oliveiram votorantim - osisoft
TRANSCRIPT
Six Sigma in Metals and
Mining Production using
PI-ProcessBook and
PI-SQC Session
Alexandre Oliveira
Votorantim Metais Zinco S/A
Ricardo C. Vieira
Cybertécnica (OSISoft partner in Brazil)
Copyright © 2006 OSIsoft, Inc. Company Confidential. All rights reserved. 2
Table of Contents
• Objective
• The Votorantim Group
• Continuous Improvement Strategy
• Managing Process Inputs
• DMAIC – An Approach to Reduce Variability
• Process Focus of Six Sigma
• Define Phase
• Measure Phase
• Analyze Phase
• Improve Phase
• Control Phase
• Implementing the PI ProcessBook
• Benefits for Our Business
Copyright © 2006 OSIsoft, Inc. Company Confidential. All rights reserved. 3
Objective
This presentation have the main objective to present how Votorantim
Metais Zinco implemented the Process Management Road Map based on
Six Sigma Methodology and supported by OSISoft PI ProcessBook helping
control the process in order to be stable and capable focused on production
volume, yield and process efficiency outputs.
P R O C E S SPRODUCT
OR
SERVICE
ENVIONMENTMETHOD
MEASUREMENTMATERIALSMACHINE
Expected Result
We know that each parcel
varies by the time
LABOUR
LSL USL
TARGET
INPUTS
INPUTS
OUTPUT
Control
Volume
Copyright © 2006 OSIsoft, Inc. Company Confidential. All rights reserved. 4
Votorantim Group
Is one of the largest economic groups in Brazil, with an annual gross income of USD 7.0 billion in 2003.
The Group’s companies are market leaders or have outstanding participation in the production of cement, cellulose, paper, aluminum, zinc, nickel, long steel, bio-oriented polypropylene film, chemicals and orange juice.
The Group also has an outstanding participation in the electric energy sector, directly generating this important power to supply its industries, and indirectly through interests in public distribution service and sale of electricity.
In 2001, the group started its internationalization process, acquiring cement companies in Canada and the US.
The search for new business that generate long-term value has also been a concern of the Votorantim Group, which has been adding new activities, including biotechnology and information technology.
Loyal to its high social and environmental awareness, the Votorantim Group has allocated resources to these areas, investing in the improvement of the communities where it operates through several education, cultural, health and environment programs.
Find out more about the Votorantim Group at http://www.votorantim.com/site/en_default.asp.
Copyright © 2006 OSIsoft, Inc. Company Confidential. All rights reserved. 5
Votorantim Metais
• Votorantim Metais’ operations are focused on mining and metallurgy of zinc, nickel and
long steel.
• The company’s activities in these markets are supported by a solid operational structure,
which includes eight proprietary plants and mines, located in the states of São Paulo,
Rio de Janeiro, Minas Gerais and Goiás, and in Lima, Peru, which employ more than
7,000 direct workers and 1,500 permanent indirect ones.
• It is leader in the production of zinc and electrolyte nickel in Latin America and the third
largest producer of long steel in the Country.
• In 2004, the company reported net earnings of USD 1.25 billion, 56 % higher than in
2003. Between 2002 and 2004, it invested USD 920 million in the expansion of its
production capacity, acquisition of companies, technological modernization, energy
generation and environmental initiatives.
• In order to reach an internationally recognized quality standard, Votorantim Metais
continually invests in the expansion of its production capacity, in employee personal and
professional growth, proprietary generation of at least 50% of electric energy,
development of proprietary technologies and mineral research and appropriate
environmental management, which enables the company to operate in a responsible
manner among the communities where it has a presence.
Copyright © 2006 OSIsoft, Inc. Company Confidential. All rights reserved. 6
Votorantim Metais
Central Office
Steel
Nickel
Zinc
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Fundamental Concepts
– Measuring variation means that we can clearly define how well we are meeting CTQ requirements.
