This document summarizes a seminar report on Six Sigma's DMAIC methodology and a case study on improving liquid metal yield at Ispat Industries Limited. [1] The report defines DMAIC as Six Sigma's five-step approach to drive variation out of processes. [2] It then details a case study where the team aimed to increase liquid metal yield from a baseline of 86.1% to a target of 87.88% through the DMAIC framework. [3] Key factors analyzed included hot metal percentage, scrap percentage, and arcing oxygen levels, with a test restricting oxygen leading to a 0.4% yield improvement.

1. A Seminar Report on
Six Sigma – DMAIC Methodology
(A case study)
Presented by,
Bharath M – 1MS09IM401

2. Contents :
Overview of Six Sigma
Introduction – DMAIC Methodology
Define
Measure
Analyze
Improve
Control
Case Study
• Ispat Industries Limited

3. What is SIGMA ?
Sigma is the Greek letter representing a
statistical unit of measurement that defines
the standard deviation of a population. It
measures the variability or spread of the
data.
6 Sigma is also a measure of variability. It is a name given to indicate
how much of the data falls within the customers requirements. The
higher the process sigma, the more of the process outputs, products
and services, meet customers requirements – or, the fewer the defects.

4. Six Sigma focuses on the Reduction of Variation that generates defects
for customers.

5. Defect Reduction due to Variation is Achieved by Eliminating Root Causes
of Variation that …
 Reduce the amount of variation in the process output and/or
 Move the mean performance of the process output.
Reducing The Process Output Variation

6. Moving The Mean

7. Six Sigma Tools: The DMAIC Process
Six Sigma’s DMAIC toolkit is without question the most effective
process improvement framework known in industry today, and
teams that learn and apply this methodology will achieve
unprecedented success.
What is DMAIC?
DMAIC is the five-step approach that makes up the Six Sigma tool
kit, and its sole objective is to drive costly variation from manufacturing and
business processes. The five steps in DMAIC are Define, Measure, Analyze,
Improve, and Control. As the backbone of the Six Sigma methodology,
DMAIC delivers sustained defect-free performance and highly competitive
quality costs over the long run.

8. DMAIC Overview
DMAIC Phase Problem Solving Roadmap
Define What are the various opportunities
Which opportunity we will work on
Who will work on the realization of opportunity
By When the problem will be eliminated
Measure What is the Project Y
What is the specification on Y
What are the issues with Measurements on Y
Analyze What is the existing baseline on Y
How much we want to improve
What are the possible Xs impacting Y
Improve What are the probable Xs impacting Y
Where we should set these Xs
Control How the results will be sustained in the long run

14. Case Study :
“ Improvement In Liquid Metal Yield ”
Ispat Industries Limited, Dolvi, Navi Mumbai

15. D M A I C
• Project Title : Improvement in liquid metal Yield
• Business Champion : B.K.Singh
• Project Champion : Alok Chandra
• Black Belt : B.K.Devangan
• Project Launch date : 25/04/2008
• Target Closure date : 30/09/2008
• Estimated financial Gains : Rs. 111.62 Rs. Crore (Yearly)

16. Month wise trend of LM Yield
88
86.9
87
86.4
86.3
86.1
86 85.6
85.3
85
84
83
Oct'07 Nov'07 Dec'07 Jan'08 Feb'08 March'08

17. CTQ Drill down
HSM Contribution
Raw Material Direct Cost Overheads
Conversion Cost Yield Cost

18. Project Selection Criteria
• Business CTQ : Increase in productivity
• Customer : Business
• Customer CTQ : Increase in LM Production
• Internal CTQ : Increase in LM Yield at EAF
Problem Statement
Average Yield for the period of Oct’07 to March’08 is 86.1% .
Yield loss is the main contributor in direct cost at EAF. Our objective is to reduce
Yield loss by analyzing critical parameter or any other Innovation in process.

19. Project Time Schedule
DMAIC Phase Target Completion date Actual Completion date
Define 30/04/2008 30/04/2008
Measure 15/05/2008 15/05/2008
Analyze 31/07/2008 31/07/2008
Improve 31/08/2008 31/08/2008
Control 30/09/2008 30/09/2008

20. Team Charter
S.N. Team member Emp. Dept Name Role in the dept Responsibility in 6 sigma
name Code project
1 J P sahay 7747 SMS Oprn Shift in-charge Feedback on process and
SMS Oprn implementation of
countermeasure
2 R N Yadav 7568 SMS Oprn Shift in-charge Feedback on process and
SMS Oprn implementation of
countermeasure
3 Bharath M 11413 SMS Oprn SMS Oprn (Tech. Analysis and implementation
cell) of countermeasure
4 Swathi 11336 Technology & Technical analysis
Siddabathula Innovation Data collection and analysis

21. Operational Definition
In the process of steel making ,the total liquid metal extracted from the
raw material is called as Yield. So yield is basically output divided by input
In any process.
At Steel Melt Shop
Yield (%) = [LM produced (Ton) / Total Charge (ton)]*100

22. Project Scope
Longitudinal:
Start – Electric Arc Furnace
End – Electric Arc Furnace
Lateral:
Liquid metal yield in Shell–1,2,3 & 4

