Statistical Process Control_quality Focus Paper_Stephanie Thomas_GM588

STATISTICAL PROCESS CONTROL 1

AN ANALYSIS IN QUALITY MANAGEMENT

Statistical Process Control

An Analysis in Quality Management

Stephanie Thomas

Keller Graduate School of Management

Managing Quality_GM588

Professor Chester Legenza

June 6 2010

Author Note

Correspondence concerning this paper should be addressed to Stephanie Thomas 1885 Manor

Drive Apt A Union NJ 07083 amibriaolcom 917-291-0178



Introduction

Statistical Process Control is a quality assurance organizing system primarily utilized in

manufacturing Its methods and tools can be effectively employed in industries such as

Customer Service Hospitals Fire and Police Departments and Media Services Its major

philosophy of evaluation of process variation and its causes ensures product efficiency while

minimizing production output waste

(Raczynski 2009)This maturity model was developed by in the 1920rsquos by Dr Walter Sheward

of Bell Laboratories and expanded by Dr W Edwards Deming Dr Demingrsquos contribution is

credited for introducing the methodology to the Japanese after WWII

Its premise is the identification of process variation and characteristic measurement through

evaluating two classes of variation causes normal and assignable Normal causes of process

variation can be defined as changes due to common causes (random variation in processes

existing in all processes and does not require special attention) Normal causes could be a

temporary slowdown in production due to the utilization of seasonal employees Assignable

causes are unexpected problems adversely affecting processes and will cause them to go out of

control An example of this is a manufacturing machine due to erroneous specification is thrown

offline which causes defective product output The variation suggests there is abnormal

occurrence to the process and should be eradicated



Certain improvement tools are used in the monitoring and controlling of SPC and are essential

in maintaining continuous improvement efforts in managing quality throughout the process life

cycle

Control Charts and Histograms are the more common of these tools and serves as a visual

representation in monitoring the extent to which products meet specifications It detects whether

a process is statistically stable A change to normal process variance is demonstrated by shifts

within control limits These shifts will show its severity and assist the adjuster to where the

process should be controlled

(Quinn-Curtis Inc) httpwwwquinn-curtiscomOpening20X-Bar20R2JPG652010

20419 PM



Those applying SPC to industrial organizations in general have built process improvements on

top of SPC The focus of SPC is on removing variation caused by assignable causes As defined

here SPC is not intended to lower process variation resulting from natural causes Many

corporations however have extended their SPC efforts with Six Sigma programs Six Sigma

provides continuous process improvement and attempts to reduce the natural variation in

processes Typically Six Sigma programs use the ldquoSeven Tools of Qualityrdquo (Table 5) The

Shewhart Cycle (Figure 4) is a fundamental idea for continuous process improvement (DACS

2010)

Table 5 The Seven Tools of QualityTool Example of Use

Check Sheet To count occurrences of problems

Histogram To identify central tendencies and any skewing to one side or the other

Pareto Chart To identify the 20 of the modules which yield 80 of the issues

Cause and Effect Diagram

For identifying assignable causes

Scatter Diagram For identifying correlation and suggesting causation

Control Chart For identifying processes that are out of control

Graph For visually displaying data eg in a pie chart



Table 4 Some Applications of Statistics in Software EngineeringPhase Use of Statistics

Requirements Specify performance goals that can be measured statistically eg no more than 50 total field faults and zero critical faults with 90 confidence

Design Pareto analysis to identify fault-prone modules Use of design of experiments in making design decisions empirically

Coding Statistical control charts applied to inspections

Testing Coverage metrics provides attributes Design of experiments useful in creating test suites Statistical usage testing is based on specified operational profile Reliability models can be applied

Based on [Dalal et al 1993]

Literature ReviewThe Significance of SPC Examination

The driving force of successful organizations is their ability to ensure the highest possible level

of customer satisfaction through performance improvement The study of SPC and its effective

utilization well enable these organizations to accomplish the stabilization of product

development customer services or any output production process

Benefits of SPC are

Signals when a problem with the process has occurred

Detects assignable causes of variation

Accomplishes process characterization

Reduces need for inspection

Monitors process quality

Provides mechanism to make process changes and track effects of those

changes(Thompson)



Once a process is stable (assignable causes of variation have been eliminated) provides process

capability analysis with comparison to the product tolerance

Statistical techniques provide an understanding of the business baselines insights for process

improvements communication of value and results of processes and active and visible

involvement SPC provides real time analysis to establish controllable process baselines learn

set and dynamically improve process capabilities and focus business on areas needing

improvement SPC moves away from opinion-based decision making (Radice 2000)

