KR101527080B1 - Digital Manufacturing Optimization System for Scenario Simulation and Discovering Bottleneck Processes Based - Google Patents

Abstract

The present invention relates to a digital manufacturing optimization system including scenario simulation and bottleneck process information processing.
The digital manufacturing optimization system of the present invention includes: (A) receiving a control variable value and a performance characteristic value for each control variable according to a scenario; (B) performing a simulation for each of the scenarios and generating at least one simulation analysis information according to a result of the simulation; (C) deriving at least one bottleneck process from the simulation analysis information, generating predetermined diagnostic information for the bottleneck process, and (D) processing line balancing information of the processes. And performs information processing.
By implementing the present invention, it is possible to simulate a plurality of scenarios to find a bottleneck process as a result of a simulation, to minimize the overall productivity deterioration due to the bottleneck process, to generate an optimal scenario through t-test, , The user can greatly assist in reviewing and adopting the alternative scenarios. By comparing and analyzing the performance characteristics of the users through the change of the facility capability in each process, the user can review the alternative scenarios efficiently And the like.

Description

TECHNICAL FIELD The present invention relates to a digital manufacturing optimization system including a scenario simulation and a bottleneck process information processing,

The present invention relates to a digital manufacturing optimization system, and more particularly, to a digital manufacturing optimization system including scenario simulation and bottleneck process information processing.

Traditionally, the domestic manufacturing industry has a low value-added industrial structure with low wages, but continues to grow as it plays a leading role in Korea's economic growth and overseas exports. However, most manufacturing companies in Korea are focusing on external growth rather than maximizing profits or minimizing costs, so that there are overlapping investments and unplanned investments due to low technological entry barriers as a whole. Therefore, due to the excessive production capacity, the operating rate of the manufacturing plant and the manufacturing facility is frequently lowered, and the international competitiveness is gradually weakening.

Therefore, the management and production environments of small and medium sized manufacturing companies in Korea are getting worse and there are many cases in which the investment in the manufacturing industry is decreased and the production base is transferred to overseas. For example, PCB manufacturing companies and bicycle manufacturing companies in many domestic manufacturing industries have moved their manufacturing plants to China and continue to expand their production in China. If the hollowing of manufacturing companies that manufacture high-tech products from such a simple assembling process accelerates, the manufacturing industry in Korea will be hit hard and the manufacturing competitiveness of the nation will be lowered.

However, as described above, manufacturing industries such as automobile, shipbuilding, semiconductor, and shipbuilding still have global competitiveness and high technology and play a role of Korea's strong growth engine . One of the reasons for this is the investment activity of information technology (IT) for securing the competitiveness and technological power of the manufacturing industry, that is, investing in the development of manufacturing-related analysis technology and analysis system using IT.

There is a need to maximize manufacturing process or production performance with limited resources or capital in the manufacturing enterprise. For this purpose, comparative analysis functions for each manufacturing process scenario are essential. However, existing simulation packages express the comparative advantage of scenarios through the difference of average values. However, since the random variable value is used in the nature of the simulation, it is required to compare the values of the scenario values through statistical verification. In particular, it can help users to review and adopt alternative scenarios by generating optimal scenarios based on tests and showing the difference of statistical values between scenarios. In addition, flexible adjustment parameters and performance characteristics It has been urgently required to develop a technology and a system capable of performing an optimal process simulation based on the control parameters and the set performance characteristics.

The first problem to be solved by the present invention is, in detail, a digital manufacturing optimization system including a scenario simulation based bottleneck process information processing.

A second problem to be solved by the present invention is to provide an information processing method of a digital manufacturing optimization system including a scenario simulation-based bottleneck process information processing.

