e3sconf icimece2023 02043

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Acceptance Sampling MIL-STD 105E for Quality Control: A Case Study

Deadila Defiatri^1*, and Retno Wulan Damayanti^1†

1Industrial Engineering Dept. Sebelas Maret University, Surakarta, Indonesia

Abstract. — The development of the garment industry in Indonesia is getting higher every year. The garment industry plays a role as a contributor to the country's foreign exchange and is relied upon to meet national clothing needs. With the increasing demand for clothing, the garment industry is competing to provide and offer good quality products at affordable prices. The company faces obstacles in products that do not comply with the specifications or standards set by the company. The finished product inspection process is carried out on each production line. Therefore, product defects that pass quality control are still found until the Final QC stage. This problem is caused by the company's absence of fixed quality standardization in each production process so that defective products enter the finishing process. The use of the sampling method at the Company is currently not effective enough to minimize found product defects at this stage, data is collected through requests for historical data on product defects in the company and also by making direct observations. One of the causes of product defects is operators who work under pressure because they have to achieve production targets. If the use of this method can be realized, there is no need to carry out 100% inspections but to adjust the number of product requests from buyers. This can help to reduce workload and the number of workers in product quality inspection thereby increasing productivity.

Application of the Acceptance Sampling system based on MIL-STD 105E on products is beneficial in determining product quality. By applying this method, it is easier for companies to detect the number of product defects with large production volumes

1 Introduction

The Central Statistics Agency (BPS) noted that the gross domestic product (GDP) at constant prices from the textile and apparel industry amounted to IDR 34.85 trillion in the third quarter of 2022. This value grew 8.09% compared to the same period the previous year.

With the increasing need for clothing, the garment industry is competing to provide and offer good quality products at affordable prices.

Product quality is the ability of a product to perform its functions, including durability, reliability, accuracy, and product repair [1]. Product quality is determined by several factors: the process of making products and equipment, the settings used in the process, aspects of sales, changes in consumer demand, and the role of inspection [2]. Good product quality is synonymous with the absence of product defects, and this depends on the inspection process and the production process.

Product quality is key to customer satisfaction and company success. A robust quality control system ensures superior quality. In general, to ensure product quality is determined by the quality control system.

Companies must carry out quality control of defective products to obtain products with quality according to standards. A quality product is defined where the quality standard is zero defects. Good quality

* Corresponding author: [email protected]

† Corresponding author: [email protected]

products are perfect, do not have defects, and are what consumers expect. Defective products are always found in every production process so companies must minimize the number of defects. Companies can increase effectiveness, efficiency, and productivity in preventing defective or failed products to reduce waste in terms of material use, the time required in production, and labor.

One of the obstacles faced by the company is products that do not comply with the specifications or standards set by the company. This company's types of defective products are major and minor. Major defective products that do not meet specifications will be returned to the company to be replaced with new products and minor defects will be returned to the product department for rework.

The finished product inspection process is carried out on each production line. There are 3 quality control processes, namely quality control 1 (QC 1) after the sewing process, quality control 2 (QC 2) after the ironing stage, and final quality control (QC Final) after the finishing stage. Stages of QC 1 and QC 2 have different standards that don't even line up. Therefore, up to the Final QC stage, product defects that pass quality control are still found. This problem is caused by the absence of fixed quality standardization by the company in each production process so that defective products

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enter the finishing process. The use of the sampling method at the Company is currently not effective enough to minimize found product defects.

Based on the problem description, the company needs alternative methods to improve. The acceptance sampling method based on MIL-STD 105E can be an alternative used in making decisions about the products produced by the company.

2 Methodology

This study was started conducted to determine the causes of defective products. The observation process is carried out by observing the product quality inspection process in the garment. After conducting a literature review consisting of journals, scientific papers, and internal articles related to quality, defect products, research methods, and alternative solutions that can be used, especially with the Acceptance Sampling method based on MIL-STD 105E. Problem identification is known by looking at field conditions and production processes at the company and continuing by defining goals, benefits, and problem boundaries.

After that, data was gathered on product defects through historical data requests and direct observations.

Observations were made by looking directly at the production process that occurred and checking the quality control of the products produced in the company.

In addition, interviews were conducted with several parties, such as the quality control manager and workers in the garment. The study conducted interviews using systematically arranged questions to gather supporting data. The collected data will be processed and analyzed to improve product quality control using Acceptance Sampling based on MIL-STD 105E. [3]

3 Result And Analysis

3.1 MIL-STD 105E

The use of the MIL-STD 105E method begins with determining the lot size. Based on the data, the number of product requests is 15,000 pcs. The lot size is determined based on the capacity of one production line and maximum delivery time. In this study, the buyer gave an estimated processing time of one month so that product production in a day is at least 500 pcs. Based on the defect data, Table 1 contains information that the minimum production quantity is 505 pcs, so the product sampling process uses the latter's size code table with level II supervision level with a normal single sampling distribution set in lots with the letter J of 80 samples with an AQL determination of 2.5% with 27 lots. So far, if the product produced is worse than the expected AQL quality, the inspection changes to a more stringent inspection, and if the product produced is worse than normal quality, the repair changes to a loose repair [4].