These benefits of SPC cannot be obtained immediately by all organizations SPC requires

defined processes and a discipline of following them It requires a climate in which personnel

are not punished when problems are detected It requires management commitment (Demmy

1989)

The study of Statistical Process Control in organizations comes from the necessity for companies

that are vulnerable to process changes In management decisions SPC incorporates a wide range

of real time analysis to assist by establishing controllable process baselines something concrete

Current SPC Relevance

SPC relevancy is apparent in that currently its tools and procedures are sought by many

organizations to eliminate waste and create effective process procedures even in our current

business society



For instance in the Health and Human Services Departments SPC is seriously incorporated in

placing the correct amount of services in the areas where it is most needed In Banking its

invaluably is sought after to trim the waste operate more efficiently with less personnel

Statistical Process Control Best Practices

In SPC the incorporated best practices are as diverse as the different types of organizations that

have integrated the methodology This is an example of these in developing software projects

1 Development process - It is important to choose the appropriate development lifecycle process

to the project at hand because all other activities are derived from the process For most modern

software development projects some kind of spiral-based methodology is used over a waterfall

process There are several choices including the Rational Unified Process (RUP) IBMreg Global

Services Method and eXtreme Programming (XP) Having a process is better than not having

one at all and in many cases it is less important on what process is used than how well it is

executed The commonly used methodologies listed above all contain guidance about how to

execute the process and templates for artifacts In addition the RUP has a series of books that

describe the best practices for using RUP [1][2][3][4] although if you do not choose to use RUP

these books still provide an excellent source of best practices It is also possible to add plugins to

the RUP For a list of available plug-ins see Plug-in Central

2 Requirements - Gathering and agreeing on requirements is fundamental to a successful

project This does not necessarily imply that all requirements need to be fixed before any

architecture design and coding are done but it is important for the development team to

understand what needs to be built Quality requirements are broken up into two kinds functional



and non-functional A good way to document functional requirements is using Use Cases Note

that Use Cases are used for non-OO projects A definitive book on the subject of use cases is by

Armour and Miller [5] Non-functional requirements describe the performance and system

characteristics of the application It is important to gather them because they have a major impact

on the application architecture design and performance See the non-functional requirements

checklist on the Construx Web site

3 Architecture - Choosing the appropriate architecture for your application is key Many times

IBM is asked to review a project in trouble and we have found that the development team did not

apply well-known industry architecture best practices A good way to avoid this type of problem

is to contact IBM Our consultants can work side by side with your team and ensure that the

projects get started on the right track Tried and true practices are called patterns and they range

from the classic Gang of Four [6] patterns Java patterns [7] to EJB design patterns [8] Suns

equivalent is the Core J2EE Patterns catalog [9] Many projects fail as discussed in the

introduction The study of these failures has given rise to the concept of antipatterns They are

valuable because they provide useful knowledge of what does not work and why

4 Design - Even with a good architecture it is still possible to have a bad design Many

applications are either over-designed or under-designed The two basic principles here are Keep

it Simple and information hiding For many projects it is important to perform Object-Oriented

Analysis and Design using UML There are many books on UML but we recommend UML

User Guide [11] and Applying UML and Patterns [12] Reuse is one of the great promises of

OO but it is often unrealized because of the additional effort required to create reusable assets



Code reuse is but one form of reuse and there are other kinds of reuse that can provide better

productivity gains

5 WebSphere application design - IBM has extensive knowledge of the best practices and design

patterns for the WebSphere product family Each project is different and our consultants have the

experience to help you There is still a tremendous return on investment (ROI) even if you only

use the consultants for a short time because you save the costs later in the project Our experts

have also published a great deal of this wisdom including considerations for high-performance

Web sites and guidelines for autonomic computing

6 Construction of the code - Construction of the code is a fraction of the total project effort but

it is often the most visible Other work equally important includes requirements architecture

analysis design and test In projects with no development process (so-called code and fix)

these tasks are also happening but under the guise of programming A best practice for

constructing code includes the daily build and smoke test Martin Fowler goes one step further

and suggests continuous integration that also integrates the concept of unit tests and self-testing

code Note that even though continuous integration and unit tests have gained popularity through

XP you can use these best practices on all types of projects I recommend using standard

frameworks to automate builds and testing such as Ant and JUnit

7 Peer reviews - It is important to review other peoples work Experience has shown that

problems are eliminated earlier this way and reviews are as effective or even more effective than

testing Any artifact from the development process is reviewed including plans requirements

architecture design code and test cases Karl Wiegers paper on the Seven Deadly Sins of