According to an aspect of the present invention, there is provided an information processing method of a digital manufacturing optimization system for registering and processing at least two scenarios, the digital manufacturing optimization system comprising: (A) Obtaining a performance characteristic value; (B) performing a simulation for each of the scenarios and generating at least one simulation analysis information according to a result of the simulation; (C) deriving at least one bottleneck process from the simulation analysis information, generating predetermined diagnostic information for the bottleneck process, and (D) processing line balancing information of the processes. This paper presents an information processing method of a digital manufacturing optimization system.

It is preferable that the control variable is at least one of a number of facilities, a number of workers, and a work method for each process, or the performance characteristic value is at least one of a delivery completion rate, a work in process (WIP) level, .

Preferably, the simulation analysis information includes comparison information of a sequence of scenario values through statistical verification.

The simulation analysis information is preferably capable of generating a difference in average value between scenarios to support review or selection of a self-made alternative scenario using the digital manufacturing optimization system.

Preferably, the simulation analysis information provides visualized chart information according to a performance characteristic value.

Preferably, the derivation of the bottleneck process is performed by including task unit information and process transfer information, and calculating a standard operation time and a process delivery time according to the task unit information to derive a predetermined bottleneck process in the process .

It is preferable that a process having the highest cycle time among the processes is selected in order to derive the bottleneck process of the preset reference.

Further comprising the step of: (E) deriving at least one or more abnormal processes after (C) or before (C), and deriving the abnormal process is characterized in that E_TAT + t _{R- 2S / Sqrt (R)} value exceeds the maximum allowable working time.

E_TAT is the actual average work time, R is the number of work repetitions measured to calculate the actual work time, s is the standard deviation of the actual work time, and t (1-R) is the t distribution freedom.

The line balancing information process may be a process of finding at least one scenario in which a bottleneck process and an abnormal process do not occur or are minimized based on a plurality of simulations or that the workplace configuration time is less than a cycle time of a bottleneck process, It is preferable to configure the worksite to have time to perform line balancing.

The derivation of the bottleneck process is selected as the process having the highest cycle time in the process, or that the values of E_TAT + t _{R-1 and 1-? / 2S / Sqrt (R)} If the working time is exceeded and the value of E_TAT + t _{R-1, 1 -? / 2S / Sqrt (R)} approaches within a predetermined value in the theoretical cycle time, the process is preferably processed by the bottleneck process .

E_TAT is the actual average work time, R is the number of work repetitions measured to calculate the actual work time, s is the standard deviation of the actual work time, and t (1-R) is the t distribution freedom.

(E) performing a resource relocation through a result of line balancing, wherein the resource relocation is performed in response to at least one or more optimized scenarios, And an input time interval, and at least one of the optimized scenarios is generated through line balancing.

According to an aspect of the present invention, there is provided an information processing method of a digital manufacturing optimization system for registering and processing at least two scenarios, the digital manufacturing optimization system comprising: a scenario processing module; Bottleneck processing module; And a DB unit, wherein the scenario processing module includes: a scenario registration module that registers a scenario related to at least two manufacturing processes to be a simulation target; A control variable registration module for registering control variables for various processes constituting each scenario; A performance characteristic value registration module for registering various performance characteristic values; A scenario configuration module including a function of combining or combining existing scenarios or newly created scenarios; And a scenario driving module for performing simulation according to at least two separate scenarios, wherein the bottleneck processing module includes a bottleneck process discovery module for discovering a process that becomes a bottleneck in a manufacturing process, and a bottleneck process discovery module for finding an optimal condition for line balancing Line balancing module; Wherein the DB unit stores scenario data, control variable data, and performance characteristic value data.

The present invention has the following special effects.

First, simulations for multiple scenarios can be performed to find the bottleneck process as a result of the simulation, and the overall productivity degradation due to the bottleneck process can be minimized.

Second, by creating an optimal scenario through t-test and showing the difference in average value between each scenario, the user can greatly assist in reviewing and adopting alternative scenarios.

Third, it is possible to provide alternative scenarios review efficiently by comparing and analyzing the performance characteristics of users through the change of facility capacity in each process.

