuously. The inventory information includes the appropriate capacity of inventory that results in zero operational loss of the resource E of the process area. The inventory information may also be information that associates the appropriate capacity of inventory with the identifier of the manufacturing line P and the identifier of the process area S for multiple manufacturing lines P and multiple process areas S.

[0033] The evaluation unit 3 performs evaluations under the control of the control unit 6. Based on the lot arrival variation characteristics to the process area S and the capacity variation characteristics of the process area S, the evaluation unit 3 can determine the lot stay variation characteristics within the process area S.

[0034] For example, the evaluation unit 3 obtains WIP information for period T from the storage unit 2. Based on the WIP information, the evaluation unit 3 can identify the actual number of work-in-progress lots in each process area S of the manufacturing line P. For each process area S, the evaluation unit 3 determines the variation characteristics of lot arrivals to the process area S based on the actual number of work-in-progress lots in the process area S. For each unit of time, the evaluation unit 3 analyzes the number of work-in-progress lots for each resource E as input and determines the total number of work-in-progress lots for multiple resources E within the process area S. The evaluation unit 3 performs statistical processing over multiple unit times to determine the distribution of the total number of work-in-progress lots. The evaluation unit 3 extracts the characteristics of this distribution (e.g., mean, variance) and can use them as parameters that indicate the variation characteristics of lot arrivals to the process area S.

[0035] The evaluation unit 3 uses the average arrival time interval t of the process area S over a period T as a parameter that indicates the variation characteristics of lot arrivals to the process area S. a Further calculations may be made. The evaluation unit 3 determines the time interval between lot arrivals to resource E for each unit of time and determines the average time interval for multiple resources E within the process area S. The evaluation unit 3 averages the time interval over multiple unit times and determines the average time interval t a We seek.

[0036] The evaluation unit 3 obtains throughput information for period T from the storage unit 2. Based on the throughput information, the evaluation unit 3 can identify the actual number of processing lots for each process area S of the manufacturing line P. Based on the actual number of processing lots for each process area S, the evaluation unit 3 determines the capacity fluctuation characteristics of each resource E in the process area S.

[0037] The evaluation unit 3 analyzes the number of processing lots for each resource E as output for each unit of time and calculates the total number of processing lots for multiple resources E within the process area S. The evaluation unit 3 performs statistical processing over multiple unit times to determine the distribution of the total number of processing lots. The evaluation unit 3 extracts the characteristics of this distribution (e.g., mean, variance) and can use them as parameters that indicate the capacity fluctuation characteristics of the process area S.

[0038] The evaluation unit 3 uses the average processing time t of the process area S over a period of T as a parameter indicating the capacity fluctuation characteristics of the process area S. m Further calculations may be made. The evaluation unit 3 determines the processing time of a lot by resource E for each unit of time and determines the average processing time for multiple resources E within the process area S. The evaluation unit 3 averages the processing time over multiple unit times and determines the average processing time t m We seek.

[0039] The evaluation unit 3 acquires WIP information for period T and throughput information for period T from the storage unit 2. Based on the WIP information, the evaluation unit 3 can identify the actual number of work-in-progress lots in each process area S of the manufacturing line P. Based on the throughput information, the evaluation unit 3 can identify the actual number of processed lots in each process area S of the manufacturing line P. For each process area S, the evaluation unit 3 determines the lot dwell time fluctuation characteristics within the process area S based on the actual number of work-in-progress lots and the actual number of processed lots in the process area S.

[0040] The evaluation unit 3 analyzes the number of work-in-progress lots for each resource E as input, the number of processed lots for each resource E as output, and the number of lots stayed as the difference for each unit of time. The evaluation unit 3 calculates the total number of lots stayed for multiple resources E within the process area S. The evaluation unit 3 performs statistical processing over multiple unit times to determine the distribution of the total number of lots stayed. The evaluation unit 3 extracts the characteristics of this distribution (e.g., mean, variance) and can use them as parameters to indicate the variability of the lot stay in the process area S.

[0041] For example, the evaluation unit 3 may determine the distribution of the number of occurrences of the number of lots staying in the process area S, as shown by the bar graph in Figure 5(a). The evaluation unit 3 then determines an approximation curve for the distribution of the number of occurrences of the number of lots staying in the process area, as shown by the dotted line in Figure 5(a).

[0042] The evaluation unit 3 may obtain the probability distribution shown by the solid line in Figure 5(b) by converting the vertical axis from the number of occurrences to the probability of occurrence for the approximation curve shown by the dotted line in Figure 5(a). The evaluation unit 3 extrapolates the portion with negative lot numbers from this probability distribution, assuming it follows a normal distribution N, as shown by the dotted line in Figure 5(b). The evaluation unit 3 then applies the characteristics shown in Figure 5(c) to the extrapolated probability distribution (for example, mean m, variance σ 2 ) may be extracted. That is, the evaluation unit 3 determines the distribution of the probability of occurrence of the number of lots in stay as a normal distribution N(m,σ). 2 ) can be used as an approximation. m corresponds to the mean of the probability distribution. σ represents the standard deviation of the probability distribution. 2 This represents the variance of the probability distribution.

[0043] The evaluation unit 3 uses the following equation 2 as a coefficient of variation c of the number of lots that stay in the process area S, which represents the variation in the number of lots that stay in the process area S. q It is possible to find this. c q =σ / m···Formula 2

[0044] As shown in Equation 2, the coefficient of variation of stay c q This is the ratio of the standard deviation σ of the probability distribution to the average lot size m, and it represents the magnitude of variability expressed by normalizing the standard deviation σ by the average lot size m.

[0045] The evaluation unit 3 analyzes the parameters of the distribution N of the probability of the number of stray lots occurring (for example, the mean m and variance σ obtained from the distribution shown in Figure 5(c)). 2 ) and the coefficient of variation c of stay shown in Equation 2 q The and are supplied to the calculation unit 81. The evaluation unit 3 calculates the average processing time t of resource E. m This is supplied to the calculation unit 82.

[0046] The evaluation unit 3 may further determine the average operating rate u of the process area S as a parameter indicating the dwell time variation characteristics of the process area S. The evaluation unit 3 determines the cumulative operating time of resource E for each unit time and determines the average cumulative operating time for multiple resources E within the process area S. The evaluation unit 3 averages the multiple unit times over time, divides the obtained value by the period T, and determines the average operating rate u.

[0047] The average utilization rate u of resource E represents the average proportion of time during which resource E is operational within a given unit of time. The average utilization rate u of resource E can also be expressed as the average load factor of resource E. The average load factor represents the average load on resource E within a given unit of time.

[0048] For example, if the operation of resource E in process area S can be approximated by the M / M / 1 model in queuing theory, the evaluation unit 3 can use the average utilization rate u of resource E to determine the average lot size m, as shown in the following equation 3. m = u / (1 - u) ... Formula 3

[0049] The evaluation unit 3 uses the average utilization rate u of resource E to calculate the variance σ as shown in the following equation 4. 2 It is possible to find this. σ 2 =u / (1-u) 2 ...Formula 4

[0050] According to equations 3 and 4, the normal distribution shown in Figure 5(c) can be expressed by the following equation 5.

number

[0051] According to equations 2, 3, and 4, the evaluation unit 3 calculates the coefficient of variation c q This can also be calculated as shown in the following equation 6. c q = 1 / √(u) ··· Formula 6

[0052] Alternatively, the average utilization rate u is given by the average arrival time interval t, as shown in Equation 7. a and average machining time t m It can be approximated by the ratio of . u=t m / t a ...Formula 7

[0053] According to equations 6 and 7, the evaluation unit 3 calculates the coefficient of variation of stay c qThis can be calculated using formula 8. c q =√(t a / t m )···Formula 8

[0054] The evaluation unit 3, for example, obtains the mean m and variance σ from the distribution shown in Figure 5(c). 2 Instead, we use the mean value m shown in Equation 3 and the variance σ shown in Equation 4. 2 The following may be supplied to the calculation unit 81. The evaluation unit 3 calculates the coefficient of variation of stay c shown in formula 2. q Instead, use the coefficient of variation c of stay shown in Equation 6 or Equation 7. q The data may be supplied to the calculation unit 81.

[0055] The calculation unit 81 calculates parameters (for example, mean m, variance σ) that represent the distribution N of the probability of lot size occurrence. 2 ) and the coefficient of variation of stay c q Based on this, we determine the probability P of operational loss occurring.

[0056] Here, it can be estimated that operational loss for resource E occurs when the number of occupant lots for resource E becomes negative, as shown by the hatched areas in Figure 5(c). The calculation unit 81 can determine the probability P of operational loss as the integral value of the portion shown by the hatched areas in Figure 5(c) using the following equation 9.

number

[0057] In equation 9, Erf is the error function and represents the integral value over a given range in a normal distribution. Erf(1 / {c q √(2)) represents the integral value for lot numbers ranging from 0 to 2 × m.

[0058] Furthermore, if the operation of resource E in process area S can be approximated by the M / M / 1 model in queuing theory, the calculation unit 81 may determine the probability P of operation loss using the following formula 10.

number

[0059] The calculation unit 81 supplies the probability P of occurrence of operational loss, determined by formula 9 or formula 10, to the calculation unit 82 along with the identification information of the process area S.

[0060] The calculation unit 82 calculates the average processing time t of the process area S. m The appropriate capacity of inventory B in process area S is determined according to the probability P of operational losses occurring in process area S.

[0061] For example, the calculation unit 82 calculates the average processing time t of the process area S. m Depending on the probability P of operational loss occurring in the process area S, the number of lots Z at which the utilization rate of resource E in the process area S becomes zero during period T may be determined as shown in Equation 1. The probability P in Equation 1 is the coefficient of variation c of stay, as shown in Equation 9. q This includes the following: As a result, the calculation unit 82 can determine the number of lots Z at which the utilization rate of resource E in the process area S becomes zero, taking into account the variation in the arrival of lots to resource E (see Figure 2(a)) and the variation in the processing capacity of resource E (see Figure 2(b)) and the resulting fluctuation in the inventory quantity (see Figure 2(c)).

[0062] Alternatively, the calculation unit 82 may use the following equation 11 obtained by substituting equation 9 into equation 1.

number

[0063] The calculation unit 82 calculates the average processing time t of the process area S. m Depending on the probability P of operational loss occurring in the process area S, the number of lots Z at which the resource utilization rate of the process area S during period T becomes zero may be determined using Equation 11. The number of lots Z shown in Equation 11 is determined by the coefficient of variation c. qThis includes the following: As a result, the calculation unit 82 can determine the number of lots Z at which the utilization rate of resource E in the process area S becomes zero, taking into account the variation in the arrival of lots to resource E (see Figure 2(a)) and the variation in the processing capacity of resource E (see Figure 2(b)) and the resulting fluctuation in the inventory quantity (see Figure 2(c)).

[0064] Furthermore, if the operation of resource E in process area S can be approximated by the M / M / 1 model in queuing theory, the calculation unit 82 may use the following equation 12 obtained by substituting equation 10 into equation 1.

number

[0065] The calculation unit 82 calculates the average processing time t of the process area S. m Depending on the probability P of operational loss occurring in the process area S, the number of lots Z at which the utilization rate of resource E in the process area S becomes zero during period T can be determined using equation 12. The number of lots Z shown in equation 12 is determined by the coefficient of variation c. q √(t) a / t m This includes the following: The calculation unit 82 can determine the number of lots Z at which the resource utilization rate of the process area S becomes zero, taking into account the variation in the arrival of lots to resource E (see Figure 2(a)) and the variation in the processing capacity of resource E (see Figure 2(b)) and the resulting fluctuation in the inventory quantity (see Figure 2(c)).

[0066] The calculation unit 82 may set the appropriate capacity of inventory B in the process area S to "at least Z lot size" according to the lot size Z obtained by formula 1, formula 11, or formula 12. The calculation unit 82 stores "at least Z lot size" as the calculation result 2b of the appropriate capacity of inventory B in the storage unit 2.

[0067] Furthermore, a configuration including calculation units 81 and 82 may be configured as the calculation unit 8.

