Integrating Six-Sigma Methods and Lean Principles to
106
Integrating Six-Sigma Methods and Lean Principles to Reduce Variation and Waste in Delivery Performance to the Customer (Production System) By E. Dan Douglas Submitted to the System Design and Management Program in Partial Fulfillment of the Requirements for the Degree of Master of Science in Engineering and Management BARKER At The MASSACHUSETTS INSTITUTE OF TECHNOLOGY Massachusetts Institute of Technology APR 17 2003 February 2003 LI I LIBRARIES 2003 E. Dan Douglas. All rights reserved The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part. Signature of Author: E. Dan Douglas System Design and Management Program January 2003 Certified by: Prof. Thomas Roemer Thesis Supervisor MIT Sloan School of Management; Operations Management Accepted by: Steven D. Eppinger Co-Director, LFM/SDM GM LFM P~so of Management Science and Engineering Systems Accepted by: Paul A. Lagace Co-Director, LFM/SDM Professor of Aeronautics & Astronautics and Engineering Systems 1
Integrating Six-Sigma Methods and Lean Principles to
Integrating Six-Sigma Methods and Lean Principles to Reduce
Variation and Waste in
Delivery Performance to the Customer (Production System)
By
E. Dan Douglas
Submitted to the System Design and Management Program in Partial
Fulfillment of the Requirements for the Degree of
Master of Science in Engineering and Management BARKER
At The MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Massachusetts Institute of Technology APR 17 2003 February 2003
LI
I LIBRARIES 2003 E. Dan Douglas. All rights reserved
The author hereby grants to MIT permission to reproduce and to
distribute publicly paper and electronic copies of this thesis
document in whole or in part.
Signature of Author:
January 2003
Thesis Supervisor MIT Sloan School of Management; Operations
Management
Accepted by:
GM LFM P~so of Management Science and Engineering Systems
Accepted by:
Professor of Aeronautics & Astronautics and Engineering
Systems
1
2
Integrating Six-Sigma Methods and Lean Principles to Reduce
Variation and Waste in
Delivery Performance to the Customer (Production System)
By
E. Dan Douglas Submitted to the System Design and Management
Program on December 13, 2002 in
Partial Fulfillment of the Requirements for the Degree of
Master of Science in Engineering and Management
Abstract Shortening order-to-delivery (OTD) times is a strategic
business goal for
companies in many industries and the automotive industry in
particular. Advantages of
shorter delivery times include lower inventory levels, less
obsolescence, and the ability
to respond more quickly to changing markets. As a consequence, many
companies are
in the process to reduce average OTD times and employ sophisticated
measurement
systems to determine the average delivery time from customer order
to customer
delivery.
While reducing OTD times may lead to considerable efficiency
improvements
within an enterprise, the customers may still benefit very little
from such improvements.
As a consequence such strategies often fail to exploit the key
strategic advantage of
increasing customer satisfaction. The customer's focus is not so
much on average
delivery times, but on variation around the average delivery times.
In many scenarios,
low variation is much more crucial to the customer than a low
average OTD time. Yet
many measurement systems today have an almost exclusive focus on
averages.
This is also the case in the system being studied here. In the case
of automotive
original equipment manufacturers (OEMs), only some customers
receive the vehicle
when they expect the vehicle due to the current system
architectures and the system
dynamics of these architectures and processes. Some customers
receive vehicles
early, while other customers receive vehicles late. The customers
experience the
3
variation from the average and are not satisfied with current
delivery performance. Delivery variation must be reduced to better
meet individual customer needs.
The production system in automotive manufacturing presents a unique
opportunity to study both the source and impact of delivery time
variation and compare it to average delivery performance. The
thesis investigates delivery variation performance in the
production system of a major OEM. The relationship of the
production system to the OTD system is discussed. The production
system is decomposed into three subsystems: order, build, and test,
inspect, rework (TIR). The TIR subsystem was determined to be the
largest contributor to delivery time variation in the production
system. Data was collected and analyzed at the system level and the
subsystem level to enable a comparison between the average delivery
performance and the delivery variation performance. The TIR system
has the most delivery variation, but the best average delivery
performance. The significance is that the TIR subsystem was not
seen as a source of customer dissatisfaction for delivery of
vehicles from the production system. Good performance in average
vehicle build time was assumed to yield good delivery performance
and customer satisfaction. The possible causes for this variation
are outlined for the TIR subsystem.
The TIR subsystem is also the production subsystem with the most
waste from the Lean enterprise perspective. The waste in the TIR
subsystem causes variation in delivery. This variation then causes
waste in the relation between the enterprise and customer. There is
a dynamic in which the internal waste causes increased waste
outside the production system. The cost for this waste is incurred
by the customer and enterprise and is discussed as part of this
work.
Thesis Supervisor: Prof. Thomas Roemer Title: MIT Sloan School of
Management; Operations Management
4
Acknowledgements
The Massachusetts Institute of Technology (MIT) System Design
and
Management (SDM) Program provided me with the opportunity to
improve my
knowledge and skill in business management, leadership,
engineering, and system
design over the past two years. The completion of program
requirements along with a
full work schedule required many long days and short nights. The
opportunity was made
possible by the support of those around me.
I would first like to recognize Sharon, my wife, who supported me
and sacrificed
her own needs during this time. Sharon worked to make sure the time
I spent with her
and Zoe, my daughter, was quality time, not time spent taking care
of the many
distractions of everyday life. Sharon has supported me throughout
and has been flexible
to the needs of my school and work schedules. Thank you, Sharon,
for taking care of
Zoe and me.
My daughter Zoe turned four this fall. I was out of town for four
months last fall
when Zoe turned three. Zoe I appreciate your patience with dad
while I have been
away. I see how upset it makes you when dad does not come home at
night. Hopefully
the long periods away will be few now that school has ended. We
have more time to
laugh and play and learn.
I would also like to thank Thomas Roemer, my thesis supervisor, for
the time we
spent together working on my thesis material and other subjects of
interest to me.
Thomas's operations expertise, previous works, and interest in the
automobile industry
allowed insight into the subject of this thesis. Thomas provided me
with ideas and
insight that made a difference in the way I think about the subject
matter.
The work I completed in the SDM program was made easier by the
support of my
plant management, product design management and peers. I never
questioned their
dedication to make me successful. They provided me with financial
support to complete
the SDM program and with individual support when there was more
work to be
completed than hours in the day. I would like to especially
recognize the persons in the
5
IT department that services my facility for their support of my
crazy requests and for
developing tools to help gather and analyze data for this thesis.
The IT team has
provided outstanding customer service and team members have become
friends I look
forward to seeing each day. Thank you.
Last, I would like to thank my peers in the SDM program. You give
new meaning
to the phrase "work hard, play hard". It is good to know you can
have so much fun and
receive so much support from those who are going through similar
challenges as
yourself. Thank you for helping me to keep things in
perspective.
6
Biography
E. Dan Douglas is a Certified Six-Sigma Master Black Belt and a
Product
Engineering Manager at a major automobile manufacturing company. He
is a leader in
the operating team for two manufacturing and assembly facilities
within the company.
Dan has held various positions within the company for the past 10
years including
design engineer, development engineer, engineering supervisor,
Six-Sigma black belt,
manufacturing plant resident engineer, and program manager. Dan has
worked on
several different vehicle systems and subsystems at the
company.
Dan worked for the DANA Corporation for five years prior to joining
his current
company. Dan held engineering positions in several different
departments while he was
at DANA.
Dan graduated from Kettering University in 1993 as a Bachelor of
Science in
Mechanical Engineering.
Table of Contents A b s tra c t
...........................................................................................................................
3
Acknowledgem ents
.....................................................................................................
5
A sim ple
case........................................................................................
17
3. Delivery variation
background.............................................................
21
3.2. State of the current production system
................................................. 23
3.3. Critical to delivery
(CTD)......................................................................
25
3.3.2. Delivery tim e
variation.........................................................................
27
3.4. Who are the customers and what do the customers
want?..................30
3.5. The cost of delivery
variation................................................................
