2007 Solache Orozco May Alejandro Berumen Davila Mario Guel Hernandez SIX SIGMA CERTIFICATION UNIVERSIDAD VIRTUAL SIX SIGMA

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UNIVERSIDAD VIRTUAL

SIX SIGMA PROJECT

SIX SIGMA BLACK BELT CERTIFICATION

Leonardo Alejandro Berumen Davila Mario Guel Hernandez

Javier Solache Orozco

May 2007

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MOLD DEFECT REDUCTION FOR THE ALUMINUM WHEELS

BY:

Leonardo Alejandro Berumen Dávila Mario Guel Hernández

Javier Solache Orozco

SIX SIGMA PROJECT PRESENTED TO: ITESM FACULTY

THIS WORK IS A PARTIAL REQUIREMENT TO OBTAIN THE BLACK BELT CERTIFIED IN SIX SIGMA

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DEDICATION

Leonardo To my Wife for her unwavering faith in me as an academic and for giving me the

knowledge, confidence, and encouragement to succeed.

Javier To my Wife - Socorro, for her patience, understanding and support in this project.

For Javier, Eugenia, Alan, and Alexa.

To the humble, courageous, "great" ones among me.

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ACKNOWLEDMENTS

We would like to acknowledge the SUPERIOR INDUSTRIES OF MEXICO for their on-going support and advice in making this research project possible: Special thanks to Heriberto Rodriguez owner process, Guillermo Calderon, Jose Antonio Banderas and Carlos Jimenez.

Thanks go out to:

Dr. Leonel Quintanilla and his wisdom, patience, guidance, and tutelage through the dreaded art of interpreting statistics.

All the team members, for adding daily knowledge, a little bit of sanity, and a lot of hard work.

Last, but never least of all, to everybody who was involve on this project, support, and encouragement on the journey to achieve and pursuit of higher goals and excellence. When we started taking the obstacles of this project presented, we took them as signs of potential to defeat, we saw who we're through our eyes and found out, once again, that just about anything is possible.

Thank you.

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SUMMARY

The mold defect appears on the face of the wheels as lack of foundry material mainly on the spokes, windows, hub area, and bead of the wheels.

The purpose of this project is to improve product quality by reducing mold defects. This involved identifying and isolating the primary causes of problems that directly affect the cost of poor quality, determine the root causes and implement effective corrective actions.

On the last semester basis, there was a total of $ 149, 835 USD as a result of scrap. The initial goal is to reduce the cost of poor quality (COPQ) by 50% to achieve annual savings of $ 74,917.

After the changes performed up to know, we have a weekly savings of 1560.00 US. Dollars, for a total at least of 78,540.00 because of some of the changes are being implemented or the effect of the implemented changes is not jet reflected.

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CONTENTS

DEDICATION

ACKNOWLEDMENTS ii

SUMMARY iii

CONTENTS ^IV

CHAPTER 1. INTRODUCTION 1.1 Company High-Lights 1.2 Six Sigma Project Objective

1 2 4

CHAPTER 2. LITERATURE REVIEW 5

CHAPTER 3. THE BREAKTROUGH STRATEGY

3.1. DEFINE PHASE 8

3.1.1. Cover Sheet 8 3.1.2. Project Charter

3.1.3. Cost Savings Tracking Sheet 3.1.4. Tollgate Review Plan

3.1.5. Project Action Plan (Gantt Chart 3.1.6. Defect Example

3.1.7. Process Model (SICOP or Map L1/L2/L3

3.2. MEASURE PHASE 16

3.2.1. Data Collection Plan 3.2.2. Base Line Data 3.2.3. M S A - G a g e Study

3.2.4. Brainstorming - Root Cause 3.2.5. Organization Causes

3.2.6. Cause Investigation

3.3. ANALYZE PHASE 19

3.3.1. Brainstorming - Solutions 3.3.2. Solution Evaluation / Selection 3.3.3. Solution Details

3.4. IMPROVE PHASE 31

3.4.1. Pilot Plan

3.4.2. Improvement Actions 3.4.3. Improvement Verification

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3.5. CONTROL PHASE 36 3.1.1. Permanent Controls

3.1.2. Celebrate Success (Close and Handoff)

CHAPTER 4. RECOMMENDATIONS 40

REFERENCES v

VITA

Javier Solache vi

Leonardo Berumen vii

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CHAPTER 1. INTRODUCTION

Six Sigma is as much about people excellence as it is about technical excellence. Employees often wonder how they are going to solve a difficult problem, but when they are given the tools to ask the right questions, measure the right things, correlate a problem with a solution and plan a course of action, they can find solutions to the problem more easily. Therefore, with Six Sigma, the company's Superior corporate culture shifts to one that includes a systematic approach to problem solving and a pro-active attitude among employees.