1 and 12 are diagrams illustrating an exemplary configuration of a digital manufacturing optimization system of the present invention.
2 is a diagram of an exemplary information processing method of the digital manufacturing optimization system of the present invention.
3 is a diagram illustrating an exemplary UI for a scenario registration module of the scenario processing module of the present invention.
FIG. 4 is a diagram illustrating an exemplary configuration UI of the control variable registration module of the scenario processing module of the present invention.
5 is a diagram illustrating an exemplary configuration UI of a performance characteristic value registration module of the scenario processing module of the present invention.
6 is a diagram illustrating an exemplary UI of a scenario configuration module of the scenario processing module of the present invention.
7 is a diagram illustrating an exemplary result UI of the information processing of the scenario driving module of the scenario processing module of the present invention.
FIG. 8 is a diagram illustrating an exemplary scenario extraction process according to an embodiment of the present invention.
9 is a diagram of a method for processing information according to an embodiment of the bottleneck processing module of the present invention.
10 is a conceptual diagram illustrating a load chart configuration object for each process.
11 is a diagram showing one embodiment of the load chart of the analysis results of the process.

Hereinafter, the present invention will be described in more detail with reference to the drawings.

1 is a diagram of an exemplary configuration of a digital manufacturing optimization system 1000 of the present invention.

The digital manufacturing optimization system 1000 of the present invention may process information while communicating with at least one user computer 3000 through a wired / wireless network 2000 as illustrated in FIG. 1, Or may operate within user computer 3000. The digital manufacturing optimization system 1000 may also exchange various information from at least one third system 4000. The digital manufacturing optimization system 1000 of the present invention registers (S11) control variables and performance characteristic values constituting a scenario and a scenario to be used, performs simulation for each of a plurality of scenarios (S12), generates analysis information (S13). The bottleneck process information is generated (S14), the overall productivity deterioration of the bottleneck process is diagnosed (S15), and the line balancing information of the processes is processed (S16).

The digital manufacturing optimization system 1000 includes a scenario processing module 1100, a bottleneck processing module 1200, and a DB portion 1300. The scenario processing module 1100 includes a scenario registration module 1110 for registering a scenario related to at least two manufacturing processes to be a simulation target, a control variable registration module for registering control variables for various processes constituting each scenario A performance characteristic value registration module 1130 for registering various performance characteristic values, a scenario configuration module 1140 for combining or combining existing scenarios or newly generated scenarios, and a scenario driving module 1140 for performing simulation according to at least two or more individual scenarios 1150).

Meanwhile, the bottleneck processing module 1200 included in the digital manufacturing optimization system 1000 includes a bottleneck process discovery module 1210 for discovering a bottleneck process, a line for finding an optimal condition for line balancing, A balancing module 1220 and a resource relocation module 1230 that generates resource relocation information as a result of line balancing.

The DB 1300 of the digital manufacturing optimization system 1000 includes a scenario DB 1310 that stores information on scenarios, a control variable DB 1320 that stores information on the control variables, And a performance characteristic value DB 1330 that stores information about the performance characteristic value.

FIG. 3 illustrates an exemplary UI for the scenario registration module 1110 of the present invention. The scenario registration module 1110 registers at least one or more scenarios. The user of the digital manufacturing optimization system 1000 may load an existing AS-IS model that has been driven, or may register it as a new scenario.

FIG. 4 illustrates an exemplary configuration UI of the control variable registration module 1120 of the present invention. The regulatory variable registration module 1120 registers regulatory variables to be analyzed in a newly registered scenario. For example, the number of facilities, the number of workers, and the method of work for each process can be examined as a scenario, and these are examples of control variables.