34
3.6. System
costs........................................................................................
35
3.7. M otivation to drive change in the system
............................................ 42
4. M ethods
..............................................................................................
45
4.1.1. Define
phase.......................................................................................
47
4.1.3. Analyze
phase.....................................................................................
50
9
4.1.5.
Controlphase........................................................................................52
4.2.1. Elimination of waste
..............................................................................
54
4.2.2. Value stream mapping
.........................................................................
57
4.3. Integrating Lean and Six-Sigma to find sources of variation
and waste ... 58
5. Automotive production system analysis
............................................... 59
5.1. Automobile OEM production system
description.................................. 59
5.2. Process maps and system decomposition
........................................... 60
5.2.1. Order-to-Delivery process p
............................................................
61
5.2.2. Production process map
.......................................................................
61 5.3. Delivery time variation measurement
system..................63
5.3.1. Measurement system operational
definition......................................... 63
5.3.2. Measurement system process
map......................................................66
5.4. Current performance
...........................................................................
67
5.5. Cost associated with the current performance
..................................... 70
5.6. Analysis of variation in the current system
........................................... 72 5.7. TIR subsystem
possible sources of variation...................80
5.7.1. TIR subsystem
decomposition............................81
5.7.2.
TRsubsystemprocessmap.............................................................
83
5.7.3. Cause and effect diagram for the TIR
subsystem................84 5.7.4. C&E
marix......................................86
5 .7 .4 . C & E m atrix
...............................................................................................
86 5.8. Analysis of variation in the TIR
subsystem....................90
5.8.1. Measurement system for key possible sources of variation in
the TIR
subsystem
...........................................................................................
. 92
5.8.2. Target reduction in variation in the TIR subsystem
.............................. 92
6. Conclusions and recommendations
..................................................... 93
6 .1. C
onclusions.........................................................................................
. 93
6.2.
Recommendations...................................................................................96
G lo s s a ry
......................................................................................................................
10 1 R e fe re n ce s
..................................................................................................................
10 3 E n d n o te s
.....................................................................................................................
1 0 5
10
Table
11
Figure
Figure
Figure
Figure
Figure
Figure
Figure
Figure
Figure
Figure
Figure
Figure
Figure
1
2
3
4
5
6
7
8
9
10
11
12
13
of Figures Order-to-Delivery system at the first level of
decomposition..................22
OTD System with production system at the second level of
decomposition
...........................................................................................................
. . 2 5
Customer and enterprise
costs.............................................................
35
Example of an initial OTD delivery variation plot
................................... 49
Example of an improved OTD variation plot as compared to the
Measure
P ha se
................................................................................................
. . 5 3
System dynamic waste / variation
loop................................................. 56
Overview of the automotive Order-to-Delivery system
.......................... 61
High level process map of the overall automotive production system
from
the time the plant receives the order until the order is
shipped............. 62
Days of delivery variation (SPAN) for 95% of the
volume......................65
Measurement system map
....................................................................
66
Variation from manufacturing order date to actual ship date for one
day of
production system orders (short term variation for the entire
production
process) 8 days
..................................................................................
. . 68
Overall production system variation for 95% of the total population
over 100
days of production
orders......................................................................69
Overall delivery average (expectation) for 95% of the population
over 100
days of production orders
......................................................................
70
Relative inventory cost for delivery
variation......................................... 71
Variation from manufacturing order date to start of production date
for one
day of production (short term variation for the order subsystem of
the
production system )
...............................................................................
73
Figure
Figure
16
17
Figure 18 Variation from start of build date to end of build date
for one day of
production (short term variation for the build subsystem of the
production
syste m
)..................................................................................................
. 74
Figure 19 Variation for the test, inspect, and rework subsystem
prior to the actual ship
d a te
.....................................................................................................
. . 7 5
Figure 20 Delivery variation performance for the entire system and
each of the three
subsystems at the first level of
decomposition...................................... 76
Figure 21 Average delivery performance for the production system
and each of the
three subsystems at the first level of decomposition
............................ 77
Figure 22 Correlation between order and build subsystem delivery
variation........78
Figure 23 Correlation between order and TIR subsystem delivery
variation..........79
Figure 24 Correlation between build and TIR subsystem delivery
variation .......... 80
Figure 25 Process map for the TIR subsystem (some steps aggregated
to simplify the
process m
ap).......................................................................................
. . 84
Figure 26 Cause and effect diagram for the TIR
subsystem..................................86
Figure 27 TIR subsystem cause and effects matrix with the top 6
possible causes
h ig h lig hte d
.............................................................................................
. 8 9
Figure 28 Problems per 100 vehicles built measured in a major
inspection area......91
Figure 29 System dynamic waste / variation
loop................................................. 95
Figure 30 Expected long term performance of delivery variation in
the production
system with and without waste in the system
............................................ 96
Figure 31 Example of a TIR subsystem delivery performance
chart......................97
12
Shortening order-to-delivery (OTD) times is a strategic business
goal for
companies in many industries and the automotive industry in
particular. Advantages of
shorter delivery times include lower inventory levels, less
obsolescence, and the ability
to respond more quickly to changing markets. As a consequence, many
companies are
in the process to reduce average OTD times and employ sophisticated
measurement
systems to determine the average delivery time from customer order
to customer
delivery.
While reducing OTD times may lead to considerable efficiency
improvements
within an enterprise, customers may still benefit very little from
such improvements. As
a consequence such strategies often fail to exploit the key
strategic advantage of
increasing customer satisfaction. For example, a restaurant may
have very short
average delivery times between customer orders and delivery,
indicating that crucial
resources, such as available space are efficiently employed. The
sole focus on average
delivery times however cannot distinguish between a system where
allindividual dishes
arrive in a short time and a system where the dessert arrives
before the appetizer. In
other words, the customer's focus is not so much on average
delivery times, but on
variation around the average delivery times. In many scenarios, low
variation is much
more crucial to the customer than a low average OTD. Yet many
measurement systems
today have an almost exclusive focus on averages.
13
This is also the case in the system being studied here. In the case
of automotive
original equipment manufacturers (OEMs), only some customers
receive the vehicle
when expected due to the current system architectures and the
system dynamics of
these architectures and processes. Some customers receive vehicles
early, while other
customers receive vehicles late. The customers experience the
variation from the
average and are not satisfied with current delivery performance.
Delivery variation must
be reduced to better meet individual customer needs.
The OTD system has a few key subsystems at the first level of
decomposition of
the system architecture. These subsystems are the order system, the
production
system, and the distribution system. The production system in
particular has been
optimized to provide the lowest average vehicle build time, but
delivery variation in the
overall production subsystem is not emphasized. The production
system in automotive
manufacturing presents a unique opportunity to study both the
source and impact of
delivery time variation and compare it to average delivery
performance.
1.2. Research
The research follows the Six-Sigma DMAIC (Define, Measure, Analyze,
Improve,
and Control) methodology to quantify average delivery performance
and delivery
variation performance in the production system. The define,
measure, and analyze
(DMA) phases will be used to guide collection and analysis of the
data from the
production system of an automotive OEM. Lean enterprise principles
and tools are
integrated with Six-Sigma methods to identify the sources of
variation and help define
waste elimination that will improve delivery variation
performance.
14
The improve and control (IC) phases are not part of this thesis.
These two
phases are highly dependent on resources for implementation that
are outside the
control of the author. Recommendations will be made about how the
improve and
control phases could be completed at the end of the thesis.
1.3. Thesis outline
The thesis begins with a discussion of average performance and
variation
performance in delivery of products and services in section two.
Section three covers
general background of the order-to-delivery (OTD) system using
several industries for
illustration. The expectations and costs for the key stakeholders
are also discussed in
section three. Section four contains an outline of the analysis
method using the Six-
Sigma DMAIC process and Lean principles and tools. Section five
contains the specific
data for the automotive OEM production system being used as a case
in this research.
The conclusions and recommendations in section six summarize the
significant results
of the data analysis and outline the significant opportunities for
improvement.