[0068] Management system 1 can be implemented using the hardware shown in Figure 6. Figure 6 is a diagram showing the hardware configuration of management system 1.

[0069] The management system 1 has the following hardware configuration: a processor 17, ROM (Read Only Memory) 18, RAM (Random Access Memory) 13, a human interface 14, a communication interface 15, a storage device 16, and a bus 19.

[0070] The processor 17 includes a CPU (Central Processing Unit), etc. The processor 17 corresponds to the control unit 6, evaluation unit 3, calculation unit 81, and calculation unit 82. The control unit 6, evaluation unit 3, calculation unit 81, and calculation unit 82 are deployed and functionally configured on RAM 13 either all at once during compilation or sequentially as processing progresses, by the execution of a management program PG by the processor 17.

[0071] ROM18 stores static data. ROM18 corresponds to storage unit 2.

[0072] RAM13 can temporarily store information and provides a work area and other resources to the processor 17. RAM13 corresponds to the storage unit 2.

[0073] The human interface 14 acts as an intermediary between humans and computers. The human interface 14 has an input device 14a and an output device 14b.

[0074] The input device 14a includes devices capable of receiving requests from a human, such as a keyboard, mouse, or touch panel. The input device 14a corresponds to the acquisition unit 5.

[0075] Output device 14b is a device capable of outputting visual and / or auditory information to humans, such as a display, printer, indicator, or speaker.

[0076] The communication interface 15 can connect to an external device via a communication medium. When an external device is connected via a communication medium, the communication interface 15 can receive information from the external device or transmit information to the external device.

[0077] The storage device 16 is a device capable of storing information non-volatilely, such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive). The storage device 16 stores programs and various data necessary to operate the processor 17. The storage device 16 may also store a management program PG. The storage device 16 corresponds to the storage unit 2.

[0078] The processor 17, ROM 18, RAM 13, human interface 14, communication interface 15, and storage device 16 are connected to each other via a bus 19 so that they can communicate with one another.

[0079] Next, the general operation of management system 1 will be explained using Figure 7. Figure 7 is a flowchart illustrating the general operation of management system 1.

[0080] In the management system 1, the acquisition unit 5 acquires parameters 2a (ST1). For example, the acquisition unit 5 acquires parameters 2a in response to user input or via a communication medium such as a wired or wireless communication line. Parameters 2a include the number of resources, WIP history, throughput history, etc., for each process area S of the manufacturing line P. The storage unit 2 may receive parameters 2a from the acquisition unit 5 and store them as a database. The database includes resource information, WIP information, throughput information, etc.

[0081] The evaluation unit 3 determines the variation characteristics of the process area S (ST2). The variation characteristics are those related to lot variation.

[0082] For example, the evaluation unit 3 acquires WIP information for period T and throughput information for period T from the storage unit 2. Based on the WIP information, the evaluation unit 3 can identify the actual number of work-in-progress lots in each process area S of the manufacturing line P. Based on the throughput information, the evaluation unit 3 can identify the actual number of processed lots in each process area S of the manufacturing line P. For each process area S, the evaluation unit 3 determines the lot dwell time fluctuation characteristics within the process area based on the actual number of work-in-progress lots and the actual number of processed lots in the process area S.

[0083] The calculation unit 8 calculates inventory information for the process area S based on the variation characteristics obtained in ST2 (ST3). The inventory information may also be information regarding the inventory B that should be set up within the process area S.

[0084] For example, the calculation unit 8 may determine, as inventory information, the appropriate capacity of inventory that allows the resource E of the process area S to operate continuously, according to the fluctuation characteristics of the lot dwell time within the process area S. The calculation unit 8 may also determine, as inventory information, the appropriate capacity of inventory B that results in zero operational loss of the resource E of the process area S, according to the fluctuation characteristics of the lot dwell time within the process area S. The calculation unit 8 supplies the calculation result 2b of the appropriate capacity of inventory B for each process area S to the storage unit 2. The storage unit 2 stores the calculation result 2b of the appropriate capacity of inventory B for each process area S.

[0085] The control unit 6 notifies the user of inventory information for each process area S (ST4). For example, the control unit 6 may notify the user of the calculation result 2b of the appropriate capacity of inventory B for each process area S by visual and / or auditory means, depending on when the calculation result 2b is stored in the storage unit 2, or in response to a request from the user.

[0086] In ST2 shown in Figure 7, ST11 to ST13 shown in Figure 8 may also be performed. Figure 8 is a flowchart of the process (ST2) for determining the variation characteristics.

[0087] The evaluation unit 3 determines the distribution of the probability of occurrence of the number of lots staying in the process area S (ST11). For example, the evaluation unit 3 may determine the distribution of the number of occurrences of the number of lots staying in the process area as shown by the bar graph in Figure 5(a). The evaluation unit 3 determines an approximation curve for the distribution of the number of occurrences of the number of lots staying in the process area as shown by the dotted line in Figure 5(a). The evaluation unit 3 may also change the vertical axis from the number of occurrences to the probability of occurrence for the approximation curve shown by the dotted line in Figure 5(a) and determine the probability distribution shown by the solid line in Figure 5(b).

[0088] The evaluation unit 3 extrapolates the distribution obtained in ST11 to the side where the number of lots remaining is negative (ST12). For example, assuming that the probability distribution obtained in ST11 follows a normal distribution, the evaluation unit 3 extrapolates the portion where the number of lots is negative, as shown by the dotted line in Figure 5(b).

[0089] The evaluation unit 3 extracts the characteristics of the extrapolated distribution (ST13). For example, the evaluation unit 3 extracts the characteristics of the extrapolated probability distribution (e.g., mean m, variance σ). 2 Alternatively, the evaluation unit 3 may extract the average utilization rate u of resource E in the process area S, and the mean m and variance σ of the probability distribution are obtained using formulas 3 and 4. 2 You may also request this.

[0090] The evaluation unit 3 uses the coefficient of variation c of the number of lots staying in the process area S as a coefficient representing the variation in the number of lots staying in the process area S, as shown in Equation 2. q The following can be determined: Alternatively, the evaluation unit 3 obtains the average utilization rate u of resource E in the process area S, and the coefficient of variation c of stay is calculated using formula 6. q You may also request this.

[0091] The evaluation unit 3 analyzes the parameters of the distribution N of the probability of lot size occurrence (for example, mean m, variance σ). 2 ) and the coefficient of variation of stay c q The and are supplied to the calculation unit 81.

[0092] In ST3 shown in Figure 7, ST21 to ST23 shown in Figure 9 may also be performed. Figure 9 is a flowchart of the process for obtaining inventory information (ST3).

[0093] The evaluation unit 3 determines the average processing time t of resource E in the process area S. m Find (ST21).

[0094] For example, the evaluation unit 3 calculates the processing time of a lot by resource E for each unit of time and calculates the average processing time for multiple resources E within the process area S. The evaluation unit 3 then averages the processing time over multiple unit times and calculates the average processing time t m The evaluation unit 3 determines the average processing time t of resource E in the process area S. m This is supplied to the calculation unit 82.

[0095] The calculation unit 81 calculates parameters (for example, mean m, variance σ) that represent the distribution N of the probability of lot size occurrence. 2 ) and the coefficient of variation of stay c q Based on this, the probability of occurrence P of operational loss is determined (ST22). The calculation unit 81 can determine the probability of occurrence P of operational loss as the integral value of the portion shown by the hatched area in Figure 5(c) using equation 9. Alternatively, the calculation unit 81 may determine the probability of occurrence P of operational loss using equation 10. The calculation unit 81 supplies the probability of occurrence P of operational loss determined by equation 9 or equation 10, along with the identification information of the process area S, to the calculation unit 82.

[0096] The calculation unit 82 calculates the average processing time t of the process area S. m The appropriate capacity of inventory B in process area S is determined according to the probability P of operational losses occurring in process area S (ST23).

[0097] In ST23, the following ST24 and ST25 may be performed. For example, the calculation unit 82 calculates the average processing time t of the process area S. m Depending on the probability P of operational loss occurring in the process area S, the number of lots Z at which the resource utilization rate of the process area S becomes zero during period T is determined as shown in Equation 1 (ST24). Alternatively, the calculation unit 82 calculates the average processing time t of the process area S. mDepending on the probability P of operational loss occurring in the process area S, the number of lots Z at which the resource utilization rate of the process area S becomes zero during period T can be determined using formula 11. Alternatively, the calculation unit 82 calculates the average processing time t of the process area S. m Depending on the probability P of operational loss occurring in the process area S, the number of lots Z at which the resource utilization rate of the process area S becomes zero during period T can be determined using formula 12.

[0098] The calculation unit 82 determines the appropriate capacity of inventory B in the process area S to be "at least Z lot size" according to the lot size Z obtained in ST24 (S25). The calculation unit 82 stores "at least Z lot size" as the calculation result 2b of the appropriate capacity of inventory B in the process area S in the storage unit 2.

[0099] As described above, in the first embodiment, in the manufacturing line P management method, the variation characteristics of the number of lots staying in the process area S are determined based on the variation characteristics of the lot arrival at the process area S and the variation characteristics of the capacity of the process area S. For example, the distribution of the probability of occurrence of the number of staying lots is determined, and the distribution is extrapolated to the side where the number of staying lots is negative, and the characteristics of the distribution including the coefficient of variation are extracted. Inventory information regarding the inventory B that should be set up in the process area S is determined according to the variation characteristics of the number of lots staying in the process area S. For example, using the coefficient of variation, the part of the extrapolated distribution where the number of staying lots is negative is integrated, and the number of lots Z at which the utilization rate of resource E in the process area S becomes zero is determined. Then, the appropriate capacity of inventory B is set to be equal to or greater than the determined number of lots. In other words, the appropriate capacity of inventory B is determined in a way that takes into account the variation in the number of inventory (see Figure 2(c)) corresponding to the variation in the lot arrival at resource E (see Figure 2(a)) and the variation in the processing capacity of resource E (see Figure 2(b)), and this can be notified to the user. This allows the user to be encouraged to set up inventory B at the appropriate capacity. Therefore, in each manufacturing line P, the operational loss of resources E in the process area S can be effectively suppressed, and lots can be processed efficiently.

[0100] (Second embodiment) Next, we will describe the manufacturing line management method according to the second embodiment. The following description will focus on the differences from the first embodiment.

[0101] In the first embodiment, a manufacturing line P is exemplified in which inventory B may be provided for each process area S, while in the second embodiment, a manufacturing line P is exemplified in which inventory B is shared among multiple process areas S. A collection of multiple process areas S that share inventory B will be called an inventory sharing unit U.

[0102] For example, as shown in Figure 10, a shared inventory unit U1 may be provided in the manufacturing line P. Figure 10 is a diagram showing the configuration of the manufacturing line P in the second embodiment. The shared inventory unit U1 includes multiple process areas S2 to S4. The multiple process areas S2 to S4 share inventory B3. The shared inventory unit U1 has a time constraint T q A constraint time T is set. q This is the time required to guarantee quality. Constraint time T q This is also called Q-Time. In manufacturing line P, the average dwell time W of a lot within the shared inventory unit U1 is... q The time constraint is T q It will be managed as follows:

[0103] Inventory B3 may be located between multiple process areas S2-S4, for example, between process area S2 and process area S3. Inventory B3 may have a number of Kanbans N1-N2 corresponding to its capacity. Each Kanban N is associated with a lot from the process area S1 immediately preceding the inventory sharing unit U1. When a lot enters inventory B3, a Kanban N is placed in inventory B3. When a lot is taken out of inventory B3 and fed into resource E, the Kanban N is returned to the process area S1 immediately preceding the inventory sharing unit U1. A number of lots corresponding to the returned Kanban N are processed in process area S1. This allows the process area S1 immediately preceding the inventory sharing unit U1 to be instructed to process the amount by which inventory B's inventory count has decreased, and the lots processed in process area S1 can be used to replenish inventory B and restore it to its original inventory count. In manufacturing line P, the maximum inventory quantity L of inventory B3 is... qis estimated, and the upper-limit inventory quantity L q is used to control the number of kanbans, which is the number of kanban cards N, so that the number of kanbans is L q .