– By observing or measuring the process over time you can determine the mean and standard deviation, and therefore the performance of the process against customer requirements.
– Sigma requires that we measure two elements:
• Process Performance
• CTQ requirements
– The goals of Sigma Business Improvement are to center the process well within CTQ requirements through reducing variation, first by eliminating special causes of variation, and then the common causes.
leading to …
Copyright © 2006 OSIsoft, Inc. Company Confidential. All rights reserved. 8
… Quantum Change
Time
Defe
cts
and W
aste
Process Improvement
Current State
New State
Improvement Period
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The starting point – Main vision:
– What is important for the stakeholders?
– What we need to keep and improve our market share at all metals business?
– How maximize Business Value Added?
Main indicators Deployed from the stakeholders visions & requirements
– Production Volume;
– Overall Yield;
– OEE (Overall Equipment Efficiency) – Quality, Performance, Availability and Usage.
These are the new CTB’s – Critical for our Business!
Continuous Improvement Strategy
Copyright © 2006 OSIsoft, Inc. Company Confidential. All rights reserved. 10
Continuous Improvement StrategyThe Continuous Improvement Strategy is compromised of four elements:
• Business
• Technical
• Cultural
• Implementation
It encompasses comprehensive and proven set of tools and techniques applied in a consistent, systematic fashion to enable us to better solve problems and optimize processes in all functional areas.
The major focal points are:
1) Eliminating waste
2) Reducing variability
3) Driving innovation and growth
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How implement the Vision (our needs):
Deployed from the stakeholders to the Plant Operations
• Understand the process flow;
• Identify the main outputs that impacts at our CTB’s (Process Y’s);
• Identify the “vital feel” variables (Process inputs – X’s) whose
impacts these Y’s;
• Reduce or remove special causes from the vital X’s;
• Keep the vital X’s under control Y’s in accordance with the goals;
• Continuous process improvement focused at business challenges.
Continuous Improvement Strategy
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The Process Management Road Map
Why?
Process control optimization and the principal these controls
became more effective and improve the performance. Obtain a new
stabilization level for the plant processes
How?
Guaranty that all processes in the plant will be mapped and the
variation sources are well identified, analyzed, and controlled,
resulting on a stabilized operation.
The management way is defined by the Technology vs. Control
Matrix vision.
Managing Process Inputs
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Managing Process Inputs
The basic premise of variation reduction is that sources of variation can
be:
• Identified
• Quantified
• Eliminated or Controlled
Few Input Variables typically have an
extraordinary influence on the Output
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DMAIC – An Approach to Reduce Variability
• DMAIC = Define, Measure, Analyze, Improve & Control
• Customers react to variances not averages … Customers remember
what they react to … Averages tell little about customer experience…
• Businesses do not excel managing averages…Businesses are negatively
impacted by extremes in the variation of a process
• The variance in a process must be minimized to drive dramatic
improvements in performance
LSL USL
PoorProcess
Capability Increases Cost
LSL USL
ExcellentProcess
CapabilityReduces Cost
Average Average
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DMAIC is a Proven Methodology to Achieve
Cost ReductionDefine Customer Requirements
Clearly Scoped Projects (Aligned to