Successfully Six Sigma programs also had contributed to the overall sense of pride of the Superior Company's employees.

Six Sigma had helped us to transforms the mindset and works on major business issues like:

targets, so that the company is constantly striving for improvement.

© Data-based decision making: Decisions are made based on critical analysis of facts and data. However, this does NOT mean it will negatively impact to a company's ability to make quick decisions. In contrast, by smoothly applying the DMAIC principles, the decision makers are more likely to have the data they need in order to make well informed decisions.

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1.1 Company High-Lights

Superior Industries de Mexico S.A. de C.V.

Lou Borick founded Superior Industries in 1957.

Steve Borick named C E O starting January 2005.

Superior Industries is the world's largest producer of cast aluminum road wheels servicing original equipment manufacturers worldwide:

& Employs a highly optimized low-pressure casting and light forging technologies, which offer structural and styling flexibility required to meet multiple platform demands.

® These processes offer high volume efficiency, manufacturing consistency, repeatability, and most of all quality.

Superior Industries is currently operating seven low-pressure cast aluminum wheel plants in the US, Mexico and Europe and new plant is in production

launching in Chihuahua, Mex. (Plant # 10).

O in 1993 Mexico operations started with plant 7 and in 2001 a second plan was build in Mexico (plant 9)

3 In this year a new plant (10) is being starting production

Superior Industries has achieved several supplier awards recognizing its excellence. The awards were:

«* In 2003, 2004, 2005, and 2006 Nissan North America awarded "Zero Defects" and "Quality Master Award".

O In 2006 named by Ford "Strategic Supplier"

& In 2002, Superior Industries achieved a new supplier award recognizing our excellence. The awards were the Ford Motor Company' Silver World Excellence Award.

® In 2004, Gold Award from Daimler-Chrysler.

• General Motors Supplier of the Year Award (2000, 2001, 2002, 2003, 2004, 2005, and 2006).

@ In 2003, Fayetteville received the V P P Gold Star Recognition Award from OSHA.

Superior Industries Mexico was awarded Self-audit status (VPP) from

"Mexico Labor Ministry" (Secretaria del Trabajo y Prevision Social) reached on 2004.

Achieved on 2005 "Clean Industry Recognition" from the Chihuahua Government" (Currently pursuing environmental excellence status).

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MISSION STATEMENT

To design and manufacture "Superior" quality aluminum wheels and other automotive component products for our customers worldwide. To continue to be the performance leader in our industry to provide a maximum return to our shareholders; provide a competitively priced quality product for our customers; and create growth opportunities for our employees with particular emphasis on a team effort among our people through open communication, smart thinking and cooperation.

Quality Vision.

Provide a supporting role to the Corporation for reducing waste, optimizing processes, and maintaining our position as the quality leader in our commodity. To clearly define the roles and functions of the Quality Organization

Address: Plant 9: Nicolas Gogol 11354 Chihuahua, Chih. CP.31109 Information of the contact in the company

Jose Luis Salgado, Operations Manager, isalgado(5).supind.com Tel. 614 4290960

Automotive Industry actual conditions

a) The modalities of price reductions "imposed" by the clients affect the margins sensibly due to the Asian competition.

b) High sensibility of the clients to the quality problems

c) Lack of competitiveness at world level with the Asian market generating closing plants of aluminum wheels in USA.

d) It is a tendency to the drop of penetration of our main clients in the market.

e) Change in the design of new vehicles (fashion / stile, size of wheels).

f) Customer expectations

g) Quality and delivery always improving.

h) More flexibility and faster new products development.

i) Cost atractive.

j) Increase the innovation in the new products

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1.2 Six Sigma Project Objective

Project Name: Mold defect reduction Project description

The mold defect appears on the face of the wheels as lack of foundry material mainly on the spokes, windows, hub area, and bead of the wheels.