[0104] In process area S2, resources E 21 to E 23 are arranged. Resources E 21 to E 23 constitute a resource group M2. In process area S3, resources E 31 to E 33 are arranged. Resources E 31 to E 33 constitute a resource group M3. In process area S4, resources E 41 to E 43 are arranged. Resources E 41 to E 43 constitute a resource group M4.

[0105] Each production line P as shown in FIG. 10 can be managed by a management system 101 as shown in FIG. 11. FIG. 11 is a diagram showing the functional configuration of the management system 101.

[0106] The management system 101 includes an evaluation unit 1031, an evaluation unit 1032, and a calculation unit 108 instead of the evaluation unit 3 and the calculation unit 8 (see FIG. 4).

[0107] The evaluation unit 1031 obtains a parameter indicating arrival fluctuation characteristics of lots to the shared inventory unit U and supplies the parameter to the calculation unit 108. The evaluation unit 1032 obtains a parameter indicating capacity fluctuation characteristics of lots at the shared inventory unit U and supplies the parameter to the calculation unit 108. The calculation unit 108 obtains inventory information using the parameter indicating the arrival fluctuation characteristics of the shared inventory unit U and the parameter indicating the capacity fluctuation characteristics of the shared inventory unit U. The calculation unit 108 may obtain, as inventory information, the upper-limit inventory quantity L of inventory B in the shared inventory unit U q . The calculation unit 108 may set an appropriate number of kanbans to L according to the upper-limit inventory quantity L of the inventory B q according to the upper-limit inventory quantity L of inventory B, the appropriate number of kanbans is set to L qThis may also be done. The calculation unit 108 uses parameters that indicate the arrival variation characteristics of the inventory sharing unit U and parameters that indicate the capacity variation characteristics of the inventory sharing unit U to determine the average dwell time W of the inventory sharing unit U as inventory information. q The calculation unit 108 calculates the average dwell time W of the shared inventory unit U. q The time constraint is W q You may also determine whether or not the condition is met.

[0108] For example, the evaluation unit 1031 obtains WIP information for period T from the storage unit 2. Based on the WIP information, the evaluation unit 1031 can identify the actual number of work-in-progress lots in the inventory sharing unit U. Based on the actual number of work-in-progress lots in the inventory sharing unit U, the evaluation unit 1031 determines the variation characteristics of lot arrivals to the inventory sharing unit U.

[0109] The evaluation unit 1031 evaluates each leading resource E as an input at each unit time interval. 21 ~E 23 Analyze the number of batches in progress, and the leading multiple resources E 21 ~E 23 The total number of work-in-progress lots is determined. The evaluation unit 1031 performs statistical processing over multiple unit time periods to determine the distribution of the total number of work-in-progress lots. The evaluation unit 1031 then analyzes the characteristics of that distribution (for example, the mean value m). a , variance σ a 2 ) can be extracted and used as a parameter to show the variation characteristics of lot arrivals to the inventory sharing unit U.

[0110] The evaluation unit 1031 uses the coefficient of variation c of arrival, as shown in the following formula 13, as a coefficient representing the variation in the number of work-in-progress lots of the inventory sharing unit U. a It is possible to find this. c a =σ a / m a ...Formula 13

[0111] As shown in Equation 13, the coefficient of variation upon arrival c a σ is the standard deviation of the probability distribution. a and average lot size m a This is the ratio to the standard deviation σa average lot size m a This shows the magnitude of the variation when normalized.

[0112] The evaluation unit 1031 uses the average arrival time interval t of the inventory sharing unit U over a period T as a parameter that indicates the variation characteristics of lot arrivals to the inventory sharing unit U. a Further requests may be made. The evaluation unit 1031 evaluates the leading resource E for each unit of time. 21 ~E 23 Determine the arrival time interval of lots to multiple resources E 21 ~E 23 The average arrival time interval is calculated. The evaluation unit 1031 averages the time over multiple unit times and calculates the average arrival time interval t. a We seek.

[0113] The evaluation unit 1031 analyzes the parameters of the distribution of the probability of occurrence of the number of work-in-progress lots (for example, the mean value m). a , variance σ a 2 ) and the coefficient of variation upon arrival c shown in Equation 13 a and average arrival time interval t a The and are supplied to the calculation unit 108.

[0114] The evaluation unit 1032 obtains throughput information for period T from the storage unit 2. Based on the throughput information, the evaluation unit 1032 can identify the actual number of processing lots for the inventory sharing unit U. Based on the actual number of processing lots for the inventory sharing unit U, the evaluation unit 1032 determines the capacity fluctuation characteristics of each resource E for the inventory sharing unit U.

[0115] The evaluation unit 1032 evaluates each of the trailing resources E as an output for each unit of time. 41 ~E 43 Analyze the number of lots processed, and multiple resources E 41 ~E 43 The total number of processed lots is determined. The evaluation unit 1032 performs statistical processing over multiple unit time periods to determine the distribution of the total number of processed lots. The evaluation unit 1032 then analyzes the characteristics of that distribution (for example, the mean value m). m , variance σ m2 ) can be extracted and used as a parameter to show the capacity fluctuation characteristics of the inventory sharing unit U.

[0116] The evaluation unit 1032 uses a coefficient representing the variation in the number of processing lots of the inventory sharing unit U, as shown in the following equation 14, which is the coefficient of capacity variation c. m It is possible to find this. c m =σ m / m m ...Formula 14

[0117] As shown in Equation 14, the coefficient of variation of capacity c m σ is the standard deviation of the probability distribution. m and average lot size m m This is the ratio to the standard deviation σ m average lot size m m This shows the magnitude of the variation when normalized.

[0118] The evaluation unit 1032 uses the average processing time t of the inventory sharing unit U over a period T as a parameter indicating the capacity fluctuation characteristics of the inventory sharing unit U. m Further requests may be made. The evaluation unit 1032 evaluates each resource E per unit time. 21 ~E 23 ,E 31 ~E 33 ,E 41 ~E 43 The processing time for a lot is determined by multiple resources E within the inventory sharing unit U. 21 ~E 23 ,E 31 ~E 33 ,E 41 ~E 43 The average processing time for is calculated. The evaluation unit 1032 calculates the average processing time over multiple unit times and calculates the average processing time t. m We seek.

[0119] The evaluation unit 1032 analyzes the parameters of the distribution of the probability of the number of processed lots occurring (for example, the mean value m). m , variance σ m 2 ) and the coefficient of variation of capacity c shown in Equation 14 m and average machining time tm The and are supplied to the calculation unit 108.

[0120] The calculation unit 108 calculates the coefficient of variation c of arrival for the shared inventory unit U. a and average arrival time interval t of inventory sharing unit U a Using this, a parameter a represents the average arrival time interval that takes arrival variability into account. R This can be calculated using the following formula 15.

number

[0121] The calculation unit 108 calculates the capacity variation coefficient c of the inventory sharing unit U. m and average processing time t of inventory sharing unit U m Using this, parameter b represents the average processing time that takes capacity fluctuations into account. R This can be calculated using the following formula 16.

[0122]

number

[0123] The calculation unit 108 calculates the average arrival time interval t of the inventory sharing unit U. a and average processing time t of inventory sharing unit U m Using these, the average utilization rate u of the inventory sharing unit U is calculated using the following formula 17.

[0124]

number

[0125] In equation 17, k represents the number of resources in the inventory sharing unit U (9 in the case of Figure 10). λ is the average arrival rate of resources to the inventory sharing unit U, and is calculated by the following equation 18. λ = 1 / t a ...Formula 18

[0126] The calculation unit 108 calculates the average arrival time interval t of the inventory sharing unit U. a and average processing time t of inventory sharing unit Um And the parameter a shown in equation 15 R and the parameter b shown in equation 16 R Using these, the parameter φ is determined by the following equation 19. The parameter φ is a parameter relating to the ratio of the average arrival time interval considering variation to the average processing time considering variation.

number

[0127] The calculation unit 108 calculates the average arrival time interval t of the inventory sharing unit U. a and average processing time t of inventory sharing unit U m And the parameter a shown in equation 15 R and the parameter b shown in equation 16 R Using the average utilization rate u shown in Equation 17 and the parameter φ shown in Equation 19, the probability P A The value of 0 is calculated using the following formula 20. Probability P A A value of 0 represents the probability that the number of lots remaining in the inventory sharing unit U is zero. Probability P A 0 corresponds to the probability that the inventory quantity of inventory B3 is zero.

number

[0128] In equation 20, Π represents the product of sets, and Σ represents the sum of sets. λ represents the average arrival rate of resources to the shared inventory unit U, k represents the number of resources in the shared inventory unit U, and r represents the capacity of inventory B3 (2 units in the case of Figure 10).

[0129] When the number of resources k=1 within the inventory sharing unit U, the calculation unit 108 calculates the probability P using the following simplified formula 21, which is derived from formula 20. A You may also choose 0.

number

[0130] The calculation unit 108 calculates the average arrival time interval t of the inventory sharing unit U. aand average processing time t of inventory sharing unit U m And the parameter a shown in equation 15 R and the parameter b shown in equation 16 R The probability P shown in equation 20 A Using 0, the probability P A k The probability P is calculated using the following formula 22. A k P is the probability that the number of lots remaining in the shared inventory unit U is k. A k This corresponds to the probability that the inventory quantity of inventory B3 is zero.

number

[0131] In equation 22, Π represents the product of sets, and k represents the number of resources within the inventory sharing unit U.

[0132] The calculation unit 108 calculates the average arrival time interval t of the inventory sharing unit U. a and average processing time t of inventory sharing unit U m And the parameter a shown in equation 15 R And the parameter φ shown in Equation 19 and the probability P shown in Equation 20. A Using 0, the probability P A k+r The probability P is calculated using the following formula 23. A k+r P is the probability that the number of lots remaining in the inventory sharing unit U is k+r. A k+r This corresponds to the probability that the quantity of inventory B3 is r.

number

[0133] In equation 23, Π represents the product of sets. k represents the number of resources within the inventory sharing unit U. r represents the capacity of inventory B3.

[0134] The calculation unit 108 uses the parameter a shown in equation 15. RAnd the parameter φ shown in Equation 19 and the probability P shown in Equation 22 A k And the probability P shown in equation 23 A k+r Using the above, the upper limit of inventory quantity L in inventory B3 is calculated using the following formula 24. q We seek.

number

[0135] In equation 24, λ represents the average arrival rate of resources to the inventory sharing unit U, k represents the number of resources in the inventory sharing unit U, and r represents the capacity of inventory B3.

[0136] The calculation unit 108 calculates the upper limit inventory quantity L shown in formula 24. q Depending on the situation, determine the appropriate number of Kanban boards L q Alternatively, the calculation unit 108 calculates the upper limit inventory quantity L shown in formula 24. q Depending on the situation, determine the appropriate number of Kanban boards L q You may also use +α, where α is the number of Kanban margins.

[0137] The calculation unit 108 calculates the probability P shown in equation 23 based on Little's Law. A k+r And the upper limit inventory quantity L shown in formula 24. q Using this, the average dwell time W of lots within the inventory sharing unit U1 q This can be calculated using the following formula 25.

number

[0138] In equation 25, λ represents the average arrival rate of resources to the inventory sharing unit U.

[0139] Furthermore, the calculation unit 108 has a constraint time T q This may be set. The calculation unit 108 calculates the average stay time W q The time constraint is T q You may also determine whether or not the condition is met.

[0140] The calculation unit 108 calculates the upper limit inventory quantity L of inventory B3. q , appropriate number of Kanban L q (or L q +α), average stay time W q The calculation result 2c, including the above, is stored in the storage unit 2. Alternatively, the calculation unit 108 calculates the upper limit inventory quantity L of inventory B3. q , appropriate number of Kanban L q (or L q +α), average stay time W q The calculation result 2c, including the result of the constraint time determination, is stored in the storage unit 2.

