JP2011065337A - Traceability system and manufacturing process failure detecting method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a traceability system and a manufacturing process failure detecting method for detecting the failure of a manufacturing process at an early stage. <P>SOLUTION: As a predetermined detection condition in order to detect the failure, the number of times in which occurrence of defectives increases in a row on the manufacturing lot basis for defective factors for each of the defective components is set up. When a percentage of the defectives exceeds the predetermined number of times, the manufacturing process is determined as the failure and shown to the user. This makes the manufacturing process failure caused by the defective with tendency of increasing, although small in number, to be detected. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a traceability system and a manufacturing process abnormality detection method applied in a manufacturing line in which a plurality of parts are processed and assembled through a plurality of processes to produce a product.

  The traceability system has “manufacturing information collection function”, “manufacturing information accumulation / management function”, “manufacturing information display / retrieval function”, and “manufacturing information analysis / analysis function”. It is a system that supports the pursuit of the cause when it occurs and supports the identification of the spillover range of products that use the component that caused the failure.

  “Manufacturing information display / retrieval function” is a method for presenting manufacturing history information that is stored and managed. “Manufacturing information defined in advance by searching by period or lot designation (multiple sets can be defined. ) "By manufacturing management (manufacturing instruction number, etc.)", "narrow down the list of manufacturing information items as search items", "ascending or descending as a specified item of manufacturing information items that are listed" "Production information analysis / analysis function" uses various analysis methods (such as graph display) rather than simply presenting production history information that is stored and managed. There is a function that supports analysis.

  In a conventional traceability system, the number of defects and the defect rate are naturally collected as one piece of manufacturing information as defect data, and are subject to the "Production Information Display / Search Function" and "Manufacturing Information Analysis / Analysis Function". .

  Further, as disclosed in Japanese Patent Application Laid-Open No. 2009-15532 (Patent Document 1), the condition that the defect indicated by the inspection result is a serious defect, the condition that the same defect is continuously generated, and the same defect are preliminarily provided. There is a method of acquiring inspection data that meets the conditions by setting the condition that discontinuous or different defects occur continuously or discontinuously, but this reduces the amount of inspection data (image) to be acquired However, the present invention does not provide an effect of facilitating detection of anomalies caused by defects that tend to increase although slightly in each production management unit such as a production lot.

  Furthermore, Japanese Patent Application Laid-Open No. 2007-157781 (Patent Document 2) discloses a method of determining whether or not the monitoring failure rate has increased for each production lot and identifying a defective part. This method was determined to be abnormal when the defect rate exceeded a threshold value using standard deviation.

JP 2009-15532 A JP 2007-157781 A

  When handling defect data, which is manufacturing information, using the manufacturing information display / retrieval function and manufacturing information analysis / analysis function of the conventional traceability system, for example, a list or graph is displayed for each manufacturing management unit such as a manufacturing lot. When a large number of defective parts suddenly occur, it can be detected relatively easily. On the other hand, although the number of defects is not so large for each production management unit such as a production lot, production management such as a production lot is performed. Although there is a slight increase in the number of defects per unit, there is a possibility that it will lead to a serious deterioration of the defect rate later, but there is a problem that it is difficult to detect.

  Similarly, in the example of Patent Document 2, even if the defect rate for each production management unit such as a production lot is slightly increased, an abnormality cannot be detected as long as it is a numerical value within a threshold (a numerical value within a variation range).

  The present invention has been made to solve the above-described problems, and an object of the present invention is to provide a traceability system and a manufacturing process abnormality detection method for detecting an abnormality at an early stage.

  In order to solve the above-described problems, the present invention includes an abnormality detection condition registration unit that registers in advance in a database as an abnormality determination value of a manufacturing process, a manufacturing information storage / management unit that stores and manages manufacturing information in the database, Obtaining from the database a counter value in which the defect rate continuously increases in units of production lots for each defect cause of defective parts, comparing the counter value with the abnormality determination value, and determining abnormality, abnormality determination means If the result of the determination means is abnormal, the apparatus includes condition determination result presentation means for presenting the determination result to the user.

  Preferably, the abnormality determination means holds a defect rate obtained by moving average based on defect data (number of defects, number of production) in units of a plurality of production lots in a storage area of the database, and the defect rate of the current moving average Is equal to or higher than the defective rate of the previous moving average, the counter is incremented by 1, and the counter value is compared with the abnormality determination value to determine abnormality. If the defective rate of the current moving average is not equal to or higher than the defective rate of the previous moving average, the counter is cleared.