5, an exemplary configuration UI of the performance characteristic value registration module 1130 of the present invention is illustrated. The performance characteristic value registration module 1130 registers at least one performance characteristic value to be compared through the adjustment parameter. Since the scenario processing module 1100 is subject to the manufacturing process, the delivery characteristic ratio, the level of work in process (WIP), the total production amount, and the like can be examples of the performance characteristic values. The scenario management module 1150 generates a plurality of scenarios having different adjustment variable values of the same conditions based on the loaded base scenarios through the performance characteristic value registration module 1130 of the present invention Simulation can be done for each scenario.

FIG. 6 illustrates an exemplary UI of the scenario configuration module 1140 of the present invention. The scenario configuration module 1140 can configure a plurality of scenarios by changing control variables and performance characteristic values in a previously activated AS-IS model, and provides an environment for rapidly simulating the scenarios. As illustrated in Fig. 6, currently four scenarios are registered, at least one control variable is allocated to each scenario, and evaluation items of the simulation are also defined.

FIG. 7 shows an exemplary result UI of the information processing of the scenario driving module 1150 of the present invention. As illustrated in FIG. 7, the scenario driving module 1150 performs simulation by reflecting control variable values and performance characteristic values for each scenario, and provides simulation execution results through a visual UI. The scenario driving module 1150 may perform a simulation for each scenario to generate a Box chart according to a primary selected performance characteristic value. In the Box chart, the average value of the performance characteristics according to the simulation driving, the upper and lower limits of the T distribution are indicated by BOX, and the maximum value and the minimum value of the simulation can be indicated by a line. As can be seen from FIG. 7, the scenario driving module 1150 of the present invention allows the user to define or select the Bigger is better and the Smaller is better, .

Next, an explanation will be given of the process of extracting the scenario among the best scenarios with reference to FIG. The scenario driving module 1150 extracts a t distribution value for each scenario (S21), estimates the average value difference (distance) between scenarios (S22), and searches for a scenario to be included in the optimal scenario configuration set S23). At this time, the t distribution table is preferably stored in the DB unit 1300.

In order for the scenario driving module 1150 to operate, the following four kinds of information are required. The number of scenarios K, the confidence level 1-α (0.95 in Arena), the error tolerance (indifference zone) δ, and the replication R _i in the i th scenario. At this time, R _i need not be the same in all scenarios. For example, R = 10, 1-α = 0.95, K = 3, Y ₁ = 10, Y ₂ = 8, Y ₃ = 5, S _i ² = 10 for all i may be an example.

An example of information processing of the scenario driving module 1150 will be described in detail.

And extracts t distribution values for each scenario. The t distribution value is determined as follows.

Set t _i = t _{1- (1-α) 1 / (k-1)} , Ri-1

In the case of the above example, Set t _i = t _{1- (0.95) 1 / (3-1)} , 10-1, that is, t _i = t _0.025 , 9 is 2.25.

Next, an average value difference (distance) between scenarios is estimated. Estimate the distance as follows.

W _ij = Sqrt ((t _i ² S _i ² / R _i ) + (T _j ² S _j ² / R _j ))

In the case of the above example, W _ij = sqrt ((2.252 × 10/10) +2.252 × 10/10)) = 3.18.

Next, a scenario to be included in the optimal scenario configuration set is searched. The scenario is searched as follows.

Y _i > Y _j - (W _ij -δ) ⁺ , for all j is not i

In the above example, Y _i > = Y _j - (3.18 -δ) ⁺ for all i is not j

FIG. 9 is an example of an actual drive result log file.

Next, the bottleneck processing module 1200 of the present invention will be described. The bottleneck processing module 1200 analyzes the manufacturing process simulation and computes the process load of each process (S31), discovers the bottleneck process (S32) in consideration of compliance with the delivery date, and determines whether the bottleneck process is a factor (S33), and performs line balancing of processes around the cycle time of the bottleneck process (S34).

The bottleneck processing module 1200 of the present invention is characterized in that analysis can be performed when only task unit information and process transfer information are defined. Because of this characteristic of the bottleneck processing module 1200, the result of the simulation model developed in any type of simulation package is advantageous in that it can be analyzed only when task unit information and process transfer information are defined.