[0141] Furthermore, the operation of management system 1 differs from that of the first embodiment in the following respects.

[0142] In ST2 shown in Figure 7, the evaluation units 1031, 1032 and the calculation unit 108 determine the retention fluctuation characteristics of lots of the inventory sharing unit U.

[0143] For example, in ST2, steps ST31 to ST35 shown in Figure 12 may be performed. Steps ST31 to ST32 and ST33 to ST34 may be performed in parallel with each other.

[0144] In ST31-ST32, the evaluation unit 1031 obtains WIP information for period T from the storage unit 2. Based on the WIP information, the evaluation unit 1031 can identify the actual number of work-in-progress lots in the inventory sharing unit U. Based on the actual number of work-in-progress lots in the inventory sharing unit U, the evaluation unit 1031 determines the variation characteristics of lot arrivals to the inventory sharing unit U.

[0145] In ST31, the evaluation unit 1031 determines the distribution of the probability of occurrence of work-in-progress lots. For example, the evaluation unit 1031 obtains WIP information for period T from the storage unit 2. Based on the WIP information, the evaluation unit 1031 can identify the actual number of work-in-progress lots in the inventory sharing unit U. Based on the actual number of work-in-progress lots in the inventory sharing unit U, the evaluation unit 1031 determines the variation characteristics of lot arrivals to the inventory sharing unit U. For each unit of time, the evaluation unit 1031 uses the leading resource E as input. 21 ~E 23Analyze the number of batches in progress, and the leading multiple resources E 21 ~E 23 The total number of work-in-progress lots is determined. The evaluation unit 1031 performs statistical processing over multiple unit time periods to determine the distribution of the total number of work-in-progress lots.

[0146] In ST32, the evaluation unit 1031 evaluates the characteristics of the distribution obtained in ST31 (for example, the mean value m a , variance σ a 2 ) can be extracted and used as a parameter to show the variation characteristics of lot arrivals to the inventory sharing unit U.

[0147] The evaluation unit 1031 uses the coefficient of variation c of arrival, as shown in Equation 13, as a coefficient representing the variation in the number of work-in-progress lots of the inventory sharing unit U. a It is possible to find this.

[0148] The evaluation unit 1031 uses the average arrival time interval t of the inventory sharing unit U over a period T as a parameter that indicates the variation characteristics of lot arrivals to the inventory sharing unit U. a Further requests may be made. The evaluation unit 1031 evaluates the leading resource E for each unit of time. 21 ~E 23 Determine the arrival time interval of lots to multiple resources E 21 ~E 23 The average arrival time interval is calculated. The evaluation unit 1031 averages the time over multiple unit times and calculates the average arrival time interval t. a We seek.

[0149] The evaluation unit 1031 analyzes the parameters of the distribution of the probability of occurrence of the number of work-in-progress lots (for example, the mean value m). a , variance σ a 2 ) and the coefficient of variation upon arrival c shown in Equation 13 a and average arrival time interval t a The and are supplied to the calculation unit 108.

[0150] In ST33-ST34, the evaluation unit 1032 obtains throughput information for period T from the storage unit 2. Based on the throughput information, the evaluation unit 1032 can identify the actual number of processing lots for the inventory sharing unit U. Based on the actual number of processing lots for the inventory sharing unit U, the evaluation unit 1032 determines the capacity fluctuation characteristics of each resource E in the inventory sharing unit U.

[0151] In ST33, the evaluation unit 1032 determines the distribution of the probability of the number of processed lots occurring. For example, the evaluation unit 1032 obtains throughput information for period T from the storage unit 2. Based on the throughput information, the evaluation unit 1032 can identify the actual number of processed lots in the inventory sharing unit U. Based on the actual number of processed lots in the inventory sharing unit U, the evaluation unit 1032 determines the capacity fluctuation characteristics of each resource E in the inventory sharing unit U.

[0152] The evaluation unit 1032 evaluates each of the trailing resources E as an output for each unit of time. 41 ~E 43 Analyze the number of lots processed, and multiple resources E 41 ~E 43 The total number of processed lots is determined. The evaluation unit 1032 performs statistical processing over multiple unit time periods to determine the distribution of the total number of processed lots.

[0153] In ST34, the evaluation unit 1032 evaluates the characteristics of the distribution obtained in ST33 (for example, the mean value m m , variance σ m 2 ) can be extracted and used as a parameter to show the capacity fluctuation characteristics of the inventory sharing unit U.

[0154] The evaluation unit 1032 uses a coefficient representing the variation in the number of processing lots of the inventory sharing unit U, as shown in equation 14, which is the coefficient of capacity variation c. m It is possible to find this.

[0155] The evaluation unit 1032 uses the average processing time t of the inventory sharing unit U over a period T as a parameter indicating the capacity fluctuation characteristics of the inventory sharing unit U. m Further requests may be made. The evaluation unit 3 evaluates each resource E per unit time.21 ~E 23 ,E 31 ~E 33 ,E 41 ~E 43 The processing time for a lot is determined by multiple resources E within the inventory sharing unit U. 21 ~E 23 ,E 31 ~E 33 ,E 41 ~E 43 The average processing time for is calculated. The evaluation unit 3 averages the processing time over multiple unit times and calculates the average processing time t. m We seek.

[0156] The evaluation unit 1032 analyzes the parameters of the distribution of the probability of the number of processed lots occurring (for example, the mean value m). m , variance σ m 2 ) and the coefficient of variation of capacity c shown in Equation 14 m and average machining time t m The and are supplied to the calculation unit 108.

[0157] In ST35, once the calculation unit 108 has completed obtaining parameters indicating arrival variability characteristics from the evaluation unit 1031 and parameters indicating capacity variability characteristics from the evaluation unit 1032, it uses these parameters to determine the dwell variability characteristics of the inventory sharing unit U. The calculation unit 108 may also determine the distribution of the probability of inventory levels occurring in the inventory sharing unit U as the dwell variability characteristics of the inventory sharing unit U.

[0158] The calculation unit 108 calculates the coefficient of variation c of arrival for the shared inventory unit U. a and average arrival time interval t of inventory sharing unit U a Using this, a parameter a represents the average arrival time interval that takes arrival variability into account. R This can be calculated using formula 15.

[0159] The calculation unit 108 calculates the capacity variation coefficient c of the inventory sharing unit U. m and average processing time t of inventory sharing unit U m Using this, parameter b represents the average processing time that takes capacity fluctuations into account. R This can be calculated using formula 16.

[0160] The calculation unit 108 calculates the average arrival time interval t of the inventory sharing unit U. a and average processing time t of inventory sharing unit U m Using these, the average utilization rate u of the inventory sharing unit U is calculated using formula 17.

[0161] The calculation unit 108 calculates the average arrival time interval t of the inventory sharing unit U. a and average processing time t of inventory sharing unit U m And the parameter a shown in equation 15 R and the parameter b shown in equation 16 R Using these, the parameter φ is determined by equation 19.

[0162] The calculation unit 108 calculates the average arrival time interval t of the inventory sharing unit U. a and average processing time t of inventory sharing unit U m And the parameter a shown in equation 15 R and the parameter b shown in equation 16 R Using the average utilization rate u shown in Equation 17 and the parameter φ shown in Equation 19, the probability P A The value of 0 is calculated using formula 20. Probability P A A value of 0 represents the probability that the number of lots remaining in the inventory sharing unit U is zero. Probability P A 0 corresponds to the probability that the inventory quantity of inventory B3 is zero.

[0163] The calculation unit 108 calculates the average arrival time interval t of the inventory sharing unit U. a and average processing time t of inventory sharing unit U m And the parameter a shown in equation 15 R and the parameter b shown in equation 16 R The probability P shown in equation 20 A Using 0, the probability P A k The probability P is calculated using formula 22. A k P is the probability that the number of lots remaining in the shared inventory unit U is k. A k This corresponds to the probability that the inventory quantity of inventory B3 is zero.

[0164] The calculation unit 108 calculates the average arrival time interval t of the inventory sharing unit U. a and average processing time t of inventory sharing unit U m And the parameter a shown in equation 15 R And the parameter φ shown in Equation 19 and the probability P shown in Equation 20. A Using 0, the probability P A k+r The probability P is calculated using formula 23. A k+r P is the probability that the number of lots remaining in the inventory sharing unit U is k+r. A k+r This corresponds to the probability that the quantity of inventory B3 is r.

[0165] In ST3 shown in Figure 7, the calculation unit 108 obtains inventory information for the inventory sharing unit U based on the retention fluctuation characteristics of the inventory sharing unit U obtained in ST2.

[0166] For example, in ST3, ST41 to ST46 shown in Figure 13 may be performed.

[0167] In ST41, the calculation unit 108 calculates the parameter a shown in equation 15. R And the parameter φ shown in Equation 19 and the probability P shown in Equation 22 A k And the probability P shown in equation 23 A k+r Using and , the upper limit of inventory quantity L in inventory B3 is calculated using formula 24. q We seek.

[0168] In ST42, the calculation unit 108 calculates the upper limit inventory quantity L shown in formula 24. q Depending on the situation, determine the appropriate number of Kanban boards L q Alternatively, the calculation unit 108 calculates the upper limit inventory quantity L shown in formula 24. q Depending on the situation, determine the appropriate number of Kanban boards L q You may also use +α, where α is the number of Kanban margins.

[0169] In ST43, the calculation unit 108 calculates the probability P shown in equation 23 based on Little's Law. A k+r And the upper limit inventory quantity L shown in formula 24.q Using this, the average dwell time W of lots within the inventory sharing unit U1 q This is calculated using formula 25.

[0170] In ST44, the calculation unit 108 calculates the average dwell time W q and time constraint T q Compared with the average stay time W q The time constraint is T q Determine whether or not it is the following.

[0171] The calculation unit 108 calculates the average stay time W. q The time constraint is T q If the following conditions are met (Yes in S44), it is determined that the inventory sharing unit U satisfies the time constraint (S45). The calculation unit 108 calculates the upper limit inventory quantity L of inventory B3. q , appropriate number of Kanban L q (or L q +α), average stay time W q The calculation result 2c, which includes the determination that the time constraint has been met, is stored in the storage unit 2.

[0172] The calculation unit 108 calculates the average stay time W. q The time constraint is T q If it exceeds (No in S44), it is determined that the inventory sharing unit U does not satisfy the time constraint (S46). The calculation unit 108 calculates the upper limit inventory quantity L of inventory B3. q and the appropriate number of Kanbans L q (or L q +α) and average stay time W q The calculation result 2c, which includes the determination that the time constraint is not met, is stored in the storage unit 2.

[0173] As described above, in the second embodiment, in the method for managing the manufacturing line P, the variation characteristics of the lot's stay within the shared inventory unit U are determined based on the variation characteristics of the lot's arrival at the shared inventory unit U and the variation characteristics of the capacity of the shared inventory unit U. For example, the probability of the inventory quantity occurring in inventory B of the shared inventory unit U is determined using the coefficient of variation of arrival and the coefficient of variation of capacity. The upper limit of inventory quantity L in inventory B3 is determined according to the variation characteristics of the lot's stay within the shared inventory unit U. q and the appropriate number of Kanbans L qThis requires that the maximum inventory quantity L of inventory B3 be determined in a manner that takes into account the variation in the arrival of lots to the inventory sharing unit U and the variation in the processing capacity of the inventory sharing unit U. q and the appropriate number of Kanbans L q (or L q This requires (+α) and can be communicated to the user. This prevents setting a theoretically impossible number of Kanbans, reduces the burden of Kanban management, and allows for flexible setting of the number of Kanbans in accordance with the interaction and / or allocation of upstream and downstream processes. As a result, lots can be processed efficiently on manufacturing line P.

[0174] Furthermore, in the second embodiment, in the method for managing the manufacturing line P, the average dwell time W of the lots in the inventory sharing unit U is determined according to the dwell time variation characteristics of the lots in the inventory sharing unit U. q This requires an average lot dwell time W that takes into account variations in the number of lots due to variations in the arrival of lots to the inventory sharing unit U and variations in the processing capacity of the inventory sharing unit U. q The average stay time W is required. q The time constraint is T q The result of determining whether or not the condition is met can be communicated to the user. This allows the capacity of inventory B to be increased if the constraint time of inventory sharing unit U is not effectively met, thereby increasing the constraint time T q This allows us to encourage users to make improvements to satisfy these requirements. As a result, lots can be processed appropriately and efficiently on manufacturing line P.

