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
LI
I LIBRARIES
February 2003
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
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.
7
8
Table of Contents
A bs tra c t ...........................................................................................................................
3
Acknowledgem ents .....................................................................................................
5
Biography ........................................................................................................................
7
Table of Contents ........................................................................................................
9
Table of Figures........................................................................................................
11
Introduction .........................................................................................
13
1.1.
M otivation............................................................................................
13
1.2.
Research...............................................................................................
14
1.3.
Thesis outline .......................................................................................
15
Average perform ance and perform ance variation .................................
17
1.
2.
2.1.
The difference between average performance and performance variation:
A sim ple case........................................................................................
2.2.
17
Expected delivery performance and delivery performance variation........18
Delivery variation background.............................................................
21
3.1.
Three segments of the order-to-delivery (OTD) system .......................
21
3.2.
State of the current production system .................................................
23
3.3.
Critical to delivery (CTD)......................................................................
25
3.3.1.
W hat is delivery tim e?.........................................................................
26
3.3.2.
Delivery tim e variation.........................................................................
27
3.
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
M ethods ..............................................................................................
45
Six-Sigm a and DMAIC .........................................................................
45
4.1.1.
Define phase.......................................................................................
47
4.1.2.
M easure phase .....................................................................................
48
4.1.3.
Analyze phase.....................................................................................
50
4.1.4.
Im prove phase .....................................................................................
51
4.
4.1.
9
4.1.5.
4.2.
Controlphase........................................................................................52
Lean principles .....................................................................................
54
4.2.1.
Elimination of waste ..............................................................................
54
4.2.2.
Value stream mapping .........................................................................
57
4.3.
5.
Integrating Lean and Six-Sigma to find sources of variation and waste ... 58
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
61
5.2.2.
Production process map .......................................................................
5.3.
p ............................................................
61
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
TIR subsystem possible sources of variation...................80
5.7.
5.7.1.
TIR subsystem decomposition............................81
5.7.2.
TRsubsystemprocessmap.............................................................
5.7.3.
Cause and effect diagram for the TIR subsystem................84
5 .7 .4 .
5.8.
5.8.1.
5.7.4. C&E marix......................................86
C &E matrix ............................................................................................... 86
Analysis of variation in the TIR subsystem....................90
Measurement system for key possible sources of variation in the TIR
subsystem ...........................................................................................
5.8.2.
83
. 92
Target reduction in variation in the TIR subsystem ..............................
92
Conclusions and recommendations .....................................................
93
6 .1.
C onclusions.........................................................................................
. 93
6.2.
Recommendations...................................................................................96
6.
G lo s s a ry ......................................................................................................................
10 1
R efe re n ces ..................................................................................................................
10 3
E n d n ote s .....................................................................................................................
1 05
10
Table of Figures
Figure 1
Order-to-Delivery system at the first level of decomposition..................22
Figure 2
OTD System with production system at the second level of decomposition
. . 25
...........................................................................................................
28
Figure 3
Variation to Customer Expectation ........................................................
Figure 4
Variation measure in the production system..........................................30
Figure 5
Customer and enterprise costs.............................................................
35
Figure 6
Example of an initial OTD delivery variation plot ...................................
49
Figure 7
Example of an improved OTD variation plot as compared to the Measure
P ha se ................................................................................................
. . 53
Figure 8
System dynamic waste / variation loop................................................. 56
Figure 9
Overview of the automotive Order-to-Delivery system ..........................
Figure 10
High level process map of the overall automotive production system from
61
the time the plant receives the order until the order is shipped............. 62
Figure 11
Days of delivery variation (SPAN) for 95% of the volume......................65
Figure 12
Measurement system map ....................................................................
Figure 13
Variation from manufacturing order date to actual ship date for one day of
66
production system orders (short term variation for the entire production
process) 8 days ..................................................................................
Figure 14
. . 68
Overall production system variation for 95% of the total population over 100
days of production orders......................................................................69
Figure 15
Overall delivery average (expectation) for 95% of the population over 100
days of production orders ......................................................................
70
Figure 16
Relative inventory cost for delivery variation......................................... 71
Figure 17
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 ) ...............................................................................
11
73
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
system )..................................................................................................
Figure 19
Variation for the test, inspect, and rework subsystem prior to the actual ship
da te .....................................................................................................
Figure 20
. 74
. . 75
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 .............................................................................................
. 89
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 ............................................
Figure 31
96
Example of a TIR subsystem delivery performance chart......................97
12
1. Introduction
1.1.
Motivation
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 SixSigma 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
2. Average performance and performance
variation
2.1.
The difference between average performance
and performance variation: A simple case.
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.
2.2.
Expected delivery performance and delivery
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:
21
1. Order system (Customer order to start of production)
2. Production system (Start of production to start of distribution)
3. Distribution system (Start of distribution to delivery to customer)
Order-to-Delivery System
Order System
Figure 1
Production System
Distribution System
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
Order subsystem (Production orders are received)
o
Build subsystem (The product is manufactured)
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
I
Order Sy stem
M
I
Order-I-,Delivery
System
I
Production
Distribution Systerm
System
I
-iI
Production
Order
Figure 2
Manufacturing
Build
Test,
Inspect,
Rework
Plant
Shipping
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
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
On-Time Product Delivery
Figure 3
Variation to Customer Expectation
28
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
Order System
Production
Distribution System
System
Days
Production
Order
Plant
Shipping
I
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.