15
16
variation
The important difference between average performance and
variation
performance is easier to understand when illustrated using a
familiar experience. A
common situation used to explain the difference is the example of a
group of students in
a classroom. The room is kept at 50 degrees (F) for the first four
hours of the day. Most
students find this too cold and uncomfortable. The students go to
lunch and while they
are at lunch, the heating system turns on and heats the room to 90
degrees. The
students return from lunch and spend the next four hours in 90
degree heat. The
students are uncomfortable in the heat all afternoon.
The first four hours at 50 degrees and the second four hours at 90
degrees gives
an average temperature of 70 degrees. The students were
uncomfortable throughout
the day in a classroom that had an average temperature of 70
degrees. 70 degrees is
normally a very comfortable temperature for the environment, except
the students did
not experience the average temperature. The students experienced
the variation around
the average temperature of 70 degrees in the classroom. The
variation in this example
was -20 degrees in the morning and +20 degrees in the
afternoon.
Students in the classroom expect a temperature of 70 degrees. A
variation of one
or two degrees on either side is probably acceptable to the
students. Beyond a few
17
degrees of temperature variation the students will start to
experience more discomfort
with greater variation from the 70 degree average. Having little
temperature variation in
this case is important to the students.
Variation is a consideration in systems that complete a process
more than once.
Temperature variation in a class room was considered in the
previous example. Other
cases involve machining parts (variation of critical dimensions),
preparing food
(variation of ingredients), and sorting luggage going to different
airliners (variation of
quality) for example. These systems are all subject to variation.
There are several cases
of variation in delivery systems for products and services too. Two
such cases are
outlined in section 2.2.
performance variation
Delivery variation occurs when a product or service is not
delivered at the
expected (or average) delivery time. A product delivered before the
expected delivery
time for the system is early; deliveries after the expected
delivery time are late
deliveries. A couple common examples of delivery variation that
will help to clarify this
concept are presented in this section
The first example is that of a customer buying a new car from a car
retailer.
Customers are told to expect a new car to be delivered in six weeks
(for example) when
a customer orders a new car. The expected delivery time allows the
customer time to
sell their old car, prepare a new loan, and prepare to insure the
new car.
18
What happens if the car is early? The customer probably has a used
car they
were targeting to sell in the sixth week, but not a couple weeks
early. The customer
does not likely have the financing and insurance prepared for an
early delivery either.
What happens when the car is delivered late? The customer has sold
the used
car and has to find another way to get around. The loan and
insurance terms may have
already started, or the terms may change based on market
conditions.
Either early or late delivery creates a problem for individual
customers buying a
car. The car retailer gives the six week estimate based on their
experience of the
average delivery time for all of their customers. Only some of
their customers
experience this average delivery time. Many of their customers
experience either early
or late deliveries. The car retailer thinks they are performing
well based on the average,
but the customers do not.
The second example is for a person purchasing a house. When a
person
purchases a house, the person expects to close (or take ownership)
on a predetermined
date. Several items must be completed for the closing to take
place. The loan, title, and
closing documents must all be ready. Homeowners insurance needs to
be in place too.
The transfer of ownership will be delayed if any of these items are
not ready at
closing. Some closings are delayed due to one or more items being
delivered late. The
buyer already has money invested in other parts of the closing
package that cost the
home buyer when the closing is delayed.
19
On average, the loan, title, closing documents, and insurance are
ready for the
closing. Why are closings delayed? There is variation in the
delivery for each of these
items. The person purchasing the home may experience average
performance on most
of these deliveries, but variation on one item can cause the
closing to be delayed. The
loan company, title company, mortgage broker, and insurance company
all deliver
average performance, but the individual home buyers do experience
variation around
the expected deliveries. No wonder it can be so frustrating to buy
a home.
The variation in both of these cases comes from the system in place
to deliver
these products. Each system is composed of several subsystems. It
becomes difficult to
predict individual delivery performance when one or more of the
subsystems are a large
source of variation. The goal of this thesis is to analyze the
variation in a production
delivery system and compare it to the average delivery performance
of the production
delivery system. Corrective actions can be taken in the system to
reduce the delivery
variation performance once the source of delivery variation is
determined. The customer
experiences expected delivery performance when this variation is
reduced to an
acceptable level.
The OTD system, delivery time and delivery variation, and the
impact on key
stakeholders are discussed further in section three.
20
3. Delivery variation background
Delivery variation occurs when a delivery process is repeated in a
system as
discussed in the preceding section. Section three provides
background on the OTD
system. The meaning of delivery time and the importance to the
customer is explored
further. The costs for delivery variation are presented for the two
key stakeholders, the
customer and the enterprise. Much of the discussion in this section
is from the point of
view of these two key stakeholders in the system.
3.1. Three segments of the order-to-delivery (OTD)
system
The OTD system includes all the subsystems it takes to complete the
delivery of
the product. The span of the OTD system extends from the time when
the customer
ordered the product until the customer received the product. The
OTD system
completes its intended function quickly in some cases. The order is
usually delivered in
a few short minutes in the fast food business for example. The OTD
system takes much
longer to complete its intended function for some larger more
complicated products. The
OTD system may take several years to complete the process for an
airplane order.
Even though the specific systems for hamburgers and airplanes are
very different, the
systems have similarities at high levels of OTD system
decomposition. The customer
orders a product, the product is made, and then the product is
delivered in both of these
systems. A generic OTD system can be decomposed into three
different subsystems in
each of these enterprises:
2. Production system (Start of production to start of
distribution)
3. Distribution system (Start of distribution to delivery to
customer)
Order-to-Delivery System
Figure 1 Order-to-Delivery system at the first level of
decomposition
Decomposition diagrams are a system tool used to break down a
complex
system to enable understanding of how the system is constructed.
The system is placed
at the zero level and each time the system is decomposed one level
lower, the system
is said to be decomposed to the next level. The system can be
aggregated by starting at
the lowest level and working toward the system or zero level.
The first level decomposition for the OTD system is shown in Figure
1. The three
major subsystems can be decomposed further to allow easier
comprehension of the
particular OTD system of interest. The decomposition should be
conducted to an
elemental level where the persons investigating variation in the
system can better
understand the complexity of the system they are a part of.
What takes place in each of these subsystems? The order system
starts with the
interaction of the customer and the enterprise (or agent of the
enterprise). The customer
22
places an order in this interaction. The enterprise then routes the
order through internal
channels to prepare the order for production. The process enters
the production system
when the order is ready for release to the production system. The
product is scheduled
for production, manufactured, assembled, tested, inspected, and
reworked in the
production system. The product is shipped and enters the
distribution system once the
production organization is satisfied with the product. The
distribution system delivers the
product to specific customers or agents of the enterprise
(retailers for example) who will
deliver the product to the customer. The customer receives the
product they ordered at
the start of the OTD process and the delivery is complete for this
customer / enterprise
interaction.
The research in this thesis will focus on the production subsystem
in the OTD
system. The customers, products, and enterprises involved in the
data collection are
from an automobile original equipment manufacturer (OEM) and
operate in a complex
OTD system. Automobile OEMs are enterprises that design, develop,
produce,
distribute, finance, service cars and trucks for a wide range of
customers. Honda,
General Motors, Toyota, BMW, Ford, Volkswagen, Fiat, Kia, and
Nissan are all
examples of automobile OEMs. The production subsystem in this
automotive case is
very complex and will be decomposed two to three levels to help
understand this portion
of the overall system.
3.2. State of the current production system
The current production system generally contains four elements at
the second
level of decomposition of the OTD system:
23
o TIR subsystem (The product is tested, inspected, and
reworked)
o Shipping subsystem (The product is shipped)
What occurs in each of the subsystems of the production system? The
necessary
materials are procured and the unit or batch is scheduled for
production on a certain
date and time when the order is received from the order subsystem
in the OTD system.