  Further preferably, when the defect rate of the current production lot is equal to or higher than the defect rate of the previous production lot, the abnormality determination unit adds 1 to the counter and compares the counter value with the abnormality determination value to determine abnormality. . In addition, when the defect rate of the current production lot is not equal to or higher than the defect rate of the previous production lot, the counter is cleared.

  According to the present invention, in the production line, the number of defects is not so large when viewed for each production management unit such as a production lot, but abnormalities due to defects that are slightly increasing for each production management unit such as a production lot are increasing. It can be easily detected.

  As a result, it is possible to detect an abnormality that may later lead to a serious deterioration of the defective rate, and to obtain an effect of preventing the abnormality from expanding by taking a proactive measure.

It is a figure which shows the structure of the traceability system which concerns on embodiment of this invention. It is a figure which shows the function structure which concerns on embodiment of this invention. It is a figure which shows the example of the separate abnormality detection condition setting which concerns on embodiment of this invention. It is a figure which shows the example of the defect data actually collected by the traceability system which concerns on embodiment of this invention. It is a figure which shows the example of the flowchart which detects abnormality of the manufacturing process in which a defect generate | occur | produces in manufacturing management units, such as an individual manufacturing lot, concerning embodiment of this invention. It is a figure which shows the example of the continuous increase abnormality detection condition setting which concerns on embodiment of this invention, an actual continuous increase generation lot number counter, and defect memory information. It is a figure which shows the example of the defect data actually collected by the traceability system which concerns on embodiment of this invention. It is a figure which shows the example of the flowchart which detects the abnormality of the manufacturing process which generate | occur | produces while a defect increases in manufacturing management units, such as a continuous manufacturing lot, concerning embodiment of this invention. It is a figure which shows the example of a transition of the counter information which concerns on embodiment of this invention, and an information storage area.

FIG. 1 shows a system configuration diagram of a traceability system applied in a production line in which a plurality of parts are processed and assembled through a plurality of processes to produce a product.
The traceability system consists of one or more trace terminals (10) installed in each process (such as “part manufacturing” → “part acceptance” → “assembly / inspection” → “product storage” → “product receipt”). A data collection device such as a personal computer that collects various manufacturing information of the line to be called, a DB server (20) that stores the various manufacturing data collected, and a "manufacturing information display / search function" based on the data in the DB server And a traceability server (30) which is an application server for providing “analysis / analysis function of manufacturing information”. These terminals, the server, and the server are connected via a network (60) such as a LAN or the Internet.

  In addition, by arranging the Web terminal (40) on the network, the server provision screen ("manufacturing information display / search function", "manufacturing information analysis / analysis function", "threshold judgment function") can be accessed via the network. It is possible to distribute by the method.

  Further, the trace terminal (10) provides an interface for connecting with manufacturing information input / output devices (RFID reader / writer, bar code reader, keyboard, touch panel, various sensors, etc.) (50) for collecting manufacturing equipment and manufacturing information. In addition, it is possible to easily connect with various manufacturing information input / output devices (50) by defining and customizing the collecting means for each manufacturing process.

FIG. 2 is a diagram showing an example of the functional configuration of the present invention.
In the example shown in FIG. 2, the traceability server (30) includes an abnormality detection condition registration unit, an abnormality determination unit, and a determination result presentation unit, and the DB server (20) includes a manufacturing information storage / management unit and a database. .

The abnormality detection condition registration unit registers the abnormality determination value in the DB in advance.
The abnormality determination unit takes out the manufacturing information (defective data) and the abnormality determination value via the manufacturing information storage / management unit of the DB server, updates the information storage area on the DB, and determines the abnormality. The information storage area on the DB is updated in accordance with the determination result, and the determination result presenting unit is notified if the abnormality determination value is abnormal.

The determination result presentation unit presents to the user of the traceability system that an abnormality has been detected based on the notification information from the abnormality determination unit.
An embodiment of a method for detecting an abnormality in an individual manufacturing process that occurs for each failure factor in each defective part in product manufacturing in a manufacturing management unit such as a manufacturing lot will be described with reference to FIGS.

  The example shown in FIG. 3 is a setting example for detecting an abnormality that occurs for each production management unit such as an individual production lot in one product type. First, a field for setting a unique numerical value as a setting number is prepared. By setting a plurality of setting numbers, a plurality of threshold conditions can be set in one product type.