3.4.
Who are the customers and what do the
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
30
o
End customer (Retail customers)
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 WalMart. 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
Customer Hard
Costs
o Storage space
o Extra handling
and product
movement
o Product
damage and
loss
o Financing cost
o Rental cost
o Unscheduled
down time
(including
layoffs) and
overtime
o Obsolescence
cost
o Large incoming
product
inventories
o Duplication of
costs for early
deliveries
Figure 5
3.6.
Customer Soft
Costs
o Lost sales
o Project delay
cost
o Poor
customer
satisfaction
Enterprise Hard
Costs
o Storage space
o Extra handling
and product
movement
o Product
damage and
loss
o Financing
Finished goods
inventory costs
o Obsolescence
cost
o Overtime to
make-up for
late units
o Work-in
progress
inventory
(WIP)
o Added capacity
to eliminate
late deliveries
Enterprise Soft
Costs
o Lost sales
o Cost of being
unable to
launch new
products
o Added
system
complexity to
expedite
orders
Customer and enterprise costs
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
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.1 2
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.1 4
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.
46
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?1 5
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 financially1 6 . 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 goal1 7 . The team will develop a high-level process map and a list of
what is important to the customer1 8 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.
47
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 validated2 1 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 place 2 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.
Initial Delivery Time Variation Capability
0
0.9
-
-5
0.80
x o0.7-
E0.6-
0 0.5
* 0.41
0.3SPAN
E 0.2U0 0.1
00
0
0
5
0*
15
10
Days
20
14.5 days
(P = 9Eno)
Figure 6
Example of an initial OTD delivery variation plot
49
25
30
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 data2 3 . 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 output 25 . 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.2 7
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
phase2 8 . 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
0
4
0.
0.90.8-
0.0.7S0.60 0.5a 0.4-
0.3E 0.2-
SPAN
-
%U0 .1
*
0.0
0
5
15
10
Days
Figure 7
20
25
5.7 days
Example of an improved OTD variation plot as compared to the
Measure Phase
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
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
o
Inappropriate processing
"
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 delivery 34. 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
00000
Production
System
system
Waste
Delivery
Variation
External
I
Customer or
Enterprise
Waste
Figure 8
System dynamic waste / variation loop
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
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 on3 6 . 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.
Customer
Customer
WantOrder from
Comapny
Order
Processing
CustomerI
Plant
Order
Distribution +Plant
Fulfilled Fuliled ~ Shipping
Manufacturing
Plant Order
Manufacturing
________
Figure 9
Overview of the automotive Order-to-Delivery system
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
Production
Order
Manufacturing
Assembly Line 2
Figure 10
Test, Inspection,
Rework
Plant
Shipping
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
Product Delivery Variation
95%
1.0
p
(Cumulative
%of the
Population)
Days of
2.5%
2.5%
Delivery
Variation
(SPAN)
0.0
Product Delivery (Days)
Early Product Delivery
Figure 11
Late Product Delivery
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
5.3.2.
Measurement system process map
The process map for the measurement system at this
2 nd
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.
Manufacturing
Assembly Line 1
Production
O r d erP
Manufacturing
Assembly Line 2
Order to
Build
Build
Test, Inspection,
Rework
l n
Shipping
Test, Inspection, Rework
Production Process
Figure 12
Measurement system map
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
subsystem is from the barcode scanner at the end of production to the time the unit is
shipped. The measure shows how long the TIR subsystem took to complete for the unit.
Each of these subsystems can be further decomposed to a more elemental level.
The subsystem with the most variation is chosen for further decomposition. Working on
the subsystem with the most variation will allow resources to improve the production
system performance by focusing on a smaller subsystem.
5.4.
Current performance
Current performance for the production system is determined for each day of
orders. Figure 13 depicts the measurement for a single day of production. The average
delivery time is the average for all 1000 units ordered on this day. The variation for 95%
of the population is measured to be eight days as shown in the figure.
67
Variation from Manufacturing Plant Order Date to Actual Ship Date
(one ay - 1000 units)
0 0
1 -
0
0
0
-
-
-
-
P
-
-
-
-
-
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0 - 0
0 000
0
5
10
5
( 0d s
(P = 95%)
Figure 13
I
I
I
I
20
25
30
35
40
Days
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
The delivery time average and variation for the overall production system were
then collected over 100 days (around 100,000 vehicles). Variation and average values
were determined for each day of orders and are plotted in Figure 14 and Figure 15
respectively. Both the average and variation in the system change from time-to-time
depending on how well the system is running. How well the system is running depends
on the performance of the three subsystems. These overall performance plots do show
that there is variation in the system, but do not lend insight to the problems that cause
68
the variation. The problems will start to surface as the analysis goes deeper into the
subsystems.