The product is then manufactured and is "completed" with the
exception of tests,
inspection, and rework. The unit is delivered for shipping to the
customer (distribution)
when test, inspection, and rework are satisfactory. The second
level of decomposition in
Figure 2 depicts these four subsystems. Each of these second level
subsystems can be
decomposed further if necessary to aid in understanding a specific
production system.
24
Plant Shipping
Figure 2 OTD System with production system at the second level of
decomposition
Delivery time in the production system is the time it takes from
production order
to plant shipping in Figure 2. Average delivery time for the
production system is a
measure of the expected delivery time based historical performance.
Performance
around this average delivery time indicates how much variation
there is in this
production system.
3.3. Critical to delivery (CTD)
Critical to delivery is a term used to signify items that are
critical to the customer
and enterprise with respect to delivery. Delivery time (DT) and DT
variation are critical
to the customer for delivery in the OTD system
25
-iI
3.3.1. What is delivery time?
Delivery time is the amount of time it takes to deliver the product
to the customer
after the order is placed in the order system. DT for the hamburger
example used
before is the time it takes to complete the customer order at the
counter, a couple
minutes. DT for the airplane example is the time in months from
contract approval to
delivery of the airplane.
The many possible sources of variation in the OTD system are hard
to visualize
at the first level of decomposition. The challenge of predictably
delivering a product to
the customer becomes more evident when the system is further
decomposed to a
second, third, or fourth level. More complex OTD systems tend to
have more
opportunity for variation in delivery. The increased opportunity
comes from the larger
quantity of system inputs that have an effect on delivery
variation. The following quote
describes a familiar realization for someone who has been a part of
a complex OTD
system.
Many companies have trouble delivering products on time.'
Performing as expected on deliveries is difficult when we look at
how hard it is to
meet schedules in the order, production, or distribution subsystems
alone. Each
subsystem presents unique opportunities to miss a delivery
expectation. The impact of
one system delivering early or late is amplified by subsequent
systems when
subsystems are linked as they are in the serial OTD system.
26
Delivery time for the production system is the time from the
production order, or
production kick-off, until the product leaves the production system
for the distribution
system. Delivery stability and predictability are desirable, but
tough to attain in the
production system. The distribution system can use a standard
operating system when
the production system is predictable and stable. When there is
variability in production
delivery time, the distribution system must try to make-up for this
variation in order to
meet expected overall delivery time.
Section 3.4 describes why understanding the OTD system and the
variation in
the OTD system is important for the enterprise's customers. The
common customers
and what they expect of the OTD system is also part of section
3.4.
3.3.2. Delivery time variation
What then is delivery time variation? At the system level, delivery
time variation is
the difference in delivery time from order to delivery for one unit
or batch when
compared to other units or batches. For example consider a person
buying a new boat.
The customer is told at the time of order that the new boat will be
delivered in 42 days
(or 6 weeks). The first customer actually receives the boat in 42
days and is satisfied
with the delivery. The delivery met the first customer's
expectations. The next customer
orders a boat and is told to expect delivery in 42 days. This
customer receives the boat
in 72 days. The second customer is not satisfied with delivery
because the season
ended and they will now pay for the boat all winter before getting
to use the boat. The
third customer is also told that they should expect to receive the
new boat in 42 days.
Only this time the boat shows up in just 12 days. Customer three is
not satisfied with the
27
delivery performance of the boat enterprise. The customer has
another boat they will be
making payments on for the expected 42 days. They were planning to
sell their current
boat toward the end of the expected 42 days, but did not expect
delivery in 12 days.
There are three customers with average delivery at the expected 42
days, but only one
of the three is satisfied with the delivery performance.
The boat company (the enterprise) on the other hand believes they
have
performed quite well with an average on-time delivery of exactly 42
days. Figure 3
shows the delivery variation time scale as it relates to customer
expectations. Deliveries
to the left of the expected delivery in this figure are early
deliveries; those to the right
are later than expected. Early and late deliveries are both
undesirable to the customer.
Product Delivery Variation to Customer Expectation
Early Product Delivery Late Product Delivery
0
Figure 3 Variation to Customer Expectation
The concept of delivery variation in the overall OTD system can be
carried into
each of the three subsystems of the process. The production system
needs to meet
delivery expectations of the enterprise in order for the OTD system
to meet customer
expectations for delivery of the product.
Figure 4 shows the concept of delivery variation for the production
system.
Average delivery performance expectations may be met by the
population distribution
shown in the figure, but variation in the production system creates
problems for overall
OTD system performance. The purpose of this research is to develop
a method to find
and eliminate the sources of variation in the production system
that impacts the overall
OTD system. The following quote illustrates this point:
Identifying and correcting the root cause impediments needs to be
done in order
to achieve significant improvements in order-to-delivery
performance.
29
Figure 4 Variation measure in the production system
If production system variation is so easy to see, why do so many
enterprises
have variation problems? Many enterprises rely on average delivery
performance to
indicate how they are performing. These enterprises miss the point
that customers
experience delivery variation around the average, not the average
delivery
performance.
customers want?
An enterprise may have several types of customers. The following
list includes
many of the more common customers of the OTD system:
i Retailers
o Commercial customers
o System integrators (enterprises who purchase product to build
their own product)
A common customer is the retailer who buys a product or products
from the
enterprise with the intent to resell the product to the end
customer. An example of a
retailer is a clothing store that buys clothing from the producer
for resale to retail
customers.
The end customer, or retail customer, purchases the product with
the intent of
using the product. The end customer does not purchase the product
with the intent of
reselling the product short term, although they may eventually sell
the product at some
salvage value in the future. We are all retail customers from
time-to-time when we buy
our groceries, homes, and vacations for example.
The commercial customer is a large volume customer who buys the
product for
use in their commercial business. Many times commercial customers
are fleet or large
account customers. The automotive business has many examples of
fleet customers for
cars and trucks, such as rental car companies, taxi companies, and
police departments.
Automotive OEMs also have large account customers such as
universities and
municipalities who use cars and trucks to conduct their
business.
The system integrator is a customer who buys the product with the
intent to use
the product as part of a larger system they are producing for sale.
An example of a
system integrator would be an airliner manufacturer who buys
engines from an engine
builder, installs them on the airplane system, and sells the
airplane.
31
The customers in each of these cases are very different, but have a
common
need for reliable delivery performance from the enterprise they buy
products from.
Customers simply want what they want, when they want it3. An
example of this concept
is found in the delivery of food products to customers.
The problems in the grocery sector.... In food, the stumbling
blocks are more
about fulfillment getting customers what they want when they want
it. In the area
of fresh produce, the highest margin part of any supermarket
business, delivery,
is time-critical and produce is easily damaged.4
Having the right produce on the shelf when the customer is looking
for the
specific item can mean the difference between sale and no sale in
this delivery
example. Variation in OTD systems can cause the enterprise to lose
a potential sale or
worse, lose customer loyalty. Jack Welch realized this at GE when
he reflected on why
Six-Sigma was not as successful as he would have liked. He changed
the focus of the
Six-Sigma effort at GE when he realized this. Jack Welch discusses
this in his book
"Jack: Straight From the Gut":
It was Piet who came up with the answer to why our customers
weren't feeling
Six Sigma improvements. Piet's reason was simple: He got all of us
to
understand that Six Sigma was about one thing - variation! We had
all studied it,
including me... But we never saw it the way Piet laid it out. He
made the
connection between averages and variation. It was a
breakthrough.
We got away from averages and focused on variation by tightening
what we call
"span". We wanted the customer to get what they wanted when they
wanted it.
32
Span measures the variance, from the exact date the customer wants
the
product, either in days early or days late. Getting span to zero
means the
customer always gets the products when they ask for them.
Jack Welch was able to change the way the customer perceived
delivery of GE
products through this change in focus using both Six-Sigma and Lean
processes and
tools. The success at GE is clear in the following excerpt from
Jack Welch's
autobiography.