The purpose of this project is to improve product quality by reducing mold defects. This involved identifying and isolating the three primary causes of problems that directly affect the cost of poor quality, determine the root cause and implement effective corrective actions. On the last semester basis, there was a total of $ 149, 835 USD as a result of scrap. The initial goal is to reduce the cost of poor quality (COPQ) by 50% to achieve annual savings of $ 74,917

This is one of the main defects in the Plant Scope: All the wheels produced in the Plant Project Objective is 50% reduction for this defect.

Key Words

0 Cast: To take a form in a mold

O Deburr: To remove the burrs from a wheel.

O Expedition EB: Type of wheel

® F21: Drag damage during removal the wheel from a mold

O Final Inspection: The place in the process to make a final evaluation of the wheel

® Fluoroscope: An instrument used for observing the internal structure of a wheel by means of X-rays

0 Heat Treat: To treat wheels by heating and cooling in a way that will produce desired properties

O Machining face wheel: Wheel with a finish as machined cover just by acrylic

O Mold shop: The place where the molds are prepared 0 MRB:

9 Picker: Fluoroscope wheel for immediate feed back to the casting operators

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CHAPTER 2. LITERATURE REVIEW: SIX SIGMA THEORIES

Six Sigma has been a popular management philosophy for years. Motorola first made Six Sigma popular in the 1980s. AlliedSignal embraced it in the early 1990s and then General Electric made it the most popular management philosophy in history. Like anything that becomes popular, misconceptions abound relative to how to implement Six Sigma. Particularly since this management philosophy is based on facts and data being used to make decisions in the organization, a host of statisticians have developed new careers teaching and consulting in this discipline. However, most statisticians are skilled in the theory of Six Sigma. To make Six Sigma a success in your organization, it must affect everyone in the organization.

Everyone in an organization must be involved and affected by ix Sigma, regardless of their position in the organization. Unlike the approaches many take that imply Six Sigma is some mystic set of skills available only to those with advanced college degrees, Six Sigma must be available to everyone in the organization, where certain skills are practiced by all.

Six Sigma is teaching everyone in the organization to become more effective and efficient. Unfortunately, most organizations are highly ineffective and inefficient. This means they have unhappy customers and waste considerable money because their processes do not run at optimum. The path to becoming more effective and efficient using Six Sigma contains three components. The first component deals with the strategy of Six Sigma. The strategy of Six Sigma is called Business Process Management. This strategic component is the responsibility of executive management. Thus, if you hear your company has embraced Six Sigma it may be several months before you see the results of your management's initial work. To have you become acquainted with what your management has done to create the Business Process Management system.

The second component of Six Sigma deals with the tactics of how project teams improve a broken process. It utilizes a methodology similar to the scientific method you learned in school. The scientific method refers to defining and measuring a problem, analyzing its root cause, and testing theories of improvement.

Six sigma is a statistical concept that measures a process in terms of defects.

Achieving six sigma means your processes are delivering only 3.4 defects per million opportunities (DPMO)—in other words, they are working nearly perfectly.

Sigma (the Greek letter a) is a term in statistics that measures something called standard deviation. In its business use, it indicates defects in the outputs of a process, and helps us to understand how far the process deviates from perfection.

A sigma represents 691462.5 defects per million opportunities, which translates to

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a percentage of non-defective outputs of only 30.854%. That's obviously really poor performance. If we have processes functioning at a three sigma level, this means we're allowing 66807.2 errors per million opportunities, or delivering 93.319% non-defective outputs. That's much better, but we're still wasting money and disappointing our customers. How well are your processes operating? Are they three sigma? Four sigma? Five? Most organizations in the world are operating at three to four sigma quality levels. That means they could be losing up to 25% of their total revenue due to processes that deliver too many defects—defects that take up time and effort to repair as well as creating unhappy customers. Is that good enough? The answer is simple. No it's not when you could be doing a lot better.