Delivery Variation
120
|-Overall VariationI
-
100
U,
-
80
60
-
0
Cu
Cu
.4-a
4-a
40
-
Cu
U,
20
IVA
0
0
20
40
60
80
100
Order Day
Figure 14
Overall production system variation for 95% of the total population
over 100 days of production orders
69
Average Delivery
120
-Overall
100 _
Average
-
80
CU
-
60
G)
40
-
CU)
-
20
0
0
20
40
60
80
100
Order Day
Figure 15
Overall delivery average (expectation) for 95% of the population over
100 days of production orders
Three spikes in the data in Figure 14 are from periods with an assignable cause.
The cause of the three largest spikes is known (assignable) and will not be discussed
here. These spikes can be removed from the data when analysis of common cause
variation (normal to the system)
5.5.
Cost associated with the current performance
Variability in the current production system leads to system costs for both the
customer and enterprise. A conservative estimate of the cost of delivery variation for the
70
enterprise is to look at the cost of inventory in the production system. Inventory level
data is compared to the amount of delivery variation to get an idea of the increase in
inventory cost for larger delivery variations. The data is normalized by giving the lowest
inventory order date a value of one and then plotting relative inventory levels for the
other order dates. The relationship of cost and delivery variation for this system is
shown in Figure 16. There appears to be a relationship between inventory cost and
delivery variation in the graph. More data points would be needed for days with high
variation before we can determine if the relationship is statistically valid. This cost
estimate is very conservative compared to the actual costs considered in section 3.5 on
the costs of delivery variation.
Relative Cost of Delivery Variation
5
4-
12-
s
.
0
0
10
20
30
40
Delivery Variation
Figure 16
Relative inventory cost for delivery variation
71
50
60
The non-value added cost from the Lean manufacturing perspective is much
higher than the inventory cost alone. Other costs associated with delivery variation
include labor, material, facility, and obsolescence costs. The magnitude of these costs
can be in the millions to tens of millions (dollars) for an automobile OEM production
system. The actual numbers for these costs are hard to determine because many of the
costs are hidden in the normal operating cost for the production system.
5.6.
Analysis of variation in the current system
We analyze variation in the order, build, and TIR subsystems to determine what
causes the variation in the production system. The order, build, and TIR subsystems are
analyzed for each day using the method outlined in the operational definition for the
measurement system. The examples that follow for each subsystem are made from
data collected on the same order date.
The variation in the production order system was determined for each day of
orders as depicted in Figure 17 for 95% of the population. The variation for the order
subsystem on this day was 1.7 days. The order variation was determined for all 100
days in the data set. The average order time was also determined for the data set.
72
Variation from Manufacturing Plant Order to Build Start Date
(one day t 1000 units)
-
*0
*
o 0
1.0
0.90.8-
-
-
0.7
0.6P0.50.4
0.3
0.20.1
-
A
0.0
0
Figure 17
2
41
1.7 days
(P = 95%'
I____________
3
8
10
12
14
Days
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)
The variation for the build subsystem was likewise determined and is depicted for
the same day in Figure 18. The average and variation in the build subsystem was
analyzed for all 100 order days too.
73
Variation from Build Start Date to Build End Date
(one day - 1000 units)
1.0
0.9
VO
0
P O
0
0 0
0
O
0
-
-
0
0.8
-
0.70.6P 0.50.40.30.20.1
0.0
r-
0
Figure 18
----- lu
I
2
IIII
8
4
6
2.6 days
(P = 95%)
10
12
14
16
Days
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 system)
The variation in the TIR subsystem was also determined for the 100 days of data.
An example of the variation analysis for 95% of the population is shown in Figure 19 for
the same day as the order and build subsystems.
74
Variation from Start of Test, Inspection, and Rework Date to Ship Date
(one day - 1000 units)
0 00
00000
p
0
10.90.80.70.60.50.40.3
0.2
0.1
0
5
10
15
25
30
35
Days
(P = 95%)
Figure 19
20
Variation for the test, inspect, and rework subsystem prior to the
actual ship date
The variation for all 100 days was then plotted as a run chart. The run chart in
Figure 20 shows how the variation in all three subsystems compares to the overall
variation in the production system. Relative variation was determined by giving the
lowest variation a value of one unit on the chart. The run chart shows that the test,
inspect, rework subsystem is consistently the subsystem with the largest variation. The
amount of TIR subsystem variation normally approaches that of the overall system. The
TIR subsystem will be the focus for efforts to reduce delivery variation in the production
system. Working to reduce delivery variation in the TIR subsystem will enable the
biggest delivery variation performance improvement in the production system.
75
Delivery Variation
120
--Overall
Variation
------ Order Variation
--- Build Variation
- - TIR Variation
100 -
-
.2 80
60
40
-
40
>
CU
-
20
0
20
40
60
80
100
Order Day
Figure 20
Delivery variation performance for the entire system and each of the
three subsystems at the first level of decomposition
Periodic shapes are observed in the order and build subsystem variation run
charts. The periodic nature of the delivery variation chart for the order subsystem comes
from the method used to process orders. The periodic nature of the delivery variation for
the build system comes from the length of the build process and where certain vehicles
are during non-production periods like weekends.