We used Six Sigma and a customer-oriented perspective including
span to guide
us. That reduced the delivery span from 15 days to 2. Now customers
really felt
the improvement because orders arrived closer to their want
dates.6
Other companies are seeking to give the customer this predictable
delivery
service like GE. Wal-Mart has led the retailing charge to give the
customer what they
want when they want it. Alcoa also has added a focus on the
customer similar to Wal-
Mart. Alcoa General Manager, Giulo Casello, speaking of a new world
headquarters site
with added manufacturing facilities:
"It gives us the ability to give the customer what they want, when
they want it,"
said Giulio Casello, the division's general manager. "It really
allows us to get
everyone focused on the same goals."7
Some enterprises have realized that giving customers high delivery
service levels
is not only an advantage in the marketplace, but it is critical to
survival. These
33
companies understand that in the end, customers get what they want
and customers
will go elsewhere if they are not satisfied.
3.5. The cost of delivery variation
Costs associated with delivery variation generally fall into two
categories,
customer cost and enterprise cost. Customer costs are those
incurred by customers
that are associated with delivery time variation as the name
implies. Enterprise costs
are those incurred by the enterprise that is associated with
delivery time variation. There
are "hard" cost and "soft" cost for both the customers and
enterprises. Hard costs are
those that are easily quantified in real dollars. An example of a
customer hard cost
associated with delivery time is the cost for a customer to rent a
replacement product in
place of a product that is being delivered late. The rental cost is
a hard cost; cash is
spent on the rental expense. Soft costs are those that are more
difficult to assign a
dollar figure to. Lost sales due to poor delivery time performance
is an example of an
enterprise soft cost; customers become frustrated and buy from
another enterprise.
Delivery variation will create both customer and enterprise costs
in the transaction in
many instances. Common customer and enterprise costs are tabulated
in Figure 5.
34
3.6. System costs
The customer and enterprise have similar costs depending on who is
responsible
for each expense per the agreement made between the two. The cost
of delivery
variation can be thought of as a system cost. The system cost
remains whether the
customer or the enterprise pays for an expense. Higher system cost
is an indication of
inefficiency or waste in the system. The enterprise suffers lower
profits or raises the
price to the customer if they incur the expense. The customer may
look for a different
35
Customer Hard Customer Soft Enterprise Hard Enterprise Soft Costs
Costs Costs Costs
o Storage space o Lost sales o Storage space o Lost sales o Extra
handling o Project delay o Extra handling o Cost of being
and product cost and product unable to movement o Poor movement
launch new
o Product customer o Product products damage and satisfaction
damage and o Added loss loss system
o Financing cost o Financing complexity to o Rental cost Finished
goods expedite o Unscheduled inventory costs orders
down time o Obsolescence (including cost layoffs) and o Overtime to
overtime make-up for
o Obsolescence late units cost o Work-in
o Large incoming progress product inventory inventories (WIP)
o Duplication of o Added capacity costs for early to eliminate
deliveries late deliveries
Figure 5 Customer and enterprise costs
enterprise to work with if they incur too much system cost. The
customer will choose to
operate in a system that is more efficient and has less
waste...
System costs for those outlined in Figure 5 are discussed
below:
Storage space
Storage space costs for the system can occur when a new product is
delivered
early and the customer does not have current storage space for the
product that was
delivered early. This may seem like a small problem, but think
about the case of the
automobile fleet customer. Finding space to store a few thousand
vehicles is difficult
and expensive if the automobiles show up a few weeks early. The
storage space cost
includes the cost to set-up, insure, and secure the storage
space.
Storage space rented or purchased to hold extra units can be costly
when extra
product is warehoused by the enterprise to protect for delivery
variation. The cost can
be high even if building space is available. Special racks,
environmental conditioning,
and warehouse staff may be needed to maintain these extra units.
This was the case at
Porsche in the early 1990s when Porsche was loosing money in their
production
operations. The production floor for Porsche looked like a spare
parts storage
warehouse at the time and led to waste and inefficiency in the
system 8.
Extra handling and product movement
Extra handling and moving cost come from the movement of the
product due to
the fact that it is not at the right place at the right time. An
early shipment of a 53' trailer
of goods to a customer many times means the customer will have to
move the product
out of the way until they are ready to use the product and then
move the product to the
36
area it will be used. The people, facilities, and equipment
necessary to handle and
move the product cause expense for the customer.
Extra units in the production system and frequent schedule changes
cause extra
handling and product movement in the enterprise system. Extra
handling and
movement can be an expense of the enterprise as it is for the
customer.
Product damage and loss
Damage and loss due to theft, accidents, and unexpected natural
occurrences
such as storms are possible when inventories are carried to protect
against delivery
variation. The cost can be high when damage and loss occur. A car
dealer that has an
unfortunate situation where vehicles are damaged due to a hail
storm will incur costs to
repair the vehicles and may experience sales losses due to the
damage to the vehicles.
New vehicle customers tend to shy away from vehicles that were
previously damaged
and repaired.
Opportunities for damage or loss in the production system are
presented by extra
handling, product movement, and storage. Work in progress (WIP) is
dropped, spilled
on, contaminated, and run into in workplace accidents. Damaged or
lost product cause
the enterprise to further vary from the delivery schedule due to
the time needed to repair
or remake product for the losses. The waste associated with loss
and damage is costly
for the enterprise.
Financing cost
Financing costs depend on the contract terms and how early or late
the product
is. Sometimes the customer pays even when the product is delivered
early; other times
37
payments begin for the customer and the product arrives late. A
contract that is prepaid
and is then delivered late is an example of a customer who is
financing a product that
they do not posses. The expense can range from a small down payment
to the full cost
of the product.
WIP inventory is many times financed by an enterprise and ties-up
assets that
would otherwise be used to produce more product or develop new
products. Financing
costs are also incurred if the facility is changed or added to in
order to improve delivery
variation performance.
Rental cost
Rental costs are those the customer pays to rent a product to use
in place of the
purchased product that is late for delivery. Automobile customers
will sometimes have
to rent a car to use for the period between selling their old car
and receiving their new
car. Delivery variation can create this unexpected expense.
Unscheduled down time and overtime
Unscheduled down time (including layoffs) and overtime may occur
when a
system integrator experiences early or late deliveries. Part
shortages will sometimes
force an enterprise who is the customer in this case, to halt their
operations until the
needed product is available to integrate into the system they are
building for sale. The
employees will sometimes be laid-off until the product is available
from the supplier.
Overtime begins when the product finally does arrive and the system
integrator has to
work extra hours to meet their product delivery schedule.
Obsolescence cost
38
Obsolescence cost are realized when a particular product that has
been stored in
inventory to protect for delivery variation becomes unusable or
obsolete. Obsolescence
can be caused by changes in technology, competitive products, aging
phenomenon,
and a shrinking customer base. The photographic industry provides a
good example of
obsolescence cost. Many camera and lens manufacturers changed the
lens mount
architecture when they switched from manual to auto focus cameras.
Camera shops
(customer of the manufacturers) experienced obsolescence costs for
the older systems
because the technology had changed the architecture of the cameras.
Photographers
purchased much less of the manual focus technology and the several
camera shops
experienced obsolescence costs for the product that sat aging in
inventory.
Large incoming product inventories
Large incoming inventories are owned by either the customer or
enterprise. The
main reason for these incoming inventories is to protect for
delivery variation from the
enterprise. Large incoming inventories are often used by system
integrators to protect
against part shortages. Inventory costs may be expected to be less
expensive than
halting production operations for a part shortage due to delivery
variation. The inventory
costs are factored into the cost of doing business. Just-in-time
(JIT) delivery practices
have attempted to reduce these on-site part inventories.
Duplication of costs for early deliveries
Duplication of cost for early delivery occurs when the current
product is being
paid for through the expected delivery date of the replacement
product and the
replacement product arrives early with payments that begin at
delivery. The customer
pays for both the current and replacement product in this case. A
person buying an
39
automobile and selling their current automobile is an example where
duplication costs
may be incurred. The customer will pay duplicate costs until the
older car is sold or
disposed of when the replacement car arrives early and the old one
is still in the
customer's possession.