Business Strategy)
Define
Mistake Proof the Solution (Poka-Yoke)
Standardize The Process Control
Validate and Pilot the Solution
Implement Improvement PlanImprove
Identify, Verify, and Quantify Root Causes
Establish Improvement TargetsAnalyze
Calculate Sigma Level
Determine Process Capability
Actual Process Performance
Measure
ANALYZE
MEASURE
DEFINE
CONTROL
IMPROVE
RECOGNIZE
STANDARDIZE / INTEGRATE
DMAIC = Define, Measure, Analyze, Improve & Control
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Six Sigma Problem Solving Approach
Practical Problem Statistical Problem
Statistical SolutionPractical Solution
Measure Analyze
ImproveControl
y f x x xk= ( , ,..., )1 2
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Process Focus Of Six Sigma
• Inputs (X1, X2 . . . Xn)
• Independent
• Cause
• Symptom
• Control
• Output
• Dependent on Input
• Effect
• Problem
• Monitor
ProcessX’s Y
Determining the critical X’s & controlling the X’s to guarantee the Y’s
How Y=f(X) Relates To A Process
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Dynamics of the Optimization Model
The Funnel Effect
Optimized Process
10 - 15 X’s
8 - 10 KPIVs
4 - 8 Key KPIVs
2 - 5 Key KPIVs
30 - 50 Inputs (X)
Eliminate Waste
Identify Process CTQ (y)Define Phase
Measure Phase
Analyze Phase
Improve Phase
Control Phase
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The Define Phase
• Identify process to be improved
• Identify the customers, their needs and requirements
• Quantify the gap(s) between process outputs and customer requirements
• Define the performance standards or measures
• Establish project objectives
• Ensure resources are in place for the improvement project
Primary responsibility of Project Champion and BB
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Measure Phase
Recuperação de
Zinco – PH 7
Efluentes
Cal
Decantador
Floculante
Rio
São Francisco
Tratamento
Residual
PH - 9
Decantador
Cal Floculante
Dique de
Segurança
Filtração
Tratamento
Residual
Filtração
Lama PH7
7
Filtrado Primário +
Sol. Secundária
Repolpamento
da Lama PH 7
Control Volume
Process Flow
Diagram
Copyright © 2006 OSIsoft, Inc. Company Confidential. All rights reserved. 21
Measure Phase
Etapas
1) % de sólidos
2) Ph da polpa de alimentação
3) Temperatura
4) Pressão de alimentação
5) % de sólidos no under dos
ciclones
6)Nível dos tanques de alimentação
7) Temperatura da polpa
8) % de sólidos no over
9) % de sólidos na alimentação
13)Espessura da torta
14)Vida útil dos tecidos
15)Amostra não representativa
16) Sequência de funcionamento do
filtro
21)Pressão de água de
pressurização
MAPEAMENTO DO PROCESSO
Filtração do silicato
X's Y
Recebimento de polpa
Concentrado (205 B/C)
Ciclonagem
Filtração do silicato
Repolpamento
Umidade na torta
TÍTULO: CÓDIGO:
ÁREA: GREEN BELT: patrocinador:
10 10 10 10 10
10 10 7 10 9
3 5 3 3 4 40
5 3 3 3 4 40
7 7 10 7 8
7 7 7 7 7
7 10 10 10 9
10 10 10 10 10
5 10 10 3 7
7 10 7 5 7
10 5 7 5 7
10 10 10 5 9
7 7 10 7 8
5 3 3 3 4
7 10 7 10 9
7 7 10 10 9
7 7 10 7 8
7 10 10 10 9
5 10 10 10 9
3 3 3 3 3
5 10 10 3 7
5 7 7 7 7
7 10 10 10 9
3 10 10 5 7
3 3 3 3 3
5 7 3 3 5
R E A C
45
LEGENDA: 10 - Correlação Muito Forte 7 - Correlação Forte 5- Correlação Moderada 3- Correlação fraca
40
Polpa de silicato/precipitado com pH entre 2,8 - 3,285
Tipo de neutralizante empregado 85
Agitação 78
30
Receb
imen
to d
a p
olp
a
de p
. d
e f
err
o(T
Q 4
68) Polpa de silicato com pH entre 2,8-3,2
73
Vazão de precipitado de ferro 70
pH na polpa de precipitado de ferro 88
Agitação
Temperatura da polpa
Tipo de neutralizante empregado 70
Sintonia da malha de controle de pH
Contaminação do neutralizante
Padronização do set point da malha de controle
Temperatura da polpa
Agitação 77,5
PESO 10 TOTAL
Po
ten
cia
is X
's d
o P
rocesso
de R
ed
ução
das p
erd
es d
e z
inco
in
so
lúvel n
a n
eu
tralização
.