The run chart for average delivery performance does not indicate that the TIR
subsystem is a problem for delivery within the enterprise. Figure 21 is the run chart for
the 100 days of data used in this analysis. More resources have traditionally been
76
focused on the order and build subsystems due to the enterprise focus on average
delivery performance. The TIR subsystem normally has the smallest average delivery
time impact; the exact opposite conclusion from what we saw when studying variation at
the subsystem level.
Average (Expected) Delivery
50
-Overall
45-
----- Order Average
40
--- Build Average
- - TIR Average
Average
-
a>35
-
20 -25
CU-20
-
15
0
0
20
40
60
80
100
Order Day
Figure 21
Average delivery performance for the production system and each of
the three subsystems at the first level of decomposition
The study of variation at the subsystem level has led us to understand that most
of the variation is coming from the TIR subsystem. The build and order subsystem are
more interesting to the enterprise for their average performance impact, but individual
customers care about the variation performance in the TIR subsystem.
77
The subsystem variation data was also used to see if subsystem variation is
related to variation found in prior subsystems. The 100 days of data was used to check
for correlation between the order and build, order and TIR, and build and TIR
subsystems. No relationship was found in this analysis as depicted in Figure 22 - Figure
24.
Correlation Between Order Variation and Build Variation
7 1
6
5
R2 = 0.1716
0
CU
4
.r
CU 3
-0
--
-.
S
e
2
1
0
-r
0
2
4
6
Order Variation
Figure 22
Correlation between order and build subsystem delivery variation
78
8
Correlation Between Order Variation and TIR Variation
60n
-
50
*
-
c 40
0
4-0
-
30
R
0.0054
01.
-
20
0
2
0
10
i
II
0
2
4
6
Order Variation
Figure 23
Correlation between order and TIR subsystem delivery variation
79
8
Correlation Between Order Variation and Build Variation
-
60
*
a
-
40
.
0
50
0
0
0
30
-
CU
R2 = 0.0005
-
.
20
.0
*0
0
00
0
.
.0
S
I.
0
U
0
1
.
0jW
'
-.
-
10
*
..
-.
2
3
0
4
5
6
7
Build Variation
Figure 24
5.7.
Correlation between build and TIR subsystem delivery variation
TIR subsystem possible sources of variation
The variation analysis at the production subsystem level indicates that the source
of much of the delivery variation in the production system is in the TIR subsystem. The
next step is to perform variation analysis on the TIR subsystem. The steps in conducting
the analysis start much the same as the production subsystem analysis. The TIR
subsystem is first decomposed and mapped to enable an understanding of complexity
in the subsystem and to show possible sources of variation.
80
5.7.1.
TIR subsystem decomposition
The TIR subsystem consists of several smaller elements at the next level of
decomposition. The smaller elements fall into four categories:
u
Vehicle Test
L3
Vehicle Inspection
L
Vehicle Rework
L
Vehicle Inventory
There are five different vehicle tests in the subsystem that range from simple
subsystem functional tests to full vehicle operational tests. The time to complete the
tests is standardized and is fairly short. Every vehicle completes all five tests and is
either passed to the next point in the process or sent for further evaluation or repair
(rework). Examples of these tests that are common among OEMs are dynamometer roll
system and functional tests, water leak tests, and electrical system tests.
The 12 different inspections in the system range from static craftsmanship
evaluations to dynamic evaluations on the road. Some of the 12 inspections are
standard for every vehicle. Other inspections are conducted on an audit basis, so only
some of the vehicles produced each day go through the evaluation. The vehicles that do
go through the non-standard evaluations tend to stay in the system longer and therefore
may be a source of variation in the TIR subsystem. The vehicles that pass each
inspection are sent to the next step in the process. The vehicles that do not pass are
sent for rework. Examples of inspections that are common among OEMs are body gap
and flushness inspections, suspension alignment audits, paint finish inspections, and
81
fluid level inspections for engine oil, transmission fluid, cooling fluid, steering fluid and
brake fluid.
Seven different rework areas are present in the TIR subsystem. Each rework
area consists of operators with specialized skills and tools to perform certain types of
repairs and rework. The numbers of repair persons and spots for vehicles to be
reworked depends on the service area. Almost all the service areas have an inventory
that feeds the service area. The inventory and variation in the extent of the repair in the
rework area are a possible source of variation. Rework completed in an OEM
production facility will include paint blemish repairs, damaged wire repairs, powertrain
repairs, and fluid level repairs to name a few.
The 13 inventory areas in the TIR subsystem feed both standard and
nonstandard test, inspection, and rework areas. The inventory is handled on a first-infirst-out (FIFO) or last-in-first-out (LIFO) basis depending on the arrangement of the
storage area and the work practices of the person delivering a vehicle to inventory or
taking a vehicle from inventory. Inventory can be a source of variation in the TIR
subsystem, especially in areas that practice LIFO inventory control. LIFO causes the
first vehicle in to be the last vehicle out. There is delivery variation between the first and
last vehicles brought into inventory.
LIFO inventory control practices depend on the arrangement of the storage area
and the work practice of the employees in the area. Sometimes the conversion from
LIFO to FIFO can be as simple as teaching the workforce about the importance of FIFO
vehicle handling. Other times the change can be expensive because facility changes
82
must occur to enable FIFO inventory control. The cost of variation must be weighed
against the cost of changing the facility if this is the case.