Tightened inspections found several rejected lots so researchers stated that the company must use strict inspections to minimize the level of defects. This indicates that the company must pay attention to every

level of conditions in the production process to control product quality [5]

Table. 1. Inspection Product Result

The results of product inspection with a single sampling plan are shown in Table 2 below.

Table. 2. Product Inspection Result In The Single Sampling Plan

The inspection decision is accepted if the number of defects (d) is greater than the number of rejects is 7 (according to Single Sampling Plan for Normal Inspection data). Based on Table 2, level shifts occur in the first five lots where 2 or more decisions are rejected resulting in a level shift from normal to tight. Five lots were discovered among the following 10, with decisions being accepted successively and the conditions being lowered from tightened to normal.

Buyer QTY Style

TNI AD 15000 Kemeja

Loreng Quantity Quantity

Inspection

Quantity of Defect

600 80 4

660 80 7

613 80 1

551 80 6

568 80 7

505 80 7

535 80 5

505 80 2

570 80 2

545 80 3

505 80 1

555 80 3

505 80 6

540 80 2

551 80 3

555 80 7

575 80 3

507 80 2

620 80 5

555 80 6

555 80 4

545 80 8

650 80 5

520 80 7

560 80 3

530 80 5

520 80 8

qty Date Inspection Type n Ac Re d Defect Decision

600 28-May 1 NORMAL 80 5 6 4 0.05 ACCEPT

660 30-May 2 NORMAL 80 5 6 7 0.09 REJECTED

613 1-Jun 3 NORMAL 80 5 6 1 0.01 ACCEPT

551 2-Jun 4 NORMAL 80 5 6 6 0.08 REJECTED

505 4-Jun 6 TIGHTENED 80 3 4 7 0.09 REJECTED

535 6-Jun 7 TIGHTENED 80 3 4 5 0.06 REJECTED

505 7-Jun 8 TIGHTENED 80 3 4 2 0.03 ACCEPT

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Table. 3. 3 Result For A Single Sampling Plan MIL- STD105E

The probability of error is 1% or 0.01, which means that the probability of acceptance is 0.99816 or 99% in Table 3. This means that if the probability of defects in the lot is 0.01, then the probability of acceptance of Pa for the lot is 0.993960, so the lot received by the company is 27 × 0.993960 = 26 and 1 lot will be inspected 100%, and returned to the company with 0%

defects. Figure 1 describes a single sampling plan on an operating characteristic curve.

Fig. 1. Single Sampling P Operating Characteristic (OC) Curve

The Pa value in this study is relatively large, therefore, it has a significant impact on the subsequent AOQ values. In a [6] study, the value of Pa is also relatively high. This indicates that as the initial quality value increases, the probability of acceptance also increases. The purpose of this research is to reduce the number of units that are not suitable for the final inspection.

Fig. 2. MIL-STD 105E AOQ curve single sampling plan The AOQ value is calculated using the probability of defects (p, probability of acceptance (Pa), lot size (N), and number of samples (n). In the calculation with N of 505 and n of 80, the AOQ in the third lot is 0.0167. From this AOQ value, AOQL (average outgoing quality limit) or the maximum value of AOQ values can be determined. The AOQL obtained from the numerical calculation is 0.0330 from a p of 0.02 and a Pa of 0.994.

Based on Figure 2, incoming quality has a presentation (p) of an error proportion of 1%, so the percentage of nonconforming in AOQ is 0.84%.

According to the calculation, the AOQL or Average Outgoing Quality Limit, which represents the worst possible average quality produced, is 0.0330 or 3.3%.

The study conducted by [7] shows the value of the number of defects per unit to identify lots that are not by standards. The AOQ value will help the company track the products produced. The higher the AOQ value, the more likely discrepancies exist.

Fig. 3. ATI MIL-STD 105E single sampling plan curve The average number inspected is close to a sample of n = 80 pieces of clothing. The ATI curve will form an asymptote. According to Figure 3, if there is a 2% error rate, a Pa value of 0.993860 will give an ATI value of 82, indicating that the product sample is of good quality as the average number inspected is close to the sample size. These values suggest that when the probability of defects is higher, the probability of receiving samples becomes less. As a result, the ATI value becomes even more important as it indicates that more re-examination is required for a product that is rejected by the sampling plan.