The central idea of Six Sigma management is that if you can measure the defects in a process, you can systematically figure out ways to eliminate them, to approach a quality level of zero defects. So, in short, Six Sigma is several things:

1. A statistical basis of measurement: 3.4 defects per million opportunities 2. A philosophy and a goal: as perfect as practically possible

3. A methodology 4. A symbol of quality

The Six Sigma methodology uses statistical tools to identify the vital few factors, the factors that matter most for improving the quality of processes and generating bottom-line results. It consists of four or five phases:

I. Define the projects, the goals, and the deliverables to customers (internal and external).

II. Measure the current performance of the process.

III. Analyze and determine the root cause(s) of the defects.

IV. Improve the process to eliminate defects.

V. Control the performance of the process.

Six Sigma statistical tools work like magic to uncover what you don't know. Yet you don't have to be a statistician to use them: you focus on selecting tools, using them, and analyzing data and let the specific software do the calculations. The five- phase process of DMAIC, described earlier in this chapter, uses a collection of tools and is a logic filter to lead you to the vital few factors affecting your process outcomes:

Define. Determines the project goals and deliverables to customers. (Internal and external)

Measure. Identifies one or more product or service characteristics, maps the process, evaluates measurement systems, and estimates baseline capability.

Analyze. Evaluates and reduces the variables with graphical analysis and hypothesis testing and identifies the vital few factors for process improvement.

Improve. Discovers variable relationships among the vital few, establishes operating tolerances, and validates measurements.

Control. Determines the ability to control the vital few factors and implements process control systems.

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Six Sigma is not another quality program. That's an important point to emphasize. Businesses exist for one purpose—to profitably serve customers. So it follows that any problem-solving initiative should do the same. Six Sigma uses your resources to fix identifiable, chronic problems. It proves its value by connecting outcomes to your bottom line. Quality programs lay a valuable foundation in creating a quality mindset. But ask yourself if any you've experienced have generated specific financial results like Six Sigma. It's very possible you'll answer,

"No," since a primary criterion for selecting Six Sigma projects is to return money to your balance sheet as the result of full-time efforts by dedicated resources.

Six Sigma is not theory. It's a practice of discovering the vital few processes that matter most. It defines measures, analyzes, improves, and controls them to tie quality improvement directly to bottom-line results.

Six Sigma is an active, involved effort that puts practical tools to work to root defects at all levels of your organization. It's not a theoretical exercise: you don't think about Six Sigma—you do it. Since the success of Six Sigma is directly linked to monetary outcomes, it generates real-world results. It uses the most readily available resources in an organization—its human assets. That means that positive, tangible results consistently show up wherever and whenever people are engaged in implementing Six Sigma techniques.

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3.1 DEFINE P H A S E

In the Define phase, we create a high-level process map to get an overview of the steps, events, and operations that make up the process. This helped us understand the process and verify the scope we defined in our charter. It is particularly important that our high-level map reflects the process as it actually is, since it serves as the basis for more detailed maps.

Pareto Analysis

We prioritized the opportunities for improvement by using Superior 2006 second semester scrap report. Cost was analyzed in order to know if the problem could be performed as a Six Sigma project.

Foundry (Total

shots) 1119322 9.21

Machining (Gross

mach) 1069676 1.8

Paint 1181961 2.6

Costs

RW 4.45 USD Foundry scrap 8.90 USD Machining USD

scrap 15.74

Final line USD

scrap 18.85

90% of defects %scrap

reduction Main

Defects (Scrap) RW

Wheels %

Scrap 50%

Reduction per

defect Cumm Scrap %

13.61

unit cost

scrap Total

F3 8388 0.75 0.37 13.24 13.24 23.30 195,440

F20 1780 0.16 0.08 13.53 13.16 23.30 41,474

F9 1437 0.13 0.06 13.55 13.09 23.30 33,482

F6 878 0.08 0.04 13.57 13.05 23.30 20,457

Others 1330 0.12 0.06 13.55 12.99 23.30 30,989

F21 293 0.03 0.01 13.60 12.98 23.30 6,827

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F11 261 0.02 0.01 13.60 12.97 23.30 6,081 14367

l/lachining

•Virgin)

F16 4690 0.42 0.21 13.40 12.76 15.74 73,821

F11 4301 0.38

n : 0.19

n^IR 13.42 12.57 15.74

1 R 7 A

67,698

G1 ouoo

2898 u.OJ

0.26 U. I D

0.13 13.48 ^{I O.HrO} 12.27 15.74 45,615 ^01.