The decomposition is at the third level of the OTD system and the first level of
the TIR subsystem. Grouping the elements for the TIR subsystem in this way gives
consistency of level in the analysis. The analysis is most easily conducted when the
elements we are studying are similar in the subsystem. We want to compare one
inventory area to others, or to test, inspection, or rework areas. The decomposition
helps us avoid studying elements at this level with elements one level up or down in the
decomposition. The analysis might yield misleading results if we were to compare an
inventory area's performance to an individual work element conducted in a repair area.
5.7.2.
TIR subsystem process map
The next step is to map the elements from the decomposition diagram to better
understand the subsystem and the interactions between elements. The process map for
the TIR subsystem is shown in Figure 25. The map in Figure 25 shows the relationship
between the four categories of elements. Inspections three through 12, inventories six
through 13, and rework six and seven are shown as one process block to simplify the
map for illustration purposes in this thesis. The purpose of the map is to show how
complex the TIR subsystem is between the completion of the build and the shipping of
the vehicle. The process map is used to help identify possible sources of variation in the
TIR subsystem.
83
Build
cormplete
Inerbxy 2 -b
Invntory 3
Test 1
Test 2
Test 3
Rewkork 2
Irertory
-
Revrk i
inventcry 4
Irspect bn I
F rocess map
a ggregated at this point
Test,4
]nspectin
3-12f
Irentbry
6-13/
Irvenbory 4
-
Irspectkon 2
Rework 6-7
Wend to
Dis ribution
System
Figure 25
Inrerery 5
Test 5
Rekrk 3
Process map for the TIR subsystem (some steps aggregated to
simplify the process map)
5.7.3.
Cause and effect diagram for the TIR subsystem
A cause and effect diagram is a tool used to help gather ideas about what
possible causes might contribute to a certain effect. The diagram is constructed by
placing the effect with groupings of possible causes pointing to the effect. There are
standardized groupings of causes such as manpower, methods, and machines, but
84
other groupings can be used if they make more sense in the system effect being
studied.
With the process map as a guide, a cause and effect diagram can be constructed
to list the several possible sources of variation in the TIR subsystem. Each of the
elements in the TIR subsystem decomposition are listed here as possible sources. The
groupings for the possible causes follow the element types from the TIR subsystem
decomposition. Other possible sources of variation can be added by the team as the
team develops new ideas about the causes of the delivery variation. A generic cause
and effect diagram for the system of interest is pictured in Figure 26.
85
Cause and effect diagram for TIR sub-system
Test
Rew ork
Rework 1
Test 1
Rework 2
Test 2
Rework3
Rework4
Test 3
Rework 6
Test 4
Rework 6
Rework?7
Test S
4 entory13
hvertory12
Inantoryl11
hspeodon 12
hspeodon 11
hspecion 10
h
rpedon
0
hspeoiion 8
hspection 7
hspection 6
hspecion 5
hspection 4
hspection 3
Vehicle Deliver/ Variation
ertrv
hertory 1
hentory
hktory
hnrtory
hxertor5
n nmntry4
hwetry3
tnertcor2
tuertorvl
hspecdion2
ispelon 1
Inventory
Figure 26
ispect
Cause and effect diagram for the TIR subsystem
5.7.4.
C&E matrix
Searching for the largest sources of variation from the 37 possible sources listed
in the cause and effect diagram would take a long time and a lot of resources. There is
a Six-Sigma tool called a cause and effect matrix that will help narrow the search to only
the most likely sources. Studying a few likely sources is a better use of scarce
resources and takes less time to complete. Studying sources that are not likely would
86
be a form of waste and would contradict the very Lean principle the analysis is working
to eliminate.
The cause and effect matrix works by listing the possible causes and related
effects and weighting each cell in the matrix to determine the most likely causes to
include in the analysis. The possible causes are called inputs and the effects are called
outputs. The nomenclature comes from the fact that we are trying to understand the
transfer function between the inputs and the outputs. This is often written as:
Y = f(x) or the output is a function of the input
Notice we are working with effects (or outputs) in the cause and effect matrix.
This is because we want to make sure we understand all the important effects that may
be impacted by changing one or more of the possible causes (as discussed in the
section on Six-Sigma methods). The outputs are placed along the top of the matrix and
each output is given a score from one to ten based on the relative importance of the
output (a score of ten being most important). The inputs come from working with the
decomposition diagram, process map, and cause and effect diagram. Each input is then
given a score for the likelihood that the input would affect the output at each intersection
in the matrix. The cells are multiplied and added across the input row to give a total
score for the input. The inputs with the highest score become the inputs of most interest
for the initial study as seen in Figure 27
The scores for each cell are based on the expertise and opinions of those on the
team. It might not seem very scientific, but usually yields a list of likely causes that will
be studied first. The short list of causes is easier for the team to work with than a longer
87
list that may range from 30 to 100 inputs. The next set of possible output variation
causes can be selected for study if the first set is not determined to be the majority of
the cause when the initial analysis is complete.