pré
neu
tralização
da p
olp
a d
e
silic
ato
(TQ
401)
Variação da vazão da polpa de silicato 100
Variação da acidez na polpa de silicato 92,5
Teor de ZnH+
MATRIZ DE PRIORIZAÇÃO DO PROCESSO: PROBLEMA PRIORITÁRIO
Perda de zinco insolúvel acima da meta na etapa de neutralização
Angelo F. Martins Alexandre D. de Oliveira
Padronização do set point da malha de controle
MATRIZ DE CAUSA E EFEITO
Projeto Seis Sigma
CMM-TM
12/09/05
Reduzir o teor de zinco insolúvel na torta dos filtros.
UGB - Processos
Neu
tralização
fin
al d
a p
olp
a
silic
ato
/p. d
e f
err
o (
TQ
469)
Agitação
Polpa de silicato/precipitado com pH 3,4 - 3,6
Vazão da polpa silicato/precipitado de ferro
Tem
po
de r
esid
ên
cia
(TQ
s470,4
15,4
14 e
Adição de vapor no tanque 413/414(bombeamento)
Contaminação do neutralizante
Sintonia da malha de controle de pH
Temperatura da polpa
Controle de nível TQ 413/414
70
93
65
70
30
93
100
70
78
93
88
Process Mapping
C&E Matrix
Copyright © 2006 OSIsoft, Inc. Company Confidential. All rights reserved. 22
Measure PhaseP
erc
ent
Part-to-PartReprodRepeatGage R&R
100
50
0
% Contribution
% Study Var
Sam
ple
Range
0,10
0,05
0,00
_R=0,0383
UCL=0,1252
LCL=0
1 2 3
Sam
ple
Mean
1,00
0,75
0,50
__X=0,8075UCL=0,8796
LCL=0,7354
1 2 3
Part
10987654321
1,00
0,75
0,50
Operator
321
1,00
0,75
0,50
Part
Avera
ge
10 9 8 7 6 5 4 3 2 1
1,00
0,75
0,50
Operator
1
2
3
Gage name:
Date of study :
Reported by :
Tolerance:
Misc:
Components of Variation
R Chart by Operator
Xbar Chart by Operator
Response by Part
Response by Operator
Operator * Part Interaction
Gage R&R (ANOVA) for Response
Gage R&R
analysis
Measuring the y
and X’s variation
Copyright © 2006 OSIsoft, Inc. Company Confidential. All rights reserved. 23
Measure Phase
pH over
Pe
rce
nt
87654
99
95
90
80
70
60
50
40
30
20
10
5
1
Mean
0,560
5,987
StDev 0,9544
N 30
AD 0,300
P-Value
Probability Plot of pH overNormal
696672648624600576
LSL USL
Process Data
Sample N 29
StDev (Within) 18.9653
StDev (O v erall) 15.9191
LSL 600
Target *
USL 700
Sample Mean 602.172
Potential (Within) C apability
C C pk 0.88
O v erall C apability
Pp 1.05
PPL 0.05
PPU 2.05
Ppk
C p
0.05
C pm *
0.88
C PL 0.04
C PU 1.72
C pk 0.04
O bserv ed Performance
PPM < LSL 448275.86
PPM > USL 0.00
PPM Total 448275.86
Exp. Within Performance
PPM < LSL 454402.19
PPM > USL 0.12
PPM Total 454402.31
Exp. O v erall Performance
PPM < LSL 445726.50
PPM > USL 0.00
PPM Total 445726.50
Within
Overall
Process Capability of C1
Zst = 4,5
Zs
hif
t =
1,5 AB
C D
Normality Test for
y’s
Capability
Analysis
Tech vs Control
Matrix
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Analyze Phase
vazão
pH
ov
er
400350300250200150100
8
7
6
5
4
S 0,460730
R-Sq 77,5%
R-Sq(adj) 76,7%
Regression
95% CI
Fitted Line PlotpH over = 3,544 + 0,01090 vazão
y f x x xk= ( , ,..., )1 2
Flow
0,70
0,45
0,20
[Zn]
128
124
120
[H+]
84
81
78
Temp
184182180
90
85
80
0,700,450,20 128124120 848178
Yield
Matrix Plot of Flow; [Zn]; [H+]; Temp; Yield
Regression
Analysis
Statistically links key input variables with key output variable
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Improve Phase
Validate and Implement Solution using tools such as:
– Optimization DOEs