Lost sales
Lost sales occur when the product is not at the right place at the
right time for the
end customer to purchase. Some customers will not wait for a
product from a specific
enterprise if they know they can go to another enterprise to have
their need fulfilled.
Once the enterprise looses this customer, they incur expense to
find another customer
for the product that was late for delivery. The cost of loosing a
fleet of product sales can
be very high for expensive products like cars, large medical
equipment, and airplane
engines for example.
Project delay cost
Project delay costs are those where a major project is delayed due
to early or
late delivery. Many times project delays affect parts of the
project that are not directly
tied to the product that experienced delivery variation. For
example, a building project
can be delayed when the escalator does not arrive on time. The
persons who were
doing the escalator work are certainly affected, but so are the
persons who must wait to
close the side of the building that the escalator will come in
through and the persons
who were to lay tile once the escalator was installed. Project
delay costs can become
very large with only a slight variation in delivery.
Overtime to make-up for late units
40
The first cost that is easily quantified is the cost of overtime
worked and paid to
make-up for units that are late for delivery. A common action
production managers take
when deliveries are running behind schedule is to work more hours.
Sometimes these
hours are not only worked on units that are late. Units are built
early along with the late
units. The enterprise is paying overtime for early units too in
this situation. Many
businesses do not have tight enough control on overtime to make
sure the overtime is
only used for late orders.
Work-in progress (WIP) inventory
Work in progress (WIP) inventory is also an area where delivery
variation causes
increased cost for the enterprise. Units are juggled in the
schedule to try and meet
delivery dates. WIP inventory increase as jobs are juggled in the
production system.
Sometimes WIP inventory is greater than the actual demand for the
product. The
surplus is partly caused by problems associated with delivery time
variation.
Cost of being unable to launch new products
The opportunity cost of not being able to design, develop, and
produce new
products due to the losses of time and money spent on delivery time
variation also costs
the enterprise. New products receive less attention because scarce
resources are busy
working on current product delivery problems.
Finished goods inventory
The enterprise will build a large (sometimes expensive) finished
goods inventory
in an attempt to always meet customer demand. The inventory also
creates problems
with storage space costs, obsolescence costs, handling and product
movement costs,
41
and damage and loss costs similar to those experienced by the
customer. Finished
goods inventories are managed by the enterprise to provide the
customer with fast,
reliable service.
Added capacity to eliminate late deliveries
A cost the enterprise experiences is the cost of adding capacity to
the production
system to meet delivery expectations. Managers of production
systems that consistently
miss delivery targets will many times believe the problem is a lack
of capacity and will
try to solve their problem by adding capacity. It is the production
system architecture
and the process that is the problem, not the size or capacity of
the system.
3.7. Motivation to drive change in the system
The motivation to reduce delivery time variation for the customer
and the
enterprise is illustrated through this discussion of costs and
expectations. Who bears
the increased cost is really irrelevant because the system cost
will likely be higher and
the system will likely be less efficient when there is more
delivery variation. The hard
part now is to understand how change can be made in the production
system.
An influential change that can be made is to base the objectives
and rewards of
those managing the production system partially on a delivery
variation target. Managers
will resist change if their internal objectives do not align with
customer expectations.
Managers reach high levels in their organization because they have
delivered on key
objectives they were measured against in the past. Managers deliver
on these
42
measured objectives for personal benefit even when the measurements
do not drive
behavior that benefits the customer and enterprise:
For instance, if the success of the shop floor is measured simply
by efficiency,
utilization and/or standard hours of output, you can be sure that
parts will be
produced even when they are not needed. The result: too much
inventory of
unneeded material and possibly shortages of what is needed.9
Reduced inventory and improved customer service level are mutual
goals. The
system costs for the customer and enterprise are reduced when both
work to reduce the
inventory in the system and eliminate other system costs incurred
due to early or late
delivery. In the past, it was thought that large inventories need
to be in place to meet
high customer service levels. Now the enterprise metric to drive
low delivery variation
and low inventories aligns with the customer expectation for better
delivery
performance.
43
44
4. Methods
The data in this research is collected and analyzed using the
Six-Sigma DMAIC
methodology as a framework and tools from both Six-Sigma and Lean
to perform the
actual analysis in section five. The purpose of section four is to
give the reader a
background in the methods that will be used in section five to
analyze the automotive
OEM case data.
4.1. Six-Sigma and DMAIC
Six-Sigma is a methodical process that uses several different tools
to improve
processes and systems. The goal of the Six-Sigma process is to make
a measurable
improvement to a process, system, or product and to maintain the
improvement long
term. Some of the tools used in the Six-Sigma process are familiar
to those in different
industries where methodical problem solving tools have been used in
the workplace.
Many of the statistical tools used in Six-Sigma are new to those
who are trained in the
Six-Sigma process.
Persons who are trained in the use of these processes and tools are
given a
designation that indicates their ability to apply both the method
and tools. The three
levels of certification are for Green Belts, Black Belts, and
Master Black Belts. The
Green Belt is the first level in Six-Sigma ability. Green Belts
receive general training that
allows them to apply simple tools to their day-to-day job.10 Black
Belts have more in
depth training and apply the Six-Sigma tools and processes to more
difficult problems
as their primary function." Master Black Belts complete additional
training in Six-Sigma
45
methods and tools after they have already been certified as a black
belt. In addition,
Master Black Belts deploy training for the enterprise in Six-Sigma,
act as a champion for
Black Belt and Green Belt Projects, and define projects with key
enterprise leaders.12
The Six Sigma breakthrough strategy can be applied at different
levels in an
organization and will yield different results. Six-Sigma tools and
processes can be used
at the business level, the operations level, or the process level.
A project to reduce the
variation in delivery time in the production process takes place at
the operations level.
Jack Welch used Six-Sigma at GE at all three levels and talks
specifically about the
operations and process levels in his autobiography:
Plant managers can use Six Sigma to reduce waste, improve
product
consistency, solve equipment problems, or create capacity.14
The Six-Sigma process is broken down into steps known in the
Six-Sigma
community as DMAIC. The Acronym stands for:
u D: Define
L M: Measure
u A: Analyze
Li 1: Improve
L C: Control
The DMAIC process takes a problem all the way from problem
definition through
improvement and control of the process. The DMAIC process is
described in the next
five sections.
4.1.1. Define phase
The define phase of a Six-Sigma project is where the team working
on the
problems or opportunity answer a few simple questions:
u What is the problem or opportunity?
Li What is the defect?
L Who are key stakeholders?15
o What is the goal?
The team will use a few documents to help answer these questions at
the start of
every project. A business case is used to help understand how the
problem or
opportunity impacts the business financially16. The team will
document how much the
problem currently costs, what they expect to gain by improving upon
the problem, and
what they expect to spend fixing the problem. Once the business
case is complete and
the team decides with the approval of the appropriate enterprise
leader, the team will
develop a charter that includes the business case, a statement of
the problem, and a
statement of the goal17 . The team will develop a high-level
process map and a list of
what is important to the customer18 from the charter. The list of
what is important to the
customer may focus on quality, cost, function, delivery, or other
characteristics of the
enterprise / customer relationship.
The charter for a delivery variation project would include a
statement about
delivery time variation, how it impacts the customer and the
enterprise, and what the
associated costs are.
4.1.2. Measure phase
The measure phase uses the stakeholder analysis, charter, and high
level
process map from the define phase to aid in selecting what is
critical to the customer.
The critical characteristic list may include CTQ (critical to
quality), CTC (critical to cost),
and CTD (critical to delivery) characteristics' 9. The critical
characteristics in this
research of delivery time variation in a production system are CTD
and CTC; critical to
both delivery and cost.
The next step is to contemplate how best to measure and then
develop a
measurement for these characteristics. Sometimes there are existing
measurement
systems to measure these characteristics, other times the team has
to develop a way to
measure the critical characteristics. Performance standards for the
measurement
should be validated if they already exist, or developed if they did
not20 . Performance
standards can be thought of as a specification for the measurement
the team is using.