The standard work time and the process delivery time according to the task unit information are calculated by calculating the standard work time according to the average work time of the task unit configuration resources and the process delivery time through the process allocation of the remaining time .

According to the result of the analysis, the bottleneck process is selected as the bottleneck process having the highest cycle time among the processes constituting the model. In addition to the bottleneck process, .

Line balancing is performed according to the cycle time of the bottleneck process, and line balancing is performed by configuring the workplace so that the workplace configuration time has a serial task execution process cycle time shorter than the bottleneck process cycle time.

FIG. 10 is a conceptual diagram illustrating a load chart constructing entity according to a process; FIG.

 Maximum allowable working time (process delivery) = Standard process time for the process + Slack Time / Total number of processes. Slack Time = Due Date Cumulative Lead Time have.

As shown in FIG. 10, the process delivery margin level in Step ₁ is a value obtained by subtracting E_TAT + t _{R -1, 1 -? / 2S / Sqrt (R)} from the maximum allowable work time. The actual load level is E_TAT - t _{R -1, 1 - α / 2S / Sqrt (R)} minus the standard work time.

E_TAT is the actual average work time, which is averaged by measuring a predetermined number of jobs (for example, 10 times) in one process. R is the number of work repetitions measured to calculate actual work time. For example, if 10 working hours are measured and the actual working time average (E_TAT) is given, R becomes 10. s is the standard deviation. For example, the standard deviation value measured in task 10 is s. The value of t (1-R) can be obtained by using the degree of freedom in the t distribution table provided in the statistical table, etc., with the t distribution free.

In FIG. 10, the gray interval (interval between E_TAT + tR _{-1 and 1-? / 2S / Sqrt (R) to} E_TAT-tR _{-1 and 1-? / 2S / Sqrt} , And it is understood as a statistical average working time interval as a box showing the maximum distribution and the minimum distribution time in the statistical distribution (T distribution) in the actual average working time.

11 is an exemplary embodiment of a load chart of the analysis result of each process.

The theoretical cycle time (total operation time / target production amount) is compared with the maximum average operation time for each process, and the maximum value is set as the line balancing reference time. In this case, step 1 is not a bottleneck process or an abnormal process because the interval between the theoretical cycle time and the maximum average work time is very large. However, Process 2 is the bottleneck process because it has the smallest interval between the theoretical cycle time and the maximum average work time. On the other hand, since the interval between the theoretical cycle time and the maximum average operation time is very small, the process 3 can be processed as an abnormal process.

As it is shown in the step 2 of Fig. 11, a digital production optimization system 1000 includes the gray zone for each step _{(E_TAT + t R -1, 1-} α / 2S / Sqrt (R) _in E_TAT - t _{R -1 , 1 -? / 2S / Sqrt (R) )} and finds a process in which the value of E_TAT + tR _{-1 and 1-? / 2S / Sqrt (R)} exceeds the maximum allowable working time . The process 2 has a high possibility of affecting the delivery date and the E_TAT + t _{R-1 and 1-α / 2S / Sqrt (R)} values are set to a value set in the theoretical cycle time (total operation time / (For example, within 50% of the standard deviation s value of the actual working time), it can be recognized as a bottleneck process. On the other hand, in the same process as the process 3, the values of E_TAT + tR _{-1 and 1-? / 2S / Sqrt (R)} exceed the maximum allowable working time. Both the bottleneck process and the abnormal process can be warned by the abnormal process.

