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

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


ObjectiveThis 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 SP R O C E S SPRODUCT PRODUCT

OR OR SERVICESERVICE

ENVIONMENTMETHOD

MEASUREMENT MATERIALS MACHINE

Expected Result

We know that each parcel varies by the time

LABOUR

LSL USL

TARGET

INPUTS

INPUTS

OUTPUT

Control Volume


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.

http://www.votorantim.com/site/en_default.asp

http://www.votorantim.com/site/en_default.asp


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.


Votorantim Metais

Central Office

Steel

Nickel

Zinc


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 …


… Quantum Change

Time

Def

ects

and

Was

te

Process Improvement

Current State

New State

Improvement Period


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


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


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


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


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


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


DMAIC is a Proven Methodology to Achieve Cost Reduction

Define Customer RequirementsClearly Scoped Projects (Aligned to Business Strategy)

Define

Mistake Proof the Solution (Poka-Yoke) Standardize The Process Control

Validate and Pilot the Solution Implement Improvement Plan

Improve

Identify, Verify, and Quantify Root CausesEstablish Improvement TargetsAnalyze

Calculate Sigma LevelDetermine Process CapabilityActual Process Performance

Measure

ANALYZE

MEASURE

DEFINE

CONTROL

IMPROVE

RECOGNIZE

STANDARDIZE / INTEGRATE

DMAIC = Define, Measure, Analyze, Improve & Control


Six Sigma Problem Solving Approach

Practical Problem Statistical Problem

Statistical SolutionPractical Solution

Measure Analyze

ImproveControl

y f x x xk ( , ,..., )1 2


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


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


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


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


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

Rec

ebim

ento

da

polp

a de

p. d

e fe

rro(

TQ 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


Agitação 77,5

PESO 10 TOTAL

Pot

enci

ais

X's

do

Pro

cess

o de

Red

ução

das

per

des

de z

inco

inso

lúve

l na

neut

raliz

ação

.

pré

neut

raliz

ação

da

polp

a de

si

licat

o(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

tral

izaç

ão fi

nal d

a po

lpa

silic

ato/

p. d

e fe

rro

(TQ

469

)

Agitação

Polpa de silicato/precipitado com pH 3,4 - 3,6

Vazão da polpa silicato/precipitado de ferro

Tem

po d

e re

sidê

ncia

(T

Qs4

70,4

15,4

14 e

Adição de vapor no tanque 413/414(bombeamento)

Contaminação do neutralizante

Sintonia da malha de controle de pH


Controle de nível TQ 413/414

70

93

65

70

30

93

100

70

78

93

88

Process Mapping

C&E Matrix


Measure PhaseP

erce

nt

Part-to-PartReprodRepeatGage R&R

100

50

0

% Contribution

% Study Var

Sam

ple

Ran

ge 0,10

0,05

0,00

_R=0,0383

UCL=0,1252

LCL=0

1 2 3

Sam

ple

Mea

n

1,00

0,75

0,50

__X=0,8075UCL=0,8796

LCL=0,7354

1 2 3

Part10987654321

1,00

0,75

0,50

Operator321

1,00

0,75

0,50

Part

Ave

rage

10 9 8 7 6 5 4 3 2 1

1,00

0,75

0,50

Operator

1

23

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


Measure Phase

pH over

Perc

ent

87654

99

95

90

80

70

605040

30

20

10

5

1

Mean

0,560

5,987StDev 0,9544N 30AD 0,300P-Value

Probability Plot of pH overNormal

696672648624600576

LSL USLProcess Data

Sample N 29StDev(Within) 18.9653StDev(Overall) 15.9191

LSL 600Target *USL 700Sample Mean 602.172

Potential (Within) Capability

CCpk 0.88

Overall Capability

Pp 1.05PPL 0.05PPU 2.05Ppk

Cp

0.05Cpm *

0.88CPL 0.04CPU 1.72Cpk 0.04

Observed PerformancePPM < LSL 448275.86PPM > USL 0.00PPM Total 448275.86

Exp. Within PerformancePPM < LSL 454402.19PPM > USL 0.12PPM Total 454402.31

Exp. Overall PerformancePPM < LSL 445726.50PPM > USL 0.00PPM Total 445726.50

WithinOverall

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


Analyze Phase

vazão

pH

ov

er

400350300250200150100

8

7

6

5

4

S 0,460730R-Sq 77,5%R-Sq(adj) 76,7%

Regression95% 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

800,700,450,20 128124120 848178

Yield

Matrix Plot of Flow; [Zn]; [H+]; Temp; Yield

Regression Analysis

Statistically links key input variables with key output variable


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


Control Phase

690675660645630615600

LSL USLProcess Data

Sample N 29

StDev(Within) 7.14055

StDev(Overall) 8.63629

LSL 600

Target *USL 700

Sample Mean 645.753

Potential (Within) Capability

CCpk 2.33

Overall Capability

Pp 1.93

PPL 1.77PPU 2.09

Ppk

Cp

1.77Cpm *

2.33

CPL 2.14CPU 2.53

Cpk 2.14

Observed Performance

PPM < LSL 0.00

PPM > USL 0.00PPM Total 0.00

Exp. Within Performance

PPM < LSL 0.00


Exp. Overall Performance

PPM < LSL 0.06


WithinOverall

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


Implementing the PI ProcessBook

Question

How does the RTPM can help Votorantim

Metais implement 6?


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


Off Line SPC

Control System

Data Report

Statistical Software

Exceptions Report

1 Day


Real Time SPC

Exception ReportControl Chart

PI Server

Clients


SPC Displays and Reports



Control Charts Histogram


What is a Contol Chart?

Upper Specification Limit

Lower Specification Limit

Average or Target Value

Upper Control Limit

Lower Control Limit

}

}

2

1

6



Statistics Exceptions





Process Tag

Annotations



SPC Display

Excel Report


Example

Before PI

64%

After PI

70%

Zinc

H2O

Mixture

Vacuum

Reject

Zinc Filtering

9.4%

Plus Zinc


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.

Documents

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