The specification is likely to have a target with some tolerance
band around the target.
The performance standard for delivery variation will be the
allowable days of variation
for the particular OTD system (these standards will vary depending
on the customer and
the product).
The measurement system is then validated21 for the measurements
that are
chosen to check the quality of the data used to determine the
performance of the
current process. The analysis that is used will depend on the type
of data being
collected (variable, ordinal, or attribute) and the operational
definition of the
measurement system. The measurement system analysis (MSA) will
check how well
48
the measurement system is able to discriminate the variation in the
production system
as compared to the variation in the measurement system. The risk of
not validating the
measurement system is that the team may not be able to quantify the
difference if the
measurement system contains too much error.
Baseline data on current system performance can be collected once
a
measurement system is in place2 2 . The baseline data defines the
number of days of
variation for a certain percentage (P) of the population in the
case of the production
system and delivery variation. An example of this performance
measure is shown in
Figure 6.
0 -5 0.9 -
0 5 10 15 20 25 30 Days 14.5 days
(P = 9Eno)
Figure 6 Example of an initial OTD delivery variation plot
49
Figure 6 is a graph of delivery dates for a large number of units
from a production
system. The definition of this measurement system requires that
delivery variation is
determined when the top and bottom 2.5% of the population are
eliminated from the
measure. The 14.5 days of delivery variation in this example are
measured after the
elimination of the top and bottom 2.5%. Said differently, 95% of
the population has a
delivery variation of 14.5 days as measured by the span.
The baseline data will help the team understand the problem better.
The team
can develop a more focused problem statement with the knowledge
they gain from
collecting the baseline data23 . The team will also evaluate
whether the current data
pinpoints problem locations or occurrences24 .
4.1.3. Analyze phase
The analyze phase of the Six-Sigma process is where the function Y
= f(X) is
explored in detail. The Y in the equation represents the output and
the f(X) represents
the function of the inputs. Another way to say Y = f(X) is the
output is a function of the
inputs. The team develops a list of possible X's, and then works to
build a relationship
between the important inputs (important Xs) and the output (Y). The
idea is to find the
sources (inputs) of variation that are causing variation in the
output25 . Another way of
thinking of the analyze phase is to identify root cause(s) and then
to confirm them with
data26 . Once the important Xs or causes are known (with
statistical validation), the
system can be changed and the effect evaluated in the improve
phase.
50
The possible causes or inputs in the production system of the OTD
System
include:
... acquiring materials, scheduling production and other
information are often big
contributors to overly long order-to-delivery cycle times.27
Data mining such as regressions and variation analysis are often
performed to
help define the critical few inputs to the system that control the
output.
Multiple outputs should be evaluated at this point in the analysis
and again in the
improve phase. Each input may affect one or more outputs. It is
important to evaluate
other outputs with data or engineering judgment to ensure the
customer and enterprise
do not get an unexpected result in another output when changing one
of the inputs.
Other outputs that are commonly important in the production system
are cost and
quality.
4.1.4. Improve phase
The improve phase starts once the source of variation is known. The
team will
develop and test changes to the system or process to validate the
desired change in the
output. The team tests and implements solutions that address root
cause in this
phase28 . The team will also develop new operating tolerances for
the improved
system 29. The team will asses the variation in the improved system
to see if the new
performance meets stakeholder expectations.
51
In the case of the OTD system, we will look to see if the delivery
time variation
has been reduced. We will do this at the system level and at the
subsystem level for all
subsystems that were changed.
4.1.5. Control phase
The control phase starts by measuring the new process performance
(once the
measurement system has been revalidated with the new process
parameters)30. The
new process performance is evaluated to see if the performance
improvement meets
project performance targets (and the project is closed) or if
additional changes will need
to be made to meet the performance targets of the project.
52
Improved Delivery Time Variation Capability
Delivery variation after improvement to key inputs is shown in
Figure 7 for the
example started in the measure phase. The variation for 95% of the
population was
reduced from 14.5 days to 5.7 days by finding and eliminating
sources of delivery
variation in the production system. The methods and tools used to
improve this example
are the same as those employed in the actual automotive OEM case
used in this
research.
Process controls and a monitoring system are then put in place to
make sure the
gains that were achieved by the team are maintained3 1 . A key
portion of the process
53
0.0 *
Days 5.7 days
Figure 7 Example of an improved OTD variation plot as compared to
the Measure Phase
control plan is to make sure the process owner buys into and owns
the maintenance of
the change. The project results are documented, learning is shared
with other
organizations that might benefit, and recommendations are made for
future work in the
project area.
4.2. Lean principles
Using Lean principles in combination with the Six-Sigma process as
discussed in
the preceding sections provides a systematic method to find and
eliminate waste
associated with delivery variation. Waste elimination and value
stream mapping are
respectively the Lean principle and tool combined with the
Six-Sigma method to provide
better delivery variation performance and improved customer
satisfaction.
4.2.1. Elimination of waste
Waste elimination is at the very center of Lean enterprise
principles. The
elimination of waste in the system allows the enterprise to perform
its intended function
with a minimum of energy and resources. The Lean enterprise has an
advantage over it
competition because it has sought and eliminated waste from its
system. The concept is
highlighted in the book Manufacturing Operations and Supply Chain
Management - The
Lean Approach3 2
The understanding of what waste is and how to remove it is
fundamental to the
creation of a Lean enterprise or supply chain; however, many
managers and
companies often do not understand or realize the importance of the
concept.
54
Types of waste are often categorized or classified to simplify the
understanding.
A popular waste classification for a production system was
developed by Toyota.
Toyota classifies wastes in each of their production systems in the
following seven
categories:
Seven wastes identified in the Toyota Production System (TPS)
33:
" Overproduction
" Waiting
" Transportation
" Inventory
" Motion
" Defects
Waste can be identified and eliminated. An example of
transportation waste in
the TPS classification would be when incoming parts are stored and
then moved to the
operation. Extra movement of parts occurs. The elimination of the
waste would be to
find a way to move the necessary parts directly from the incoming
supply truck to the
operation.
A system dynamic occurs between waste and variation when we
consider
product delivery34. Waste in the production system causes variation
in delivery from the
production system. The variation in production delivery then causes
additional waste for
the customer or enterprise. The external waste for the customer or
enterprise
55
sometimes comes full circle and causes waste in the production
system. This dynamic
is depicted in the causal loop diagram in Figure 8.
Production
System
Waste
Production
system
Delivery
Variation
External
The system dynamic becomes clearer with an example. An
automobile
production system contains multiple reworks for quality defects
(waste - defects) that
cause variation in delivery of a vehicle to a customer that buys
hundreds of vehicles
each year. The customer has to adjust (waste - waiting) their
planned use because the
vehicle is delivered late. The customer asks for the next vehicle
to be delivered earlier
56
00000
I
than actually needed to allow time for late delivery. The customer
is gaming the system.
The enterprise experiences waste in their production system when
they pull one vehicle
from the build schedule (waste - inappropriate processing) and
replace it with this new
customer order. The vehicle that is pulled is now subject to
delivery variation due to
waste. The waste / variation loop is real and costly for both the
customer and the
enterprise.
Section 4.2.2 discusses a valuable Lean tool called a value stream
map to help
identify the waste in a system. The mapping tool with the principle
of identifying and
eliminating waste are two powerful yet simple elements of a Lean
enterprise.
4.2.2. Value stream mapping
Value stream mapping is a simple to use Lean tool employed to help
identify the
process flow and resulting waste in the system. A values stream map
is simply a map
that depicts the current process including any rework or handling
for the current
production system. The detailed map is completed by a team of
people involved with
the current process so as to reflect the reality of the current
system.
Elements of the system are next labeled as value added, non value
added, or
non value added, but necessary for the enterprise. Value added
steps are then
identified as those that add value from the customer point of view
35 . These may be
items that add form or function to the product or are steps that
the customer is willing to
pay for. Non-value added, but necessary steps are also identified.