Next, the line balancing method performed by the line balancing module 1220 will be described. As long as the average work time exceeds the maximum allowable work time, there are two ways to solve this problem. One is to delay the delivery date, and the faster the work speed, the lower the average work time. Most companies can not postpone the deadline, so they use the average down time. The easiest way to do this is to have two people do the work for one person (because two are created at the same time, Work time is reduced to half), and equipment with good process capability is introduced. However, because it is difficult to use immediately due to the cost problem, it is possible to increase the maximum allowable working time by increasing the free time for the abnormal process by slightly reducing the working time in the other process. Since we can not know how much to reduce and how much to reduce in a process, we can create several scenarios for situations that can be reduced in other processes, and then use simulations to validate them and find ways to reduce line balancing . In the present invention, simulation can be performed for various scenarios, and through this, at least one or more scenarios in which the bottleneck process and the abnormal process do not occur or can be minimized based on a plurality of simulations can be found. Through this, the balancing of processes, that is, line balancing, in which the bottleneck process and the abnormal process do not occur or is minimized is realized. The line balancing module 1220 performs a role of finding at least one or more scenarios in which the bottleneck process and the abnormal process do not occur or are minimized based on a plurality of simulations. Through the above-described line balancing, at least one or more optimized scenarios are generated, and resources to be input to various processes constituting the optimized scenario are relocated on the basis of the optimized scenarios. In response to the optimized scenario, the resource relocation module 1230 performs a function of varying resources to be input for each process, and determining a type and amount of resources and an input time interval to be input to the entire scenario.

Table 1 below describes various terms used in the specification of the present invention.

Terms Hangul name Explanation slack time Free time DueData Cumulative lead time from the current time to the due date minus the total lead time Cumulative Lead Time Total Time Time required to produce and sell to customers Load Chart Load chart A chart charting the time required for production of each product by process. This term refers to the chart in Figure 2 E-TAT  Actual average working time Average work time per process performed in actual site. Standard working time Standard working time Time spent by the company in the process agreed with the union. The time that each worker has agreed to work without overdoing this time Actual load level Actual load level The minimum value of the average working time minus the standard working time means that this value becomes (+), which means that even during the earliest working hours, the standard working time is exceeded. being Fair delivery margin Fair delivery margin It means that there is still time to meet the deadline, which means that the maximum value of the average working time minus the maximum working time. Theoretical Cycle time Theoretical Cycle Time Working hours required per product to meet delivery dates within operating hours (factory operating hours). If you only fit the theoretical cycle time per operation hour, you can meet the delivery date. The reason for the theoretical reason is that this does not include the time to move the process to the next process or the preparation time for the next process. So, if the working time goes beyond the theoretical cycle time, there will surely be problems. Average working time Average working time Actual working time considering distribution in average working time. (Minimum distribution, actual average working time, maximum distribution). Therefore, the actual work is done within the average work time box. Maximum average work time The maximum value of the average operation time for all processes. Here, the distribution value of the actual flat average working time Maximum allowable working time Maximum allowable working time The maximum allowable working time in each process, and the delay in operation than the maximum allowable working time, may affect the next process. Maximum allowable working time = standard working time + (allowance / total number of processes) 주기 시간 Cycle time Time required in the process to make a product. There is usually a tendency to look like standard work time.

The present invention can be utilized throughout the manufacturing industry including the process.

31: Load simulation model to perform comparison evaluation
41: Whether or not the evaluation items are affected and the extent of the impacts.
51: Registration of one or more evaluation items to be a scenario evaluation scale
61: Scenario to be compared
62: regulatory variable
63: Evaluation items
71: Comparison of evaluation items - Ranking rank
101: Maximum allowable working time (fair delivery time)
102: Fair delivery margin level
103: actual load level
104: Standard working time
1000: Digital Manufacturing Optimization System
1100: scenario processing module
1110: scenario registration module
1120: Control variable registration module
1130: performance characteristic value registration module
1140: Scenario configuration module
1150: scenario driving module
1200: Bottleneck module
1210: bottleneck process discovery module
1220: Line balancing module
1230: resource relocation module
1300: DB section
1310: Scenario DB
1320: Control variable DB
1330: Performance characteristic DB
2000: Wired and wireless network
3000: user computer
4000: Third System

Claims (12)