[0175] (Third embodiment) Next, a method for managing the manufacturing line according to the third embodiment will be described. The following description will focus on the differences from the first and second embodiments.

[0176] In the first embodiment, the management of a manufacturing line P focusing on the lot flow in each process area S is exemplified, while in the third embodiment, the management of a manufacturing line P focusing on the lot flow in multiple reentrant process areas S is exemplified.

[0177] For example, as shown in Figure 14, the manufacturing line P has multiple reentrant process areas S X ~S Z A feature may be provided. Figure 14 shows the configuration of the manufacturing line P in the third embodiment.

[0178] Multiple reentrant process areas S X ~S Z In each of these, the same resource E can repeatedly process the same lot. The repeated processing may be performed by changing the recipe (processing conditions). In the manufacturing line P, as shown by the arrows in Figure 14, from the immediately preceding process area S0 to the first process area S X The lot is put into process area S. X →Process area S Y →Process area S Z When the processing is carried out sequentially by resource E, the lot is processed in process area S X Returning to process area S again X The material is processed by resource E and then transported to the subsequent process area S (not shown). Multiple reentrant process areas S X ~S Z A collection of resources E is also called a job shop.

[0179] Each process area S X ~S Z It has inventory B and resource group M. Process area S X Inventory B X and resource group M X It has. Resource group M X is, k X Includes individual resource E. Process area S. Y Inventory B Y and resource group M Y It has. Resource group M Y is, k Y Includes individual resource E. Process area S. Z Inventory B Z and resource group M Z It has. Resource group M Z is, k ZIncludes individual resource E.

[0180] Each manufacturing line P, as shown in Figure 14, can be managed by the management system 201, as shown in Figure 15. Figure 15 is a diagram showing the functional configuration of the management system 201.

[0181] The management system 201 has evaluation units 2031, 2032, and 208 instead of evaluation unit 3 and calculation unit 8 (see Figure 4).

[0182] The evaluation unit 2031 evaluates each reentrant process area S X ~S Z The calculation unit 2032 calculates parameters that show the variation characteristics of lot arrivals to the process area S and supplies them to the calculation unit 208. X ~S Z Parameters showing the capacity fluctuation characteristics are obtained and supplied to the calculation unit 208. The calculation unit 208 calculates each process area S X ~S Z Parameters showing arrival variation characteristics and each process area S X ~S Z Using parameters that show the capacity fluctuation characteristics, inventory information is obtained. The calculation unit 208 obtains inventory information for each process area S X ~S Z The maximum inventory quantity L for inventory B. X ~L Z The calculation unit 208 calculates the upper limit inventory quantity L of inventory B. X ~L Z Accordingly, the appropriate capacity of inventory B is L X ~L Z This is also acceptable. The calculation unit 208 calculates each process area S X ~S Z Parameters showing arrival variation characteristics and each process area S X ~S Z Using parameters that show the capacity fluctuation characteristics, inventory information is obtained for each process area S. X ~S Z Average length of stay W X ~W Z You may also calculate the average stay time W. X ~W ZThis corresponds to the time the lot remains in inventory B.

[0183] For example, the evaluation unit 2031 acquires WIP information for a predetermined period from the storage unit 2. Based on the WIP information, the evaluation unit 2031 evaluates each process area S X ~S Z The actual number of work-in-progress lots can be identified. The evaluation unit 2031 can identify each process area S X ~S Z Based on the actual number of work-in-progress lots, each process area S X ~S Z Determine the arrival variation characteristics of the lot.

[0184] The evaluation unit 2031 evaluates each process area S X ~S Z As a parameter indicating the variation in lot arrival characteristics, each process area S over a predetermined period is used. X ~S Z Average arrival time interval t aX ~t aZ The evaluation unit 2031 may determine each process area S for each unit time. X ~S Z The time interval between lots arriving at resource E is determined, and the average arrival time interval for multiple resources E is calculated. The evaluation unit 2031 calculates the time average over multiple unit times for each process area S. X ~S Z Average arrival time interval t aX ~t aZ The evaluation unit 2031 determines the value of each process area S. X ~S Z Average arrival time interval t aX ~t aZ The reciprocal of the average arrival rate λ X ~λ Z We seek.

[0185] The evaluation unit 2031 has multiple reentrant process areas S X ~S Z From the average arrival rate λ0 of the immediately preceding process area S0, each process area S is calculated using the following formula 26. X ~S Z Average arrival rate λ X ~λZ You may also request this.

number

[0186] The evaluation unit 2031 determines the average arrival time interval t aX ~t aZ This is supplied to the evaluation unit 2032.

[0187] The evaluation unit 2032 acquires throughput information for a predetermined period from the storage unit 2. Based on the throughput information, the evaluation unit 2032 evaluates each process area S X ~S Z The actual number of processing lots can be identified. The evaluation unit 2032 can identify each process area S X ~S Z Based on the actual number of processing lots, each process area S X ~S Z Determine the capability fluctuation characteristics of each resource E.

[0188] The evaluation unit 2032 evaluates each process area S X ~S Z As a parameter that shows the capacity fluctuation characteristics, each process area S over a predetermined period X ~S Z Average processing time t mX ~t mZ Further calculations may be made. The evaluation unit 2032 determines the processing time of each lot by each resource E for each unit time, and for each process area S X ~S Z The average processing time for multiple resources E within the process area S is calculated. The evaluation unit 2032 calculates the average processing time for multiple unit times and calculates the average processing time for each process area S. X ~S Z Average processing time t mX ~t mZ We seek.

[0189] The evaluation unit 2032 acquires resource count information from the storage unit 2 and, according to the resource count information, evaluates each process area S X ~S Z Number of resources k X ~k ZThe evaluation unit 2032 identifies the average arrival time interval t from the evaluation unit 1031. aX ~t aZ The evaluation unit 2032 obtains the average processing time t. mX ~t mZ and average arrival time interval t aX ~t aZ and the number of resources k X ~k Z Using this, each process area S X ~S Z Average utilization rate u X ~u Z This can also be calculated using the following formula 27.

number

[0190] The evaluation unit 2032 evaluates each process area S at each unit time interval. X ~S Z The evaluation unit 2032 analyzes the number of processing lots for each resource E as an output and calculates the total number of processing lots for multiple resources E. The evaluation unit 2032 performs statistical processing for multiple unit times and calculates the distribution of the total number of processing lots. The evaluation unit 2032 analyzes each process area S X ~S Z Regarding the characteristics of its distribution (for example, the mean m mX ~m mZ , variance σ mX 2 ~σ mZ 2 ) are extracted, and each process area S X ~S Z This can be used as a parameter to indicate the performance fluctuation characteristics.

[0191] The evaluation unit 2032 evaluates each process area S X ~S Z The coefficient representing the variation in the number of processing lots is the coefficient of capacity variation c, as shown in the following equation 28. mX ~c mZ It is possible to find this. c mX =σ mX / m mX ,c mY =σmY / m mY ,c mZ =σ mZ / m mZ ...Formula 28

[0192] The evaluation unit 2032 evaluates the average operating rate u X ~u Z and average machining time t mX ~t mZ and the coefficient of change of ability c mX ~c mZ These are supplied to the evaluation unit 2031 and the calculation unit 208, respectively.

[0193] The evaluation unit 2031 acquires resource count information from the storage unit 2 and, according to the resource count information, evaluates each process area S X ~S Z Number of resources k X ~k Z The evaluation unit 2031 identifies the average operating rate u from the evaluation unit 2032. X ~u Z and the coefficient of change of ability c mX ~c mZ The evaluation unit 2031 obtains the number of resources k. X ~k Z and average arrival rate λ X ~λ Z and average utilization rate u X ~u Z and the coefficient of change of ability c mX ~c mZ Using these, the coefficient of variation upon arrival, c, is as shown in equations 29 to 31 below. aX ~c aZ You may also request this.

number

number

number

[0194] Here, equations 29 to 31 represent multiple reentrant process areas S X ~SZ This corresponds to the lot flow in [the system]. Therefore, equations 29 to 31 form a circular reference, as indicated by the arrows in Figure 16(a). This circular reference can be solved mathematically, as shown in Figure 16(b). Equation 29 to c aY Solving for and substituting into equation 30, and then substituting equation 31 into equation 30, we obtain the coefficient of variation upon arrival c shown in equation 32. aZ It can be rewritten as a single equation.

number

[0195] Other arrival coefficients of variation c aX , coefficient of variation upon arrival c aY Similarly, this can also be expressed as a single formula. As a result, the evaluation unit 2031 can determine the coefficient of variation upon arrival c aX ~c aZ It is possible to find this.

[0196] The evaluation unit 2031 has a resource count of k X ~k Z and average arrival rate λ X ~λ Z and the coefficient of variation upon arrival c aX ~c aZ The and are supplied to the calculation unit 208.

[0197] The calculation unit 208 has a resource count of k. X ~k Z and average utilization rate u X ~u Z and average machining time t mX ~t mZ and the coefficient of variation upon arrival c aX ~c aZ and the coefficient of change of ability c mX ~c mZ Using this, each process area S X ~S Z Average length of stay W X ~W Z This can be calculated using the following formula 33.

number

[0198] The calculation unit 208 calculates the average arrival rate λ based on Little's Law. X ~λ Z And the average stay time W shown in equation 33. X ~W Z Using this, each process area S X ~S Z The maximum inventory quantity L for inventory B. X ~L Z This can be calculated using the following formula 34.

number

[0199] The calculation unit 208 calculates the upper limit inventory quantity L shown in formula 34. X ~L Z Depending on the process area S X ~S Z The appropriate capacity of inventory B is L X ~L Z Let's assume that.

[0200] The calculation unit 208 calculates each process area S X ~S Z Appropriate capacity L for inventory B X ~L Z , each process area S X ~S Z Average length of stay W X ~W Z The calculation result 2d, including the result, is stored in the storage unit 2.

[0201] Furthermore, the operation of the management system 201 differs from that of the first embodiment in the following respects.

[0202] In ST2 shown in Figure 7, the evaluation units 2031, 2032 and the calculation unit 208 have multiple reentrant process areas S X ~S Z Determine the lot arrival variation characteristics and capacity variation characteristics for each process area S in the process.

[0203] For example, in ST2, steps ST51 to ST57 shown in Figure 17 may be performed. Steps ST51 to ST52 and ST53 to ST57 may be performed in parallel with each other.

[0204] In ST51 to ST52, the evaluation unit 2031 acquires WIP information for a predetermined period from the storage unit 2. Based on the WIP information, the evaluation unit 2031 evaluates each process area S X ~S Z The actual number of work-in-progress lots can be identified. The evaluation unit 2031 can identify each process area S X ~S Z Based on the actual number of work-in-progress lots, each process area S X ~S Z Determine the arrival variation characteristics of the lot.

[0205] In ST51, the evaluation unit 2031 evaluates each process area S X ~S Z As a parameter indicating the variation in lot arrival characteristics, each process area S over a predetermined period is used. X ~S Z Average arrival time interval t aX ~t aZ The evaluation unit 2031 may determine each process area S for each unit time. X ~S Z The time interval between lots arriving at resource E is determined, and the average arrival time interval for multiple resources E is calculated. The evaluation unit 2031 calculates the time average over multiple unit times for each process area S. X ~S Z Average arrival time interval t aX ~t aZ The evaluation unit 2031 determines the value of each process area S. X ~S Z Average arrival time interval t aX ~t aZ The reciprocal of the average arrival rate λ X ~λ Z We seek.

[0206] The evaluation unit 2031 has multiple reentrant process areas S X ~S Z From the average arrival rate λ0 of the immediately preceding process area S0, according to formula 26, each process area S X ~S Z Average arrival rate λ X ~λ ZYou may also request this.