  Next, conditions for detecting defects are set for each setting No. First, a defective part is set. This is the name of the part that is assembled to the product. This time, data meaning part names such as R1, R2, and R3 is designated.

  Next, a failure factor corresponding to the defective part in each row is set. This time, data indicating a failure factor such as failure A, failure B, and failure C is designated. As in this example, it is possible to set different failure factors for the same defective component, or to set the same failure factors for different defective components.

  Next, a defect rate threshold value is set for each setting No. condition set so far. The defect rate threshold is used to set what percentage or more the defect rate in a production management unit such as a production lot is determined as abnormal. These conditions are stored in the DB in advance (before starting production of the production lot).

  The example shown in FIG. 4 is an example of defect data actually collected by the traceability system. For each production management unit such as a production lot, the number of produced defective parts, the cause of the defect, and the number of defects are shown for the number of production. The production lot No. 1 manufactured 1000 units, one defect factor A for the defective component R1, two defect factors D for the defective component R2, and the production lot No. 2 shows that 1000 units were manufactured and four defect factors A of the defective component R1 were generated.

  Data (manufacturing lot number, number of production, defective parts, defect factor, number of defects) is stored in the DB when manufacturing of a production control unit such as a manufacturing lot is completed, and at the same time, the defect rate (number of defects / number of production × 100 ) Is also calculated and stored in the DB.

FIG. 5 is an example of the processing flow of the “threshold determination function” processed by the traceability server (30).
In FIG. 5, first, individual abnormality detection conditions including a combination of a setting number, a defective part, a failure factor, and a failure rate threshold shown in FIG. 3 are registered in the DB in advance (step [501]).

Next, data collection of a production management unit such as a production lot is started in the traceability system (step [502]).
Next, in the traceability system, when the data collection of the production management unit such as the production lot is finished and a defect occurs in the production management unit such as the corresponding production lot, the production management unit such as the production lot shown in FIG. The defect data of the combination of the number of production, defective parts, defect factor, defect number and defect rate is stored in the DB (step [503]).

  Next, it is determined whether or not the defect data is stored in the DB in the production management unit such as the corresponding production lot. If the defective data is not stored in the DB, the process proceeds to the next data collection step [502] (step [504]).

  Next, if any defect data is stored in the DB, the condition determination is performed on the individual abnormality detection condition registered in the DB in Step [501] and the defect data stored in the DB in Step [503].

  For example, in the case of the example 410 in FIG. The set condition of 1 and the defective part match R1 and the cause of the defect is defect A, but the defect rate is 0.1, which is smaller than 0.4 of the defect rate threshold of the set value, so it is determined that there is no abnormality.

  In the case of the example 420 in FIG. 2 and no. 3 is the same defective part, but does not meet the condition of the failure factor (defect B, defect C).

In the case of the examples 410 and 420 in FIG. 4, since there is no abnormality, nothing is executed and the process proceeds to the next data collection step [502].
In the case of the example of 430 in FIG. One defective part matches R1, the cause of defect is defect A, and the defect rate threshold is 0.4 or more, and is determined to be abnormal.

  In the case of the example of 430 in FIG. 4, since it is abnormal, the process proceeds to step [506] (step [505]), and the traceability server (30) forcibly “defective part: R1, defect factor: defect A, defect rate threshold: The fact that an abnormality meeting the condition of 0.4 has been detected ”is presented to the user of the traceability system, and the process proceeds to the next data collection step [502].

  Further, similarly to the Web terminal (40) arranged on the intranet, the server-provided screen function is compulsorily used via the network by the server system function “Defect part: R1, Defect cause: Defect A, Defect rate threshold: 0. Is delivered to the user of the traceability system (step [506]).

Next, an embodiment of a method for detecting an abnormality in a manufacturing process that occurs while continuously increasing in a manufacturing management unit such as a plurality of manufacturing lots will be described.
In addition to the individual manufacturing lot abnormality detection condition setting shown in FIG. 3, the setting in FIG. 6 further includes consecutive defects in a plurality of manufacturing lots for each failure factor for each setting No. condition. A threshold for determining an abnormality due to an increase is added.