F7 2750 0.25 0.12 13.49 12.15 15.74 43,285

F5 2684 0.24 0.12 13.49 12.03 15.74 42,246

F8 2541 0.23 0.11 13.50 11.92 15.74 39,995

F21 20461 0.18 0.09 13.52 11 15.74 32.204

F25 1383 0.12 0.06 13.55 11.76 15.74 21,768

26959

•Virgin)

F16 2987 0.27 0.13 13.48 11.63 18.85 56,305

F11 2065 0.18 0.09 13.52 11.54 18.85 38,925

F20 1071 0.10 ; 0.05 13.56 ¹ 20,188

F17 653 0.06 0.03 13.58 11.46 18.85 12,309

F4 515 0.05 0.02 13.59 11.44 18.85 9,708

F5 483 0.04 0.02 13.59 11.42 18.85 9,105

G1 481 0.04 0.02 13.59 11.40 18.85 9,067

F1 319 0.03 0.01 13.60 11.38 18.85 6,013

8574 4.46 2.23

Problem Description

Through the use of these Pareto charts (Fig. 3.1.1), we were able to identify that this defect is one of the primary causes of scrap generation. This became the focus of our Six Sigma project.

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Six Sigma project charter

After project selection, project cover sheet, project contents and project charter (Fig. 3.1.2) were made and then formed the project team.

Ng. scrap by Detect

Gantt chart

A Gantt chart (Fig. 3.1.3) is a visual project planning device used for production scheduling. A Gantt chart graphically displays time needed to complete tasks.

In order to follow a sequence, a Gantt chart was developed; it helps team members to perform activities punctually.

Fig. 3.1.2 Project Charter

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D Task name Srait Finish % completed 1 Stage 0: Information analysis

2 information review of the problem 15-Jan-07 100%

3 Problem *vheei style and process 15-Jan-07 i S2C07 100%

4 Stage J: Define - Project purpose and scope, to identify critical to quality fCTQ's)

5 Project selection (Y) 100':;

6 Project evaluation cost (Finance) 100%

7 Project charter 100%

8 Problem statement 100%

9 Project scope 100%

10 Process sigma level 100%

11 Team Members 100%

12 Financial benefits 100%

13 Project charter validation 100%

14 S i P O C (High level processflow) 100 = 3

15 Process capability 100%

16 Voice of the customer |¥QC) 100%

17 Critical to Quality (CTQ's) Jara-07 100%

13 Stage!!.- Measure - Data collection and real process information

19 Process flow 29-Jar,-07

20 Detailed process flow 100%

21 Process R T Y 100%

22 Data collection plan 100%

23 Type of data (Discrete or continuous) 100%

24 Sample size 100%

25 Measurement system 100%

26 Record sheet 100%

27 ;^ c s records 100%

28 G a q e R & R ll "

29 Stage ill.- Analyze - Root cause analysis

30 Process flow diagram 12-Fet-07 li 100?'!

31 Value added and non value added activities 12-Feb-07 2/16/2007 ! 0 0 %

32 Key process input and outputs variables 12 •-. 3/30*2007 100%

33 variables identification MfA Hi A

34 Stage IV,- Improve - Root cause solution implementation 35 identify means to remove the causes of the defects

7 2007 100%

36 Solution validation 9-Feb-07 4 / 1 * 2 0 0 7 100%

37 Modify the process to stay within the acceptable range 9-Feb-07 4/13/2007 100%

38 cost'benefit analysis

39 Stage V.- Control- Determine how to maintain the improvements

40 Control plan 9-Feb-07 4/13/2007 100?.

41 Project hand off 4/t6i2007 50/2007

Fig. 3.1.3 Gantt Chart

The multi-functional core team is formed of Production, Product Engineering and Quality personnel. The support team involved Industrial Engineering, Maintenance and mold shop.

The team meets a minimum of once a week from start to completion of the project (January 2007 - May 2007).

SIPOC

A SIPOC was made in order to document the process as a high level and visually show the process, identify boundaries, identify inputs and outputs

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S U P P L I E R INPUT P R O C E S S OUTPUT C U S T O M E R

Meltinq Melted

Aluminum

Mold shop Mold

Facilities Pressured Air, Molded

Wheel Maintenance Water, Liqht,

etc Molding Molded

Wheel Fettling C F E

A C H E S O N Mold Paint O R P A C Special Paint

Process Capability

In order to know if a process consistently makes a product that meets a customer specified specification range (Tolerance).