[0207] The evaluation unit 2031 determines the average arrival time interval t aX ~t aZ This is supplied to the evaluation unit 2032. ST52 will be discussed later.

[0208] In ST53 to ST57, the evaluation unit 2032 acquires throughput information for a predetermined period from the storage unit 2. Based on the throughput information, the evaluation unit 2032 evaluates each process area S X ~S Z The actual number of processing lots can be identified. The evaluation unit 2032 can identify each process area S X ~S Z Based on the actual number of processing lots, each process area S X ~S Z Determine the capability fluctuation characteristics of each resource E.

[0209] In ST53, the evaluation unit 2032 evaluates each process area S X ~S Z As a parameter that shows the capacity fluctuation characteristics, each process area S over a predetermined period X ~S Z Average processing time t mX ~t mZ Further calculations may be made. The evaluation unit 2032 determines the processing time of each lot by each resource E for each unit time, and for each process area S X ~S Z The average processing time for multiple resources E within the process area S is calculated. The evaluation unit 2032 calculates the average processing time for multiple unit times and calculates the average processing time for each process area S. X ~S Z Average processing time t mX ~t mZ We seek.

[0210] In ST54, the evaluation unit 2032 acquires resource count information from the storage unit 2, and according to the resource count information, each process area S X ~S Z Number of resources k X ~k Z The evaluation unit 2032 identifies the average arrival time interval t from the evaluation unit 1031.aX ~t aZ The evaluation unit 2032 obtains the average processing time t. mX ~t mZ and average arrival time interval t aX ~t aZ and the number of resources k X ~k Z Using this, each process area S X ~S Z Average utilization rate u X ~u Z This can also be calculated using formula 27.

[0211] In ST55, the evaluation unit 2032 determines the distribution of the probability of occurrence of the number of processing lots. For example, the evaluation unit 2032 determines the distribution of the probability of occurrence of each process area S for each unit time. X ~S Z The number of processing lots for each resource E as an output is analyzed, and the total number of processing lots for multiple resources E is determined. The evaluation unit 2032 performs statistical processing over multiple units of time to determine the distribution of the total number of processing lots.

[0212] In ST56, the evaluation unit 2032 evaluates each process area S X ~S Z Regarding this, the characteristics of the distribution obtained with ST55 (for example, the mean m mX ~m mZ , variance σ mX 2 ~σ mZ 2 ) are extracted, and each process area S X ~S Z This can be used as a parameter to indicate the performance fluctuation characteristics.

[0213] In ST57, the evaluation unit 2032 evaluates each process area S X ~S Z The coefficient representing the variation in the number of processing lots is the coefficient of capacity variation c, as shown in Equation 28. mX ~c mZ It is possible to find this.

[0214] The evaluation unit 2032 evaluates the average operating rate u X ~u Zand average machining time t mX ~t mZ and the coefficient of change of ability c mX ~c mZ These are supplied to the evaluation unit 2031 and the calculation unit 208, respectively.

[0215] In ST52, the evaluation unit 2031 acquires resource count information from the storage unit 2, and according to the resource count information, each process area S X ~S Z Number of resources k X ~k Z The evaluation unit 2031 identifies the average operating rate u from the evaluation unit 2032. X ~u Z and the coefficient of change of ability c mX ~c mZ The evaluation unit 2031 obtains the number of resources k. X ~k Z and average arrival rate λ X ~λ Z and average utilization rate u X ~u Z and the coefficient of change of ability c mX ~c mZ Using these, the coefficient of variation upon arrival, c, is as shown in equations 29 to 31. aX ~c aZ You may also request this.

[0216] The evaluation unit 2031 has a resource count of k X ~k Z and average arrival rate λ X ~λ Z and the coefficient of variation upon arrival c aX ~c aZ The and are supplied to the calculation unit 208.

[0217] In ST3 shown in Figure 7, the calculation unit 208 obtains inventory information based on the arrival fluctuation characteristics and capacity fluctuation characteristics obtained in ST2.

[0218] For example, in ST3, ST61 to ST63 shown in Figure 18 may be performed.

[0219] In ST61, the calculation unit 208 has a resource count of k. X ~k Z and average utilization rate uX ~u Z and average machining time t mX ~t mZ and the coefficient of variation upon arrival c aX ~c aZ and the coefficient of change of ability c mX ~c mZ Using this, each process area S X ~S Z Average length of stay W X ~W Z This is calculated using formula 33.

[0220] In ST62, the calculation unit 208 calculates the average arrival rate λ X ~λ Z And the average stay time W shown in equation 33. X ~W Z Using this, each process area S X ~S Z The maximum inventory quantity L for inventory B. X ~L Z This can be calculated using formula 34.

[0221] In ST63, each process area S X ~S Z The appropriate capacity of inventory B is L X ~L Z Let's assume that.

[0222] The calculation unit 108 calculates each process area S X ~S Z Appropriate capacity L for inventory B X ~L Z , each process area S X ~S Z Average length of stay W X ~W Z The calculation result 2d, including the result, is stored in the storage unit 2.

[0223] As described above, in the third embodiment, in the method for managing the manufacturing line P, each reentrantable process area S X ~S Z Lot arrival variation characteristics and reentrantable process area S X ~S Z Depending on the capacity fluctuation characteristics, the upper limit of inventory B is L. X ~LZ The appropriate capacity of inventory B is L X ~L Z This is said to be the case. As a result, for each reentrantable process area S, the upper limit of inventory B L is set in a way that takes into account the variation in inventory levels corresponding to the variation in lot arrival and processing capacity. q This is required, and it can be communicated to the user.

[0224] Furthermore, in the third embodiment, in the method for managing the manufacturing line P, each reentrantable process area S X ~S Z Lot arrival variation characteristics and reentrantable process area S X ~S Z Depending on the capacity fluctuation characteristics, each process area S X ~S Z Average length of stay W X ~W Z This requires that, for each reentrantable process area S, the average dwell time W for each process area S can be determined in a way that takes into account variations in inventory levels due to variations in lot arrival and processing capacity, and this can be communicated to the user.

[0225] (Fourth embodiment) Next, we will describe the manufacturing line management method according to the fourth embodiment. The following explanation will focus on the differences from the first to third embodiments.

[0226] In the first embodiment, the management of the manufacturing line P is illustrated by focusing on the lot flow in each process area S, while in the fourth embodiment, the management of the manufacturing line P is illustrated by focusing on the lot flow in two adjacent process areas S that become bottlenecks.

[0227] For example, as shown in Figure 19, the manufacturing line P is a bottleneck in two adjacent process areas S. 101 ,S 102 Let's consider the case that includes this. Figure 19 shows the configuration of the manufacturing line P in the fourth embodiment.

[0228] Adjacent 2-process area S 101 ,S 102 The lot is processed sequentially. Process area S 101 This is the forward process area S 101 Also known as process area S 102 This is the rear process area S 102 It shall also be called

[0229] Each process area S 101 ,S 102 It has inventory B and resource group M. Process area S 101 Inventory B 101 and resource group M 101 It has. Resource group M 101 This includes multiple resources E. Process area S 102 Inventory B 102 and resource group M 102 It has. Resource group M 102 This includes multiple resources E.

[0230] Adjacent 2-process area S 101 ,S 102 If the capabilities are equivalent, the lot is in an adjacent 2-process area S 101 ,S 102 Flow can be difficult in between. Lots are in adjacent 2-process area S. 101 ,S 102 In order for the process to flow smoothly in between, the downstream process area S 102 Inventory B 102 It is desirable to curb the depletion of these resources.

[0231] Each manufacturing line P, as shown in Figure 19, can be managed by a management system 301, as shown in Figure 20. Figure 20 is a diagram showing the functional configuration of the management system 301.

[0232] The management system 301 has evaluation units 3031, 3032, 3081, 3082, and 3083 instead of evaluation unit 3 and calculation unit 8 (see Figure 4).

[0233] The evaluation unit 3031 is located in the adjacent two-process area S101 ,S 102 Parameters indicating the variation characteristics of lot arrivals to are obtained and supplied to calculation units 3081 and 3082. Evaluation unit 3032 evaluates adjacent two process areas S 101 ,S 102 The parameters showing the capacity fluctuation characteristics are determined and supplied to the calculation units 3081 and 3082. The calculation unit 3081 calculates the parameters showing the forward process area S 101 Parameters showing arrival variation characteristics and the forward process area S 101 Using parameters that show the capacity fluctuation characteristics, inventory information is obtained. The calculation unit 3081 obtains inventory information from the forward process area S 101 Inventory B 101 The average inventory quantity L1 may also be calculated. The calculation unit 3082 calculates the average inventory quantity L1 of the downstream process area S. 102 Parameters showing arrival variation characteristics and the downstream process area S 102 Using parameters that show the capacity fluctuation characteristics, inventory information is obtained. The calculation unit 3082 obtains the inventory information from the downstream process area S 102 Inventory B 102 The average inventory quantity L2 may also be calculated. The calculation unit 3083 calculates the average inventory quantity L2 of the downstream process area S. 102 Inventory B 102 The average inventory level L2 is in the forward process area S. 101 Inventory B 101 We need to find the conditions under which the average inventory quantity L1 is greater than the average inventory quantity L1.

[0234] For example, the evaluation unit 3031 acquires WIP information for a predetermined period from the storage unit 2. Based on the WIP information, the evaluation unit 3031 evaluates each process area S 101 ,S 102 The actual number of work-in-progress lots can be identified. The evaluation unit 3031 can identify each process area S 101 ,S 102 Based on the actual number of work-in-progress lots, each process area S 101 ,S 102 Determine the arrival variation characteristics of the lot.

[0235] The evaluation unit 3031 evaluates the forward process area S as input at each unit time. 101The number of work-in-progress lots for each resource E is analyzed, and the total number of work-in-progress lots for multiple resources E is determined. The evaluation unit 3031 performs statistical processing over multiple unit time periods to determine the distribution of the total number of work-in-progress lots. The evaluation unit 3031 then analyzes the characteristics of this distribution (for example, the mean value m). a1 , variance σ a1 2 ) can be extracted and used as a parameter to show the variation characteristics of lot arrivals to the inventory sharing unit U.

[0236] The evaluation unit 3031 is located in the forward process area S. 101 The coefficient of variation c of arrival, as shown in the following equation 35, represents the variation in the number of work-in-progress lots. a1 It is possible to find this. c a1 =σ a1 / m a1 ...Formula 35

[0237] Similarly, the evaluation unit 3031 evaluates the downstream process area S 102 The coefficient of variation c of arrival, as shown in the following equation 36, represents the variation in the number of work-in-progress lots. a2 It is possible to find this. c a2 =σ a2 / m a2 ...Formula 36

[0238] Here, adjacent 2-process area S 101 ,S 102 If the capabilities are equivalent, the coefficient of variation upon arrival is c a1 ,c a2 Since they are approximately equal, we can express this as shown in the following equation 37, c a I'll leave it at that.

number

[0239] The evaluation unit 3031 is located in the forward process area S. 101 As a parameter indicating the variation in lot arrival characteristics, the forward process area S over a predetermined period. 101 Average arrival time interval t a1The evaluation unit 3031 may determine the process area S in front of it at each unit time. 101 The time interval between lots arriving at resource E is determined, and the average arrival time interval for multiple resources E is determined. The evaluation unit 3031 averages the time over multiple unit times and determines the forward process area S 101 Average arrival time interval t a1 We seek.

[0240] Similarly, the evaluation unit 3031 evaluates the downstream process area S 102 Average arrival time interval t a2 We seek.

[0241] Here, adjacent 2-process area S 101 ,S 102 If the capabilities are equivalent, the average arrival time interval t a1 ,t a2 Since they are approximately equal, we can express this as shown in the following equation 38: a I'll leave it at that.

number

[0242] The evaluation unit 3031 calculates the coefficient of variation upon arrival, c a and average arrival time interval t a These are supplied to calculation units 3081 and 3082, respectively.