P 100Po n nPo Pa 100pa Po*Pa 100Po*Pa AOQ ATI AOQL

0 0 80 0 1.000 100.0000 0.0000 0.0 0.0000 80 0.0330

0.01 1 80 0.8 1.000 99.9816 0.0100 1.0 0.0084 80.0783 0.0330 0.02 2 80 1.6 0.994 99.3960 0.0199 2.0 0.0167 82.5671 0.0330 0.03 3 80 2.4 0.964 96.4327 0.0289 2.9 0.0243 95.1608 0.0330 0.04 4 80 3.2 0.895 89.4592 0.0358 3.6 0.0301 124.798 0.0330

0.05 5 80 4 0.785 78.5130 0.0393 3.9 0.0330 171.32 0.0330

0.06 6 80 4.8 0.651 65.1006 0.0391 3.9 0.0329 228.322 0.0329 0.07 7 80 5.6 0.512 51.1861 0.0358 3.6 0.0302 287.459 0.0302 0.08 8 80 6.4 0.384 38.3744 0.0307 3.1 0.0258 341.909 0.0258 0.09 9 80 7.2 0.276 27.5897 0.0248 2.5 0.0209 387.744 0.0209

0.1 10 80 8 0.191 19.1236 0.0191 1.9 0.0161 423.725 0.0161

0.11 11 80 8.8 0.128 12.8387 0.0141 1.4 0.0119 450.436 0.0119 0.12 12 80 9.6 0.084 8.3815 0.0101 1.0 0.0085 469.379 0.0085 0.13 13 80 10.4 0.053 5.3387 0.0069 0.7 0.0058 482.311 0.0058 0.14 14 80 11.2 0.033 3.3274 0.0047 0.5 0.0039 490.859 0.0039 0.15 15 80 12 0.020 2.0341 0.0031 0.3 0.0026 496.355 0.0026 0.16 16 80 12.8 0.012 1.2222 0.0020 0.2 0.0016 499.806 0.0016 0.17 17 80 13.6 0.007 0.7231 0.0012 0.1 0.0010 501.927 0.0010 0.18 18 80 14.4 0.004 0.4218 0.0008 0.1 0.0006 503.207 0.0006 0.19 19 80 15.2 0.002 0.2430 0.0005 0.0 0.0004 503.967 0.0004

0.2 20 80 16 0.001 0.1384 0.0003 0.0 0.0002 504.412 0.0002

0.21 21 80 16.8 0.001 0.0780 0.0002 0.0 0.0001 504.669 0.0001 0.22 22 80 17.6 0.000 0.0435 0.0001 0.0 0.0001 504.815 0.0001 0.23 23 80 18.4 0.000 0.0241 0.0001 0.0 0.0000 504.898 0.0000 0.24 24 80 19.2 0.000 0.0132 0.0000 0.0 0.0000 504.944 0.0000 0.25 25 80 20 0.000 0.0072 0.0000 0.0 0.0000 504.969 0.0000 0.26 26 80 20.8 0.000 0.0039 0.0000 0.0 0.0000 504.983 0.0000 0.27 27 80 21.6 0.000 0.0021 0.0000 0.0 0.0000 504.991 0.0000

0.0000 20.0000 40.0000 60.0000 80.0000 100.0000 120.0000

0 5 10 15 20 25 30

Operating Characteristic Curve for Single Sampling Plan

0.0 1.0 2.0 3.0 4.0 5.0

0 10 20 30

Average Outgoing Quality (AOQ) Curve

0 100 200 300 400 500 600

0 5 10 15 20 25 30

Average Total Inspected (ATI)

Curve

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Fig. 4. Product Process Capability

Figure 4 explains that the Cp values obtained were 0.96 and Cpk were 0.93. This process speed produces output far from the target. In a study by [8], it was found that the production process may have irregularities due to a Cpk < Cp value.

These calculations indicate an unstable production process, resulting in deviation from target specifications. Research conducted by [9] shows that the process capability values of bookmark products from small industries are below the required standards, with Cp < 1. Meanwhile, according to [10], the cement packing process has a process capability value also below 1. This means that the process still produces products that do not comply with the specifications set by the company. Companies should assess each step of garment production.

3.2 Cause and Effect Diagram

The primary causes of defects in products are missed stitches, skip stitches, and uneven fabric. These causes can be categorized into four groups: man, material,

machine, and environment.

Fig. 5. Cause and Effect Diagram

3.2.1 Man

Defective products by men or humans are operators who are not careful, are in a hurry, and work under pressure.

In a day, operators will work for 8 hours according to their respective shifts. Every day, the company sets a production target based on product demand and manufacturing capabilities. This target must be achieved by each operator every day. If the operator is can not to reach the target, the operator must accept the

consequence in the form of additional working hours. A similar incident was described by [11], who stated that the cause of product defects was less careful operators and machine conditions were not good due to lack of maintenance.