The cause and effect matrix (C&E matrix) for the TIR subsystem is shown in
Figure 27. The outputs for the C&E matrix include delivery variation, total cost, and
quality. Changing an input in the subsystem is likely to affect all three of these outputs.
The output weighting in the C&E matrix for the TIR subsystem is completed from the
customer's perspective. Quality is more important than delivery which is more important
than cost for automobiles as judged by the person or team conducting the analysis.
The inputs for the TIR subsystem C&E matrix are taken directly from the C&E
diagram. Each input is given a score based on the likelihood that it affects the given
output of quality, cost, and delivery. The values in the cells are multiplied by the output
weighting and summed across the input row to give a total score for each input. The
scores are then arranged so the largest score is at the top of the list and the smallest
score is at the bottom. The top six likely causes of variation are then selected for
analysis. The tops six in the TIR subsystem are as follows: Rework 3, Rework 1,
Inspect 4, Inspect 5, Inspect 12, and Inventory 8.
These inputs now become the focus of the analysis for which measurement
systems will be developed, changes will be made, and improvement will be verified for.
The measurement systems for these elements are not available and the analysis of
these elements is not part of this thesis. The steps to develop measurement systems
and conduct analysis at this level are the subject of the next section of this thesis.
88
Cause and Effects Matrix for the TIR Sub-System
Foutputs (Y's)
10
9
4)
Figure 27
a
10
9
9
8
7
10
8
10
4
6
9
4
8
8
3
7
8
2
6
3
8
2
3
8
9
9
9
5
2
8
8
2
1
1
1
2
2
0
0
0
Possible X's
Rework 3
Rework 1
Inspect 4
Inspect 5
Inspect 12
Inventory 8
Rework 2
Inventory 7
Rework 4
Inspect 9
Inventory 9
Inspect 3
Inventory 11
Inventory 12
Test4
Inventory 13
Inventory 10
Inspect 1
Rework 5
Inspect 2
Inventory 6
Inspect 6
Inspect 11
Rework 7
Inventory 2
Inventory 3
Inventory 4
Rework 6
Test1
Inventory 1
Inventory 5
Inspect 10
Test3
Inspect 7
Inspect8
Test2
Test5
7
0
C
9
8
10
10
9
3
5
3
6
8
3
8
4
4
9
4
3
9
4
8
3
9
8
2
1
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1
3
8
1
1
7
8
8
8
7
7
I-
0l
0
-
Weighting for Outputs
6
5
2
1
2
6
5
5
7
1
3
2
2
2
1
3
3
2
4
2
2
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1
3
3
3
3
5
1
3
3
2
1
1
1
1
1
222
196
195
179,
167
162
157
155
145
141
132
130
126
126
124
124
123
122
122
121
116
115
114
113
112
112
112
110
105
103
103
102
96
96
96
95
95
}
Top possible
causes
TIR subsystem cause and effects matrix with the top 6 possible
causes highlighted
89
5.8.
Analysis of variation in the TIR subsystem
Actual variation analysis was not possible as part of this thesis. The method for
conducting this analysis is the subject of this section. The analysis consists of
developing and validating a measurement system that will provide trusted data for the
six areas listed in the preceding section. The next step is to collect and analyze data to
determine which of the six elements has a significant amount of variation. The source of
variation for each element can be analyzed and likely includes resource limitations,
wasteful processes, problems with worker skill levels, and variation in the quantity and
type of problems generated in upstream systems. Improvements are then developed
and tested to ensure the desired reduction in delivery variation. Training would be
administered or new workers would be employed as a possible improvement if worker
skill level were found to be the issue in a rework area.
Data for the number of daily problems found in the largest inspection area were
compared to the delivery variation in the TIR subsystem (in the absence of delivery
variation data for the specific inspection area) to see if problems created in the build
subsystem cause delivery variation in the TIR subsystem. The number of problems per
100 vehicles was collected over a 50 day period and compared to TIR delivery variation
for the same time period. The graph for the problem rate is shown in Figure 28.
90
Problems Identified In A Major Inspection Area
-
180
-
160
-
co
140
CD
-
2 120
U,
-
o 100
S80
-
C
E
60
0-
40
-
-
-D
-0
-
20
0
0
10
30
20
40
50
Build Day
Figure 28
Problems per 100 vehicles built measured in a major inspection area
The problem rate appears fairly stable over the period as compared with delivery
variation in the TIR subsystem. The standard deviation for problems during the period
was 13 % of the average. The standard deviation for delivery variation during the period
was 72% of the average. We do not conclude that the delivery variation in the TIR
subsystem is caused by the daily change in problems found in this large inspection
area. We would not rule this out in other areas and would need to collect data to
determine if there is a relationship in other areas.
91
5.8.1.
Measurement system for key possible sources
of variation in the TIR subsystem
The measurement system for the elements of interest needs to be able to
quantify the amount of variation in time for different vehicles that are processed through
the rework area. The system also needs to identify the number of vehicles that are
processed through the area. The simple solution is to provide a handheld barcode
scanner for the vehicle to be scanned when they enter and leave the rework area. The
amount of variation caused in each area can be determined given this information.
5.8.2.