  In addition, in order to analyze the increasing tendency of the defect rate of consecutive production lots, and the continuous defect increase occurrence lot number counter that counts the number of lots that occurred while the defects actually increased for each setting No condition In addition, an information storage area for storing the defect rate and the defect rate moving average of a plurality of continuous production lots (three production lots in FIG. 6) is provided on the DB.

The counter is cleared to 0 as an initial value, and all information storage areas are set to 0 as an initial value.
For example, this time, a simple moving average (simple average with no weighting of the latest n pieces of data) is used as the moving average calculation method, and n = 3 is designed.

  The example shown in FIG. 7 is an example of defect data actually collected by the traceability system. For each production management unit such as a production lot, the number of produced defective parts, the cause of the defect, and the number of defects are shown for the number of production.

  The production lot No. No. 1 manufactured 1000 units, one failure factor A for defective component R1, two failure factors E for defective component C2, In No. 2, 1000 units are manufactured, the failure factor A of the defective component R1 is 2, the failure factor E of the defective component C2 is 3, the production lot No. No. 3, 1000 units are manufactured, the defect factor A of the defective component R1 is three, the defect factor E of the defective component C2 is one, the production lot No. In No. 4, 1000 units are manufactured, the failure factor A of the defective component R1 is 2, the failure factor E of the defective component C2 is 1, the production lot No. No. 5 shows that 1000 units were manufactured and three defect factors A for the defective component R1 and one defect factor E for the defective component C2 occurred.

Data is stored in the DB when manufacturing of a manufacturing management unit such as a manufacturing lot is completed, and at the same time, a defect rate (number of defects / number of units produced × 100) is also calculated and stored in the DB.
FIG. 8 is an example of a process flow of a “threshold determination function” for determining whether or not defects tend to increase in a plurality of continuous production lots processed by the traceability server (30).

FIG. 9 is an example showing changes in the values of the counter and the information storage area corresponding to the progress of each production lot when the processing flow of FIG. 8 is explained for the collected data of FIG.
In FIG. 8, first, the same as in FIG. 3, the individual abnormality detection condition consisting of items of setting No., defective part, defect factor and defect rate threshold, and the setting No., defective part, defect factor and defect rate added in FIG. A continuous increase abnormality detection condition comprising items of a threshold and a threshold value for the number of consecutive defective increase occurrence lots is registered in the DB in advance (before the production of a production lot) for each product type. (Step [801])
Next, data collection of manufacturing management units such as manufacturing lots is started in the traceability system. (Step [802])
Next, in the traceability system, when the data collection of the production management unit such as the production lot is finished and a defect occurs in the production management unit such as the corresponding production lot, the production management unit such as the production lot shown in FIG. Then, defect data including items of production number, defective parts, defect factor, number of defects and defect rate is stored in the DB. (Step [803])
Next, it is determined whether or not the defect data is stored in the DB in the production management unit such as the corresponding production lot. If the defective data is not stored in the DB, the process proceeds to step [806]. (Step [804])
If any defect data is stored in the DB, the condition of the individual abnormality detection condition registered in the DB in step [801] and the defect data stored in the DB in step [803] are determined.

For example, the production lot No. 1, when the example 710 in FIG. The set condition of 1 and the defective part match R1 and the cause of failure is the defect A, but the defect rate is 0.1, which is smaller than the defect rate threshold value 0.4 of the set value. Proceed to [806]. (Step [805])
Next, in step [806], the information storage area on the DB is updated. First, the defect rate data is updated. In that case, the corresponding defect rates for the three latest lots in a production management unit such as a production lot are stored in three areas for storing the defect rates in the information storage area.

  Production lot No. 1 and the example of 710 in FIG. 7 is collected, as shown in FIG. 0.0 is stored in the defective rate of the previous two lots, 0.0 is stored in the defective rate of the previous lot, and 0.1 is stored in the defective rate of the latest lot.

Furthermore, 0.00, which is the moving average information stored in the defective rate moving average [current], is stored in the defective rate moving average [previous]. Stores 0.03 calculated from the defect rate 0.0 of the previous lot, the defect rate 0.0 of the previous lot, and the defect rate 0.1 of the latest lot, and proceeds to step [807]. (Step [806])
Next, in step [807], abnormality determination is performed by the defective rate moving average stored in the information storage area on the DB. Here, if the value of the defective rate moving average [current] is equal to or higher than the defective rate moving average [previous], it is determined to be abnormal, and otherwise, it is determined not to be abnormal.