These are steps that
are not of direct value to the customer, but necessary for the
enterprise. An example of
57
no-value added, but necessary are steps completed to fulfill
regulatory requirements.
The last category identified is the steps that are truly non-value
added. The non-value
added steps of interest in the production system concerning
delivery are those that
cause variation in the delivery system or delay expected delivery.
Non-value added
steps of this type are targeted for elimination to improve delivery
performance.
4.3. Integrating Lean and Six-Sigma to find sources
of variation and waste
The combination of Lean principles and tools and Six-Sigma methods
is useful in
quantifying variation in delivery performance. System decomposition
diagrams are
utilized in constructing the process map for a given system (a
production system in this
case). Six-Sigma measurement systems are then set-up to measure the
delivery
performance at key points along subsystems boundaries. The data
from these
measurements enables analysis that indicates the source of
variation. Waste is often
found to be this source of variation.
The combined method outlined here will be used in section 5 for an
automotive
OEM production system. The purpose of the case is to show how
subsystem mapping
and measurement systems (or the integration of Lean and Six-Sigma)
can lead to a
strategic delivery performance advantage for the enterprise and
better customer
satisfaction for delivery of products.
58
5. Automotive production system analysis
Some of the key steps in the data analysis as well as the actual
data for the
automobile OEM production system being studied are presented in
section five. The
data has been normalized due to the confidential nature of the data
in the competitive
automotive industry. The production system is described using
system decomposition
diagrams and process maps. The system is analyzed using actual
normalized data from
an automotive production system.
5.1. Automobile OEM production system description
The scale of an automobile OEM production system is helpful to
understand in
looking at the specific case. The magnitude of the production
system being studied in
the case is typical of several OEMs and is represented by the
following:
* 200,000 to 300,000 vehicles per year are produced
* Vehicles sell mostly in the $20,000 to $30,000 range
* The manufacturing facility encloses one to two million square
feet
* The work force numbers 2,000 to 3,000
* 1,000 vehicles might be produced on an average day
* Thousands of parts are assembled to create the finished
product
L Annual sales are in the $4 billion to $9 billion range depending
on sales volume
and the average selling price
u Profits may range from $80 million to $600 million depending on
sales and cost
U Customers include dealerships (retailers), retail customers
(end-users), fleets
(commercial), and system integrators
59
The opportunity to improve profits by eliminating waste is expected
to be very
large for the production system described above. Waste elimination
occurs through
delivery variation reduction in this case. A small change in per
vehicle cost performance
can make a big change in profitability for the OEM. Saving four to
five dollars on each
vehicle sold can translate into a million dollars in increased
profit. The cost to the
customers can be significant as well. A customer who depends on
timely delivery of
vehicles can incur tens of thousands of dollars in cost when their
operations are
interrupted due to delivery variation.
5.2. Process maps and system decomposition
Process maps are used to help the researcher and the research team
better
understand the system they are working on36. Process maps have a
rational boundary
for which items not included will be considered outside influences
on the system 37. A
boundary is chosen to include items which are likely active
elements of the system and
exclude elements that are outside the system being considered. An
example of an
element inside the production system is the assembly line. A
railway subsystem that
delivers vehicles would be outside this production system boundary,
but would be inside
a rational boundary for the entire OTD system.
The process map is completed by the most experienced and
knowledgeable
persons working in the area of interest, in this case the
production system. The maps
used for this production system are of the current state, but maps
can also be used to
depict the future state as well.
60
5.2.1. Order-to-Delivery process map
The first process map developed and used is the high-level process
map for the
OTD system shown in Figure 9. The system decomposition diagram
developed in
section three was used to aid construction of the process map. The
process starts with
the customer want and customer order. The process goes through
order processing and
into the production system from there. Once the product completes
the production
process, it enters distribution and the overall cycle is completed
when the customer
receives the product.
Comapny
Fuliled ________ ~ Shipping PlantFulfilled
5.2.2. Production process map
Although the overall process map helps us understand how the
production
system fits into the OTD process it does not provide an
understanding of the production
system. More detailed process maps have been developed for the
production system at
61
further levels of decomposition. The high level production process
map for the
automotive production system being studied is shown in Figure
10.
Manufacturing Assembly Line 1
Manufacturing Test, Inspection, Plant Assembly Line 2 Rework
Shipping
Figure 10 High level process map of the overall automotive
production system from the time the plant receives the order until
the order is shipped
The process starts by receiving the order in production. The
product then goes
through the build process before entering the test, inspect, and
rework (TIR) subsystem.
The product is ready for distribution once the product completes
the TIR processes.
The high-level process map leads to a natural segmentation at
subsystem
boundaries for measuring system performance. The first task will be
to determine what
part of the system the variation comes from. Three segments were
measured to better
understand this variation:
o Order (variation from production order date to the start of the
build)
o Build (variation from the start to the end of the build)
o TIR (variation from the end of build, through test, inspection,
and rework, until the product is shipped)
62
A measurement system was needed to help quantify the system
variation and
the variation in these three steps once these measurement points
were determined.
5.3. Delivery time variation measurement system
The measurement of delivery time is necessary to allow analysis
that leads to the
elimination of the waste that causes the variation. The measurement
of delivery time
implies that the person collecting the data will gather data for
individual units or batches
at the start and again at the end of the process. The date the
order is received and the
date the order is shipped are these endpoints in the automotive
production system. The
data that is collected in this case is the date, time, and
individual identification number
(or specific ID) of the unit being ordered and produced. Each unit
is measured for
delivery performance in the automotive production system.
5.3.1. Measurement system operational definition
Collecting the date, time, and specific ID for each unit in this
case is completed
with the help of information technology and barcode technology. The
date information
for the order is recorded and stored on a central database when it
is received from the
upstream OTD order system. The vehicle builds start at some point
after this order date.
The unit is built and the barcode is read at the end of the
process. The vehicle
specific ID is read from the barcode when the unit is shipped. The
barcode for each unit
is read by a barcode scanner. The data is then sent to and stored
in a central database
with a common or central clock.
63
These two measures provide enough information to determine the
production
delivery time for each unit. Several units for a particular order
date can then be
combined to determine the delivery variation for orders placed on a
particular day. The
delivery variation for a day is depicted in Figure 11. The
population is rank ordered from
the shortest to the longest delivery time. The variation for 95% of
the population is then
determined as the difference between the value for 97.5% of the
population and 2.5% of
the population as shown in the figure.
Why use 95% of the population instead of 100%? The correct
population
threshold will be determined based on an understanding of the
current enterprise and
the goal for delivery variation reduction. In a business where
every unit must be
delivered on time, 100% would be used. 90% or 95% is used in a
business where there
are five or ten in 100 exceptions that enterprise leaders are
willing to accept. Using
100% in these cases would tend to skew the data to exaggerate the
magnitude of the
delivery variation. The population threshold level is something
that the team will have to
set and was chosen to be 95% in this automotive OEM case.
64
Early Product Delivery Late Product Delivery
Figure 11 Days of delivery variation (SPAN) for 95% of the
volume
The team was also able to measure important subsystems of the
production
system with the same type of measurement system and the same data
analysis as
shown in Figure 11. Measure the overall production system process
performance and
compare that to the performance of the subsystems at the next lower
level to determine
which subsystem has the most opportunity for improvement. Working
on improvements
without this type of analysis may mean the team works hard to
improve the system by
working in the area with the least opportunity for improvement
38.
65
1.0
5.3.2. Measurement system process map
The process map for the measurement system at this 2nd level of
decomposition
in the production system (the production system is at the 1st level
of decomposition in
the OTD system) is shown in Figure 12.
The time measurement for each unit from order to shipping (the
overall
production system) is shown in the figure with the three subsystem
measurements
depicted above it. The first measure is from the order until the
barcode scanner at the
start of production reads and records data for the unit. The second
subsystem is from
the start of production barcode scanner to the end of production
scanner. The last
66
l nManufacturing Test, Inspection, Assembly Line 2 Rework
Shipping
Order to Build Test, Inspection, ReworkBuild
Production Process
Figure 12 Measu