Target reduction in variation in the TIR
subsystem
The data collected by the handheld barcode scanner can then be analyzed using
the same combination of Lean principles and tools and Six-Sigma methods as was
done in this thesis for the order, build, and TIR subsystems. Compare the variation
caused by each element to the total variation for the same population of vehicles in the
entire TIR subsystem in this case. If the variation is large relative to the overall TIR
delivery variation, then the element becomes an area where changes are made or new
systems or processes are tried to reduce the variation in the TIR subsystem.
92
6. Conclusions and recommendations
6.1.
Conclusions
Average delivery time in the production system is important to the enterprise.
Lower average delivery times generally lead to lower cost to the enterprise.
Sophisticated measurement systems are in place at automotive OEMs to measure
average delivery performance. The average performance of the build subsystem is the
primary focus of the OEM since it is the subsystem where materials and other resources
are employed for the longest time. Average performance of the build subsystem
continues to be important to automotive OEMs for its impact on enterprise cost.
The measurement systems that are in place to measure average delivery are
also capable of capturing data that allows analysis of delivery variation performance.
Delivery variation is especially important to individual customers as well as the
enterprise. Each customer uses their own delivery experience as the basis for judging
the delivery performance of the enterprise. Customers expect to get what they want,
when they want it. System costs for both the customer and enterprise increase with
delivery variation.
The test, inspect, rework (TIR) subsystem was determined to be the largest
source of delivery variation in this automotive OEM production system. Variation in the
TIR subsystem does not appear to be stable throughout the measurement period used
in this thesis. The TIR subsystem has traditionally received less attention with regards
to delivery than the build subsystem. This is because TIR subsystem average delivery
93
performance is relatively short compared to build subsystem delivery performance.
Average delivery and delivery variation lead the enterprise to work on two different
subsystems in this automotive OEM case study.
TIR delivery variation performance can be improved with a systematic approach
to find and eliminate variation and waste. The systematic approach recommended in the
thesis uses Six-Sigma methodology in combination with Lean principles and tools. The
combined approach leads to analysis of the most important inputs to the subsystem.
The system is decomposed to a level where the relationship between the inputs and
output is understood using this systematic method.
The sources of variation within the TIR subsystem are still to be determined. The
relation of variation between subsystems did not show correlation. Many times the
inputs that need to be improved are sources of waste. This is expected to be the case in
the TIR subsystem. Improving variation in the TIR subsystem will improve delivery
performance in the production system and ultimately delivery to the customer in the
OTD system.
The system dynamic loop can be improved by reducing delivery variation in TIR
subsystem inputs. The system dynamic waste / variation loop is pictured in Figure 29.
94
Production
Production
System
system
Waste
Delivery
Variation
External
Customer
or
Enterprise
Figure 29
System dynamic waste / variation loop
Delivery variation will be improved in the production system when waste in the
TIR subsystem is reduced. The improvement in delivery variation will cause a reduction
in waste that is external to the production system. The external waste reduction is a
benefit to both the customer and the enterprise. The external reduction in waste
associated with delivery variation will help reduce the gaming that goes on in the OTD
system that causes the production system to incur waste. This waste elimination comes
in the form of less expediting and fewer schedule changes in the production system.
Both the customer and the enterprise benefit from the reduction of waste that causes
delivery variation in the TIR subsystem.
95
The long term variation performance in the production system depends upon how
often the system dynamic loop is repeated and whether waste is increasing or
decreasing in the system. The general shape of the performance curve for delivery
variation is pictured in Figure
3039.
Expected Performance of Production
System Delivery Variation (based on
system dynamics)
w/o waste
reduction
E c
co
a)0
o'
reduction
Time
Figure 30
6.2.
Expected long term performance of delivery variation in the
production system with and without waste in the system
Recommendations
The most likely sources of variation in the test, inspect, and rework (TIR)
subsystem should be analyzed and improved to reduce delivery variation in the
96
production system. A chart can be used to report the average delivery performance and
the delivery variation performance in the TIR subsystem and allow management to
better see the variation in the subsystem. The chart provides visual cues that the
system is either in or out of control. An example of such a chart is found in Figure 31
15
TIR Sub-System AverageNariation
Performance Management Chart
-
Example: TIR sub-system
delivery became unstable and
corrective actions were taken
---
97.5%
Average
- -2.5%
10C',
5
01
Figure 31
11
21
31
41
51
61
71
Day Entered TIR Sub-System
81
91
Example of a TIR subsystem delivery performance chart
The chart shows that delivery variation increased mid-way through the period
used in the example. The team took action to return control to delivery variation
performance and verified the improvement using the chart. Maintaining similar charts of
performance for the most important inputs will make it easier to determine which input
97
went out of control to cause this change in delivery variation performance in the TIR
subsystem.
Delivery variation performance will need to be made an objective of the leaders
who are responsible for the production system in the enterprise. The focus on reducing
variation in and stabilizing the system will be lost unless performance targets are set in
a person's objectives and the person is rewarded for meeting or exceeding the
objectives. The performance to target becomes measurable through the use of the
variation management chart described above. The same type of measure and objective
is currently used to achieve build subsystem average delivery performance.