Binomial distribution is used due it is usually associated with recording the number of defective items out of the total number sampled.

Two process capabilities are shown, historical used as reference (Fig. 3.1.4).

Two process points are being measure due to defect registration (Fig. 3.1.5).

Historical data

Fig. 3.1.4 First Slope Capability 2006 2nd Semester.

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Fig. 3.1.5 Final Inspection Process Capability 2006 2nd Semester.

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Voice of the Customer

V O C focused feedback in the context of the customer's experience with the product or service, providing us clues about theirs expectations, perceptions and needs.

This Standard sets the Criteria that is to be used forjudging acceptable cosmetic Appearance of Wheels.

PAINTED & MACHINED FACED W H E E L S

ZONE B

Blemish Type Description / Definition Max Size mm Max# Max Size

mm Max#

Dirt or Foreign Particles

Visible foreign Material in the form of lint, dirt or

dust found in the paint and clear coat layers

1.0mm 3 1.5mm 1 per

window

Out gassing/

Finish Bubbles/

Clear coat Pops

Bubble remnants or small holes in the

surface finish.

1.0mm 3 1.5mm 1 per

window Out gassing/

Finish Bubbles/

Clear coat Pops

Bubble remnants or small holes in the

surface finish. >0.5mm 5 in 25mm x25mm Sq >0.5mm

8 in 25mm x25mm

Sq Scratches Scratches in Clear coat

visible at Arms Length.

5mm long x0.5mm

wide 2 5mm long

x0.5mm wide

1 per window

Chip whip Scratches under Clear Coat from Machining

Chips

Allowed Not Not

Allowed Not

Allowed Not Allowed Paint Runs &

Sags Visible at Arms Length. Not

Allowed Not

Allowed Not Allowed

Orange Peel Visible at Arms Length. Not

Allowed Not

Allowed Not Allowed Damage

(Dents) Anomalies in substrate

under Clear coat 0.5mm

Allowed Not

1 per window

Machining line

Machine lines on surface, visible at arm's

length. (Not Applicable to Painted wheels)

Allowed Not Not

Allowed Not

Allowed Not Allowed

Table 3.1.1 Painted & Machined Faced Wheels

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Critical to Quality (CTQ)

In order to identify measurable CTQ characteristic that will be improved, some pictures were taken (Fig. 3.1.6). It helps to determine the specification limits for our Y, also, any characteristic that satisfies or dissatisfies customer.

Fig. 3.1.6 Pictures taken from machine shop scrap and final inspection rework.

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In the Measure and Analyze phases, we created a detailed process map to help us identify problems in the process. Our improvement project is focus on addressing these problems

Process Map

First, we mapped the process to fully understand the activities and sequence of steps in order to identify Key Process Input and Output Variables (KPIVs and KPOVs). Value stream mapping objective is to optimize the process and waste

o l i m i n a t i n n

3.2 M E A S U R E P H A S E

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Type of data (Discrete or continuous) Definitions:

Continuous data is numerical information such that the numbers represent quantities. Thus, the number 14 for continuous data is twice the amount as the number 7. In theory, fractions of these quantities make sense, e.g., 1.3 days or 3.17 inches. In practice, if fractions of quantities, e.g., counts, do not make sense but the number of possible values is large, one can treat the data as continuous- like.

Discrete data is non-quantitative information. If numerical, the numbers are labels, e.g., social security numbers, rather than quantities. For this reason, a finite number of values are possible and the values cannot be subdivided meaningfully.

For example, the number of parts damaged in shipment produces discrete data because parts are either damaged or not damaged.

Based on the above definitions, our product data will be defined as discrete due they are classified as good or bad.

Detailed Process Map

On this map, we can realize all factors involved on the manufacturing.

Flow diagram for three coat wheels (Fig. 3.2.2)

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18

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Measurement System Analysis

Attribute Gage Repeatability & Reproducibility (Gage R & R) studies were performed to identify and correct measurement errors.

This Six Sigma tool increased the confidence in ensuring only good products are sending to our internal and external customers, consequently, also resulting to quick savings in material by reducing the probability of rejecting good products.