3.2.2 Material

Material requirements are an essential factor for operators. If the material does not meet quality standards, the resulting product will be suboptimal.

Good material supports process productivity. The cause of this product defect is because there are several damaged materials, such as broken threads, and broken and blunt needles during the sewing process. This causes some stitches to skip and also miss.

3.2.3 Machine

The main factor in the problem is the age and lack of maintenance of the machine. Machines that have been in use for a long time will show signs of fragility, so they often break down and are not as effective as they used to be. According to a case study presented by [12], sewing operators at PT Globalindo Intimates Garment are not only responsible for the sewing process, but also required to have knowledge of standard sewing machine set-up and double-check the sewing machine used.

Before sewing, re-checking is allowed only up to 10 minutes.

3.2.4 Environment

The operator often makes mistakes in sewing due to poor lighting and high temperature in the workplace. In addition, such a work environment does not provide comfort to the operator when working.

3.3 Solutions of Defect

After analyzing product defects using cause-and-effect diagrams, the company formulates corrective actions. In this study, the activities carried out at the improvement stage were determining solutions or actions to overcome the problem of product incompatibility during the striped shirt production process. It is essential for companies to oversee the implementation of standard operating procedures (SOP) on every production line.

Proper monitoring can ensure that the correct procedures are being followed consistently, which can help maintain product quality and safety standards. With clear SOPs, operators have a reference for working procedures. This suggestion can be implemented by printing the SOP and displaying it prominently in the production area. In addition, good coordination between operators is crucial to produce a high-quality product without rushing the job. Companies can also hold regular briefings with operators so they understand what needs to be done and understand the flow of the production process well. In addition, it is crucial to maintain good coordination with material suppliers, including those who provide threads and needles.

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Companies can also carry out inspections of the materials used. Good and quality material will support the effectiveness of the production process so that no material is damaged when used. Scheduling thread and needle replacement can be done by the company to prevent delays caused by brittle materials.

In order to minimize product defects, it is necessary to perform maintenance on the production machinery.

Companies need to check the machines used periodically. Companies can determine important dates for the replacing of spare parts on machines so they don't disrupt the production process. The company is also expected to have good coordination with the spare parts warehouse so that it follows the replacement schedule date. Companies can enforce machine cleaning rules, such as removing excess thread, as a form of maintenance after use.

The department's hot atmosphere causes operators to easily become exhausted and dehydrated. The company is expected to add cooling devices, such as fans, to garments to improve operator comfort by facilitating airflow. Companies can improve their lighting by adding lamps at multiple points. On-the-job lighting will help operators see the product they are about to manufacture. The company should regularly check the quality of the lights and increase the light intensity if found decreased. This can be achieved by providing spare lamps that can be replaced at any time.

3.4 Implications of Applying the Acceptance Sampling Methode based on MIL-STD 105E in the Company

Applying the acceptance sampling method based on MIL-STD 105E is beneficial when a company inspects a large number of lots. It is impossible for companies to test each product continuously. When producing items in large quantities, it is necessary to inspect a significant number of products with a sizable workforce. One of the causes of product defects is operators who work under pressure because they have to achieve production targets. If this method is used, instead of 100%

inspections, the number of product requests can be adjusted based on buyer needs. This can increase productivity by reducing workload and the need for as many product quality inspections. MIL-STD 105E is a widely used sampling system for accepting properties (attributes) due to its simplicity.

In addition, using this method provides more information, such as knowing how far quality deviations occur. If multiple lots are rejected due to a high number of product defects, it indicates an unstable production process in that lot. Companies will find it easier to track if the quality deviation is large enough. This implies that the company is accountable for delivering quality that satisfies the customer [13].

However, implementing this method requires careful consideration and significant company-wide changes. The use of the current sampling method has been around for a long time and staff is used to using it.

In addition, there is no staff knowledge regarding acceptance sampling MIL-STD 105E. Most of the staff ranges in age from 40 to 60 years, which is a less

productive and less adaptive age that requires more effort to make a new change in the inspection process.

The inspection process modifications will inevitably affect the department's mechanism. Companies need training for staff in the garment department to be able to implement this method properly.

4 Conclusion

The most common defects found in camouflage shirt products produced by the company are missed stitches, skip stitches, and uneven fabric. In addition, there are several types of defects, namely bursting, broken fabric, unequal pocket distances, lacking buttons, dirty, deformed, long, sticks that do not meet, wrinkled pockets, improper button positions, not straight collars, and inadequate bartack. This type of defect is considered major and requires repair or rejection of the product. The causes of defects in the products produced by the company are human error, machine maintenance, and an uncomfortable environment for operators. Application of the Acceptance Sampling system based on MIL-STD 105E on products is beneficial in determining product quality. Companies can more easily detect product defects in high volume production using this method.

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