Six Sigma And Customer Facing Processes(1).pdf

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Six-Sigma and Customer-Facing Processes

September 2003

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Table of Contents

Introduction

Six-Sigma and Its Impact

What is Six-Sigma?

Statistical Definition

Process Definition—The DMAIC Process

Application of Six-Sigma to Customer-Facing Processes

Is It Applicable?

To Which Processes Should One Apply Six-Sigma?

Implementation Process for a Six-Sigma Program

Key Success Factors

Conclusion

Appendix: Design for Six Sigma (DFSS)

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Introduction

Six-Sigma (or 6- s ) has been a widely used management tool to drive quality and process

improvement over the past 20 years. Championed by companies such as GE, this method-

ology has gained widespread acceptance, particularly in manufacturing processes to drive

toward “zero defects.” Hundreds of companies have deployed it for thousands of processes

to create billions of dollars of value for their shareholders. The impact of Six-Sigma is far-

reaching and is not limited only to bottom-line improvement. It has helped create common

terminology, common ways of defining and measuring key performance indicators (KPIs),

and a fact-based thinking that creates a common platform for scalability.

While the success of Six-Sigma is widely recognized within the manufacturing processes,

Six-Sigma also applies to other areas of an organization. As companies and management

have gained more experience with the concept, they have started using Six-Sigma for many

nonmanufacturing industries and processes such as accounts payable, R&D efficiency, and

so on, to drive substantial improvements in the bottom line and customer satisfaction.

However, Six-Sigma appears to have been underemployed for customer-facing processes 1

and functions, and its use is not well understood. If judiciously chosen and relentlessly

driven from the top management, deployment of Six-Sigma to customer-facing processes

could create a significant competitive advantage and have a strong bottom-line impact.

As companies are increasingly outsourcing noncore activities such as manufacturing and

transaction processing, one of the last bastions of sustainable competitive advantage is their

relationship with customers, employees, and partners. It is important to keep in mind that

customer-facing processes are a conduit between a company and these key constituents. As

such, consistency in meeting or exceeding customer expectations across every interaction

with customers, employees, and partners will impact a company’s success.

Many of the world’s leading companies such as GE, American Express, and JP Morgan have

used Six-Sigma methodologies to improve customer-facing processes, and the successes of

these companies clearly cannot be ignored.

The core emphasis of this paper is to provide an executive overview of when and how

Six-Sigma can be applied to customer-facing processes. In addition, it provides a brief

overview of the Six-Sigma methodology; a brief introduction to the Six-Sigma concept,

from both a mathematical and a process perspective; and outlines why, where, and how

Six-Sigma can be applied to customer-facing processes. In addition this paper outlines

some of the key success factors for the implementation of Six-Sigma to customer-facing

processes and key conclusions.

The term “customer-facing processes” has been used in this paper for processes that are customer-,

employee-, or partner-facing. It is used to differentiate these processes from “back-end processes”

such as manufacturing processes, transaction processing, and so on.

SIX-SIGMA AND CUSTOMER-FACING PROCESSES

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Six-Sigma and Its Impact

What Is Six-Sigma?

Six-Sigma techniques strive to reduce variability, with the underlying philosophy that

variability is undesirable. As an example, consider a retail bank, where a customer has

applied for an automobile loan. While the customer is applying for the loan, the bank

assures the customer that it will get back to the customer within one day. However,

because the bank has not controlled the variability of its loan application process, it

often takes more than a day, and sometimes as long as a week, to get back to the customer,

due to “unforeseen” circumstances such as the social security number being incorrectly

communicated by the bank to the credit rating agency or the bank teller noting down

the wrong spelling of the customer’s name. The customer in this case would be very

dissatisfied and, given the number of retail banking choices available, is likely to take

his or her business elsewhere.

Such incidences are frequent in everyday life. Oftentimes variability in these processes is

due to “common” or “natural” causes, that is, causes that are either unforeseen or cannot

be helped. For example, a flash blizzard that keeps all employees away from work for two

days without any electricity is a natural cause, and a small shift in machine tolerances is a

common cause. These causes are difficult to eliminate and will be present in some form

in any process. However, more often than not, variations occur due to “assignable” causes.

These are causes that occur due to human or machine errors, which, if identified and

eliminated, can significantly reduce the variability in the process. The objective of the

Six-Sigma methodology is to reduce such variability.

The definition of Six-Sigma varies, but the concept is the same—it is the application of

statistical techniques to detect, correct, and continuously improve variability or defects

in processes. The analysis for Six-Sigma could be understood at two different levels: a

statistical level and a process level.

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Statistical Definition

Consider a process 2 whose output is measured through some metric (cycle time, success

rate, and so on) that has mean “µ” (see Figure 1). Customers of this process expect the

output to be between the lower control limit (LCL) and the upper control limit (UCL).

The lower the variation of the process—that is, the lower the standard deviation, “ s ”—

the higher the chances that the output will lie between LCL and UCL. A process with 6- s

capability implies that only 3.4 times in a million occurrences 3 will the output be outside

the desired limits. Keep in mind that Six-Sigma is an arbitrary limit and, depending on

the process, requirements may be 4- s or 8- s . That said, to put Six-Sigma into perspec-

tive, consider what would happen if all processes were to be only 4- s —there would be

two unsafe landings at Chicago’s O’Hare International Airport every day (see Figure 1).

Most processes in everyday life lie between 2- s and 3- s .

Lower Control

Limit (LCL)

Upper Control

Limit (UCL)

At four-sigma (99.9% perfect) level performance, one should expect:

• Two unsafe landings at the O’Hare International Airport in Chicago every day

• 16,000 pieces of lost mail by the U.S. Postal Service every hour

• 50 newborns dropped by their doctor at birth every day

• 32,000 missed heartbeats per person each year

Figure 1: Statistical Definition of Six-Sigma.

For most executives, it may not be necessary to understand the mathematical details,

but rather to understand the underlying components that drive continuous improvement

of processes.

For a more detailed introduction to statistical definitions, refer to www.isixsigma.com/library/

content/Six-Sigma-newbie.asp or Operations Management: Strategy and Analysis by Lee. J. Krajewski

and Larry Ritzman.

Strictly speaking, 3.4 errors per million is based on allowance of 1.5 s variation to the mean, m.

While this is based on certain assumptions, most Six-Sigma practitioners use the 3.4 errors

per million as the standard.

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