Analyzing the enhancement of production efficiency using FMEA through simulation-based optimization technique: A case study in apparel manufacturing

Figures & data

Table 1. Occurrence scale

Range	Possible failure rate	Probability of failure
1	<1 in 1,500,000	Nearly impossible
2	1 in 150,000	Remote
3	1 in 15,000	Low
4	1 in 2.000	Relatively low
5	1 in 400	Moderate
6	1 in 80	Moderately high
7	1 in 20	High
8	1 in 8	Repeated failure
9	1 in 3	Very high
10	>1 in 2	Extremely high

Table 2. Severity scale

Range	Probability of failure	Effect of failure
1	None	Be unnoticed and not affect the performance
2	Very minor	Failure may have minor effects on the customer’s process or product
3	Minor	Cause a minor nuisance but can be overcome with no performance loss
4	Very low	Cause minor performance loss
5	Low	Failure creates enough of a performance loss to cause the customer to complain
6	Moderate	Failure results in a subsystem or partial malfunction of the product
7	High	Failure causes a high degree of customer dissatisfaction
8	Very high	Failure renders the unit inoperable or unfit for use
9	Extremely high	Failure involves hazardous outcomes
10	Dangerously high	Failure could injure the customer or an employee

Table 3. Detection certainty scale

Range	Probability of failure	Detection approach
1	Almost certain	The defect is obvious or there is 100% automatic Inspection with regular calibration
2	Very high	All units are automatically inspected
3	High	100% inspection surrounding out of control conditions
4	Moderately high	Statistical Process Control is used with an immediate reaction to out of control conditions
5	Moderate	Some Statistical Process Control is used in process and product is final inspected off line
6	Low	Manual inspection with mistake proofing modifications
7	Very low	All units are manually inspected
8	Remote	Product is accepted based on no defectives in a sample
9	Very remote	Occasional units are checked for defects
10	Almost impossible	The product is not inspected or the defect caused by failure is not detectable

Figure 1. FMEA implementing cycle.

Figure 2. Simulation-optimization implementing cycle.

Table 4. Selected factory main properties

Establishment year	1997
Product type	Any kinds of apparel, including pants, T-shirts, and so on
Number of operators	152
Level of annual production in unit number of apparels	600,000

Table 5. RPN value for each station

Station	1	2	3	4	5	6	7
RPN value	48	84	60	288	30	27	180

Figure 3. Simulated sewing section.

Figure 4. Station sample.

Figure 5. Optimal solution for the developed model.

Figure 6. Main results of the optimization.

Figure 7. Comparison between control percent’s, before and after optimization.

Figure 8. Comparison between percent of accepted and rejected products, before and after optimization.