  Production lot No. 1 and collected in the example 710 in FIG. The value of 1 defective rate moving average [current] is 0.03, which is 0.00 or more, which is the value of defective rate moving average [previous], so it is determined as abnormal, and the process proceeds to step [808].

If it is determined that there is no abnormality with respect to all setting Nos, all the consecutive defect increase occurrence lot number counters are cleared to 0, and the process proceeds to the next data collection step [802]. (Step [807])
Next, in step [808], the consecutively increasing failure occurrence lot number counter corresponding to the abnormality is incremented by 1, and the other consecutive failure increasing occurrence lot number counter not corresponding to the abnormality is cleared to 0, and the process proceeds to step [809].

For example, the production lot No. 1 and the example of 710 in FIG. 7 is collected, as shown in FIG. 1 is incremented by 1 and the setting No. The counters 2 to 4 are cleared to 0 because they do not correspond with no abnormality. (Step [808])
Next, in step [809], abnormality determination is performed based on the continuous increase abnormality detection condition registered in the DB in step [801].

  For example, the production lot No. 1 and the example of 710 in FIG. The continuous defect increase occurrence lot counter of 1 is 1, and the setting No. Since it is smaller than the threshold value 5 for the number of consecutive defective increases, it is determined that there is no abnormality, and the process proceeds to the next data collection step [802].

  Next production lot No. 2, when the example 730 in FIG. 7 is collected, similarly, as shown in FIG. 9, the information storage area on the DB is updated in step [806]. 0.0 is stored in the defective rate of the previous lot, 0.1 is stored in the defective rate of the previous lot, and 0.2 is stored in the defective rate of the latest lot.

  Furthermore, 0.03, which is the moving average information stored in the defective rate moving average [current], is stored in the defective rate moving average [previous], and the defective rate moving average [current] is newly updated by two previous items. 0.10 calculated from the defective rate 0.0 of the previous lot, the defective rate 0.1 of the previous lot, and the defective rate 0.2 of the latest lot is stored.

In step [807], the value of the defective rate moving average [current] is 0.10, which is not less than 0.03 which is the value of the defective rate moving average [previous].
In step [808], as shown in FIG. By incrementing only the counter of 1 by 1, the continuously increasing defect occurrence lot number counter becomes 2, and in step [809], since the consecutive defect increase occurrence lot number counter is 2, the setting No. Since it is smaller than the threshold value 5 for the number of consecutive defective increases of 1, it is determined that there is no abnormality, and the process again proceeds to step [802].

  Next production lot No. 3, when the example 750 in FIG. 7 is collected, similarly, as shown in FIG. 9, the information storage area on the DB is updated in step [806]. The defect rate of the previous lot is 0.1, the defect rate of the previous lot is 0.2, the defect rate of the latest lot is stored as 0.3, and the defective rate moving average is stored. The moving average information 0.10 stored in [Current] is stored in the defective rate moving average [Previous], and the defective rate moving average [Current] contains a new defective rate 0 for the previous two lots. .1 and 0.20 calculated from the defective rate 0.2 of the previous lot and the defective rate 0.3 of the latest lot are stored.

  In Step [807], the value of the defective rate moving average [current] is 0.20, which is not less than 0.10 which is the value of the defective rate moving average [previous], so that it is determined as abnormal. In Step [808], As shown in FIG. By incrementing only the counter of 1 by 1, the continuously increasing defect occurrence lot number counter becomes 3, and in step [809], the continuous defect increase occurrence lot number counter is 3, so the setting No. Since it is smaller than the threshold value 5 for the number of consecutive defective increases of 1, it is determined that there is no abnormality, and the process again proceeds to step [802].

  Next production lot No. 4, when the example of 770 in FIG. 7 is collected, similarly, as shown in FIG. 9, the information storage area on the DB is updated in step [806]. 0.2 is stored in the defective rate of the previous lot, 0.3 is stored in the defective rate of the previous lot, and 0.2 is stored in the defective rate of the latest lot.

  Furthermore, 0.20 which is the moving average information stored in the defective rate moving average [current] is stored in the defective rate moving average [previous], and the defective rate moving average [current] is newly added two pieces before. 0.23 calculated from the defective rate 0.2 of the previous lot, the defective rate 0.3 of the previous lot and the defective rate 0.2 of the latest lot is stored, and in step [807], the defective rate moving average is stored. Since the value of [Current] is 0.23 and the defective rate moving average [Previous] is 0.20 or more, it is determined as abnormal.