Three areas for future study emerged from the research conducted to
complement this thesis:
" Cost to the customer
"
Delivery variation in other industries
" Measurement systems
The actual cost of delivery time variation to the customer is an area where little
has been published. The knowledge gained from a study of these costs would allow an
enterprise to determine the amount of resources to use in reducing delivery variation
and waste. The initial work might be to study costs for a single industry by using several
enterprise and customer case studies, such as the automotive industry. Future work
could compare costs across different industries.
98
Delivery variation in other production systems would be of interest to see where
variation originates in other production systems. Systems with both large and small
amounts of variation are of interest and may lead to an understanding of how some
production system architectures naturally lead to better delivery variation performance.
The results of the study would lead to system architecting principles that yield a
competitive delivery performance advantage for the enterprise.
Measurement systems that allow for ease of measurement in the production
system might also be an area for future research. Measurement systems that are
inexpensive and easy to use for several different types of products would make it
possible for more production systems to measure and understand both average delivery
performance and delivery variation performance.
99
100
Glossary
OEM:
Original equipment manufacturer
OTD:
Order to delivery
CTQ:
Critical to quality characteristic
CTC:
Critical to cost characteristics
CTD:
Critical to delivery characteristics
Delivery time
DT:
DMAIC:
6-Sigma define, measure, analyze, improve, and control
project phases
Production Order Date:
The date the manufacturing plant receives the order from the
internal order process from which the order will be produced
Off Line Date:
The date the product is complete with the exception of test,
inspection, and repair
Ship Date:
The date the product is released to the distribution system
and is sold by the manufacturer
Span:
Measure of variation in product delivery dates compared to
customer expectation (sum of days early and days late)
MSA:
Measurement system analysis (similar to gauge R&R)
Specific ID:
ID for a specific unit of product that differentiates the single
unit from all others. An example would be a serial number on
a computer or other product
Time Stamp:
Date and time that a specific unit passed a certain point in
the process. Used in calculating the overall process variation
or the variation contributed by sub processes
Cause & Effect Diagram:
Diagram used to map possible causes of a problem
Cause & Effect Matrix:
Matrix that ranks most likely causes based on there
importance to selected outputs
TIR:
The test, inspect, rework subsystem in the production
system used in the case study
System Decomposition:
The breakdown of a system structure into subsystems that
are smaller than the prior level. Decomposition ends when
the system is broken down to the elemental level
Expected Value:
The average value for a population (of units in this case)
101
102
References
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103
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104
Endnotes
1 Donovan,
Michael R. The Order to Delivery Cycle: Quick Response is Essential. R. Michael Donovan
Co., 1998
2 ibid
3 Pine, Joseph B. Mass Customization: The New Frontier in Business Competition. Boston: Harvard
Business School Press, 1999
4 Voyle, Susanna. TESCO: Food e-tailers in search of a recipe. The Financial Times, February 15, 2000
5 Welch, Jack, and Byrne, John A. Jack: Straight From the Gut. New York: Warner Books, 2001. Pg. 337
6 ibid., 337
7 Copeland, Dave. Alcoa showcases chemicals facility. Pittsburgh Tribune-Review, Thursday, June 27,
2002
8 Mudd, Tom. Back in High Gear. Industry Week, February 21, 2000
9 Donovan. The Order to Delivery Cycle
10 Harry, Mikel J. and Schroeder, Richard. Six Sigma : The Breakthrough Management Strategy
Revolutionizing the World's Top Corporations. New York: Doubleday, 2000. Pg. 195 - 196
11 ibid., 195
12
13
14
15
16
17
ibid., 193
ibid., 108 -109
Welch. Jack: Straight From the Gut. Pg. 339
Nightingale, Deborah. "16.852J Integrating the Lean Enterprise" MIT, Cambridge. Fall 2001
Rath & Strong. Six Sigma Pocket Guide. Lexington, MA: Aon Consulting Worldwide, 2002, Pg. 5
ibid., 5
18 ibid., 5
19 Harry. Six Sigma. Pg. 129
20 ibid., 129
21 ibid., 129
22 Rath. Six Sigma Pocket Guide. Pg. 6
23 ibid., 6
24 ibid., 6
25 Harry. Six sigma. Pg. 129
26 Rath. Six Sigma Pocket Guide. Pg 6
27 Donovan. The Order to Delivery Cycle.
28 Rath. Six Sigma Pocket Guide. Pg. 6
29 Harry. Six Sigma: Pg. 129
30 ibid., 129
31 Rath. Six Sigma Pocket Guide. Pg. 6
32 Taylor, David and Brunt, David. Manufacturing Operations and Supply Chain Management, The Lean
Approach. London: Thomson Learning, 2001. Pg. 79.
33 Ibid., 81
34 Hines, Jim. "15.983 System Dynamics Foundations" MIT, Cambridge. Summer 2002
35 George, Michael L. Lean Six Sigma, Combining Six Sigma Quality with Lean Speed. New York:
McGraw-Hill, 2002. Pg. 51-56.
36 Nightingale. "16.852J Integrating the Lean Enterprise"
37 Crawley, Edward F. "16.882 System Architecture" MIT, Cambridge. Fall 2001
38 Nightingale. "16.852J Integrating the Lean Enterprise"
39 Hines. "15.983 System Dynamics"
105