[0243] The evaluation unit 3032 acquires throughput information for period T from the storage unit 2. Based on the throughput information, the evaluation unit 3032 evaluates the two adjacent process areas S 101 ,S 102 The actual number of processing lots can be identified. The evaluation unit 3032 identifies the adjacent two-process area S 101 ,S 102 Based on the actual number of processing lots, adjacent 2-process area S 101 ,S 102 Determine the capability fluctuation characteristics of each resource E.

[0244] The evaluation unit 3032 evaluates the forward process area S as an output for each unit of time. 101The number of lots processed by each resource E is analyzed, and the total number of lots processed for multiple resources E is determined. The evaluation unit 3032 performs statistical processing over multiple unit time periods to determine the distribution of the total number of lots processed. The evaluation unit 3032 then analyzes the characteristics of that distribution (for example, the mean value m). m1 , variance σ m1 2 ) is extracted, and the forward process area S 101 This can be used as a parameter to indicate the performance fluctuation characteristics.

[0245] The evaluation unit 3032 is located in the forward process area S. 101 The coefficient representing the variation in the number of processing lots is the coefficient of capacity variation c, as shown in the following equation 39. m1 It is possible to find this. c m1 =σ m1 / m m1 ...Formula 39

[0246] Similarly, the evaluation unit 3032 evaluates the downstream process area S 102 The coefficient representing the variation in the number of processing lots is the coefficient of capacity variation c, as shown in the following equation 40. m2 It is possible to find this. c m2 =σ m2 / m m2 ...Formula 40

[0247] Here, adjacent 2-process area S 101 ,S 102 If the abilities are equivalent, the coefficient of variation of ability c m1 ,c m2 Since they are approximately equal, we can express this as shown in the following equation 41, c m I'll leave it at that.

number

[0248] The evaluation unit 3032 is located in the forward process area S. 101 The parameter that shows the capacity fluctuation characteristics is the forward process area S over period T. 101 Average processing time t m1Further calculations may be made. The evaluation unit 3032 determines the processing time of each lot by each resource E for each unit time, and the forward process area S 101 The average processing time for multiple resources E within the system is determined. The evaluation unit 3032 averages the processing time over multiple unit times and calculates the average processing time for the preceding process area S. 101 Average processing time t m1 We seek.

[0249] Similarly, the evaluation unit 3032 evaluates the downstream process area S 102 Average processing time t m2 We seek.

[0250] Here, adjacent 2-process area S 101 ,S 102 If the capabilities are equivalent, the average processing time t m1 ,t m2 Since they are approximately equal, we can express this as shown in the following equation 42: m I'll leave it at that.

number

[0251] The evaluation unit 3032 uses the coefficient of variation of capacity c m and average machining time t m These are supplied to calculation units 3081 and 3082, respectively.

[0252] The calculation unit 3081 calculates the coefficient of variation upon arrival, c a and average arrival time interval t a and the coefficient of change of ability c m and average machining time t m Using the forward process area S 101 The average inventory quantity L1 is calculated using the following formula 43. The average inventory quantity L1 is equal to the inventory B per unit time. 101 This is the number of items in stock.

number

[0253] The calculation unit 3081 is located in the forward process area S 101The average inventory quantity L1 is supplied to the calculation unit 3083.

[0254] The calculation unit 3082 calculates the coefficient of variation upon arrival, c a and average arrival time interval t a and the coefficient of change of ability c m and average machining time t m Using this, the downstream process area S 102 The average inventory quantity L2 is calculated using the following formula 44. The average inventory quantity L2 is equal to the inventory quantity B per unit time. 102 This is the number of items in stock.

number

[0255] The calculation unit 3082 is located in the downstream process area S. 102 The average inventory quantity L2 is supplied to the calculation unit 3083.

[0256] As mentioned above, the lot is in adjacent two-process area S. 101 ,S 102 In order for the process to flow smoothly in between, the downstream process area S 102 Inventory B 102 It is desirable to curb the depletion of these resources.

[0257] The calculation unit 3083 is located in the forward process area S 101 Average inventory level L1 and downstream processing area S 102 Depending on the average inventory level L2, the downstream process area S 102 The average inventory level L2 is in the forward process area S. 101 Calculate the conditions under which the average inventory quantity L1 will be greater than the average inventory quantity L1.

[0258] The calculation unit 3083 calculates the coefficient of variation upon arrival, c a and the coefficient of change of ability c m You can also calculate how the relationship between average inventory quantity L1 and average inventory quantity L2 changes while varying the relative magnitudes of the two.

[0259] For example, the average inventory quantity L1 shown in formula 43 and the average inventory quantity L2 shown in formula 44 are given by t m =10,ta =12,c m =1 is substituted, and when ca is changed around 1, the average inventory quantity L1 and the average inventory quantity L2 change as shown in FIG. 21, respectively. FIG. 21 shows two adjacent process areas S 101 , S 102 , which is a diagram showing inventory fluctuations. In FIG. 21, the vertical axis represents the average inventory quantity, and the horizontal axis represents the magnitude of the arrival variation coefficient ca. Since the capacity variation coefficient c m =1 is fixed, the range where the arrival variation coefficient ca <1 on the horizontal axis corresponds to the magnitude relationship of "arrival variation coefficient ca" < "capacity variation coefficient c m ". The range where the arrival variation coefficient ca>1 on the horizontal axis corresponds to the magnitude relationship of "arrival variation coefficient ca"> "capacity variation coefficient c m ".

[0260] As shown in FIG. 21, when the arrival variation coefficient c in each area S 101 , S 102 becomes larger than 1, the average inventory quantity L1 of the upstream process area S a becomes dramatically larger than the average inventory quantity L2 of the downstream process area S 101 Thus, it can be seen that when the arrival variation coefficient c 102 is relatively larger than the capacity variation coefficient c a , lots tend to accumulate excessively in the inventory B m of the upstream process area S 101 , and lots are likely to be depleted in the inventory B 101 of the downstream process area S 102 . 102

[0261] On the other hand, when the arrival variation coefficient c in each area S 101 , S 102 becomes smaller than 1, the average inventory quantity L1 of the upstream process area S a becomes smaller than the average inventory quantity L2 of the downstream process area S 101 , but the difference between the two is relatively small. Accordingly, when the arrival variation coefficient c 102 is relatively smaller than the capacity variation coefficient c a , lots are distributed in the two adjacent process areas S m , S 101 102 ​​The process flows smoothly in between, and in the rear process area S 102 Inventory B 102 It can be seen that the depletion of resources can be mitigated.

[0262] Based on this calculation result, the calculation unit 3083 calculates the downstream process area S 102 The average inventory level L2 is in the forward process area S. 101 The condition for the average inventory quantity L1 to be greater than the following is shown in equation 45. c a <c m ...Formula 45

[0263] The conditions shown in formula 45 apply to adjacent two-process area S 101 ,S 102 When we show each of these, we get the following equation 46. c a1 <c m1 ,c a2 <c m2 ...Formula 46

[0264] The calculation unit 3083 may, instead of the conditions shown in formula 45, determine the conditions shown in formula 46.

[0265] The calculation unit 3083 is located in the forward process area S 101 Average inventory level L1, downstream process area S 102 The average inventory quantity L2 and the calculation result 2e, which includes the conditions shown in formula 45 or formula 46, are stored in the storage unit 2.

[0266] Furthermore, the operation of the management system 301 differs from that of the first embodiment in the following respects.

[0267] In ST2 shown in Figure 7, the evaluation units 3031 and 3032 are located in adjacent two-process area S 101 ,S 102 We will determine the arrival variation characteristics and capacity variation characteristics.

[0268] For example, in ST2, steps ST71 to ST74 shown in Figure 22 may be performed. Steps ST71 to ST72 and ST73 to ST74 may be performed in parallel with each other.

[0269] In ST71-ST72, the evaluation unit 3031 acquires WIP information for a predetermined period from the storage unit 2. Based on the WIP information, the evaluation unit 3031 evaluates each process area S 101 ,S 102 The actual number of work-in-progress lots can be identified. The evaluation unit 3031 can identify each process area S 101 ,S 102 Based on the actual number of work-in-progress lots, each process area S 101 ,S 102 Determine the arrival variation characteristics of the lot.

[0270] In ST71, the evaluation unit 3031 determines the distribution of the probability of occurrence of work-in-progress lots. For example, the evaluation unit 3031 determines the distribution of the probability of occurrence of the forward process area S as input for each unit time. 101 The number of work-in-progress lots for each resource E is analyzed, and the total number of work-in-progress lots for multiple resources E is determined. The evaluation unit 3031 performs statistical processing over multiple unit time periods to determine the distribution of the total number of work-in-progress lots.

[0271] In ST72, the evaluation unit 3031 evaluates the characteristics of the distribution obtained in ST71 (for example, the mean value m) a1 , variance σ a1 2 ) can be extracted and used as a parameter to show the variation characteristics of lot arrivals to the inventory sharing unit U.

[0272] The evaluation unit 3031 is located in the forward process area S. 101 The coefficient of variation c, as shown in Equation 35, represents the variation in the number of work-in-progress lots. a1 It is possible to find this.

[0273] Similarly, the evaluation unit 3031 evaluates the downstream process area S 102 The coefficient of variation c, as shown in Equation 36, represents the variation in the number of work-in-progress lots. a2 It is possible to find this.

[0274] Here, adjacent 2-process area S 101 ,S 102 If the capabilities are equivalent, the coefficient of variation upon arrival is ca1 , c a2 are approximately equal, and as shown in Mathematical Expression 37, this c a is set as follows.

[0275] The evaluation unit 3031 is configured to, for the preceding process area S 101 as a parameter indicating arrival fluctuation characteristics of lots at, obtain an average arrival time interval t of the preceding process area S 101 over a predetermined period. The evaluation unit 3031 is configured to obtain an arrival time interval of lots at the resource E in the preceding process area S a1 for each unit time, and obtain an average arrival time interval for a plurality of resources E. The evaluation unit 3031 is configured to time-average the values over a plurality of unit times to obtain an average arrival time interval t 101 of the preceding process area S 101 average arrival time interval t a1 .

[0276] Similarly, the evaluation unit 3031 is configured to obtain an average arrival time interval t 102 average arrival time interval t a2 for the subsequent process area S

[0277] The evaluation unit 3031 is configured to supply an arrival variation coefficient c a and an average arrival time interval t a to calculation units 3081 and 3082, respectively.

[0278] In steps ST73 to ST74, the evaluation unit 3032 is configured to acquire throughput information for a period T from the storage unit 2. Based on the throughput information, the evaluation unit 3032 can specify actual results of the number of processed lots in two adjacent process areas S 101 , S 102 . The evaluation unit 1032 is configured to obtain capacity fluctuation characteristics of each resource E in two adjacent process areas S 101 , S 102 based on the actual results of the number of processed lots in the two adjacent process areas S 101 , S 102 .

[0279] In ST73, the evaluation unit 3032 determines the distribution of the probability of occurrence of the number of processing lots. For example, the evaluation unit 3032 determines the distribution of the forward process area S as the output for each unit time. 101 The number of lots processed by each resource E is analyzed, and the total number of lots processed for multiple resources E is determined. The evaluation unit 3032 performs statistical processing over multiple units of time to determine the distribution of the total number of lots processed.

[0280] In ST74, the evaluation unit 3032 evaluates the characteristics of the distribution obtained in ST73 (for example, the mean value m) m1 , variance σ m1 2 ) is extracted, and the forward process area S 101 This can be used as a parameter to indicate the performance fluctuation characteristics.

[0281] The evaluation unit 3032 is located in the forward process area S. 101 The coefficient of variation c, as shown in Equation 39, represents the variation in the number of processing lots. m1 It is possible to find this.

[0282] Similarly, the evaluation unit 3032 evaluates the downstream process area S 102 The coefficient of variation c, as shown in Equation 40, represents the variation in the number of processing lots. m2 It is possible to find this.

[0283] Here, adjacent 2-process area S 101 ,S 102 If the abilities are equivalent, the coefficient of variation of ability c m1 ,c m2 Since they are approximately equal, we can express this as shown in equation 41, c m I'll leave it at that.