In step [808], as shown in FIG. By incrementing only one counter by 1, the continuously increasing defect occurrence lot counter becomes 4.
In Step [809], since the continuous defect increase occurrence lot counter is 4, the setting No. Since it is smaller than the threshold value 5 for the number of consecutive defective increases of 1, it is determined that there is no abnormality, and the process again proceeds to step [802].

  Next production lot No. 5, when the example 790 of FIG. 7 is collected, similarly, as shown in FIG. 9, the information storage area on the DB is updated in step [806]. 0.3 is stored in the defective rate of the previous lot, 0.2 is stored in the defective rate of the previous lot, and 0.3 is stored in the defective rate of the latest lot.

  Furthermore, 0.23, which is the moving average information stored in the defective rate moving average [current], is stored in the defective rate moving average [previous], and the defective rate moving average [current] is newly updated by two previous items. 0.27 calculated from the defective rate 0.3 of the previous lot, the defective rate 0.2 of the previous lot, and the defective rate 0.3 of the latest lot is stored. In step [807], the defective rate moving average is stored. Since the value of [Current] is 0.27 and the defective rate moving average [Previous] is 0.23 or more, it is determined as abnormal.

In step [808], as shown in FIG. By incrementing only the counter of 1 by 1, the continuously increasing defect occurrence lot number counter becomes 5, and in step [809], since the consecutive defect increase occurrence lot number counter is 5, the setting No. Therefore, it is determined that there is an abnormality, and the process proceeds to Step [810]. (Step [809])
Next, in step [810], continuous production lots are not so many as in the examples of 710, 730, 750, 770, and 790 in FIG. If it is determined that there is an abnormality, the traceability server (30) forcibly detects “abnormality that meets the conditions of“ defective part: R1, failure factor: defect A, continuous defect increase lot number threshold: 5 ”. ] To the users of the traceability system.

In addition, the Web terminal (40) arranged on the intranet is also compulsorily forced by the server-provided screen function via the network via the Web method “Defect parts: R1, Defect cause: Defect A, Number of consecutive defects increasing. Threshold value: An anomaly that meets the condition of 5 was detected ”and delivered to the user of the traceability system. (Step [810])
In this example, the simple moving average was used as an example of the method for determining the increasing tendency of abnormalities, but other moving averages such as a weighted moving average and an exponential moving average that calculate the average with different weights applied to individual defect rates are used. Other determination methods are also conceivable, such as determination by using or simply comparing and determining the increase / decrease in the defect rate of the previous time and this time.

10 Trace terminal 20 DB server 30 Traceability server 40 Web terminal 50 Manufacturing information input / output device 60 Network

Claims (4)

In a traceability system applied in a production line that processes and assembles multiple parts through multiple manufacturing processes,
An abnormality detection condition registration means for registering in advance in the database as an abnormality determination value of the manufacturing process;
Manufacturing information storage / management means for storing / managing manufacturing information in the database;
Obtaining a counter value in which the defect rate continuously increased in units of production lots for each defect cause of defective parts from the database, comparing the counter value with the abnormality determination value, and determining an abnormality;
A traceability system comprising: a condition determination result presenting means for presenting the determination result to the user in the case of an abnormality as a result of the abnormality determination means.   The abnormality determination means holds a defect rate obtained by moving average based on defect data (number of defects, number of production) in units of a plurality of production lots in a storage area of the database, and the defect rate of the current moving average is moved last time. 2. The traceability system according to claim 1, wherein when the average defect rate is equal to or greater than one, the counter value is incremented by 1, and the counter value is compared with an abnormality determination value to determine abnormality. The abnormality determining means adds 1 to the counter value when the defect rate of the current production lot is equal to or higher than the defect rate of the previous production lot, and compares the counter value with the abnormality determination value to determine abnormality. The traceability system according to claim 1.
A manufacturing process abnormality detection method in a traceability system applied in a manufacturing line that processes and assembles a plurality of parts through a plurality of manufacturing processes,
Obtains a counter value in which the defect rate has been continuously increased in units of production lots for each defect cause of defective parts from the database in which manufacturing information is stored and managed, and compares the counter value with the abnormality determination value to determine abnormality. And
A manufacturing process abnormality detection method, wherein, when a determination result is abnormal, the determination result is presented to a user.