Software Reviews explains the correct ways to perform peer reviews Peer reviews are helpful in

trying to produce software quality at top speed

8 Testing - Testing is not an afterthought or cutback when the schedule gets tight It is an

integral part of software development that needs to be planned It is also important that testing is

done proactively meaning that test cases are planned before coding starts and test cases are

developed while the application is being designed and coded There are also a number of testing

patterns that have been developed

9 Performance testing - Testing is usually the last resort to catch application defects It is labor

intensive and usually only catches coding defects Architecture and design defects may be

missed One method to catch some architectural defects is to simulate load testing on the

application before it is deployed and to deal with performance issues before they become

problems

10 Configuration management - Configuration management involves knowing the state of all

artifacts that make up your system or project managing the state of those artifacts and releasing

distinct versions of a system There is more to configuration management than just source

control systems such as Rational Clearcase There are also best practices and patterns [13] for

configuration management

11 Quality and defects management - It is important to establish quality priorities and release

criteria for the project so that a plan is constructed to help the team achieve quality software As

the project is coded and tested the defect arrival and fix rate can help measure the maturity of

the code It is important that a defect tracking system is used that is linked to the source control



management system For example projects using Rational ClearCase may also use Rational

ClearQuest By using defect tracking it is possible to gauge when a project is ready to release

12 Deployment - Deployment is the final stage of releasing an application for users If you get

this far in your project - congratulations However there are still things that can go wrong You

need to plan for deployment and you can use a deployment checklist on the Construx Web site

13 System operations and support - Without the operations department you cannot deploy and

support a new application The support area is a vital factor to respond and resolve user

problems To ease the flow of problems the support problem database is hooked into the

application defect tracking system

14 Data migration - Most applications are not brand new but are enhancements or rewrites of

existing applications Data migration from the existing data sources is usually a major project by

itself This is not a project for your junior programmers It is as important as the new application

Usually the new application has better business rules and expects higher quality data Improving

the quality of data is a complex subject outside the scope of this article

15 Project management - Project management is key to a successful project Many of the other

best practice areas described in this article are related to project management and a good project

manager is already aware of the existence of these best practices Our recommended bible for

project management is Rapid Development by Steve McConnell [14] Given the number of other

checklists and tip sheets for project management it is surprising how many project managers are

not aware of them and do not apply lessons learned from previous projects such as if you fail

to plan you plan to fail One way to manage a difficult project is through timeboxing



16 Measuring success - You can measure your development process against an industry standard

known as the Capability Maturity Model (CMM) from the Software Engineering Institute at

Carnegie Mellon University Most projects are at level 1 (initial) If you implement the best

practices described above and the guidelines in the companion article Guide to Running

Software Development Projects then you could be well on the way to achieving a higher

maturity level and a successful project (Perks 2003)

Errors in Implementation and Control

Generally errors are made during these processes are caused by lack of information and

procedure unclear specification improper training none existent documentation and inaccurate

calculation of data In manufacturing missing incorrect parts or incorrect processing affects the

efficiency level of production These errors can affect cost inefficiency by causing a high rework

level

According to Douglass C Fair (Fair 2006) some mistakes made during the SPC implementation

and control processes are not training everyone in SPC who is responsible for maintaining

quality processes lack of documentation (charts) Segregating control charts from the people

who utilize them hampering the effectiveness of the SPC lead by not allowing full

communication of adverse occurrences and using SPC for quality control without justification

Errors caused by lack of knowledge to prioritize processes negative reaction of operators and

middle managers lack of knowledge on what to measure and how to measure in a certain

process lack of SPC training and an inadequate measurement system in place



Application Demonstration

This analysis gathered from a scholarly source written by (DQ Cai 2001) discusses the

application and effects of SPC (Statistical Process Control) combined with EPC (Engineering

Process Control) in a CMIS (Computer Integrated Manufacturing System) environment