[0284] The evaluation unit 3032 is located in the forward process area S. 101 The parameter that shows the capacity fluctuation characteristics is the forward process area S over period T. 101 Average processing time t m1 Further calculations may be made. The evaluation unit 3032 determines the processing time of each lot by each resource E for each unit time, and the forward process area S 101The average processing time for multiple resources E within the system is determined. The evaluation unit 3032 averages the processing time over multiple unit times and calculates the average processing time for the preceding process area S. 101 Average processing time t m1 We seek.

[0285] Similarly, the evaluation unit 3032 evaluates the downstream process area S 102 Average processing time t m2 We seek.

[0286] Here, adjacent 2-process area S 101 ,S 102 If the capabilities are equivalent, the average processing time t m1 ,t m2 Since they are approximately equal, we can express this as shown in equation 42, t m I'll leave it at that.

[0287] The evaluation unit 3032 uses the coefficient of variation of capacity c m and average machining time t m These are supplied to calculation units 3081 and 3082, respectively.

[0288] In ST3 shown in Figure 7, the calculation units 3081, 3082, and 3083 are located in the adjacent two-process area S. 101 ,S 102 I would like to request inventory information.

[0289] For example, in ST3, steps ST81 to ST83 shown in Figure 23 may be performed. Steps ST81 and ST82 may be performed in parallel with each other.

[0290] In ST81, the calculation unit 3081 calculates the coefficient of variation upon arrival c a and average arrival time interval t a and the coefficient of change of ability c m and average machining time t m Using the forward process area S 101 The average inventory quantity L1 is calculated using formula 43. The average inventory quantity L1 is the inventory quantity B per unit time. 101 This is the number of items in stock.

[0291] The calculation unit 3081 is located in the forward process area S101 The average inventory quantity L1 is supplied to the calculation unit 3083.

[0292] In ST82, the calculation unit 3082 calculates the coefficient of variation upon arrival c a and average arrival time interval t a and the coefficient of change of ability c m and average machining time t m Using this, the downstream process area S 102 The average inventory quantity L2 is calculated using formula 44. The average inventory quantity L2 is the inventory quantity B per unit time. 102 This is the number of items in stock.

[0293] The calculation unit 3082 is located in the downstream process area S. 102 The average inventory quantity L2 is supplied to the calculation unit 3083.

[0294] In ST83, the calculation unit 3083 calculates the forward process area S 101 Average inventory level L1 and downstream processing area S 102 Depending on the average inventory level L2, the downstream process area S 102 The average inventory level L2 is in the forward process area S. 101 Calculate the conditions under which the average inventory quantity L1 will be greater than the average inventory quantity L1.

[0295] The calculation unit 3083 calculates the coefficient of variation upon arrival, c a and the coefficient of change of ability c m You can also calculate how the relationship between average inventory quantity L1 and average inventory quantity L2 changes while varying the relative magnitudes of the two.

[0296] Based on the calculation results, the calculation unit 3083 determines the downstream process area S 102 The average inventory level L2 is in the forward process area S. 101 The condition for the average inventory quantity L1 to be greater than the condition shown in formula 45 is sought.

[0297] The calculation unit 3083 may determine the conditions shown in equation 46 instead of the conditions shown in equation 45.

[0298] The calculation unit 3083 is located in the forward process area S 101Average inventory level L1, downstream process area S 102 The average inventory quantity L2 and the calculation result 2e, which includes the conditions shown in formula 45 or formula 46, are stored in the storage unit 2.

[0299] As described above, in the fourth embodiment, in the method for managing the manufacturing line P, adjacent two process areas S 101 ,S 102 Lot arrival variation characteristics and adjacent 2-process area S 101 ,S 102 Inventory information is obtained based on the capacity fluctuation characteristics. For example, the forward process area S 101 Parameters showing arrival variation characteristics and the downstream process area S 102 Parameters showing arrival variation characteristics and the forward process area S 101 Parameters showing the capacity fluctuation characteristics and the downstream process area S 102 Using parameters that show the capacity fluctuation characteristics, the forward process area S 101 Inventory B 101 Average inventory level L1 and downstream processing area S 102 Inventory B 102 The average inventory quantity L2 and the average inventory quantity L2 can be calculated. This allows for the calculation of the two adjacent process areas S. 101 ,S 102 Variation in lot arrival and adjacent two-process area S 101 ,S 102 The adjacent two-process area S takes into account fluctuations in inventory levels corresponding to variations in processing capacity. 101 ,S 102 Inventory B 101 ,B 102 The average inventory levels L1 and L2 can be calculated and communicated to the user.

[0300] Furthermore, in the fourth embodiment, in the method for managing the manufacturing line P, adjacent two process areas S 101 ,S 102 The backward process area S 102 Inventory B 102 The average inventory level L2 is in the forward process area S. 101 Inventory B 101 This allows us to determine the conditions under which the average inventory quantity L1 of a lot is greater than the average inventory quantity L1.101 ,S 102 This allows us to encourage users to make improvements to ensure a smoother flow between processes. As a result, production line P can process lots efficiently.

[0301] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims of the invention and its equivalents. [Explanation of Symbols]

[0302] 1, 101, 201, 301 Management system, 2 Storage unit, 3, 1031, 1032, 2031, 2032, 3031, 3032 Evaluation unit, 5 Acquisition unit, 6 Control unit, 8, 81, 82, 108, 208, 3081~3083 Calculation unit.

Claims

1. In a manufacturing line where multiple process areas, each containing multiple resources, are arranged, the variation in lot arrivals to the process areas and the variation in the capacity of the process areas are used to determine the variation in lot stay within the process areas. In accordance with the aforementioned staging variation characteristics, inventory information regarding the inventory to be provided within the process area is obtained, A method for managing a manufacturing line equipped with the following features.

2. To obtain the aforementioned inventory information, This includes determining the appropriate inventory capacity that allows the resources of the process area to operate continuously, in accordance with the aforementioned stabilization characteristics. A method for managing a manufacturing line according to claim 1.

3. To obtain the aforementioned inventory information, This includes determining the appropriate inventory capacity such that there is zero operational loss of resources in the process area, according to the aforementioned dwell time fluctuation characteristics. A method for managing a manufacturing line according to claim 1.

4. Determining the appropriate capacity of the aforementioned inventory is: Based on the aforementioned capacity fluctuation characteristics, the average processing time of the resources in the process area is determined, Based on the aforementioned stabilization characteristics, the probability of resource loss in the process area is determined, The appropriate inventory capacity is determined according to the average processing time and the probability of occurrence. including A method for managing a manufacturing line according to claim 1.

5. Determining the appropriate capacity of the aforementioned inventory is Using the average processing time and the probability of occurrence, determine the number of lots at which the resource utilization rate of the process area becomes zero. The appropriate inventory capacity shall be greater than or equal to the lot size requested above, including The method for managing a manufacturing line according to claim 4.

6. Determining the aforementioned stay variation characteristics is: To determine the distribution of the probability of the number of remaining lots occurring, Extrapolating the aforementioned distribution to the side where the number of lots stayed is negative, Extracting the features of the extrapolated distribution, Includes, Determining the probability of the aforementioned operational loss occurring is: This includes integrating the portion of the extrapolated distribution where the number of dwell lots is negative. The method for managing a manufacturing line according to claim 4.

7. Extracting the aforementioned features means This includes determining the coefficient of variation of the distribution, The aforementioned integration is performed as follows: This includes integrating the portion of the extrapolated distribution where the number of dwell lots is negative, using the coefficient of variation. The method for managing a manufacturing line according to claim 6.

8. Each of the aforementioned process areas is provided with an inventory sharing unit that includes two or more process areas that share inventory. Determining the aforementioned stay variation characteristics is: This includes determining the staging characteristics of lots within the shared inventory unit based on the lot arrival variability characteristics to the shared inventory unit and the capacity variability characteristics of the shared inventory unit. To obtain the aforementioned inventory information, This includes determining the upper limit of inventory in the inventory sharing unit according to the fluctuation characteristics of the lot's presence within the inventory sharing unit. A method for managing a manufacturing line according to claim 1.

9. In the aforementioned multiple process areas, Kanbans associated with lots being fed into the inventory sharing unit from the process area immediately preceding the inventory sharing unit can be placed in the inventory. To obtain the aforementioned inventory information, This further includes setting the determined upper limit of inventory to the appropriate number of Kanban signs. The method for managing a manufacturing line according to claim 8.

10. To obtain the aforementioned inventory information, This further includes determining the average time spent in the inventory sharing unit according to the average number of lots that should remain in the inventory sharing unit. The method for managing a manufacturing line according to claim 8.

11. Determining the aforementioned stay variation characteristics is: Using the coefficient of variation of arrivals relating to the distribution of lot arrival variations to the shared inventory unit and the coefficient of variation of capacity relating to the distribution of capacity variations of the shared inventory unit, the probability of the inventory quantity occurring in the inventory is determined. To obtain the aforementioned inventory information, This includes determining the upper limit of the inventory in the inventory sharing unit according to the probability of the inventory quantity occurring in the inventory. The method for managing a manufacturing line according to claim 8.

12. Determining the aforementioned stay variation characteristics is: Using the coefficient of variation of arrivals relating to the distribution of lot arrival variations to the shared inventory unit and the coefficient of variation of capacity relating to the distribution of capacity variations of the shared inventory unit, the probability of the inventory quantity occurring in the inventory is determined. To obtain the aforementioned inventory information, This includes determining the maximum inventory quantity of the inventory shared unit and the average dwell time of the inventory shared unit, based on the probability of the inventory quantity occurring in the inventory. The method for managing a manufacturing line according to claim 8.

13. The aforementioned inventory sharing unit has a time constraint set. To obtain the aforementioned inventory information, The further includes determining whether the average stay time satisfies the constraint time. The method for managing a manufacturing line according to claim 12.

14. The aforementioned plurality of process areas include two or more process areas that are reentrantable. Determining the aforementioned stay variation characteristics is: This includes determining the lot arrival variation characteristics for each process area in the two or more process areas and the capacity variation characteristics for each process area in the two or more process areas, To obtain the aforementioned inventory information, This includes determining the maximum inventory quantity for each of the two or more process areas in accordance with the arrival fluctuation characteristics and capacity fluctuation characteristics. A method for managing a manufacturing line according to claim 1.

15. Determining the aforementioned stay variation characteristics is: This includes determining the coefficient of variation of arrivals relating to the distribution of variations in lot arrivals to each process area in the two or more process areas, and the coefficient of variation of capacity relating to the distribution of variations in capacity of each process area in the two or more process areas. To obtain the aforementioned inventory information, This includes determining the maximum inventory quantity for each process area in the two or more process areas based on the aforementioned coefficient of variation in arrivals and the aforementioned coefficient of variation in capacity. A method for managing a manufacturing line according to claim 14.

16. To obtain the aforementioned inventory information, This further includes setting the determined upper limit of inventory to the appropriate inventory capacity for each of the two or more process areas. A method for managing a manufacturing line according to claim 14.

17. To obtain the aforementioned inventory information, This further includes determining the average dwell time of lots within each of the two or more process areas, in accordance with the arrival variation characteristics and capacity variation characteristics. A method for managing a manufacturing line according to claim 14.

18. The aforementioned multiple process areas are, The first process area and A second process area following the first process area, Includes, To obtain the aforementioned inventory information, This includes determining the average inventory quantity for the first process area and the average inventory quantity for the second process area, respectively, based on the arrival variability characteristics of the first process area, the capacity variability characteristics of the first process area, the arrival variability characteristics of the second process area, and the capacity variability characteristics of the second process area. A method for managing a manufacturing line according to claim 1.

19. To obtain the aforementioned inventory information, This further includes determining the conditions under which the average inventory quantity in the second process area is greater than the average inventory quantity in the first process area, based on the arrival variability characteristics of the first process area, the capacity variability characteristics of the first process area, the arrival variability characteristics of the second process area, and the capacity variability characteristics of the second process area. The method for managing a manufacturing line according to claim 18.

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