Fig. 3.2.3 Gage R&R Attribute Risk Analysis

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Cause and Effect Diagram (Fishbone)

The team used several graphical tools to visualize the problem and brainstorm the solution.

3.3 A N A L Y Z E P H A S E

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Fig. 3.3.2 Final Inspection Rejections F20 & F21

Fig. 3.3.3 First Slope F20 & F21 per wheel style

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Fig. 3.3.4 F20 & F21 Final Inspection per wheel style

Fig. 3.3.5 Scrap F20 & F21 per Inspection Point

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Fishbone Diagram

This fishbone diagram identified the primary causes of defects. This further reinforces the focus of our improvement efforts and in much more detail.

Fig. 3.3.6 Fishbone Diagram fro Mold Defect

A mold shop supervisor was integrated to the team. We realized he could help on this problem due the most of cases the mold before running is strongly related with none defects operation.

The team decided to analyze a specific part number. Using Pareto chart we identified the part number with the highest problem. At the chart attached (Fig.

3.3.7), Expedition part number was selected to analyze in detail.

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Fig. 3.3.7Pareto Chart of F20 & F21 First Slope in Expedition E/B Wheel

Fig. 3.3.8 F20 & F21 Expedition Rejection in First Slope & Final Inspection

Rejection in first slope and final inspection for Expedition wheel.

The team identified some primary causes listed as follows:

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1. Defects classification by quality. Final inspection and MRB.

2. Bad inspection mold. The mold had defects before running at casting deck.

3. Polish at mold shop after running. The molds are not well polished during maintenance at mold shop after production.

4. Lack of cosmetic inspection at casting deck.

Actions already taken for the above mentioned:

1. Different pictures were taken in both areas in order to identify those defects that were identified as a defect mold only Final inspection and MRB were advised. The purpose of this is to classify the defects correctly in order to know the problem as it is.

Fig. 3.3.9 Mold Defects

2. Bad inspection mold. The mold had defects before running at casting deck. The attached report is being performing in order to detect molds with defects when it starts running or during normal operation.

Currently all molds with defects before running will send back to mold shop for repairing.

Under normal operation a report had been done. It was indicating molds that needed to be repaired immediately because the damage on it, it was marked as "R" on report in the case of severe damage.

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3. Polish at mold shop after running. Normally all molds are sending back to mold shop for normal maintenance, several times molds are not well polished during maintenance because lack of feedback from production. Currently all Expedition molds are sending to mold shop with an as-cast wheel. It helps to mold shop technician to detect mold defects easier.

4. Lack of cosmetic inspection at casting deck. Some inspections are performing by casting technicians but they were not doing that well due other duties. Six activities were analyzed in order to eliminate them or enlarge samples frequency. Cpk's and stability were developed:

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Fig. 3.3.10 Process Capability specific on casting machines

Fig. 3.3.11 Process Capability Mg Percent on Casting Machine

Industrial Engineering study also was performed in order to make sure cosmetic inspection could be improved without affectation in other duties.

Process capability specific gravity on casting machines (using 95.0% confidence)

Frequency: Two times per shift per casting machine

Process capability Mg percent on casting machine (using 95.0% confidence)

Frequency: Once per shift per casting machine

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Hypothesis Testing

After measurement system corrections a testing the significance of improvements was performed, it was necessary for us to convert a practical issue to a statistical analysis. In short, "Hypothesis Testing gave us clear indication if what we are changing or modifying really works,"

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Fig. 3.3.12 Hypothesis Testing

Two-Sample T-Test and CI: Status 2, C7 Two-sample T for Status 2

C7 N Mean StDev S E Mean

Before 5 1.440 0.872 0.39

After 6 0.770 0.279 0.11

Difference = mu (before) - mu (after) Estimate for difference: 0.670000

95% lower bound for difference: -0.015293

T-Test of difference = 0 (vs. >): T-Value = 1.79 P-Value = 0.05 DF = 9 Both use Pooled StDev = 0.6174

On this statistical analysis we are comparing means before and after improvements were made, p value tells us with 95% of confidence that there is sufficient evidence to reject Ho, in others words, there is statistical difference among those two periods, mu before is bigger than after.