23. Financial benefit calculations
Business Target LM Yield – 87.88 % with the following charge-mix :
Hot Metal - 50.8%
Prime DRI – 31.38%
Cold briquette – 2.98%
Scrap – 14.85%
Baseline LM Yield is 86.1% with 51.4 % Hot Metal & 10.04% Scrap
New baseline is 86.4% LM yield with the charge mix as per ABP.
Saving – 1.48 % Yield improvement
– 111.62 Rs. crore/annum [with 3.18 mtpa LM prodn]

24. SIPOC
Charging
Blast LF
Hot Metal Blowing
Furnace Liquid Caster
DRI Arcing
SIP Metal Mill
Scrap Tapping
Scrap Yard Slag Yard
Suppliers Inputs Processes Output Customer

25. Measurement System Design
Unit of measurement : %
Data Type : Continuous type
Data collected for base lining : June-08
Source of data : EAF heat report

26. Process Map

27. Graphical Summary
Summary for Yield
A nderson-D arling N ormality Test
A -S quared 1.80
P -V alue < 0.005
M ean 85.950
S tD ev 0.884
V ariance 0.782
S kew ness -0.024032
Kurtosis 0.804470
N 81
M inimum 83.430
1st Q uartile 85.205
M edian 86.070
3rd Q uartile 86.315
84 85 86 87 88
M aximum 88.100
95% C onfidence Interv al for M ean
85.755 86.146
95% C onfidence Interv al for M edian
86.010 86.160
95% C onfidence Interv al for S tD ev
95% Confidence Intervals
0.766 1.046
Mean
Median
85.7 85.8 85.9 86.0 86.1 86.2

28. Process Capability
Process Capability of Yield
LSL Target
P rocess D ata W ithin
LS L 85.21000 Ov erall
Target 87.88000
USL * P otential (Within) C apability
S ample M ean 85.95049 Cp *
S ample N 81 C PL 0.76
S tD ev (Within) 0.32458 C PU *
S tD ev (O v erall) 0.88696 C pk 0.76
C C pk 2.74
O v erall C apability
Pp *
PPL 0.28
PPU *
P pk 0.28
C pm 0.42
84 85 86 87 88
O bserv ed P erformance E xp. Within P erformance E xp. O v erall P erformance
P P M < LS L 246913.58 P P M < LS L 11262.44 P P M < LS L 201895.58
PPM > USL * PPM > USL * PPM > USL *
P P M Total 246913.58 P P M Total 11262.44 P P M Total 201895.58
Defects – LM Yield less than 85.21%

29. Process Improvement Target
Business Target LM Yield – 87.88 % with the following charge-mix :
 Hot Metal - 50.8%
 Prime DRI – 31.38%
 Cold briquette – 2.98%
 Scrap – 14.85%

30. Identification of sources of variation in Y thru brain storming
Factors affecting LM yield :
1. % Hot Metal
2. % Prime DRI
3. % Scrap
4. % Coal base DRI
5. % HBI
6. HM quality
7. DRI quality
8. Process Type
9. Coke cons.
10. Oxygen in Arcing

31. Analyze : % Yield Vs Process type
Boxplot of Yield vs Process type
90.0
88.5
Yield
86.9496 86.9343
87.0
86.0683
85.778 85.6989
85.5
84.0
l s c p p
ta es r a a
e c -A cr cr
tM ro on s
ut
s
Ho tp -C ith o
% oj
e
A rc c
w i th
00 -C ar c
w
1
Arc
C on ar
C on
Process type

32. Analyze : % Yield Vs Process type
One-way ANOVA: Yield versus Process type
Source DF SS MS F P
Process type 4 649.87 162.47 32.70 0.000
Error 3314 16466.19 4.97
Total 3318 17116.06
S = 2.229 R-Sq = 3.80% R-Sq(adj) = 3.68%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev -----+---------+---------+---------+----
100% Hot Metal 23 86.950 3.066 (--------------*--------------)
Arc-Cojet proces 126 86.068 1.244 (-----*------)
Arc-Con-Arc 549 86.934 1.866 (--*--)
Conarc with scra 2088 85.778 2.207 (-*)
Conarc without s 533 85.699 2.740 (--*--)
-----+---------+---------+---------+----
85.80 86.40 87.00 87.60

33. Analyze : %Hot Metal Vs Yield
Boxplot of Yield vs HM Category
90.0
88.5
87.0 86.7183
Yield
85.8407
85.5
84.7214
84.0
82.5
1. Less than 45% HM 2. 45 to 55% HM 3. More than 55% HM
HM Category

34. Analyze : %Hot Metal Vs Yield
One-way ANOVA: Yield versus HM Category
Source DF SS MS F P
HM Category 2 1221.69 610.85 106.84 0.000
Error 3424 19575.53 5.72
Total 3426 20797.23
S = 2.391 R-Sq = 5.87% R-Sq(adj) = 5.82%
Level N Mean StDev
1. Less than 45% 430 84.721 2.777
2. 45 to 55% HM 2068 85.841 2.284
3. More than 55% 929 86.718 2.431
Individual 95% CIs For Mean Based on
Pooled StDev
Level --+---------+---------+---------+-------
1. Less than 45% (---*---)
2. 45 to 55% HM (-*)
3. More than 55% (-*--)
--+---------+---------+---------+-------
84.60 85.20 85.80 86.40

35. Impact of arcing oxygen on LM yield

36. Remarks :
Test heat taken to restrict the oxygen injection in Arcing phase upto 200 t
of total charge ,which lead to improvement in LM yield by 0.4%.