- Open Access
- Total Downloads : 11
- Authors : Jitender Kumar, Mukesh Verma, Atul Aggarwal
- Paper ID : IJERTCONV1IS02031
- Volume & Issue : NCEAM – 2013 (Volume 1 – Issue 02)
- Published (First Online): 30-07-2018
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License: This work is licensed under a Creative Commons Attribution 4.0 International License
IMPLEMENTATION OF DMAIC APPROACH TO MINIMIZE THE DEFECTS RATE OF PRODUCT IN TEXTILE PLANT
IMPLEMENTATION OF DMAIC APPROACH TO MINIMIZE THE DEFECTS RATE OF PRODUCT IN TEXTILE PLANT
1Jitender Kumar, 2Mukesh Verma, 3Atul Aggarwal
1M.Tech Scholar, SSIET, Dera Bassi
2,3 Associate Prof., ME Deptt. SSIET , Dera Bassi
email@example.com, firstname.lastname@example.org, email@example.com
The DMAIC is a financial improvement strategy for an organization and now a days it is being used in many industries. Basically it is a quality improving process of final product by reducing the defects; minimize the variation and improve capability in the manufacturing process. The objective of DMAIC is to increase the profit margin, improve financial condition through minimizing the defects rate of product. It increases the customer satisfaction, retention and produces the best class product from the best process performance.
LITERATURE REVIEW Motorola was the first organisation to use the term DMAIC in the 1980s as part of its quality performance measurement and improvement program. Recent
DMAIC success stories, primarily from the likes of General Electric, Sony, Allied Signal, and Motorola, have propagated the use of quality tools for gaining the
knowledge. Some of the pioneering companies, which use DMAIC methodology, are ABB, General Electric (GE), Allied Signal and Texas Instruments. General Electric spent 500 million dollar on DMAIC works in 1995 and gained more than 2 billion dollar from that investment. In 2001 Horel shows that the Six Sigma improvement methodology has received considerable attention recently, not only in the statistical and quality literature, but also within general business literature. In published discussions, terms such as Black Belt(BB), Master Black Belt, and Green Belt have frequently been used indiscriminately, without any operational definitions provided. Ponce in 2004 shows that six- sigma knowledge characteristics, and their impact on performance and gains, have not yet been addressed regardless of its knowledge content.  in 2005 Kundi studied the implementation of Six Sigma in the UK organizations. Sokovic in 2006 explained that Six sigma is an effective way to find out where are the greatest process needs and which are the softest points of the process. Also, Six sigma provide measurable indicators and adequate data for analytical analysis. Systematic application of Six Sigma DMAIC tools and methodology within an automotive parts production results with several achievements. Reduced tool expenses for 40 %, Reduced costs of poor quality (CORQ) for 55 %, and reduced labours expenses for 59
%. Also, the significant results are achieved by two indexes that are not dependent on the volume of production: Production time reduction for 38 %, and Index cost/volume reduction for 31 %. Generally, improvements through reduced Production time, Control time, Material and Internal scrap will give annual benefits of $ 72 000. Expected annual benefits of external clamping system application is $100 000..In 2009 Naidu implement the DMAIC in garment
industry. The focus was exporting the final product to European countries. It was operating at a percentage defective of 4.42. After implementing the DMAIC methodology the percentage defective is reduced to
In all processes the smallest variation in quality of raw material, production conditions, operator behavior and other factors can result in a cumulative variation (defects) in the quality of the finished product. DMAIC approach aims to eliminate these variations and to establish practices resulting in a consistently high quality product. Therefore, a crucial part of DMAIC work is to define and measure variation with the intent of discovering its causes and to develop efficient operational means to control and reduce the variation.
The expected outcomes of DMAIC efforts are faster and more robust product development, more efficient and capable manufacturing processes, and more confident overall business performance.
Last section of yarn manufacturing process where auto cone machines are installed and take an input material from combing process in the form of Lap. Then the lap is converted into thread. It gives yarn on paper cone after passing detecting instrument as a output. The yarn which is obtained from winding section is able to sell the customers. So DMAIC approach is implemented to the winding section.
D- DEFINE: The definition of the problem is the first and the most important step of any DMAIC project because a good understanding of the problem makes the job much easier. The problem found is rejection due to defects in winding process.
CARDING DRAW FRAME WINDING SECTION
Fig. 1 Chart between Defect and Sigma Level
M-MEASURE : Measure the performance of the process by collecting the data and also write down the importance of different critical defects regarding to customer value. Techniques used are :
Cause and Effect Analysis
Data Collection Plan
Fig. 2. Defects in Winding Section
ANALYSIS: Analyse the root causes of the process whether it can be improved or redesigned the process. There are different parameters involve in this phase which are given below.
CRITICAL SUCCESS FACTORS STRENGTH OF YARN
Strength of yarn depends on twist of yarn, as the twist increases the strength is also increases up to a certain limit.
CV OF YARN
CV of yarn is the variation of different parameters like, strength, count etc. and profitability of the plant. Evening shift has more defects as compared to morning
All overhauling is done mostly in morning shift by the maintenance team and restart the machine in evening shift .
and night shift. The night shift has minimum defects during manufacturing process.
CHANGE SHIFT SHIFT
MORE DEFECT DEFECT
Fig.5. Defect Cross % in Product Change Shift
The improvement of process is calculated by the help of Design of Experiment. In order to improve the process, some settings are change which are the sever effect on the defects of final product.
Fig. 3. Shift Wise Defect Chart
EXTRA DEFECT DEFECT
Fig. 4. Defect Cross % in Overhauling Shift
In this normal plot, some significant factors are shown which causes major effects on the defects on the product in the winding process.
Speed of winding machine 3- Diskof machine
4- Suction mouth gauge Parameters
Scan Cuts = 37
Speed = 700
Disk = 1 (Good) Guage = -1 (< 6mm)
Speed is already slow so no big influence on defect.
By deeply analyzing this problem, whenever change the product at machine or run the machine after overhauling chances of Stitch defects increases in first shift. Up till second shift things get normalized.
1 SIGMA LEVEL
APR MAY JUN JUL AUG SEP
Fig. 6. Bar Chart after Improvement
Scan-Cuts and Disk life are most important factors. They need to be controlled to achieve optimum results best Scan-Cuts are below 40.
Condition of Disk should be good always and the suction mouth gauge should be less then 6 mm
C-CONTROL In control phase, the process will be check by applying the control charts whether it is control or not. Variation of whole process should be in control limits for control process. Statistical process control is used to monitoring the consistency of process and makes the process is under control.
Data of defects %age shows that the process is under control and there is not any point in this graph which is out of control limits.
Fig. 7. X-bar R chart of Defect %
RESULTS: The defects has been reduced from 13012 to 185. The sigma level has been increased from 3.81 to 5.10.
It is necessary to work in a systemic way and try to improve financial condition of the organization. I have also implemented DMAIC tool in our report to highlight the clear understanding about the problems and importance of critical success factors to the quality of final yarn product.
Anup A. Junankar, P.N Shende Minimization Of Rewok In Belt Industry Using Dmaic International Journal of Applied Research in Mechanical Engineering, Volume-1, Issue-1, 2011
Chethan Kumar C S, Dr. N V R Naidu, Dr. K Ravindranath Performance improvement of manufacturing industry by reducing the Defectives using Six Sigma Methodologies IOSR Journal of Engineering (IOSRJEN) Vol. 1, Issue 1, pp. 001-009 
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