– Action Plan Based on 5W+2H
– Alternative Solutions Matrix
– Cost Benefit Analysis
– Piloting Solution
– Implementation
Optimized Process
From 8 - 10 KPIVs
to
4 - 8 Leverage KPIVs
Continue Eliminating Waste
Copyright © 2006 OSIsoft, Inc. Company Confidential. All rights reserved. 26
Control Phase
690675660645630615600
LSL USL
Process Data
Sample N 29
StDev (Within) 7.14055
StDev (O v erall) 8.63629
LSL 600
Target *
USL 700
Sample Mean 645.753
Potential (Within) C apability
C C pk 2.33
O v erall C apability
Pp 1.93
PPL 1.77
PPU 2.09
Ppk
C p
1.77
C pm *
2.33
C PL 2.14
C PU 2.53
C pk 2.14
O bserv ed Performance
PPM < LSL 0.00
PPM > USL 0.00
PPM Total 0.00
Exp. Within Performance
PPM < LSL 0.00
PPM > USL 0.00
PPM Total 0.00
Exp. O v erall Performance
PPM < LSL 0.06
PPM > USL 0.00
PPM Total 0.06
Within
Overall
Process Capability of After Improvement
New Capability
Analysis
Zst = 4,5
Zs
hif
t =
1,5 AB
C D
Revised Control
Plan
New Tech vs
Control Matrix
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Implementing the PI ProcessBook
Question
How does the RTPM can help Votorantim
Metais implement 6?
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RTPM Implementation
• 7 PI Servers, 45000 Tags
• 186 Clients (ProcessBook, Data Link and SQC)
• 24 Interfaces (OPC, DDE, RelDB, PItoPI)
TM
VZ
MA
JF
BM
NQ
SM
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Off Line SPC
Control System
Data Report
Statistical Software
Exceptions Report
1 Day
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Real Time SPC
Exception ReportControl Chart
PI Server
Clients
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SPC Displays and Reports
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SPC Displays and Reports
Control Charts Histogram
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What is a Contol Chart?
Upper Specification Limit
Lower Specification Limit
Average or Target Value
Upper Control Limit
Lower Control Limit
}
}
2
1
6
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SPC Displays and Reports
Statistics Exceptions
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SPC Displays and Reports
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SPC Displays and Reports
Process Tag
Annotations
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SPC Displays and Reports
SPC Display
Excel Report
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Example
Before PI
64%
After PI
70%
Zinc
H2O
Mixture
Vacuum
Reject
Zinc Filtering
9.4%
Plus Zinc
Copyright © 2006 OSIsoft, Inc. Company Confidential. All rights reserved. 39
Benefits for the Business
• Standardized control processes for all Metals Plants;
• Process operations focused on KPIV’s which impacts on y’s;
• Only one way to understand the Process Variations;
• Quick response when out of control “signals” appears at select control charts:
– Process Tag Annotations
– Control Charts views
– OCAP shortcut
• Easy way to manage the data – displays and reports;
• PI ProcessBook data base complete usage for Continuous Improvement strategies;
• Turn into reality the SPC for plant floor: Operators and Supervisors day-by-day tool – the DMAIC Control phase.