An information processing method of a digital manufacturing optimization system for registering and processing at least two scenarios, the digital manufacturing optimization system comprising:
(A) receiving control variable values and performance characteristic values for each control variable by scenario;
(B) performing a simulation for each of the scenarios and generating at least one simulation analysis information according to a result of the simulation;
(C) deriving at least one bottleneck process from the simulation analysis information, and generating predetermined diagnostic information for the bottleneck process; and
(D) processing the line balancing information of the processes,
Wherein the bottleneck process is performed by including task unit information and process transfer information and calculating a standard operation time and a process delivery time according to the task unit information to derive a predetermined bottleneck process in the process, A method for processing information in a digital manufacturing optimization system. The method according to claim 1,
The control variable may be at least one of a facility number, a worker number, and a work method for each process,
Wherein the performance characteristic value is at least one of a delivery completion rate, a work in process (WIP) level, and a total production amount. The method according to claim 1,
Wherein the simulation analysis information includes comparison information about a sequence of scenario values through statistical verification. The method according to claim 1,
Wherein the simulation analysis information generates a difference in average value between scenarios to support review or selection of a self-selected scenario using the digital manufacturing optimization system. The method according to claim 1,
Wherein the simulation analysis information provides visualized chart information according to a performance characteristic value. delete The method according to claim 1,
Wherein the step of deriving the bottleneck process of the predetermined criterion is to select a process having the highest cycle time among the processes. The method according to claim 1,
After the step (C) or before the step (C)
(E) deriving at least one or more ideal processes
Deriving the abnormal process
E_TAT + t _{R-1, 1-? / 2S / Sqrt (R)} exceeds the maximum allowable working time.
E_TAT is the actual average work time, R is the number of work repetitions measured to calculate the actual work time, s is the standard deviation of the actual work time, and t (1-R) is the t distribution freedom. The method according to claim 1,
The line balancing information processing is to find at least one or more scenarios on which a bottleneck process and an abnormal process do not occur or minimize based on a plurality of simulations,
Wherein the line balancing is performed by configuring the work site so that the work site configuration time has a serial workload execution process cycle time that is equal to or less than the cycle time of the bottleneck process. The method according to claim 1,
The derivation of the bottleneck process is a process having the highest cycle time in the process,
_{E_TAT + t R-1, 1-} α / 2S / Sqrt (R) value exceeds the maximum allowed work time, and _{E_TAT + t R -1, 1- α} / 2S / Sqrt (R) for at least one step Wherein the process is performed by the bottleneck process when the value is within a predetermined value in the theoretical cycle time.
E_TAT is the actual average work time, R is the number of work repetitions measured to calculate the actual work time, s is the standard deviation of the actual work time, and t (1-R) is the t distribution freedom. 9. The method of claim 8,
(E) performing a resource relocation through the result of line balancing,
The resource relocation is to determine the type and amount of resources and the input time interval of the resource to be input into the entire scenario, corresponding to at least one or more optimized scenarios,
Wherein the optimized scenario is generated through at least one line balancing. An information processing method of a digital manufacturing optimization system for registering and processing at least two scenarios, the digital manufacturing optimization system comprising:
Scenario processing module;
Bottleneck processing module; And
A DB portion,
The scenario processing module
A scenario registration module for registering a scenario related to at least two manufacturing processes to be a simulation target;
A control variable registration module for registering control variables for various processes constituting each scenario;
A performance characteristic value registration module for registering various performance characteristic values;
A scenario configuration module including a function of combining or combining existing scenarios or newly created scenarios; And
And a scenario driving module that performs simulation according to at least two separate scenarios,
The bottleneck processing module
A bottleneck process discovery module that finds a process that becomes a bottleneck in the manufacturing process; and
A line balancing module for finding at least one or more scenarios for line balancing; / RTI >
Wherein the DB unit stores scenario data, control variable data, and performance characteristic value data.

Images