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

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

1erjitender11@gmail.com, 2vermamukesp9@gmail.com, 3atulengineer74@gmail.com

1. INTRODUCTION

   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.

2. 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. [9] 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.[4].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

   1.95. [1]

3. PROBLEM FORMULATION

   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.

   DEFECT %	0.11	0.03	0.10	1.06	0.32
   SIGMA LEVEL	4.64	4.93	4.59	3.8	4.91

   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.

4. METHODOLOGY

   DMAIC APPROACH

   WINDING SECTION

   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.

   6

   5

   4

   3

   2

   1. DEFECTS %

      0

      BLOW ROOM

      CARDING DRAW FRAME WINDING SECTION

      PACKING

      SIGMA LEVEL

      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

      • MSA

      • Process Capability

        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.

        • Process Analysis

        • Regression analysis

      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 .

      1.8

      1.6

      1.4

      1.2

      1

      0.8

      and night shift. The night shift has minimum defects during manufacturing process.

      1800

      0.6

      0.4

      0.2

      0

      PRODUCT NORMAL

      EXTRA DEFECT

      DEFECT

      1600

      1400

      1200

      1000

      800

      CHANGE SHIFT SHIFT

      600

      400

      200

      0

      MORE DEFECT DEFECT

      Fig.5. Defect Cross % in Product Change Shift

      I-IMPROVE

      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.

      3

      2.5

      2

      1.5

      1

      0.5

      0

      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.

      1. Scan cuts

      2. Speed of winding machine 3- Diskof machine

      4- Suction mouth gauge Parameters

      Global Solution

      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.

      6

      5

      4

      3

   2. DEFECT%

   1 SIGMA LEVEL

   MARCH

   APR MAY JUN JUL AUG SEP

   0

   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.

5. CONCLUSION

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.

REFERENCES

1. 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[10]

2. 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 [1]

3. Hongbo Wang A Review of Six Sigma Approach: Methodology, Implementation and Future Research[11]

4. Kunal Ganguly Improvemt process for rolling mill through the DMAIC Six Sigma approach International Journal for Quality research UDK- 378.014.3(497.11) Short Scientific Paper (1.03)[6]

5. Linda Whicker A conceptual model for the application of Six Sigma methodologies to supply chain improvement International Journal of Logistics: Research and Applications Vol. 8, No. 1, March 2005, 5165.[2]

6. M. Sokovi Six Sigma process improvements in automotive parts production Journal of Achievements in Materials and Manufacturing Engineering vol 19 issue no. 1 Nov 2006 [4]

7. Obaidullah Hakeem Khan Kundi A Study of Six Sigma Implementation and Critical Success Factors Pakistans 9th International Convention on Quality Improvement November 14-15, 2005 [26]

8. R.Veeramani C.Dhanapal Application of six-sigma in controlling cotton contamination and vendor quality ratings [8]

9. Roger W. hoerl Six Sigma Black Belts: What Do They Need to Know? Journal of Quality Technology Vol. 33, No. 4, October 2001[12]

10. Silvia Ponce And Sid-Ali Zahaf Knowledge communities, a key element in six-sigma implementation strategies and deployment HEC MONTREAL PP 4-6[9].