SPC more identifiably used in manufacturing environments and is effective in maintaining

product variation The automation of SPC in an automated manufacturing environment holds

many issues as well as advantages

Traditional SPC methods compromised in an automated system such as manual equation

analysis and chart interpretation due to the ability of computation and interpretation through

computer automation However the capability of automation allows for an abundance of data

methods and computations that are stored and delivered in record time This advantages save

physical resources response time and money

There may no longer be an urgent demand for engineers to fully understand the statistical

background necessary in a manual SPC environment CMIS will be able to generate charts and

signals after completing a background analysis The issue of data independence in manual SPC is

non-existent in CMIS because of the ability of data co-existence and integration

Combining SPC and EPC methodology is complementary because SPC will improve

performance but cannot maintain it In contrast EPC can maintain performance but cannot

improve it



Conclusion and Reflection

In most SPCEPC CMIS environments strengthens the stability overall due to

more robust and effective output

operational errors are minimized

high level of process stability

reduction of process variability

reduction in system re-adjustment time

outstanding computation power

an abundance of available data

Industry and organizations that incorporate these methodologies stand a greater chance of being

successful in regards to customer satisfaction and accurate throughput These elements are

essential in maintaining a high level of quality in production



References

DQ Cai M X (2001) SPC in an Automated Manufacuring Environment International

Journal of Computer Integrated Manufacturing 206-211

DACS (2010) Statistical Process Control Retrieved from goldpracticescom

httpwwwgoldpracticescompracticesspcindexphp

Demmy W (1989)

Demmy W (1989) Statistical Process Control in Software Quality Assurance Proceedings of

the IEEE National Aerospace and Electronics Conference(NAECON 1989 (pp pp 1585-1590)

Fair D C (2006) Inside Quality Insider Retrieved from The Top 10 SPC Mistakes

httpwwwqualitydigestcominsidequality-insider-articletop-10-spc-mistakes

Perks M (2003) IBM Retrieved from ibmcom

httpwwwibmcomdeveloperworkswebspherelibrarytecharticles0306_perksperks2html

Quinn-Curtis Inc (nd) QCSPCChart CF - SPC Control Chart Tools for Net Compact

Framework Retrieved from quinn-curtiscom

httpwwwquinn-curtiscomQCSPCChartCFProdPagehtm

Raczynski B (2009) Is Statistical Process Control Applicable to Software Development

Processes

Radice R (2000) Statistical Process Control in Level 4 and 5 Organizations Worldwide

Proceedings of the 12th Annual Software Technology Conference



Thompson B M (nd) Center for System Reliabulity Retrieved from reliabilitysandiagov

httpreliabilitysandiagovManuf_StatisticsStatistical_Process_Control

statistical_process_controlhtml

1 Ambler Scott and Constantine Larry The Unified Process Inception Phase ISBN 1929629109

2 Ambler Scott The Unified Process Elaboration Phase ISBN 1929629052

3 Ambler Scott and Constantine Larry The Unified Process Construction Phase ISBN 192962901X

4 Ambler Scott and Constantine Larry The Unified Process Transition and Production Phases ISBN 157820092X

5 Armour Frank and Miller Granville Advanced Use Case Modeling ISBN 0201615924

6 Gamma E Helm R Johnson R and Vlissides J Design Patterns ISBN 0201633612

7 Grand Mark Patterns in Java ISBN 0471258393

8 Marinescu Floydd EJB Design Patterns ISBN 0471208310 PDF file

9 Alur D Crupi J Malks D Core J2EE Patterns ISBN 0130648841 also see httpjavasuncomblueprintscorej2eepatterns

10 IBM Redbooks Search for patterns AND e-business

11 Booch G Rumbaugh J and Jacobson I The Unified Modeling Language User Guide ISBN 0201571684

12 Larman Craig Applying UML and Patterns ISBN 0130925691

13 Berczuk Stephen and Appleton Brad Software Configuration Management Patterns ISBN 0201741172

14 McConnell Steve Rapid Development ISBN 1556159005





Introduction

Statistical Process Control is a quality assurance organizing system primarily utilized in

manufacturing Its methods and tools can be effectively employed in industries such as

Customer Service Hospitals Fire and Police Departments and Media Services Its major

philosophy of evaluation of process variation and its causes ensures product efficiency while

minimizing production output waste

(Raczynski 2009)This maturity model was developed by in the 1920rsquos by Dr Walter Sheward

of Bell Laboratories and expanded by Dr W Edwards Deming Dr Demingrsquos contribution is

credited for introducing the methodology to the Japanese after WWII

Its premise is the identification of process variation and characteristic measurement through

evaluating two classes of variation causes normal and assignable Normal causes of process

variation can be defined as changes due to common causes (random variation in processes

existing in all processes and does not require special attention) Normal causes could be a

temporary slowdown in production due to the utilization of seasonal employees Assignable

causes are unexpected problems adversely affecting processes and will cause them to go out of

control An example of this is a manufacturing machine due to erroneous specification is thrown

offline which causes defective product output The variation suggests there is abnormal

occurrence to the process and should be eradicated





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20419 PM










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Documents

Statistical Process Control_quality Focus Paper_Stephanie Thomas_GM588