Ho: Ha: before

before after after

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Correlation Analysis

With this analysis, we were able to determine that there was a correlation between the final inspection and first inspection detection. It is known that good detection at first inspection correlates with final inspection because the wheels are not sending with defects until final inspection. (Pearson Correlation = 0.806).

Correlations: first slope rejection, final inspection rejection

Pearson correlation of first slope and final inspection rejections = 0.503 P-Value = 0.096

P-value tells us that at this time there is not correlation among those two inspection points so far. It is assumed that efforts made in first slope will reduce rejections on final inspection. A correlation is desirable.

In order to detect problems on wheels with mold defects, a record sheet was designed. It helps on tracking (e.g. wheel style, mold letter, casting machine date, shift and defect zone on wheel)

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The reports were used temporarily in these areas, first slope and final inspection.

Some wheel defects were analyzed during this study.

Fig 3.3.14 Mold Defects

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3.4 IMPROVE PHASE

An action plan was established for each wheel style in function of the problem on it.

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B E N C H M A R K I N G

Hungary plant is performing a rework in all wheels as a normal operation. This is to make sure good wheels at final inspection.

Mold defect is one of the main reasons of this rework.

Boron nitrate paint is being used by production in casting decks. This special paint helps the wheel during molding. It reduces mold defects.

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Process capability

A process capability is calculated one time all the possible solutions were implemented, on this case not all improvements are implemented yet.

Process capability is calculated in first slope inspection and final inspection.

Fig. 3.4.1 Actual Process Capability First Slope F20 & F21

Fig. 3.4.2 Actual Process Capability Final Inspection F20 & F21

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Financial analysis

As a rule, finance is the in charge of projects evaluation; we have established that project must be tracked by finance at least six months after project closing.

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In control phase, some rules were already established. All molds are being repaired if any defect is detected at casting deck. Quality inspection is more powerful than before.

Fig. 3.5.1 Daily Quality Inspection

A feasibility check list was created for new programs; it will help us since mold design to avoid problems on production.

3.5 C O N T R O L P H A S E

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Fig. 3.5.2 Feasibility Check List

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New records were implemented in casting decks, these records will help on defect detection besides casting deck personnel evaluation.

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Fig. 3.5.5 Reworks Tracking by Operator

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RECOMMENDATIONS

At the present the automotive industry has been an evolution in the aspects as Quality - Cost and Delivery, now the final customer determines the market laws to meet their expectations, that's why the Quality, Cost and Delivery of products and services have had to improve substantially.

As part as supplier's requirements, the ISO-TS16949 standard calls for continuous improvement methodologies and a structure in the supply chain companies that include Six Sigma and lean manufacturing.

In spite of the obstacles we had as far as time for the development of the project, the impacts in the plant were very positive in the economic aspect as well as the cultural change.

During the development of the project we found opportunity areas that were not considered initially, these arose due to the depth of the analysis that methodology six sigma demand. An additional benefit was achieved "yield" at final line from 87 to 94 percent.

Six sigma has demonstrated that its application is very useful as well as the support of the top management, to be open to the change and to be conscientious of the necessity of the improvement.

This project improvement shall be applied in the other plants of the corporation. Superior Industries has an internal web page in order to place all best proactive from different departments. This project will be placed a soon as we close it.

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REFERENCES

Breygfogle ill, Forrest w. (1999). implementing Six Sigma, Smarter Solutions

using statistical Methods. John Wiley & Sons. New York

Harry, Mikel J. (2000). The Vision of Six Sigma: A Roadmap for Breakthroug. 4a ed. Sigma Publishing Company. Phoenix, Arizona.

Costich-Sicker, Therese (2002) The Six Sigma Memory Jogger II G O A L / Q P C Salem NH.

Brassard, Michael (2002) The Black Belt Memory Jogger G O A L / Q P C Salem NH.

Mireles Linares, Juan Jose A S Q MBB (2007) A S Q conference Six Sigma introduction an executive

Coracides, Mario (2005) G E Capital Courier Simulation v 3.0.1

McCarty Thomas, Daniels Lorraine, Bremer Michel and Gupta Praveen. (2005) The Six Sigma Black Belt handbook Motorola University.

McGraw Hill USA

Eckes, George (2003) Six Sigma for everyone John Wiley & Sons, Inc. USA

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